researchportal.port.ac.uk · II ABSTRACT Inflammatory bowel disease (IBD) is a series of disorders...
Transcript of researchportal.port.ac.uk · II ABSTRACT Inflammatory bowel disease (IBD) is a series of disorders...
BACTERIAL INVOLVEMENTS IN ULCERATIVE COLITIS: MOLECULAR AND
MICROBIOLOGICAL STUDIES
Samia Alkhalil
A thesis submitted in partial fulfilment of the requirements for the award of the degree of Doctor of Philosophy of the University of Portsmouth
Institute of Biomedical and biomolecular Sciences School of Pharmacy and Biomedical Sciences
October 2017
I
AUTHORS’ DECLARATION
I declare that whilst registered as a candidate for the degree of Doctor of
Philosophy at University of Portsmouth, I have not been registered as a
candidate for any other research award. The results and conclusions
embodied in this thesis are the work of the named candidate and have not
been submitted for any other academic award.
Samia Alkhalil
II
ABSTRACT
Inflammatory bowel disease (IBD) is a series of disorders characterised by chronic
intestinal inflammation, with the principal examples being Crohn’s Disease (CD) and
ulcerative colitis (UC). A paradigm of these disorders is that the composition of the colon
microbiota changes, with increases in bacterial numbers and a reduction in diversity,
particularly within the Firmicutes. Sulfate reducing bacteria (SRB) are believed to be
involved in the etiology of these disorders, because they produce hydrogen sulfide
which may be a causative agent of epithelial inflammation, although little supportive
evidence exists for this possibility. The purpose of this study was (1) to detect and
compare the relative levels of gut bacterial populations among patients suffering from
ulcerative colitis and healthy individuals using PCR-DGGE, sequence analysis and
biochip technology; (2) develop a rapid detection method for SRBs and (3) determine
the susceptibility of Desulfovibrio indonesiensis in biofilms to Manuka honey with and
without antibiotic treatment.
Mucosal biopsy DNA from 4 colitis patients and one healthy individual was used to
amplify 16S rRNA fragments, which were separated either by DGGE or by molecular
cloning prior to sequencing, and dissimilatory sulphite reductase (dsr) gene fragments,
which were cloned and sequenced. The number of bands separated by DGGE varied
from 3 to 12 for an individual. The profiles from the UC patients had a greater similarity
than the one from the healthy individual. In total, 25 bands were excised for sequence
analysis but only 4 produced usable sequences. The 16S rRNA gene library comprised
250 clones, the sequences of which showed great changes in diversity from the healthy
individual to the UC patients. The sequences from the healthy individual represented
members of the Bacteroidetes, Clostridiaceae, Ruminococcaceae, Enterobacteriaceae,
Coriobacteriaceae and Lactobacillaceae, whereas those form the UC patients comprise
only members of the Enterobacteriaceae. The library for the dsr gene comprised 30
clones, with sequences from the healthy individual representing members of the
Desulfobacteraceae (45%), and the Desulfovibrionaceae (33%), with minor components
from the Desulfohalobiaceae, Desulfomicrobiaceae and Desulfonatronaceae. Sequences
from the UC patients were less diverse, being composed primarily of
Desulfovibrionaceae sequences.
III
Oligonucleotide probes targeting SRB and other bacterial species found to be involved in
UC were developed or selected from published sources. Cassettes with multiple probes
were used to evaluate the hybridisation probes for inclusion in a biochip. Validated
probes (23), with similar binding profiles, were then used to assess the levels of SRB and
other bacteria associated with UC in healthy control and UC patient’s samples. The DNA
from the healthy individual gave a strong hybridisation signal for Desulfovibrio piger,
and weaker signals for Escherichia coli, Bacteroides fragilis, and a Clostridium species,
whereas the signals Desulfobacter and Desulfovibrio gigas were present in all of the
samples from the UC patients. Signals were also recorded for species of Fuscobacterium,
which have been implicated in colitis.
In this investigation, procedure for rapid detection and quantification of SRB was
developed. The SRB growth was monitored in the presence and absence of Escherichia
coli BP, the copy number of the dsrA and adenosine-5′-phosphosulfate reductase (aps)
genes was detected in DNA and RNA purified from different ages of SRB culture using
TaqMan qPCR. The result showed that in the presence of E.coli BP enhanced the growth
of SRB and allowed the detection of SRB in low dilutions comparing with the growth of
SRB only.
E. coli and SRB are both implicated in colonic infections. They pose several mechanisms
for immune evasion and antibiotic resistance, one of these being their ability to grow in
a biofilm. The present investigation has highlighted that Manuka honey and antibiotic
treatment might have a protective role against SRBs and enteric bacteria. However,
mixed bacterial population in biofilms exhibit greater resistance to both treatments, and
this needs to be taken into account when devising a treatment based on ingestion of
honey.
The present investigation were conducted as a baseline study for determination the role
of SRB in the pathogenesis of colitis through the use of novel methodology allowing for
rapid detection and screening investigations into species and genera of interest present
in colonic mucosa; that may allowed in further investigation to evaluate the bacterial
role in UC through extensive studies in broad multinational cohort of patients suffering
from this UC.
IV
ACKNOWLEDGEMENTS
First of all, all thanks and gratitude go to Almighty ALLAH for guiding and inspiring me
during this research process. Secondly, I offer my sincerest gratitude to my parents who
supported me during my study, without them and their help this mission would have
been impossible. My deepest gratitude to my husband Tariq for his patience and support
while I’m doing this research and to my sons, Mohammed, Abdulaziz and my daughter
Monera with love to all of you.
I would like to express my thanks to my first supervisor Dr. Vitaly Zinkevich for giving
me the opportunity to give a great hand in this project, this thesis would not be achieved
without his presence and support.
I am sincerely grateful to my supervisor Dr. Julian Mitchell for his supervision,
motivation for friendly guidance and enormous support and help during the stages of
this thesis. Without his constructive feedback and tremendous support this thesis would
not have seen the light of day. Thank you, Dr. Julian for all positive energy that you gave
me.
I would like to tank Dr. Irina Bogdarina for her continues help and also Dr. Nelly
Sapojnikova who was always available for help and Dr. Tamar Kartvelishvili for the
warm welcome and for their assistance during the stay in the University of Tbilisi.
Thanks to my sponsor Ministry for Higher Education in Saudi Arabia for their financial
and academic support represented by Saudi Arabian Cultural Bureau in London.
Finally, I offer my regards and blessings to all of those who have supported me and
given assistance to me in any respect during the completion of this thesis.
V
LIST OF ABBREVIATIONS
APS Ammonium persulphate
APS Adenosine phosphosulfate
ATCC American type culture collection
ATP Adenosine triphosphate
BLAST Basic Local Alignment Search Tool
bp Base pairs
cDNA Complementary DNA
CD Crohn’s Disease
Ct Cycle threshold
Cy3 Cyanine 3
DGGE Denaturing gradient gel electrophoresis
DIEA Diisopropylethyl amine
DMF Anhydrous amine free N,N-dimethylformamide
DNA Deoxyribonucleic acid
dsDNA double-stranded DNA
Dsr Dissimilatory sulphite reductase
EDTA Ethyleno-diamino tetra-acetic acid
FAM 6-carboxy-fluorescein, a blue fluorescent dye
FISH Fluorescence in situ hybridization
GIT Gastrointestinal Tract
H2S Hydrogen sulphide
IDT Integrated DNA Techenlogy
IPTG Isopropyl-β-D-thiogalactopyranoside
kb Kilobase pairs
LB Lysogeny or Luria Broth
MBC Minimum Bactericidal Concentration
VI
MGO Methylglyoxal
MIC Minimum Inhibitory Concentration
mRNA Messenger RNA
MW Molecular weight
NA Avogadro’s number
PCR Polymerase chain reaction
PFGE Pulsed Filed gel Electrophoresis
QPCR Quantitative PCR
QS Quorum sensing
RNA Ribonucleic acid
rpm Revolutions per minute
rRNA
RT
Ribosomal RNA
Room Temperature
sp. Species
SRB Sulfate-reducing bacteria
ssDNA single stranded DNA
TAMRA Tetramethylrhodamine
TEMED N,N,N’,N’Tetramethylethylenediamine
Tm Melting temperature, the temperature at which a
DNA double helix dissociates into single strands
Tris-HCl
UC
Tris (hydroxylmethyl) aminomethane
Ulcerative Colitis
UV Ultra Violet light
VM Vitamin Medium
VMI Vitamin Medium with Iron
VMR Vitamin Medium with Reduced iron
X-Gal 5-bromo-4-chloro-3-indolyl-β-D-
galactopyranoside
%(v/v) Percentage volume/volume
VII
%(w/v) Percentage mass/volume
VIII
TABLE OF CONTENTS
AUTHORS’ DECLARATION…………………………………………….……………………………………………..I ABSTRACT .............................................................................................................................. II ACKNOWLEDGEMENTS .................................................................................................... IV LIST OF ABBREVIATIONS ................................................................................................... V TABLE OF CONTENTS ...................................................................................................... VIII
LIST OF FIGURES ................................................................................................................ XII LIST OF TABLES ............................................................................................................. XVIII DISSEMINATION ............................................................................................................... XXI CHAPTER 1: INTRODUCTION ............................................................................................... 1
Section 1 .................................................................................................................................. 1
The Microbiota of the Human Large Intestine ....................................................................... 2
1.1 .1 General Overview ................................................................................................. 2
1.1.2 Development of the intestinal Microbiota ................................................................ 3
1.1.3 Normal intestinal microbiota a substantial factor in health ...................................... 5
1.1.4 Intestinal Microbiota in Disease ................................................................................. 5
1.1.4.1 Ulcerative colitis (UC) ........................................................................................ 5
1.1.4.2 Role of bacteria in UC ........................................................................................ 8
1.1.4.3 Bacterial composition and UC ........................................................................... 9
1.1.5 Dysbiosis of the intestinal microbiota in UC ............................................................ 10
1.1.6 Miscellaneous Bacterial/Host Product in Colitis ...................................................... 12
Section 2 ................................................................................................................................ 14
The contribution of sulphate reducing bacteria in UC ......................................................... 14
1.2.1 Sulphate Reducing Bacteria in the colon ................................................................. 14
1.2.2 Interaction of SRB with other organisms ................................................................. 16
1.2.3 Role of SRB in the aetiology of UC ........................................................................... 17
1.2.4 Sulphate Reducing Bacteria, H2S, and Colitis – A potential Nexus .......................... 18
1.2.4 Genetic Diversity of SRB – 5’-phosphosulphate reductase (apsr1) and dissimilatory
sulphite reductase (dsrAB) ................................................................................................ 20
Section 3 ................................................................................................................................ 22
Molecular techniques for the detection and identification bacteria in human colon ......... 22
1.3.1 General Overview ..................................................................................................... 22
1.3.2 Polymerase chain reaction Amplification (PCR) ....................................................... 26
1.3.3 16S rRNA Gene Sequencing and Phylogenetic analysis ........................................... 27
1.3.4 Denaturing Gradient Gel Electrophoresis (DGGE) ................................................... 28
IX
1.3.5 Pulsed field gel electrophoresis (PFGE).................................................................... 30
1.3.6 Quantitative PCR (qPCR) .......................................................................................... 31
1.3.7 Microarray ................................................................................................................ 32
Section 4 ................................................................................................................................ 35
The effect of antibiotics and honey on the growth of bacterial biofilms ............................. 35
1.4.1 Bacterial biofilms ...................................................................................................... 35
1.4.2 The aspects of bacterial Quorum Sensing in the colon ........................................... 36
1.4.3 Existing UC therapies ................................................................................................ 37
1.4.4 Traditional medicine as a potential treatment for UC ............................................. 39
AIMS OF THE RESEARCH ................................................................................................... 41
CHAPTER 2: MATERIALS AND METHODS ............................................................................ 42
Molecular characterisation of mucosa-associated bacterial communities from human
colon ...................................................................................................................................... 43
2.1.1 Bacterial growth conditions ..................................................................................... 43
2.1.1.1 Purification of genomic bacterial DNA ........................................................... 44
2.1.2 Polymerase chain reaction (PCR) ............................................................................. 45
2.1.2.1 PCR for bacterial 16S rRNA genes .................................................................. 45
2.1.2.2 PCR detection of SRB (for dsr, Desulfovibrio 16S rRNA and aps genes) ..... 45
2.1.2.3 Positive controls for PCR reactions ................................................................ 47
2.1.3 Analysis of PCR products using agarose gel electrophoresis ................................... 48
2.1.4 Cloning: construction of genomic libraries and sequencing .................................... 48
2.1.4.1 Ligation of plasmid DNA and competent cells transformation .................... 48
2.1.4.2 Purification of plasmid DNA ........................................................................... 49
2.1.4.3 Purification of PCR product and Sequencing ................................................. 50
2.1.5 Denaturing Gradient Gel Electrophoresis (DGGE) ................................................... 50
2.1.6 DNA extraction from DGGE gels and Sequencing .................................................... 51
2.1.7 Phylogenetic analysis ............................................................................................... 52
Section 2 ................................................................................................................................ 53
Probe designing and testing ................................................................................................. 53
2.2.1. DNA probes design .................................................................................................. 53
2.2.2 Cassette construction for fine-tuning of biochips .................................................... 54
2.2.3 Development of a biochip using dendrimeric matrix .............................................. 54
2.2.4 Hybridization ............................................................................................................ 55
2.2.5 Genomic DNA preparation for hybridization on a biochip ...................................... 57
X
2.2.5.1 DNA amplification ............................................................................................ 57
2.2.5.2 Purification of amplified products ................................................................. 57
2.2.5.3 DNA Fragmentation ......................................................................................... 58
2.2.5.4 DNA labelling and hybridization .................................................................... 58
Section 3 ................................................................................................................................ 60
Detection and quantification of sulphate reducing bacteria using microbiological
and quantitative PCR techniques .......................................................................................... 60
2.3.1 Bacterial culture preparation ................................................................................... 60
2.3.2 Purification of total bacterial RNA ........................................................................... 61
2.3.2 .1 cDNA Synthesis ............................................................................................... 62
2.3.3 Preparation of PCR fragment of the apsA and the dsrA genes ............................... 62
2.3.4 Purification of DNA fragments from the agarose gel ............................................... 63
2.3.5 Quantitative real-time PCR (Q PCR) ......................................................................... 63
2.3.6 Measurements of hydrogen sulfide (H2S) ................................................................ 64
2.3.7 Pulsed field gel electrophoresis (PFGE).................................................................... 64
2.3.7.1 Preparation of Agarose Embedded Bacterial DNA ....................................... 64
2.3.7.2 Restriction Enzyme Digestion of Plugs .......................................................... 66
2.3.7.3 Casting the gel .................................................................................................. 66
2.3.7.4 Removing and staining the gel ........................................................................ 66
Section 4 ................................................................................................................................ 68
The effect of Manuka honey and antibiotics on the growth of pure and mixed sulphate
reducing bacterial biofilms ................................................................................................... 68
2.4.1 Honey samples ......................................................................................................... 68
2.4.2 Antibiotics................................................................................................................. 68
2.4.3 Bacterial growth conditions ..................................................................................... 68
2.4.4 Preparation of honey samples ................................................................................. 69
2.4.5 Determination of the Minimum Inhibitory Concentration (MIC) and Minimum
Bactericidal Concentration (MBC) of honey and antibiotics ............................................. 69
2.4.6 Preparation of Methylglyoxal (MGO) ....................................................................... 71
CHAPTER 3: RESULTS AND DISCUSSION ....................................................................... 72
Section 1 ................................................................................................................................ 73
Analysis of Biopsy DNA samples using 16S rRNA and DSR genes sequences ....................... 73
3.1.1 PCR for bacterial 16S rRNA genes ............................................................................ 73
3.1.2 DGGE analysis of microbial communities in the biopsy samples............................. 78
XI
3.1.3 Diversity analysis of PCR products of bacterial 16S rRNA and dsr genes by
sequencing ........................................................................................................................ 83
3.1.4 Phylogenetic analysis .............................................................................................107
Section 2 ..............................................................................................................................120
Design of a Biochip for screening of mucosa associated bacteria in healthy and colitis
patients ...............................................................................................................................120
3.2.1 Oligonucleotide probe design ................................................................................120
3.2.2 Fine-tuning of biochips using cassette approach ...................................................128
3.2.3 Analysis of DNA samples of healthy control and patients suffering from UC using
biochip .............................................................................................................................134
Section 3 ..............................................................................................................................144
Detection and quantification of SRB using microbiological and quantitative PCR techniques
............................................................................................................................................144
3.3.1 The growth of different age of Desulfovibrio species culture in VMR and VMI media
............................................................................................................................................144
3.3.2 Quantification of D. indonesiensis culture using q PCR ............................................154
3.3.3 Quantification of Hydrogen sulfide produced by D. indonesiensis grown with and
without E. coli BP .............................................................................................................159
3.4 Genome Sizes of D. indonesiensis estimated by Pulsed-Field Gel Electrophoresis ..161
Section 4 ..............................................................................................................................165
The effect of antibiotics and Manuka honey on the growth of sulfate reducing bacterial
biofilm .................................................................................................................................165
3.4.1 The MIC and MBC values of different Manuka honey on D. indonesiensis in pure
culture and mixed culture with E. coli BP .......................................................................166
3.4.2 The susceptibility of D. indonesiensis in pure culture and mixed culture with E. coli
BP when treated with Manuka honey and antibiotics ...................................................177
CHAPTER 4: GENERAL DISCUSSION AND CONCLUSION ......................................... 188 FUTURE WORK ................................................................................................................... 196 REFERENCES ....................................................................................................................... 199
References .............................................................................................................................. 200 APPENDICES ........................................................................................................................ 240
Appendix 1: Growth media composition ........................................................................241
Appendix 2: Phylogenetic analysis ..................................................................................244
Appendix 3: Taxonomic report ........................................................................................275
XII
LIST OF FIGURES
Figure 1.1.1 The link between global diet, bacterial composition and the risk of colonic
disease development (Simpson & Campbell, 2015; Vipperla K & O’Keefe,
2016)………………………………………………………………………………………………………………………...7
Figure 1.2.1. Metabolic pathway used by SRBs to reduce sulphate (adapted Carbonero
et al., 2012); APS: adenosine 5’-
phosphosulfate…………………………………………………………………………………………………...……19
Figure1.2.2. Sulphide as a Mucus Barrier-Breaker in IBD (Adapted from Ijssennagger et
al., 2016)…………..…………………………………… …………………………………………………………….…20
Figure 1.3.1. Schematic diagram of molecular techniques used for colonic bacterial
analysis (adapted from Muyzer & Smalla, 1998; Vaughan et al.,
2000)………………………………………………………………………………………………………………………26
Figure 1.3.2. The principles of DGGE………………………………………………………………………..30
Figure 1.3.3. Comparison of and A) SYBR®-Green, B) TaqMan®- Based Detection
Workflows Adapted from (Cao & Shockey, 2012)………………………………………………………32
Figure 1.3.4. Principle of the microarray technique (adapted from Namsolleck et al,
2004)………………………………………………………………………………………………………………………34
Figure 1.4.1 biofilm formation – 1. Cell adhesion 2. Formation of micro colonies, 3.
Accompanied by EPS production. 4. The formation and maturation5. The dispersion
cells from biofilm (Kjelleberg et al., 2007)…………………………………………………………………35
Figure 2.2.1. Photograph for the hybridization chamber………………………………………......56
Figure 3.1.1. A photograph of agarose gel showing the PCR amplified products of
16SrRNA gene (550 bp) with primers: 341F+GC and 907R. Lane 1: PCR products of
Patient 1 suffering from UC; lane 2: PCR products of Patient 2 suffering from UC; lane 3:
PCR products of Patient 3 suffering from UC ; lane 4: PCR products of Patient 4 suffering
from UC; lane 5: PCR products of Healthy volunteer; lane 6: 1 kb DNA Ladder; lane 7:
Negative control; lane 8: DNA D. alaskensis……………………………………………………………….74
Figure 3.1.2. A photograph of agarose gel showing the PCR amplified products of
16SrRNA gene (1500 bp) with primers: 341F+GC and 907R. Lane 1: PCR products of
Patient 1 suffering from UC ; lane 2: PCR products of Patient 2 suffering from UC; lane 3:
PCR products of Patient 3 suffering from UC ; lane 4: PCR products of Patient 4 suffering
from UC; lane 5:PCR products of Healthy volunteer; lane 6: 1 kb DNA Ladder; lane 7:
Negative control; lane 8: DNA D. alaskensis……………………………………………………………….75
Figure 3.1.3. A photograph of agarose gel showing the PCR amplified products of
desulfovibrios 16S rRNA gene (135 bp) with primers: DSV691F and DSV826R. Lane 1:
PCR products of Patient 1 suffering from UC ; lane 2: PCR products of Patient 2 suffering
from UC; lane 3: PCR products of Patient 3 suffering from UC ; lane 4: PCR products of
XIII
Patient 4 suffering from UC; lane 5: 100 bp DNA Ladder; lane 6: Negative control; lane
7: PCR products of DNA D. vulgaris; lane 8: PCR products of Healthy volunteer…………..76
Figure 3.1.4. A photograph of agarose gel showing the PCR amplified products of dsrA
gene (221 bp) with primers: DSR1F+and DSR-R Lane 1: PCR products of Patient 1
suffering from UC ; lane 2: PCR products of Patient 2 suffering from UC; lane 3: PCR
products of Patient 3 suffering from UC ; lane 4: PCR products of Patient 4 suffering from
UC; lane 5: 100 bp DNA Ladder; lane 6: Negative control; lane 7: PCR products of DNA D.
vulgaris; lane 8: PCR products of Healthy volunteer…………………………………………………..77
Figure 3.1.5. DGGE profile demonstrating diversity of bacterial populations in biopsy
samples obtained from healthy volunteer and patients suffering from UC based on PCR
products of 550 bp of bacterial 16S rRNA. Lane 1: UC Patient 1, Lane 2: UC Patient 2,
Lane 3: UC Patient 3, Lane 4: UC Patient 4, Lane 5: Negative control, Lane 6: healthy
volunteer…………………………………………………………………………………………………………………80
Figure 3.1.6. UPGMA dendogram showing similarity of DGGE profiles of microbial
communities in all colonic biopsy samples of UC patients and healthy control used in
this study. Bands from each sample were coded and a UMPGA tree was produced using
the distance algorithm described by Nei and Li (1979) and executed in PAUP* 4.0. P1:
UC patient number 1, P2: UC patient number 2 , P3: UC patient number 3 , P4: UC patient
number 4 , H: healthy individual……………………………………………………………………………….81
Figure 3.1.7. A histogram demonstrating the Family level of bacterial composition
present in healthy patient and patients suffering from UC (16S rRNA gene)………………84
Figure 3.1.8. Proportion of bacterial genera present in the colon of UC patients and
healthy control (16S rRNA)………………………………………………………………………………………93
Figure 3.1.9. A histogram demonstrating the Family level of SRB composition present in
healthy patient and patients suffering from UC (dsrA gene)……………………………………….96
Figure 3.1.10. Distribution of genera from heathy and UC patients. The numbers above
each histogram represents the number of species identified as Desulfovibrio from each
patient…………………………………………………………………………………………………………………….98
Figure 3.1.11. Species of Desulfovibrio present in a healthy individual and four UC
suffers……………………………………………………………………………………………………………………105
Figure 3.1.12. A maximum likelihood phylogenetic tree (model TN93) showing the
relationships between derived 16S rRNA gene sequences of known taxa belonging to
Firmicutes and those of random clones from healthy control; the support value
estimated with 100 bootstrap replicates using PhyML. Bootstrap values which above 50
only shown…………………………………………………………………………………………………………….109
Figure 3.1.13. A maximum likelihood phylogenetic tree (model TN93) showing the
relationships between derived 16S rRNA gene sequences of known taxa belonging to
Bacteroidetes and those of random clones from healthy control; the support value
estimated with 100 bootstrap replicates using PhyML. Bootstrap values which above 50
only shown…………………………………………………………………………………………………………….112
XIV
Figure 3.1.14. A maximum likelihood phylogenetic tree (model TN93) showing the
relationships between derived 16S rRNA gene sequences of known taxa belonging to
Proteobacteria and those of random clones from healthy control; the support value
estimated with 100 bootstrap replicates using PhyML. Bootstrap values which above 50
only shown…………………………………………………………………………………………………………….113
Figure 3.1.15. A maximum likelihood phylogenetic tree (model TN93) showing the
relationships between derived 16S rRNA gene sequences of known taxa belonging to
Actinobacteria and those of random clones from healthy control; the support value
estimated with 100 bootstrap replicates using PhyML. Bootstrap values which above 50
only shown……………………………………………………………………………………………………………114
Figure 3.1.16. A maximum likelihood phylogenetic tree (model TN93) showing the
relationships between derived 16S rRNA gene sequences of known taxa belonging to
Proteobacteria and those of random clones from patients suffering from UC the support
value estimated with 100 bootstrap replicates using PhyML. Bootstrap values which
above 50 only
shown……………………………………………………………………………………………………………………116
Figure 3.1.17. Deltaproteobacteria maximum-likelihood (model TN93) dsr gene based
phylogenetic tree. The tree reflected the relative placement of the dsr gene clones from
healthy control and patients suffering from UC among sulfate-reducing
Deltaproteobacteria. Numbers placed above the branches were the support values
estimated with 100 bootstrap replicates using PhyML. Bootstrap values which above 50
only shown…………………………………………………………………………………………………………….118
Figure 3.2.1. The composition of the ss
cassettes………………………………………………………………………………………………………..………127
Figure 3.2.2. Schematic diagram and the results of the designed cassette N1
hybridization as signal to noise (S/N) ration. The purple circles are the probes position
markers the gray circles are the probes. The arrangement of the probes was as follows:
A1, A2, A3, A4,B1,C1,D1, E1 – Probes position markers; B2,C2 – Dslpig for Desulfovibrio
piger ;D2,E2 – Dslgig for Desulfovibrio gigas ;B3,C3 – Dslsp for Desulfovibrio sp; D3,E3 –
S1 for SRB; B4,C4 -16S Negative control; D4,E4 – were left empty to give background
signal.…………………………………………………………………………………………………………………….129
Figure 3.2.3. Schematic diagram and the results of the designed cassette N2
hybridization as signal to noise (S/N) ration. The purple circles are the probes position
markers the gray circles are the probes. The arrangement of the probes was as follows:
A1, A2, A3, A4, B1, C1, D1, E1 – Probes position markers; B2, C2 – Dsbub for
Desulfobulbus; D2, E2 – Dsbac for Desulfobacter; B3, C3 – Dsldes for Desulfovibrio
desulfuricans; D3, E3 – Desfot for Desulfotomaculum; B4, C4 -16S Negative control; D4,
E4 – were left empty to give background signals.…………………………………………………....130
Figure 3.2.4. Schematic diagram and the results of the designed cassette N3
hybridization as signal to noise (S/N) ration. The purple circles are the probes position
markers the gray circles are the probes. The arrangement of the probes was as follows:
A1, A2, A3, A4, B1, C1, D1, E1 – Probes position markers; B2, C2 –Ent1 for
XV
Enterobacteriaceae; D2, E2 –EC for E.coli; B3, C3 – Bacfr for Bacteriodes fragilis; D3, E3
–Bacsp for Bacteriodes sp; B4, C4 -16S Negative control; D4, E4 – were left empty to
give background signals.…………………………………………………………………………………………130
Figure 3.2.5. Schematic diagram and the results of the designed cassette N4
hybridization as signal to noise (S/N) ration. The purple circles are the probes position
markers the gray circles are the probes.The arrangement of the probes was as follows:
A1, A2, A3, A4, B1, C1, D1, E1 – Probes position markers; B2, C2 – Cldif for Clostridium
difficile; D2, E2 –Clsp1 for Clostridium sp; B3,C3 –Fusv for Fusobacterium varium; D3,
E3 –Bif for Bifidobacterium; B4, C4 -16S Negative control; D4, E4 – were left empty to
give background signals.…………………………………………………………………………………………131
Figure 3.2.6. Schematic diagram and the results of the designed cassette N5
hybridization as signal to noise (S/N) ration. The purple circles are the probes position
markers the gray circles are the probes. The arrangement of the probes was as follows:
A1, A2, A3, A4, B1, C1, D1, E1 – Probes position markers; B2, C2 –Fusnu for
Fusobacterium nucleatum; D2, E2 –Rum for Ruminococcus; B3, C3 –Fusn for
Fusobacterium necrophorum; D3, E3 –Lac for Lactobacillus; B4, C4 -16S Negative
control; D4, E4 – were left empty to give background signals.…………………………………..131
Figure 3.2.7. Schematic diagram and the analysis of the designed cassettes
hybridization with biochip. The results of the hybridization as signal to noise (S/N)
ration. The data presented are mean
values±SD.……………………………………………………………………………………………………………..133
Figure 3.2.8. Schematic diagram and the hybridization analysis of the DNA obtained
from healthy individual. The hybridization results represented as signal to noise (S/N)
ratio.……………………………………………………………………………………………………………………...135
Figure 3.2.9. Schematic diagram and the hybridization analysis of the DNA obtained
from UC patient 1. The hybridization results represented as signal to noise (S/N)
ratio…...…………………………………………………………………………………………………………………136
Figure 3.2.10. Schematic diagram and the hybridization analysis of the DNA obtained
from UC patient 2. The hybridization results represented as signal to noise (S/N)
ratio…...……………………………………………………………………………………………………...…………..137
Figure 3.2.11. Schematic diagram and the hybridization analysis of the DNA obtained
from UC patient 3. The hybridization results represented as signal to noise (S/N) ratio.
…...…………………………………………………………………………………………………….………………….138
Figure 3.2.12. Schematic diagram and the hybridization analysis of the DNA obtained
from UC patient 4. The hybridization results represented as signal to noise (S/N) ratio.
…...…………………………………………………………………………………………………….…………………...139
Figure 3.2.13. Heat map of bacteria detected in the samples of healthy individual and
UC patients. Cells are coloured according to the S/N ratio calculated for each sample and
probe as per the key provided.
………………………………………………………………………………………………….…………………………..141
XVI
Figure 3.3.1. Growth of 7days old D. indonesiensis culture in VMR medium in the
presence and absence of E.coli BP. (A) Growth of pure D. indonesiensis, (B) Growth of D.
indonesiensis with E.coli BP. The black precipitate at the bottom of the vials indicates
SRB growth.………………………………………………………………………….……………………………….146
Figure 3.3.2. Growth of 3 months old D. indonesiensis culture in VMR medium in the
presence and absence of E.coli BP. (A) Growth of pure D. indonesiensis, (B) Growth of D.
indonesiensis with E.coli BP. The black precipitate at the bottom of the vials indicates
SRB growth.……………………………………………………………………….………………………………….147
Figure 3.3.3. Growth of 1 year old D. indonesiensis culture in VMR medium in the
presence and absence of E.coli BP. (A) Growth of pure D. indonesiensis, (B) Growth of D.
indonesiensis with E.coli BP. The black precipitate at the bottom of the vials indicates
SRB growth.……………………………………………………………………….…………………………………..149
Figure 3.3.4. Growth of 7 days old D. indonesiensis culture in VMI medium in the
presence and absence of E.coli BP. (A) Growth of pure D. indonesiensis, (B) Growth of D.
indonesiensis with E.coli BP. The black precipitate at the bottom of the vials indicates
SRB growth.…………………………………………………………………….……………………………………150
Figure 3.3.5. Growth of 3 months old D. indonensiensis culture in VMI medium in the
presence and absence of E.coli BP. (A) Growth of pure D. indonensiensis, (B) Growth of
D. indonensiensis with E.coli BP. The black precipitate at the bottom of the vials
indicates SRB growth.…………………………………………………………………….……………………...152
Figure 3.3.6. Growth of 1 year old D. indonesiensis culture in VMI medium in the
presence and absence of E.coli BP. (A) Growth of pure D. indonesiensis, (B) Growth of D.
indonesiensis with E.coli BP. The black precipitate at the bottom of the vials indicates
SRB growth.…………………………………………………………………….……………………........................153
Figure 3.3.7. Standard curve for apsA
gene……………………………………………………………………………………………………..……….……….155
Figure 3.3.8. Standard curve for dsrA
gene…………………………………………………………………………………………………………….………...156
Figure 3.3.9. The concentration of H2S of 7 days D. indonesiensis culture with and
without E.coli BP. three independent experiments were done in triplicate. The data
presented are mean values±SD.…………………………………………………….……….........................159
Figure 3.3.10. Pulsed-field gel sizing of SRB genomes from (a) D. indonensiensis and (b)
E.coli: Lanes (1) Schizosaccharomyces pombe size range 3.5-5.7 Mb; (2) lambda ladder
size range 0.05–1 Mb ;(3) D. indonensiensis digested – PmeI; (4) D. indonensiensis; (5)
D. indonensiensis digested-NotI; (6) Saccharomyces cerevisiae ladder size range 240-
2200 Kb; (7) E.coli digested – PmeI; (8) E.coli; (9) E.coli digested – NotI;(10) D.
indonensiensis. Sizes of selected DNA standards (BIO-RAD DNA Size Markers–Yeast
Chromosomal; BIO-RAD DNA Size Markers lambda and BIO-RAD DNA Size Markers–S.
pombe Chromosomal DNA) are indicated…………………………………………………….………….163
XVII
Figure 3.4.1. The MIC values for E. coli and D. indonesiensis grown as pure and mixed
population (the honey was added simultaneously with culture and then incubated for 7
days) when treated with Manuka honey UMF® 5+, UMF® 15+, UMF® 25+ and
commercial honey. The MIC values are the average from 5 independent experiments and
are displayed with standard deviation.………………………………………………….………………...167
Figure 3.4.2. The MBC values for E. coli and D. indonesiensis grown as pure and mixed
population (the honey was added simultaneously with culture and then incubated for 7
days) when treated with Manuka honey UMF®5+, UMF®15+, UMF®25+ and
commercial honey. The mean MBC values were calculated from 5 independent repeat
experiments and shown with standard deviation……………………………………………….……170
Figure 3.4.3: The MIC value of Manuka UMF® 5+, UMF® 15+, UMF® 25+ and
commercial honey for E. coli and D. indonesiensis grown as pure and mixed population
(the honey was added after incubating the culture for 6 days). The mean values were
calculated form 5 independent experiments and shown with standard deviation……..173
Figure 3.4.4. The MBC value of Manuka UMF® 5+, UMF® 15+, UMF® 25+ Manuka
honey and commercial honey for E. coli BP and D. indonesiensis grown as pure and
mixed population (the honey was added after incubated the culture for 6 days). The
mean values were calculated from 5 independent experiments and shown with standard
deviation.…………………………………………….………………………………………………….……………175
Figure 3.4.5. The MIC value of rifaximin, metronidozole alone and in combination,
rifaximin+ metronidozole in the presence of Manuka UMF® 15+ (the antibiotics and
honey were added simultaneously to the cultures), rifaximin+ metronidozole in the
presence of Manuka UMF® 15+ (the antibiotics were added after 6 days incubation with
Manuka UMF® 15+). Mean values were estimated from 5 independent experiments and
are shown with standard deviation.………………………………………….……………………………..179
Figure 3.4.6. The MBC value of rifaximin, metronidozole alone and in combination,
rifaximin+ metronidozole in the presence of Manuka UMF® 15+ (adding the antibiotics
and honey simultaneously), rifaximin+ metronidozole in the presence of Manuka UMF®
15+ (adding antibiotics after 6 days of incubation with Manuka UMF® 15+). The mean
values were estimated from 5 independent experiments and are shown with standard
deviation………………………………………….…………………………………………………………………….182
Figure 3.4.7: The MIC value of Methylglyoxal (MGO) of D. indonensiensis as pure and
mixed culture with E.coli BP. The mean values were estimated from 5 independent
experiments and are shown with standard deviation.….…………………………………….…….185
Figure 3.4.8. The MBC value of Methylglyoxal (MGO) of D. indonensiensis as pure and
mixed culture with E.coli BP. Mean MBC values were estimated from 5 independent
experiments and are shown with standard deviation.….…………………………………..………186
XVIII
LIST OF TABLES
Table 1.2.1 The Characteristics and metabolic activities of some key genera of SRB in human colon………………………………………………………………………………………….…………………15 Table 1.3.1. The role of intestinal bacteria in human health and disease……………….......23 Table 2.1.1. Sequence of primers used in PCR……………………………………………….………….47 Table 3.1.1. Sequencing results of bacterial 16S rRNA from the bands cut from DGGE gel……………………………………………………………………………………………………………………………83 Table 3.1.2. The species level of bacterial composition present in the colon of UC patients and healthy control (16S rRNA)…………………………………………………………………...88 Table 3.1.2 contd.……………………………………………………………………………………………………89 Table 3.1.2 contd.……………………………………………………………………………………………………90 Table 3.1.2 contd..………………………………………………………………………………………………..….91 Table 3.1.3 The level of SRB species present in the colon of UC patients and healthy individual (dsr gene) ……………………………………………………………………………………………..100 Table 3.1.3 contd.………………………………………………………………………………………………….101 Table 3.1.3 contd..…………………………………………………………………………………………………102 Table 3.1.3 contd..…………………………………………………………………………………………………103 Table 3.2.1. List of design probes……………………………………………………………………..…….122 Table 3.2.1. Cont.…………………………………………………………………………………….……………123 Table 3.2.2. List of published DNA probes.……………………………………………………………...124 Table 3.3.1. Comparison of 7 days old D. indonesiensis culture growth efficiency in VMR medium with and without of E. coli BP. The symbol (+) means growth of D. indonesiensis represented by blackening of the medium, and (-) means no growth. Several independent experiments were done in triplicate.………………………………………145 Table 3.3.2. Comparison of 3 months old D. indonesiensis culture growth efficiency in VMR medium with and without of E. coli BP. The symbol (+) means growth of D. indonesiensis represented by blackening of the medium, and (-) means no growth….147 Table 3.3.3. Comparison of 1 year old D. indonesiensis culture growth efficiency in VMR medium with and without of E. coli BP. The symbol (+) means growth of D. indonesiensis represented by blackening of the medium, and (-) means no growth. Several independent experiments were done in triplicate.……………………………………….148 Table 3.3.4. Comparison of 7 days old D. indonesiensis culture growth efficiency in VMI medium with and without of E. coli BP. The symbol (+) means growth of D. indonesiensis represented by blackening of the medium, and (-) means no growth. Several independent experiments were done in triplicate.………………………………………150
XIX
Table 3.3.5. Comparison of 3 months old D. indonesiensis culture growth efficiency in VMI medium with and without of E. coli BP. The symbol (+) means growth of D. indonesiensis represented by blackening of the medium, and (-) means no growth…..151 Table 3.3.6. Comparison of 1 year old D. indonesiensis culture growth efficiency in VMI medium with and without of E. coli BP. The symbol (+) means growth of D. indonesiensis represented by blackening of the medium, and (-) means no growth. Several independent experiments were done in triplicate……………………………….……….153 Table 3.3.7. Quantification of the dsrA and ApsA genes in genomic DNA purified from 7 days old D. indonesiensis cultures using TaqMan qPCR. Experiment and statistical analysis was based on several independent experiments.…………………...…………………...157 Table 3.3.8. A comparison of the copy number of dsrA and apsA genes detected in genomic DNA isolated from 7 days, 3months and 1 year old D. indonesiensis cultures using Kruskal-Wallis statistic test based on ten replicates……………….……………………….157 Table 3.3.9. Quantification of the dsrA and ApsA genes in RNA purified from 7 days, 3 months old D. indonesiensis cultures using TaqMan qPCR. Experiment and statistical analysis was based on several independent experiments.………………………………………...158 Table 3.3.10. A comparison of the copy number of dsrA and apsA genes detected in transcripts in total RNA isolated from 7 days and 3 months old D. indonesiensis cultures using Mann-Whitney test statistic based on ten replicates of dsr and aps genes………..158 Table 3.3.11. Estimated gel size Measurement of the genomes from (a) D. indonensiensis and (b) E.coli: (3) D. indonensiensis digested – PmeI; (5) D. indonensiensis digested-NotI; (7) E.coli digested – PmeI; (8) E.coli; (9) E.coli digested – NotI.……………………………………………………………………………………………………………..………..163 Table 3.4.1: A comparison of the MIC values obtained when different concentrations of Manuka and commercial honey were applied using Kruskal-Wallis test statistics. Only comparisons which reached significance are shown………………………………………………..169 Table 3.4.2: A comparison of the MBC values obtained when different concentrations of Manuka and commercial honey were applied using Kruskal-Wallis test statistics…….171 Table 3.4.3: A comparison of the MIC values obtained when different concentrations of Manuka and commercial honey were applied using Kruskal-Wallis test statistics……..174 Table 3.4.4. A comparison of the MBC values obtained when different concentrations of Manuka and commercial honey were applied using Kruskal-Wallis test statistics……..176 Table 3.4.5. A comparison of the MIC values obtained when different concentrations antibiotics and UMF®15+ were applied Kruskal-Wallis test statistics………………………180 Table 3.4.6. A comparison of the MBC values obtained when different concentrations antibiotics and UMF®15+ were applied using Kruskal-Wallis test statistics……………..183
XX
Table 3.4.7. A comparison of the MIC values obtained when different concentrations of Methylglyoxal (MGO) were applied using Mann-Whitney test statistics.……………………186 Table 3.4.8. A comparison of the MBC values obtained when different concentrations of Methylglyoxal (MGO) were applied using Mann-Whitney test statistics……………………187
XXI
DISSEMINATION
1. Faculty of Science Research Degree Students, Poster, June 2012, University of
Portsmouth.
2. Faculty of Science Research Degree Students Poster, Jun 2013, University of
Portsmouth.
3. Nutrition Society conference, Diet, gut microbiology and human health, Oral
Presentation, December 2013, Royal College of Surgeons, London, UK.
4. Seventh Saudi Students Conference (SSC2014), Edinburgh international Conference
Centre (EICC), Poster, Feb 2014, Edinburgh, UK.
5. Alkhalil S, Chopra M and Zinkevich V. Antibacterial activity of Manuka honey on the
growth of pure and mixed bacterial biofilms formed by sulphate reducing bacteria and
Escherichia coli. Proceedings of the Nutrition Society. (2014), 73 (OCE1), E36, Abstract.
6. Zinkevich, V., Sapojnikova, N., Mitchell, J., Kartvelishvili, T., Asatiani, N., Alkhalil, S., Al-
Humam, A. A. (2014). A novel cassette method for probe evaluation in the designed
biochips. PLoS One, 9(6), e98596. doi:10.1371/journal.pone.0098596
7. Training on biochip technology, Tbilisi University, Georgia.
8. The 8th Saudi Students Conference (SSC2015), Imperial College London, Poster, Feb
2015, London, UK.
9. The 9th Saudi Students Conference (SSC2016). The International Convention Centre,
University of Birmingham, Poster, Feb 2016, Birmingham, UK.
CHAPTER 1: INTRODUCTION
1
CHAPTER 1: INTRODUCTION
CHAPTER 1: INTRODUCTION
2
Section 1
The Microbiota of the Human Large Intestine
1.1 .1 General Overview
The human body plays host to a complex community consisting of taxa from across the
tree of life. Bacteria are the major microbial organism present in the gut, however, many
other organisms can be found such as protozoa, fungi and viruses. The interactions of
these microbes with each other and the host have a profound influence on physiology in
both health and disease (Macpherson & Harris, 2004; O’Hara & Shanahan, 2006;
Clemente et al., 2012).
The microorganisms that coexist peacefully with their hosts are referred to as normal
microflora. It is estimated that in humans this microflora community contains between
1013 to 1014 bacteria, 10 times greater than the number of human cells present in the
body (Hooper & Gordon, 2001).
Almost the entire human body, including the respiratory and urogenital tracts and the
skin, are colonized by microbiota, however, it is the gastrointestinal tract (GIT) which is
most heavily colonized due to the abundance of nutrients as a food source. Within the
GIT the colon has been found to contain more than 70% of the total number of microbes
in human body (Whitman et al., 1998; Ley et al., 2006). This complex microbial
community within the GIT plays a crucial role in human health with an influence on host
metabolism, physiology, protection, nutrition and immune function (Ley et al., 2008).
Therefore any alteration, imbalance (dysbiosis) or change in bacterial function within
the GIT ecosystem, or changes in bacterial interactions with each other can impact on
human health and is thought to contribute to the development of key intestinal diseases
such as inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn’s
disease (Underwood, 2014; Carding et al., 2015).
Bacteria are usually found in a dynamic environment containing numerous different
microbial species populations in which frequent interactions occur (Shoaie et al., 2013).
Therefore, a better understanding of the interactions between bacteria will help to
improve our understanding of how bacterial genetic information can impact on human
health and disease. Several studies have highlighted a link between intestinal bacteria
CHAPTER 1: INTRODUCTION
3
and human disease (Frank et al., 2007; Guinane & Cotter, 2013; Carding et al., 2015).
Additionally, determining the growth conditions required for culturing single isolates
remains challenging. Although much progress has been made using culture-based
methods to characterize bacterial diversity within the GIT, with over 400-500 different
microbial species fully identified and assessed (Hiergeist et al., 2015), it is thought that
this method has only identified a small fraction of the full diversity existing within the
GIT.
Molecular ecological studies allow for more accurate analysis of the microbiota within
the GIT, revealing that the intestinal bacterial ecosystem is more complex and diverse
than previously expected with the vast majority of microbial species remaining
uncultivated (Zoetendal et al., 2004). Most of these studies rely on analysis of the highly
conserved ribosomal RNA (rRNA) sequences of bacteria. Molecular approaches used to
study the microbial population colonizing the human colon can facilitate both high-
detail and high-throughput analysis of phylogenetic diversity (Rajendhran &
Gunasekaran, 2011). Molecular techniques previously used include temperature or
denaturing gradient gel electrophoresis (TGGE or DGGE), quantitative polymerase chain
reaction (qPCR), and Microarray technology (Houpikian & Raoult, 2002).
The estimation of the structure of the bacterial population in the large intestine is
moving closer to absolute quantification (Swidsinski et al., 2005; Casen et al., 2015).
However, whilst absolute quantification of bacterial populations may soon be possible,
the major future goal of colonic bacterial studies will be to investigate the interactions of
these bacteria with each other and the host, and how these interactions can impact on
human health and disease. To this end recent major advances have been made in colonic
microbiota studies aiming to determine the identity of specific bacterial groups that may
contribute to host colonic diseases (Guinane & Cotter, 2013; Ohland & Jobin, 2015).
1.1.2 Development of the intestinal Microbiota
As well as bacteria the gut microflora also contains archaea, fungi and viruses,
advancement in sequencing technologies and the use of metagenomics approaches are
helping to characterise these total populations; including identifying various factors
involved in their sustenance (Hoffmann et al., 2013). All humans are born with an
almost sterile colon, however both bottle and breastfed infants rapidly start
CHAPTER 1: INTRODUCTION
4
accumulating Gram-positive and Gram-negative intestinal bacteria (Koenig et al., 2011).
The early colonizers are typically Enterococci and Enterobacteria, which are mainly
facultative anaerobes (Zetterstrom et al., 1994; Dogra et al., 2015; Avershina et al.,
2016). Both these bacterial species significantly reduce oxygen pressure (pO2) within
the GIT creating an environment suitable for the growth of other anaerobes mainly
Bacteroides and Bifidobacteria (Roberts et al., 1992; Knol et al., 2005). It has been
observed that bacterial populations in the colon varies among infants depending on
region of birth and type of feeding, i.e. breastfeeding or formula milk (Gomez-Llorente et
al., 2013; Patel et al., 2015). In adults, colonic microorganisms are mainly anaerobic in
nature and the population is typically stable (Hoffmann et al., 2013). Differences do exist
in the diversity of bacterial population among individuals from different geographical
regions of the world. An evaluation of 13,355 prokaryotic ribosomal RNA genes from
multiple colonic mucosal sites and faeces from healthy subjects demonstrated
significant intersubjective variability as well as the differences between the stool and
mucosal bacterial population (Eckburg et al., 2005).
The composition and functionality of the human intestinal microbiota has long been of
great interest. More than 1000 different species have been reported to colonize the
human intestine, with the majority being of unknown species belonging to anaerobic
strains (Bäckhed et al., 2005; Zoetendal et al., 2006; Rajilić-Stojanović et al., 2007;
Jandhyala et al., 2015). Microbial colonization occurs throughout the length of the large
intestinal gut with marked differences in the density and composition of colonization.
The colonization of bacteria in the large intestine is influenced by many factors such as
host secretions, environmental conditions, substrate availability, transit rates, and the
organization of the gut wall. Thus, the large intestine mostly supports the establishment
of a dense population dominated by anaerobic bacteria (Graf et al., 2015). Information
on the composition of colonic microbiota is derived mainly from molecular
characterisation of faecal samples, which do not usually reflect mucosa-associated
bacteria. Consequently, relatively little is known about the composition of the microflora
of the large intestine.
CHAPTER 1: INTRODUCTION
5
1.1.3 Normal intestinal microbiota a substantial factor in health
Bacterial colonization of the human colon plays a crucial role in modulating the
immunological, physiological and metabolic activities in the human body which can
have a large impact on both health and disease. Bacteria can play a beneficial role by
contributing to the host gut defence system and by supporting and maintaining normal
gut function (Jandhyala et al., 2015). The metabolic processes of primary fermenters
lead to the production of key nutrients that the human body is unable to manufacture or
derive itself such as vitamins (vitamin K, vitamin B12 and folic acid), amino acids, short-
chain fatty acids (SCFA), and yielding some gases such as hydrogen (H2), Methane (CH4)
and carbon dioxide (CO2) and hydrogen sulphide (H2S) (Wong et al., 2006; Fischbach &
Sonnenburg, 2011). These products are important to the host but are also used as a
carbon and energy source for other bacteria within in the human colon. Therefore, these
fermenting bacteria must maintain the balance between oxidation and reduction
process whilst at the same time producing the required energy (Macfarlane &
Macfarlane, 2011). In addition to this, bacteria can breakdown non-digestible foods,
such as resistant starches and dietary fibres generating energy (Parker, 1976; Bergman,
1990; Maier et al., 2015).
The disposal of the hydrogen produced by bacteria in the human colon during anaerobic
fermentation along with the final products produced from their metabolism plays a key
role in optimal large bowel function and has an important impact on host health (Conlon
& Bird, 2015). The major protective role of resident bacteria is through barrier effect,
colonizing the intestinal mucosa thus leaving no space for the pathogenic bacteria
attachment and subsequent proliferation (Kamada et al., 2013).
1.1.4 Intestinal Microbiota in Disease
1.1.4.1 Ulcerative colitis (UC)
UC is both an acute and chronic disorder of the large intestine, and one of the main
forms of IBD. UC is marked by an inflammation affecting the mucosal layer of the colon
and rectum (Di Sabatino et al., 2012). Patients with UC present symptoms such as
idiopathic diarrhoea, abdominal pain, fever, rectal bleeding and weight loss; these
CHAPTER 1: INTRODUCTION
6
usually range from mild to severe depending on the extent and severity of the
inflammation (Zhang et al., 2015).
Epidemiological studies have found that the occurrence of UC is between 1.5 to 24.5 per
100,000 cases, with wide variation depending upon geographical location. A significant
increase in the occurrence of UC during the last 20 years has also been described
(Cosnes et al., 2011; Burisch & Munkholm, 2013; da Silva et al., 2014), with a higher
prevalence amongst people from the West, potentially indicating a strong influence of
the western lifestyle on UC prevalence (Pajares & Gisbert, 2001; Suk-Kyun et al., 2001)
(Figure 1.1.1).
CHAPTER 1: INTRODUCTION
7
Figure1.1. 1 The link between global diet, bacterial composition and the risk of colonic disease development (Simpson & Campbell, 2015; Vipperla K & O’Keefe, 2016)
CHAPTER 1: INTRODUCTION
8
Although the cause of UC remains uncertain, several factors are recognized to have a
role in the development of the disease. Among these factors, the involvement of bacteria
in the pathogenesis of UC has been strongly implicated. However, because there appears
to be no single established cause of UC, this condition seems to have multi-factorial
disease susceptibility (Fries & Comunale, 2011). The pathogenesis of UC and underlying
mechanisms of the disease are an active area of investigation, with several longitudinal
studies describing the mechanisms underlying the pathogenesis of this disease.
However, the exact aetiological factors associated with UC are still unknown.
1.1.4.2 Role of bacteria in UC
The involvement of intestinal bacteria in the aetiology of UC is supported by studies that
have shown that the inflammation occurs in regions of the intestine with the greatest
concentration of bacteria, and the observation that germ-free animals do not develop
the disease (Thompson-Chagoyan et al., 2005; Nell et al., 2010; Nagalingam et al., 2011;
Jiminez et al., 2015). In humans, altered bacterial combinations combined with high
numbers of bacteria in UC patients compared with healthy subjects have been reported
by several studies (Cummings et al., 2003; Round & Mazmanian, 2009; Matsuoka &
Kanai, 2015).
Further evidence supporting the role of bacteria in UC comes from studies where the
efficacy of using antibacterial agents as a therapy was assessed (Azad Khan et al., 1977;
Sutherland et al., 1993; Moshkovska & Mayberry, 2007; Freeman, 2012). A meta-
analysis evaluating 40 years of available scientific literature (1966 -2006) relevant to
the efficacy of antibacterial therapies in UC suggested significant clinical remission
among UC patients taking antibacterial therapy (Rahimi et al., 2007). Another later
meta-analysis described a similar benefit for antibacterial treatment in the clinical trials
for UC (Wang et al., 2012).
The key role of bacteria in the aetiology of UC was first proposed in the 1950’s (Marshall
et al., 1950, Seneca & Henderson, 1950). In the following decades, some studies
demonstrated that it was not just overall bacterial load, but also the types of bacteria
associated with UC. Changes in the anaerobic environment of the colon, associated
bacterial population and the generation of short chain fatty acids as fermentation
CHAPTER 1: INTRODUCTION
9
products which used as energy source for colonic epithelial cells brought the concept of
colitis as an energy deficiency diseases to the fore (Roediger, 1980). This was confirmed
by several studies suggesting that bacteria disturb the synthesis of short chain fatty
acids generation from carbohydrates during the pathogenesis of UC (Kim, 1998). The
question of whether a specific pathogen could cause UC has been increasingly
investigated. Although several bacteria have been reported to contribute to UC
susceptibility, to date no specific bacteria have been confirmed to be solely responsible
for the disease.
1.1.4.3 Bacterial composition and UC
The human colon contains a rich microbial population, predominated by 4 divisions
including Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria. Most bacteria
within the colon have been identified as belonging to the Firmicutes and Bacteroides.
Firmicutes Represent ~64% of the microbiota community, whereas Bacteroidetes ~23%
of the normal microbiota (Eckburg et al., 2005; Mao et al., 2015).
There have been numerous attempts to discern the bacteria involved in UC. However,
due to the invasive nature of the procedures, initial efforts were limited to evaluating
the microbial content of faecal material. With the availability of colonoscopy, it has been
easier to collect samples from the colon for further evaluation. A study focusing on the
colon in UC reported that the bacterial population were mainly Bacteroides (Poxton et
al., 1997). In this study the bacterial population present in biopsy samples of colon
mucosa, collected during colonoscopy from the proximal colon and rectum of 12
patients, (six with UC and six controls without inflammation) was characterized. The
study revealed no difference in the bacterial counts from proximal colonic and rectal
biopsy samples. Overall, the study reported 235 isolates representing 11 species of
Bacteroides. These species were uniformly distributed along the colon and differed from
the communities recovered from faeces (Zoetendal et al., 2002).
Technological limitations also hampered the identification of bacterial species. Whilst
initially cell culture techniques were used; the emergence of genomic techniques
broadened the evaluation process. Subsequent to the completion of human genome
CHAPTER 1: INTRODUCTION
10
project several techniques like fluorescent in-situ hybridization (FISH), restriction
fragment length polymorphism (RFLP), denaturing gradient gel electrophoresis (DGGE),
quantitative dot blot hybridization, and large-scale 16S rDNA sequencing emerged as
powerful techniques able to overcome issues with conventional microbiological plating
methods (Mai & Morris, 2004).
A GI Health Foundation sponsored supplement published in the American Journal of
Gastroenterology in 2012 stated that there is a need for “additional therapeutic
strategies as the overall potential of emerging techniques have not yet been validated by
controlled clinical trials. These include blocking attachment of adherent bacteria, such
as adherent/invasive E. coli; enhancing defective bacterial killing in genetically
susceptible hosts; and faecal transplant to correct dysbiosis” further highlighting the
role of bacteria in the pathogenesis of UC (Sartor & Mazmanian, 2012).
1.1.5 Dysbiosis of the intestinal microbiota in UC
Microbial diversity within the bacterial communities residing in the colon plays a
significant role in modulating human health. A recent review concludes that “A healthy
gut environment is regulated by the balance of its intestinal microbiota, metabolites, and
the host's immune system. Imbalance of these factors in genetically susceptible persons
may promote a disease state. Manipulation of the intestinal microbiota with probiotics,
which can selectively stimulate the growth of beneficial bacteria, might help to maintain
a healthy intestinal environment or improve diseased one” (Kataoka, 2016).
It is important to note that culture-independent techniques, such as the molecular
methodologies previously described have significantly aided our understanding of the
composition and diversity of colonic microbial populations. The compositional diversity
of gut can be evaluated at two levels. Firstly, diversity amongst resident bacteria of the
gut; and secondly, the transient bacteria introduced from external environments
through ingestion or other sources such as the regulary influx of bacteria by intake of
food. Therefore, understanding the factors influencing the diversity of the mucosa
associated bacteria is essiential in order to underlying the pathology and a critical next
CHAPTER 1: INTRODUCTION
11
step in further evaluating the contribution of bacteria to establishing and promoting the
chronicity of colonic disease.
In 2007 the number of bacterial species described to be present in the large intestine
was approximately 400, although it was reconginsed that this did not describe the full
extent of bacteiral species present in the gut (Rajilić-Stojanović et al., 2007). Three years
later, a catalogue of microbial genes present in the human gut, established by
metagenomics sequencing, suggested that more 1000 species-level phylotypes were
present in the GI tract of humans (Qin et al., 2010). It is important to mention here that
the metagenomics analysis used for predicting phylotypes is based on non-redundant
genes contained by an average-sized genome, so it is possible that this number will be
revised as more information becomes available.
The linkage between bacteria and UC is well established, however, the exact species or
strains involved in the pathogenesis of UC remain unknown limiting the potential for
targeted therapies. An increase in overall bacterial population and reduction in
protective bacteria like lactobacilli and bifidobacteria has been reported (Cummings et
al., 2003). Additionally, probiotic modulation of bacterial populations identified key
changes in the microbial ecosystem and certain bacterial species that may be involved in
UC (Gerritsen et al., 2011).
Interestingly Sasaki and Klapproth (2012) performed an elaborate study to evaluate
imbalances in bacterial population during the pathogenesis of colitis. Based on their
evaluation of existing literature they conclude that “quantitative and qualitative
microbial imbalance in UC, defined as dysbiosis, has been characterized by an increase
in Rhodococcus spp., Shigella spp., and Escherichia spp., but a decrease in
certain Bacteroides spp. More specifically, Campylobacter spp., Enterobacteriae, and
enterohepatic Helicobacter were more prevalent in a tissue sample from UC patients
subjected to molecular detection methods, but not controls. Also, serologic testing
identified Fusobacterium varium as a potential contributor to the intestinal
inflammation in UC. Interestingly, in-situ hybridization studies have shown anti-
inflammatory Lactobacillus spp. and Pediococcus spp. were absent in the patients’
samples suffreing from UC. Therefore, dysbiosis is a factor in the pathogenesis of UC”.
CHAPTER 1: INTRODUCTION
12
1.1.6 Miscellaneous Bacterial/Host Product in Colitis
The human colon harbours both pathogenic and non-pathogenic bacteria. In
deciphering the etiologic elements involved in colitis, several bacteria and their
products have been thought to be involved in either the initiation or overall
pathogenesis of the disease. Nitric oxide (NO) one of the bacterial metabolic products
was considered to participate in human colitis related inflammation. Neut et al (1997)
compared the quantity and quality of nitrate reducing bacteria in diversion colitis (colon
inflammation occurring among patients undergoing ileostomy or colostomy) with
healthy controls. The relative percentage of nitrate reducing bacteria showed
statistically significant elevation among individuals suffering from diversion colitis.
Indirect observation has also provided support for the role of bacterial populations in
the induction of colitis. The therapeutic use of bacterial populations as probiotics,
mainly Escherichia coli strain Nissle 1917, is acknowledged as one of the most important
uses of bacteria (Konturek et al., 2009, Sha et al., 2014, Souza et al., 2016). It is believed
that in healthy individuals their intestinal microflora occupies all the available space
inhibiting colonization by pathogenic bacteria. Under certain conditions, when the
normal intestinal microflora is disturbed it provides an opportunity for either the
opportunistic or disease-causing bacteria to find an attachment place followed by
colonization leading to disease like colitis and others.
Johansson et al., (2014) have provided substantial evidence regarding the involvement
of bacteria in UC. They proposed that the whole human sigmoid colon inner mucous
layer, which is typically recalcitrant to microorganisms, becomes permeable providing
access to the bacteria causing colitis and associated dysfunctions. These findings are
mainly based on observations from a mouse model which revealed the presence of
bacteria in epithelial areas supporting the idea of bacterial penetration. The breakage of
colon mucus barrier and colitis has been supported by several studies (Chen et al.,
2014). The existing animal colitis models including the dextran sulphate colitis model
(Johansson et al., 2010) and Muc2 deficient mice model closely match human colitis
(Wenzel et al., 2014). Using these models no direct evidence exists to support barrier
CHAPTER 1: INTRODUCTION
13
crossing by the sulphate-reducing bacteria. However, this might be a very plausible
mechanism to be explored in future studies for providing a proof of concept.
CHAPTER 1: INTRODUCTION
14
Section 2
The contribution of sulphate reducing bacteria in UC
1.2.1 Sulphate Reducing Bacteria in the colon
The human gut microbiota helps in extracting nutrients and energy from our daily diet.
A variety of dietary components not metabolized in the small intestine are metabolized
by fermentation when they reach the colon, and this is driven by bacteria. Maintaining
the redox balance is a major problem faced during fermentation. The H2 produced
during the fermentation must be consumed, otherwise its high concentrations leads to
several adverse effects such as the accumulation of toxic compounds like butyrate.
Overcoming such toxicity by H2 consuming bacteria is likely to be a significant role in the
gut. (Gibson et al. 1990; Macfarlane & Macfarlane, 2012). Approximately 30-50% of
individuals in the West harbour bacteria capable of producing methane from H2 in their
gut. These individuals have methanogenic bacteria in their large intestine converting
hydrogen into methane and are called methanogenic individuals (Triantafyllou et al.,
2014). However, these are not the only bacteria capable of consuming hydrogen.
In the human colon, the H2 consuming microbial population (hydrogenotrophic) can be
split into three classes; methanogens, acetogens and sulphate reducing bacteria (SRB).
This classification is based on the products they release upon utilizing hydrogen. The
methanogens produce methane, acetogens acetate and SRBs H2S. and of the three
classes the SRB are the most efficient in utilizing sulphate (Gibson et al., 1988a). It has
also been reported that the human colon mainly harbours Desulfovibrio of the class δ-
Proteobacteria, the main genus of SRB (Scanlan et al., 2009). Sulphate reducing bacteria
(SRB) are a group of unrelated microorganisms that can utilize oxidized sulphur
compounds, as final electron acceptors; during growth under anaerobic conditions.
Through utilizing H2 SRB produce methane, acetate and H2S according to the following
equation: 4 H2 + SO42- + H+ → HS- + 4 H2O (Plugge et al. 2011 ; Carbonero & Gaskins,
2014).
CHAPTER 1: INTRODUCTION
15
In general, the SRB are a highly diverse group of microorganisms including
Desulfovibrio, Desulfomicrobium, Desulfobulbus, Desulfobacter, Desulfococcus,
Desulfosarcina, Desulfonema, Desulfotomaculum, and Thermodesulfobacterium. The
metabolic activities and characteristics of main key genera of SRB that present in the
human colon are sumrise in Table 1.2.1.
Table 1.2.1 The Characteristics and metabolic activities of some key genera of SRB in
human colon
Genus Characteristics and metabolic activities
Desulfovibrio The most predominant genus in the large intestine (64–81%), commonly utilize lactate, ethanol, and hydrogen as electron donors for sulfate reduction. The SRB use sulfate as a terminal electron acceptor for respiration with typical electron donors consisting of H2, ethanol, lactate, succinate and other organic acids, with the concomitant production of H2S which is toxic to colonic epithelial cells leading to several colonic disease including UC (Wagner et al., 1998, Carbonero et al., 2012)
Desulfobacter Desulfobacter utilizes only acetate as electron donor and oxidizes it to CO2 via the citric acid cycle only 9–16% of this group are present in human colon mucosa (Carbonero et al., 2012).
Desulfobulbus Desulfobulbus present in the human intestinal mucosa in 5–8%. Desulfobulbus utilize propionate and hydrogen and can ferment organic materials under certain conditions (Kuever et al., 2005)
Desulfomicrobium Desulfomicrobium reduce sulfate to hydrogen sulfide. They oxidize simple organic compounds in the human intestine (lactate, pyruvate, ethanol, formate and hydrogen) incompletely, with the formation of acetate as an end product. They can grow using sulfate as an electron acceptor during sulfate anaerobic respiration. Desulfomicrobium are often found in the intestines of both humans and animals (Kushkevych, 2014).
Desulfococcus Desulfococcus are found as one of SRBs members that associated with mucosa in human colon. This group can oxidized organic compounds such as carbohydrates, fatty acids, alkanes, aromatic hydrocarbons completely to CO2 (Kushkevych, 2016).
Desulfosarcina One of the sulfate reducer that plays a crucial role in sulfur cycle.They can grow autotrophically with hydrogen gas as electron donor and CO2 sole carbon source. Desulfosarcina has been implicated to have role in colon cancer (Huycke & Gaskins, 2004).
Bilophila
Bilophila are common inhabitant of the human colon and has been reported as infective agent. Bilophila are differs from the other colonic associated members of Desulfovibrionaceae by not being able to reduce sulfate and the absences of desulfoviridin. In addition, this group are non-saccharolytic bacteria, which produce acetic acid as major and succinic and lactic acids as minor fermentation products from peptone-yeast broth. (Baron et al., 1989; Marchesi et al., 2016).
CHAPTER 1: INTRODUCTION
16
Desulfuromonas Desulfuromonas are asporogenous bacteria that oxidize organic substrates completely to CO2. This group of bacteria use sulfur as an electron acceptor. It has been found that the growth of Desulfuromonas in human colon is associated with excessive protein ingestion (protein diet) as these metabolites products will promote the overgrowth of this group in human colon which may lead to the inflammation (Muyzer & Stams, 2008 ;Kushkevych,2013)
SRB groups found in the gut are mainly of the genera Desulfobacter, Desulfovibrio,
Desuflbulbus, Desulfomonas, Desulfomicrobium (Gibson, 1990). Reports have also shown
that that among these five genera the Desulfovibrio are predominant in the human colon
(Gibson et al., 1988c ; Willis et al., 1997). Desulfovibrio are characterized by the presence
of a pigment, desulfoviridin which displays differential fluorescence; red in alkaline and
blue-green in acidic under long wavelength UV 365 millimicron (Warren et al., 2005).
Additionally, these bacteria are all anaerobic, Gram-negative bacilli. Two desulfovibrio
species the D. piger and D. fairfieldensis were first identified in 1976 from human faeces.
These species are unique to the human intestinal tract and have never been isolated
from outside the human body (Gibson et al., 1988b). Desulfomonas pigra was the first
SRB identified that was later renamed as Desulfovibrio piger (Loubinoux et al., 2002).
Following this identification several other species belonging to the genera Desulfobacter
and Desulfobulbus were also isolated and characterized (Gibson et al., 1993b). The
presence of SRB in human faeces varies geographically, and may be associated with
dietary habits or, perhaps, certain genetic factors. This presumption is based on a study
reporting the differential carriage rates of SRB between the UK and South Africa faecal
donors. With over 70% of UK stool samples containing SRB compared to only 15% of
South African samples ( Section 1, Figure 1.1.1) (Gibson et al., 1988c) .
1.2.2 Interaction of SRB with other organisms
Co-existence of SRB with other organisms has been observed in natural and artificial
environments. SRB can coexist and interact with several anaerobic bacteria through
using an alternative electron donor or can compete by consuming the same substrate
(Dar et al., 2008). A number of processes, such as conjugation, multicellular symbiosis,
quorum sensing and niche adaption, combating host defence mechanisms, production of
CHAPTER 1: INTRODUCTION
17
secondary metabolites, benefit from inter- and intra-species co-operation (Williams et
al., 2007). It is feasible that the interactions between different metabolic groups of
bacteria in the colon may impact on human health, however before such a hypothesis
can be formed it is necessary to have a greater knowledge about the exact nature of the
bacterial populations and their possible interactions.
1.2.3 Role of SRB in the aetiology of UC
SRB are capable of metabolizing hydrogen as a source of energy and fall into the
category of hydrogenotrophic bacteria. The H2S produced by SRB is considered a
primary pathogenic mechanism in UC. The high production of acetate and H2S, both of
which have a deleterious effect on the intestinal epithelial cells if they are not removed
quickly (Kushkevych, 2014). The cells lining the colon can detoxify this gaseous
material, however in UC patients this detoxification system is deteriorated so that H2S
accumulates. This accumulation can lead to several other pathologies including an
increase in the epithelial permeability and barrier function of the colon thus impacting
its defensive and protective role, as well as inhibiting butyrate oxidation (Pitcher &
Cummings, 1996). There are several strains of SRB present in the colon, however, some
of these have demonstrated a relatively higher production of H2S identifying them as a
potential nexus within the aetiology of UC. Kushkevych (2013) compared the production
of H2S amongst SRB strains and revealed that Desulfovibrio sp. strain Vib-7 produced the
highest concentration (up to 3.23 mM) of H2S, consuming 99% of sulphate presented in
the medium. In vitro characterization of SRB isolates demonstrated that rates of H2S
production were higher in SRBs isolated from UC patients compared to those obtained
from healthy ones (0.55 versus 0.25 mM). Furthermore, faecal samples of UC patients
mainly contain SRB of the genus Desulfovibrio (Gibson et al., 1991).
Although the pathogenesis of UC remains poorly understood several studies have linked
SRB with the development of UC. Loubinoux et al (2002) compared the presence of SRB
in the faeces of patients with IBD to healthy volunteers and demonstrated that the
amount of Desulfovibrio piger (formerly Desulfomonas pigra) was significantly increased
in IBD patients (55%) when compared with healthy individuals (12%) or patients with
other symptoms (25%) (p<0.05). A study comparing the levels of these groups of
CHAPTER 1: INTRODUCTION
18
bacteria in various parts of the colon of 25 healthy subjects and using sequence analyses
of genus specific genes revealed that mucosa-associated SRBs were related to
Desulfovibrio piger, Desulfovibrio desulphuricans and Bilophila wadsworthia (Nava et al.,
2012).
It has been proposed that a higher dietary sulphate intake promotes SRB associated
with UC, however, a study using Genome-wide transposon mutagenesis and insertion-
site sequencing, RNA-Seq, with mass spectrometry in a murine model revealed that
“genes involved in hydrogen consumption and sulphate reduction are necessary for its
colonization, varying dietary-free sulphate levels did not significantly alter levels of D.
piger, which can obtain sulphate from the host in part via cross-feeding mediated by
Bacteroides-encoded sulphatases” (Rey et al., 2013).
As SRB play important roles associated with hydrogen removal in the intestines, it is
unsurprising that they have been linked to UC syndrome (Levine et al., 1998; Jørgensen
& Mortensen, 2001; Nyangale et al., 2012). The use of molecular techniques has aided
the identification of pathogens, and techniques such as PCR have identified the relative
richness of SRB in individuals with colitis issues (Zinkevich & Beech, 2000).
1.2.4 Sulphate Reducing Bacteria, H2S, and Colitis – A potential Nexus
SRB use sulphate as an electron acceptor in their energy supply pathway producing H2S
as a metabolite (Attene-Ramos et al., 2007) (Figure 1.2.1).
CHAPTER 1: INTRODUCTION
19
Figure 1.2.1.Metabolic pathway used by SRBs to reduce sulphate (adapted Carbonero et al., 2012); APS: adenosine 5’-phosphosulfate
H2S produced from SRB is hypothesized to disrupt mucin polymers of the gut
epithelium. These polymers contain disulphide bonds and sulphide produced by SRB is
thought to reduce these bonds, leading to breakage of mucus barrier thus allowing
pathogenic bacteria access to the gut epithelium leading to the development of
inflammation (Ijssennagger et al., 2016) (Figure 1.2.2). The penetration of hydrogen
sulphide through the cell membrane is due to its high solubility in lipophilic solvents
and it exerts an inhibitory effect on butyrate production (Rowan et al., 2009; Cuevasanta
et al., 2012). Butyrate oxidation is the main source of energy the epithelial cells of the
gut, when inhibited the resulting energy deficient environment leads to apoptosis of the
cells and induction of inflammation ( Moore et al., 1997; Babidge et al., 1998).
H2S has further deleterious effects on host cellular metabolism including activation of
adenosine 5’-triphosphate (ATP) dependent potassium channels, inhibition of
mitochondrial cytochrome c oxidase and induction of DNA damage (Szabó, 2007; Popov,
2013). Higher concentrations of H2S in the millimolar range lead to oxidative stress
through the formation of a complex with the ferric ion of cytochrome c oxidase, which
ultimately inhibits cellular respiration (Caro et al., 2011). In support of this, a recent
study by Beaumont et al (2016) reported that colonocyte exposure to low
concentrations of the sulphide donor NaHS reversibly inhibited colonocyte
mitochondrial oxygen consumption, concomitantly enhancing the expression of hypoxia
inducible factor 1alpha (Hif-1alpha) gene along with inflammation-related genes such as
those for inducible nitric oxide synthase (iNos) and interleukin-6 (Il-6).
CHAPTER 1: INTRODUCTION
20
It is important to mention here that the effect of H2S on bacterial cells are contrasting
compared to host epithelial cells and is time and environment dependent (Wang , 2012).
Figure 1.2.2. Sulphide as a Mucus Barrier-Breaker in IBD (Adapted from Ijssennagger et al., 2016)
1.2.4 Genetic Diversity of SRB – 5’-phosphosulphate reductase (apsr1) and
dissimilatory sulphite reductase (dsrAB)
The SRB have two essential genes which allow them to utilise sulphate as a metabolite,
adenosine 5’-phosphosulphate reductase (apsr1) and dissimilatory sulphite reductase
(dsrAB). Both are highly conserved among SRB and have potential as biomarker. There
are limited studies describing the role of both apsr1 and dsrAB in the overall pathogenic
mechanisms of these bacteria. However, dsrAB was used to confirm SRB in colonic
biopsy samples (Nava et al., 2012). Through utilizing dsrAB 1.9 kb amplicon a unique gel
retardation-based technique, involving fingerprinting methodology, efficiently increases
the chance of detecting rare and novel SRB (Wagner et al., 2005). Additionally, there are
several interesting studies describing the role of the dsrAB gene in characterizing
changes in the environment and various ecosystems (Perez-Jimenez et al., 2001; Perez-
Jimenez & Kerkhof, 2005). There are several ongoing metagenomics and molecular
analysis to evaluate the role of gut-residing SRB in the pathogenesis of IBDs underway.
CHAPTER 1: INTRODUCTION
21
However, there is limited literature relevant to both these genes as far as their
involvement in colitis disorders is concerned.
CHAPTER 1: INTRODUCTION
22
Section 3
Molecular techniques for the detection and identification bacteria
in human colon
1.3.1 General Overview
The human colon harbours hundreds of different species of bacteria (Poxton et al.,
1997). Earlier research on the detection of colonic bacteria mainly involved traditional
culture techniques for the isolation of bacteria followed by their characterization.
Analysis of the microbial community in the human gut was largely dependent on the use
of enrichment procedures and the ability to grow strict anaerobes. The isolates were
identified and characterized by various phenotypic methods. In a pioneering study,
Moore & Holdeman (1974) analysed the faecal flora of 20 male Japanese-Hawaiians. The
total number of different bacterial species was estimated to exceed 400, although the
actual number of identified species was only 113. Subsequent studies confirmed the
great diversity of intestinal bacteria. The Bacteroides, Bifidobacterium, Clostridium,
Eubacterium, Fusobacterium, Lactobacillus, Peptostreptococcus, Ruminococcus and
Streptococcus were the first dominant taxa identified (Eckburg et al., 2005; Martínez et
al., 2013; Donaldson et al., 2016). Altough the exact diversity and function of the
bacteria in human colon continue to be revealed; the role of these microbial
communities in colonic health and disease has received considerable attention. It
thought that certain bacteria inhabit the colon play a role in the onset of some colonic
disease, whereas other bacteria are considered protective. The role of some key bacteria
in the colon is shown in Table 1.3.1.
CHAPTER 1: INTRODUCTION
23
Table 1.3.1. The role of intestinal bacteria in human health and disease
Bacterial genera Role of bacteria in the human colon
Bacteroides
A dominant genus present in the human colon as a normal microflora, however, causes disease if get into the bloodstream either through surgery or invades various tissues. Suggesting the role of these bacteria as an aetiological agent in infection diseases such as UC (Waidmann et al.,2003; Singh et al., 2013)
Bifidobacterium The first bacteria to colonize the human colon and are believed to have positive benefits on their host health. They have important probiotic and protection role in the colon from pathogens invasion by competitive exclusion. They involved in the promoting of the immune system and in the digestion of energy substrates. (Toumi et al., 2013; Satish et al., 2017).
Campylobacter Involved in IBD through producing toxins in the oral cavity. Enteric Campylobacter infections produce an inflammatory, bloody diarrhea (Man et al.,2010 ; Zhang et al.,2014)
Clostridium Play a crucial role in colonic homeostasis through the interaction with the other bacterial populations. They provide specific and fundamental maintenance functions in the colon. However, some Clostridium species such as C. difficile is considered as the most important cause of infectious of the human colon; they linked to large bowel malignancies. Additionally has been associated with immunosuppression. These bacteria increased sharply among UC patients (Zhang et al.,2007 ; Marteau, 2009; Kolho et al., 2015; Autenrieth & Baumgart, 2017; Sokol et al., 2017)
Escherichia Commensal strains of Escherichia are involved in the homeostasis of human colon. However, in case of dysbiosis most beneficial bacteria are damaged, favouring the overgrowth of pathogenic strains that exert toxic effects on the colonic epithelium and causes chronic diarrhea. Some of Escherichia strains have been associated with IBD by promoting the inflammation leading to the development of colon cancer (Waidmann et al., 2003; Joossens et al., 2011)
Faecalibacterium Faecalibacterium are one of the most abundant bacterial members in the human colon found in ileal, colonic, and rectal biopsies from healthy individuals. These organisms produce butyrate and have anti-inflammatory properties. Lower levels of these bacteria are associated with Crohn’s disease and ulcerative colitis (Martinez-Medina et al., 2006; Hansen et al., 2012; Machiels et al.,2014; Vrakas et al.,2017)
CHAPTER 1: INTRODUCTION
24
Lactobacillus
Lactobacillus in the colon has a very important role in the host health promoting properties. They are commonly used as probiotics, which are known as live microorganisms that give a health benefit on the host by creating balanced microbial populations in human colon. In addition, protect the host against colonic infections and tumor by their antibacterial properties and immune system activation. (Ott et al., 2008)
Peptostreptococcus Induces intracellular cholesterol biosynthesis and degrade complex indigestible compounds in the colon. However, they can become pathogenic to their host. Numbers of these organisms were found in IBD and Colorectal cancer patients (Verma et al., 2010; Zhang et al.,2013)
Ruminococcus Dominant members of the human intestinal bacteria that play a key role in the digestion of the complex carbohydrate present in the high fibre dietary to provide the energy that is necessary for their host. (Joossens et al., 2011)
Eubacterium Eubacterium is considered as a key orgnasim within the human colon that has very important role in the intestinal metabolic balance with final impact on host health. They utilize glucose and ferment acetate and lactate, to form butyrate and hydrogen supporting the trophic interactions of microbes in the human colon (Engels et al, 2016).
Fusobacterium
Part of the normal flora of the human colon. However; Fusobacterium recently found to be in a high abundance in colon cancer patients suggesting that they may provide an important contribution in disease stages (Kostic et al., 2012).
Streptococcus Part of the commensal human intestinal microbiota which involved in the physiological health by promoting the immunomodulatory functions (Jandhyala et al., 2015).
CHAPTER 1: INTRODUCTION
25
Characterization of SRB was initially dependent on 1) phenotypic characteristics of
bacteria involving nutritional aspects and morphology and 2) biochemical
characterization, mainly the presence of desulphoviridin or lipid fatty acids. The advent
of molecular techniques for evaluating bio-ecological microbial communities of the gut
has provided a wealth of information beneficial for overall understanding the role of
resident gut bacteria in human health and disease (Furrie, 2006).
There are advantage and disadvantages to both the classical bacterial culturing
techniques and more modern molecular techniques. The choice of techniques is
dependent the question being addressed. For example, techniques involving
fingerprinting are good for evaluating bacterial community structure; dot blots and
fluorescent in situ hybridization are helpful to measure the density of a bacterial
species. Whereas, techniques like microarrays can be used to identify pathogenesis
mechanisms as well as for bacterial identification purposes. Molecular techniques based
microbial diagnostic have emerged as an active discipline, and several commercial
companies have developed tools for the identification and characterization of bacterial
species (Namsolleck et al., 2004) (Figure 1.3.1)
CHAPTER 1: INTRODUCTION
26
Figure 1.3.1. Schematic diagram of molecular techniques used for colonic bacterial
analysis (adapted from Muyzer & Smalla, 1998; Vaughan et al., 2000)
1.3.2 Polymerase chain reaction Amplification (PCR)
PCR was developed by Kary B. Mullis in 1983, and rapidly became a fundamental tool in
molecular biology. Without specifically isolating the target bacteria; PCR can detect
specific nucleic acid of individual bacterial species from a mixture of nucleic acids of
different sources (Mullis, 1990). In theory, the products generated by PCR reflect the
mix of microbial gene signatures present within a sample. Amplifying conserved genes
with PCR like 16S rRNA obtained from a sample, is a widespread practice for studying
colonic microbial ecology mainly because conserved genes are 1) universal in
prokaryotes, 2) are functionally and structurally conserved, 3) are composed of both
highly conserved and variable regions (Hugenholtz, 2002). An alternative is to use
specially designed primers that exclusively target regions of DNA that encode functional
genes, verifying the presence of specific metabolic pathways. Several primers are
available that specifically target key species of colonic mucosa bacteria. PCR products
CHAPTER 1: INTRODUCTION
27
that result from amplifying DNA obtained from colonic samples are generally analysed
by using 1) the clone library method, 2) DNA micro-arrays, 3) genetic fingerprinting or
through a combination of any of these (Coenye & Vandamme, 2003; Frank et al., 2007).
There are several types of PCR available to investigate populations of microbes both
qualitatively and quantitatively. In addition to being able to detect the genetic signature
of bacteria that cannot be cultivated by conventional means, the advantage of PCR
techniques which includes real-time PCR, quantitative PCR is that the technology is very
sensitive and demonstrates selectivity (Ott et al., 2004).
1.3.3 16S rRNA Gene Sequencing and Phylogenetic analysis
During the last decade, it has become apparent that there has been a significant
miscalculation of the taxonomic and phylogenetic diversity of GIT microbiota. This
situation arose from the combination of being unable to isolate and cultivate microbes
together with the inability to identify new species. The technological development of
using 16S rRNA gene sequences to identify and classify bacteria has transformed
taxonomy. By comparing the rRNA sequence, taxonomists can determine the
evolutionary relationships between specimens. The extent of sequence differences in
different samples provides a form of measurement of evolutionary distance. Some
regions in the rRNA molecules are highly conserved, whereas others display high
degrees of variability. Evolutionarily closer samples demonstrate greater homology
(Ritari et al., 2015). This method allows organisms to be differentiated at phylogenetic
levels ranging from species to domain, with relationships visualised by creating a
phylogenetic tree (Janda & Abbott, 2007).
The 16S rRNA genes display a relatively slower rate of evolution making them ideal for
use in phylogenies (Woese & Fox, 1977; Janda & Abbott, 2007). The usefulness of the
16S rRNA in detecting clinically relevant bacterial species can be ascertained from the
fact that there are more than three 16S database used for taxonomic classification of
microbial species. Among these the ribosome database project (RDP) (Bacci et al., 2015),
SILVA (Quast et al., 2013), and Greengenes (DeSantis et al., 2006) have received
relatively more attention.
CHAPTER 1: INTRODUCTION
28
Today, a library of tens of thousands of 16S rRNA gene sequences is available; novel
sequences that have been isolated can be compared to these known existing sequences
making this technique an effective tool in the identification of novel human GIT bacteria
(Lahti et al., 2014).
The phylogenetic identification of cultures of hundreds of obligate anaerobes has been
achieved by sequencing short fragments of amplified rDNA (approximately 500 bases);
these sequences includes variable regions V1, V2 and V3 and V6 that are used for
diagnosis. More than 90% of the organisms have easily been identified as belonging to
recognised species, but many novel isolates remain unmatched (Schloss & Westcott,
2011). Detailed phylogenetic analysis has demonstrated that these isolates belong to
previously unknown species or genera with many new GIT species being officially
recorded; meanwhile the research into the phenotypes of the unknown isolates
continues (Li et al., 2014). It has been reported that 16S rRNA sequencing technology
has been helpful mainly in the identification of bacteria with an unusual phenotypic
profile, rare and hard to culture bacterial species. According to very conservative
estimates; 29 bacterial species, out of 215 belonged to new genus, which were achived
between 2001 and 2007 (Woo et al., 2008). Additionally, the 16S rRNA technique has
been helpful in defining the changes in bacterial characteristics over time. Therefore, it
is important to mention here that one should be cautious in describing the data
generated from the 16S rRNA sequencing strategy particularly in clinical settings
(Christophersen et al., 2011; O'Sullivan et al., 2014).
1.3.4 Denaturing Gradient Gel Electrophoresis (DGGE)
Molecular methods that have been developed to study the microbial composition are
referred to fingerprinting. A common feature of all molecular fingerprinting methods is
that they depend upon multi-template PCR reactions to assess microbial community
structures or ‘fingerprints’. Fingerprinting techniques have been widely applied to the
ecology of colonic microbes; the methods are also proving invaluable to understanding
the composition and dynamics of GIT microbes (Suchodolski et al., 2004).
CHAPTER 1: INTRODUCTION
29
Denaturing gradient gel electrophoresis (DGGE), introduced in 1993 by Muyzer et al.
can be used to analyse the diversity of complex microbial ecosystems. This method,
demonstrated in Figure 1.3.2, separates 16S rRNA fragments that have been amplified
by PCR on polyacrylamide gels that contain a gradient of denaturing agents. Hetero-
duplexes of different amplicons (with different G/C content) are migrated to different
positions; thereby fragments of the same length but different sequences can be
differentiated. The result is a pattern of bands in the gel with each band representing
hypothetically, a single species. The bands separated through this methodology can then
be identified through sequence analysis by extracting DNA fragments from the gel
(Muyzer et al. 1993). A benefit associated with DGGE is several samples can be analyzed
on a single gel run. In addition to the biases presented by the PCR technique and DNA
extraction inadequacies, DGGE is also vulnerable to general biases related to sample
handling, storage and type (Green et al. 2006). Furthermore, there are some DGGE
specific limitations, including the co-migration of DNA fragments with different
sequences and limited detection sensitivity for under-represented species where only
the dominant species of the community are displayed. In addition, relatively small
fragments of DNA can be difficult to separate, as are the molecules generated by the
different rRNA operons of a single organism.
Irrespective of its shortcomings and despite the advancement of high-throughput
sequence analysis techniques, DGGE is the current best method available to obtain an
accurate outline of the composition of a microbial communities; from this, sequence
analyses can be explored in greater detail (Possemiers et al., . 2004; Vanhoutte et al.,
2004).
CHAPTER 1: INTRODUCTION
30
Figure 1.3.2. The principles of DGGE
1.3.5 Pulsed field gel electrophoresis (PFGE)
Pulsed field gel electrophoresis (PFGE) is a reliable and fast method to resolve DNA
molecules larger than 30 kb in an electric field that periodically changes direction. This
technique can be used to estimate genome size of a microorganism. (Alduina &
Pisciotta., 2015). The PFGE fingerprinting method is commonly used by the
epidemiologists for differentiating pathogenic bacterial strains from the non-pathogenic
counterparts (Falsafi et al., 2014). In addition, it is also possible to use PFGE to compare
the genome of bacterial species. The PGFE of linearized full-length chromosomal DNA of
Desulfovibrio desulphuricans, Desulfovibrio vulgaris, and Desulfobulbus propionicus
(Devereux et al., 1997). There are several studies describing the utility of PFGE for
comparative studies of bacterial diversity (Said et al., 2010; Fendri et al., 2013; Castiaux
et al., 2014).
CHAPTER 1: INTRODUCTION
31
1.3.6 Quantitative PCR (qPCR)
Quantitative PCR (qPCR) technique is used for direct quantification of the number of
bacteria present in a sample. qPCR uses fluorescent reporter molecules that can be 1)
fluorescent dye-tagged probes made of sequence-specific oligonucleotides, together
with a quencher (e.g. Molecular Beacons, TaqMan probes (hydrolysis probes) or
(Scorpions). 2) Nonspecific DNA binding dyes, for example SYBR Green, which when
bound to double-stranded DNA (dsDNA), emit fluorescence (Brukner et al., 2015).
DNA binding dyes, such as SYBR Green present an easy to use, cost-effective
visualisation tool and are a popular choice for optimising qPCR reactions. Fluorescence
increases in proportion with the concentration of dsDNA, which presents the dye with
more binding sites. This feature provides information about the accumulation of PCR
products as the fluorescence signal increases in line with the augmented levels of dsDNA
and this can be directly measured (Figure 1.3.3). SYBR Green is a reasonably
straightforward method; however, non-specificity is a limitation that is inherent to
assays that rely upon dyes binding to DNA. The binding of dye molecules that increases
in parallel with the increase in dsDNA means that reaction specificity can only be
governed by the primers (Giglio et al., 2003; Agrimonti et al., 2013).
The other most commonly employed detection chemistry for qPCR is linear probes, such
as TaqMan. As well as PCR primers, an oligonucleotide probe is incorporated into the
reaction in this detection chemistry. Attached to the 5’ end of the probe is fluorescent
reporter dye, such as FAM, and at the 3’ end is a quencher, for example TAMRA. TaqMan
system developed by relies on the release and detection of a fluorescent signal following
the cleavage of a fluorescent labelled probe by the 5-exonuclease activity of Taq
polymerase. In the intact state, the fluorescent signal on the probe, such as 6-
carboxyfluorescein (6- FAM), is quenched by the proximity on the probe of a second dye,
6-carboxy-tetramethylrhodamine (TAMRA). The rapid detection of fluorescence is
achieved after each round of amplification as fluorescent dye is released(Figure 1.3.3)
(Heid et al., 1996; Kang et al., 2007; Kubota et al., 2014; Wang et al., 2014).
CHAPTER 1: INTRODUCTION
32
qPCR allows for high throughput analysis of numerous samples. Provisional upon the
creation of a universal probe for use with real-time PCR, conserved regions of 16S rRNA
theoretically enable the detection and enumeration of all complex populations of
bacteria (Farrelly et al., 1995). A functional gene specific for a given group or species to
increase specificity was also applied in this technique (Nadkarni et al., 2002). qPCR has
been used to identify and evaluate the distribution of faecal desulfovirbios in healthy
samples also determining Desulfovibrio numbers in crypt-associated mucous (Fite et al.,
2004; Rowan et al., 2010).
Figure 1.3.3. Comparison of and A) SYBR®-Green, B) TaqMan®- Based Detection
Workflows Adapted from (Cao & Shockey, 2012)
1.3.7 Microarray
A biochip is a collection of miniaturized test sites (microarrays) arranged on a solid
surface allowing in a single experiment to analyse the expression of thousands of genes,
or the same gene from thousands of organisms in parallel.
CHAPTER 1: INTRODUCTION
33
Total DNA or RNA is isolated from clinical samples and then fragmented. The fragments
are amplified by PCR and simultaneously labelled using labelled nucleotides (referred to
as DNA arrays) or the fragments are directly labelled chemically (referred to as RNA
arrays) and hybridized to oligonucleotide probes which immobilized on support matrix,
termed probes. After that the immobilized probes are washed to remove excess product
before the probe/target complex can be visualized with a fluorescent scanner
(Namsolleck et al., 2004) (Figure 1.3.4).
In standard oligonucleotide or cDNA microarray probes are organised directly on a solid
surface. Either a collection of organised spotted probes immobilized on a solid surface
like glass microscope slide, nylon membrane or silicon or alternatively a bead array
which involves a collection of microscopic polystyrene beads, each with a specific probe.
(Lyons, 2003; Mah et al., 2004; Mocellin et al., 2005).
2D solid substrate arrays rely on probes bound in a two-dimensional fashion directly to
the surface. However, in 3D solid substrate arrays the probes are bound in multiple
layers within a three-dimensional microdroplet. A 3D format increases the surface area
and increase probe quantities, therefore increasing the sensitivity of the microarray
(Tang et al., 2009; Bumgarner, 2013).
A review of the literature indicates that micro-array systems have been used to analyse
the diversity of bacterial populations based on their functional genes or the 16S rRNA
gene (Cho & Tiedje 2001; Paliy et al., 2009). Microarrays have also previously been used
in the study of the composition of bacteria present in the human colon (Wang et al.,
2004; Kim et al., 2005; Palmer et al., 2006).
For characterization of bacterial composition several types of microarrays have been
used. Genome arrays which are established using pure bacterial culture genomic DNA
(Wu et al., 2004). Functional gene arrays which contain genes encoding key enzymes
that are involved in the metabolic pathway of the bacteria which allow controlling any
physiological changes within bacterial population (Wu et al., 2001). Phylogenetic
oligonucleotide arrays are used for analysis of bacteria structure and variance and their
probes are designed from rRNA sequence database (Loy et al., 2002).
CHAPTER 1: INTRODUCTION
34
Figure 1.3.4. Principle of the microarray technique (adapted from Namsolleck et al,
2004).
CHAPTER 1: INTRODUCTION
35
Section 4
The effect of antibiotics and honey on the growth of bacterial biofilms
1.4.1 Bacterial biofilms
Bacterial populations utilize several strategies to preserve their existence in adverse
environments. One such strategy is the formation of bacterial biofilms in which a group
of bacteria form a multicellular structure, protecting individuals through the production
of extracellular polymeric substances (EPS) (Figure 1.4.1) (Kjelleberg et al., 2007).
Whilst biofilms have been investigated those present on living tissues, and in particular
those of the gut, have not been explored in detail (Dongari-Bagtzoglou, 2008). The
human colon harbours a huge number of bacterial including both beneficial bacteria,
and under certain circumstances pathological or opportunistic bacteria which can give
rise to conditions such as UC (Sasaki and Klapproth, 2012). As far as the formation of
biofilms in the colonic bacterial population is concerned, there have been relatively few
studies ( Probert & Gibson, 2002; Macfarlane & Dillon, 2007; von Rosenvinge et al.,
2013). Whilst the usage of antibiotics for controlling a particular bacterial species within
colon can be effective; they demonstrate reduced effect against bacterial biofilms and
can impact on key metabolic activities, such as fermentation and enzyme synthesis
(Newton et al., 2013).
Figure 1.4.1 biofilm formation – 1. Cell adhesion 2. Formation of micro colonies, 3. Accompanied by EPS production. 4. The formation and maturation5. The dispersion cells from biofilm (Kjelleberg et al., 2007).
CHAPTER 1: INTRODUCTION
36
1.4.2 The aspects of bacterial Quorum Sensing in the colon
Quorum sensing (QS) is a mechanism that can regulate the bacterial colonization and
the dissemination in the gut by coordinating gene expression and cell behaviour
according to the population density of the gut bacteria (Yang and Jobin, 2014). This
phenomenon involves bacterial release of various classes of signalling molecules,
“autoinducers”, into their environment such as acyl-homoserine lactones (AHLs) (Fuqua
et al., 1994). The accumulation of these autoinducer molecules then enable a single cell
to sense the number of bacteria (cell density) and also measure the number
(concentration) of the molecules within a population (Miller and Bassler, 2001;
Thompson et al., 2015). In this way, the bacteria share information about cell density
and regulate individual gene expression by cell-to-cell communication (Rutherford and
Bassler, 2012). When the signalling molecules attain a critical threshold concentration,
the targeted QS genes are activated or repressed (Jacobi et al., 2012).
The role of QS in the growth and survival of colonic bacteria is likely to be important. It
is believed that upon colonization of the gut bacteria undergo metabolic changes where
genes involved in transportation and the metabolism of carbohydrate and amino acid
become highly induced (Yuan et al., 2008; Alpert et al., 2009). The colon microbiota is
known to consist of a wide variety of bacteria with a variety of roles in the gut ranging
from modulation of the immune system to protection against pathogens, and nutrient
absorption (Poxton et al., 1997). The diversity of the colonic bacteria determine the
state of the human gut, with a high diversity being associated with a healthy human gut.
On the other hand, a reduction in diversity is associated with dysbiosis where an
abnormal ratio of commensal and pathogenic bacterial species exists in the colon,
favouring the emergence and augmentation of certain bacterial species in the gut which
is considered to be the hallmark of the colonic disease, such as UC (Manichanh et al.,
2006; Bäckhed et al., 2012; Belizário and Napolitano, 2015). Because of the diversity and
the increase in number of the bacteria inhabiting the colon there is a need for a survival
mechanism that would enhance the growth in the presence of bacterial competition. QS
could be such a mechanism. Bacteria motility, surface proteins biofilm formation, and
virulence are also controlled by the secretion of QS signalling molecules which are
released into the colon environment (Di Cagno et al 2011; Piras et al., 2012; Shao et al.,
2012; Thompson et al., 2015). It has been reported that chemicals mediating QS are also
CHAPTER 1: INTRODUCTION
37
pathogenic factors for certain bacterial species and that their levels define the relative
bacterial number particularly in diseases like UC (Raut et al., 2013). Several studies have
described the role of QS the in their pathogenesis. It is worth mentioning here that one
of the emerging bacterial species appearing during the pathogenesis of UC was
Enterococcus faecalis. According to these studies, microbes utilize several QS regulatory
proteins including gelatinase, serine protease, enterocin O16 and cytolysin as the
primary pathogenic attributes ( Zhou et al., 2016; Ali et al., 2017). Among these various
molecular moieties, gelatinase has been associated with the disruption of intestinal
epithelial barriers thus providing pathogenic bacteria to invade intestinal barrier
(Maharshak et al., 2015). Furthermore, data from animal model studies showed that
bacterial invasion of intestinal barrier is dependent on the quorum-sensing
transcriptional regulator SdiA and bacterial species lacking this regulatory protein have
relatively lesser capability to penetrate into the deep layers of the gut (Sharma and
Bearson, 2013; Sharma and Casey, 2014). The bacteria involved in UC are no exception
to this control. These bacteria, amongst others, synchronize their social behavior, to
communicate among themselves, and to regulate gene expression in response to their
population density (Rutherford and Bassler 2012). In additiona, antibiotic treatment
eliminates vulnerable bacteria from the bacterial population in the gut, leaving resistant
bacteria to grow, multiply and perform their physiological functions. It is possible,
therefore, that the acquired and intrinsic resistance to a variety of antibiotics in UC
could be associated with QS mechanisms of the bacteria involved (Miller et al., 2014; Ali
et al., 2017).
1.4.3 Existing UC therapies
The UC symptoms are mainly inflammation of the colon associated with diarrhoea with
blood. Symptoms are mainly a refractory and hard to treat even with conventional
medications. Mostly anti-inflammatory drugs prescribed like aminosalicylates and
corticosteroids followed by a variety of immune system suppressors that reduce
inflammation. The most prominent immunosuppressants in practice are azathioprine,
mercaptopurine, cyclosporine, infliximab, adalimumab and golimumab and vedolizumab
(Monterubbianesi et al., 2014; Rietdijk & D'Haens, 2014; Kim et al., 2015; Shahidi et al.,
2016). With our increasing understanding of the gut microbiome and the role of
CHAPTER 1: INTRODUCTION
38
bacterial biofilms colonic disease; it is timely to evaluate usage of antibiotics amongst
UC patients. In treating UC clinicians must account for numerous factors when designing
a therapeutic regime. It has been observed that alongside bacterial associated colitis, the
Clostridium difficile secreted toxins (CD toxins) can also induce antibiotic-associated
colitis, or pseudomembranous colitis (PMC) (Kawaratani et al., 2010).
Two prominent antibiotics rifaximin and metronidazole are commonly prescribed to
treat UC. However, the usage of metronidazole is controversial, whilst considered as a
cost-effective drug which inhibits the nucleic acid synthesis by disrupting the DNA of
microbial cells resulting in bacterial cell death with good activity against pathogenic
anaerobic bacteria (Lofmark et al., 2010). Issues remain surrounding the generation of
resistant bacteria involved in UC with a capability to produce nitroreductase (Rafii et al.,
2003). Because of this and due to other factors, including genetic susceptibility, and
abnormal immune response potentially due to Clostridium difficile colonisation,
metronidazole is often administered in conjuction with other agents. A case report
suggested that vancomycin and metronidazole when administered in conjunction were
helpful in overcoming refractory UC (Miner et al., 2005). A more in depth study of
metronidazole-ciprofloxacin administration demonstrated that the antibiotic pairing
transiently disrupted the colon bacterial population with selective disruption of
bacterial biofilms associated with UC. The beneficial bacterial population return to
normal within a week upon the cessation of antibiotic administration (Swidsinski et al.,
2008).
Rifaximin is another antibiotic that selectively targets the microbial population involved
in IBD acts by inhibiting RNA synthesis in susceptible bacteria by binding to the β-
subunit of bacterial RNA polymerase (Guslandi, 2011). This antibiotic has a very
promising in vitro antimicrobial profile when compared with other antibiotics like
ampicillin-sulbactam, neomycin, nitazoxanide, teicoplanin, and vancomycin. A study
comparing rifaximin with all these antimicrobial pharmacological agents revealed its
inhibitory effect on 90% of the anaerobic bacterial strains evaluated in this study.
Furthermore, the minimal inhibitory concentration was 0.25 microgram/ml comparable
with teicoplanin and vancomycin, however, less than those of nitazoxanide and
ampicillin-sulbactam (Finegold et al., 2009). Rifaximin is approved in the United States
for the treatment of travellers’ diarrhoea and colonic infections. Due to its safety profile
and beneficial impact on gastroenteritis and associated disorder, this antibiotic is
CHAPTER 1: INTRODUCTION
39
approved in more than 30 other countries to treat a variety of gastrointestinal disorders
(Koo & DuPont, 2010).
Rifaximin has shown very promising data, with remission in 59% of individuals with
Crohn’s disease and 76% with UC (Guslandi, 2011). Promising results with rifaximin
have also led to the development of rifaximin-extended intestinal release variants
(Scribano, 2015) as well as a nano-suspension formulation instead of orally
administered tablets (Newton & Kumar, 2016). IBD are associated with gut microbiota
dysbiosis, and a review of existing studies suggested that, besides direct bactericidal
effect, the rifaximin can also modulate the gut environment maintaining normal
microflora (Newton & Kumar, 2016). However, these findings and the proposed
mechanism of action of rifaximin are yet to be validated.
1.4.4 Traditional medicine as a potential treatment for UC
A wide variety of traditional medicines are also used to treat UC. The most popular
include aloe Vera gel, wheat grass juice, Boswellia serrata, and bovine colostrum enemas
(Ke et al., 2012). A proposed advantage of using herbal remedies is reduced side effects
when compared with synthetic medicine. However, there is limited evidence about the
efficacy of herbal medicine, although its use is widely accepted for herbal remedies due
to relative safety and cost benefits. A series of clinical trials using herbal medicine in UC
have also been reported (Salaga et al., 2014, Teschke et al., 2015, Triantafyllidi et al.,
2015). The use of honey as a natural remedy for UC is increasing. Natural honey showed
highly promising results in protecting rodents from acetic induced colitis. Honey is
composed of a mixture of sugars and as such the control in this study involved glucose,
fructose, sucrose and maltose mixture showing no significant protective effect
confirming that natural honey has certain essential elements inhibiting the induction of
colitis (Mahgoub et al., 2002).
Intriguingly a similar antibacterial effect was observed with a natural product, honey, at
a low concentration of 0.5 % (v/v). At this dose biofilm formation in enterohemorrhagic
Escherichia coli O157: H7 was significantly reduced, without inhibiting the growth
commensal E. coli K-12 biofilm formation (Lee et al., 2011). Follow-up studies compared
Manuka honey (MH) alone or in combination with sulfasalazine a medicine used for
colitis treatment. MH showed an additive effect when used as combination therapeutics
CHAPTER 1: INTRODUCTION
40
thus confirming its efficacy and safety profile without any interactions with a
conventional drug (Medhi et al., 2008).
Besides benefitting morphological and histological scores, biochemical parameters like
lipid peroxidation were also improved with combination therapeutic utilizing MH
(Medhi et al., 2008). The use of honey in the treatment of colitis has been reported both
with the oral administration or rectal administration. A study compared the benefits of
honey, prednisolone and disulfiram in the trinitrobenzene sulfonic acid induced colitis
rat model. Intriguingly honey showed a higher potential to treat chronic colitis when
compared with prednisolone (Bilsel et al., 2002).
The emerging therapeutic potentials of honey in UC are due to its potential in
overcoming inflammation and oxidative stress. A recent study has evaluated the benefits
of using honey in dextran sodium sulfate (DSS) induced colitis model. The study
revealed that honey-treated UC animals showed improvements in both microscopic and
macroscopic scores. A significant down-regulation of oxidative, inflammatory and
apoptotic markers manifest the beneficial impact of honey in UC (Nooh & Nour-Eldien,
2016). This study supports further investigations into the effect of honey alone, or in
combination with other conventional medications, to treat UC.
41
AIMS OF THE RESEARCH
The exact causes of ulcerative colitis (UC) are unknown, with possible candidates being
an autoimmune response, genetics and diet/environment. It is the contention of this
thesis that the microflora of the colon plays an important role in the development of UC,
in particular that the Sulfate Reducing Bacteria (SRB) are the primary etiological agent
involved in UC. A sub-hypothesis of the current study is that the consumption of Mauka
honey can be an effective approach in the control of possible UC-causing bacteria. The
major hypothesis and the associated sub-hypothesis are linked with the following
research questions:
Does bacterial diversity change in patients suffering from UC, with particular
reference to SRBs?
Is there any link between SRB and the presence of specific bacterial population
that can help in early detection of SRB?
Can the use of Manuka honey improve the efficiency of antibiotics in controlling
the bacteria involve in UC?
The overall aim of the study is to estimate the relative levels of SRB, compared to
predominant species colonizing the mucosal surface of the human colon, in healthy and
patients suffering from UC, and to design procedures for the detection and
quantification of SRBs.
Specific aims of the investigation were as follows:
Test bacterial DNA samples obtained from patients suffering from UC and
healthy controls.
Identify existing oligonucleotide (DNA) probes for selected bacteria known to be
implicated in the aetiology of UC; and designing de-novo probes where required.
Evaluate DNA probes using a cassette based approach for fine-tuning of biochips.
Test DNA probes for hybridization with bacterial DNA samples obtained from
patients suffering from UC and healthy controls.
Evaluate a sensitive method for rapid enumeration of SRB
Investigate the influence of honey on the growth of SRB biofilms in the presence
of antibiotics to determine whether this agent influences bacterial growth and
activity.
CHAPTER 2: MATERIALS AND METHODS
42
CHAPTER 2: MATERIALS AND METHODS
CHAPTER 2: MATERIALS AND METHODS
43
Section 1
Molecular characterisation of mucosa-associated bacterial
communities from human colon
2.1.1 Bacterial growth conditions
The bacterial strains used in this study were Desulfovibrio alaskensis NCIMB 13491,
Desulfovibrio indonensiensis NCIMB 13468, Desulfovibrio vulgaris (strain Hildenborough,
NCIB 8303), Desulfovibrio vietnamensis DSMZ 10520, and Escherichia coli (BP) obtained
from the University of Portsmouth, School of Pharmacy and Biomedical Sciences
bacterial culture collection. The Desulfovibrio bacteria were used because they represent
the main genus of SRB that found in the human colon and known to be implicated in UC.
Moreover, unpublished data reported a level of serum antibody against D. indonensiensis
in UC patients (Zinckivch, Unpublished). E. coli (BP) was used because previous
experiments have shown that the presence of E. coli in the human colon correlates
significantly with SRB presence (Zinckivch, Unpublished).
SRB strains (Desulfovibrio sp.) were cultivated anaerobically in Vitamin Medium with
iron (VMI medium) or Vitamin medium with reduced iron (VMR) at 37°C for 7 days. It
has been found that these media supplemented with lactate and sulfate which can
ofered a higher sensitivity of SRB detection (one to two orders of magnitude) (Zinkevich
et al., 1996; Zinkevich & Beech, 2000). E. coli BP were cultivated aerobically at 37°C for
18 h in either Lysogeny Broth (LB) medium which is the most commonly used a
nutrient-rich medium for fast growth of E.coli (adapted from Bertani, 1951) (Fisher,
UK); or in MacConkey Agar which is usually used for the differentiation and
identification of enteric bacteria based on their lactose fermentation (MacConkey,
1905). The bacterial inoculum was adjusted for all experiments of approximately 2X108
cells/ml for E. coli BP and 7X106 cells/ml for SRB. The composition of VMI, VMR, LB
medium nutrient broth and MacConkey Agar is shown in Appendix 1.
CHAPTER 2: MATERIALS AND METHODS
44
2.1.1.1 Purification of genomic bacterial DNA
Chromosomal DNA was extracted from bacterial cultured cells using QIAGEN DNeasy
Blood and Tissue Kit (QIAGEN, UK) following the manufacturer’s instructions (DNeasy
Blood and Tissue Handbook 07/2006 p.29-44). The bacterial cells harvested in
eppendorf tubes (1.5 ml) by centrifugation at 13 000 x g for 10 min at room tempreture
(Heraeus Fresco 21, Thermo Scientific, UK) and were resuspended in 180 μl of Tissue
Lysis buffer (ATL buffer). 20 µl of Proteinase K was added to the samples and mixed
thoroughly by vortexing and incubated at 56°C from 1-3 h until the samples completely
lysed. The samples were vortexed occasionally (every hour). After incubation the
samples were vortexed for 15 seconds before adding 200µl of Lysis buffer (AL buffer)
and mixed thoroughly by vortexing. Then 200µl of 100% ethanol was added to the
samples and mixed again thoroughly by vortexing. The mixture was placed into the
DNeasy Mini Spin column (placed in a 2 ml collection tubes) and centrifuged at 6 000 x g
for 1 min at room tempreture, the flow through was discarded and the DNeasy Mini Spin
column was placed in a new collection tube. 500 µl of Wash buffer 1 (buffer AW1) was
added to the column, which was then centrifuge at 6 000 x g for 1 min at at room
tempreture, the flow through was discarded and the DNeasy Mini Spin column placed in
a new collection tube. 500 µl of Wash buffer 2 (buffer AW2) was added to the column,
which was then centrifuged at 13 000 x g for 3 min at room tempreture and the flow
through was discarded. Finally, the DNA was eluted by placing DNeasy Mini Spin column
in a clean 1.5 ml Eppendorf tubes after adding the 50 µl of Elution buffer (buffer AE)
directly onto the centre of DNeasy membrane, incubated at room temperature for 1 min
and then centrifuged at 6 000 x g for 1 min at room temperature.
Bacterial DNA isolated following the sampling of specimens of colonic mucosa which
were taken during colonoscopy from the proximal colon of one healthy individual and
four patients suffering from UC. The samples were kept at -20°C until used (Zinkevich &
Beech, 2000). These samples were provided by Dr. Zinkevich.
CHAPTER 2: MATERIALS AND METHODS
45
2.1.2 Polymerase chain reaction (PCR)
Aliquots of 2-10 ng of DNA were added to a PCR master mixture and the volume
adjusted to 25 µl or 50 µl with nuclease-free water (SIGMA). The PCR products were
analysed by 0.9 or 1.2 % agarose gel electrophoresis. Standard PCR reactions were
carried out in the RoboCycler® Gradient 96 (Stratagene, UK) or T100™ Thermal Cycler
(Bio-Rad, UK). Touch down PCR reactions were carried out in the T100™ Thermal Cycler
(Bio-Rad, UK). All primers used in this study were purchased from Life Technologies,
UK. GoTaq® Green Master Mix was purchased from Promega, UK.
2.1.2.1 PCR for bacterial 16S rRNA genes
The V3, V4 and V5 region of the bacterial 16S rRNA gene was amplified using the
primers pair 341F+GC and 907R (Table 2.1.1). Final reactions contained: 2x GoTaq®
Green Master Mix and 10 µM of each oligonucleotide primers. Reactions for the V3, V4
and V5 region were initially denatured at 94°C for 4 min; followed by a touch down PCR:
20 cycles of 94°C for 1 min, 63-54 °C for 1 min and 72°C for 1 min followed by 15 cycles
of 94°C for 1 min, 53°C for 1 min and 72°C for 1 min plus an additional 10-min cycle at
72°C.
Approximately 1.5kb of 16S rRNA gene was amplified using primers 8F and 1492R
(Table 2.1.1). Final reactions contained: 2x GoTaq® Green Master Mix and 10 µM of
each oligonucleotide primers. Reactions were initially denatured at 94°C for 2 min;
followed by 30 cycles of 94°C for 30s, 55°C for 1 min and 72°C for 45s followed by a final
extension step at 72°C for 15 min.
2.1.2.2 PCR detection of SRB (for dsr, Desulfovibrio 16S rRNA and aps
genes)
The dsr, Desulfovibrio 16S rRNA and aps genes were amplified using the primers set
DSR1F+,DSR-R, DSV691F, DSV826R and Aps F, Aps R respectively (Table 2.1.1).
Reactions contained: 2x GoTaq® Green Master Mix and 10μM of each oligonucleotide
primers. Reactions for the dsr were initially denatured at 95°C for 3 min; followed by 35
cycles of 95°C for 15s, 67°C for 30s and 72°C for 30s followed by a final extension step at
CHAPTER 2: MATERIALS AND METHODS
46
72°C for 10 min. The reactions for the Desulfovibrio 16S rRNA gene were initially
denatured at 95°C for 3 min; followed by 35 cycles of 95°C for 30s, 62°C for 1min and
72°C for 45s followed by a final extension step at 72°C for 10 min. The reactions for the
Aps gene were initially denatured at 95°C for 3 min; followed by 34 cycles of 95°C for
15s, 59°C for 1min and 72°C for 1 min followed by a final extension step at 72°C for 10
min.
CHAPTER 2: MATERIALS AND METHODS
47
Table 2.1.1. Sequence of primers used in PCR
Primer Sequences of + and – Primers
(nucleotide)
Gene Target Reference
8F 5'-AGAGTTTGATCCTGCCTCAG-3'
Bacterial
16S rRNA
Turner et al.,
1999
1492R 5'-GGTTACCTTGTTACGACTT-3'
341F+GC
5’-
CGCCCGCCGCGCGCGGGCGGGGCGGGGGCA
CGGGGGGCCTACGGGAGGCAGCAG-3’
Muyzer et
al., 1998
907R 5’-CCGTCAATTCMTTTGAGTTT-3’
DSR1F+
5’-ACSCACTGGAAGCACGGCGG-3’ dsrA Kondo et al.,
2006
DSR-R 5’ -GTGGMRCCGTGCAKRTTGG-3’
DSV691F 5′-CCGTAGATATCTGGAGGAACATCAG-3′ Desulfovibrio
16S rRNA
Fite et al.,
2004 DSV826R 5′-ACATCTAGCATCCATCGTTTACAGC-3′
APS F 5’-CGCTGGCAGATGATGATCAA-3’ apsA This study
APS R 5’-ATGCGGTTGGGCTCGTT-3’
Legend: R=G/A, Y=T/C, W=A/T, I = Inosine, S=G /C , M= A/C , K= G/T.
2.1.2.3 Positive controls for PCR reactions
Genomic DNA, extracted from laboratory type strains obtained from University of
Portsmouth strains collections were used as positive controls and Nuclease free Water
as negative controls for all PCR reactions. Details of laboratory type strains and
microbial cultivation are given in subsection (2.1.1) and media composition in Appendix
1.
CHAPTER 2: MATERIALS AND METHODS
48
2.1.3 Analysis of PCR products using agarose gel electrophoresis
All the amplified PCR products (10 µl) were analysed on either 0.9 % (w/v) or 1.2%
(w/v) agarose gel made by dissolving the appropriate amount of agarose (Sigma
Chemical Ltd, UK) in 1xTAE (20 mM Tris acetate, 10 mM sodium acetate, 0.5 mM Na2-
EDTA) buffer. DNA molecular weight markers (1 kb DNA ladder and 100 bp, Promega,
UK) were used for size determination of the PCR products. The gels were run in a
horizontal electrophoresis chamber (Horizontal gel electrophoresis unit, Scie-Plas, UK).
A voltage of 50 V was applied for the first 10 min and 140 V for the remaining 40 min.
The gels were stained with the SYBR® Safe DNA gel stain (Life Technologies), (6 µl of
stain added into 100 ml of agarose gel) and was photographed under UV light using
molecular imager® Gel Doc™ XR+ Imaging system (BIORAD).
2.1.4 Cloning: construction of genomic libraries and sequencing
The total bacterial DNA extracted from biopsy sample was used as the template for PCR
amplification of 16S rRNA and dsrA genes, which were subsequently cloned using
pGEM®-T Easy Vector cloning system (Promega, UK) and TOPO® TA Cloning® Kit for
Sequencing (life technologies, UK).
2.1.4.1 Ligation of plasmid DNA and competent cells transformation
The ligation reaction (final volume 10 µl) was prepared using 50 ng (1 µl) of pGEM®-T
Easy vector, 5 µl of 2X rapid ligation buffer, 3 µl of insert (PCR product) and 1 µl of T4
DNA ligase. The molar ratio of vector to the insert of 1:3 was used as recommended by
the manufacturer (technical manual no. 042).
The ligation reactions were incubated at RT for 1 h then centrifuged and 2 µl of the
ligation reaction was added to sterile 1.5 ml tube placed on ice. 50 µl of JM109 high
efficiency competent cells (Messing et al., 1981) were carefully transferred to ligated
mixture and incubated on ice for 20 min.
The cells were heat-shocked for 50 seconds at 42°C in a water bath and were
immediately returned to the ice for 2 min. Room temperature SOC medium (950 µl)
CHAPTER 2: MATERIALS AND METHODS
49
(Sigma, UK) was and cells were incubated at 37°C for 1.5 hours with aeration (~150
rpm).
100 µl of each transformation culture were plated onto LB/Ampicillin/IPTG/X-Gal
plates in duplicate. The solid LB medium was supplemented with 100 µg ampicillin ml-
1, 100 µg X-Gal ml-1 and 0.5 mM IPTG. After incubation overnight at 37°C the plates
were placed at 4°C overnight for better blue/white screening.
TOPO cloning reaction (final volume 6 µl) was prepared according to manufacture
instruction. 4 µl of PCR product, 1 µl of salt solution and 1 µl TOPO®TA vector, was
mixed gently and incubated for 5 minutes at RT. 2 µl of the TOPO® cloning reaction was
added to One Shot® TOP10 chemically competent cells (Life Technologies, UK), mixed
gently and incubated on ice for 30 minutes. The cells were heat-shocked for 30 seconds
at 42°C without shaking. The tube was immediately transferred to the ice for few
minutes. 250 µl of room temperature S.O.C medium was added to the cells and shaked
horizontally (200rpm) at 37°C. 100 µl of each transformation culture were plated onto
LB/Ampicillin plates in duplicate and incubated overnight at 37°C .
2.1.4.2 Purification of plasmid DNA
The white bacterial colonies were picked from the plates and inoculated into sterile
universal tubes containing 5 ml of LB supplemented with 100 µg/ml ampicillin (Fisher,
UK) and incubated overnight at 37°C with aeration (~200 rpm).
Recombinant plasmid DNA was purified using Zyppy™ Plasmid Miniprep Kit (Zymo
Research, Irvine, CA) following the manufacturer’s instructions. 3 ml of E.coli LB culture
harvested in Eppendorf tubes (1.5 ml) by centrifugation at 11 000 x g for 1 min
(Heraeus Fresco 21, Thermo Scientific, UK). Bacterial cells were re-suspended in 600 µl
water. 100 µl of 7X Lysis Buffer was added and mixed by inverting the tube 4-6 times
until complete lysis was obtained. 350 µl of Neutralization Buffer was added and mixed
by inverting the tube several times until the neutralization was completed. The samples
were span at 11,000 x g for 4 minutes.
The supernatant (~900 µl) was transferred into the Zymo-Spin™ IIN column without
disturbing the cell debris pellet. The column centrifuged 11,000 x g for 15 seconds and
flow through was discarded (this step was repeated until all supernatant has been
loaded). 200 µl of Endo-Wash Buffer was added to the column and centrifuged for 30
CHAPTER 2: MATERIALS AND METHODS
50
seconds then 400 µl of Zyppy™ Wash Buffer and centrifuged for 1 minute. Finally the
plasmid DNA was eluted by placing Zymo-Spin™ IIN column into a clean 1.5 ml
Eppendorf tube and then 30 µl of PCR grade H2O was added directly onto the
membrane, incubated at RT for 1 min and then centrifuged at 11 000 x g for 1 min.
Insertion of the PCR product of interest into the plasmid was verified by PCR using
primers 8F and 1492R and DSR1F+ and DSR-R (Table 2.1.1) and by agarose gel
electrophoresis analysis (subsection 2.1.3).
2.1.4.3 Purification of PCR product and Sequencing
Amplified PCR products of plasmid DNA (bacterial 16S rRNA and dsr genes) were
purified using QIAquick® PCR purification kit (Qiagen, UK) following the
manufacturer’s instructions (quick-start protocol, 09/2011 p.1-2) before to be sent to
the GATC Biotech UK (Cambridge, UK) DNA sequencing service. 5 volumes of buffer PB
was mixed with 1 volume of the PCR reaction applied onto the QIAquick column and
centrifuged for 1 min. Flow through was discarded. The column was washed using 750
µl buffer PE and centrifuged for 1 min. After flow through was discarded the QIAquick
column was centrifuged once more for 1 min to remove residual wash buffer. Finally,
the PCR product was eluted with 50 µl of ultra-pure water. Purified PCR products were
sent for DNA sequencing (GATC Biotech, Cambridge, UK). The DNA fragments were
identified by sequencing using PCR primers (subsections 2.1.2.1 and 2.1.2.2) and
homology searches in BLAST of the GenBank.
2.1.5 Denaturing Gradient Gel Electrophoresis (DGGE)
PCR products (bacterial 16S rRNA gene) were separated on denaturing gradient gel
(INGENYphorU, Ingeny International BV, The Netherlands). 30 µl of PCR products were
loaded on 6% (w/v) polyacrylamide gel in 0.5x TAE buffer. The electrophoresis was run
at 60°C using the gel containing 30% to 80% urea-formamide denaturing gradient
(100% corresponded to 7M urea and 40% (v/v) formamide) increasing in the direction
of electrophoresis. The gel was run for 15 min at 200 V and for 20 hours at 90 V.
CHAPTER 2: MATERIALS AND METHODS
51
After electrophoresis the gel was removed gently from the tank, placed in appropriate
container and stained using 12 µl of SYBR Safe DNA gel stain (Life technologies, UK) in
750 ml of double distilled water for 20 minutes in darkness, then rinsed thoroughly with
distilled water. The gel was photographed under UV light using Alpha Innotech Gel
Documentation System (Alpha Innotech Corporation, USA).
2.1.6 DNA extraction from DGGE gels and Sequencing
All bands were cut out from the DGGE gels using a sterile scalpel, transferred into a
sterile 1.5 ml Eppendorf tubes containing 40 µl of ultra-pure water (Machado et al.,
2007). The samples were incubated for 24 h at 4°C, followed by 1 hour incubation at
37°C. Samples were then centrifuged at 13 000 x g for 1 min (Heraeus Fresco 21,
Thermo Scientific, UK). The supernatants (5 µl) were used for PCR re-amplification as
described above (subsection 2.1.3.1). Re-amplified PCR products were purified using
QIAquick® PCR purification kit (Qiagen, UK) (as described in subsection 2.1.4.3) and
sent for DNA sequencing (GATC Biotech, Cambridge, UK). The DNA fragments were
identified by sequencing using PCR primers (subsections 2.1.2.1) and homology
searches in BLAST of the GenBank.
CHAPTER 2: MATERIALS AND METHODS
52
2.1.7 Phylogenetic analysis
Sequences were aligned using Muscle as implemented in SeaView ver4.0 (Galtier et al.,
1996; Edgar, 2004). Phylogenetic trees were constructed using the maximum likelihood
optimal criteria which are part of SeaView ver4.0. The Tamura and Nei 1993 (TN93)
model was the substitution model employed using a rate category of 4. Trees were
constructed using BioNJ method (Gascuel, 1997) with NNI tree searching operations.
Support for nodes in the produced trees was estimated with a bootstrap analysis,
implemented in SeaView ver4.0. A bootstrap search of 100 repetitions was used in this
analysis.
The DGGE gel was scored for the presence and absence of bands for each sample tested.
A comparative table was constructed with band presence score as 1, and absence scored
as 0. The comparative table was used to construct a matrix from which genetic distance
was calculated according to the algoritm of Nei and Li (1979) as applied in PAUP* 4.0
(Swofford, 2003). The genetic distance was used to construct a UPGMA tree.
CHAPTER 2: MATERIALS AND METHODS
53
Section 2
Probe designing and testing
2.2.1. DNA probes design
The groups of colonic bacteria that can be involved in the etiology and pathogenesis of
UC were used as targets for Probes design. The NCBI database
(http://www.ncbi.nlm.nih.gov) was used to derive sequences for probe design
(Appendix 3). The complete nucleotide sequences for each functional and structural
gene were downloaded and saved in FASTA format. The sequences were loaded in
SeaView software (Galtier et al., 1996). Region variations in the sequences were
identified from multiple alignments generated by Clustalo (Sievers et al., 2011) or
Muscle (Edgar, 2004). Based on final alignments, conserved parts of the sequences of
each gene were chosen and used for probe designing using the Oligo 7 program
(Rychlik, 2007). Published probes and an established rRNA-targeted oligonucleotide
probe database named 'probeBase' (Loy et al., 2007) were also used for probe selection.
The probe design meets the following well-established requirements:
Unique to the target agent.
The homogeneous predicted melting temperatures (Tm) of the probes.
The homogeneous length of oligonucleotides and for the developing array the
probe size is preferentially limited to between 18 and 25 bases.
Hybridization kinetics of nucleic acids is temperature dependent, and the specificity and
efficiency depend on the hybridization temperature. One of the main problems with
designing oligonucleotide microarrays is to achieve nearly identical melting
temperatures for all probes on the array. There are several approaches for the
equalizing of the probes hybridization ability. In the first stage, the oligonucleotide
probes should be design with the same predicted melting temperature (Tm±2⁰C) by
using one of the algorithms based on thermodynamic properties of nucleic acid duplex
formation and dissociation in solution. Mainly all the algorithms are based on the
nearest neighbour model (Breslauer et al., 1986).
CHAPTER 2: MATERIALS AND METHODS
54
The predicted Tm is calculated using Integrated DNA Techenlogy (IDT) OligoAnalyzer
3.1program (http://eu.idtdna.com/analyzer/Applications/OligoAnalyzer/Default.aspx).
For the Tm calculation, the program demands not only the oligonucleotide sequence,
but the buffer conditions as well. Tm and consequently the hybridization temperature
depend on the buffer composition. The length of each probe was established ranging
from 18 to 25 bases. The GC content was calculated and only probes with GC content
between 40 and 60% were selected for further analysis. The designed probes were
synthesized and C6-Amine-group was inserted at 5´-end during the synthesis (Sigma-
Aldrich, UK). All designed probes were verified against the GeneBank nucleic acid
database for specificity using the BLAST program (Altschul et al., 1990).
2.2.2 Cassette construction for fine-tuning of biochips
Cassette approach is a novel method used for evaluation designed probes in single
reaction. The cassette comprising all of the probes in equimolar proportions for a quick
reveal the probes unsuitable for inclusion in the biochip (Zinkevich et al., 2014). Single
stranded (ss) DNA cassette was used as the targets for hybridization with the probes
that were coupled to the matrix so that the hybridization capacity of each probes to be
evaluated. The ss DNA cassette is a lineal array of sequences complimentary to the
studied set of probes. Cy3 fluorescent dye was inserted at the 5´-end of the ss cassette
during the synthesis. Each DNA cassettes was constructed to contain the four chosen
oligonucleotides and were synthesized by Bioneer Corporation (Daejeon, Republic of
South Korea). The size of the cassette was varied from 93 to 100 bp.
2.2.3 Development of a biochip using dendrimeric matrix
Ready to use dendrimeric matrix activated for the probes immobilization was provided
by Dr Nelly Sapojnikova (I. Javakhishvili Tbilisi State University, Andronikashvili
Institute of Physics). Dendrimeric matrix is formed by the microscopic glass slide
treatment in a special manner (Anne-Marie et al., 2006).
The oligonucleotide probes were immobilized onto the activated slides using spotting
pins (V&P Scientific, Inc, San Diego, USA). 200 nl/spot of oligonucleotide probes with
two replicate spots of each probe being applied to activated slides. Concentrations of
oligonucleotide probes were 250 pmol/0.1µl in 1% Diisopropylethyl amine (DIEA)
CHAPTER 2: MATERIALS AND METHODS
55
reagentPlus®, 99% (Sigma-Aldrich, UK), in water. The slides were then incubated
overnight in a humid chamber at 37⁰C and subsequently washed with nuclease-free
sterile water then with 100 % ethanol. The surface of the glass slides with the
immobilized probes was deactivated by treatment with a solution made of 6-amino-1-
hexanol 97% (50mM), (Sigma Adrich, UK) and DIEA (150 mM) in 20ml of anhydrous
amine free N,N-dimethylformamide (DMF) molecular biology grade (Applichem,
Germany) for 2 hours, in order to prevent the binding of the fluorescently labelled DNA
with the matrix surface. Finally, the deactivated glass slides with the immobilized
probes (biochips) were washed with DMF, acetone, water and dried. The biochips were
then ready to be used in hybridization experiments and can be stored at 4ᵒC until use at
least for 6 months.
2.2.4 Hybridization
The hybridization buffer for the ss-DNA cassette was SSARC (4xSSC [600 mM NaCl, 60
mM Na-citrate], 7.2% (v/v) Na-sarcosyl). The solution of Cy3-labelled ss cassette (100
pmol/µl) was prepared by dissolving the dry pellet in nuclease free water. The cassette
quantity in the hybridization buffer was 100 pmol. The sample and the slide with
immobilized probes were preheated to the hybridization temperature (45⁰C) using
chilling/heating block (Cole-Parmer®,USA) for 5 minutes. The fluorescent samples
solution was loaded onto the biochip and covered immediately with the cover slip. The
final volume of the hybridization solution was 10 µl. The volume of the hybridization
solution (10 µl) is from the calculation 2 µl per 1 cm2 of the covered square. The area
under a cover slip was 2.2cm x 2.2cm=4.84 cm2.
Before placing the biochip in the hybridization chamber (Arrayit, USA) 10 µl of nuclease-
free water was added to one of the grooves to keep 100 % humidity in the chamber
during the hybridization reaction. The slide was immediately placed in the hybridization
chamber covered by the chamber lid and sealed by screws (Figure 2.2.1). The
hybridization proceeds at 45°C for 4 hours. Slide was removed from the hybridization
chamber and immersed in the washing buffer 1 (2XSSC/0.2% SDS) in a Petri dish for
removing the cover slip. Then the slide was placed in a Falcon tube for washing in buffer
1 by shaking for 2 minutes on a shaker multi Bio 3D (BIOSAN, Latvia). Then the buffer 1
was discarded and next wash was performed in the washing buffer 2 (0.2XSSC/0.2%
CHAPTER 2: MATERIALS AND METHODS
56
SDS) and the washing buffer 3 (0.2XSSC) subsequently on a shaker multi Bio 3D
(BIOSAN, Latvia) for 2 minutes each at 30 rpm at room temperature. The slide was dried
by centrifugation in a Microarray High Speed Centrifuge (ArrayIt, USA), and visualized
using a Portable Imager 5000 (Aurora Photonic, USA) with 532 nm green Laser and 580
nm filter. The signal intensity of each point on the biochip was calculated using
MicroChip Imager software (Aurora Photonics, USA).
Figure 2.2.1. Photograph for the hybridization chamber
CHAPTER 2: MATERIALS AND METHODS
57
2.2.5 Genomic DNA preparation for hybridization on a biochip
DNA purified from the bacterial strains described in subsection 2.1.1 and the genomic
DNA from biopsy samples subsection 2.1.1.1 was used in the hybridization procedure.
2.2.5.1 DNA amplification
Genomic DNA from bacteria and from biopsy samples were amplified using illustra™
GenomiPhi HY DNA Amplification Kit (GE Healthcare, Life Science, USA). 22.5 µl of
sample buffer was added to 2.5 µl of 10 ng DNA and mixed thoroughly. The samples
were heated at 95 ᵒC for 3 min and immediately chilled on ice. The master mix was
prepared by combine 22.5 µl of reaction buffer and 2.5 µl of enzyme mix on ice .25 µl of
prepared master mix was transferred to each cooled samples on ice. The samples were
incubated at 30ᵒC for 4 hours. The reaction was stopped by incubation at 65°C for 10
min and followed by immediate chilling on ice for 10 min. The extent of amplification
was verified using 1.5% agarose gel in 1X TAE buffer. The amplified reactions were
store at -20 °C until used.
2.2.5.2 Purification of amplified products
The amplified products were purified using PureLink Quick PCR Purification Kit
(Invitrogen, USA). Four volumes of PureLink® Binding Buffer (B2) with isopropanol
was added to one volume of the amplified product and mixed well. The samples were
loaded into the PureLink® Spin Column placed in collection tube. The column was
centrifuged at 10,000 × g for 1 min at room temperature and the flow through was
discarded. 650 µl of washing buffer with ethanol was added to the column and
centrifuged at 10,000 × g for 1 min at room temperature and the flow through was
discarded. The column was centrifuged again at maximum speed (13,000× g) at room
temperature for 3 min. Finally, the DNA was eluted by placing the PureLink® Spin
Column in a clean 1.5 ml Eppendorf tubes after that 50 µl elution Buffer (10 mM Tris-
HCl, pH 8.5) was added directly onto the centre of column , incubated at room
temperature for 1 min and then centrifuged at 13, 000 x g for 2 min.
CHAPTER 2: MATERIALS AND METHODS
58
2.2.5.3 DNA Fragmentation
The amplified purified products were fragmented using NEBNext® dsDNA
Fragmentase® (New England biolabs, UK). The fragmentation reaction was prepared by
mixing 1-3 µg of amplified purified products, 2µl 10X reaction buffer v2, 2µl of
NEBNext® dsDNA Fragmentase and 4 µl of 200 Mm MgCl2. The volume was adjusted to
20 µl by nuclease-free water. The reaction was incubated at 37°C for 25 min and the
enzyme was inactivated by heating the reaction for 5 min at 65°C.
The restirictase FastDigest SaqAI (Thermo Scientific, USA) was used for the
fragmentation of the amplified purified products as well. . The fragmentation reaction
was prepared by mixing 3 µg of amplified purified products, 3 µl 10xSaqAI Buffer, 3 µl of
SaqAI enzyme. The volume was adjusted to 30 µl by nuclease-free water. The reaction
was incubated at 37°C 30 min, and the enzyme was inactivated by heating the reaction
for 5 min at 65°C. The digestion conditions were selected to form a range of fragment
sizes from 50 to 200 bp. The fragments sizes were checked in 1.5 % agarose gel in
1XTAE buffer. The fragmented DNA was purified using PureLink Quick PCR Purification
Kit (Invitrogen, USA) as described in subsection 2.2.5.2
2.2.5.4 DNA labelling and hybridization
Fragmented DNA was labelling using BioPrime® Plus Array CGH Genomic Labelling
System (Invitrogen, USA) Alexa Fluor® 555-labeled primers and nucleotides. For
labelling reaction approximately 1 µg of fragmented DNA and 20 µl of Alexa Fluor® 555
Panomer™ 9(31 nmole was re-suspended in 110 µl of 2.5X Reaction Buffer) were mixed
and nuclease free water was added up to 44 µl, . The reaction was incubated at 95°C for
10 minutes then chilled immediately on an ice and was protected from light, for 5
minutes. One ice, 5 µl of 10X Nucleotide Mix with Alexa Fluor® 555-aha-dCTP and 1 µl
Exo-Klenow Fragment were added to the mixture with DNA to reach the final volume 50
µl, The isothermal amplification of DNA and simultaneous labelling in the samples
proceeds at 37ᵒC for 8 hours. The amplified and labelled products were purified using
BioPrime® purification Module (Invitrogen, USA). 200 µl of Binding Buffer B2 was
added to to each tube contained the labelled samples. The samples were loaded onto the
PureLink™ Spin Column and centrifuged at 10,000×g for 1 minute, then flow-through
CHAPTER 2: MATERIALS AND METHODS
59
was discarded and the column was placed back in the tube. 650 µl of Wash Buffer W1
was added to the column and centrifuged at 10,000×g for 1 minute, then flow-through
was discarded and the column was placed back in the tube. The samples were spin at
maximum speed (14,000 xg) for an additional 3 minutes to remove any residual wash
buffer. Finally, the Spin Column was placed in a new, sterile amber collection tube and
55 µl of elution buffer E1 was added to the centre of column, incubated at room
temperature for 1 minute and centrifuged at maximum speed (14,000 xg) for 2 minutes.
The yield of amplified labelled purified DNA was estimated by spectrophotometer
(NanoDrop, Technologies Inc.). The DNA was precipitated by adding 10 volumes of 2%
LiClO4 in acetone for overnight at - 20°C. Centrifugation was performed to pellet DNA at
14,000 x g for 30 min, the supernatant was discarded, and the pellet was washed with 1
ml acetone, and then centrifuged at 14,000xg for 20 min. The pellet was dried on air or
at 37°C 10-20 min until the red/pink colour was clearly visible. The pellet was dissolved
in the hybridization buffer (1 M GuSCN (guanidine thyocianate), 5 mM EDTA, 50 mM
HEPES (pH 7.5), 0.2 mg/ml BSA (bovine serum albumin) (Sigma, UK), then was heated
at 95°C for 5 min followed by immediate chilling on ice for 2 min. The fluorescent
samples solution was loaded onto the slide of immobilized probes and covered
immediately with the cover slip. The cover slip was prepared by soaking in washing
detergent for 20 min, and then extensively rinsed in distillate water; after that, the cover
slip was soaked in the concentrated sulfuric acid for 20 min, and rinsed extensively in
distillate water. Finally, the cover slip was rinsed in 95% ethanol and dry at 80°C. The
area under a cover slip was 2.2cm x6.4cm=14.08 cm2; the volume of the hybridization
buffer (30 µl) is calculated from 2 µl per 1 cm2. Before placing the biochip in the
hybridization chamber 10 µl of nuclease-free water was added to one of the grooves to
keep 100 % humidity in the chamber during the hybridization reaction. The samples
were hybridized at 25°C for 4 hours followed by washing in 4xSSC; 7.2 % Sarcosyl for 30
sec. The slide was dried by centrifugation in a Microarray High Speed Centrifuge
(ArrayIt, USA), and visualized using a Portable Imager 5000 (Aurora Photonic, USA)
with 532 nm green Laser and 580 nm filter. The signal intensity of each point on the
biochip was calculated using MicroChip Imager software (Aurora Photonics, USA).
CHAPTER 2: MATERIALS AND METHODS
60
Section 3
Detection and quantification of sulphate reducing bacteria using microbiological and quantitative PCR techniques
2.3.1 Bacterial culture preparation
Desulfovibrio indonesiensis NCIMB 13468 were cultivated anaerobically in Vitamin
Medium Reducing iron (VMR medium) at 37°C for 7 days. E. coli BP was cultivated
anaerobically in VMR medium at 37°C overnight (~17-18 h). The bacterial inoculum
was adjusted for all experiments of approximately 2X108 cells/ml for E. coli BP and
7X106 cells/ml for D. indonesiensis. Seven days old culture of D. indonesiensis was used
for subculturing D. indonesiensis in different time of growth for 7 days, 3 months and 1
year in VMR and VMI media at 37 °C. The SRB growth stages divided into exponential
phase, stable state and decline period. It has been found that the number of SRB
increased rapidly (from day ~3-30) achieving the maximum in day seven. After that the
growing process of SRB reached the stable state (~2-6 months) before it started the last
stage which is the decline phase (Butlin et al., 1949; Zhang et al., 2011; Mahat et al.,
2015). In these experiments three growth stages (7days, 3 months and 1 year old
culture) were used. This is meant to observe the stage by stage progress of SRB gene
expression over time as well as to evaluate the ability of E.coli BP to induce the growth
of SRB in low cell density. Each age of D. indonesiensis cultures (7 days, 3months, and 1
year) were serially diluted in VMR and VMI media. 0.5ml of E. coli BP was inoculated to
each dilution of different age D. indonesiensis cultures and incubated anaerobically at
37°C for 28 days and the growth of D. indonesiensis , was monitored every 24 hours
visually for the production of black iron sulfide. The blackening of the medium is evidence
of SRB growth in the medium (Vester & Ingvorsen, et al., 1998). Viable count of E. coli
BP was determined before co-culture with D. indonesiensis by ten-fold serially diluting
0.1 ml of E.coli BP culture in 0.9 % NaCl; and plated on a freshly prepared MacConkey
agar and incubation at 37°C. The colonies were counted and expressed as colony
forming unit per ml of culture (cfu/ml). The composition of VMR, VMI and MacConkey
agar is shown in Appendix 1.
CHAPTER 2: MATERIALS AND METHODS
61
Genomic DNA was extracted from 5ml of D. indonesiensis original culture cells which
was used for serial dilution (of all ages) QIAGEN DNeasy Blood and Tissue Kit (QIAGEN,
Germany) following the manufacturer’s instructions (as described in subsection 2.1.1.1).
2.3.2 Purification of total bacterial RNA
RNA was purified from D. indonesiensis original culture which were used for serial
dilution (of all ages) using the QIAGEN RNeasy Mini kit according to the manufacturer’s
instructions (RNAprotect bacteria reagent Handbook, 07/2001 p.13-35). Before
proceeding to RNA purification 4 ml of D. indonesiensis culture were stabilized by adding
1 volume of the culture to 2 volumes of RNAprotect bacteria solution placed in a
reaction tube. The samples were mixed by vortexing for 5 seconds, incubated for 5 min
at room temperature and centrifuged for 10 min at 5000x g (Heraeus Fresco 21, Thermo
Scientific, UK). The supernatant was discarded. 200µl of TE buffer containing lysozyme
(1mg/ml) was added to the samples and mixed by vortexing for 10 seconds then
incubated for 5 min at room temperature. 700 µl of buffer RLT containing β-
Mercaptoethanol (10µl β-ME per 1ml buffer RLT), (sigma Aldrich, UK) was added to the
samples and votexed vigorously then centrifuged at the maximum speed (13000xg) for
2 min. The supernatant was collected in a new eppendorf tube and 500 µl of 100%
ethanol was added to the lysate, mixed by votexing. The lysate containing ethanol was
applied to RNeasy mini column placed in a 2ml collection tube and centrifuged for 15
seconds at 8000x g. DNase digestion was performed during RNA purification using
RNase-free DNase set (QIAGEN, UK). 350 µl of RW1 buffer was added into the RNeasy
mini column and centrifuged for 15 seconds at 8000 x g and the flow through was
discarded. 80 µl of DNase I incubation mixture was added into each RNeasy silica-gel
membrane, and incubated in the room temperature for 15 min. 350 µl of buffer RW1
was added into the RNeasy column , incubated at the room temperature for 5 min and
centrifuged for 15 seconds at 8000 x g. The RNeasy column was transferred into new
collection tube, washed by 500 µl RPE buffer and centrifuged for 15 seconds at 8000 x g
and the flow through was discarded. RNeasy column was washed again with 500 µl RPE
buffer and centrifuged for 2 min at 8000 x g. The RNeasy column was placed in new 2 ml
collection tube and centrifuged for 1min at the maximum speed (14000 x g). Finally,
RNA was eluted with 40 µl RNase free water onto the centre of RNeasy silica-gel
CHAPTER 2: MATERIALS AND METHODS
62
membrane was placed in a new 1.5 ml Eppendorf tube and centrifuged for 1 min at 8
000 x g.
2.3.2 .1 cDNA Synthesis
Random primers (2.5 µl, 60 µM, Promega, UK) were annealed to RNA (10 µl, approx. 1-2
µg) in a total volume of 15 µl at 70°C for 10 minutes and immediately placed on ice for 5
minutes. 2.5 µl of 5x buffer for MMLV, 1µl of 10mM dNTP mix, 1µl RNase inhibitor, 4.5 µl
of water and 1µl of MMLV reverse transcriptase (Promega, UK) were mixed and added
to each RNA/primer mixture; the total volume of each reaction was 25 μl. Reverse
transcription was carried out at 37°C for 60 minutes. The reaction was stopped by
heating for 15 minutes at 70°C. An equal volume of H2O was added to each cDNA. A
negative control was included by omitting MMLV-RT from the reaction mixture. cDNA
samples were stored at -20°C before real-time PCR analysis.
2.3.3 Preparation of PCR fragment of the apsA and the dsrA genes
The apsA and dsrA genes fragments were used for standard curve generation for qPCR.
PCR fragments were amplified (five tubes reactions; final volume of 50 µl) using
genomic DNA of D. indonesiensis as a template as describes in subsection 2.1.2.2. The
PCR products were verified in 1.2% agarose gel. The rest of PCR reactions of were
combined and DNA was precipitated. One tenth of the PCR products volume of 3M
Sodium Acetate pH 5.2 was added (Sigma adrich, UK). 2.5 volumes of 100 % ethanol was
added to the samples and placed in a -20°C overnight. DNA was precipitated by
centrifugation at 14 000 x g for 10 min, washed twice with 400µl of 70% ethanol and
centrifuged at 14 000 x g for 10 min each. After removal of ethanol the pelleted DNA was
air dried and resuspended in 25 µl of nuclease free water. The DNA samples were mixed
with 6X loading dye (Promega, UK) and analysed on 1.2 % agrose gel in 1 X TAE buffer
(90 min, 100 V). The gel was photographed after staining as described in section 2.1.3.
CHAPTER 2: MATERIALS AND METHODS
63
2.3.4 Purification of DNA fragments from the agarose gel
PCR fragments of dsrA gene (221 bp) and apsA gene (135 bp) were cut off from agarose
gel and purified using QIAquick ® Gel Extraction kit (Qiagen, UK) according to the
manufacturer's instructions (QIAquick Spin Handbook 04/2014 p24-25). 3 volumes of
Buffer QG were added to 1 volume of gel slice (100 mg ~ 100 μl). The gel slice was
incubated at 50°C for 10 min (until agarose has completely dissolved). The tube was
vortexed occasionally. 1 gel volume of isopropanol was added to the sample and mixed
thoroughly. The samples were applied to the QIAquick column, centrifuged for 1 min,
washed with 500 µl of buffer QG, and then washed with 750 µl of buffer PE. After
centrifugation the flow-through was discarded. The column was centrifuged for an
additional 1min at 14 000 x g to dry the column. DNA was eluted with 50 µl of Buffer EB
(10mMTris·Cl, pH 8.5).
2.3.5 Quantitative real-time PCR (Q PCR)
All real-time experiments were performed on Stratagene Mx3005P (Agilent
Technologies, UK). A quantitative real-time PCR assay targeting two functional genes of
SRB dsrA and apsA was used to determine the copy numbers of these genes and finally
enumerate the amount of SRB. Specific primers DSR-F1, DSR-R (Table 2.1.1) and
TaqMan probe (6FAM) - 5’-CCGATAACRCYGCCGCCGTAACCGA - 3’ - (TAMRA) (Bourne et
al., 2011) were used for dsrA gene. For the apsA gene; TaqMan assay using the primers
APS-F, APS-R (Table 2.1.1) and the probe – (6FAM-5’ CGCATCATGGAGCGCATGTTCATC-
3’ - TAMRA); (This study).
Standard curves for quantification of the dsrA and the apsA genes were constructed with
10-fold dilutions of gel purified PCR fragments from 10-1 – 10-10. PCR reaction contained
5 µl of 2 X Brilliant Q PCR master mix (Agilent, UK), 0.5 µl of 10 µM of the DSR or APS
forward and reverse primers mix, 0.25 µl of 5 uM specific dsr or aps TaqMan probes, 2 µl
of template, plus nuclease-free water to bring the total volume to 10 µl. The reaction
condition for amplification was 95°C for 3 min followed by 40 cycles of 95°C for 15 sec,
67°C for 30 sec and 72°C for 30 sec. All runs included a no template control (NTC).
Standard curves were obtained by plotting the Ct (threshold cycle) values of 10-fold
dilutions of the dsrA and the apsA fragments. Ct values for each well were calculated
CHAPTER 2: MATERIALS AND METHODS
64
using the manufacturer’s software. Three experiments in triplicate were performed for
each gene. For data analysis, Ct and DNA quantity in ng were exported into a Microsoft
Excel Word spread sheets. The copy number was calculated on the basis of the
measured concentration of standard (ng/ µl), an average molecular mass of 660 for a
base pair in a double-stranded DNA and the size of PCR fragment (221 bp for dsrA gene
and 135 bp for apsA) and Avogadro’s number (6,02 x 1023 mol-1). Normality of data was
assessed using the Shapiro-Wilk normality test. The data was found to be not normally
distributed; therefore Kruskal-Wallis with Dunn’s multiple comparisons or Mann-
Whitney statistical tests were performed to compare the SRB g DNA copy numbers and
the gene expression in different time of growth.
2.3.6 Measurements of hydrogen sulfide (H2S)
The serial dilution of a 7 days old D. indonesiensis culture was grown in either VMR or
VMI medium at 37°C for 7 days. The last two vials of the serial dilution which gave
positive indication of D. indonesiensis growth as pure and mixed culture with E. coli BP
were used for measuring H2S concentration using H2S meter (Sensorcon, Inc. ,USA),
with a lower detection limit of 1 ppm. Three independent experiments were done in
triplicate. The averages of the 3 readings of each repeats were taken and the mean
values were determined. Error bars indicate standard deviation. * indicate significance
(p ≤ 0.05).
2.3.7 Pulsed field gel electrophoresis (PFGE)
The Pulsed Field Gel Electrophoresis (PFGE) was performed according to manufacture
instruction (CHEF-DR®II pulsed field electrophoresis systems, instruction manual and
application guide p12-23) with some minor modification adapted from (Devereux et al.,
1997; Ribeiro et al., 2009) See below.
2.3.7.1 Preparation of Agarose Embedded Bacterial DNA
The bacterial culture used in this study was D. indonesiensis and E.coli BP. D.
indonesiensis was grown anaerobically in VMR medium at 37°C for 24h, 48h and 6 days
and E. coli BP aerobically in LB Broth for 4h at 37°C. 180 g/ml chloramphenicol (sigma
CHAPTER 2: MATERIALS AND METHODS
65
Aldrich, UK) was added to the bacterial cultures and continued to grow for 1 hour. A
twenty-fold dilution of the above bacterial suspension was made and small amount of
this suspension was placed on the hemocytometer for counting bacterial cells under
light microscope with a 40 X objective. Approximately 5X10⁸ bacterial cells were taken
for each ml of agarose plugs to be made. The cells were harvested from culture by
centrifugation at 10,000 x g for 5 minutes at 4 °C. The pellet was washed twice with TE
buffer(10 mM Tris-HCl and 1 mM EDTA, pH 8.0) (sigma, UK) then the cells were
resuspend in one-half the final volume of plugs to be made using Cell Suspension Buffer
(10 mM Tris, pH 7.2, 20 mM NaCl, 50 mM EDTA) and the cell suspension were
equilibrated to 50 °C. 2% of low melt agarose (CleanCut agarose, Biorad, US) was
prepared and equilibrated to 50 °C in a water bath. The cell suspension combined with
an equal volume of 2% CleanCut agarose and mixed gently but thoroughly. The
cell/agarose mixtures were kept at 50 °C, and the mixture was transferred to plug molds
which were placed initially on ice using sterile transfer pipettes. The agarose was
allowed to solidify by placing the molds at 4 °C for 10–15 min. Using 50 ml of falcon
tube, 5ml of lysozyme buffer was transferred (10 mM Tris, pH 7.2, 50 mM NaCl, 0.5%
Brij 58, 0.2% sodium deoxycholate, 0.5% sodium lauryl sarcosine, 1 mg/ml lysozyme),
(sigma, UK) for each ml of agarose plugs. The solidified agarose plugs were pushed
using the snap off tool with plug molds into the 50 ml centrifuge tube containing
lysozyme buffer then the plugs Incubated for 1 hour at 37 °C without agitation. The
lysozyme buffer was removed and the plugs was rinsed with 25 ml of 1x wash buffer
(20 mM Tris, pH 8.0, 50 mM EDTA) (sigma, UK). 5 ml of Proteinase K Reaction Buffer
(100 mM EDTA, pH 8.0, 0.2% sodium deoxycholate, 1% sodium lauryl sarcosine, 1
mg/ml Proteinase K) (sigma, UK) was used for each ml of agarose plugs. The plugs were
transferred to the tubes contain the Proteinase K Reaction Buffer then were Incubated
at 50°C until the plug goes clear after 2 hours-overnight without agitation. The plugs
were washed with sterile water twice, followed by 6 washes with 50 ml of 1X wash
buffer (20 mM Tris, pH 8.0, 50 mM EDTA)(sigma, UK) 1 hour each at room temperature
with gentle agitation. The plugs were kept in TE buffer and store at 4°C until used.
CHAPTER 2: MATERIALS AND METHODS
66
2.3.7.2 Restriction Enzyme Digestion of Plugs
Tow restriction enzymes were used for plugs digestion PmeI which recognizes
5’GTTT↓AAAC 3’ sites and NotI recognizes 5’GC↓GGCCGC 3’ sites (Thermo scientific, US).
In a sterile 1.5 ml microcentrifuge tube one plug was placed with 1 ml of 1X Buffer B for
PmeI or 1X Buffer H for Not I then were incubated at room temperature for 1 hour with
gentle agitation. The buffer was aspirated off then 0.3 ml of fresh 1X Buffer was added
and 50U of the restriction enzyme Pme I or Not I per 100 µl plug and was incubated at
37°C for 2 hours. After the incubation time, the buffer was removed and 1 ml of
UltraPure™ 0.5 X TBE Buffer (made from 10X stock solution:1.0M Tris, 0.9M boric Acid,
0.01M EDTA pH 8.0)(Invitrogen by Life Technologies, UK) was added and was incubated
for 30 minutes at 37°C with gentle agitation.
2.3.7.3 Casting the gel
The gel was run using CHEF-DR® II Pulsed Field Electrophoresis Systems (Biorad, US).
3 litters of 0.5X TBE was poured into in the electrophoresis chamber and the buffer was
allowed to circulate at ~ 0.75 L/min to equilibrate to the desire temperature (14°C).
The plugs were placed on each tooth of the comb then 1% Pulsed Field Certified™agrose
gel (Biorad, USA) in 0.5X TBE was poured gently and allowed to solidify for 15 minutes.
The comb was removed gently and the gel wells was filled with 1% agrose gel in 0.5X
TBE allowed to solidified for 10 minutes and placed in the electrophoresis chamber
containing 0.5X TBE. The run time was 22 h at 6 V/cm with a 60 to 120 second switch
time ramp at an included angle of 120°. Three markers were used as as size standards in
the gel; Lambda ladder 1000 kb, Saccharomyces cerevisiae 2200 kb and
Schizosaccharomyces pombe 5.7 mb (Biorad, USA).
2.3.7.4 Removing and staining the gel
After the run was completed the gel was removed gently and slided off into 0.5µg/ml of
SYBR® Safe DNA gel stain (Life Technologies) in 0.5 X TBE buffer and the gel was left in
dark for 20 min then destained in 0.5 X TBE for 5min. Finally, the gel was photographed
under UV light using molecular imager® Gel Doc™ XR+ Imaging system (BIORAD). The
CHAPTER 2: MATERIALS AND METHODS
67
genome size was estimated based on the observation of linearized chromosomal DNA
which migrated into the agarose gel during PFGE.
CHAPTER 2: MATERIALS AND METHODS
68
Section 4
The effect of Manuka honey and antibiotics on the growth of pure
and mixed sulphate reducing bacterial biofilms
2.4.1 Honey samples
Manuka honey of different strength: UMF® 5+, UMF® 15+, UMF®25+ and (Comvita), as
commercial honey (Rowse) were compared for their effects on biofilm formation. The
methylglyoxal (MGO) content of Manuka honey is used to define the antimicrobial
activity of this honey and is referred to as the unique Manuka Honey Factor (UMF®)
(Mavric et al., 2008). All samples were diluted in VMR medium for use in all assays and
stored in the dark at 4°C. All honey concentrations are expressed as % w/v. To
distinguish the antimicrobial effect of honey from any osmotic effects of sugars, an
artificial honey, simulating the sugar composition of honey, 30.5% glucose, 37.5%
fructose and 1.5% sucrose dissolved in sterile water (Shannon et al., 1979) were also
tested.
2.4.2 Antibiotics
The antibiotics used in this study included rifaximin and metronidazole (sigma aldrich,
UK). The two antibiotics most commonly used as antimicrobial drugs in the treatment of
patients suffering from UC (Wang and Yang , 2012).
2.4.3 Bacterial growth conditions
The bacterial strains which were used in this study were D. indonesiensis and E. coli BP.
Approximately 2X108 cells/ml for E. coli BP and 7X106 cells/ml for D. indonesiensis had
been used as adjusted inoculum for all the further studies. Both strains were
subcultured anaerobically in VMR medium at 37oC. E.coli BP culture was ten-fold
serially diluted in 0.9 ml of 0.9 % NaCl; then plated on MacConkey agar and incubated at
37°C for 24 h for counting of viable bacterial cells before inoculation. The initial D.
CHAPTER 2: MATERIALS AND METHODS
69
indonesiensis inoculum size was determined using a haemocytometer. The result of
counting was expressed as cells per millilitre (cells/ml).
2.4.4 Preparation of honey samples
Stock solution of the honey were prepared in VMR medium (w/v %). Honey solution
was kept in closed jars in dark by covering the jars with aluminium foil. Prepared honey
solution was placed in shaker incubator innova 4000 (New Brunswick Scientific, USA) at
220 rpm for approximately 1 h at 37oC to obtain a homogenous solution. Aseptically, the
prepared honey solution was then pre-filtered with 0.45μm Nalgene syringe pre-filters
(Themo Scientific, USA) into new autoclaved-containers in order to purify the highly
viscous solutions and prevent clogging of final filtration. Pre-filtrated honey solution
was then subsequently filtered with sterile 0.22μm pore sized "Millex Syringe
Filters"(Merck Chemicals Ltd.) into autoclaved glass vials and degassed under a stream
of nitrogen (N2) for 30 mins, sealed and kept in the 4⁰C until further use.
2.4.5 Determination of the Minimum Inhibitory Concentration (MIC) and
Minimum Bactericidal Concentration (MBC) of honey and antibiotics
The Minimum Inhibitory Concentration (MIC) which is the lowest concentration of an
antimicrobial agent required to inhibit the visible growth of bacteria; and the Minimum
Bactericidal Concentration (MBC) which is the lowest concentration of antimicrobial
treatment that required to kill the bacteria was determined. The effect of honey,
rifaximin and metronidazole alone and in combination against D. indonesiensis as pure
and mixed culture with E. coli BP in biofilms were examined. These experiments were
performed in different aspects. First, for the evaluation of the ability of the honey to
prevent the biofilms formation; the honey concentrations were prepared in increments
of 5 in the range of 0- 60 % (w/v) in VMR medium. D. indonesiensis was inoculated as
pure and mixed culture with E.coli BP (approximately 106 cells/ml for D. indonesiensis
and 108 cells/ml for E. coli BP) simultaneously with the honey and incubated
anaerobically at 37⁰C for 7days. After 7 days incubation the vials were treated carefully
in order to not disrupted the biofilms. The supernatant solution was gently removed by
syringe and the uniform biofilms that been adhere, colonize in the vials were rinsed by
CHAPTER 2: MATERIALS AND METHODS
70
0.5 ml of fresh VMI medium. 1 ml of the solution was transferred into fresh VMI medium
and ten fold serial dilutions were performed. The media with cultures were incubated at
37⁰C for 7days. The second experiment was performed to assess wither the honey was
able to eradicate the already established biofilms. The culture of pure and mixed D.
indonesiensis with E.coli BP was allowed to grow at 37⁰C for 6 days. Then different
concentrations of honey (0- 60 % (w/v) were added to the bacterial cultures and
incubated at 37⁰C overnight. The bacterial biofilms assessments were repeated as
described above for all experiments. Third, the effect of antibiotics on the growth of
pure and mixed D. indonesiensis biofilms was evaluated. Antibiotics solutions were
prepared from stock solutions of 25mg/ml and diluted to 256- 1 µg/ml (v/v) through
doubling dilution in VMR medium. D. indonesiensis was inoculated as pure and mixed
culture with E. coli BP in different concentrations of antibiotics alone and in
combination then incubated anaerobically at 37⁰C for 7days before the biofilms were
assed as described above. Fourth, the synergistic effect of honey and antibiotics was
investigated. The concentration of Manuka UMF®15+ that inhibited the growth of pure
and mixed D. indonesiensis biofilms was used to be tested with different concentrations
of antibiotics to assess whether manuka honey will be able to improve the efficiency and
reduce the MIC value of the antibiotics. Manuka UMF®15+ was used to be tested
synergistically with antibiotics because it is commercially available. The volume of
antibiotics necessary to achieve the required concentrations (256- 1 µg/ml (v/v) was
aseptically added simultaneously into sterile vials containing the bacterial culture and
the required Manuka UMF®15+ concentration in VMR medium then incubated
anaerobically at 37⁰C for 7days. In the fifth experiment, to evaluate whether manuka
honey can inhibit the growth of biofilms therefore the antibiotics will have better effect
on the weak biofilms. As described above the required Manuka UMF®15+ concentration
in VMR medium were added simultaneously to the pure and mixed D. indonesiensis. The
cultures were incubated with Manuka UMF®15+ at 37⁰C for for 6 days; then to achieve
the required concentrations of the antibiotics combination; appropriate volume was
added. The cultures were incubated overnight before the biofilms were assesd.
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71
2.4.6 Preparation of Methylglyoxal (MGO)
Methylglyoxal (MGO) is the major antibacterial component of Manuka honey. MGO is
found in high concentration in the nectar of manuka flowers and belived to give manuka
honey its antibacterial properties (Mavric et al., 2008). In order to assess the
contribution role of MGO to the activity of Manuka honey this experiment was
performed. MGO levels can be used to estimate the UMF based on the relationship
between MGO and UMF identified previously (Adams et al., 2008; Cokcetin et al., 2016).
MGO levels in Manuka honey found to be ranging from 38 to 1123 mg/kg. Manuka
UMF®15+ was predicted to has the mnimum MGO content of approximately 514 mg/kg
(Adams et al., 2008; Atrott and Henle, 2009; Roberts et al., 2015). MGO (40 % solution in
water (w/w) (Sigma-Aldrich, UK) was diluted in VMR medium to obtain the equivalent
concentrations of MGO content in Manuka UMF®15+ (ranging from 0-0.25% v/v). D.
indonesiensis was inoculated as pure and mixed culture with E.coli BP in the different
concentrantions of MGO and incubated for 7 days at 37°C before the biofilms were assed
as described in section 2.4.5. In all experiments the mean values were estimated from
five independent experiements. All values are presented as mean standard deviation.
Normality of data was assessed using the Shapiro-Wilk normality test. In all instances
the data was found to be not normally distributed. As such Kruskal-Wallis with Dunn’s
multiple comparisons or Mann-Whitney statistical tests were performed to compare the
differences between treatments and among honey samples on the pure and mixed D.
indonesiensis biofilms as required.
.
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72
CHAPTER 3: RESULTS AND DISCUSSION
CHAPTER 3: RESULTS AND DISCUSSION
73
Section 1
Analysis of Biopsy DNA samples using 16S rRNA and DSR genes
sequences
It has been widely excepted that the bacteria that colonize the human colon play a
fundamental role in the human physiology however, little is known about the bacterial
composition that colonize mucosal surfaces in the human colon. In this investigation the
bacterial composition of the colonic mucosa associated bacteria were characterised for
microbial identification and phylogeny construction. Rapid molecular fingerprinting
analysis based on 16S rRNA and DSR genes including DGGE and sequencing were used
to detect and identified bacteria in samples obtained from patients suffering from UC
and healthy individual.
3.1.1 PCR for bacterial 16S rRNA genes
The 16S rRNA gene is used frequently as a target for many assays, and universal PCR
primers are routinely used for species identification (Weisburg et al., 1991). To confirm
that bacterial DNA has been successfully extracted and to exclude the presence of
inhibitors of the PCR reaction, the universal bacterial 16S rRNA primers were used as a
positive PCR amplification control (subsection 2.1.2.1). PCR gels demonstrating the
presence of bacterial DNA are depicted in Figures 3.1.1 and 3.1.2.
The 16S rRNA PCR products of the correct size (550 bp and 1500 bp) for DNA extracted
from the colonic mucosa of healthy volunteer and patients suffering from UC showed
that all the extracted DNA products were efficiently PCR amplifiable in all samples,
which indicated that bacteria were always present and that the PCR were conducted
without significant amounts of inhibitors in biopsy samples. Negative controls yielded
no bands, which demonstrated that contaminants were not present. In addition, the fact
that no band occurred in all samples examined confirmed that PCR reagents were free of
contaminants.
CHAPTER 3: RESULTS AND DISCUSSION
74
Figure 3.1.1. A photograph of agarose gel showing the PCR amplified products of
16SrRNA gene (550 bp) with primers: 341F+GC and 907R. Lane 1: PCR products of
Patient 1 suffering from UC; lane 2: PCR products of Patient 2 suffering from UC; lane 3:
PCR products of Patient 3 suffering from UC ; lane 4: PCR products of Patient 4 suffering
from UC; lane 5: PCR products of Healthy volunteer; lane 6: 1 kb DNA Ladder; lane 7:
Negative control; lane 8: DNA D. alaskensis
CHAPTER 3: RESULTS AND DISCUSSION
75
Figure 3.1.2. A photograph of agarose gel showing the PCR amplified products of
16SrRNA gene (1500 bp) with primers: 341F+GC and 907R. Lane 1: PCR products of
Patient 1 suffering from UC ; lane 2: PCR products of Patient 2 suffering from UC; lane
3: PCR products of Patient 3 suffering from UC ; lane 4: PCR products of Patient 4
suffering from UC; lane 5:PCR products of Healthy volunteer; lane 6: 1 kb DNA Ladder;
lane 7: Negative control; lane 8: DNA D. alaskensis
Highly specific primers for Desulfovibrios 16S rRNA gene were used to confirm the
presence of the genus of Desulfovibrio in the samples. The result showed strong PCR
positivity of the expected size and mucosa associated Desulfovibrio were present in all of
UC tissues and in healthy volunteer (Figure 3.1.3). The microbiota present in the colon
has been mostly described from faeces samples, which do not accurately represent the
mucosa-associated microbiota (Zoetendal et al., 2002; Eckburg et al., 2005). Therefore,
little is known about the diversity and ecology of mucosa-associated SRB in the human
colon. This study provides some information on the molecular characterization of
abundant of microbes that are associated with the colonic mucosa of healthy human and
UC suffers. These observations confirmed that Desulfovibrio were the predominant SRB
populations and that they were found associated with the mucosa throughout the large
intestine in both UC sufferers and healthy individual, supporting previous evidence that
indicated Desulfovibrio was the dominant genus of SRB found in human colon (Gibson et
al., 1988a; Loubinoux et al., 2002; Stewart et al., 2006; Kushkevych, 2014).
CHAPTER 3: RESULTS AND DISCUSSION
76
Figure 3.1.3. A photograph of agarose gel showing the PCR amplified products of
desulfovibrios 16S rRNA gene (135 bp) with primers: DSV691F and DSV826R. Lane 1:
PCR products of Patient 1 suffering from UC ; lane 2: PCR products of Patient 2 suffering
from UC; lane 3: PCR products of Patient 3 suffering from UC ; lane 4: PCR products of
Patient 4 suffering from UC; lane 5: 100 bp DNA Ladder; lane 6: Negative control; lane
7: PCR products of DNA D. vulgaris; lane 8: PCR products of Healthy volunteer
To further analyze the SRB population in biopsy samples of UC and healthy volunteer,
genomic DNAs from these samples were amplified using the dsr primers set DSR1F+and
DSR-R. All samples tested yielded unambiguously a positive result and distinct
amplicons of the right size were obtained and that indicated the presence of SRB in all
samples (Figures 3.1.4). DSR gene involved in dissimilatory sulphate reduction pathway
in SRB is evolutionarily conserved among SRB, including delta proteobacteria and was
selected as the preferred marker for SRB. (Wagner,1998; Ben-Dov et al.,, 2007).
CHAPTER 3: RESULTS AND DISCUSSION
77
Figure 3.1.4. A photograph of agarose gel showing the PCR amplified products of dsrA
gene (221 bp) with primers: DSR1F+and DSR-R Lane 1: PCR products of Patient 1
suffering from UC ; lane 2: PCR products of Patient 2 suffering from UC; lane 3: PCR
products of Patient 3 suffering from UC ; lane 4: PCR products of Patient 4 suffering
from UC; lane 5: 100 bp DNA Ladder; lane 6: Negative control; lane 7: PCR products of
DNA D. vulgaris; lane 8: PCR products of Healthy volunteer
Functional marker genes encoding key enzymes of characteristic of SRB metabolic
energy pathways, such as sulfate reduction, have been found to be useful in the
detection of microorganisms belonging to different ecophysiological classes in complex
microbial assemblages from diverse habitats in faeces samples. However, no
information exists about the diversity of functional genes of SRB in colonic mucosa
associated bacteria (Gibson et al., 1991; Fite et al., 2004; Wagner et al., 2005; Frank et
al., 2007; Christophersen et al., 2011; Rey et al., 2013).
Perhaps, in order to generate information on microflora of the colon, a molecular
approach was taken, involving PCR, Cloning, Sequencing and DGGE-PCR analysis, to
provide a structural and functional analysis of the colon from healthy and UC patients.
CHAPTER 3: RESULTS AND DISCUSSION
78
3.1.2 DGGE analysis of microbial communities in the biopsy samples
It is evident that there is a strong association between UC and colonic mucosa
microflora. However, the entire picture remains unclear. Using molecular techniques, to
analyze the bacteria existing in colonic mucus that were considered to be influencing the
inflammation of UC helped to clarify the relationship between colonic inflammation and
microflora diversity in UC. PCR-DGGE approach has been used extensively to analysis
human microbiota and was found that this approach is a useful tool for comparing
complex microbial community profiles present in human colonic samples (Scanlan et al.,
2006; Kinross et al., 2008).
The 16S rRNA gene profiles of the bacterial collections associated with biopsies were
generated by PCR coupled with DGGE. The PCR amplified products of 16S rRNA gene
(550 bp) were analysed with DGGE for the visualisation of DNA bands representing
dominant bacterial species in the samples. DGGE allows separation of DNA fragment
mixtures of equal length depending on their sequence. To some extent the community
profiles originating in the patients suffering from UC resembled each other as far as the
16S rRNA band numbers and migration patterns are concerned. However, significant
differences in the healthy volunteer sample profile were observed. Examples of profiles
are shown in Figure 3.1.5.
The high-density bands which represent the dominant microbial populations showed
the differences in microbial structure of the various biopsy samples. The differences in
localization of dominant bands in the adjacent lanes suggested that there were various
degrees of changes and differences in the microbial community structure between UC
patients and healthy control and, even within UC patients.
The DGGE patterns obtained from each sample contained a number of bands at
relatively different positions and with differing intensities. The community profiles of
samples obtained from UC patients number one and two (Fig. 3.1.5, lanes 1 and 2)
showed relatively few strong bands (1-3), while the profile of the samples from UC
patient number 3 and 4 (Fig 3.1.5, lanes 3 and 4) had between 5-7 dominant bands with
a greater number of weaker bands. In contrast, the lane (Fig. 3.1.5., lane 6) containing
amplicons from the healthy patient displayed up to 9 bands, representing the presence
CHAPTER 3: RESULTS AND DISCUSSION
79
of at least 9 different bacterial phylotypes. This suggests that there has been a reduction
in diversity for UC patients compared with healthy one. Several bands were shared
between all samples, and these included bands 2, 4, 10, 15 and 24. The bands 1,7,13 and
19 were present in UC patient 1, 3, 4 and healthy control. However, these bands were
absence in UC patient 2. Band 5 and 12 were present only in UC patients 3 and 4, while
band 11 was unique to UC patient 3 (Figure 3.1.5).
The lower band counts found in the UC patients indicate the possible absence or
reduction in the abundance of specific bacterial classes amongst their predominant
bacteria compared with healthy control (Figure 3.1.5). DGGE analyses in this
investigation showed that the richness of UC patient’s profiles was lower compared to
healthy individual. These results collectively are consistent with many comparative
studies of colonic microbiota of patients with UC and non-UC controls, confirming that
changes occur in the microbial communities in UC patients colon when compared to
healthy individuals (Frank et al., 2007; Arumugam et al., 2011; Rigottier-Gois, 2013).
This was subsequently confirmed by sequencing and biochip analysis (Section 2).
CHAPTER 3: RESULTS AND DISCUSSION
80
Figure 3.1.5. DGGE profile demonstrating diversity of bacterial populations in biopsy
samples obtained from healthy volunteer and patients suffering from UC based on PCR
products of 550 bp of bacterial 16S rRNA. Lane 1: UC Patient 1, Lane 2: UC Patient 2,
Lane 3: UC Patient 3, Lane 4: UC Patient 4, Lane 5: Negative control, Lane 6: healthy
volunteer
CHAPTER 3: RESULTS AND DISCUSSION
81
The dendrogram in Figure 3.1.6 was constructed based on DGGE banding similarity
patterns, and clearly shows that the banding profiles from all of the patients biopsy
samples formed a single clade, supported by a bootstrap value of 75%. Within this
patient cluster two other clusters formed, one composed of patient 1 with 2 (cluster I)
and the other containing patients 3 with 4 (cluster II), supported by bootstrap values of
80% and 81% respectively. The banding profile from the healthy individual grouped
outside from the main cluster. This result showed the distinct separation of patients
suffering from UC and healthy individual thus indicating a difference in the composition
of the intestinal microbiota present in both group.
Figure 3.1.6. UPGMA dendogram showing similarity of DGGE profiles of microbial
communities in all colonic biopsy samples of UC patients and healthy control used in
this study. Bands from each sample were coded and a UMPGA tree was produced using
the distance algorithm described by Nei and Li (1979) and executed in PAUP* 4.0. P1:
UC patient number 1, P2: UC patient number 2 , P3: UC patient number 3 , P4: UC patient
number 4 , H: healthy individual.
CHAPTER 3: RESULTS AND DISCUSSION
82
The strength of a PCR-DGGE analysis is that it is able to show the changes in structure of
dominant bacterial communities between different environments by simple changes in
the banding profiles. The bands can be identified after they have been re-amplified and
sequenced by performing a BLAST analysis using the target sequence against a
database, such as GenBank. 25 bands, identified in Fig. 3.1.6, were excised, re-amplified
and sequenced. Although 25 bands were excised, not all were successfully sequenced.
The results of 16S rRNA gene sequencing of DGGE-bands is shown in (Table 3.1.1). Only
4 bands were successfully sequenced. There are a number of reasons why the majority
of bands failed to generate a sequence: (1) insufficient DNA for sequencing, (2)
contamination of the extracted DNA with inhibitors, (3) degradation of the extracted
DNA so that the primers fail to anneal, and (4) the band may contain heterogeneous
DNA fragments that give indistinct sequences (Sekiguchi et al., 2001; Sun et al., 2013). In
addition to this, some PCR fragments may not separate during DGGE so that they form
indistinct smears of other separated PCR fragment (Kisand & Wikner, 2003). The
sequences from the 4 bands were matched with those from Escherichia coli (98%
match) for band 5, and Enterobacter aerogenes (99% match) for band 13, both of which
were present in the UC patients. The other sequences from bands 23 and 25, derived
from a healthy individual, matched those from an uncultured Ruminococcus species
(98% match) and Eggerthella lenta (99% match) resepectively. Both of these species are
Gram-positive gut inhabiting, anaerobic bacteria. Eggerthella species have been
implicated in systematic bacteremia (Lau et al., 2004), and are candidates for
involvement in UC, and the cause of liver and anal abscesses. Although the sample size
here is small, the results suggest that the microflora of UC patients is different to that of
a healthy person, with it having a lower diversity and, potentially, species that differ
considerably.
CHAPTER 3: RESULTS AND DISCUSSION
83
Table 3.1.1. Sequencing results of bacterial 16S rRNA from the bands cut from DGGE gel
Band(s)* Closest relative % Identity Accession no
5 Escherichia coli 1303 98% |HE616528.1|
13 Enterobacter aerogenes EA1509E 99% |FO203355.1|
23 Uncultured Ruminococcus sp. M6-41 98% gb|EU530431.1|
25 Eggerthella lenta strain W02018C 99% KP944193.1
*Bands are numbered as indicated on the DGGE gels shown in Figure 3.1.5
3.1.3 Diversity analysis of PCR products of bacterial 16S rRNA and dsr
genes by sequencing
Analysis of amplified bacterial 16S rRNA gene provides in depth exploring of bacterial
diversity from complex bacterial communities. In this investigation, bacterial diversity
of the mucosal biopsies from 4 UC patients and one healthy control were compared by
analysis of PCR-amplified 16S rRNA clone libraries. A total of 250 clones from the
mucosal biopsies were sequenced (control 100 clones; UC patient1 25 clones; UC
patient2 25 clones; UC patient3 50 clones; UC patient4 50 clones) to obtain an overall
estimation of bacterial diversity present in both groups.
The 16S rRNA sequence data from the healthy individual identified bacteria pre-
dominantly from families including Enterobacteriaceae, Bacterioidaceae, Clostridiacease,
Coriobacteriaceae, Ruminococcaceae, Eggerthellaceae, Lactobacilaceae and
Porphyromonoadaceae. All the four colitis patients had sequences from the
Enterobacteriaceae (Figure 3.1.7) and no other. The 16S rRNA have been used in several
studies to evaluate the bacterial diversity of the mucosal biopsies from different parts of
the gastrointestinal tract (GIT) like human jejunum, ileum, colon as well as the rectum. A
study focusing on evaluating the diversity of human GIT revealed that upon sequencing
347 clones bacteria from various phyla including Firmicutes, Bacteroidetes,
Proteobacteria, Fusobacteria, Verrucomicrobia, and Actinobacteria were observed within
one healthy individual (Wang et al., 2005). It was intriguing to note in this study that
microbial population varies from different parts of the colonic mucosa. For example
jejunum, ileum, colon, and rectum have highly diverse bacterial population. This
suggests that the differences of microbial population can occur within the patient.
CHAPTER 3: RESULTS AND DISCUSSION
84
However, other studies showed that the major group of bacteria were similar
throughout the different parts of the colon between the individuals (Eckburg et al.,
2005; Costello et al., 2009 ; Momozawa et al., 2011)
Figure 3.1.7. A histogram demonstrating the Family level of bacterial composition
present in healthy patient and patients suffering from UC (16S rRNA gene)
This data shown (Figure 3.1.7) which obtained from this study clearly demonstrate that
colitis leads to an imbalance in colon microbial population strengthening the population
of Enterobacteriaceae, whereas, bacteria from all other families had vanish among colitis
individuals. Since imbalance of normal colonic bacterial population is precondition for
colon inflammation (Gophna et al., 2006). Higher abundance of Enterobacteriaceae, in
UC patients has also been reported previously (Kotlowski et al., 2007).
Enterobacteriaceae have been observed to be active in inflammatory environments; the
pathogenic bacteria can start the inflammation process in genetic susceptibility people,
weakening the mucosal lining by the production of high concentration of butyric acid.
This leads to activation of the immune system provoking an attack on the mucosa cells,
causing a shift in the colonic commensal microbiota that favours members of the
Enterobacteriaceae (Ohkusa et al., 2003; Nedialkova et al., 2014). The imbalance of
CHAPTER 3: RESULTS AND DISCUSSION
85
microbial population in the colon leads to a metabolic imbalance which inflames the
colonic mucosa which may result in changing of bacterial composition (Ott et al., 2004).
A reduction of bacteroidetes was also observed in UC microbiota which comes in line
with our findings. However, this study found also that there was an increase in clostridia
in UC patients ( Hansen et al., 2011)
Regarding the relative abundance of various bacterial families present in the healthy
colon, it was observed that Bacteroidaceae was the most abundant bacterial family in
the colon of the healthy patient, having a 48% share of the total, followed by members
from the Clostridiaceae (16%), the Ruminococcaceae (13%), the Enterobacteriaceae
(12%), and the Coriobacteriaceae (8%). Three other families, Eggerthellaceae,
Porphyromonadaceae and Lactobacilaceae had sequences representing 1% of the total.
It is interesting to note that colitis patients having disrupted this balance of various
bacterial families and have 100% predominance of bacteria from Enterobacteriaceae
family.
Several studies have compared the bacteria present in patients with active UC and non
UC patients. Nishikawa et al (2009) shown that there was a loss of bacterial belonging
to Clostridia associated with active colitis. Ríos-Covián et al (2016) indicated that
species from Bacteriodes and Firmcute promote good health in the colon by producing
long chain fatty acid, which are an essential source of energy for epithelial cells lining
the colon. It is possible that after the inflammation and damaging the mucosa layer of
the colon, these bacteria may be depleted which result of the reduction of this bacterial
group during active UC disease.
A reduction of bacterial diversity has been observed in other studies. Walujkar et al
(2014) using 16S rRNA gene sequences reported dysbiosis and dysanaerobiosis in
microbial populations of patients suffering mild to server UC, with an overall decrease in
the proportion of obligate anaerobes and an increase in facultative anaerobes and some
aerobes. This study also aligns well with data generated from other studies that suggest
a reduction in the diversity of the colonic mucosa associated bacterial microflora (Ott et
al., 2004). The mechanisms causing this imbalance remain unknown.
The features of a healthy colon include low oxygen concentrations and the presence of
anaerobic bacteria in great numbers. The decrease of obligate anaerobes observed in
CHAPTER 3: RESULTS AND DISCUSSION
86
this study, including Bacteroidetes, Clostridium, Ruminococcus present in the colon of a
healthy individual, and the shift to facultative anaerobes, members of the
Enterobacteriaceae in the colons of patients suffreing from UC, can be explained by
increases in the levels of oxygen, which will kill the obligate anaerobes allowing
facultative anaerobes to take over (Lind Due et al., 2003; Lupp et al., 2007, Rigottier-
Gois, 2013)
Based on the information generated from sequences of 16S rRNA the next step was to
identify relative key species of each bacterium among UC patients and healthy control.
The healthy individual gut microbiome is highly complex. It has several species of
bacteria. However, the ulcerative colitis patients mainly harbor bacteria from
Enterobacteriaceae family. The major species of Enterobacteriaceae in the colon of
colitis patients varied amongst the patients. A breakdown for relative levels of each
bacterial species in healthy is shown in Table 3.1.2. In the healthy individual 16S RNA
gene sequences from Bacteriodes frgilis were recovered at a frequency of 14% followed
by 10% for Bacteroides dorei, Bacteroides vulgatus (8%), Ruminococcus torques (6%),
Clostridum ramosum (4%) and Clostridum saccharogumia , Clostridum cocleatum ,
Clostridum spiroforme and Barnesiella intestinihominis each with (3%) while the other
sequences represented the species Enterobacter aerogenes, Lactobacillus aogosae,
Faecalibacterium prausnitzii, Eggerthella lenta, klebsiella pneumoniae at a frequency of
1%.
Although all of the bacterial sequences from the colons of the UC patients represented
species of the Enterbacteriaceae, there were not a single common bacterial species
found among all the four patients. The majority of 16S rRNA gene sequences recovered
from UC patient 1 belonged to Citrobacter species, with 29% of the sequences belonging
to Citrobacter freundii, 10% to Citrobacter murliniae, and 8% each to Citrobacter braakii,
Citrobacter werkmanii and Enterobacter aerogenes in addition to other species displayed
in Table 3.1.2.
16S rRNA sequences from UC patient 2 were found to have identities similar to some of
the common species found in UC patient 1, although the relative levels varied. These
included matches for Citrobacter freundii and Enterobacter aerogenes, each representing
22% of the sequences recovered, Citrobacter braakii, representing 19%, Raoultella
CHAPTER 3: RESULTS AND DISCUSSION
87
ornithinolytica (13%), Kluyvera cryocrescens (11%) in addition to other species
including Klebsiella pneumoniae, Lelliottia amnigena, Acinetobacter calcoaceticus,
Citrobacter murliniae and Klebsiella oxytoca all showed in Table 3.1.2.
UC patient 3 showed a different profile to all of the other UC patients. UC patient 3 had
sequence matches for five species, 48% of which were for Escherichia coli followed by
28% for Shigella sonnei and 10% for Serratia marcescens, and 7% each for Shigella
flexneri, Shigella boydii (Table 3.1.2). Sequences from UC patient 4 matched those from
Enterobacter aerogenes (24% of the total), Raoultella ornithinolytica (22%), Kluyvera
cryocrescens (19%), Citrobacter freundii (12%) followed by the presence of Citrobacter
braakii, Klebsiella oxytoca and Yokenella regensburgei at 4% each with other more
species all showed in Table 3.1.2.
CHAPTER 3: RESULTS AND DISCUSSION
88
Table 3.1.2. The species level of bacterial composition present in the colon of UC patients and healthy control (16S rRNA)
Bacterial species Healthy control UC patient1 UC patient2 UC patient3 UC patient4
Bacteroides fragilis 14% - - - -
Bacteroides dorei 10% - - - -
Bacteroides vulgatus 8% - - - -
Ruminococcus torques 6% - - - -
Clostridium ramosum 4% - - - -
Clostridium spiroforme, C.cocleatum , C.saccharogumia, C.hathewayi 3% - - - -
Clostridium leptum, C. lavalense, C.asparagiforme, C. bolteae, C.
clostridioforme, C.citroniae, C.coccoides, C. aldenense
1% - - - -
Ruminococcus productus, R. gnavus 1% - - - -
Bacteroides merdae, B. eggerthii, B. gallinarum, B. clarus, B. uniformis 1% - - - -
Odoribacter splanchnicus 1% - - - -
Butyricimonas paravirosa, Butyricimonas faecihominis, Butyricimonas virosa 1% - - - -
Barnesiella intestinihominis 3% - - - -
Blautia hansenii, Blautia stercoris 1% - - - -
Lactobacillus rogosae 1% - - - -
Adlercreutzia equolifaciens 1% - - - -
Asaccharobacter celatus 1% - - - -
Gordonibacter faecihominis,G. pamelaeae 1% - - - -
Enterorhabdus mucosicola, E. caecimuris 1% - - - -
CHAPTER 3: RESULTS AND DISCUSSION
89
Table 3.1.2 cont.
Bacterial species Healthy control UC patient1 UC patient2 UC patient3 UC patient4
Parvibacter caecicola 1% - - - -
Denitrobacterium detoxificans 1% - - - -
Slackia faecicanis 1% - - - -
Faecalibacterium prausnitzii 1% - - - -
Averyella dalhousiensis 1% - - - -
Kluyvera ascorbate 1% - - - -
Hungatella effluvii 1% - - - -
Erwinia billingiae 1% - - - -
Raoultella ornithinolytica 1% 4% 13% - 22%
Pantoea agglomerans 1% - - - 3%
Lelliottia amnigena 1% - 1% - -
Eggerthella lenta,Eggerthella sinensis,Eggerthella hongkongensis 1% - - - -
Enterobacter soli 1% - - - -
Klebsiella oxytoca - - 2% - 4%
Citrobacter youngae - 3% - - -
CHAPTER 3: RESULTS AND DISCUSSION
90
Table 3.1.2 cont.
Bacterial species Healthy control UC patient1 UC patient2 UC patient3 UC patient4
Escherichia coli - - - 48%- -
Shigella sonnei - - - 28% -
Serratia marcescens - - - 10% -
Citrobacter freundii 1% 29% 22% - 12%
Citrobacter murliniae - 10% 5% - -
Citrobacter braakii 1% 8% 19% - 4%
Citrobacter werkmanii - 8% - - 3%
Enterobacter aerogenes 1% 8% 22% - 24%
Kluyvera cryocrescens 1% 6% 11% - 19%
Kluyvera intermedia - 2% - - -
klebsiella pneumoniae 1% - 1% - -
Shigella flexneri - - - 7% -
Shigella boydii - - - 7% -
Raoultella terrigena - 2% - - 3%
Raoultella planticola - - - - 1%
CHAPTER 3: RESULTS AND DISCUSSION
91
Table 3.1.2 cont.
Bacterial species Healthy control UC patient1 UC patient2 UC patient3 UC patient4
Yokenella regensburgei - 1% - - 4%
Enterobacter asburiae - 2% - - 1%
Acinetobacter calcoaceticus - 2% 4% - -
Enterobacter cloacae - 4% - - -
Enterobacter ludwigii - 3% - - -
Cedecea neteri, Cedecea lapagei - 1% - - -
Leclercia adecarboxylata - 1% - - -
Escherichia vulneris - 1% - - -
Pantoea eucalypti - 1% - - -
Proteus mirabilis - 1% - - -
Enterobacter hormaechei - 2% - - -
CHAPTER 3: RESULTS AND DISCUSSION
92
Among the four UC patients, each has certain unique bacterial species suggesting
pathogenesis of diseases varies from individual to individual. Although the species differ
between patients, similar genera are present in 3 of the patients (Figure 3.1.8). Genera
of Citrobacter, Enterobacter, Kluyvera and Roultella are present in patients 1, 2 and 4. In
contrast, patient 3 has less diversity, dominated by genera of Escherichia and Shigella.
Healthy control was dominated by high proportion of Bactriodes , Ruminococcus and
clostridium and low level of Kluyvera, Roultella and Citrobacter when compared with UC
patients.
Becker et al (2015) highlighted the unmet need to identify bacterial species involved in
UC. The authors stated “It is currently unclear whether the composition of the microbial
flora or individual bacterial strains or pathogens induces or supports the pathogenesis of
UC. Further research will be necessary to carefully dissect the contribution of individual
bacterial species to this disease and to ascertain whether specific modulation of the
intestinal microbiome may represent a valuable further option for future therapeutic
strategies.” Although this study was a pilot in nature, it does identify bacterial
populations involved in colitis disorders at the genus and family level and highlights the
need for future genomic studies to understand better diseases associated with human
colonic mucosa.
CHAPTER 3: RESULTS AND DISCUSSION
93
Figure 3.1.8. Proportion of bacterial genera present in the colon of UC patients and healthy control (16S rRNA).
CHAPTER 3: RESULTS AND DISCUSSION
94
In the literature various species have been sporadically identified as being involved in
colitis. According to Kaakoush et al (2014) bacteria from Campylobacter species are
involved in colitis disorders. Among the various bacteria adherent and invasive
Escherichia coli, Helicobacter, Fusobacteria, and Campylobacter species have also been
identified (Bohr et al., 2004; Conte et al., 2006; Minami et al., 2009; Mukhopadhya et al.,
2011; Thomson et al., 2011). Data have started to emerge to indicate that the bacterial
genotype has a significant role in its invasiveness to gut (Kaakoush et al., 2014). Murine
model data further revealed that it is not only the presence of a particular bacterial
species that is important but it is the interaction of microbes with other species present
in the gut that determine the pathogenic aspects (Liang et al., 2014). A study by Winter
and Baumler (2014) agreed with our findings at the molecular level. According to this
study, high levels of facultative anaerobic bacteria mainly from the Enterobacteriaceae
family were involved in inflamed colon. In addition, Ahmed et al (2007) concluded that
pro-inflammatory bacterial species such as Shigella, Citrobacter were associated with
the early stages of inflammation, as possible bacterial operators that can drive colitis to
colon cancer in patients suffering from UC. It may be important to recognise that several
types of inflammatory pathways that could manifest, which could be influenced by
different bacteria. Lapthorne et al (2015), in an animal study, noticed a reduction in the
Enterobacteriaceae family bacteria associated with colonic disorders. This study
confirms these observations associating members of the Enterobacteriaceae with colitis,
particularly, Citrobacter and Shigella species, as well as recording that dysbiosis in
colonic mucosa is generally characterized by bacterial reduction in diversity and
decrease of obligate anaerobes in parallel with increase facultative anaerobes or the
presences of unusual aerobes bacteria.
CHAPTER 3: RESULTS AND DISCUSSION
95
In the current study, primary interest was to detect SRB in samples of healthy control
and patients suffering from UC and to estimate if there is any difrences or the precense
of specific SRB species in both groups. SRB 16S rRNA gene sequences were not detected
in this study. This might be because these bacteria were not present in high enough
quantities as aresult of their slow growth. In order to detect SRBs, a different target
gene was used which is specific to SRBs. In next set of experiments primer specific for
dissimilatory sulfite reductase gene was used to amplify dsr gene of healthy and UC
patients DNA then PCR products cloned. A total of 30 clones from the mucosal biopsies
were sequenced (control 7 clones; UC patient1 6 clones; UC patient2 7 clones; UC
patient3 5 clones; UC patient4 5 clones). The sequences were compared with database
sequences. As shown in Figure 3.1.9, SRB of five different families were identified. These
include Desulfovibrionaceae, Desulfohalobiaceae, Desulfomicrobiacease,
Desulfobacteraceae, Desulfonatronaceae, and Syntrophobacteraceae. Importantly, all
four colitis patients revealed a loss of the Desulfonatronaceae bacterial family.
Furthermore, bacteria from Syntrophobacteraceae family were only present in patient 2
and 4. Based on the data from Figure 3.1.9, it is not possible to make any conclusive
statement, except that members of the Desulfovibrionaceae and Desulfomicrobiaceae are
present in healthy and all UC patients and Desulfontronaceae was only present in
healthy control and was absence in all UC patients. The microbiome diversity appears to
be the major issue encountered by patients with colitis disorders.
CHAPTER 3: RESULTS AND DISCUSSION
96
Figure 3.1.9. A histogram demonstrating the Family level of SRB composition present
in healthy patient and patients suffering from UC (dsrA gene)
CHAPTER 3: RESULTS AND DISCUSSION
97
Sequences from Desulfovibrionaceae, Desulfobacteraceae and Desulfomicrobiaceae were
the most abundant of SRB groups present in the samples, including samples from
patients with UC and the healthy individual and which were recovered with similar
frequencies in general. The exception being sequences from patient 1 which showed the
absence of Desulfobacteraceae .
The gut microbiome is highly diverse in nature, and overall bacterial population varies
between individuals. A dietary habit of the people and environmental factors affects the
composition of bacterial populations. The diversity of SRBs, however, is limited to two
families and 2 main genera. In total, sequences from 14 genera were detected from the
patients and the healthy individual. The presence of sequences from two genera,
Desulfovibrio and Desulfomicobium, were recovered from all patients and the healthy
individual. All of the other genera were present in one, two, three or four of the
individuals sampled. The greatest number of species recovered, according to the dsr
sequences, was found within the Desulfovibrio. Sequences representing 18 Desulfovibrio
species were recovered from the healthy individual. In three of the four patients, this
number increased to 20, 27 and 28 (Figure 3.1.10). Only in patient 1 did the number of
Desulfovibrio species sequences drop, to 5. The diversity of SRBs in this individual was
greatly reduced compared to the other patients, reflecting the dominance of 7 species in
total. Sequences from the other genus present in all of the individuals matched
Desulfomicrobium species. The healthy individual had sequences representing 3 species
of Desulfomicrobium. This number increased in two UC patients, stayed the same in one
and decreased in the other (Figure 3.1.10).
CHAPTER 3: RESULTS AND DISCUSSION
98
Figure 3.1.10. Distribution of genera from heathy and UC patients. The numbers above
each histogram represents the number of species identified as Desulfovibrio from each
patient.
CHAPTER 3: RESULTS AND DISCUSSION
99
The levels and diversity of SRB in UC patient 1 are of interest. The clinical data relevant
to disease severity and how long the patient has been suffering from the disease was
not described for the patients. In future, it will be quite intriguing to link bacterial
population particularly SRB with the UC pathogenic aspect and disease stage. It is a
quite possible that the levels and type of bacteria species might be defining factors in
the disease outcomes. In comparing with health control, it is quite evident that existing
population of SRB was significantly elevated in this patient, although the diversity was
low. However, the other three individuals also manifested emergence of new bacterial
species.
The healthy individual had dsr sequences representing a complex and diverse SRB
population with no significant increase for any specific species. The sequence identities
recovered from UC patient 1 are shown in Table 3.1.3, along with those from the other
patients. Seven species of bacteria were identified including Desulfohalobium retbaense
(15%), Desulfomicrobium salsuginis (15%), Desulfovibrio burkinensis (14%),
Desulfovibrio intestinalis (14%), Desulfovibrio carbinolicus (14%), Desulfovibrio
magneticus (14%), and Desulfovibrio desulfuricans (14%). Twenty-one new sequences
were recovered from the rest of the patients (2 to 4), representing 21 new species. The
diversity from patients 2, 3 and 4 resembled that of the healthy individual. The largest
group of sequences representing single species were recovered from UC patient 2, and
these were for Desulfovibrio gigas and Desulfovibrio hydrothermalis, both representing
7% of the total sequences recovered.
CHAPTER 3: RESULTS AND DISCUSSION
100
Table 3.1.3 The level of SRB species present in the colon of UC patients and healthy individual (dsr gene)
Bacterial species Healthy control UC patient1 UC patient2 UC patient3 UC patient4
Desulfovibrio africanus 7% - 5% 4% 4%
Desulfovibrio intestinalis 6% 14% 5% 3% 4%
Desulfovibrio gabonensis 6% - 2% 1% 3%
Bilophila wadsworthia 6% - 5% 1% 4%
Desulfovibrio vulgaris 4% - 2% 1% 3%
Desulfovibrio piger 4% - 5% 1% 3%
Desulfovibrio desulfuricans 4% 14% 5% 3% 3%
Desulfovibrio fructosivorans 3% - - 1% 1%
Desulfovibrio fairfieldensis 3% - 2% 1% 3%
Desulfovibrio simplex 3% - - 1% 3%
Desulfovibrio burkinensis 3% 14% 2% 4% 3%
Desulfovibrio oxyclinae 3% - 2% 3% 3%
Desulfovibrio oceani 3% - - 1% 1%
Desulfovibrio magneticus 3% 14% 2% 4% 3%
Desulfovibrio halophilus 2% - 2% 3% 3%
Desulfovibrio longus 2% - - 1% 1%
Desulfovibrio aminophilus 2% - - 3% 1%
Desulfovibrio termitidis 1% - 2% 3% 3%
CHAPTER 3: RESULTS AND DISCUSSION
101
Table 3.1.3 cont
Bacterial species Healthy control UC patient1 UC patient2 UC patient3 UC patient4
Desulfovibrio carbinolicus 2% 14% 3% 4% 3%
Desulfovibrio alkalitolerans 1% - - 3% 1%
Desulfovibrio alaskensis 2% - 3% 3% 1%
Desulfovibrio piezophilus - - 5% 3% 4%
Desulfovibrio portus - - 2% 1% 3%
Desulfovibrio hydrothermalis - - 7% 1% -
Desulfovibrio gigas - - 7% 4% 4%
Desulfovibrio zosterae - - 3% 3% 1%
Desulfovibrio salexigens - - 5% 1% 3%
Desulfovibrio aespoeensis - - 3% - 1%
Desulfovibrio acrylicus - - - 3% 1%
Desulfovibrio aerotolerans - - - 3% 1%
Desulfovibrio carbinoliphilus - - - 3% 1%
Desulfovibrio cuneatus - - - 3% 1%
Desulfohalobium retbaense 1% 15% - 3% 1%
Desulfomicrobium salsuginis 1% 15% 3% 3% 3%
Desulfosalsimonas propionicica 1% - - 3% 1%
CHAPTER 3: RESULTS AND DISCUSSION
102
Table 3.1.3 cont
Bacterial species Healthy control UC patient1 UC patient2 UC patient3 UC patient4
Desulfonatronospira thiodismutans 2% - - - -
Desulfatibacillum alkenivorans 3% - - 1% 3%
Desulfococcus oleovorans 4% - 2% - 1%
Desulfomicrobium apsheronum 1% - - 2% -
Desulfatitalea tepidiphila 2% - - - -
Desulfonatronovibrio halophilus 2% - - - -
Desulfomicrobium aestuarii 1% - - 3% 1%
Desulfonatronum thioautotrophicum 1% - - - -
Desulfonatronum lacustre 1% - - - -
Desulfatibacillum aliphaticivorans 3% - - 1% 1%
Desulfobacter vibrioformis 3% 2% - -
Desulfobacter postgatei 2% - 2% - 1%
Desulfobacter latus 2% - 2% - 1%
Thermodesulforhabdus norvegicus - - 3% - 1%
Desulfosarcina variabilis - - 3% - 2%
Desulfomicrobium escambiense - - 2% 2% 3%
Desulfomicrobium baculatum - - 2% 2% 1%
CHAPTER 3: RESULTS AND DISCUSSION
103
Table 3.1.3 cont
Bacterial species Healthy control UC patient1 UC patient2 UC patient3 UC patient4
Desulfomicrobium thermophilum - - - 3% 2%
Desulfomicrobium orale - - - 3% 2%
Desulfomicrobium salsuginisstrain - - - - 2%
CHAPTER 3: RESULTS AND DISCUSSION
104
The number of dsr sequences that represented a single species totaled 36 for the
healthy individual, 7 for patient 1, 31 for patient 2, 42 for pateint 3 and 47 for pateint 4.
This confusing data is simplified if the number of genera for each individual is
considered. The healthy individual had sequences representing 11 genera, whereas the
UC patients had 4, 7, 6 and 9 for patients 1, 2, 3 and 4 respectively. A reduction in
diversity for each individual patient. This reduction coincides with an increase in the
propotion of sequences representing the genus Desulfovibrio. In the healthy individual,
53% of sequences recovered belonged to Desulfovibrio, this proportion increased to
71% and 70% in patients 1 and 3, and to 66% and 67% in patients 4 and 2.
Sequences representing 32 species of Desulfovibrio were recovered from all individuals.
Of these, only 5 sequences were present in the healthy individual and UC patients, and
these represented the species Desulfovibrio intestinalis, Desulfovibrio desulfuricans,
Desulfovibrio burkinensis, Desulfovibrio carbinolicus and Desulfovibrio magnetus (Figure
3.1.11). Sequences present in the healthy individual and three of the patients included
those presenting the species Desulfovibrio fairfieldensis, Desulfovibrio termitidis,
Desulfovibrio alaskensis and Desulfovibrio halophilus. Sequences representing species
only found in three of the UC suffers included: Desulfovibrio piger, D. piezophilus, D.
gigas, D. zosterae, and D. salexigens. The only other species present as a dsr sequence in
the healthy individual and three of the UC patients was Bilophila wadsworthia.
CHAPTER 3: RESULTS AND DISCUSSION
105
Figure 3.1.11. Species of Desulfovibrio present in a healthy individual and four UC suffers
CHAPTER 3: RESULTS AND DISCUSSION
106
Jia et al (2012) reported that distribution of SRB was reduced among patients suffering
from UC. This study concluded that for a healthy functioning colon two bacterial species
were needed, Desulfovibrio piger and B. wadsworthia, where their presence was noted at
levels of 93.5% in the healthy control group. In the present study, a slightly different
response was observed. There was a reduction in diversity of SRBs at the genera level
associated with UC, possibly at the expense of an increase in the diversity of one genus,
Desulfovibrio. Desulfovibrio piger sequences were not present in the healthy individual
but those for Bilophila wadsworthia were. In UC patient 1, which exhibited the greatest
diversity loss, B. wadsworthia was absent, but it was present in the other three UC
patients. This study suggests that it is, perhaps, the loss of diversity at the genera level
that is important in the development of UC, allowing one group of SRBs to become
dominant.
There are several studies describing dysbiosis, but these have produced different sets of
information about the bacterial populations. Ott et al (2004) studied the dynamics of
the mucosa-associated flora in the UC patients using 16S rRNA sequences, reporting the
loss of bacterial diversity including Prevotella sp, Porphyromonas sp, however, none of
the bacteria described in this study were reported in the UC patients. It is quite possible,
that the amplification strategies as well as the quality of DNA also impact the overall
recovery of bacterial species for 16S rRNA and these findings still awaits confirmation
with future studies. No SRB sequences were detected in the current study when the 16S
rRNA gene was targeted.
Although 16S rRNA is widely used for bacterial species characterization from
environments, it is possible that some species sequences are missed in such studies,
particularly those from rare or low abundant species. Targeting functional genes in PCR
amplifications may be the only option to get conclusive information about rare bacterial
species. There are several reports highlighting this issue. Salzman et al (2002) studied
the characterization and presence of Lactobacillus acidophilus from mouse small
intestine, large intestine and faeces. 16S rRNA gene sequences were helpful in the
identification of this particular bacterium, but also picked out certain other bacterial
species. Similarly, there are several other reports in the literature in which gene-specific
CHAPTER 3: RESULTS AND DISCUSSION
107
PCR has been the final resort in the characterization of bacterial species (Parra et al.,
1991; Spierings et al., 1992; Gloux et al., 2011).
The major focus of interst in this study was on sulfate reducing bacteria, and to our
surprise, it was not possible to get a reliable picture of sulfate reducing bacteria in the
UC and healthy individual using 16S rRNA gene sequences. However, a gene specific
PCR involving dsr gene was selected to find out their potential contributions in UC.
This study based on combining 16S rRNA bacterial identification strategy combined
with specific gene for SRB (dsr gene) utilization in evaluating the relative levels of colon
microbiota about UC has opened up a new avenue of research. It has identified that
bacterial diversity changes dramatically in UC sufferers at several levels; there is an
overwhelming increase in the number of species from the Enterobacteriaceae, and an
increase of species from Desulfovibrio.
3.1.4 Phylogenetic analysis
The sequences of 16S rRNA and dsrA genes obtained by cloning and sequencing were
used for Phylogenetic analysis construction. All sequences and their closest relatives
were aligned using Muscle implemented in Seaview (Galtier et al., 1996; Edgar, 2004)
(Appendices 2). Based on the sequence result from 16S rRNA; the majority of the
inhabitants of the colon of UC patient and healthy control belonged to Firmicutes,
Bacteroidetes, Actinobacteria and Proteobacteria. These bacteria have been recorded as
present in healthy colon by other studies, supporting our findings (Eckburg et al., 2005;
Huse et al., 2012). Phylogenetic analyses were performed for each of these bacterial
groups, with the significance of each clade estimated by bootstrapping.
The taxa in the Firmicutes 16S rRNA Phylogenetic tree (Figure 3.1.12) divide into three
deeply branched clades, named Group-A, Group-B, and Group-C. Group-C contained
members of the class Negativicutes and sequences from clones were absent from this
group. Within Group-B, representing class Bacilli and Clostridia, 5 out of 27 clones from
the healthy colon sample (K53, K55, K23, P81, and P68) were grouped with sequences
from Clostridium ramosum. Most of the clones, 22 out of 27 clones from the healthy
control sample, belonged to the larger group, Group-A. Within the Group-A, 2 clones
(K24 and K31) were grouped with Clostridium lavalense and 2 clones (C_P2100 and K8)
CHAPTER 3: RESULTS AND DISCUSSION
108
with sequences from Hungatella hathewayi. 7 clone sequences (p87, p28, p69, p21, p67,
k10, and k5) formed a distinct clade that was closely related to Ruminococcus torques
sequences. 2 clones (K29 and P24) had sequences that grouped with sequences from
Clostridium nexile. 2 clone sequences (K30 and K50) formed a clade with those from
Ruminococcus gnavus. 2 clones (P41 and P47) formed their own clade, which was
closely related to Subdoligranulum variabile sequences. 5 clones (K53, k55, k23, p81,
and p68) were clustered with Clostridium raosum. Clostridia species participate in
immune homeostasis, inhibition of autoimmunity and harmful inflammation to colonic
mucosa, and they also contribute to the overall maintenance of colon function, however,
it still unknown if the reduction of Clostridia species in the colon can lead to chronic
inflation in UC patients (Frank et al., 2007; Rajilic et al., 2007). Clones K25, P76, K13,
K52, and K32 were classified as Blautia coccoides, Eubacterium eligens, Clostridium
leptum, Faecalibacterium prausnitzii, Flavonifractor plautii respectively; which may
indicate that these clones are belonging to these bacteria.
CHAPTER 3: RESULTS AND DISCUSSION
109
Figure 3.1.12. A maximum likelihood phylogenetic tree (model TN93) showing the
relationships between derived 16S rRNA gene sequences of known taxa belonging to
Firmicutes and those of random clones from healthy control; the support value
estimated with 100 bootstrap replicates using PhyML. Bootstrap values which above 50
only shown
CHAPTER 3: RESULTS AND DISCUSSION
110
Both S. variabile and Faecalibacterium prausnitzii are abundant in the healthy human
colon and they have an important function in human health and protection against
pathogenic bacteria (Nava et al., 2005).
The taxa representing the order Bacteroidales formed a maximum likelihood tree
(Figure 3.1.13) that divided into four deeply branched clades named group-A to group-
D. Most of the clones, 37 out of 43 Bacteroidales clones from healthy control sample,
belonged to the larger group, group-A. The Bacteroidales, an order within the phylum
Bacteroidetes, contains four families including the Bacteroidaceae, the
Porphyromonaceae, the Rikenellaceae, and the Prevotellaceae. Members of the
Bacteroidales have been shown to predominate and occupy a vital niche at the colonic
mucosal surface, which gives these bacteria the ability to interact with the host and
therefore they have role(s) in the human health either beneficial or detrimental
(Hooper et al., 2003; Mazmanian et al., 2005). Within the group-A, containing general
Bacteroides taxa, clones and reference taxa formed seven independent sub-clades
termed BF1, BF2, BA1, BV, BD, BU, and BA2. Sequences from 14 out of 43 clones (p45,
p46, k26, p44, p82, p25, p64, k4, p89, k16, k51, k6, p49, and p26) formed clades with
BF1 and BF2 subgroups with various B. fragilis strains. Sub-clade BA1 comprised 6
sequences out of 43 clones (k2, k3, k1, p78, p212, k28), which was basal to clades BF1
and BF2. Blast searching results showed k2 and k3 clones had 99% sequence
homologies with B. fragilis, as did sequences from clones p78, p212 and k28 at 97%,
96% and 98% homology matches respectively. It would appear that clades BF1, BF2 and
BA1 contain strains of B. fragilis, with the exception k1 clone, which had 99% sequence
homology with that from Bacteroides clarus. Poxton et al (1997) found mucosa
associated B. fragilis in the colon representing 42% of total bacteroides isolated, which
is in agreement with the high frequency of this species in healthy human colon of this
study.
Clade BV contained 5 clone sequences (k54, p85, p48, p83, p86) with representatives of
B. vulgatus sequences at a basal location, while clade BD containing 7 clone sequences
(k22, p29, p66, p62, p88, p65, p710) with a basal B. dorei sequence. The taxonomic
affiliation of the BA2 clade, which contains 4 clone sequences (p77, k27, p61, and p410)
only, was hard to infer from the tree. The blast searching results showed that the
CHAPTER 3: RESULTS AND DISCUSSION
111
sequence from clone P410 had 96% sequence identity with that from B. uniformis.
Whereas, the sequence from clone P61 had 99% sequence homology with that from B.
fragilis. Clones k27 and p77 were closely related to B. vulgatus. Clade BA2 is sister to
clade BU, which contained 1 clone (p412) and many B. uniformis strains. In this study
the dominating species including B. thetaiotaomicron, B. vulgatus, B. uniformis and B.
fragilis which were observed to be commonly present in the healthy control sample
came in line with other studies (Frank et al., 2007;Noor et al., 2010; Sekirov et al., 2010).
The protection role of B. vulgatus and B. ovatus against the development of colitis and
intestinal inflammation has previously been demonstrated (Waidmann et al., 2003;
Conte et al., 2006; Hudcovic et al., 2009).
The remaining 3 clades (group B, group C and group D) all contained clone sequences.
In general, group-B contained members of the Parabacterioides and clone k15, which
grouped with the 16S rRNA sequence from Parabacteroides merdea. Group-C contained
member of the Barnesiella as a sister group to three clones (P211, P611, p210) where
sequences from Barnesiella viscericola represented to nearest taxonomic relation. Basal
to this main clade was the sequence from clone L31 and Barnesiella intestinihominis.
Group-D contained members of the Butyricimonas and clone p27, which formed a clade
with the sequence from Butyricimonas virosa. This species has been recorded before as
being a member of the human colonic mucosa, and reinforces the usefulness of 16S
rRNA sequences to provide a rapid identification of environmental bacteria, providing a
broader understanding of unknown bacteria present in mucosa of human colon (Toprak
et al., 2015; Mehta et al., 2015; Enemchukwu et al., 2016).
CHAPTER 3: RESULTS AND DISCUSSION
112
Figure 3.1.13. A maximum likelihood phylogenetic tree (model TN93) showing the
relationships between derived 16S rRNA gene sequences of known taxa belonging to
Bacteroidetes and those of random clones from healthy control; the support value
estimated with 100 bootstrap replicates using PhyML. Bootstrap values which above 50
only shown
CHAPTER 3: RESULTS AND DISCUSSION
113
The 16S rRNA phylogenetic tree of Proteobacteria in healthy control sample (Figure
3.1.14) appeared divided into two major branches named Group-A, Group-B, containing
the Gamma Proteobacteria and the Delta Proteobacteria, as an outgroup. Group A
comprised members of the orders Pseudomonadales and Enterobacterales, and two
clones from the healthy control sample (P79 and P610) which grouped inside of the
Enterobacterales subgroup, closely related to the sequences of Enterobacter aerogenes.
Enterobacter aerogenes is recognised as part of the normal intestinal flora, however
under certain conditions these bacteria have been found to infect the human colon and
lead to several symptoms that can cause debilitation of the host colon that leads to
inflammation (Grimont & Grimont, 2006).
Figure 3.1.14. A maximum likelihood phylogenetic tree (model TN93) showing the
relationships between derived 16S rRNA gene sequences of known taxa belonging to
Proteobacteria and those of random clones from healthy control; the support value
estimated with 100 bootstrap replicates using PhyML. Bootstrap values which above 50
only shown
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114
The last bacterial group from the healthy individual was the Actinobacteria and the
phylogenetic tree for this is shown in Figure 3.1.15. It contained three major branches
Group-A, Group-B, and Group-C corresponding to the Coriobacteriia, Bifidobacteriales,
and Actinobacteri. Within the Group-A, one clone (K14) from healthy control sample
formed a clade with Eggerthella lenta. E. lenta found as member of normal bowel
mucosa their presence contributes to the host defense through the anti-inflammation
activity that certain E. lenta strains have however their pathogenicity is not as low as
previously suggested (Kageyama et al., 1999; Saunders et al., 2009; Longman & Littman,
2015).
Figure 3.1.15. A maximum likelihood phylogenetic tree (model TN93) showing the
relationships between derived 16S rRNA gene sequences of known taxa belonging to
Actinobacteria and those of random clones from healthy control; the support value
estimated with 100 bootstrap replicates using PhyML. Bootstrap values which above 50
only shown
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115
Sequences recovered from UC suffers gave homology matches to members of the
Enterobacteriaceae, and a phylogenetic tree (Figure 3.1.16) based on maximum
likelihood analysis was constructed using clone rRNA sequences and public available
reference sequences for this group. The clone sequences from all patients suffering from
UC grouped into three distinct groups termed Group-A, Group-B, and Group-C. Group-A
contained 11 clone sequences (C_23/24, C_23/32, C_A1A6, C23/22, C23/33, C_A1A17,
C_A1A20, C_A1A15, C_A1A11, C_A1A18, and C_A1A14), which formed a clade with
sequences from Escherichia coli. The clones belonged to Group-A , were all from patient
3. Group-B contained 10 clone sequences formed a clade with those from Citrobacter
freundii and these sequences were from patients 2 and 4. Group-C contained 7 clones
from patient 1 and patient 2 and these sequences also formed a clade with sequences
from Citrobacter freundii. Several studies have observed imbalances in the bacteria
present in the colon of patients suffering from UC such as the increase of certain group
of bacteria (Proteobacteria) which was observed this study (Ott et al., 2004; Frank et al.,
2007; Packey& Sartor., 2009). The taxonomic differences between control and UC
patients were distinguished in this study and our results are similar in terms of the
reduction the reduction of biodiversity of Firmicutes, Bacteroidetes and Actinobacteria
in patients of UC and an increase in members of Gamma Proteobacteria was also
observed (Sokol & Seksik, 2010).
CHAPTER 3: RESULTS AND DISCUSSION
116
Figure 3.1.16. A maximum likelihood phylogenetic tree (model TN93) showing the
relationships between derived 16S rRNA gene sequences of known taxa belonging to
Proteobacteria and those of random clones from patients suffering from UC the support
value estimated with 100 bootstrap replicates using PhyML. Bootstrap values which
above 50 only shown
CHAPTER 3: RESULTS AND DISCUSSION
117
SRB belong to the mucosa associated bacteria of both humans and animals (Cummings
et al., 2003), and 67- 91% of all dominant SRB species are belonging to Desulfovibrio
genus (Kushkevych, 2013). A pathogenic role in UC disease may came from this genus of
SRB, however not one 16S rRNA sequence for these species was detected from healthy
or UC patients. One reason that could explain this absence at the 16S rRNA level is the
rarity of SRB cells, sequences from which might be out-completed in PCR amplifications
by those from commoner species. In order to characterise the SRBs present in the
human colon, a different gene was targeted, one that would be present pre-dominantly
in SRB cells – the dissimilatory sulfite reducatase (dsr) gene. Dissimilatory sulfate
reduction and the production of hydrogen sulphide by SRB in the human colon have
been proposed as one of the main causes of colonic mucosa inflammation (Rowan et al.,
2009). Previous studies on the diversity of SRB in complex microbial communities such
as human colon have been based on 16SrRNA gene analysis (Deplancke et al., 2000;
Nava et al., 2012; Kushkevych, 2014). The dsr gene has been widely used as ideal
phylogenetic marker gene for assessing the diversity of SRB in complex environments,
other than the human colon (Wagner et al., 1998; Loy et al., 2004; Nakagawa et al.,
2004).
The dsr gene phylogenetic tree (Figure 3.1.17) was constructed from an alignment of
taxa including dsr sequences from cloned PCR products. A maximum likelihood tree
divided the taxa into five deeply branched clades named group-A to group-E. The
Group-A, Group-B, Group-C, Group-D, and Group-E clades corresponding to the
Desulfovibrionales, Desulfobacterales, Desulfovibrionales, Desulfarculales, and
Syntrophobacterales taxonomic lineage respectively. The dsr gene clone sequences were
from both healthy (clones F1, F4, F6, and E5) and UC patients (clones A7, B8, B6, C4,
C11, C1, D4, D12, and D5) and all were placed inside the larger clade, the Group-A for
the Delsulfovibrionales, but not at the same location. All the clone sequences from
healthy control samples were clustered together, and were closely related to
Desulfovibrio aminophilus and Desulfovibrio longus. All the clone sequences from three
patient samples were clustered together with Desulfovibrio alaskensis G20, a gram-
negative mesophilic sulfate-reducing bacterium. The presence and increased members
of the genus of Desulfovibrio subspecies (Specifically, strains of D. piger and D. vulgaris)
were reported as human opportunistic pathogen in UC patients (Gibson et al., 1990;
Zinkevich & Beech, 2000; Rowan et al., 2009; Rowan et al., 2010).
CHAPTER 3: RESULTS AND DISCUSSION
118
Figure 3.1.17. Deltaproteobacteria maximum-likelihood (model TN93) dsr gene based
phylogenetic tree. The tree reflected the relative placement of the dsr gene clones from
healthy control and patients suffering from UC among sulfate-reducing
Deltaproteobacteria. Numbers placed above the branches were the support values
estimated with 100 bootstrap replicates using PhyML. Bootstrap values which above 50
only shown
CHAPTER 3: RESULTS AND DISCUSSION
119
Based on the phylogenetic analysis obtained from this study distinct bacterial taxa
accounted the differences between healthy control and UC patients the overall bacterial
profile showed a major shifts in the bacterial taxa that discriminated mucosal
communities in healthy control and UC patients samples. The ability of 16S rRNA gene
to distinguish SRB species was limited however the functional gene, dsr, was able to
distinguish ecologically distinct bacterial populations (Kondo et al., 2006).
CHAPTER 3: RESULTS AND DISCUSSION
120
Section 2
Design of a Biochip for screening of mucosa associated bacteria in
healthy and colitis patients
The low-density biochips have been developed to analyse a relatively small number of
genes. A notable example being the usage of a biochip to monitor the common intestinal
pathogenic bacteria (Jin et al., 2005). These multi target biochips are possibly ideal for
detecting pathogens that interact to produce symptoms and as such would be of great
use for rapidly screening the bacterial populations of the colonic mucosa in UC. In this
chapter a set of oligonucleotide probes targeting some of bacteria known to be involved
on UC was designed. The hybridization potential of each probe was evaluated using
cassette method (Zinkevich et al., 2014); and subsequently the probes were used to
design biochips for bacterial detection and identification on healthy and UC patients
samples.
3.2.1 Oligonucleotide probe design
The colonic bacteria, which are likely to be implicated in the etiology and pathogenesis
of UC, were selected and the DNA probes for targeting these bacterial genes were
designed. The selection of these bacteria was based on previous studies as well as on
the analysis of biopsy samples of healthy individual and UC patients from this thesis
(Cummings et al., 2003; Sasaki & Klapproth, 2012; Kushkevych, 2013; Matsuoka &
Kanai, 2015). Additional probes obtained from ProbeBase an rRNA-targeted
oligonucleotide probe database were also included in the analysis (Loy et al., 2007).
Designed and published probes shown in Table 3.2.1 and Table 3.2.2.
The probes were designed from the alignments of species- or genus-specific variable
regions. The sequences of selected marker genes for a range of taxa were obtained from
NCBI. Alignment maps were generated to allow for the detection of conserved
sequences which would allow for the generation of taxa probes. All probes were
checked for their specificity and taxon coverage by BLAST searches against the GenBank
database. Probe length is important to modulate specificity and sensitivity, with longer
CHAPTER 3: RESULTS AND DISCUSSION
121
probes (40-70 mer) are less specific but more sensitive; whereas, shorter probes (> 20
mer) can differentiate SNPs in target sequences (Relógio et al., 2002).
CHAPTER 3: RESULTS AND DISCUSSION
122
Table 3.2.1. List of design probes
Probe name Targeted bacteria Gene name Probe sequence(5’-3’) Tm °C
Dslpig Desulfovibrio piger dissimilatory sulfite reductase beta-subunit-dsrB TCAAAAAGAATTTCGGCAAGTGGCT 56.1
Dslgig Desulfovibrio gigas Sulfate adenylyltransferase-sat CCTGAAGATCGAAGAAAAGTACG 55.7
Dsbub Desulfobulbus Sulfate adenylyltransferase-sat GATCATTATTTCGTCAAAGAGAACG 54.4
Dsbac Desulfobacter Sulfate adenylyltransferase-sat AAGATCGCTATTGAAGTATGTGACG 56.1
Dslsp Desulfovibrio sp adenosine-5'-phosphosulfate reductase alpha-
subunit-aprA
TCATGATCAACGGTGAATCCTACAA 56.1
Dsldes Desulfovibrio
desulfuricans
dissimilatory sulfite reductase alpha –subunit-
dsrA
CAAGTACTATCACAGCAAATTCCTG 56.1
Desfot Desulfotomaculum Sulfate adenylyltransferase-sat CTGAAGAATACAAAGGTGTTTACCT 54.4
S1 SRB dissimilatory sulfite reductase alpha –subunit-dsrA TAAGTTCTACACCTCCGAATACCT 55.9
Ent1 Enterobacteriaceae β-galactosidaes-β-gal TATTCGATTTCTGATAAGAGCGTGG 56.1
EC E. coli β-galactosidaes-β-gal GGAAAACACCTAACGCATATTAACG 56.1
Bacfr Bacteriodes fragilis Neuraminidase gene-nanH GACAAGGATTCTACCAGCTTTATAC 56.1
CHAPTER 3: RESULTS AND DISCUSSION
123
Table 3.2.1. Cont.
Probe name Targeted bacteria Gene name Probe sequence(5’-3’) Tm °C
Bacsp Bacteriodes sp beta-isopropylmalate
dehydrogenase -leuB
TGTATTTCGGTGAGAAGTATCAGGA 56.1
Cldif Clostridium difficile Toxin Clostridium difficile gene-
TcdC
GTGAAGCGTTCTTAGTCAATACACT 56.1
Clsp1 Clostridium sp [Fe-Fe]-hydrogenases- hydA GGGGATAGCTAGTGGATTTAGTATA 56.1
Fusv Fusobacterium varium Fusobacterium adhesin A-fadA TAACTATAAAGTATCAGGAGGGCTT 54.4
Fusnu Fusobacterium nucleatum Fusobacterium adhesin A-fadA GATTTAATGAAGAAAGAGCACAAGC 54.4
Fusne Fusobacterium necrophorum Fusobacterium adhesin A-fadA CTATTATTTCGGTCAAAGTTCCACA 54.4
Ent2 Enterobacteriaceae β-galactosidaes-β-gal GGCTGAACAACAAGAAGATTTACAC 56.1
Clsp2 Clostridium sp [Fe-Fe]-hydrogenases- hydA GACCGTATAAGAGATAACTATGAAG 54.4
CHAPTER 3: RESULTS AND DISCUSSION
124
Table 3.2.2. List of published DNA probes
Probe name Targeted bacteria Gene name Probe sequence(5’-3’) Tm °C Reference
Rum Ruminococcus 16SrRNA
CCA ATT GGA AAC GAT TGT TAA TAC C 54.4 Harmsen et al., 2002
Bif Bifidobacterium CATCCGGCATTACCACCC 56.9 Reichardt et al., 2011
Lac Lactobacillus CCG TCA ACC CTT GAA CAG TT 55.2 Demaneche et al., 2008
SR2 SRB aprA GGCCTGTCCGCCATCAATA 57.1 Zinkevich & Beech,
2000
16S Bacteria 16S rRNA CCTACGGGAGGC AGCAG 55 Muyzer et al.,1993
CHAPTER 3: RESULTS AND DISCUSSION
125
Probes used in this study vary between 18 and 25 oligigonucleotides in length. The
probes with similar Tm values (Tm±2°C) were also considered. The identical
hybridization behaviour of the oligonucleotide probes immobilized onto a solid surface
have to be achieved and controlled. Five cassettes were constructed. Each cassette
contains four of the chosen oligonucleotide probes and functions as the target DNA in
hybridisation reactions with the biochip were constructed. Therefore, appropriate and
poorly performing probes can be rapidly identified in the hybridization reactions. The
composition of the ss cassettes is shown in Figure 3.2.1.
CHAPTER 3: RESULTS AND DISCUSSION
126
Cassette 1- 5’- piger (25/56.1)* gigas (23/55.7) * Desulfovibrio sp (25/56.1)* SRB1
(24/55.9) *- 3’
Cassette 1 - SEQUENCE
5’TCAAAAAGAATTTCGGCAAGTGGCTCCTGAAGATCGAAGAAAAGTACGTCATGATCAACG
GTGAATCCTACAATAAGTTCTACACCTCCGAATACCT3’
Cassette 1_COMPL
5’ CY3-
AGGTATTCGGAGGTGTAGAACTTATTGTAGGATTCACCGTTGATCATGACGTACTTTTCTTC
GATCTTCAGGAGCCACTTGCCGAAATTCTTTTTGA 3’ (97 bp)
________________________________________________________________________
Cassette 2 -5’- Desulfobulbus (25/54.4)* Desulfobacter (25/56.1)* Desulfovibrio
desulfuricans (25/56.1)* Desulfotomaculum (25/54.4)*- 3’
Cassette 2 – SEQUENCE
5’ GATCATTATTTCGTCAAAGAGAACGAAGATCGCTATTGAAGTATGTGACG
CAAGTACTATCACAGCAAATTCCTGCTGAAGAATACAAAGGTGTTTACCT 3’
Cassette 2_COMPL
5’ CY3-
AGGTAAACACCTTTGTATTCTTCAGCAGGAATTTGCTGTGATAGTACTTGCGTCACATACTT
CAATAGCGATCTTCGTTCTCTTTGACGAAATAATGATC’3 (100 bp)
_____________________________________________________________
Cassette 3 -5’ Enterobacteriaceae (25/56.1)* E.coli (25/56.1)* Bacteriodes fragilis
(25/56.1)* Bacteriodes sp (25/56.1)*-3’
Cassette 3 – SEQUENCE
5’ TATTCGATTTCTGATAAGAGCGTGG GGAAAACACCTAACGCATATTAACG
GACAAGGATTCTACCAGCTTTATAC TGTATTTCGGTGAGAAGTATCAGGA’3
Cassette 3_COMPL
5’ CY3-
TCCTGATACTTCTCACCGAAATACAGTATAAAGCTGGTAGAATCCTTGTCCGTTAATATGCG
TTAGGTGTTTTCCCCACGCTCTTATCAGAAATCGAATA’3 (100 bp)
______________________________________________________________________
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127
Cassette 4 -5’ Clostridium difficile (25/56.1)* Clostridium sp (25/56.1)* Fusobacterium
varium (25/54.4)* Bifidobacterium (18/56.9)*’3
Cassette 4 – SEQUENCE
5’ GTGAAGCGTTCTTAGTCAATACACTGGGGATAGCTAGTGGATTTAGTATA
TAACTATAAAGTATCAGGAGGGCTTCATCCGGCATTACCACCC ’3
Cassette 4_COMPL
5’ CY3-
GGGTGGTAATGCCGGATGAAGCCCTCCTGATACTTTATAGTTATATACTAAATCCACTAGCT
ATCCCCAGTGTATTGACTAAGAACGCTTCAC’3 (93 bp)
_________________________________________________________________________
Cassette 5 -5’ Fusobacterium nucleatum (25/54.4)* Ruminococcus (25/54.4)
*Fusobacterium necrophorum (25/54.4)* Lactobacillus (20/55.2)* ’3
Cassette 5 – SEQUENCE
5’ GATTTAATGAAGAAAGAGCACAAGCCCAATTGGA AACGATTGTTAATACC
CTATTATTTCGGTCAAAGTTCCACACCGTCAACCCTTGAACAGTT’3
Cassette 5_COMPL
5’ CY3-
AACTGTTCAAGGGTTGACGGTGTGGAACTTTGACCGAAATAATAGGGTATTAACAATCGTTT
CCAATTGGGCTTGTGCTCTTTCTTCATTAAATC’3 (95 bp)
______________________________________________________
Figure 3.2.1. The composition of the ss cassettes
CHAPTER 3: RESULTS AND DISCUSSION
128
3.2.2 Fine-tuning of biochips using cassette approach
A critical step in biochips design is the evaluation of probes, which are going to be used
on a biochip. These probes have to satisfy several requirements such as a uniform
melting temperature (Tm) and being a similar size so that their signal intensities are
comparable under the same hybridization conditions. Whilst in silico modelling of
biochip probes is developing rapidly there is often a large difference between the
theoretical and real world activity of probes. Approximately a quarter of probes are
non-specific when in situ (Hager, 2006). Determination of individual probe efficacy can
be time consuming and a novel cassette method for the rapid evaluation of probe
efficacy has been developed (Zinkevich et al., 2014).
This methodology was adapted to investigate the role of SRB in the development of UC.
Glass or other silica derivatives are typically used as the base material for microarrays
as they are chemically resistant and display good optical character. However, they
feature a limited DNA binding capacity resulting in a low-density surface coverage.
Various techniques have been used to improve binding efficacy, for example the
polyacrylamide gel pads coupled with 3-methyluridine probes (Zinkevich et al., 2014).
For this study a dendrimeric matrix was used which allows for a higher loading of
probes, promotes improved hybridization and are more robust allowing for several
hybridization, de-hybridization cycles (Caminade et al., 2006). Probes to detect bacterial
species or genera identified from the published literature as potentially playing a role in
UC were designed.
Single stranded (ss) cassettes were used as the targets for the hybridization with the
probes coupled into the dendrimeric matrix. The cassettes present the lineal set of
sequences complimentary to the probes. The Cy3 fluorescent dye was inserted at 5´-end
of each cassette during the synthesis.
Five cassettes were used to evaluate the hybridization potential of each probe. The
cassettes were composed of four probes; and the size of cassettes varied from 93 to 100
bp. The hybridization behaviours of the probes targeting Desulfovibrio piger,
Desulfovibrio gigas, Desulfovibrio sp and SRB were assessed using cassette N1. All of the
CHAPTER 3: RESULTS AND DISCUSSION
129
probes showed comparable hybridization capacities (Figure 3. 2.2), indicating that each
of these probes were suitable for inclusion in the biochip.
Figure 3.2.2. Schematic diagram and the results of the designed cassette N1
hybridization as signal to noise (S/N) ration. The purple circles are the probes position
markers the gray circles are the probes. The arrangement of the probes was as follows:
A1, A2, A3, A4,B1,C1,D1, E1 – Probes position markers; B2,C2 – Dslpig for Desulfovibrio
piger ;D2,E2 – Dslgig for Desulfovibrio gigas ;B3,C3 – Dslsp for Desulfovibrio sp; D3,E3 –
S1 for SRB; B4,C4 -16S Negative control; D4,E4 – were left empty to give background
signal.
Four probes for the identification of Desulfobulbus, Desulfobacter, Desulfovibrio
desulfuricans and Desulfotomaculum included in cassette N2. Only two of these probes
for Desulfobulbus and Desulfotomaculum exhibited similar hybridization profiles
however the probes for Desulfobacter and Desulfovibrio desulfuricans failed to detect
their antisense (Figure 3. 2.3)
CHAPTER 3: RESULTS AND DISCUSSION
130
Figure 3.2.3. Schematic diagram and the results of the designed cassette N2
hybridization as signal to noise (S/N) ration. The purple circles are the probes position
markers the gray circles are the probes. The arrangement of the probes was as follows:
A1, A2, A3, A4, B1, C1, D1, E1 – Probes position markers; B2, C2 – Dsbub for
Desulfobulbus; D2, E2 – Dsbac for Desulfobacter; B3, C3 – Dsldes for Desulfovibrio
desulfuricans; D3, E3 – Desfot for Desulfotomaculum; B4, C4 -16S Negative control; D4,
E4 – were left empty to give background signals.
The probes for Enterobacteriaceae, Escherichia coli, Bacteriodes fragilis and Bacteriodes
sp. in cassette N3 reveal the same binding capacity while the probe for Bacteriodes sp.
failed to give any signal (Figure 3. 2.4).
Figure 3.2.4. Schematic diagram and the results of the designed cassette N3
hybridization as signal to noise (S/N) ration. The purple circles are the probes position
markers the gray circles are the probes. The arrangement of the probes was as follows:
A1, A2, A3, A4, B1, C1, D1, E1 – Probes position markers; B2, C2 –Ent1 for
Enterobacteriaceae; D2, E2 –EC for E.coli; B3, C3 – Bacfr for Bacteriodes fragilis; D3, E3 –
Bacsp for Bacteriodes sp; B4, C4 -16S Negative control; D4, E4 – were left empty to give
background signals.
CHAPTER 3: RESULTS AND DISCUSSION
131
The hybridization efficiency of the probes for Clostridium sp, and Bifidobacterium in
cassette N4 was the same. The probes for Clostridium difficile and Fusobacterium varium
failed to hybridize with its single-stranded compliment (Figure 3. 2.5).
Figure 3.2.5. Schematic diagram and the results of the designed cassette N4
hybridization as signal to noise (S/N) ration. The purple circles are the probes position
markers the gray circles are the probes.The arrangement of the probes was as follows:
A1, A2, A3, A4, B1, C1, D1, E1 – Probes position markers; B2, C2 – Cldif for Clostridium
difficile; D2, E2 –Clsp1 for Clostridium sp; B3,C3 –Fusv for Fusobacterium varium; D3, E3
–Bif for Bifidobacterium; B4, C4 -16S Negative control; D4, E4 – were left empty to give
background signals.
The probes in cassette N5 showed comparable hybridization capacities except for probe
Lactobacillus which gave negative result (Figure 3.2.6).
Figure 3.2.6. Schematic diagram and the results of the designed cassette N5
hybridization as signal to noise (S/N) ration. The purple circles are the probes position
markers the gray circles are the probes. The arrangement of the probes was as follows:
A1, A2, A3, A4, B1, C1, D1, E1 – Probes position markers; B2, C2 –Fusnu for
Fusobacterium nucleatum; D2, E2 –Rum for Ruminococcus; B3, C3 –Fusn for
Fusobacterium necrophorum; D3, E3 –Lac for Lactobacillus; B4, C4 -16S Negative
control; D4, E4 – were left empty to give background signals.
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132
The 6 probes (for Desulfobacter; Desulfovibrio desulfuricans; Bacteriodes sp; Clostridium
difficile; Fusobacterium varium and Lactobacillus) which failed to produce any
hybridization signal were reordered and were tested again. The biochip experiment is a
multi-stage process in which the accuracy of each individual step may influence the
target detection. Therefore, to exclude the possibility of the influence of other factors
caused the detection failure, such as the probe design or the human factor; the probes
were reorder as separate sets. The newly synthesized probes showed comparable
hybridization signal with other probes (Figure 3.2.7). This indicated that the negative
results obtained with the first set of the probes occurred due to the error during the
synthesis. The probe manufacturing errors were reported previously that it is very
common (Jonathan et al., 1997; McGall et al., 1997; Jakubek & Cutler, 2012). Another
possibility is that the chips often may have different binding properties if these matrixes
manufactured on different days (batch effect). However, several fundamental
observations of microarray behaviour remain poorly understood (Hong et al., 2008).
As a result most of the probes exhibited comparable hybridization signals and were
characterized by the mean S/N ratio=7 (dashed line) (Figure 3.2.7). Only probes for
Desulfobacter, Desulfotomaculum, Enterobacteriaceae and Clostridium sp were slightly
out of the mean range ±SD. The probes, S/N ratio which were higher than the mean S/N
of the majority of the probes; were re-design and tested against DNA obtained from
patients suffering from UC and healthy individual.
CHAPTER 3: RESULTS AND DISCUSSION
133
Figure 3.2.7. Schematic diagram and the analysis of the designed cassettes
hybridization with biochip. The results of the hybridization as signal to noise (S/N)
ration. The data presented are mean values±SD.
Panel A. Schematic diagram represents the arrangement of the probes on the biochip. The probes were spotted in duplicate. The red circles are the probes position markers. B2, C2- Dslpig for D. piger; B3, C3- Dsbub for Desulfobulbus; B4, C4- Ent1 for Enterobacteriaceae; B5, C5- Cldif for C. Difficile, B6, C6- Fusnu for F. nucleatum; D2, E2- Dslgig for D. gigas ; D3, E3- Dsbac for Desulfobacter; D4, E4- EC for E.coli; D5, E5- Clsp1 for Clostridium sp.; D6, E6- Rum for Ruminococcus; F2, G2- Dslsp for Desulfovibrio sp; F3, G3- Dsldes for D. desulfuricans; F4, G4- Bacfr for B. fragilis; F5, G5- Fusv for F. varium; F6, G6- Fusn for F. necrophorum; H2, I2- S1 for SRB; H3, I3- Desfot for Desulfotomaculum; H4, I4- Bacsp for Bacteriodes sp.; H5, I5- Bif for Bifidobacterium; H6, I6-Lac for Lactobacillus.
Panel B. The analysis of the designed cassettes represents the results of the hybridization as signal to noise (S/N) ratio. 1- S1 for SRB; 2- Dslsp for Desulfovibrio sp; 3- Dslpig for D. piger; 4- Dslgig for D. gigas; 5- Dsldes for D. desulfuricans; 6- Dsbub for Desulfobulbus; 7- Dsbac for Desulfobacter; 8- Desfot for Desulfotomaculum; 9- Ent1 for Enterobacteriaceae; 10- EC for E.coli; 11- Bacsp for Bacteriodes sp.; 12- Bacfr for B. fragilis; 13- Clsp1 for Clostridium sp; 14- Cldif for C. difficile; 15- Rum for Ruminococcus; 16- Fusv for F. varium; 17- Fusnu for F. nucleatum; 18- Fusn for F. necrophorum;19- Bif for Bifidobacterium; 20- Lac for Lactobacillus
CHAPTER 3: RESULTS AND DISCUSSION
134
3.2.3 Analysis of DNA samples of healthy control and patients suffering
from UC using biochip
The designed biochip was used to study the bacterial profiling using DNA purified from
biopsy samples of healthy individual and patients suffering from UC. DNA of healthy
individual and patients suffering from UC were prepared for hybridization with the
biochip by amplification, fragmentation and labelling. Peaks equal and above a S/N of 2
were counted as positive samples.
The biochip for healthy individual sample analysis was constructed using 23 probes in
duplicate.
Probes 1 and 2 targeted SRB while probes 3-9 targeting specific SRB species. Probes 10
and above target other bacteria which may have a role in the development of UC. Probes
10 and 11 targeted Enterobacteriaceae species generically and probes 15 and 16
targeted Clostridium species.
In the healthy control, DNA produced high levels of the fluorescent signal intensity for
the probes targeting D. piger and Enterobacteriaceaee family members (Probe 2 which
was re-designed but not Probe 1) were detected. Low levels of SRB (Probe 1 and 2), E.
coli, Bacteriodes sp., Bacteriodes fragilis , Clostridium sp1 (Probe 1; but not probe 2 which
was re-designed) and Fusobacterium nucleatum were detected (Figure 3.2.8). The
obtained results showed the presence of these bacterial species in healthy individual
sample. The weak or absence of hybridization signal (probes 3, 5-10, 17-19, 23) could
mean either the absence of the appropriate group of bacteria in the studied sample or
very low presentation of these bacteria.
CHAPTER 3: RESULTS AND DISCUSSION
135
Figure 3.2.8. Schematic diagram and the hybridization analysis of the DNA obtained
from healthy individual. The hybridization results represented as signal to noise (S/N)
ratio.
Panels A. Schematic diagram represents the arrangement of the probes on the biochips of healthy individual sample. The probes were spotted in duplicate. The red circles are the probes position markers. B2, C2- S1 for SRB1; B3, C3- SR2 for SRB; B4, C4- Dslsp for Desulfovibrio sp; B5, C5- Dslpig for D. piger; B6, C6- Dslgig for D. gigas; D2, E2- Dsldes for D. desulfuricans; D3, E3- Dsbub for Desulfobulbus; D4, E4- Dsbac for Desulfobacter; D5, E5- Desfot for Desulfotomaculum; D6, E6- Ent1 for Enterobacteriaceae; F2, G2- Ent2 for Enterobacteriaceae; F3, G3- EC for E.coli; F4, G4- Bacsp for Bacteriodes sp.; F5, G5- Bacfr for B. fragilis; F6, G6- Empty; H2, I2- Clsp1 for Clostridium sp.; H3, I3- Clsp2 for Clostridium sp.; H4, I4- Cldif for C. Difficile; H5, I5- Rum for Ruminococcus; H6, I6- Fusv for F. varium; J2, K2- Fusnu for F. nucleatum; J3, K3- Fusn for F. necrophorum; J4, K4- Bif for Bifidobacterium; J5, K5- Lac for Lactobacillus. Panel B. The analysis of the designed probes represents the results of the hybridization as signal to noise (S/N) ratio. 1- S1 for SRB; 2- SR2 for SRB; 3- Dslsp for Desulfovibrio sp; 4- Dslpig for D. piger; 5- Dslgig for D. gigas; 6- Dsldes for D. desulfuricans; 7- Dsbub for Desulfobulbus; 8- Dsbac for Desulfobacter; 9- Desfot for Desulfotomaculum; 10- Ent1 for Enterobacteriaceae; 11- Ent2 for Enterobacteriaceae; 12- EC for E.coli; 13- Bacsp for Bacteriodes sp.; 14- Bacfr for B. fragilis; 15 - Clsp1 for Clostridium sp; 16: Clostridium sp2; 17- Cldif for C. difficile; 18- Rum for Ruminococcus; 19- Fusv for F. varium; 20- Fusnu for F. nucleatum; 21- Fusn for F. necrophorum; 22- Bif for Bifidobacterium; 23- Lac for Lactobacillus. The biochip for UC patients’ samples analysis was constructed using 23 probes in
duplicate. UC patient 1 sample all bacteria were detected in this sample with the
exception of Desulfobulbus and Bacteriodes sp, Clostridium sp (Probe 1 but not Probe 2)
and Clostridium difficile. SRB 1, SRB 2, D. piger, Desulfobacter, Enterobacteriaceaee
CHAPTER 3: RESULTS AND DISCUSSION
136
(Probe 1 but not Probe 2), and Fuscobacterium varium were detected at high levels
comparing with other probes (Figure 3.2.9).
Figure 3.2.9. Schematic diagram and the hybridization analysis of the DNA obtained
from UC patient 1. The hybridization results represented as signal to noise (S/N) ratio.
Panels A. Schematic diagram represent the arrangement of the probes on the biochips of UC patient 1 sample. The probes were spotted in duplicate. The red circles are the probes position markers.B2, C2- Dslpig for D. piger; B3, C3- Dslsp for Desulfovibrio sp; B4, C4- Ent2 for Enterobacteriaceae; B5, C5- Cldif for C. Difficile; B6, C6- Fusn for F. necrophorum; B7, C7- SR2 for SRB2; D2, E2- Dslgig for D. gigas; D3, E3- Dsldes for D. desulfuricans; D4, E4- EC for E.coli; D5, E5- Clsp1 for Clostridium sp.; D6, E6- Rum for Ruminococcus; D7, E7- Ent1 for Enterobacteriaceae; F2, G2- Dsbub for Desulfobulbus; F3, G3- Desfot for Desulfotomaculum; F4, G4- Bacfr for B. fragilis; F5, G5- Fusv for F. varium; F6, G6- Bif for Bifidobacterium; F7, G7- Clsp2 for Clostridium sp.; H2, I2- Dsbac for Desulfobacter; H3, I3- S1 for SRB1; H4, I4- Bacsp for Bacteriodes sp.; H5, I5- Fusnu for F. nucleatum; H6, I6- Lac for Lactobacillus; H7, I7- S1 for SRB
Panel B. The analysis of the designed probes. The hybridization results represented as signal to noise (S/N) ratio. 1- S1 for SRB; 2- SR2 for SRB; 3- Dslsp for Desulfovibrio sp; 4- Dslpig for D. piger; 5- Dslgig for D. gigas; 6- Dsldes for D. desulfuricans; 7- Dsbub for Desulfobulbus;8- Dsbac for Desulfobacter; 9- Desfot for Desulfotomaculum; 10- Ent1 for Enterobacteriaceae; 11- Ent2 for Enterobacteriaceae; 12- EC for E.coli; 13- Bacsp for Bacteriodes sp.; 14- Bacfr for B. fragilis; 15- Clsp1 for Clostridium sp; 16: Clostridium sp2; 17- Cldif for C. difficile; 18- Rum for Ruminococcus; 19- Fusv for F. varium; 20- Fusnu for F. nucleatum;21- Fusn for F. necrophorum; 22- Bif for Bifidobacterium; 23- Lac for Lactobacillus;24-S1 for SRB. In UC patient 2 sample; SRB (Probe 1 but not 2), Desulfovibrio sp, D. piger, D. gigas,
Desulfobacter, Bacteriodes sp., Bacteriodes fragilis, Desulfotomaculum,
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137
Enterobacteriaceae (Probe 1 not 2) and Lactobacillus were detected at high levels of
hybridization signals. However, low levels of Clostridium sp. were detected (Figure
3.2.10).
Figure 3.2.10. Schematic diagram and the hybridization analysis of the DNA obtained
from UC patient 2. The hybridization results represented as signal to noise (S/N) ratio.
Panels A. Schematic diagram represent the arrangement of the probes on the biochips of UC patient 2 sample. The probes were spotted in duplicate. The red circles are the probes position markers.B2, C2- Dslpig for D. piger; B3, C3- Dslsp for Desulfovibrio sp; B4, C4- Ent2 for Enterobacteriaceae; B5,C5- Cldif for C. Difficile; B6,C6- Fusn for F. necrophorum; B7,C7- SR2 for SRB2; D2,E2- Dslgig for D. gigas; D3,E3- Dsldes for D. desulfuricans; D4,E4- EC for E.coli ;D5,E5- Clsp1 for Clostridium sp.; D6,E6- Rum for Ruminococcus; D7,E7- Ent1 for Enterobacteriaceae; F2,G2- Dsbub for Desulfobulbus; F3,G3- Desfot for Desulfotomaculum; F4,G4- Bacfr for B. fragilis; F5,G5- Fusv for F. varium; F6,G6- Bif for Bifidobacterium; F7,G7- Clsp2 for Clostridium sp.; H2,I2- Dsbac for Desulfobacter; H3,I3- S1 for SRB1; H4,I4- Bacsp for Bacteriodes sp.; H5,I5- Fusnu for F. nucleatum; H6,I6- Lac for Lactobacillus; H7,I7- S1 for SRB
Panel B. The analysis of the designed probes represents the results of the hybridization as signal to noise (S/N) ratio. 1- S1 for SRB; 2- SR2 for SRB; 3- Dslsp for Desulfovibrio sp; 4- Dslpig for D. piger; 5- Dslgig for D. gigas; 6- Dsldes for D. desulfuricans; 7- Dsbub for Desulfobulbus;8- Dsbac for Desulfobacter; 9- Desfot for Desulfotomaculum; 10- Ent1 for Enterobacteriaceae; 11- Ent2 for Enterobacteriaceae; 12- EC for E.coli; 13- Bacsp for Bacteriodes sp.; 14- Bacfr for B. fragilis; 15- Clsp1 for Clostridium sp; 16: Clostridium sp2; 17- Cldif for C. difficile; 18- Rum for Ruminococcus; 19- Fusv for F. varium; 20- Fusnu for F. nucleatum;21- Fusn for F. necrophorum; 22- Bif for Bifidobacterium; 23- Lac for Lactobacillus;24-S1 for SRB.
CHAPTER 3: RESULTS AND DISCUSSION
138
In UC patient 3 high levels of fluorescent signal of the probes detecting SRB, D. piger and
D. gigas were observed. However, low levels of Desulfobulbus, Enterobacteriaceae
(Probe 1), Fuscobacterium nucleatum and Bifidobacerium were detected (Figure 3.2.11).
Figure 3.2.11. Schematic diagram and the hybridization analysis of the DNA obtained from UC patient 3. The hybridization results represented as signal to noise (S/N) ratio. Panels A. Schematic diagram represent the arrangement of the probes on the biochips of UC patient 3 sample. The probes were spotted in duplicate. The red circles are the probes position markers.B2, C2- Dslpig for D. piger; B3, C3- Dslsp for Desulfovibrio sp; B4, C4- Ent2 for Enterobacteriaceae; B5,C5- Cldif for C. Difficile; B6,C6- Fusn for F. necrophorum; B7,C7- SR2 for SRB2; D2,E2- Dslgig for D. gigas; D3,E3- Dsldes for D. desulfuricans; D4,E4- EC for E.coli ;D5,E5- Clsp1 for Clostridium sp.; D6,E6- Rum for Ruminococcus; D7,E7- Ent1 for Enterobacteriaceae; F2,G2- Dsbub for Desulfobulbus; F3,G3- Desfot for Desulfotomaculum; F4,G4- Bacfr for B. fragilis; F5,G5- Fusv for F. varium; F6,G6- Bif for Bifidobacterium; F7,G7- Clsp2 for Clostridium sp.; H2,I2- Dsbac for Desulfobacter; H3,I3- S1 for SRB1; H4,I4- Bacsp for Bacteriodes sp.; H5,I5- Fusnu for F. nucleatum; H6,I6- Lac for Lactobacillus;H7,I7-S1 for SRB
Panel B. The analysis of the designed probes represents the results of the hybridization as signal to noise (S/N) ratio. 1- S1 for SRB; 2- SR2 for SRB; 3- Dslsp for Desulfovibrio sp; 4- Dslpig for D. piger; 5- Dslgig for D. gigas; 6- Dsldes for D. desulfuricans; 7- Dsbub for Desulfobulbus;8- Dsbac for Desulfobacter; 9- Desfot for Desulfotomaculum; 10- Ent1 for Enterobacteriaceae; 11- Ent2 for Enterobacteriaceae; 12- EC for E.coli; 13- Bacsp for Bacteriodes sp.; 14- Bacfr for B. fragilis; 15- Clsp1 for Clostridium sp; 16: Clostridium sp2; 17- Cldif for C. difficile; 18- Rum for Ruminococcus; 19- Fusv for F. varium; 20- Fusnu for F. nucleatum;21- Fusn for F. necrophorum; 22- Bif for Bifidobacterium; 23- Lac for Lactobacillus;24-S1 for SRB.
CHAPTER 3: RESULTS AND DISCUSSION
139
In UC patient 4 sample SRB (with both probes), D. piger, D. gigas and Desulfobacter were
detected at high levels comparing with low levels of hybridization signals of
Enterobacteriaceae, Fusobacterium nucleatum and Fuscobacterium necrophorum (Figure
3.2.12).
Figure 3.2.12. Schematic diagram and the hybridization analysis of the DNA obtained
from UC patient 4. The hybridization results represented as signal to noise (S/N) ratio.
Panels A. Schematic diagram represent the arrangement of the probes on the biochips of UC patient 4 sample. The probes were spotted in duplicate. The red circles are the probes position markers.B2, C2- Dslpig for D. piger; B3, C3- Dslsp for Desulfovibrio sp; B4, C4- Ent2 for Enterobacteriaceae; B5,C5- Cldif for C. Difficile; B6,C6- Fusn for F. necrophorum; B7,C7- SR2 for SRB2; D2,E2- Dslgig for D. gigas; D3,E3- Dsldes for D. desulfuricans; D4,E4- EC for E.coli ;D5,E5- Clsp1 for Clostridium sp.; D6,E6- Rum for Ruminococcus; D7,E7- Ent1 for Enterobacteriaceae; F2,G2- Dsbub for Desulfobulbus; F3,G3- Desfot for Desulfotomaculum; F4,G4- Bacfr for B. fragilis; F5,G5- Fusv for F. varium; F6,G6- Bif for Bifidobacterium; F7,G7- Clsp2 for Clostridium sp.; H2,I2- Dsbac for Desulfobacter; H3,I3- S1 for SRB1; H4,I4- Bacsp for Bacteriodes sp.; H5,I5- Fusnu for F. nucleatum; H6,I6- Lac for Lactobacillus; H7,I7-S1 for SRB Panel B. The analysis of the designed probes represents the results of the hybridization as signal to noise (S/N) ratio. 1- S1 for SRB; 2- SR2 for SRB; 3- Dslsp for Desulfovibrio sp; 4- Dslpig for D. piger; 5- Dslgig for D. gigas; 6- Dsldes for D. desulfuricans; 7- Dsbub for Desulfobulbus; 8- Dsbac for Desulfobacter; 9- Desfot for Desulfotomaculum; 10- Ent1 for Enterobacteriaceae; 11- Ent2 for Enterobacteriaceae; 12- EC for E.coli; 13- Bacsp for Bacteriodes sp.; 14- Bacfr for B. fragilis; 15- Clsp1 for Clostridium sp; 16: Clostridium sp2; 17- Cldif for C. difficile; 18- Rum for Ruminococcus; 19- Fusv for F. varium; 20- Fusnu for F. nucleatum;21- Fusn for F. necrophorum; 22- Bif for Bifidobacterium; 23- Lac for Lactobacillus;24-S1 for SRB.
CHAPTER 3: RESULTS AND DISCUSSION
140
When comparing the samples, several key trends are visible (Figure 3.2.13). Pan-SRB
probes 1 and 2 detected SRB in all samples, however in UC patients 1 and 4 this was
highly elevated compared to healthy individual sample. The probe for D. piger was
detected in all samples and at elevated levels in the healthy control individual.
Conversely, the probes for D. gigas and Desulfobacter sp were detected in all UC patients
samples, albeit at low levels in some UC patients, but they were not detected in the
control sample. Whilst no other specific SRB were detected in the control sample it was
not possible to discern a trend due to a high degree of variation in UC patient samples.
The presence of D. piger was unsurprising as it has previously been described as the
most common SRB present in the human colon (Scanlan et al., 2009, Rey et al., 2013).
The converse trend was observed with D. gigas and Desulfobacter sp in this instance no
hybridization signal was detected in the control sample whereas a consistent level
signal was detected in all UC patients samples. There are no reports in the literature of
D. gigas or Desulfobacter sp specifically being associated with UC so this is a potential
novel finding. An interesting possibility is that the reduction in D. piger and increase in
D. gigas and Desulfobacter sp may be linked through mechanisms unknown,
contribution to the dysbiosis associated with UC. In addition, other explanations of the
association of D. gigas with UC patients samples that D. gigas may has physiological
features that cause inflammation or participate in the prolongation of chronic
inflammatory or may these species are preferred by inflammation condition in UC
patients (Rowan et al., 2010). However, investigation of sufficient number of samples
would be required to make a proper conclusion.
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141
Probe Target Healthy UC1 UC2 UC3 UC4
S1 SRB
SR2 SRB
Dslsp Desulfovibrio sp.
Dslpig D. piger
Dslgig D. gigas
Dsldes Desulfovibrio desulfuricans
Dsbub Desulfobulbus
Dsbac Desulfobacter
Desfot Desulfotomaculum
Ent1 Enterobacteriaceaee
Ent2 Enterobacteriaceaee
EC E. coli
Bacsp Bacteriodes sp.
Bacfr Bacteriodes fragillis
Clsp1 Clostridium sp
Clsp2 Clostridium sp
Cldif Clostridium difficile
Rum Ruminococcus
Fusv Fuscobacterium varium
Fusnu Fuscobacterium nucleatum
Fusne Fuscobacterium
necrophorum
Bif Bifidobacerium
Lac Lactobacillus
Key
>2 2-4 4-6 6-8 8+
Figure 3.2.13. Heat map of bacteria detected in the samples of healthy individual and
UC patients. Cells are coloured according to the S/N ratio calculated for each sample and
probe as per the key provided.
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142
The probes assessing Enterobacteriaceaee displayed a marked difference, with a strong
detection with Enterobacteriaceaee probe 1 in UC patients 1, 2 and 3 samples but not UC
patient 4 and control, whereas Enterobacteriaceaee probe 2 detected in the healthy
control sample at a high level, and only in UC patient 1 , 2 and 4 at a low level. These
data suggest that the pan-Enterobacteriaceae probes were detecting different, non-
overlapping populations. Enterobacteriaceae have been associated with UC previously,
although specific strains have not (Lavelle et al., 2015). These findings suggest that
investigation into specific species of Enterobacteriaceae may prove useful in
determining if certain species display a protective or deleterious effect in UC.
Clostridium difficile colonization can induce antibiotic-associated colitis (Kawaratani et
al., 2010). However, there was no noticeable trend in the detection of C. difficile with
specific probes in all samples analysed. Interestingly similar amount of Clostridium
genera with non-specific probes was detected in UC patients 1 and 2 as well as healthy
control sample in the case of the latter suggesting that the generic probes did not detect
C. difficile. Whilst C. difficile is undoubtedly pathogenic and able to induce colitis
however these species was not detected in all samples tested in this study. Therefore, it
is not possible to draw strong conclusions from the data obtained. As Clostridium genera
were detected in the healthy control it is possible that non-pathogenic genera displaces
the C. difficile in control sample. However, investigation of sufficient number of samples
would be required to make a proper conclusion.
One other key difference between the control and UC patient samples was the detection
of Fuscobacterium nucleatum probe in all UC patients except UC patient 2. The probe for
Fuscobacterium necrophorum was detected in UC patients samples together with
presence of the Fuscobacterium varium probe in UC patient 1 and UC patient 2 with
notable increase on UC patient 1. Members of the Fusobacterium genus, including
Fuscobacterium nucleatum, F. necrophorum, and Fuscobacterium varium which have
been detected in the studied samples, have been reported by previous studies as
gastrointestinal bacteria that have a significant role on UC (Ohkusa et al. 2003; Legaria
et al., 2005; Afra et al., 2013; Tahara et al., 2015). Although Fuscobacterium varium is
conserved generally to be a rare bacterium and found to be correlated with UC in a
minority cases around the world, the present study findings was in conjunction with
previous studies which demonstrated the F. varium have long been associated with the
development of UC.
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143
All samples tested in this study displayed differences in the heterogeneity of resident
bacteria, which may conform to the hypothesis that UC arises from dysbiosis
(Cummings et al., 2003). A loss of protective bacteria such as Lactobacilli and
Bifidobacteria was also proposed as a mechanism in the pathogenesis of UC, however
this was not shown in the studied samples. Indeed, all UC patients displayed a marked
increase in the detection of Bifidobacteria and Lactobacillus was detected only in UC
patient 1 and 2 in high level. The increasing level of Lactobacillus in UC patients was
previously reported (Wang et al., 2014). The presence of Bifidobacteria and the high
level of Lactobacillus in UC patients samples could be explained that these bacteria have
a role in defence through the promotion of a mucous secretion which will help to
protect colonic epithelial cells from pathogenic bacteria during inflammation (Mack et
al., 2003).
Together this suggests that whilst UC may be linked to bacteria, it is not currently
possible to identify a single bacterial species or change that is associated with UC.
Elevated levels of SRB bacteria detected in UC patients 1 , 3 and 4 suggests that SRB
may play a role in the development of UC, but low level results of SRB in patient 2
suggests that this may be limited to a subset of cases. The analysis of the patients
samples for the presence of main bacterial species or groups that may have role on UC
pathogenesis by biochip showed that hybridization signals are absent for some probes.
This may indicate that either the absence of the appropriate target group of bacteria in
the studied samples or their low abundance.
The biochip/cassette methodology used in this study would provide an ideal framework
to rapidly and accurately assess bacteria associated with UC in patient samples. In
future studies it would be worthwhile incorporating of multiple probes specific to
species of Enterobacteriaceae as the changes in the level of hybridization signals of
these bacteria were potentially the most novel finding of these experiments.
CHAPTER 3: RESULTS AND DISCUSSION
144
Section 3
Detection and quantification of SRB using microbiological
and quantitative PCR techniques
The role of SRBs, particularly Desulfovibrio species, in etiology of UC has been widely
suggested. SRBs are toxic to human epithelial intestinal cells chiefly because they
produce H2S, which damage the colonic mucosa layer leading to inflammation (Medani
et al., 2011; Chan & Wallace, 2013; Kushkevych, 2014). In order to extend
understanding further from these reports and to control SRB effects, a rapid method to
quantification and detect SRB colonisation is needed. Unfortunately, this has posed a
huge challenge. This is largely because of low recovery rate of SRB making it difficult to
recognize the early stages of infection. In this study rapid method for detection and
quantification of Desulfovibrio species was developed.
3.3.1 The growth of different age of Desulfovibrio species culture in VMR
and VMI media
Strains of Desulfovibrio species are frequently recovered at a greater frequency (when
Escherichia coli is present, even when the SRB is present at low cell densities (102-
103cells/ml) (Zinkevich, unpublished data). This suggests that a relationship might exist
between the two organisms. When both species are co-cultured together their growth
behaviour changes (Bharati et al., 1980), the growth yield constant increases and sulfate
is reduced accompanied by the total consumption of formate, lactate and alcohol. Co-
culture of SRBs with methanogens and dehalorespiring bacteria are well-known
examples of syntrophism (Drzyzga et al, 2001). The relationship with Escherichia coli
appears to lead to enhanced growth of the SRB (generation time for this organisms is
usually greater than 3-8 days) (Rabus et al., 1993; Galushko et al., 1999; Gerardi, 2003;
Ingvorsen et al., 2003). This can, potentially, provide a rapid detection of the sulphate
reducer by stimulating its growth. SRBs often operate at low cell densities and have a
slow growth (- > 28 days) which could be changed by co-culturing with E. coli so that
detection can be recorded earlier.
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145
For these experiments, the effect of E.coli BP on the growth of different age cultures (7
days, 3 months and 1-year old) of Desulfovibrio indonesiensis was assessed. 10-fold
serial dilutions of 7-day old D. indonesiensis cultures were incubated in VMR media. Two
groups of experiments were designed: (1) where E. coli BP was added into each dilution
of D. indonesiensis, and (2) where no E.coli BP was added to the dilution. The growth of
D. indonesiensis was monitored daily for 28 days and the SRB was detected by the
production of the metal sulphide in culture. The blackening of the medium was
indication of SRB growth in the medium (Andrade, et al., 2000).
The results showed that the addition of E.coli BP to the culture induced the growth of D.
indonesiensis. The growth of pure D. indonesiensis was observed in the first 5 dilutions
only, whereas in the presence of E. coli BP growth of D. indonesiensis was observed up to
10-10 (Table 3.3.1, Figure 3.3.1). These results indicate, that E. coli BP induces D.
indonesiensis growth or acts as an enhancer that activates dormant SRB. This result
suggests that inclusion of E. coli increase the detection of SRB which otherwise would
have stayed undetected due to their extremely low density.
Table 3.3.1. Comparison of 7 days old D. indonesiensis culture growth efficiency in VMR
medium with and without of E. coli BP. The symbol (+) means growth of D. indonesiensis
represented by blackening of the medium, and (-) means no growth. Several
independent experiments were done in triplicate.
7 days old
culture used for
inoculation
Dilution in VMR medium
-1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12
D. indonesiensis + + + + + - - - - - - -
D. indonesiensis
+ E.coli BP
+ + + + + + + + + + - -
CHAPTER 3: RESULTS AND DISCUSSION
146
Figure 3.3.1. Growth of 7days old D. indonesiensis culture in VMR medium in the
presence and absence of E.coli BP. (A) Growth of pure D. indonesiensis, (B) Growth of D.
indonesiensis with E.coli BP. The black precipitate at the bottom of the vials indicates
SRB growth.
To test whether E.coli BP can activate dormant SRB cells older cultures of D.
indonesiensis were used. When a 3 months old D. indonesiensis culture was used the
growth induction was observed as well. However, the difference between pure SRB
growth (10-1) and growth of SRB in the presence of E. coli BP (10-5) was 4 orders of
magnitude (Table 3.3.2, Figure 3.3.2). Although the detection level had dropped; it is
worth noting that there is a difference in detection level between D. indonesiensis with
or without the addition of E. coli BP.
CHAPTER 3: RESULTS AND DISCUSSION
147
Table 3.3.2. Comparison of 3 months old D. indonesiensis culture growth efficiency in
VMR medium with and without of E. coli BP. The symbol (+) means growth of D.
indonesiensis represented by blackening of the medium, and (-) means no growth.
Several independent experiments were done in triplicate.
Figure 3.3.2. Growth of 3 months old D. indonesiensis culture in VMR medium in the
presence and absence of E.coli BP. (A) Growth of pure D. indonesiensis, (B) Growth of D.
indonesiensis with E.coli BP. The black precipitate at the bottom of the vials indicates
SRB growth.
A similar result was obtained when a 1-year old pre-culture of D. indonesiensis was used
as the inoculum for the serial dilution in the presence or absence of E. coli BP (Table
3.3.3, Figure 3.3.3). Interestingly, this result showed that D. indonesiensis could still be
detected even though the culture was 1 year old. Although both groups, had growth it
3 months old
culture used for
inoculation
Dilution in VMR medium
-1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12
D. indonesiensis + - - - - - - - - - - -
D. indonesiensis
+E.coli BP
+ + + + + - - - - - - -
CHAPTER 3: RESULTS AND DISCUSSION
148
was evident that the addition of E. coli enhanced the level of detection of D. indonesiensis
significantly when compared to the group without. This result further suggests that E.
coli plays a very important role in re-activating SRBs. This result is consistent with the
previous results obtained when the 7-day and the 3-month old inoculates were used,
implying that E. coli BP may be acting as a growth inducer or activator, thereby making
the SRB cells detectable. All three experiments showed that although D. indonesiensis
was detectable without the inclusion of E. coli, its addition greatly increased the lower
level of detection.
Table 3.3.3. Comparison of 1 year old D. indonesiensis culture growth efficiency in VMR
medium with and without of E. coli BP. The symbol (+) means growth of D. indonesiensis
represented by blackening of the medium, and (-) means no growth. Several
independent experiments were done in triplicate.
1 year old culture
used for inoculation
Dilution in VMR medium
-1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12
D. indonesiensis + - - - - - - - - - - -
D. indonesiensis
+E.coli BP
+ + + + - - - - - - - -
CHAPTER 3: RESULTS AND DISCUSSION
149
Figure 3.3.3. Growth of 1 year old D. indonesiensis culture in VMR medium in the
presence and absence of E.coli BP. (A) Growth of pure D. indonesiensis, (B) Growth of D.
indonesiensis with E.coli BP. The black precipitate at the bottom of the vials indicates
SRB growth.
Species of Desulfovibrio have a requirement for inorganic iron to stimulate growth
(Postgate, 1965; Gibson, et al., 1999). The amount of inorganic iron present in the
medium used, VMR, to culture D. indonesiensis previously was 0.05 g of iron (II)
sulphate heptahydrate (FeSO4.7H2O). It is feasible that increasing this quantity of iron in
the media will further promote growth. If this can be achieved, then the level of
detection might be increased. For this reason, the inorganic iron in the medium was
increased to 0.5 g (medium VMI). The experiments to characterise the relationship
between E. coli BP and D. indonesiensis were repeated using VMI medium.
The cultures of D. indonesiensis used aged for 7 days, 3 months and 1 year, as before,
and growth assessed with and without E.coli BP. A difference in the growth of the 7-day
old culture of D. indonesiensis in VMI medium was observed when E.coli BP was
included in the dilution series (Table 3.3.4, Figure 3.3.4). The addition of E. coli BP
greatly enhanced the ability to detect D. indonesiensis at greater dilutions. A result that
was similar to the one obtained when VMR medium was used except that detection was
made at a greater dilution. However, the difference between the levels of detection for
the co-culture and monoculture was the same in both sets of experiments. It can be
CHAPTER 3: RESULTS AND DISCUSSION
150
concluded that the increase of inorganic iron to the medium does promote growth of the
SRB but does not influence the effect that E.coli has on growth.
Table 3.3.4. Comparison of 7 days old D. indonesiensis culture growth efficiency in VMI medium with and without of E. coli BP. The symbol (+) means growth of D. indonesiensis represented by blackening of the medium, and (-) means no growth. Several independent experiments were done in triplicate.
Figure 3.3.4. Growth of 7 days old D. indonesiensis culture in VMI medium in the
presence and absence of E.coli BP. (A) Growth of pure D. indonesiensis, (B) Growth of D.
indonesiensis with E.coli BP. The black precipitate at the bottom of the vials indicates
SRB growth.
When the age of the D. indonesiensis culture was increased to 3 months a similar result
was obtained. The level of detection of the monoculture and co-culture improved, but
the difference between the treatments did not, which remained at the 104 level (Table
3.3.5, Figure 3.3.5).
7 days old culture
used for inoculation
Dilution in VMI medium
-1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12
D. indonesiensis + + + + + + - - - - - -
D. indonesiensis +E.
coli BP
+ + + + + + + + + + - -
CHAPTER 3: RESULTS AND DISCUSSION
151
Table 3.3.5. Comparison of 3 months old D. indonesiensis culture growth efficiency in
VMI medium with and without of E. coli BP. The symbol (+) means growth of D.
indonesiensis represented by blackening of the medium, and (-) means no growth.
Several independent experiments were done in triplicate.
3 months old culture
used for inoculation
Dilution in VMI medium
-1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12
D. indonesiensis + + - - - - - - - - - -
D. indonesiensis +
E.coli BP
+ + + + + + - - - - - -
CHAPTER 3: RESULTS AND DISCUSSION
152
Figure 3.3.5. Growth of 3 months old D. indonensiensis culture in VMI medium in the
presence and absence of E.coli BP. (A) Growth of pure D. indonensiensis, (B) Growth of D.
indonensiensis with E.coli BP. The black precipitate at the bottom of the vials indicates
SRB growth.
The difference in detection levels between the two media was not observed when a 1-
year old culture of D. indonesiensis was used as the inoculum. (Table 3.3.6 and Figure
3.3.6). There was no increase in the level of detection of D. indonesiensis with or without
the inclusion of E. coli when either medium was used. This presumably reflects the
physiological state of the older culture, where fewer cells capable of responding to the
increased iron levels exist. Nevertheless, the co-cultures with E. coli BP retained their
enhanced level of detection ability; this result strongly suggests a role for E.coli in
increasing the detection of SRB in that E.coli has a commensal effect which improves the
efficiency of growth of SRB at a low density of cells.
CHAPTER 3: RESULTS AND DISCUSSION
153
Table 3.3.6. Comparison of 1 year old D. indonesiensis culture growth efficiency in VMI
medium with and without of E. coli BP. The symbol (+) means growth of D. indonesiensis
represented by blackening of the medium, and (-) means no growth. Several
independent experiments were done in triplicate.
Figure 3.3.6. Growth of 1 year old D. indonesiensis culture in VMI medium in the
presence and absence of E.coli BP. (A) Growth of pure D. indonesiensis, (B) Growth of D.
indonesiensis with E. coli BP. The black precipitate at the bottom of the vials indicates
SRB growth.
The growth experiments described above indicate that there is a significant relationship
between E. coli BP and SRBs. The addition of E. coli greatly enhanced the lower level of
detection of D. indonesiensis when compared to growth without E. coli BP in all the ages
of D. indonesiensis pre-cultures tested (7 days old, 3 month’s old and 1 year old pre-
cultures). This suggests that co-culturing SRB with E. coli provides a method to recover
aged SRB cultures and sustain their growth. Both SRB and E. coli have been reported to
1 year old culture
used for inoculation
Dilution in VMI medium
-1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12
D. indonesiensis + - - - - - - - - - - -
D. indonesiensis
+E.coli BP
+ + + + - - - - - - - -
CHAPTER 3: RESULTS AND DISCUSSION
154
be involved in UC etiology, and the relationship exhibited between the two may be a
survival mechanism.
The experiments described in this section indicate the influence of E.coli on SRB growth
and the role for E. coli as an inducer of SRB growth. This is probably because E. coli may
be producing a substance(s) capable of either enriching the medium or supporting the
growth of SRB by an unknown mechanism. The inclusion of E.coli in the culture medium
for SRB increases detection level, thereby improving identification and quantification of
SRB, and enhancing the chance of having cells physiologically capable of growth for
further investigation (Gibson et al., 1991; Kushkevyeh & Moroz, 2012). Additionally, the
inclusion of E.coli may improve the growth of SRB because it is a facultative anaerobe,
and as such it is possible that it makes use of the traces of oxygen available in the
environment, which will aid the growth of SRB, which are obligate anaerobes. E.coli has
an iron (II) transport system and its ability to obtain this crucial element for
fundamental living processes (Kammler et al., 1993). This may be the critical factor that
allowed the survival and growth of E.coli to continue and therefore improve the
efficiency of SRB growth.
3.3.2 Quantification of D. indonesiensis culture using q PCR
Viable SRB cell counts are believed to be underestimated when standard synthetic
enumeration media is used. Improved count estimates are produced when natural
media are used (Vester & Ingvorsen, 1998), presumably because of the presence of
additional nutrients not found in synthetic media, however it is not always convenient
to use natural media. Most enumeration methods using culture are evaluated by the
production of a black ferrous sulfide precipitate. This provides a useful method for
detection but an inaccurate one for quantification and it is time consuming. The slow
growth rates of SRBs and their low cell densities add to the problems associated with
their enumeration. Therefore, there is a need to identify the best method to quantify
SRB for experimental purposes. The accurate quantification of genes and their
transcripts allow an exact measurement of microbial activity and, possibly, phylogenetic
identity (Neretin et al 2003). Real time or qPCR is a specific quantification method that
accurately evaluates the number of bacteria in a culture by estimating the number of
transcripts or DNA molecules directly in an environment that can be amplified by PCR
(Wagner et al., 2005; Barton & Fauque, 2009; Christophersen et al., 2011).
CHAPTER 3: RESULTS AND DISCUSSION
155
Dissimilatory sulfite reductase (DSR) and adenosine phosphosulfate reductase (APS)
are key enzymes in sulfate reduction pathways present in SRBs. Estimation of the copy
number for the genes and transcripts for these enzymes can be related to the number of
cells in culture. Such an approach can be used to confirm the results obtained in the
previous section regarding the age of the culture and its activity.
The genomic DNA and total RNA were extracted from the samples taken from the
original culture of different ages used for serial dilution (7 days, 3 months and 1year)
and analyzed by qPCR. A series of tenfold dilutions of the target amplicons were
analyzed in parallel with DNA and RNA samples for estimations of absolute abundance
of dsrA and apsA. Ct values in each dilution were measured in triplicate using qPCR with
the apsA primers-probe set to generate the standard curve. Ct values were plotted
against the logarithm of their initial template (in ng). Efficiency of PCR for apsA was
99.3% with R2 = 0.995 and for dsrA was 100.3% with R2 = 0.992; both the reaction
efficiencies and the R values indicated the validity of the standard curves. (Figure 3.3.7;
Figure 3.3.8).
Figure 3.3.7. Standard curve for apsA gene
CHAPTER 3: RESULTS AND DISCUSSION
156
Figure 3.3.8. Standard curve for dsrA gene
The dsrA and the apsA copy numbers were expressed as number of SRB cells per ml of
D. indonesiensis culture, taking into account dilution factors and the volume of nucleic
acid extract. All calculations were based on the assumption that an average of one dsrA
gene copy (Klein et al., 2001; Kondo et al., 2008) and only one apsA (Friedrich, 2002)
gene copy per SRB cell.
For genomic DNA samples (Table 3.3.7), 2.13 x1011 cells/ml were revealed in a 7-day
old culture, based on the detection of dsrA copies in genomic DNA isolated from these
cells. SRB cultures that were 3 months old contained 6.9 x1010 cells/ml, while the 1-year
old culture gave 2.85 x1010 cells/ml. Quantification of the cells in different age SRB
cultures based on the detection of the apsA copy numbers in genomic DNA showed that
the greatest cell count was found in the 7-day old culture at 4.40 x 1011 cells/ml,
followed by the 3-month old culture at 1.68 x1011 cells/ml and the least was for the 1-
year oldlture at 7.68 x1010 cells/ml (Table 3.3.7). The same trend was shown for both
genes - the older SRB culture contained the fewest number of cells.
CHAPTER 3: RESULTS AND DISCUSSION
157
Table 3.3.7. Quantification of the dsrA and ApsA genes in genomic DNA purified from 7
days old D. indonesiensis cultures using TaqMan qPCR. Experiment and statistical
analysis was based on several independent experiments.
The age of
D. indonesiensis
culture
The copy number of dsrA and apsA genes
per ml of D. indonesiensis culture
dsr A Aps A
7 days 2.13 x1011 4.40 x 1011
3 months 6.9 x1010 1.68 x1011
1 year 2.85 x1010 7.68x1010
Furthermore, according to Kruskal-Wallis analysis (Table 3.3.8) there was a significant
difference in the copy number for the dsrA gene detected in genomic DNA between the
7-day and 1 year old cultures (P = 0.0005). On the other hand (Table 3.3.8) a
comparison of the of the copy number of aps gene detected in genomic DNA showed
significant difference between 7days and 3 month old cultures (P = 0.0373) and 7 days
and 1 year cultures (P = 0.0023). However, when comparing 3 months and 1 year old
cultures there was no significant was observed.
Table 3.3.8. A comparison of the copy number of dsrA and apsA genes detected in genomic DNA isolated from 7 days, 3months and 1 year old D. indonesiensis cultures using Kruskal-Wallis statistic test based on ten replicates. Asterisks indicate significant differences (* P ≤ 0.05; ** P ≤ 0.01; *** P ≤ 0.001).
Comparison Significant P Value
dsrA
7 Days vs. 3 Months No 0.1348
7 Days vs. 1 Year Yes 0.0005 (***)
3 Months vs. 1 Year No 0.2813
ApsA
7 Days vs. 3 Months Yes 0.0373 (*)
7 Days vs. 1 Year Yes 0.0023 (**)
3 Months vs. 1 Year No 0.9418
CHAPTER 3: RESULTS AND DISCUSSION
158
Detection of dsrA and apsA transcripts in total RNA isolated from the different age of
SRB cultures showed the same trend - a higher cell numbers in the 7-day old culture
with 1.43 x 1010 (dsr A) and 6.37 x 109 (aps A) cells/ ml of D. indonesiensis culture when
compared to the 2.85 x 109 (dsr A) and 6.98 x 108 (aps A) present in the 3-month old
culture (Table 3.3.9). The Mann-Whitney test statistic suggested that there was a
significant different between the 7-day old and 3-month old cultures of D. indonesiensis
culture for both genes (dsr A, P=0.0018 and Aps A, P=0.0079) (Table 3.3.10). This may be
due to the age of the inoculum of the cultures and the number of viable cells present.
Older cultures would be expected to have fewer viable cells.
Table 3.3.9. Quantification of the dsrA and ApsA genes in RNA purified from 7 days, 3
months old D. indonesiensis cultures using TaqMan qPCR. Experiment and statistical
analysis was based on several independent experiments.
The age of
D. indonesiensis
culture
The copy number of dsrA and apsA genes
per ml of D. indonesiensis culture
dsr A aps A
7 days 1.43 x 1010 6.37 x 109
3 months 2.85 x 109 6.98 x 108
Table 3.3.10. A comparison of the copy number of dsrA and apsA genes detected in transcripts in total RNA isolated from 7 days and 3 months old D. indonesiensis cultures using Mann-Whitney test statistic based on ten replicates of dsr and aps genes. Asterisks indicate significant differences (** P ≤ 0.01).
Comparison Significant P Value
dsrA
7 Days vs. 3 Months YES 0.0018 (**)
ApsA
7 Days vs. 3 Months YES 0.0079 (**)
CHAPTER 3: RESULTS AND DISCUSSION
159
3.3.3 Quantification of Hydrogen sulfide produced by D. indonesiensis
grown with and without E. coli BP
SRB are known to produce toxic hydrogen sulfide (H2S) in the gastrointestinal tract of
humans and animals (Reis et al., 1992). It is also believed that only very little of the H2S
is produced by dissimilatory sulfate reduction, which is integrated in to the cell for cell
synthesis, while the rest is expelled into the environment (Singh & Lin, 2015). The
production of hydrogen sulfide by D. indonesiensis was estimated in order to correlate it
to the production of ferrous sulfide. In the experiment a 7-day old culture of D.
indonesiensis was diluted serially in VMR medium and cultured with and without E. coli
BP, and the H2S concentration was measured in the vial with the lowest level of
detection (positive for culture of D. indonesiensis). This was the 10-5 dilution without the
addition of E.coli BP and the 10-10 dilution when D. indonesiensis was cultured with E.coli
BP. The average concentration of H2S present in the D. indonesiensis and E. coli culture
was 131 ppm, which was 3 times greater than the concentration (51 ppm) measured in
the D. indonesiensis culture, despite the difference in dilution (Figure 3.3.9). The
presence of E.coli BP with D. indonesiensis enhanced the H2S production when compared
to D. indonesiensis alone culture. Both data sets resulted in the same gap difference in
the detection of growth of the original experiment, a magnitude of 5 (-5 to -10). The
difference made by the addition of E. coli; this result indicates that E.coli is capable of
enhancing growth in SRB.
Figure 3.3.9. The concentration of H2S of 7 days D. indonesiensis culture with and without E. coli BP. three independent experiments were done in triplicate. The data presented are mean values ± SD. Asterisks indicate significant differences (* P ≤ 0.05).
CHAPTER 3: RESULTS AND DISCUSSION
160
Previous studies have shown that in order to determine the etiology of SRB in
gastrointestinal diseases, it is important to accurately estimate its cell growth
(Christophersen et al., 2011). Therefore in the process of determining the best method
of measuring and quantifying the number of cell growth of Desulfovibrio in a culture,
the use of qPCR was examined in this study.
The qPCR data obtained from a 7-day old pre-culture and its serially diluted post-
cultures in the vials labeled 10-1 to 10-12 resulted in a similar data to those obtained
when levels of detection in growth medium was examined. The qPCR data in this
chapter correlated with the growth data in corresponding vial. This result thus showed
that when the dsrA and aps -TaqMan qPCR were used, the number of SRB cells
estimated per ml of cultures had an impressive correlation with the data of culture
obtained from 7 days old D. indonesiensis. This result therefore supports previous data
that showed that qPCR is an accurate and time saving method of measuring and
quantifying Desulfovibrio (Christophersen et al., 2011; Neretin et al., 2003).
The H2S level in vials result in serially diluting a 7 day old pre-culture D. indonesiensis
with and without E.coli supports previous reports that H2S is a key product resulting
from the metabolic processes of SBR (Christophersen et al., 2011). Studies have also
shown that H2S is toxic to human colon; extreme levels of H2S coupled with profuse
levels of SRB have also been implicated in patients having UC (Gibson et al., 1991;
Roediger et al., 1993; Gardiner et al., 1995; Christl et al., 1996; Pitcher et al., 2000). The
findings obtained from this study showed the differences of concentration average level
of H2S in D. indonesiensis co-cultured with E. coli BP. The difference was greatly
increased ~3-folds in concentration when compared to D. indonesiensis alone which
suggest that E. coli BP supporting the growth of D. indonesiensis and therefore increase
the H2S production. During the metabolic activity, E. coli produces Lactate which is one
of the substrates used by SRB Desulfovibrio in the gut for metabolism (Barbetti et al.,
1988; Vernia et al., 1988; Bourriaud et al., 2005; Marquet et al., 2009; Kushkevych and
Moroz, 2012). Although, the presence of sulfate and lactate in the gut increases cell
growth, it also results in enhanced production of acetate and hydrogen sulfide which
are toxic to humans (Gibson et al., 1993b; Marquet et al., 2009; Rey et al., 2013).
CHAPTER 3: RESULTS AND DISCUSSION
161
Culture technique allows detecting the SRB growth in the presence of E. coli BP in low
dilutions comparing with the growth of SRB only. This suggests an underestimation of
SRB growth in agreement with other reports (Gibson et al., 1987; Levine et al., 1998;
Jørgensen, 2009). Using the dsrA-apsA TaqMan qPCR the number of SRB cells was
estimated and that was in a good correlation with the data of culture techniques. The
inclusion of E. coli BP in the culture medium for these SRB allows a faster identification
of their presence.
3.4 Genome Sizes of D. indonesiensis estimated by Pulsed-Field Gel
Electrophoresis
The genome size of D. indonesiensis was estimated by Pulsed-Field Gel Electrophoresis
(PGFE). The size was determined from the estimation of the migration of the full-length
bands relative to the sizes of DNA standard fragments. PFGE is an analytical tool
employed in estimating the actual genome size of bacteria; this method separates the
bacteria DNA into several chromosomes of various sizes that can be measured (Römling
et al., 1992; Boyle et al 1993; Devereux et al., 1997; Klaassen et al., 2002; Fernández-
Cuenca, 2004). Interestingly, PFGE resolves DNA fragments into several megabases
between 0.6 and 10 Mb thus making it easier to evaluate and also map out the bacterial
genomes physically (Römling et al., 1992).
In the past researchers have been able to use PFGE to identify other family members of
a bacterium genus. When accurately interpreted these data and structure could be used
in genetic engineering, drug and vaccine discovery and other areas of Molecular Biology
and Biotechnology (Kullen & Klaenhammer 2000; Klaassen et al., 2002; Fernández-
Cuenca, 2004). It is however worth noting that this method may not be effective in
resolving some bacterial chromosomes and in some instances the DNA degrades (Kato
et al 1996; Izumiya et al 1997; Liesegang & Tschäpe, 2002). To solve this problem some
workers recommended the use of thiourea which has the ability to neutralize the
nucleolytic peracid derivative of Tris buffer produced at the anode during
electrophoresis, thereby preventing DNA degradation but this method was not effective
for all bacterial species (Ray et al., 1995; Silbert et al., 2003). However, over the years
the use of Restriction endonuclease was discovered and tested. This involves cleaving
CHAPTER 3: RESULTS AND DISCUSSION
162
the bacterial chromosome with a restriction endonuclease that has a 6 bp or 8 bp
recognition site (genome finger printing), resulting in about three to one hundred DNA
fragments that are resolvable on the PFGE gel (Pfaller et al., 1992; Römling et al., 1992).
Summation of these fragments allows an estimation of the genome size. For this section,
the bacterial cultures used were D. indonesiensis and E. coli BP, as these were used in the
detection studies.
Figure 3.3.10 shows the PGFE gel with the following samples loaded in corresponding
wells/lane as labelled below: Lanes (1) Schizosaccharomyces pombe ladder; (2) lambda
ladder (3) D. indonesiensis DNA digested with PmeI; (4) D. indonesiensis DNA; (5) D.
indonesiensis DNA digested with NotI; (6) Saccharomyces cerevisiae ladder size range;
(7) E.coli DNA digested with PmeI; (8) E.coli DNA; (9) E.coli DNA digested with NotI.
CHAPTER 3: RESULTS AND DISCUSSION
163
Figure 3.3.10. Pulsed-field gel sizing of SRB genomes from (a) D. indonesiensis and (b)
E.coli: Lanes (1) Schizosaccharomyces pombe size range 3.5-5.7 Mb; (2) lambda ladder
size range 0.05–1 Mb ;(3) D. indonesiensis digested – PmeI; (4) D. indonesiensis ; (5) D.
indonesiensis digested-NotI; (6) Saccharomyces cerevisiae ladder size range 240-2200
Kb; (7) E.coli digested – PmeI; (8) E.coli; (9) E.coli digested – NotI;(10) D. indonesiensis.
Sizes of selected DNA standards (BIO-RAD DNA Size Markers–Yeast Chromosomal; BIO-
RAD DNA Size Markers lambda and BIO-RAD DNA Size Markers–S. pombe Chromosomal
DNA) are indicated.
Table 3.3.11 below showed the estimation of the total genome observed. E.coli was used
as control in this experiment largely because it has a known genomic size (Lukjancenko
et al., 2010).
Table 3.3.11. Estimated gel size Measurement of the genomes from (a) D. indonesiensis
and (b) E.coli: (3) D. indonesiensis digested – PmeI; (5) D. indonesiensis digested-NotI;
(7) E.coli digested – PmeI; (8) E.coli; (9) E.coli digested – NotI.
Lane 3 5 8 9
Fragments 392 181 2100 2150
512 1673 1600 1600
1965 1695 405 378
Total (Mb) 2869 3549 4105 4128
CHAPTER 3: RESULTS AND DISCUSSION
164
DNA degradation was observed when samples were analyzed by electrophoresis
without restriction digestion, as seen in the D. indonesiensis genomic DNA (lane 4
above). The reason for the degradation was unclear but this may probably be due the
large size of the genome or to the nucleolytic peracid derivatives formed by Tris buffer
at the anode during electrophoresis thus, supporting observations from other research
workers (Ray et al., 1995). The restriction enzymes PmeI and NotI were selected and
used individually to digest genomic DNA from SRB and E.coli and the resultant
fragments were separated by PFGE (Figure 3.3.10). The electrophoretic profiles and
genome fingerprinting results above showed three DNA fragments on Lane 3- SRB
digested – PmeI of a total genome size ~2.5 Mb (392 kb, 512 kb and 1965 kb
respectively). Lane 5- SRB digested-NotI resulted in a total genome size of ~3.5 Mb (181
kb, 1673 kb and 1695 kb respectively), Lane 8- E. coli resulted in a total genome size of
~4.1 Mb (2100 kb, 1600 kb and 405 kb respectively) while Lane 9- E.coli digested – NotI
resulted in a total genome size of ~4.1 Mb (2150 kb, 1600 kb and 378 kb respectively).
From the results of this study, the D. indonesiensis estimated genome size obtained
above showed a very close similarity to those obtained in previous research work
where the genome sizes of Desulfovibrio desulfuricans, Desulfovibrio vulgaris, and
Desulfobulbus propionicus were estimated to be 3.1, 3.6, and 3.7 Mb, respectively
(Devereux et al., 1997).
The results obtained in this section thus indicates that Pulse Field Gel Electrophoresis
accurately estimates the genome size of bacteria after the DNA has first been digested
with restriction enzymes; with the restriction enzymes preventing DNA degradation
during electropghoresis (Pfaller et al., 1992; Römling et al., 1992; Boyle et al 1993;
Devereux et al., 1997; Klaassen et al., 2002; Fernández-Cuenca, 2004).
CHAPTER 3: RESULTS AND DISCUSSION
165
Section 4
The effect of antibiotics and Manuka honey on the growth of
sulfate reducing bacterial biofilm
A biofilm is a microbial community formed within a complex matrix where bacteria and
other microbes co-exist and support the growth of each other (Costerton et al., 1995;
Jackson et al., 2002a). In adapting to their way of life, these bacteria have developed
intricate methods of growing and surviving by attaching themselves to and inhabiting
solid surfaces (Costerton et al., 1995, 1999; Jackson et al., 2002 a,b). Biofilms protects
bacteria from damaging circumstances, the effect of which is widespread universally
through all environments allowing biofilms to escalate bacterial infections making them
difficult to treat (Römling & Balsalobre, 2012). Previous reports have highlighted the
difficulty in treating chronic and difficult-to-treat infections caused by the bacterial
inhabitants of the colon; a source of concern here is related to one of the advantages of
that biofilms provide their ability to evade attacks from the human immune system
(Dongari-Bagtzoglou, 2008; Macfarlane et al., 2011; Willem, 2015). Antimicrobial
efficiency of Manuka honey which is produced from manuka tree (Leptospermum
scoparium) in New Zealand is known to supersede other honey and its effectiveness is
attributed to a joint action of methylglyoxal (MGO) and other compounds which give it
an extra antiseptic and therapeutic property against several antibiotic resistance
microorganisms. The MGO content of Manuka honey is used to define the antimicrobial
activity of this honey and is referred to as the unique Manuka Honey Factor (UMF®)
(Molan & Russell, 1988; Mavric et al., 2008; Maddocks et al., 2012). In the previous
sections, it was observed that UC was associated with a change in bacterial diversity
favoring members of the Enterbacteriaceae and the Desulfovibrio. Furthermore, it was
demonstrated that a relationship between E. coli and Desulfovibrio indonesiensis existed,
where co-culturing the SRB with E. coli enhanced its growth. Therefore, does the use of
manuka honey in combination with antibiotics can help in controlling the bacteria
involved in UC ?
In this section Manuka honey with different UMF number was compared with the
commercial honey for their efficacy against E. coli BP and D. indonesiensis microbes both
of which are associated with UC. Effect of honey against pure and mixed of these
CHAPTER 3: RESULTS AND DISCUSSION
166
bacteria was examined on the biofilm formation as well as disruption of the existing
biofilms. The effect of two commonly used antibiotics for UC rifaximin and
metronidazole was also examined in the presence and absence of honey.
3.4.1 The MIC and MBC values of different Manuka honey on D.
indonesiensis in pure culture and mixed culture with E. coli BP
The growth characteristics of D. indonesiensis and E. coli BP before treatment with
honey and antibiotics were determined. The growth of E. coli for all experiments ranged
between 1.95X107 to 2X108 cells/ml after 18 hours incubation at 37C; while the
growth for D. indonesiensis was 3.5X105 to 7X106 cells/ml after 6 days incubation at 37
C.
Minimum inhibitory concentrations (MIC values) and minimum bactericidal
concentration (MBC values) were determined for D. indonesiensis biofilms cultured with
and without E.coli BP when treated with different concentrations of Manuka honey
(UMF® 5+, UMF® 15+ and UMF® 25+) , artificial honey and commercial honey. Here, the
honey was added simultaneously with the D. indonesiensis biofilm with and without E.
coli; and incubated for 7 days after which the inhibitory effect of honey on biofilm
formation was examined. Although artificial honey has the same amount of sugars
presented in the honey; it exhibited no effect on the growth of pure and mixed D.
indonesiensis biofilms with E. coli BP. This result clearly suggested that sugar content is
not the only antibacterial factor however there might be other mechanisms than the
high osmotic pressure are involved in the inhibition of bacterial growth (Nassar et al.,
2012).
The data shown in Figure 3.4.1 gives the mean MIC values for D. indonesiensis, in single
and mixed culture, when incubated simulataneously with 4 different honeys, Manuka
UMF®5+, Manuka UMF®15+, Manuka UMF®25+ and commercial honey. Differences in
MIC values exist between treatments and between single culture or mixed biofilms.
CHAPTER 3: RESULTS AND DISCUSSION
167
Figure 3.4.1. The MIC values for E. coli and D. indonesiensis grown as pure and mixed population (the honey was added simultaneously with culture and then incubated for 7 days) when treated with Manuka honey UMF® 5+, UMF® 15+, UMF® 25+ and commercial honey. The MIC values are the average from 5 independent experiments and are displayed with standard deviation. Asterisks indicate significant differences (* P ≤ 0.05; ** P ≤ 0.01). Significant differences between treatments were observed when assessed via Kruskal-
Wallis test with Dunn’s multiple comparisons (Table 3.4.1) for MIC values for the SRB
when in single or mixed culture. The differences between the mean MIC values when
the SRB in single or mixed culture was treated with different grades of Manuka honey
showed some inconsistencies. The mean MIC value for all of the treatments increased
when D. indonesiensis was cultured with E. coli, however none of these differences were
significant according (Table 3.4.1). Successive treatments with higher grades of Manuka
honey, whether the SRB was in single or mixed culture, resulted in lower mean MIC
values although significance not detected indicating some differences in susceptibility to
Manuka honey (Table 3.4.1). However, a significant effect was observed when
comparing the UMF®15+ and UMF®25+ single biofilms to the commercial honey single
and mixed biofilms (UMF®15 vs CH P value of 0.009, UMF®15 vs CH Mix value of 0.003
& UMF®25+ vs CH value of 0.005, UMF® 25+ vs CH Mix value of 0.002).
CHAPTER 3: RESULTS AND DISCUSSION
168
Similarly, no significant difference between the means was found for the SRB in single
or mixed culture when the treatments were for Manuka UMF®15+ and UMF®25+ (MIC
values from 14% to 12% respectively). However, when the mean MIC values for UMF®
5+ and UMF® 25+ treatments were compared there was a significant difference
between these two concentrations (UMF®25+ had a lower MIC value) indicating that
both cultures had a greater susceptible to higher grades of Manuka honey. The mean
MIC value for all of the treatments increased when D. indonesiensis was cultured with E.
coli, however not all of these differences were significant according to Kruskal-Wallis
test statistics (Table 3.4.1). Comparison of the means for the mixed and single culture
biofilms treated with commercial honey (42% and 46% respectively) were not
significantly different at P = > 0.9999. Similarly, the difference between the mean MIC
values was not significant for D. indonesiensis in either biofilm when treated with
Manuka UMF®5, UMF®15+ and UMF®25 (Table 3.4.1).
CHAPTER 3: RESULTS AND DISCUSSION
169
Table 3.4.1: A comparison of the MIC values obtained when different concentrations of
Manuka and commercial honey were applied using Kruskal-Wallis test statistics. Only
comparisons which reached significance are shown. Asterisks indicate significant differences (** P ≤ 0.01).
Comparison Significant p-value UMF®5+ vs. UMF®5+ mix No > 0.9999
UMF®5+ vs. UMF®15+ No > 0.9999 UMF®5+ vs. UMF®15+ mix No > 0.9999
UMF®5+ vs. UMF®25+ No > 0.9999 UMF®5+ vs. UMF®25+ mix No > 0.9999
UMF®5+ vs. CH No 0.6890 UMF®5+ vs. CH mix No 0.3041
UMF®5+ mix vs. UMF®15+ No 0.3980 UMF®5+ mix vs. UMF®15+ mix No > 0.9999
UMF®5+ mix vs. UMF®25+ No 0.2703 UMF®5+ mix vs. UMF®25+ mix No > 0.9999
UMF®5+ mix vs. CH No > 0.9999 UMF®5 mix vs. CH mix No > 0.9999
UMF®15+ vs. UMF®15+ mix No > 0.9999 UMF®15+ vs. UMF®25+ No > 0.9999
UMF®15+ vs. UMF®25+ mix No > 0.9999 UMF®15+ vs. CH Yes 0.0090 (**)
UMF®15+ vs. CH mix Yes 0.0027 (**) UMF®15+ mix vs. UMF®25+ No > 0.9999
UMF®15+ mix vs. UMF®25+ mix No > 0.9999 UMF®15+ mix vs. CH No 0.4291
UMF®15+ mix vs. CH mix No 0.1805 UMF®25+ vs. UMF®25+ mix No > 0.9999
UMF®25+ vs. CH Yes 0.0053 (**) UMF®25+ vs. CH mix Yes 0.0015 (**) UMF®25+ mix vs. CH No 0.5974
UMF®25+ mix vs. CH mix No 0.2598 CH vs. CH mix No > 0.9999
In these situations, the addition of E. coli to the biofilm appears to have increased the
resistance of D. indonesiensis to the inhibitory effects of the Manuka honey. A number of
possibilities could explain these results. The apparent reduction in susceptibility of the
SRB cells to the antimicrobial component of honey could have happened because their
contact with it had been reduced. In the mixed culture, a greater number of E. coli cells
would have been present to interact with the antimicrobial agent, preventing
interaction with the SRB cells. E. coli cells in culture are sensitive to the active
ingredient in Manuka honey, perhaps more so than other bacteria, and they are
particularly sensitive to the higher grades of honey (Badawy et al., 2004; Adebolu, 2005;
Wilkinson & Cavanagh, 2005; Mandal & Mandal, 2011). Another possibility is that the
CHAPTER 3: RESULTS AND DISCUSSION
170
E.coli biofilm provides protection against the uptake of the antimicrobial agent. It has
been reported that these biofilms exhibit a greater resistance to antibiotics because
E.coli changes its expression of the rpoS gene, which codes for one of the sigma factors
(sigma 38) of RNA polymerase. This controls stationary phase genes and protects the
cell against stress. Genes activated at this time include sodC – the gene that codes for
superoxide dismutase, and gor – glutathione reductase (Corona-Izquierdo & Membrillo-
Herna’ndez 2002; Schembri et al., 2003; Ito et al 2009). Presumably the reduction in
activity against D. indonesiensis results from some degree of protection afforded by the
presence of another bacterium. In this respect it can be considered that the difference in
the inhibitory effect is due to complementary activity of the two species taken together
(Cooper et al., 1999).
The mean MBC values for D. indonesiensis biofilms cultured with and without E.coli
when treated with Manuka honey of different grades and commercial honey, which was
added simultaneously with the inoculum and cultured for 7 days, are shown in the
histogram in Figure 3.4.2.
Figure 3.4.2. The MBC values for E. coli and D. indonesiensis grown as pure and mixed population (the honey was added simultaneously with culture and then incubated for 7 days) when treated with Manuka honey UMF®5+, UMF®15+, UMF®25+ and commercial honey. The mean MBC values were calculated from 5 independent repeat experiments and shown with standard deviation. Asterisks indicate significant differences (* P ≤ 0.05; ** P ≤ 0.01).
CHAPTER 3: RESULTS AND DISCUSSION
171
The pattern for the MBC values was similar to the one observed for MIC values, although
the values were marginally greater, which was expected. No significant difference was
observed within or between Manuka honey treatments with the exception of UMF®5
mixed SRB and E. coli culture compared to UMF® 25+ pure SRB (P = 0.021) ;UMF® 15+
compared to commercial honey on pure SRB culture (P =0.0211); UMF® 15+ compared
with commercial honey on mixed culture (P =0.0017); UMF® 25+ with compared with
commercial pure SRB culture (P=0.0035) and UMF® 25+ pure SRB culture with
compared with commercial honey (P=0.0002) (Table 3.4.2).
Table 3.4.2: A comparison of the MBC values obtained when different concentrations of
Manuka and commercial honey were applied using Kruskal-Wallis test statistics. Asterisks indicate significant differences (* P ≤ 0.05; ** P ≤ 0.01; *** P ≤ 0.001).
Comparison Significant p-value
UMF®5+ vs. UMF®5+ mix No > 0.9999 UMF®5+ vs. UMF®15+ No > 0.9999
UMF®5+ vs. UMF®15+ mix No > 0.9999 UMF®5+ vs. UMF®25+ No 0.8417
UMF®5+ vs. UMF®25+ mix No > 0.9999 UMF®5+ vs. CH No > 0.9999
UMF®5+ vs. CH mix No 0.5915 UMF®5+ mix vs. UMF®15+ No 0.1025
UMF®5+ mix vs. UMF®15+ mix No > 0.9999 UMF®5+ mix vs. UMF®25+ Yes 0.0211 (*)
UMF®5+ mix vs. UMF®25+ mix No > 0.9999 UMF®5+ mix vs. CH No > 0.9999
UMF®5+ mix vs. CH mix No > 0.9999 UMF®15+ vs. UMF®15+ mix No > 0.9999
UMF®15+ vs. UMF®25+ No > 0.9999 UMF®15+ vs. UMF®25+ mix No > 0.9999
UMF®15+ vs. CH Yes 0.0211 (*) UMF®15+ vs. CH mix Yes 0.0017 (**)
UMF®15+ mix vs. UMF®25+ No > 0.9999 UMF®15+ mix vs. UMF®25+ mix No > 0.9999
UMF®15+ mix vs. CH No > 0.9999 UMF®15+ mix vs. CH mix No 0.2369 UMF®25+ vs. UMF 25 mix No > 0.9999
UMF®25+ vs. CH Yes 0.0035 (**) UMF®25+ vs. CH mix Yes 0.0002 (***) UMF®25+ mix vs. CH No 0.7854
UMF®25+ mix vs. CH mix No 0.1272 CH vs. CH mix No > 0.9999
CHAPTER 3: RESULTS AND DISCUSSION
172
The mean MBC values for D. indonesiensis present in biofilms varied when it was
present alone or with E.coli. In all cases the MBC values were greater when the SRB was
in a mixed biofilm although this effect never reached significance (Table 3.4.2).
When the grade of Manuka honey was increased to UMF® 15+, the mean MBC value was
reduced compared to UMF®5+ for both cultures (Figure 3.4.2); with the pure D.
indonesiensis biofilm having 16 % MBC mean value and the D. indonesiensis with E. coli
BP mixed having 23 % MBC mean value although the difference was not significant
according to Kruskal-Wallis test statistics (Table 3.4.2), this suggests that increase in
concentration of honey increases the susceptibility of both cultures but the inclusion of
E. coli further emphasizes the resistance of D. indonesiensis to the treatments.
Treatment with a higher UMF® 25+ Manuka honey resulted in a much lower MBC mean
value when compared to UMF® 5+ and UMF® 15+ (Figure 3.4.2); which was statistically
significant between the single UMF®25+ and the mixed UMF®5+ biofilm cultures (Table
3.4.2) according to Kruskal-Wallis test statistics. Treatment of the D. indonesiensis
biofilm resulted in a 13.8 % mean value and the mixed culture showed a 21.4 % mean
value, suggesting that the MBC value is dependent on the concentration of the Manuka
honey where the higher the grades of the honey results in a lower MBC value.
The inclusion of E. coli BP enhanced the resistance of D. indonesiensis to these
treatments. On the other hand, treatment with the commercial honey resulted in an
increased MBC mean value for both cultures when compared to their mean values
obtained when Manuka honey were used. The mean MBC values for D. indonesiensis
alone or with E. coli BP, were 45% and 53% respectively, with the difference between
these not being statistically significant (Table 3.4.2); this data also showed that the
addition of E. coli BP decreased the sensitivity of the mixed biofilm to honey thus, the
slightly higher MBC values of D. indonesiensis with E. coli BP when compared to the pure
D. indonesiensis.
In order to determine whether Manuka honey could disrupt established biofilms, the
honey was added to preformed biofilms before MIC and MBC values were estimated. D.
indonesiensis biofilms and D. indonesiensis with E. coli biofilms were formed after 6 days
growth, when various honey treatments (Manuka UMF ®5+, Manuka UMF®15+, Manuka
UMF®25+ and commercial honey) were applied to the biofilms followed by overnight
incubation. The mean MIC values for D. indonesiensis were estimated for each treatment
(Figure 3.4.3). The pattern observed was similar to the one obtained when honey was,
CHAPTER 3: RESULTS AND DISCUSSION
173
added to the growing cultures, except that the mean MIC values were greater. The mean
MIC value decreases with the different Manuka honey treatments, with the lowest
values being observed for the highest-grade honey applied to the SRB biofilm.
Figure 3.4.3. The MIC value of Manuka UMF® 5+, UMF® 15+, UMF® 25+ and commercial honey for E. coli and D. indonesiensis grown as pure and mixed population (the honey was added after incubating the culture for 6 days). The mean values were calculated form 5 independent experiments and shown with standard deviation. Asterisks indicate significant differences (* P ≤ 0.05; ** P ≤ 0.01).
CHAPTER 3: RESULTS AND DISCUSSION
174
Table 3.4.3: A comparison of the MIC values obtained when different concentrations of Manuka and commercial honey were applied using Kruskal-Wallis test statistics. Asterisks indicate significant differences (** P ≤ 0.01).
Comparison Significant p-value
UMF®5+ vs. UMF®5+ mix No > 0.9999 UMF®5+ vs. UMF®15+ No > 0.9999
UMF®5+ vs. UMF®15+ mix No > 0.9999 UMF®5+ vs. UMF®25+ No 0.8016
UMF®5+ vs. UMF®25+ mix No > 0.9999 UMF®5+ vs. CH No > 0.9999
UMF®5+ vs. CH mix No > 0.9999 UMF®5+ mix vs. UMF®15+ No 0.0767
UMF®5+ mix vs. UMF®15+ mix No > 0.9999 UMF®5+ mix vs. UMF®25+ Yes 0.0019 (**)
UMF®5+ mix vs. UMF®25+ mix No 0.1743 UMF®5+ mix vs. CH No > 0.9999
UMF®5+ mix vs. CH mix No > 0.9999 UMF®15+ vs. UMF®15+ mix No > 0.9999
UMF®15+ vs. UMF®25+ No > 0.9999 UMF®15+ vs. UMF®25+ mix No > 0.9999
UMF®15+ vs. CH No > 0.9999 UMF®15+ vs. CH mix No 0.0767
UMF®15+ mix vs. UMF®25+ No 0.4172 UMF®15+ mix vs. UMF®25+ mix No > 0.9999
UMF®15+ mix vs. CH No > 0.9999 UMF®15+ mix vs. CH mix No > 0.9999
UMF®25+ vs. UMF®25+ mix No > 0.9999 UMF®25+ vs. CH No 0.0701
UMF®25+ vs. CH mix Yes 0.0019 (**) UMF®25+ mix vs. CH No > 0.9999
UMF®25+ mix vs. CH mix No 0.1743 CH vs. CH mix No > 0.9999
Table 3.4.3 showed a comparison of the different treatments used in this experiment for
both biofilms according to Kruskal-Wallis test statistics. The MIC mean values of D.
indonesiensis alone were only significant for UMF®5+ mixed compared to UMF®25+
single (P= 0.002) although a trend of decreasing MIC with increasing Manuka
concentration as observed. The mean MIC values for the SRB alone at UMF®25+ are
significantly different compared to the values obtained for commercial honey with a
mixed biofilm, with the Manuka honey producing the lower values (P = 0.002).
In other words, the only successful treatment of the SRB in mixed culture was with the
highest grade of Manuka honey, where the mean MIC was 37%.
CHAPTER 3: RESULTS AND DISCUSSION
175
This contrast with a previous study where the reported percentage of honey needed to
completely prevent the growth of Proteus mirabilis and E. coli in biofilms was between
6-6.5 % v/v (Willix et al. 1992; Ahmed et al. 2014). This concentration was lower than
that obtained in this study for the tested honeys. These differences probably due to the
different of antibacterial activity of honey of different batch or the differences in the
tested bacteria and tested methods.
Figure 3.4.4. The MBC value of Manuka UMF® 5+, UMF® 15+, UMF® 25+ Manuka honey and commercial honey for E. coli BP and D. indonesiensis grown as pure and mixed population (the honey was added after incubated the culture for 6 days). The mean values were calculated from 5 independent experiments and shown with standard deviation. Asterisks indicate significant differences (* P ≤ 0.05)
The mean MBC values for D. indonesiensis in pure and mixed pre-formed biofilms when
treated with different grades of honey are shown in Figure 3.4.4. In general, the mean
values have increased from those observed when the honey was included with the
growing culture (Figure 3.4.2), and they were greater than the mean MIC values (Figure
3.4.3). The pattern observed was similar to the previous susceptibility results, where
greater mean MBC values were observed for the SRB in mixed culture and decreasing
mean MBC values recorded for the higher grades of Manuka honey (values of 47%, 37%
CHAPTER 3: RESULTS AND DISCUSSION
176
and 37% for pure culture biofilms treated with Manuka UMF®5+, UMF®15+ and
UMF®25+ respectively, and 57%, 45% and 41% for mixed cultures treated with Manuka
UMF®5+, UMF®15+ and UMF®25+ respectively). The greatest mean MBC values (52%
and 57% for the SRB in pure and mixed culture respectively) were for the cells in
biofilms treated with commercial honey.
Table 3.4.4. A comparison of the MBC values obtained when different concentrations of Manuka and commercial honey were applied using Kruskal-Wallis test statistics. Asterisks indicate significant differences (* P ≤ 0.05).
Comparison Significant p-value
UMF ®5+ vs. UMF®5+ mix No > 0.9999 UMF®5+ vs. UMF®15+ No > 0.9999
UMF®5+ vs. UMF®15+ mix No > 0.9999 UMF®5+ vs. UMF®25+ No > 0.9999
UMF®5+ vs. UMF®25+ mix No > 0.9999 UMF®5+ vs. CH No > 0.9999
UMF®5+ vs. CH mix No > 0.9999 UMF®5+ mix vs. UMF®15+ Yes 0.0156 (*)
UMF®5+ mix vs. UMF®15+ mix No 0.9377 UMF®5+ mix vs. UMF®25+ Yes 0.0103 (*)
UMF®5+ mix vs. UMF®25+ mix No 0.1047 UMF®5+ mix vs. CH No > 0.9999
UMF®5+ mix vs. CH mix No > 0.9999 UMF®15+ vs. UMF®15+ mix No > 0.9999
UMF®15+ vs. UMF®25+ No > 0.9999 UMF®15+ vs. UMF®25+ mix No > 0.9999
UMF®15+ vs. CH No 0.3626 UMF®15+ vs. CH mix Yes 0.0156 (*)
UMF®15+ mix vs. UMF®25+ No > 0.9999 UMF®15+ mix vs. UMF®25+ mix No > 0.9999
UMF®15+ mix vs. CH No > 0.9999 UMF®15+ mix vs. CH mix No 0.9377
UMF®25+ vs. UMF®25+ mix No > 0.9999 UMF®25+ vs. CH No 0.2644
UMF®25+ vs. CH mix Yes 0.0103 (*) UMF®25+ mix vs. CH No > 0.9999
UMF®25+ mix vs. CH mix No 0.1047 CH vs. CH mix No > 0.9999
Comparison of the mean MBC values for D. indonesiensis treated with the different
honeys using the Kruskal-Wallis test statistic (Table 3.4.4) revealed differences with the
various treatments. The trends were the same for the single culture and mixed culture
biofilms. Significant differences between the means were observed for the Manuka
CHAPTER 3: RESULTS AND DISCUSSION
177
UMF®5+ on mix SRB culture and UMF®15+ on pure SRB (P = 0.016) and UMF®25+ (P =
0.010), and the UMF®15+ and UMF®25+ on pure SRB culture compared with the
commercial honey on mixed SRB biofilms (P = 0.0156 and 0.010 respectively) (Table
3.4.4)
The susceptibility of D. indoneseisis to Manuka honey when present in an existing
biofilm, pure or mixed, was less than when the honey was added to the cells prior to the
establishment of the biofilm. The mean MIC and MBC values were greater for every
treatment, although there was still greater susceptibility when the higher grades of
honey were used. Presumably the increase in MIC and MBC mean values reflects that
greater permeability barriers exist when the biofilm is established, and that higher
concentrations of honey are needed to disrupt the biofilm. Previous studies have shown
honey’s ability to prevent the formation as well as elimination of established biofilms
(Cooper et al., 2010; Lu et al., 2014). In agreement with previous studies the results
obtained from this investigation showed that the effective concentration of honey
needed to prevent biofilm formation is lower than the concentration required to disrupt
an established biofilm (Allen et al., 1991; Probert & Gibson, 2002; Lin et al., 2011).
Manuka UMF®25+ was more effective at interrupting the growth and survival of
matured biofilm when compared to other concentrations of honey tested. This is result
agrees with previous studies by Sherlock et al (2010), who showed in their study that
the biofilm formation of E.coli is inhibited by Manuka honey UMF®25+ at
concentrations ≥ 12.5%.
3.4.2 The susceptibility of D. indonesiensis in pure culture and mixed
culture with E. coli BP when treated with Manuka honey and antibiotics
The susceptibility of D. indonesiensis to Manuka honey supplemented with antibiotics
was assessed to explore whether application of the honey improves the prevention of
SRB biofilm establishment or disruption. The antibiotics chosen for study were rifaxmin
and metronidazole, both of which are frequently used in the treatment of UC (Guslandi,
2011; Miner et al., 2005). Rifaxmin is a semi-synthetic antibiotic that binds to the Beta
sub-unit of RNA polymerase, preventing transcription. It is used as a treatment against
traveller’s diarrhoea, caused by E. coli, irritable bowel syndrome and hepatic
encephalopathy. Metronidazole is a nitroimidazole that inhibits DNA synthesis by
CHAPTER 3: RESULTS AND DISCUSSION
178
disrupting DNA. This only happens when the antibiotic is reduced, which takes place in
anaerobic cells. It, therefore, has little effect against human cells or aerobic bacteria.
The susceptibility of the SRB was tested against these antibiotics given singularly and in
mixtures with and without Manuka UMF®15+ honey.
The MIC values for D. indonesiensis in pure and mixed cultures after they had been
treated with the antibiotics rifaximin, metronidazole, and combinations of these with
and without honey (Figure 3.4.5). The greatest effect of these antibiotics was on the
pure cultures of D. indonesiensis when they were given individually or in combination,
and when the honey was added, either at the beginning of growth or after 6 days. The
greatest mean MIC value was 230.4 µg/ml, which was obtained when the SRB was in
mixed culture treated with either rifaxmin, metronidole or a combination of both. The
mean MIC values dropped considerably to 57.6, 22.4 and 22.4 µg/ml respectively when
treated with rifaxmin, metronidole and a combination of both. In this study these
antibiotics were combined in order to reduce the occurrence of antibiotic resistance to
rifaximin and metronidazole. Combination of antibiotics agents used in this way form a
synergy that increases the antimicrobial properties. The increased sensitivities to the
antibiotics provide the best way to eradicate both test bacteria, which are involved in
the etiology and pathogenesis of UC (Burke, 1997; Ohge et al., 2003; Rowan et al., 2009;
Leibovici et al., 2010; Mirsepasi-Lauridsen et al., 2016). However, what was observed is
that these two bacteria were able to increases their chance of survival at high
concentrations of both antibiotics even in combination when were grown together.
CHAPTER 3: RESULTS AND DISCUSSION
179
Figure 3.4.5. The MIC value of rifaximin, metronidozole alone and in combination, rifaximin+ metronidozole in the presence of Manuka UMF® 15+ (the antibiotics and honey were added simultaneously to the cultures), rifaximin+ metronidozole in the presence of Manuka UMF® 15+ (the antibiotics were added after 6 days incubation with Manuka UMF® 15+). Mean values were estimated from 5 independent experiments and are shown with standard deviation. Asterisks indicate significant differences (* P ≤ 0.05; ** P ≤ 0.01).
According to Kruskal-Wallis test statistics (Table 3.4.5), although a large difference
existed between the mean MIC values for D. indonesiensis in pure culture than mixed
with E. coli BP when they were grown simultaneously with rifaximin or metronidazole
alone and in combination, these values were not significant. The pure D. indonesiensis
culture was sensitive to these antibiotics while the mixed culture showed high
resistance probably because the co-culturing both bacteria enhances resistance via a
number of mechanisms (Cooper et al., 1999; Wilkinson & Cavanagh, 2005; Mittal et al.,
2015). These include speed with which they develop extracellular polymeric matrix as
well as altered activity of metabolic enzymes and the growth rate leading to a shift from
the TCA cycle to fermentation-dervied energy acquisition, endogenous oxidative stress
and their ability to transfer genes which enables them to develop resistance to the
therapeutic agents (Bradshaw et al., 1996; Shimizu, 2014).
When D. indonesiensis and cultures of D. indonesiensis and E. coli were treated with
rifaximin, metronidazole and Manuka honey 15+, added simultaneously to the culture,
the difference between the mean MIC value for the SRB in the two cultures were again
different but not significantly so according to the Kruskal-Wallis test statistics (Table
CHAPTER 3: RESULTS AND DISCUSSION
180
3.4.5). The mean MIC value was lowest when D. indonesiensis was in pure culture, with
the smallest value being 14.4µg/ml.
Table 3.4.5. A comparison of the MIC values obtained when different concentrations antibiotics and UMF®15+ were applied Kruskal-Wallis test statistics. Asterisks indicate significant differences (** P ≤ 0.01; *** P ≤ 0.001).
Treatment Significant p-value
Rif. vs. Rif. mix No > 0.9999 Rif. vs. Met. No > 0.9999
Rif. vs. Met. mix No > 0.9999 Rif. vs. Rif. + Met. No > 0.9999
Rif. vs. Rif. + Met. mix No > 0.9999 Rif. vs. Rif. + Met. + UMF®15+ No > 0.9999
Rif. vs. Rif. + Met. + UMF®15+ mix No > 0.9999 Rif. vs. UMF®15+ treated cells with Subs. Rif. + Met. No 0.5913
Rif. vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No > 0.9999 Rif. mix vs. Met. No 0.1223
Rif. mix vs. Met. mix No > 0.9999 Rif. mix vs. Rif. + Met. No 0.1223
Rif. mix vs. Rif. + Met. mix No > 0.9999 Rif. mix vs. Rif. + Met. + UMF®15+ Yes 0.0167 (**)
Rif. mix vs. Rif. + Met. + UMF®15+ mix No > 0.9999 Rif. mix vs. UMF®15+ treated cells with Subs. Rif. + Met. Yes 0.0006 (***)
Rif. mix vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No > 0.9999 Met. vs. Met. mix No 0.1223
Met. vs. Rif. + Met. No > 0.9999 Met. vs. Rif. + Met. mix No 0.1223
Met. vs. Rif. + Met. + UMF®15+ No > 0.9999 Met. vs. Rif. + Met. + UMF®15+ mix No > 0.9999
Met. vs. UMF®15+ treated cells with Subs. Rif. + Met. No > 0.9999 Met. vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No > 0.9999
Met. mix vs. Rif. + Met. No 0.1223 Met. mix vs. Rif. + Met. mix No > 0.9999
Met. mix vs. Rif. + Met. + UMF®15+ Yes 0.0167 (**) Met. mix vs. Rif. + Met. + UMF®15+ mix No > 0.9999
Met. mix vs. UMF®15+ treated cells with Subs. Rif. + Met. Yes 0.0006 (***) Met. mix vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No > 0.9999
Rif. + Met. vs. Rif. + Met. mix No 0.1223 Rif. + Met. vs. Rif. + Met. + UMF®15+ No > 0.9999
Rif. + Met. vs. Rif. + Met. + UMF®15+ mix No > 0.9999 Rif. + Met. vs. UMF®15+ treated cells with Subs. Rif. + Met. No > 0.9999
Rif. + Met. vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No > 0.9999 Rif. + Met. mix vs. Rif. + Met. + UMF®15+ Yes 0.0167 (**)
Rif. + Met. mix vs. Rif. + Met. + UMF®15+ mix No > 0.9999 Rif. + Met. mix vs. UMF®15+ treated cells with Subs. Rif. + Met. Yes 0.0006 (***)
Rif. + Met. mix vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No > 0.9999
Rif. + Met. + UMF®15 vs. Rif. + Met. + UMF®15 mix No > 0.9999
CHAPTER 3: RESULTS AND DISCUSSION
181
Treatment Significant p-value
Rif. + Met. + UMF®15 vs. UMF®15 treated cells with Subs. Rif. +
Met.
No > 0.9999
Rif. + Met. + UMF®15+ vs. UMF®15+ treated cells with Subs. Rif. +
Met. mix
No > 0.9999
Rif. + Met. + UMF®15+ mix vs. UMF®15+ treated cells with Subs.
Rif. + Met.
No 0.0947
Rif. + Met. + UMF®15+ mix vs. UMF®15+ treated cells with Subs.
Rif. + Met. mix
No > 0.9999
UMF®15+ treated cells with Subs. Rif. + Met. vs. UMF®15+ treated
cells with Subs. Rif. + Met. mix
No 0.8500
Differences in mean MIC values between the treatments were observed that were
statistically significant according to Kruskal-Wallis tests (Table 3.4.5).
Considering the susceptibility of the SRB to the antibiotics alone, the mean MIC value
recorded was 22.4 µg/ml for metronidazole, which was not significantly different to the
mean MIC value of 57.6 µg/ml for rifaximin. Similarly, when the two antibiotics were
combined in a treatment, the mean MIC value (22.4 µg/ml) was not significantly
different to that for rifaximin alone or metronidazole alone. The mean MIC value for D.
indonesiensis fell to 14.4 µg/ml when Manuka UMF®15 honey was included with the
dual antibiotic treatment of the pure biofilm, however this was not significantly
different to the metronidazole or rifaximin and metronidazole treatments. The mean
MIC values for D. indonesiensis changed considerably when the biofilm contained E. coli
as well as the SRB. The value, 230.4 µg/ml, did not change for treatments with rifaximin,
metronidazole or the mixture, but dropped significantly to 76.8 µg/ml for the treatment
with the honey (Table 3.4.5). Interestingly, the addition of honey influenced the MIC
value for the SRB in mixed culture only.
Increased sensitivity of D. indonesiensis to the honey and antibiotic treatment was
achieved by prolonged incubation of the SRB, grown with E.coli, with Manuka UMF®15
honey for 6 days before the addition of rifaximin and metronidazole. For the pure
culture biofilm, the mean MIC value for the SRB decreased to 4.8 µg/ml, whereas the
mean value dropped to 44.8 µg/ml for the mixed biofilm. This reduction for single
cultures was significant compared to previous treatments with rifaximin and
metronidazole in mixed cultures (Table 3.4.5). It is feasible, under these circumstances,
that the honey is preventing the formation and establishment of the biofilm which, in
CHAPTER 3: RESULTS AND DISCUSSION
182
turn, reduces the protection it provides, and increases the sensitivity of the cells to the
antibiotics, when they are applied.
Figure 3.4.6. The MBC value of rifaximin, metronidozole alone and in combination, rifaximin+ metronidozole in the presence of Manuka UMF® 15+ (adding the antibiotics and honey simultaneously), rifaximin+ metronidozole in the presence of Manuka UMF® 15+ (adding antibiotics after 6 days of incubation with Manuka UMF® 15+). The mean values were estimated from 5 independent experiments and are shown with standard deviation. Asterisks indicate significant differences (** P ≤ 0.01).
The mean MBC values for D. indonesiensis in pure or mixed cultures with E. coli treated
with the antibiotics, singularly and in combination, with and without Manuka UMF®15+
honey are shown in Figure 3.4.6. The overall pattern observed was similar to the one
described for mean MIC values. The mean values when the SRB biofilms were treated
with rifaximin and metronidazole were 51.2 and 70.4 µg/ml, which were not
significantly different according to Kruskal-Wallis test statistics (Table 3.4.6), while the
value when the antibiotics were used in combination was 57.6 µg/ml, which was also
not significantly different to the ones obtained for the rifaximin or metronidazole
treatments. The MBC values decreased to 14.4 and 7.2 µg/ml when the combined
antibiotic treatment included Manuka honey UMF®15+ and when the cells were treated
with Manuka UMF®15+ honey prior to combined antibiotic treatment. Both of these
treatments produced mean MBC values that were significantly different to the values
obtained with the other treatments (Table 3.4.6).
CHAPTER 3: RESULTS AND DISCUSSION
183
Table 3.4.6. A comparison of the MBC values obtained when different concentrations antibiotics and UMF®15+ were applied using Kruskal-Wallis test statistics. Asterisks indicate significant differences (* P ≤ 0.05; ** P ≤ 0.01; *** P ≤ 0.001).
Treatment Significant p-value
Rif. vs. Rif. mix No > 0.9999 Rif. vs. Met. No > 0.9999
Rif. vs. Met. mix No 0.6438 Rif. vs. Rif. + Met. No > 0.9999
Rif. vs. Rif. + Met. mix No 0.6438 Rif. vs. Rif. + Met. + UMF®15+ No > 0.9999
Rif. vs. Rif. + Met. + UMF®15+ mix No > 0.9999 Rif. vs. UMF®15+ treated cells with Subs. Rif. + Met. No > 0.9999
Rif. vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No > 0.9999 Rif. mix vs. Met. No > 0.9999
Rif. mix vs. Met. mix No > 0.9999 Rif. mix vs. Rif. + Met. No > 0.9999
Rif. mix vs. Rif. + Met. mix No > 0.9999 Rif. mix vs. Rif. + Met. + UMF®15+ Yes 0.0112 (*)
Rif. mix vs. Rif. + Met. + UMF®15+ mix No > 0.9999 Rif. mix vs. UMF®15+ treated cells with Subs. Rif. + Met. Yes 0.0017 (**)
Rif. mix vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No 0.6844 Met. vs. Met. mix No > 0.9999
Met. vs. Rif. + Met. No > 0.9999 Met. vs. Rif. + Met. mix No > 0.9999
Met. vs. Rif. + Met. + UMF®15+ No > 0.9999 Met. vs. Rif. + Met. + UMF®15+ mix No > 0.9999
Met. vs. UMF®15+ treated cells with Subs. Rif. + Met. No 0.8698 Met. vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No > 0.9999
Met. mix vs. Rif. + Met. No 0.6638 Met. mix vs. Rif. + Met. mix No > 0.9999
Met. mix vs. Rif. + Met. + UMF®15+ Yes 0.0044 (**) Met. mix vs. Rif. + Met. + UMF®15+ mix No > 0.9999
Met. mix vs. UMF®15+ treated cells with Subs. Rif. + Met. Yes 0.0006 (***) Met. mix vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No 0.3526
Rif. + Met. vs. Rif. + Met. mix No 0.6638 Rif. + Met. vs. Rif. + Met. + UMF®15+ No > 0.9999
Rif. + Met. vs. Rif. + Met. + UMF®15+ mix No > 0.9999 Rif. + Met. vs. UMF®15+ treated cells with Subs. Rif. + Met. No > 0.9999
Rif. + Met. vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No > 0.9999 Rif. + Met. mix vs. Rif. + Met. + UMF®15+ Yes 0.0044 (**)
Rif. + Met. mix vs. Rif. + Met. + UMF®15+ mix No > 0.9999 Rif. + Met. mix vs. UMF®15+ treated cells with Subs. Rif. + Met. Yes 0.0006 (***)
Rif. + Met. mix vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No 0.3526 Rif. + Met. + UMF®15+ vs. Rif. + Met. + UMF®15+ mix No 0.2704
Rif. + Met. + UMF®15+ vs. UMF®15+ treated cells with Subs. Rif. + Met. No > 0.9999
CHAPTER 3: RESULTS AND DISCUSSION
184
Treatment Significant p-value
Rif. + Met. + UMF®15+ vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No > 0.9999
Rif. + Met. + UMF®15+ mix vs. UMF®15+ treated cells with Subs. Rif. + Met. No 0.0596
Rif. + Met. + UMF®15+ mix vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No > 0.9999
UMF®15+ treated cells with Subs. Rif. + Met. vs. UMF®15+ treated cells with Subs. Rif. + Met. mix No > 0.9999
The mean MBC values increased when the treatments were against mixed culture
biofilms of D. indonesiensis and E. coli. When rifaximin was used the mean value was
204.8 µg/ml, which increased to 230.4 µg/ml when metronidazole was used. When the
antibiotics were used in combination the mean MBC value was 230.4 µg/ml. All of these
values were not significantly different according to Kruskal-Wallis test statistics (Table
3.4.6). The mean values dropped to 115.2 µg/ml when Manuka honey UMF®15+ was
included in the treatment, and to 44.8 µg/ml when the cells were treated with Manuka
honey UMF®15+ before antibiotic treatment.
The differences between the mean MBC values for the SRB in pure or mixed culture
biofilms were not significantly different when each treatment was compared (Table
3.4.6). However, there was a trend of the mixed biofilms having greater MIC and MBC
mean values than those for D. indonesiensis alone, indicating that the SRB strain was less
susceptible to the antibiotics and antibiotics/Manuka honey combinations when in
mixed culture.
A similar pattern of significance, as described for figure 3.4.5, when comparing mixed
cultures with rifaximin and metronidazole to single cultures with honey was observed.
With a significant reduction for all values observed following the addition of Manuka
honey. The lowest MBC values were obtained when Manuka honey UMF®15+ was used
to treat the cells in either the pure or mixed biofilm before antibiotic treatment.
Desulfovibrio species and E. coli have been reported to be sensitive to rifaximin and
metronidazole (Huang & DuPont, 2005; Finegold et al., 2009; Löfmark et al., 2010), and
this appears to be true for D. indonesiensis, however it also appears that the SRB
becomes less susceptible to both antibiotics when it is co-cultured with E. coli. How this
sensitivity is altered is unknown, but there does appear to be a relationship between
CHAPTER 3: RESULTS AND DISCUSSION
185
these organisms as both grow together in the human colon and they have both been
implicated in the pathogenesis of UC (Burke & Axon, 1987; Pitcher et al., 2000). E. coli
strains have a known ability to increase the cell density of other organisms by
producing a signalling factor (Scott, 2002). This signalling factor may be part of a
quorum sensing system or the production of an auto-inducer for growth. If the growth
of the SRB is stimulated by the presence of E. coli, it is possible that this could lead to the
greater production of hydrogen sulfide, which might act against the antibiotics.
In order to determine if the inhibitory effect of Manuka honey was due to its
antimicrobial agent, Methylglyoxal (MGO) (Adams et al., 2008; Mavric et al., 2008;
Müller et al., 2013), rather than some other compound found in the honey, the effect of
MGO on the pure and mixed cultures was examined.
Figure 3.4.7. The MIC value of Methylglyoxal (MGO) of D. indonesiensis as pure and mixed culture with E.coli BP. The mean values were estimated from 5 independent experiments and are shown with standard deviation. Asterisks indicate significant differences (** P ≤ 0.01). The mean MIC value for D. indonesiensis in pure culture was 0.08% v/v which increased
to 0.19% v/v when the SRB was co-cultured with E. coli (Figure 3.4.7). This indicated
that MGO had an antimicrobial effect on the pure and the mixed culture of D.
indonesiensis. The difference in MIC level between the pure D. indonesiensis and D.
indonesiensis co- cultured with E. coli BP was statistically significant according to Mann-
Whitney test statistics (P = 0.008) (Table 3.4.7).
CHAPTER 3: RESULTS AND DISCUSSION
186
Table 3.4.7. A comparison of the MIC values obtained when different concentrations of Methylglyoxal (MGO) were applied using Mann-Whitney test statistics. Asterisks indicate significant differences (** P ≤ 0.01).
Comparison Significant p-value
MGO: SRB vs SRB + EC Yes 0.008 (**)
The pattern recorded here was similar to the one observed with Manuka honey, where a
decrease in susceptibility occurred when the SRB was co-cultured with E. coli. This
result thus agreed with previous research work showed that the antimicrobial activity
of Manuka honey against bacterial cultures is due to the presence of the unique MGO as
the active antimicrobial component in Manuka honey (Adams et al., 2008; Müller et al.,
2013)
Figure 3.4.8. The MBC value of Methylglyoxal (MGO) of D. indonesiensis as pure and mixed culture with E.coli BP. Mean MBC values were estimated from 5 independent experiments and are shown with standard deviation. Asterisks indicate significant differences (* P ≤ 0.05).
The mean MBC values of MGO for D. indonesiensis in pure and mixed culture were 0.14%
v/v and 0.23% v/v respectively (Figure 3.4.8), mirroring the pattern observed for the
mean MIC values of MGO and Manuka honey. The difference between the two values
was significant according Mann-Whitney analysis (P = 0.047) (Table 3.4.8). The
magnitude of change, however, was not as great as observed for the Manuka honey.
CHAPTER 3: RESULTS AND DISCUSSION
187
Table 3.4.8. A comparison of the MBC values obtained when different concentrations of Methylglyoxal (MGO) were applied using Mann-Whitney test statistics. Asterisks indicate significant differences (* P ≤ 0.05).
Comparison Significant p-value
MGO: SRB vs SRB + EC Yes 0.047 (*)
The present study showed that Manuka honey both inhibits as well as disrupts existing
biofilms both in pure and mixed culture of Desulfovibrio with and without E. coli. The
effectiveness of this increased with the higher UMF grade of Manuka honey. The
protection of the SRB by E. coli was observed by a reduction in its susceptibility,
recorded as increases in mean MIC and MBC values. The effectiveness of any treatment
with the antibiotics rifaximin and metronidazole is enhanced if Manuka honey
UMF®15+ is applied prior to their delivery. It was also shown that the believed active
ingredient in Manuka honey, methylglyoxal, inhibited D. indonesiensis in a similar way
observed by treatment with Manuka honey, suggesting that this is indeed the active
component.
CHAPTER 4: GENERAL DISCUSSION AND CONCLUSION
188
CHAPTER 4: GENERAL DISCUSSION AND CONCLUSION
CHAPTER 4: GENERAL DISCUSSION AND CONCLUSION
189
The primary aims of the work described in this thesis were to compare and contrast the
diversity of bacteria present in the colon of healthy people with that from patients
suffering from ulcerative colitis with specific reference to SRBs, develop a system to
enhance the detection of SRBs in natural systems, and to assess the susceptibility of
Desulfovibrio indonesiensis, cultured alone and co-cultured with Escherichia coli, to
Manuka honey.
The colon harbours a complex microbial ecosystem, reaching densities of 109- 1012
cfu/ml (Blaut & Clavel, 2007) that provides anaerobic fermentative of resistant
starches, dietary fibres and unabsorbed sugars and alcohols, as well as proteins to
generate short chain fatty acids, acetate, propionate, and butyrate as well as peptides
and amino acids. Lactic acid, ethanol, succinic acid and formate are important
intermediates in this breakdown. In addition to these compounds, branch chain fatty
acids, ammonia, hydrogen sulfide, amines, phenols, indoles and mercaptanes are also
formed. This requires the cooperative action of different microbial groups. It has been
estimated that there are approximately 500 different species present in the colon, of
which 75% are believed to be uncultrable (Duncan et al., 2007). Although great
diversity of bacterial species and groups exists, dependent upon diet and health,
approximately 60-80% of colonic or faecal bacteria belong to either the Cytophaga-
Flavobacterium-Bacteroides (CFB) group or to the low GC Firmicutes. The remaining
20-25% is often represented by members of the Bacteriodetes and 3-5% by high GC
content Actinomycetes. Proteobacteria usually form a minor component of the colon
microflora.
In this study a number of molecular techniques were employed to assess the molecular
diversity of bacteria from the colon of healthy people and UC suffers. These included
PCR-DGGE, and the cloning and sequencing of 16S rRNA gene fragments, the dsr gene
fragments from colon derived DNA. PCR/DGGE profiles were used initially as a rapid
method to compare the bacterial diversity associated with UC patients and healthy
individual samples and the result showed that there was a change in microbiota
composition associated with the disease.
The 16S rRNA sequences from the healthy person provided a broad diversity that
included members from the Bacteriodaceae, Clostridiaceae, Coriobacteriaceae,
CHAPTER 4: GENERAL DISCUSSION AND CONCLUSION
190
Ruminococcaceae, Eggerthellaceae, Lactobacilaceae, Porphyromonadaceae, and the
Enterobacteriaceae, with the greatest proportion being members of the Bacteriodaceae
followed by the Clostridiaceae and Ruminococcaceae. The frequency of
Enterobacteriaceae was approximately 12 %, which is greater than previously reported
(Duncan, et al., 2007) but it is clear that this group is not one of the pre-dominant ones.
This level of diversity is consistent with previous reports (Eckburg et al., 2005; Rajilić-
Stojanović, 2007; Bik et al., 2010) and reflects the metabolic complexity expected of
bacteria present in the colon. In contrast, the mucosal bacterial community of the colon
from UC patients was not so diverse, with a total reduction in bacteria belonging to the
Firmicutes and Bacteriodetes and an increase in the number belonging to the
Enterobacteriaceae, in particular species of Citrobacter and Enterobacter.
It is tempting to speculate that the observed dysbiosis in the microflora from UC
patients maybe characteristic, and could be used as an initial marker for diagnosing the
UC disease, however not all studies report such a reduction. Hansen et al (2011)
reported a reduction in alpha diversity of colonic bacteria for suffers of Crohn’s Disease
(CD) but not for those with ulcerative colitis. In this study, 37 patients (13 with CD, 12
with UC and 12 healthy) had mucosal biopsies, from which DNA was extracted and
sequenced for 16S rRNA gene spanning V3 to V6 regions. Sequences representing
members of the Proteobacteria represented 10 % of the total for UC patients, which was
higher than that for CD patients and healthy people but the proportion for Firmicutes
and Bacterioidetes remained the same. In the present study biopsies from 4 patients
were analysed, which is less than the number examined in Hansen et al (2011). It is
possible that if a greater number of samples were analysed the recorded change in
diversity might have been less. Lennon et al (2014) reported that the mucus gel layer in
UC suffers is thinner than that of healthy people. This reduction of the layer is
accompanied by an increase of bacteria that can degrade the gel, such as species of
Rumminococcus, and the reduction in butyrate producing bacteria. The degradation of
the mucus layer releases other compounds that support the growth of other bacteria,
such as SRBs through the release of oxidised sulphur compounds.
The reduction in 16S rRNA molecular diversity in UC patients reported in the current
study clearly showed an increase in the proportion of Enterobacteriaceae at the expense
CHAPTER 4: GENERAL DISCUSSION AND CONCLUSION
191
of other bacteria commonly associated with a healthy functioning colon; however it did
not show the presence of any SRB sequences. Sulfate reducing bacteria have been
implicated in UC condition through the production of hydrogen sulphide (Pitcher &
Cummings, 1996; Rowan et al., 2009). The presence of hydrogen sulfide in the colon
(Jørgensen & Mortensen, 2001; Linden et al., 2010); Bacteria degradation of cysteine
and methionine results in the formation of hydrogen sulfide, presence as HS- at neutral
pH, and the build up of this in the colon is toxic, inhibiting butyrate oxidation. Hydrogen
sulfide can also be generated by the oxidation of lactate, ethanol, succinate and H2 when
sulfate is reduced by SRBs. Oxidised forms of sulfur are present in foods, and these can
act as the substrate for sulfide production. The absence of 16S rRNA gene sequences for
any SRB does not mean that they were not present in the biopsy samples, it is possible
that they were present but at low densities. Under these conditions, bacteria present at
greater cell densities would be always preferentially detected. In order to test whether
SRB were present in the mucosa biopsies different target genes were chosen.
Dissimilatory sulphite reductase (DSR) is a key enzyme produced by SRBs, and the dsr
AB genes make suitable targets for detection of these bacteria ( Dar et al., 2007). Few
studies have been conducted to detect mucosa associated SRBs in the colon of UC
patients (Zinkevich & Beech, 2000; Fite et al., 2004), most studies have limited the
detection of SRBs to faeces. Although the targeting of dsr A gene, including real time
PCR, to detection SRBs has been used in healthy human fecal (Christophersen et al.,
2011), it has not been used to detect them in colonic mucosa of UC patients. A variety of
morphologically and nutritionally different SRBs have been isolated from human faeces,
which belong to 5 genera with Desulfovibrio being the pre-dominant genus (Gibson et
al., 1988c). The recovery of dsr A gene sequences from the 4 biopsies of UC patients
revealed a diverse range of Desulfovibrio species, including Desulfovibrio intestinalis,
Desulfovibrio desulfuricans, Desulfovibrio burkinensis, Desulfovibrio carbinolicus,
Desulfovibrio magneticus, but with limitations for other SRB genera.
The detection of Desulfovibrio species directly with mucosal biopsy samples agrees with
the study of Loubinoux et al. (2002) where it was observed that the incident of SRBs
(specifically Desulfovibrios) was higher in the faeces of IBD (CD and UC) patients than
healthy indivuals. It is also in agreement with (Rowan et al., 2010) who used laser
CHAPTER 4: GENERAL DISCUSSION AND CONCLUSION
192
capture microdisection and PCR to show that the Desulfovibrio increased in colonic
mucosal biopsies from acute and chronic UC patients, It would appear that the mucosal
layer undergoes a dysbiosis in UC patients where the proportion of Enterobacteriaceae
and the number of Desulfovibrio species increases.
A possible link between SRB and colonic epithelial inflammation has been suggested, as
a result of hydrogen sulphide production but with little supporting evidence (Rowan et
al., 2009). Desulfovibrio sp are well documented as possible causative agent of UC and in
some extreme cases they may cause clone cancer in the individual. This makes
investigate the role of SRB important bacteria of interest. However, the growth of SRB is
slow and making the early detection these bacteria at low cell density very difficult after
their recovery from natural habitats (human colon) and therefore controlling these
bacteria is important.
The detection of SRBs and their control was investigated by the development of a
biochip to allow rapid screening and by identifying dietary additions for which SRBs
have susceptibility. The challenging step in biochip design is the selection of probes
with identical hybridisation characteristics. A novel cassette based methodology, which
allowed for a rapid validate the de-novo designed probes and those which identified
from the literature for fine-tuning of biochips, was used (Zinkevich et al., 2014) to
assess the biochip. After probe evaluation, DNA from four UC biopsies and one healthy
control were hybridised against the biochip to assess changes in bacterial population
number and heterogeneity. It was not possible to identify a particular species or genera
of bacteria that had a significant association with UC or confirm the role for SRB,
although this may be due to the relatively small samples number of the study. However,
several key trends were identified which may prove useful for further research. Possible
variations in levels of Enterobacteriaceae were detected between UC patients and the
control. Additionally, a potentially novel change in the relative amounts of D. piger and
D. gigas were detected between UC patients and the control. Whilst, unable to identify a
pathological mechanism in the literature this novel description requires further
investigation. To summarize; whilst unable to identify a single species or genera of
bacteria which underlies the eaitology of UC, this project has advanced the field and
identified several possible strands of future development. Namely, more detailed
CHAPTER 4: GENERAL DISCUSSION AND CONCLUSION
193
investigation into identifying particular Enterobacteriaceae species along with further
investigation into the interaction between D. piger and D. gigas may prove informative.
However, the precise role of SRB in UC remains unclear. Additionally, the novel, rapid
biochip/cassette based screening method represents proof of concept study and should
prove useful to expand the sample size of this research, for further validation to confirm
the clinical usefulness of this test and also for other researchers for UC diagnosis before
any test could be rolled out. As our understanding of the dysbioisis associated with UC
expands, a methodology to rapidly optimize probes for simultaneous detection bacterial
species and genera from UC patient samples will prove invaluable.
Previous observation have shown that there is substantial association between SRBs
and E. coli in the gut (Unpublished), where SRB always seem to be associated with the
presence of enteric bacteria, such as E. coli, in colonic mucosa tissue. In addition to this
it has been observed that the colonic bacteria E.coli can induce the recovery and growth
of SRBs from the colon at low cell density. Co-culturing of SRBs with E. coli might
provide a method to enhance its growth so that early detection of these bacteria can be
made. SRB may play an important role in the eitology of UC and the key to
understanding the role of SRB in UC could be in determining the real quantity of SRB
between UC and non UC patients. The results obtained in this study take a closer look at
their relationship and further emphasizes the effect of E. coli on the aiding the growth
and survival of SRBs and how they can exist together in the colon as model in vitro.
SRBs are well known to act co-operatively with other organisms as a result of their need
to act synergistically when searching for exogenous electron acceptors (Walker et al.,
2009). The nature of the relationship between E. coli and SRBs is believed to one where
the enteric stimulates the growth of the SRB. This is not unusual, E.coli has been
described to enhance growth of other organisms, such as Chorella (Higgins et al., 2015).
Co-culturing of Desulfovibrio indonesiensis with Escherichia coli BP resulted in increased
levels of detection of the SRB. The strain of E.coli, recovered from an UC biopsy sample,
was able to promote the growth of the SRB from aged pre-cultures, even when the SRB
was present in low densities. This growth study was supported by changes in the levels
of dsrA and apsA transcripts in the cultures detected by qPCR.
CHAPTER 4: GENERAL DISCUSSION AND CONCLUSION
194
This observation is novel and has not been previously reported, and the co-culturing
appears to give a novel way to detect SRBs.
The control of SRBs can be achieved by treatment with antibiotics. However the mucosa
associated bacteria are able to form biofilms which increases their chance of survival at
high concentrations of antibiotics. This may be a contributing factor to the observation
that UC patients receiving antibiotic treatment do not necessarily show any
improvements in their condition. The low level of success in using antibiotics to treat UC
suffers may be improved if the biofilm associated with the mucosal layer could be
disrupted prior to treatment. The use of honey to do this was an option here, because
honey has been widely used in treating bacterial infection (Badawy et al., 2004; Mandal
& Mandal, 2011; Maddocks et al., 2013). No such investigation examining the effect of
honey on the bacteria involved in UC and mixed biofilms in combinations with
antibiotics has previously been reported.
The effect of Manuka honey of various strengths with and without rifaximin and
Metronidazole antibiotics on the growth of D. indonesiensis alone and with E. coli was
examined. It was observed that the addition of honey to pure biofilm D. indonesiensis
reduced its MIC and MBC values in comparison to D. indonesiensis with E. coli, and that
the strength of this reduction was associated with the higher grades of Manuka honey,
in particular UMF®25+. It was shown also that the addition of the antibiotics to the
bacterial culture after incubation with Manuka honey improved the antibiotics effect on
the growth of pure D. indonesiensis biofilms and slightly for mixed D. indonesiensis with
E.coli biofilms. The honey was able to inhibit the development of biofilms rather than
inhibit the growth of establish biofilms this finding suggest that after early detection of
bacteria causing the disease the honey before starting the antibiotics course will
improve the efficiency of antibiotics used for treating the condition. This study thus
showed that although co-culturing of SRB with E. coli increases the detection level of
SRB in culture making isolation, identification quantification effective, it also increases
its resistance to antimicrobials thus, increasing their survival
CHAPTER 4: GENERAL DISCUSSION AND CONCLUSION
195
In conclusion, SRB has been linked to UC as possible causes by numbers of
investigations, but whether these bacteria are causative agents, or other bacteria such
as E. coli taking an advantage of changes in the gut intestinal bacteria and promoting the
SRB growth; will be the main challenge for the future research.
FUTURE WORK
196
FUTURE WORK
FUTURE WORK
197
Ulcerative colitis is a well characterized medical condition associated with bacterial
dysbiosis, where changes in the intestinal bacterial population influence an individual’s
health (Chen et al., 2017). It is possible that the establishment of a particular bacterial
community is a predisposing factor for this disorder. Previous work has focussed on
evaluating dynamic changes in the microbiome of the colon or identifying the
predominant species present. Individual dietary habits can influence the microflora of
the colon, and may have implications for the overall pathogenicity of the disease. The
present study consisted of two separate, yet contextually highly relevant, studies
investigating the role of sulfate reducing bacteria in the overall pathogenesis of the UC
and the role that Manuka honey might have on the colon resident bacterial populations
when used to treat SRBs. Kumamoto (2016) has previously reported that rare gut
microbes might have a significant role in the inflammatory bowel disease. This study
mainly considered the fungal population, but data presented in this thesis suggests that
it may be equally true for the bacterial population. Here, the crucial role of SRB as
producers of hydrogen sulfide fits well with the notion that microbes with a low
population density might have a significant impact on the pathogenesis of UC disease.
The microbial population changes observed in this study revealed that members of the
Enterobacteriaceae predominated the colon in UC suffers, reflecting an overall reduction
in bacterial diversity, while there was also an increase in species of Desulfovibrio.
Although a reduction in bacterial diversity has been previously observed in UC suffers,
the link between the enterics and SRBs has not been reported, particularly the induction
of Desulfovibrio species growth in the presence of Escherichia coli. Nevertheless, to make
these findings more meaningful, there is a need for further study relevant to this aspect.
The samples obtained for this study were colonic biopsies from a healthy
individual or UC suffers. Furture work should focus on samples from inflamed
and non-inflamed regions of biopsies from the same patient, and from patients
who are in the same stage of the disease. This would allow the identification of
specific strains that might be associated with the development of the disease.
Biopsy samples from different colonic regions of the same patient and taken at
different stages of the disease would also allow an assessment how SRB profiles
can change, and if there is link between particular SRB strains and bacterial
population with UC pathogenesis.
FUTURE WORK
198
The potential relationship, commensal or symbiotic, between SRBs and E.coli
needs to be investigated further. This is probably best approached by studying
the association of bacterial strains, isolated from UC suffers, in animal model
systems. By growing the strains together in vitro or in germ free animal models it
would be possible to check whether they could induce inflammation to the model
tissue, allowing this information to be translated to a clinical settings.
The co-culture of SRB strains with different organisms isolated from UC patient
at different disease stages might allow the identification of their interactions
leading to colonisation, and the reduction in bacterial diversity. This approach
might answer the questions whether different strains of SRB can transmit the
disease phenotype and if some bacteria can improve or prevent the disease.
H2S is the major byproduct of SRB growth that affects the mucosa layer of human
colon leading to the inflammation. It is suspected that anything that promotes
SRB growth will lead to an increase in the concentration of hydrogen sulphide
production. This needs to be tested in situ.
The reduction in bacterial diversity associated with UC has been reported on a
number of occasions (Zoetendal et al., 2002; Ott et al., 2004 ;Rigottier-Gois, 2013;
Wang et al., 2014; Carding et al., 2015), however the reduction observed in this
study was extreme, resulting in only members of the Enterobactericeae and
Desulfovibrio being present from UC biopsies. This may reflect a low sequence
sampling effort or that the change in diversity is progressive. In addition to this,
the relationship between E. coli and Desulfovibrio species, described in this
thesis, suggests a mechanism for the diversity reduction observed in the SRB
group. The possible progressive nature in the reduction of diversity at the
general and specific levels can be investigated further by evaluating the
relationship between diet and the microbial diversity of UC sufferers at different
levels of the disease.
The use of Manuka honey and its impact on controlling bacterial population
existing in the form of biofilms is intriguing and is of future research interest. For
the future direction clinical trials worth to conducted to evaluate the beneficial
impact of Manuka honey with and without antibiotics among patients suffering
from UC.
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APPENDICES
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APPENDICES
APPENDICES
241
Appendix 1: Growth media composition
Growth media composition used in this study is described below.
Vitamin Medium, Vitamin Medium with iron and Vitamin medium
with reduced iron (VM, VMI, VMR medium)
Medium was prepared as follows (composition per litre) (Table A.1.1).
Table A.1.1: VM, VMI and VMR medium composition.
Compound g/liter Potassium dihydrogen phosphate
(KH2PO4)
0.5 g
Ammonium chloride (NH4Cl)
1 g
Sodium sulphate (Na2SO4) 4.5 g
Calcium chloride (CaCl2∙2 H2O) 0.04 g
MgSO4∙7 H2O 0.06 g
FeSO4∙7 H2O 0.004 g*
Sodium citrate (Na3C6H5O7∙2 H2O) 0.3 g
NaCl 25.0 g
Casamino acids
2.0 g
Tryptone
2.0 g
Sodium lactate (NaC3H5O3) 6.0 g
* FeSO4∙7 H2O for VMI medium 0.5 g and for VMR 0.05 g
Medium pH was adjusted to 7.5 by adding NaOH. Sterile trace elements (1 ml stock
solution) (Table A.1.2) and 1 ml sterile vitamin stock solution (Table A.1.3) were added
to the medium. 10 ml vials were filled with a medium prepared. The medium was
subsequently degassed with an O2 free-N2 flux to achieve anaerobic condition, the vial
was covered with a stopper and crimp with aluminum and autoclaved for 30 min at
121°C.
APPENDICES
242
Table A.1.2: Modified Wolfe’s mineral elixir (per liter).
Compound g/liter C6H9NO6
1.5 g
MgSO4∙7 H2O 3.0 g
MnSO4∙ H2O 0.5 g
NaCl 1.0 g
FeSO4∙7 H2O 0.1 g
CoSO4∙7 H2O 0.1 g
NiCl2∙6 H2O 0.1 g
CuCl2∙2 H2O 0.1 g
ZnSO4∙7 H2O 0.1 g
CuSO4∙5 H2O 0.01 g
AlK(SO4) ∙12 H2O 0.01 g
H3BO3 0.01 g
NaMoO4∙2 H2O 0.01 g
Na2SeO4 0.001 g
The pH was adjusted to 6,5 to 7 then Distilled water was added up to 1L.
APPENDICES
243
Table A.1.3: Vitamin stock solution.
Vitamins Weight
Vitamin C, ascorbic acid 10 g
Vitamin B1, thiamine 60 mg
Vitamin B2, riboflavin 20 mg
Vitamin B12 5 mg
Vitamin B3, nicotinic acid 50 mg
Vitamin B5, D-pentanthonic acid 60 mg
Vitamin B6, pyridoxime 61 mg
Vitamin H , d-biotin 1 mg
Distilled water 100 ml
LB medium
LB Medium was prepared as follows (composition per litre) (Table A.1.4). Table A.1.4: Composition of LB medium.
Compound Weight
Sodium Chloride 10
Yeast Extract 5
Tryptone 10
The pH for medium was adjusted to 7.2 using 5M NaOH; then autoclaved at 121°C for 30
minutes.
MacConkey agar
Table A.1.5: Composition of MacConkey agar.
Compound Weight
MacConkey agar powder 52
Distilled H2O Up to 1L
The MacConkey agar was dissolved; then autoclaving at 121⁰C for 30 minutes.
Approximately 20 ml of MacConkey agar was poured into each Petri dish.
APPENDICES
244
Appendix 2: Phylogenetic analysis
Table A.2.1. Accession numbers of 16SrRNA sequences used for Bacteriodes phylogenic tree construction
Bacteria Accession number
Bacteroides thetaiotaomicron (ATCC 29148) 16S ribosomal RNA L16489.1
Bacteroides vulgatus 16S ribosomal RNA gene, partial sequence M58762.2
Bacteroides vulgatus strain mpk genome CP013020.1
Bacteroides fragilis gene for 16S ribosomal RNA, partial cds,
strain: JCM 17586
AB618792.1
Bacteroides dorei strain Z5 16S ribosomal RNA gene, partial
sequence
KJ145327.1
Bacteroides uniformis gene for 16S ribosomal RNA, partial
sequence, strain: EFEL002
AB908393.1
Parabacteroides distasonis gene for 16S ribosomal RNA strain:
JCM 1294
AB640686.1
Parabacteroides goldsteinii strain JCM13446 16S ribosomal RNA
gene
EU136697.1
Bacteroidetes bacterium Smarlab 3301186 16S ribosomal RNA
gene
AY538684.1
B.ovatus 16S rRNA (ATCC 8483T) X83952.1
Barnesiella viscericola strain C46 16S ribosomal RNA gene NR_041508.1
Uncultured Bacteroidetes bacterium clone M0035_096 16S
ribosomal RNA gene
EF071264.1
Butyricimonas virosa strain MT12 16S ribosomal RNA gene NR_041691.1
Parabacteroides merdae 16S ribosomal RNA gene EU722738.1
APPENDICES
245
Table A.2.1.Cont
Bacteria Accession number
Bacteroides sp. WAL 10018 16S ribosomal RNA gene, partial sequence AY608696.1
Bacteroides salyersiae strain JCM 12988 16S ribosomal RNA gene,
partial sequence
NR_112942.1
Uncultured Bacteroidetes bacterium clone M0011_019 16S ribosomal
RNA gene, partial sequence
EF071259.1
Bacteroides thetaiotaomicron strain VPI-5482 16S ribosomal RNA gene NR_074277.1
Bacteroides nordii strain JCM 12987 16S ribosomal RNA gene, partial
sequence
NR_112939.1
Bacteroides sp. Marseille-P2653 partial 16S rRNA gene, strain Marseille-
P2653
LT558803.1
Bacteroides distasonis 16S ribosomal RNA M86695.1
Bacteroides sp. WH2 16S ribosomal RNA gene, partial sequence AY895180.1
Bacteroides uniformis ATCC 8492 16S ribosomal RNA gene, complete
sequence
L16486.1
Uncultured Bacteroides sp. clone RML172 16S ribosomal RNA gene,
partial sequence
KU161522.1
Bacteroides uniformis strain ZL1 16S ribosomal RNA gene, partial
sequence
gb|JN873344.1
Bacteroides sp. WH302 16S ribosomal RNA gene, partial sequence AY895184.1
B.fragilis 16S rRNA (ATCC 25285T) X83935.1
B.caccae 16S rRNA (ATCC 43185T) X83951.1
APPENDICES
246
Table A.2.1.Cont Bacteria Accession number
Uncultured Bacteroidetes bacterium clone LI3-81 16S
ribosomal RNA gene
GU956524.1
Barnesiella intestinihominis strain JCM 15079 16S
ribosomal RNA gene
NR_113073.1
Bacteroides fragilis gene for 16S ribosomal RNA, strain:
JCM 17587
AB618793.1
Barnesiella intestinihominis strain JCM 15079 16S
ribosomal RNA gene
NR_113073.1
Bacteroides fragilis gene for 16S ribosomal RNA, strain:
JCM 17587
AB618793.1
Uncultured Bacteroides sp. clone RML151 16S ribosomal
RNA gene
KU161501.1
Bacteroides vulgatus strain W05002A 16S ribosomal RNA
gene
KP944149.1
Bacteroides vulgatus strain 39a-cc-B-5824-ARE 16S
ribosomal RNA gene, partial sequence
KR364743.1
Bacteroides fragilis strain DNF00264 16S ribosomal RNA
gene, partial sequence
KU726649.1
Bacteroides uniformis strain 19006C 16S ribosomal RNA
gene, partial sequence
KP944146.1
Uncultured Bacteroides sp. clone RML172 16S ribosomal
RNA gene, partial sequence
KU161522.1
APPENDICES
247
* 700 * 720 * 740 * 760 * 780 * 8
AY608696.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
NR_112942. : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
EF071259.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
NR074277.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
NR112939.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGTTACTGACACTGAGGCTCGAAAGTGTGGGTATCAAAC
LT558803.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
M86695.1 : GGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCCAAGCCATTACTGACGCTGATGCACGAAAGCGTGGGGATCAAAC
AY895180.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGTATCAAAC
L16486.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGTATC-AAC
KU161522.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGTATCAAAC
JN873344.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGTATCAAAC
AY895184.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
X83935.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
X83951.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGTGTAACTGACGCTGATGCTCGAAAGTGTGGGTATCAAAC
L16489.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
M58762.2 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAG-CTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC
CP013020.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC
AB618792.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
KJ145327.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC
AB908393.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGTATCAAAC
NR126194.1 : GGCGGAACAAGTGAAGTAGCGGTGAAATGCTTAGATATCACTTGGAACCCCGATAGCGAAGGCAGCTTCCCAGGCTCGATCTGACGCTGATGCGCGAGAGCGTGGGTAGCGAAC
AB640686.1 : GGCGGAATGCGTGGTGTAGCGGTGAAATGCTTAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCCAAGCCATGACTGACGCTGATGCACGAAAGCGTGGGGATCAAAC
EU136697.1 : GGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACTCCGATTGCGAAGGCAGCTTACTAAACTATAACTGACACTGAAGCACGAAAGCGTGGGGATCAAAC
AY538684.1 : GGCGGAACAAGTGAAGTAGCGGTGAAATGCTTAGATATCACTTGGAACCCCGATAGCGAAGGCAGCTTCCCAGGCTCGATCTGACGCTGATGCGCGAGAGCGTGGGTAGCGAAC
X83952.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTG--ACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
NR041508.1 : GGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGCAACGGACGCTGAGGCACGAAAGCGTGGGGATCGAAC
EF071264.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC
NR041691.1 : GGCGGAACAAGTGAAGTAGCGGTGAAATGCATAGATATCACTTGGAACCCCGATAGCGAAGGCAGCTTCCCAGGCTCGTTCTGACGCTGATGCGCGAGAGCGTGGGTAGCGAAC
EU722738.1 : GGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACTCCGATTGCGAAGGCAGCTTACTAAACCATAACTGACACTGAAGCACGAAAGCGTGGGGATCAAAC
GU956524.1 : GGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGCGACAGACGCTGAGGCACGAAAGCGTGGGTGTCGAAC
NR_113073. : GGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGTATCGAAC
AB618793.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
KU161501.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC
KP944149.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC
KR364743.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC
KU726649.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
KP944146.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGTATCAAAC
KU161522.1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGTATCAAAC
K1 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGTGTAACTGACGCTGATGCTCGAAAGTGTGGGTATCAAAC
K4 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
k16 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
k22 : GGCAGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC
k28 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
k51 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
k54 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAACTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC
p25 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
p26 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
APPENDICES
248
p64 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
p65 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAACTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC
p66 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC
p78 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
p710 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC
C_k2 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
C_k3 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
C_k6 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
C_k15 : GGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACTCCGATTGCGAAGGCAGCTTACTAAACCATAACTGACACTGAAGCACGAAAGCGTGGGGATCAAAC
C_k26 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACCGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
C_k27 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC
C_L31 : GGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGTATCGAAC
C_P29 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC
C_P44 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
C_P45 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
C_P46 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
C_P48 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAACTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC
C_P49 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
C_P61 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
C_P62 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC
C_P77 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC
C_P82 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
C_P83 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC
C_P85 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAACTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC
C_P86 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC
C_P88 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAACTGCAACTGACATTGAGGCTCGAAAGTGTGGGTATCAAAC
C_P89 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
C_PP210 : GGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGCGACAGACGCTGAGGCACGAAAGCGTGGGTATCGAAC
C_P211 : GGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGCGACAGACGCTGAGGCACGAAAGCGTGGGTATCGAAC
C_P212 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAAC
C_P410 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGTATCAAAC
C_P412 : GGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGTATCAAAC
C_P611 : GGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGCGGCAGACGCTGAGGCACGAAAGCGTGGGTATCGAAC
C_P27 : GGCGGAACAAGTGAAGTAGCGGTGAAATGCATAGATATCACTTGGAACCCCGATAGCGAAGGCAGCTTCCCAGGCTCGATCTGACGCTGATGCGCGAGAGCGTGGGTAGCGAAC
GGCgGAAttcGTGgtGTAGCGGTGAAATGCtTAGATATCACgaaGAACtCCGATtGCGAAGGCAGC Ct ctg aaCtGAC TGA GCtCGAaAGtGTGGGtatCaAAC
Figure A.2.1. Alignment showing the 16SrRNA sequences of known taxa from database belonging to Bacteriodes and those of random clones from healthy control.
APPENDICES
249
Table A.2.2. Accession numbers of 16SrRNA sequences used for Firmicutes phylogenic tree construction
Bacteria Accession number
Clostridium leptum 16S ribosomal RNA M59095.1
Clostridium sp. MB2-A37 16S ribosomal RNA gene KT935669.1
Uncultured Clostridium sp. b2-173 16S ribosomal RNA gene gb|JX576090.1
Ruminococcus bromii strain L2-63 16S ribosomal RNA gene gb|EU266549.1
Clostridium sp. JCD partial 16S rRNA gene, isolate JCD HG726040.1
Clostridium boltei partial 16S rRNA gene, strain 16351 AJ508452.1
Clostridium lavalense strain CCRI-9929 16S ribosomal RNA gene EF564278.1
Clostridium asparagiforme strain N6 16S ribosomal RNA gene NR_042200.1
Ruminococcus sp. 14565 partial 16S rRNA gene, strain 14565 AJ315980.1
Ruminococcus albus 16S ribosomal RNA gene AF079847.1
Clostridium sp. YIT 12069 gene for 16S rRNA AB491207.1
Ruminococcus flavefaciens strain JM1 16S ribosomal RNA gene AY445595.1
Ruminococcus sp. NML 00-0124 16S ribosomal RNA gene EU815223.1
Clostridium orbiscindens strain AIP162.06 16S ribosomal RNA gene EU541435.1
Clostridium sp. gene for 16S ribosomal RNA AB622833.1
Clostridium bartlettii strain WAL 16138 16S ribosomal RNA gene AY438672.1
Uncultured Clostridium sp. clone 5.17h43 16S ribosomal RNA gene JN679181.1
APPENDICES
250
Table A.2.2. Cont
Bacteria Accession number
Clostridium sp. TM-40 gene for 16S rRNA AB249652.1
Faecalibacterium prausnitzii A2165 a 16S rRNA gene AJ270469.2
Uncultured Faecalibacterium sp. M4-59 16S ribosomal RNA gene EU530347.1
Uncultured Faecalibacterium sp. M4-28 16S ribosomal RNA gene EU530316.1
Clostridium celerecrescens strain AIP 148.09 16S ribosomal RNA gene JQ246092.1
Clostridium sp. K10 gene for 16S rRNA AB610561.1
Clostridium lavalense 16S rRNA gene, strain Marseille-P2117 LT223652.1
Clostridium hathewayi strain 1313 16S ribosomal RNA gene NR_036928.1|
Clostridium citroniae strain RMA 16102 16S ribosomal RNA gene NR_043681.1
Blautia coccoides strain DSM 29138 16S ribosomal RNA gene KU196081.1
Blautia producta strain JCM 1471 16S ribosomal RNA gene NR_113270.1
Clostridium nexile 16S rRNA gene, strain DSM 1787 X73443.1
Flavonifractor plautii gene for 16S rRNA sequence, strain: MT42 AB693937.1
Hungatella hathewayi strain R4 16S ribosomal RNA gene KU234408.1
Ruminococcus gnavus gene for 16S ribosomal RNA AB910745.1
Coprococcus eutactus gene for 16S ribosomal RNA D14148.1
Gemella haemolysans ATCC 10379 16S ribosomal RNA gene L14326.1
Gemella morbillorum strain 2917B 16S ribosomal RNA gene NR_025904.1
Gemella sp. oral clone ASCF12 16S ribosomal RNA gene AY923143.1
Uncultured Gemella sp. DSD10 16S ribosomal RNA gene AY672072.1
APPENDICES
251
Table A.2.2. Cont
Bacteria Accession number
Roseburia hominis A2-183 16S rRNA gene, type strain A2-183T AJ270482.2
Parvimonas micra ATCC 33270 16S ribosomal RNA gene AF542231.1
Peptostreptococcus sp. oral clone FJ023 16S ribosomal RNA gene AY349390.1
Eubacterium ventriosum strain ATCC 27560 16S ribosomal RNA gene L34421.2
Eubacterium hallii strain ATCC 27751 16S ribosomal RNA gene L34621.2
Coprococcus catus gene for 16S rRNA AB038359.1
Eubacterium siraeum strain ATCC 29066 16S ribosomal RNA gene L34625.2
Roseburia intestinalis 16S rRNA gene, strain L1-82 AJ312385.1
Roseburia inulinivorans 16S rRNA gene, type strain A2-194T AJ270473.3
Eubacterium eligens 16S ribosomal RNA L34420.1
Clostridium sp. 14774 16S rRNA gene, strain 14774 AJ315981.1
Butyrate-producing bacterium A2-166 16S rRNA gene AJ270489.1
Butyrate-producing bacterium A2-175 16S rRNA gene AJ270485.2
Roseburia faecis strain M6/1 16S ribosomal RNA gene AY804149.1
Ruminococcus obeum ribosomal RNA (16S rRNA) gene L76601.1
Ruminococcus torques gene for 16S ribosomal RNA isolate: GIFU 12126 D14137.1
Subdoligranulum variabile 16S rRNA gene, type strain BI 114T AJ518869.1
Butyrate-producing bacterium L2-6 16S rRNA gene AJ270470.2
Dorea formicigenerans strain ATCC 27755 16S ribosomal RNA gene L34619.2
Table A.2.2. Cont
Bacteria Accession number
Dialister sp. E2_20 16S ribosomal RNA gene AF481209.1
Uncultured Dialister sp. clone oral taxon JKAS-116 16S ribosomal RNA
gene
JX548458.1
Anaerostipes hadrus 16S ribosomal RNA gene AY305319.1
Eubacterium rectale 16S ribosomal RNA L34627.1
Faecalibacterium prausnitzii strain ATCC 27768 AJ413954.1
L.lactis ribosomal RNA encoding 16S ribosomal RNA, transfer RNA X64887.1
Lactococcus lactis subsp. lactis strain NM152-3 16S ribosomal RNA
gene
HM218600.1
Streptococcus mitis 16S ribosomal RNA gene AF003929.1
Streptococcus sp. 2418 16S ribosomal RNA gene JN590018.1
Streptococcus oralis Uo5 strain Uo5 16S ribosomal RNA NR_102809.1
APPENDICES
252
* 820 * 840 * 860 * 880 * 900 *
M59095.1 : GAAGTAGAGG-TAGGCGGAATT-CCCNGTGTAGCGGT-AAATGCGTAGAGATCG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTACT-GGGCTTT-AACTGACGCTGAAGCA
KT935669.1 : GAAGTAGAGG-TAGGCGGAATT-CCCGGTGTAGCGGTGAAATGCGTAGAGATCG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGGCTTT-AACTGACGCTGAGGCA
JX576090.1 : GAAGTAGAGG-CAGGTGGAATT-CCCGGTGTAGCGGTGGAATGCGTAGAGATCG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTGCT-GGGCTTT-AACTGACGCTGAGGCA
EU266549.1 : GAAGTAGAGG-CAGGCGGAATT-CCCCGTGTAGCGGTGAAATGCGTAGAGATGG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTGCT-GGGCTTT-AACTGACGCTGAGGCA
HG726040.1 : GAAGTAGAGG-TAGGCGGAATT-CCCGGTGTAGCGGTGAAATGCGTAGAGATCG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTACT-GGGCTTT-AACTGACGCTGAGGCA
AJ508452.1 : GTCGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGAT-AACTGACGTTGAGGCT
EF564278.1 : GCAGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACTGT-AACTGACGTTGAGGCT
NR_042200. : GCAGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACTGT-AACTGACGTTGAGGCT
AJ315980.1 : GAAGTAGAGG-CAGGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTGCT-GGGCTTT-AACTGACGCTGAGGCT
AF079847.1 : GAAGTAGAGG-TAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-TCAGTGACGAA-GGC-GGCTTACT-GGGCTTT-AACTGACGCTGAGGCT
AB491207.1 : GAAGTAGAGG-CAGGCGGAATT-CCGAGTGTAGCGGTGAAATGCGTAGATATTC-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTGCT-GGGCTTT-TACTGACGCTGAGGCT
AY445595.1 : GAAGTAGAGG-TAAGCGGAATT-CCTGGTGTAGCGGTGAAATGCGTAGATATCA-GGAGGAACA-CCGGTGGCGAA-GGC-GGCTTACT-GGGCTTT-TACTGACGCTGAGGCT
EU815223.1 : GAAGTAGAGG-TTGGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-TCAGTGGCGAA-GGC-GGCCAACT-GGGCTTT-TACTGACGCTGAGGCT
EU541435.1 : GCTGGAGAGG-CAATCGGAATT-CCGTGTGTAGCGGTGAAATGCGTAGATATAC-GGAGGAACA-CCAGTGGCGAA-GGC-GGATTGCT-GGACAGT-AACTGACGCTGAGGCG
AB622833.1 : GCTGGAGAGG-CAATCGGAATT-CCGTGTGTAGCGGTGAAATGCGTAGATATACGGAGGGAACA-CCAGTGGCGAA-GGCGGGATTGCTGGGACAGT-AACTGACGCTGAGGCG
AY438672.1 : GCAGGAGAGG-AGAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTAGCGAA-GGC-GGCTCTCT-GGACTGT-AACTGACGCTGAGGCA
JN679181.1 : GCAGGAGAGG-AGAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTAGCGAAGGGC-GGCTCTCT-GGACTGT-AACTGACGCTGAGGCA
L76600.1 : GAAGTAGAGG-CAGGCGGAATT-CCCCGTGTAGCGGTGAAATGCGTAGAGATGG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTGCT-GGGCTTT-AACTGACGCTGAGGCA
EU530308.1 : GAAGTAGAGG-CAGGCGGAATT-CCCCGTGTAGCGGTGAAATGCGTAGAGATGG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTGCT-GGGCTTT-AACTGACGCTGAGGCA
EU728796.1 : A-AGTAGAGG-CAGGCGGAATT-CCCCGTGTAGCGGTGAAATGCGTAGAGATGG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTGCT-GGGCTTT-AACTGACGCTGAGGCA
M59112.2 : GTCGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CNAGTGGCGAA-GGC-GACTTACT-GGACGAT-AACTGACGTTGAGGCT
X76747.1 : GTCGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGAT-AACTGACGTTGAGGCT
HM008264.1 : GTCGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGAT-AACTGACGTTGAGGCT
AF262238.1 : GTCGGAGAGG-CAGGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTGCT-GGACGAT-GACTGACGTTGAGGCT
AF028349.1 : GTCGGAGAGG-CAGGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTGCT-GGACGAT-GACTGACGTTGAGGCT
AB117566.1 : GTCGGAGAGG-CAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTGCT-GGACGAT-GACTGACGTTGAGGCT
JN713312.1 : GTCGGAGAGG-AAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTTCT-GGACGAT-GACTGACGTTGAGGCT
NR036800.1 : GTCGGAGAGG-AAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTTCT-GGACGAT-GACTGACGTTGAGGCT
HM235650.1 : GTCGGAGAGG-AAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTTCT-GGACGAT-GACTGACGTTGAGGCT
X73441.1 : GCAGGAGAGG-ATCGTGGAATT--CCTGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCAGTGGCGAA-GGC-GACGGTCT-GGCCTGT-AACTGACGCTCATTCC
AY699288.1 : GCAGGAGAGGAATCGTGGAATTCCCATGTGTAGCGGTGAAATGCGTAGATATATGGGAGGAACA-CCAGTGGCGAAGGGC-GACGATCT-GGCCTGCAAACTGACGCTCAGTCC
NR119286.1 : GCAGGAGAGG-ATCGTGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCAGTGGCGAA-GGC-GACGATCT-GGCCTGT-AACTGACGCTCAGTCC
FJ538172.1 : GCAGGAGAGG-ATCGTGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCAGTGGCGAA-GGC-GACGATCT-GGCCTGC-AACTGACGCTCAGTCC
AB249652.1 : GCATCAGAGG-ATCGCGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCAGTGGCGAA-GGC-GGCGGTCT-GGGGTGC-AGCTGACGCTCAGTCC
AJ270469.2 : AGTGCAGAGG-TAGGCGGAATT-CCCGGTGTAGCGGTGGAATGCGTAGATATCG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTACT-GGGCACC-AACTGACGCTGAGGCT
EU530347.1 : AGTGCAGAGG-TAGGCGGAATT-CCCGGTGTAGCGGTGGAATGCGTAGATATCG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTACT-GGGCACC-AACTGACGCTGAGGCT
EU530316.1 : AGTGCAGAGG-TAGGCGGAATT-CCCGGTGTAGCGGTGGAATGCGTAGATATCG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTACT-GGGCACC-AACTGACGCTGAGGCT
JQ246092.1 : GCAGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACTGT-AACTGACGTTGAGGCT
AB610561.1 : GCAGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACTGT-AACTGACGTTGAGGCT
LT223652.1 : GCAGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACTGT-AACTGACGTTGAGGCT
NR036928.1 : GCAGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACTGT-AACTGACGTTGAGGCT
NR043681.1 : GTCGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGAT-AACTGACGTTGAGGCT
APPENDICES
253
KU196081.1 : GCCGGAGAGG-TAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGGT-AACTGACGTTGAGGCT
NR113270.1 : GCCGGAGAGG-TAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGGT-AACTGACGTTGAGGCT
X73443.1 : GTCGGAGAGG-TAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGAT-CACTGACGTTGAGGCT
AB693937.1 : GCTGGAGAGG-CAATCGGAATT-CCGTGTGTAGCGGTGAAATGCGTAGATATAC-GGAGGAACA-CCAGTGGCGAA-GGC-GGATTGCT-GGACAGT-AACTGACGCTGAGGCG
KU234408.1 : GCAGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACTGT-AACTGACGTTGAGGCT
JQ271545.1 : GCAGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACTGT-AACTGACGTTGAGGCT
AB910745.1 : GTCGGAGAGG-AAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTTCT-GGACGAT-GACTGACGTTGAGGCT
D14148.1 : ACAGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GACTTACT-GGACTGC-TACTGACACTGAGGCA
L14326.1 : GCAGGAGAGA-AAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGAGATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTTTT-GGCCTGT-AACTGACACTGAGGCG
NR025904.1 : GCAGGAGAGA-AAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGAGATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTTTT-GGCCTGT-AACTGACACTGAGGCG
AY923143.1 : GCAGGAGAGA-AAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGAGATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTTTT-GGCCTGT-AACTGACACTGAGGCG
AY672072.1 : GCAGGAGAGA-AAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGAGATTA-GGAGGAACACCCAGTGGCGAA-GGC-GGCTTTTT-GGCCTGT-AACTGACACTGAGGCG
AJ270482.2 : GTCGGAGGGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGAT-TACTGACGCTGAGGCT
AF542231.1 : GAAGGAGAGG-AAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAATA-CCGGTGGCGAA-GGC-GACTTTCT-GGACTTT-TACTGACGCTCAGGTA
AY349390.1 : GAAGGAGAGG-AAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAATA-CCGGTGGCGAA-GGC-GACTTTCT-GGACTTT-TACTGACGCTCAGGTA
L34421.2 : GTCGGAGGGG-TAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCGGTGGCGAA-GGC-GGCTTACT-GGACGAT-TACTGACGCTGAGGCT
_L34621.2 : ACAGGAGAGG-CAGGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTGCT-GGACTGT-TACTGACACTGAGGCA
AB038359.1 : TCAGGAGAGG-TAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACTGA-CAATGACGCTGAGGCT
L34625.2 : GAAGTAGAGG-CAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTGCT-GGCTTTT--ACTGACGCTGAGGCT
AJ312385.1 : GTCGGAGGGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGAT-TACTGACGCTGAGGCT
AJ270473.3 : GTCGGAGGGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGAT-AACTGACGCTGAGGCT
L34420.1 : GCAGGAGGGG-TGAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTCACT-GGACTGT-AACTGACACTGAGGCT
AJ315981.1 : GCAGGAGAGG-ATCGTGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCAGTGGCGAA-GGC-GACGATCT-GGCCTGT-AACTGACGCTCAGTCC
AJ270489.1 : ACAGGAGAGG-AAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTTCT-GGACTGA-AACTGACACTGAGGCA
AJ270485.2 : TCAGGAGAGG-TAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAG-GGCGAA-GGC-GGCTTACT-GGACTGA-CAATGACGCTGAGGCT
AY804149.1 : GTCGGAGGGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGAT-AACTGACGCTGAGGCT
L76601.1 : GCCGGAGAGG-TAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGGT-AACTGACGTTGAGGCT
D14137.1 : GT-GGAGAGG--TAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACAGT-AACTGACGTTGAGGCT
AJ518869.1 : AGTGCAGAGG-TAGGTGGAATT-CCCGGTGTAGCGGTGGAATGCGTAGATATCG-GGAGGAACA-CCAGTGGCGAA-GGC-GACCTACT-GGGCACC-AACTGACGCTGAGGCT
_AJ270470. : AGTGCAGAGG-TAGGCGGAATT-CCCGGTGTAGCGGTGGAATGCGTAGATATCG-GGAGGAACA-CCA-TGGCGAA-GGC-GGCCTACT-GGGCACC-AACTGACGCTGAGGCT
L34619.2 : GTCGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGC-TACT-GGACGAT-GACTGACGTTGAGGCT
AF481209.1 : ATCGGAGAGG-AAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGAGATTA-GGAAGAACA-CCGGTGGCGAA-GGC-GACTTTCT-GGACGAA-AACTGACGCTGAGGCG
JX548458.1 : ATCGGAGAGG-AAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGAGATTA-GGAAGAACA-CCGGTGGCGAA-GGC-GACTTTCT-GGACGAA-AACTGACGCTGAGGCG
AY305319.1 : GCAGGAGAGG-TAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-TCAGTGGCGAA-GGC-GGCTTACT-GGACTGA-AACTGACACTGAGGCA
L34627.1 : GTCGGAGGGG-TAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGAT-AACTGACGCTGAGGCT
AJ413954.1 : AGTGCAGAGG-TAGGCGGAATT-CCCGGTGTAGCGGTGGAATGCGTAGATATCG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTACT-GGGCACC-AACTGACGCTGAGGCT
X64887.1 : GCAGGAGAGG-AGAGTGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCGGTGGCGAA-AGC-GGCTCTCT-GGCCTGT-AACTGACACTGAGGCT
HM218600.1 : GCAGGAGAGG-AGAGTGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCGGTGGCGAA-AGC-GGCTCTCT-GGCCTGT-AACTGACACTGAGGCT
AF003929.1 : GCAAGAGGGG-AGAGTGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCGGTGGCGAA-AGC-GGCTCTCT-GGCTTGT-AACTGACGCTGAGGCT
JN590018.1 : GCAAGAGGGG-AGAGTGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCGGTGGCGAA-AGC-GGCTCTCT-GGCTTGT-AACTGACGCTGAGGCT
NR102809.1 : GCAAGAGGGG-AGAGTGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCGGTGGCGAA-AGC-GGCTCTCT-GGCTTGT-AACTGACGCTGAGGCT
p76 : GCAGGAGGGG-CAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTGCT-GGACTGT-AACTGACACTGAGGCT
K8 : GCAGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACTGT-AACTGACGTTGAGGCT
APPENDICES
254
k10 : GCTGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACAGT-AACTGACGTTGAGGCT
k13 : GAAGTAGAGG-TAGGCGGAATT-CCCGGTGTAGCGGTGAAATGCGTAGAGATCG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTACT-GGGCTTT-AACTGACGCTGAGGCA
k50 : GTCGGAGAGG-AAAGCGGAATT-CCTAGTGTAGCGGTGAAACGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTTCT-GGACGAT-GACTGACGTTGAGGCT
k52 : AGTGCAGAGG-TAGGCGGAATT-CCCGGTGTAGCGGTGGAATGCGTAGATATCG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTACT-GGGCACC-AACTGACGCTGAGGCT
k53 : GCAGGAGAGG-ATCGTGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCAGTGGCGAA-GGC-GACGATCT-GGCCTGC-AACTGACGCTCAGTCC
k55 : GCAGGAGAGG-ATCGTGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCAGTGGCGAA-GGC-GACGATCT-GGCCTGC-AACTGACGCTCAGTCC
p21 : GCTGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACAGT-AACTGACGTTGAGGCT
p67 : GCTGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACAGT-AACTGACGTTGAGGCT
C_k5 : GCTGGAGAGG-CAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACAGT-AACTGACGTTGAGGCT
C_k23 : GCAGGAGAGG-ATCGTGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCAGTGGCGAA-GGC-GACGATCT-GGCCTGC-AACTGACGCTCAGTCC
C_k24 : GCAGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACTGT-AACTGACGTTGAGGCT
C_k29 : GTCGGAGAGG-TAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGAT-CACTGACGTTGAGGCT
C_k30 : GTCGGAGAGG-AAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTTCT-GGACGAT-GACTGACGTTGAGGCT
C_k31 : GCAGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACTGT-AACTGACGTTGAGGCT
C_k32 : ACTGGAGAGG-CAGACGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGTCTGCT-GGACAGC-AACTGACGCTGAGGCG
C_P24 : GTCGGAGAGG-TAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGAT-CACTGACGTTGAGGCT
C_P41 : GGTGTAGAGG-TAGGCGGAATT-CCCGGTGTAGCGGTGGAATGCGTAGATATCG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTACT-GGGCACT-AACTGACGCTGAGGCT
C_P47 : GGTGTAGAGG-TAGGCGGAATT-CCCGGTGTAGCGGTGGAATGCGTAGATATCG-GGAGGAACA-CCAGTGGCGAA-GGC-GGCCTACT-GGGCACT-AACTGACGCTGAGGCT
C_P68 : GCAGGAGAGG-ATCGTGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCAGTGGCGAA-GGC-GACGATCT-GGCCTGC-AACTGACGCTCAGTCC
C_P69 : GCTGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACAGT-AACTGACGTTGAGGCT
C_P81 : GCAGGAGAGG-ATCGTGGAATT-CCATGTGTAGCGGTGAAATGCGTAGATATAT-GGAGGAACA-CCAGTGGCGAA-GGC-GACGATCT-GGCCTGC-AACTGACGCTCAGTCC
C_P87 : GCTGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACAGT-AACTGACGTTGAGGCT
C_P2100 : GCAGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACTGT-AACTGACGTTGAGGCT
C_P28 : GCTGGAGAGG-TAAGTGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACAGT-AACTGACGTTGAGGCT
C_k25 : GCCGGAGAGG-TAAGCGGAATT-CCTAGTGTAGCGGTGAAATGCGTAGATATTA-GGAGGAACA-CCAGTGGCGAA-GGC-GGCTTACT-GGACGGT-AACTGACGTTGAGGCT
g g AGaGg a g GGAATT cC GTGTAGCGGTgaAAtGCGTAGAtAT GgagGAAcA ccagtggCGAA gGC Ggc t cT GG c acTGACg TgAggc
Figure A.2.2. Alignment showing the 16SrRNA sequences of known taxa from database belonging to Firmicutes and those of random clones from healthy control.
APPENDICES
255
Table A.2.3. Accession numbers of 16SrRNA sequences used for Proteobacteria phylogenic tree construction for healthy control Bactria Accession number
Pseudomonas aeruginosa PAO1 chromosome NC_002516.2
Citrobacter freundii 16S rRNA gene, isolate MBRG 7.3 AJ514240.1
Escherichia coli CFT073 NC_004431.1
Escherichia coli O157:H7 EDL933 NC_002655.2
K.pneumoniae 16S rRNA gene X87276.1
Salmonella bongori strain BR 1859 16S ribosomal RNA
gene
AF029227.1
Y.enterocolitica (serotype 0:3) for 16S rRNA X68674.1
Enterobacter aerogenes strain BAC006 16S ribosomal
RNA gene
KU161309.1
Enterobacter sp. ChroAq 66 16S ribosomal RNA gene KU951452.1
Enterobacter xiangfangensis strain SDI-37 16S
ribosomal RNA gene
KT021517.1
Enterobacter cloacae strain KLHD_03 16S ribosomal
RNA gene
KU158291.1
Citrobacter sp. UIWRF1269 16S ribosomal RNA gene KR189389.1
Citrobacter braakii strain 519C1 16S ribosomal RNA
gene
KT764983.1
Enterobacter sp. AR15 16S ribosomal RNA gene HM027902.1
Escherichia coli strain B10 16S ribosomal RNA gene KU870318.1|
Escherichia coli str. K-12 substr. MG1655 strain K-12
16S ribosomal RNA
NR_102804.1
Escherichia coli strain ATCC 43893 16S ribosomal
RNA
HM194886.1
APPENDICES
256
Table A.2.3. Cont
Bacteria Accession number
Citrobacter freundii gene for 16S ribosomal
RNA strain: JCM 24066
AB548831.1
Klebsiella pneumoniae strain QLR-2 16S
ribosomal RNA gene
KM096434.1
Campylobacter coli 16S ribosomal RNA M59073.1
E. coli genomic sequence M87049.1
Bilophila wadsworthia ribosomal RNA
(rRNA) gene
L35148.1
Bilophila wadsworthia strain CCUG 32349
16S ribosomal RNA gene
KU749296.1
Shigella sonnei strain AK102 insertion
sequence IS1012
KU255074.1
Escherichia coli strain SYW001 16S
ribosomal RNA gene, partial sequence
EF620921.1
APPENDICES
257
* 820 * 840 * 860 * 880 * 900 *
NC002516.2 : GAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACCACCTGGACTGATACTGACACTGAGGTGCG-AAAGCGTGGGGAGCAAACAGG
AJ514240.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCA-GTGCG-AAAGCGTGGGGAGCAAACAGG
NC004431.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG
NC002655.2 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG
_X87276.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG
AF029227.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG
X68674.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG
KU234408.1 : GAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACTGTAACTGACGTTGAGGCTCG-AAAGCGTGGGGAGCAAACAGG
KU161309.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG
KU951452.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG
KT021517.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG
KU158291.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG
KR189389.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG
KT764983.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG
HM027902.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG
KU870318.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG
KU255074.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG
EF620921.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG
NR_102804. : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG
HM194886.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG
AB548831.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG
KM096434.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG
M59073.1 : GAATTGGTGGTGTAGGGGTAAAATCCGTAGAGATCACCAAGAATACCCATTGCGAAGGCGATCTGCTAGAACTCAACTGACGCTAA--TGCGTAAAGCGTGGGGAGCAAACAGG
M87049.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG
L35148.1 : GAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCG-AAAGCGTGGGTAGCAAACAGG
KU749296.1 : GAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCG-AAAGCGTGGGTAGCAAACAGG
p79 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG
C_P610 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCG-AAAGCGTGGGGAGCAAACAGG
GAATTccagGTGTAGcgGTgAAATgCGTAGAgATctggAgGAAtACCggTgGCGAAGGCGgccccCTgGAc aagACTGACgcTcAggtgCG AAAGCGTGGGgAGCAAACAGG
Figure A.2.3. Alignment showing the 16SrRNA sequences of known taxa from database belonging to Proteobacteria and those of random clones from healthy control.
APPENDICES
258
Table A.2.4. Accession numbers of 16SrRNA sequences used for Actinobacteria phylogenic tree construction
Bacteria Accession number
Corynebacterium durum 16S ribosomal RNA Z97069.1
Uncultured Corynebacterium sp. 24BA82 16S
ribosomal RNA gene
FJ976416.1
Bifidobacterium adolescentis 16S ribosomal
RNA gene
AY305304.1
Bifidobacterium sp. TM-7 gene for 16S rRNA AB218972.1
Bifidobacterium coryneforme strain ATCC
25911 16S ribosomal RNA gene
NR044690.2
Bifidobacterium sp. MRM 5.9 16S ribosomal
RNA gene
KP718941.1
Bifidobacterium breve strain KB 90 16S
ribosomal RNA gene
AY513711.1
Uncultured Bifidobacterium sp. clone
ACW_P1 16S ribosomal RNA gene
HM046575.1
Bifidobacterium minimum strain 20E 16S
ribosomal RNA gene
KC787351.1
Actinomyces graevenitzii 16S rRNA gene,
strain CCUG 27294T
AJ540309.1
Corynebacterium sundsvallense 16S rRNA
gene, strain CCUG 36622
Y09655.1
Actinomyces odontolyticus 16S rRNA X53227.1
Eubacterium aerofaciens gene for 16S rRNA AB011816.1
Eggerthella lenta gene for 16S ribosomal RNA
strain: JCM 10766
LC145578.1
Eggerthella sp. SDG-2 16S ribosomal RNA
gene
EF413638.1
Table A.2.4.Cont
Bacteria Accession number
Slackia heliotrinireducens strain JCM 14554 16S
ribosomal RNA gene
NR_113176.1
Gordonibacter pamelaeae strain 7-10-1-b 16S
ribosomal RNA gene
NR_102934.1
Adlercreutzia equolifaciens gene for 16S
ribosomal RNA strain: FJC-A10
AB306660.1
Eggerthella lenta strain W02018C 16S
ribosomal RNA gene
KP944193.1
Adlercreutzia equolifaciens gene for 16S rRNA
strain: MT4s-5
AB693938.1
Gordonibacter faecihominis strain CAT-2 16S
ribosomal RNA gene
KF785806.1
APPENDICES
259
460 * 480 * 500 * 520 * 540 * 560 *
Z97069.1 : GGTGACGGTACCGTGGAGAAGAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCAAGCGTTGTCCGGATTTACTGGGCGTAAAGAGCTCGTAGGTGGTG
FJ976416.1 : GGTGACGGTACCGTGGAGAAGAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCAAGCGTTGTCCGGATTTACTGGGCGTAAAGAGCTCGTAGGTGGTG
AY305304.1 : GGTGAGTGTACC-TTTCGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTT
AB218972.1 : GGTGAGTGTACC-TTTCGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTT
NR044690.2 : AGTGAGTGTACC-TTTCGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGC-AGCGTTATCCGGAATTATTGGGCGTAAAGAGCTCGTAGGCGGTT
KP718941.1 : AGTGAGTGTACC-CGTTGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCAAGCGTTATCCGGAATTATTGGGCGTAAAGAGCTCGTAGGCGGTT
AY513711.1 : GTTGAGTGTACC-TTTCGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTT
HM046575.1 : AGTGAGTGTACC-TTTCGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCAAGCGTTATCCGGATTTATTGGGCGTAAAGAGCTCGTAGGCGGTT
KC787351.1 : AGTGAGTGTACC-TTTCGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCAAGCGTTATCCGGAATTATTGGGCGTAAAGAGCTCGTAGGCGGTT
AJ540309.1 : GTTGAGGGTACC-TGGATAAGAAGCGCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGCGCAAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCTCGTAGGCGGCT
_Y09655.1 : TGTGACGGTAGG-TTGAGAAGAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCGAGCGTTGTCCGGAATTACTGGGCGTAAAGAGCTCGTAGGTGGTT
X53227.1 : GGTGACGGTAG--TGGGTAAGAAGCGCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGCGC-AGCGTTGTCCGGAATTATTGGGCGTAAAGGGCT--TAGGCGGT-
AB011816.1 : CAAGACTGTACC-TGCAGAAGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCGAGCGTTATCCGGATTCATTGGGCGTAAAGCGCGCGTAGGCGGCC
LC145578.1 : TTCGACGGTACC-TGCAGAAGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCGAGCGTTATCCGGATTCATTGGGCGTAAAGAGCGCGTAGGCGGCC
EF413638.1 : TTCGACGGTACC-TGCAGAAGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCGAGCGTTATCCGGATTCATTGGGCGTAAAGAGCGCGTAGGCGGCC
NR113176.1 : CATGACGGTACC-TGCAGAAGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCGAGCGTTATCCGGATTCATTGGGCGTAAAGCGCGCGCAGGCGGCC
|NR102934. : -GTGACGGTACC-TGCAGAAGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCGAGCGTTATCCGGATTCATTGGGCGTAAAGCGCGCGTAGGCGGCC
AB306660.1 : ---GACGGTACC-TGCAGAAGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCGAGCGTTATCCGGATTCATTGGGCGTAAAGCGCGCGTAGGCGGCC
_KP944193. : TTCGACGGTACC-TGCAGAAGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCGAGCGTTATCCGGATTCATTGGGCGTAAAGAGCGCGTAGGCGGCC
AB693938.1 : -TAGACGGTACC-TGCAGAAGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCGAGCGTTATCCGGATTCATTGGGCGTAAAGCGCGCGTAGGCGGCC
KF785806.1 : -TTGACGGTACC-TGCAGAAGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCGAGCGTTATCCGGATTCATTGGGCGTAAAGCGCGCGTAGGCGGCC
k14 : TTCGACGGTACC-TGCAGAAGAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCGAGCGTTATCCGGATTCATTGGGCGTAAAGAGCGCGTAGGCGGCC
GA GTAcc t gAA AAGC CCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGG GC AGCGTT TCCGGA T AtTGGGCGTAAAG GC cgtAGGcGG
Figure A.2.4. Alignment showing the 16SrRNA sequences of known taxa from database belonging to Actinobacteria and those of random clones from healthy control.
APPENDICES
260
Table A.2.5. Accession numbers of 16SrRNA sequences used for Proteobacteria phylogenic tree construction for UC patients
Bacteria Accession number
Pseudomonas aeruginosa PAO1 chromosome NC_002516.2
Citrobacter freundii 16S rRNA gene, isolate MBRG 7.3 AJ514240.1
Escherichia coli CFT073 NC_004431.1
Escherichia coli O157:H7 EDL933 NC_002655.2
K.pneumoniae 16S rRNA gene X87276.1
Salmonella bongori strain BR 1859 16S ribosomal RNA
gene
AF029227.1
Y.enterocolitica (serotype 0:3) 16SS rRNA X68674.1
Enterobacter aerogenes strain BAC006 16S ribosomal
RNA gene
KU234408.1
Enterobacter sp. ChroAq 66 16S ribosomal RNA gene KU951452.1
Enterobacter xiangfangensis strain SDI-37 16S ribosomal
RNA gene
KT021517.1
Enterobacter cloacae strain KLHD_03 16S ribosomal
RNA gene
|KU158291.1
Citrobacter sp. UIWRF1269 16S ribosomal RNA gene KR189389.1
Citrobacter braakii strain 519C1 16S ribosomal RNA
gene
KT764983.1|
Enterobacter sp. AR15 16S ribosomal RNA gene HM027902.1
Escherichia coli strain B10 16S ribosomal RNA gene KU870318.1|
Shigella sonnei strain AK102 insertion sequence IS1012 KU255074.1
Escherichia coli strain SYW001 16S ribosomal RNA
gene
EF620921.1
APPENDICES
261
Table A.2.5. Contd.
Bacteria Accession number
Escherichia coli str. K-12 substr. MG1655 strain
K-12 16S ribosomal RNA
NR_102804.1
Escherichia coli strain ATCC 43893 16S
ribosomal RNA gene
HM194886.1
Citrobacter freundii gene for 16S ribosomal RNA
strain: JCM 24066
AB548831.1
Klebsiella pneumoniae strain QLR-2 16S
ribosomal RNA gene
KM096434.1
Campylobacter coli 16S ribosomal RNA M59073.1
E. coli genomic sequence M87049.1
APPENDICES
262
* 700 * 720 * 740 * 760 * 780 * 8
C_23/22_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
C_23/24_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
C_23/32_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
C_23/33_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
C_26/36_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
C_26/41_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
C_B13U_UC_ : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
C_A1A6_UC_ : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
C_A1A11_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
C_A1A14_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
C_A1A15_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
C_A1A17_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
C_A1A18_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
C_A1A20_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
C_B1B3_UC_ : GAATTNCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
C_B1B16_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
C_6_UC_P1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
C_7_UC_P1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
C_19/14_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
C_19/20_UC : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
C_1A_UC_P1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
C_AA1_UC_P : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
C_AA4_UC_P : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
C_AA5_UC_P : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
C_AA7_UC_P : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
C_AA10_UC_ : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
C_AA20_UC_ : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
C_BB5_UC_P : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
NC002516.2 : GAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACCACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAAACAGGA
_AJ514240. : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCA-GTGCGAAAGCGTGGGGAGCAAACAGGA
NC004431.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
NC002655.2 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
X87276.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
AF029227.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
X68674.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
KU161309.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
KU951452.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
KT021517.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
|KU158291. : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
KR189389.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
KT764983.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
APPENDICES
263
HM027902.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
KU870318.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
KU255074.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
EF620921.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
NR_102804. : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
HM194886.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
AB548831.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
KM096434.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
_M59073.1 : GAATTGGTGGTGTAGGGGTAAAATCCGTAGAGATCACCAAGAATACCCATTGCGAAGGCGATCTGCTAGAACTCAACTGACGCTAATGCGT-AAAGCGTGGGGAGCAAACAGGA
M87049.1 : GAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGA
GAATTccagGTGTAGcGGTgAAATgCGTAGAgATctggAgGAAtACCggTgGCGAAGGCGgcCccCTgGAc aagACTGACgCTcAgGtGcgAAAGCGTGGGGAGCAAACAGGA
Figure A.2.5. Alignment showing the 16SrRNA sequences of known taxa from database belonging to Proteobacteria and those of random clones from UC Patients.
APPENDICES
264
Table A.2.6. Accession numbers of dsr sequences used for Deltaproteobacteria phylogenic tree construction for UC patients and healthy individua
Bacteria Accession number
Desulfomicrobium sp. MSL97 dsrA gene for
dissimilatory sulfite reductase alpha subunit
AB373130
Desulfomicrobium sp. MSL95 dsrA gene for
dissimilatory sulfite reductase alpha subunit
AB373129
Desulfomicrobium sp. MSL94 dsrA gene for
dissimilatory sulfite reductase alpha subunit
AB373128
Desulfomicrobium sp. MSL93 dsrA gene for
dissimilatory sulfite reductase alpha subunit
AB373127
Desulfomicrobium sp. MSL65 dsrA gene for
dissimilatory sulfite reductase alpha subunit
AB373124|
Desulfomicrobium escambiense dsrA, dsrB
genes for sulfite reductase alpha and beta
subunits
AB061531
Desulfomicrobium salsuginis dsrA gene
ADR28
AM493693.1
Desulfomicrobium baculatum dsrA, dsrB
genes for sulfite reductase alpha and beta
subunits
AB061530
Desulfomicrobium baculatum DSM 4028 CP001629
Desulfococcus oleovorans Hxd3 strain DSM
6200 dissimilatory sulfite reductase alpha
subunit (dsrA) and dissimilatory sulfite
reductase beta subunit (dsrB) genes
AF418201.1
APPENDICES
265
Table A.2.6.Cont Bacteria Accession number
Desulfococcus oleovorans Hxd3 strain DSM 6200
dissimilatory sulfite reductase alpha subunit (dsrA) and
dissimilatory sulfite reductase beta subunit (dsrB) genes
AF482464
Desulfococcus oleovorans Hxd3 CP000859.1
Desulfatibacillum alkenivorans dissimilatory sulfite
reductase alpha subunit (dsrA) and dissimilatory sulfite
reductase beta subunit (dsrB) genes
AY504426
Desulfatibacillum aliphaticivorans strain CV2803
dissimilatory sulfite reductase
AY215506.1
Desulfatibacillum aliphaticivorans dissimilatory sulfite
reductase alpha subunit
AY215505.1
Desulfosarcina sp. SD1 dissimilatory sulfite reductase
alpha subunit (dsrA) and dissimilatory sulfite reductase
beta subunit (dsrB) genes, partial cds
FJ416306.1
Desulfosarcina variabilis dissimilatory sulfite reductase
alpha subunit (dsrA) and dissimilatory sulfite reductase
beta subunit (dsrB) genes
AF191907
Desulfosarcina variabilis dissimilatory sulfite reductase
alpha subunit (dsrA) gene
AF360643.1
Desulfobacter latus dissimilatory sulfite reductase alpha
subunit (dsrA) and dissimilatory sulfite reductase beta
subunit (dsrB) genes
U58124.2
Desulfobacter vibrioformis dissimilatory sulfite redutase AJ250472
Desulfobacter postgatei 2ac9 strain DSM 2034
dissimilatory sulfite reductase alpha subunit (dsrA) and
dissimilatory sulfite reductase beta subunit (dsrB) genes
AF418198.1
Table. A.2.6.Cont Bacteria Accession number
Desulfobacterium cetonicum dissimilatory
sulfite reductase alpha subunit (dsrA) and
dissimilatory sulfite reductase beta subunit
(dsrB) genes
AF420282
Desulfohalobium retbaense DSM 5692 CP001735.1
Thermodesulforhabdus norvegicus
dissimilatory sulphite reductase
AJ277293.1
Desulfovibrio africanus dissimilatory sulfite
reductase alpha subunit (dsrA) and
dissimilatory sulfite reductase beta subunit
(dsrB) genes
AF271772.1
Desulfovibrio africanus dsrA, dsrB genes for
sulfite reductase alpha and beta subunits
AB061535.1
Desulfovibrio africanus subsp. uniflagellum
strain dissimilatory sulfite reductase alpha
subunit and dissimilatory sulfite reductase
beta subunit genes
EU716165
APPENDICES
266
Table. A.2.6.Cont Bacteria Accession number
Desulfovibrio africanus str. Walvis Bay NC_016629.1
Desulfovibrio alaskensis G20, complete
genome
CP000112.1
Desulfovibrio alkalitolerans DSM 16529 ctg3 NZ_ATHI01000026.1
Desulfovibrio carbinolicus strain DSM 3852
dissimilatory sulfite reductase alpha subunit
(dsrA) and dissimilatory sulfite reductase
beta subunit (dsrB) genes
AY626026
Desulfovibrio desulfuricans subsp.
desulfuricans dissimilatory sulfite reductase
A subunit gene, partial cds
AY820828
Desulfovibrio desulfuricans strain F28-1
dissimilatory sulfite reductase alpha subunit
(dsrA) and dissimilatory sulfite reductase
beta subunit (dsrB) genes
DQ092635.1
Desulfovibrio desulfuricans dissimilatory
sulfite reductase alpha subunit (dsrA) and
dissimilatory sulfite reductase beta subunit
(dsrB) genes
AF273034
Desulfovibrio desulfuricans subsp.
desulfuricans dissimilatory sulfite reductase
subunit A (dsrA) gene, partial cds
AY015495
Table. A.2.6.Cont
Bacteria Accession number
Desulfovibrio fairfieldensis strain CCUG 45958,
complete genome
CP014229
Desulfovibrio gabonensis strain DSM 10636
dissimilatory sulfite reductase alpha subunit
(dsrA) and dissimilatory sulfite reductase beta
subunit (dsrB) genes
AY626027 2
Desulfovibrio gigas DSM 1382 = ATCC 19364 CP006585
Desulfovibrio halophilus strain DSM 5663
dissimilatory sulfite reductase alpha subunit
(dsrA) and dissimilatory sulfite reductase beta
subunit (dsrB) genes
AF482461.1
Desulfovibrio intestinalis dsrA, dsrB genes for
sulfite reductase alpha and beta subunits
AB061539
Desulfovibrio intestinalis strain DSM 11275
dissimilatory sulfite reductase alpha subunit
(dsrA) and dissimilatory sulfite reductase beta
subunit (dsrB) genes
AF418183
Desulfovibrio magneticus RS-1 DNA AP010904
Desulfovibrio oxyclinae strain DSM 11498 AY626033.1
APPENDICES
267
dissimilatory bisulfite reductase alpha subunit
(dsrA) gene
Desulfovibrio piezophilus C1TLV30 FO203427.1
Desulfovibrio piger ATCC 29098 strain DSM 749
dissimilatory sulfite reductase alpha subunit
(dsrA) and dissimilatory sulfite reductase beta
subunit (dsrB) genes
AF482462
Desulfovibrio portus dsrA gene for dissimilatory
sulfite reductase alpha subunit
AB373125
Desulfovibrio salexigens DSM 2638 CP001649
Desulfomonile tiedjei DSM 6799 CP003360
Table. A.2.6.Cont Bacteria Accession number
Desulfovibrio sp. CME3 dissimilatory sulfite
reductase alpha subunit (dsrA) gene
AF360649
Desulfovibrio sp. dissimilatory sulfite reductase
alpha subunit gene
DSU58116
Desulfovibrio sp. enrichment culture clone 83
dissimilatory sulfite reductase (dsrAB) gene
HM800739 1
Desulfovibrio sp. J2, complete genome CP014206
Desulfovibrio sp. LVS-10 partial rA gene for
dissimilatory sulfite reductase alpha subunit,
strain LVS-10
AM494494
Desulfovibrio sp. LVS-13 gene for dissimilatory
sulfite reductase alpha subunit, strain LVS-13
AM494495
Desulfovibrio sp. LVS-21 partial rA gene for
dissimilatory sulfite reductase alpha subunit
AM494497
Desulfovibrio sp. sul5 dissimilatory sulfite
reductase subunit A (dsrA) and dissimilatory
sulfite reductase subunit B (dsrB) genes
KJ801800.1
Desulfovibrio sp. wp6 dissimilatory sulfite
reductase subunit A (dsrA) and dissimilatory
sulfite reductase subunit B (dsrB) genes
KJ801801
Desulfovibrio termitidis dsrA, dsrB genes for
sulfite reductase alpha and beta subunits
AB061542.1
Desulfovibrio termitidis HI1 strain DSM 5308
dissimilatory sulfite reductase alpha subunit
(dsrA) and dissimilatory sulfite reductase beta
subunit (dsrB) genes
AF418185
Desulfovibrio vulgaris subsp. vulgaris
dissimilatory sulfite reductase subunit A
DQ826729
Desulfovibrio vulgaris RCH1 CP002297.1
APPENDICES
268
Table. A.2.6.Cont Bacteria Accession number
Desulfovibrio vulgaris DP4 CP000527
Desulfovibrio vulgaris subsp. vulgaris str.
Hildenborough
AE017285
Desulfovibrio vulgaris dissimilatory sulfite reductase
alpha
U16723.1
Desulfovibrio zosterae strain DSM 11974
dissimilatory sulfite reductase alpha subunit (dsrA)
and dissimilatory sulfite reductase beta subunit (dsrB)
genes
AY626028.2
Desulfovibrio simplex dsrA, dsrB genes for sulfite
reductase alpha and beta subunits
AB061541
Desulfovibrio fructosivorans dsrA, dsrB genes for
sulfite reductase alpha and beta subunits
AB061538
Desulfovibrio aerotolerans dissimilatory sulfite
reductase alpha subunit (dsrA) and dissimilatory
sulfite reductase beta subunit (dsrB) genes
AY749039.1
Desulfovibrio aminophilus strain DSM 12254
dissimilatory sulfite reductase alpha subunit (dsrA)
AY626029
Desulfovibrio simplex dissimilatory sulfite reductase
alpha
U78738
Desulfovibrio aespoeensis Aspo-2 CP002431
Desulfovibrio longus dsrA, dsrB genes for sulfite
reductase alpha and beta subunits
AB061540
Desulfarculus baarsii DSM 2075 CP002085
Desulfovibrio fructosivorans JJ strain DSM 3604
dissimilatory sulfite reductase alpha subunit (dsrA)
AF418187
Desulfobacula toluolica Tol2 FO203503
APPENDICES
269
* 240 * 260 * 280 * 300 * 320 * 340
AB373130 : ------------------------------------------ATGTTCCCCGGCGTGGCCCACTTCCACACCGTACGTGTGGCCCAGCCTTCCGGCATGTACTACACTTCCGAA
AB373129 : ------------------------------------------ATGTTCCCCGGCGTGGCCCACTTCCACACCGTACGTGTGGCCCAGCCTTCCGGCATGTACTACACTTCCGAA
AB373128 : ------------------------------------------ATGTTCCCCGGCGTGGCCCACTTCCACACCGTACGTGTGGCCCAGCCTTCCGGCATGTACTACACTTCCGAA
AB373127 : ------------------------------------------ATGTTCCCCGGCGTGGCCCACTTCCACACCGTACGTGTGGCCCAGCCTTCCGGCATGTACTACACTTCCGAA
_AB373124| : ------------------------------------------ATGTTCCCCGGCGTGGCCCACTTCCACACCGTACGTGTGGCCCAGCCTTCCGGCATGTACTACACTTCCGAA
AB061531 : ------------------------------------------ATGTTCCCCGGCGTTGCTCATTTTCATACCATCCGTGTCGCTCAGCCCGCGGGCATGTACTACACCACGGAG
AM493693.1 : ------------------------------------------ATGTTCCCCGGCGTTGCTCATTTTCATACCATCCGTGTCGCTCAGCCCGCGGGCATGTACTACACCACGGAG
_AB061530 : -ATGGCGGCGGCGTTATCGGCCGTTACTGTGACCAGCCCCAGATGTTCCCCGGTGTTGCTCATTTCCACACCGTTCGTGTCGCTCAGCCTGCGGGCATGTACTACACGACTGAT
CP001629 : TATGGCGGCGGCGTTATCGGCCGTTACTGTGACCAGCCCCAGATGTTCCCCGGTGTTGCTCATTTCCACACCGTTCGTGTCGCTCAGCCTGCGGGCATGTACTACACGACTGAT
AF418201 : ------------------------------------------------------------------------ATGCGCGTGGCCCAGCCCGCGGGCAAGTTCTACACCACCAAG
_AF482464 : ------------------------------------------------------------------------ATGCGCGTGGCCCAGCCGC---GCAAGTTCTACACCACCAAG
CP000859 : TACGGCGGCGGCGTTATCGGCCGTTACTGCGACCAGCCCCAGCAGTTTCCCGGCGTGGCTCACTTTCACACCATGCGCGTGGCCCAGCCCGCGGGCAAGTTCTACACCACCAAG
_AY504426 : ------------------------------------------ATGTTCCCGGGCGTTGCTCACTTTCACACCATCCGCGTGAACCAGCCCGCCGGTAAGTTCTACAACACCGAA
AY215506.1 : ------------------------------------------ATGTTCCCGGGCGTTGCTCACTTTCACACCATCCGCGTGAACCAGCCCGCCGGTAAGTTCTACAACACCGAA
AY215505 : ------------------------------------------ATGTTCCCGGGCGTTGCTCACTTTCACACCATCCGCGTGAACCAGCCCGCCGGTAAGTTCTACAACACCGAA
FJ416306 : ------------------------------------------------------------------------ATGCGTATCAACCAGCCCGGCGGAAAATACTACACCACTGAT
AF191907 : ------------------------------------------------------------------------ATGCGTATCAACCAGCCTGGCGGGAAATACTATACCTCCGAT
AF360643 : ------------------------------------------------------------------------ATGCGTATCAACCAGCCTGGCGGGAAATACTACACCACCGAT
DLU58124 : ------------------------------------------------------------------------ATGCGTGTAAACCAGCCCTGCGGTAAATATTACAGCACTGAA
_AJ250472 : TACGGCGGCGGCGTTATCGGCCGTTACTGCGACCAGCCCAAACTGTTCCCCGGTGTTGAACATTTTCACACCATGCGCGTAAATCAGCCCTGCGGTAAGTACTACAGCACAGAA
_AF418198 : ------------------------------------------------------------------------ATGCGTGTAAACCAGCCTTGCGGTAAATACTATAGCACCGAG
_AF420282 : -ATGGCGGTGGTGTTATCGGTCGTTACTGTGACCAGCCCGAAATGTTCCCCGGCGTGGCCCACTTTCATACCATGCGCATCAACCAGCCCGGCGGCAAATATTACACCACCGAG
_CP001735. : TATGGTGGCGGCGTTATCGGCCGGTACTGTGATCAGCCGGAAATGTTCCCCGGCGTGGCCCACTTCCACACCATGCGTGTCAACCAGCCTGCCGGCAAGTACTACACCAGTGAG
AJ277293.1 : TACGGCGGCGGCGTTATCGGACGATACTGCGACAGACCCGACCTGTTTCCCAATGTTGCCCATTTCCACACGATGCGTGTTAATCAGCCGGCAAGCAAGTTTTACACTACCGAC
AF269147 : TACGGCGGCGGCGTTATCGGCCGTTACTGCGACCAGCCCGAAATGTTCCCCGGCGTGGCTCACTTCCACACCGTCCGCGTGGCTCAGCCCTCCGGTAAGTACTACTCCACCGAA
_CP003221 : TACGGCGGCGGCGTTATCGGCCGTTACTGCGACATGCCCCAGCAGTTCCCCGGCGTGGCCCACTTCCACACCGTGCGCGTGAACCAGCCGTCCGGCAAGTACTACACGACCAAG
AF327308 : ---------------------------------ATGCCCCAGCAGTTCCCCGGCGTGGCCCACTTCCACACCGTGCGCGTGAACCAGCCGTCCGGCAAGTACTACACGACCAAG
AB061535 : ------------------------------------------ATGTTCCCCGGCGTGGCTCACTTCCACACCATGCGTGTGGCCCAGCCTGCGGGCAAGTACTACAACAGCAAG
AF271772 : ---------------------------------ATGCCCCAGCAGTTCCCCGGCGTGGCCCACTTCCACACCGTGCGCGTGAACCAGCCGTCCGCGAAGTACTACACGACCAAG
EU716165 : ---------------------------------ATGCCCCAGCAGTTCCCCGGCGTGGCCCACTTCCACACCGTGCGCGTGAACCAGCCGTCCGGCAAGTACTACACGACCAAG
____NC_016 : TACGGCGGCGGCGTTATCGGCCGTTACTGCGACATGCCCCAGCAGTTCCCCGGCGTGGCCCACTTCCACACCGTGCGCGTGAACCAGCCGTCCGGCAAGTACTACACGACCAAG
CP000112.1 : TACGGCGGCGGCGTTATCGGCCGTTACTGTGACCAGCCCGAAATGTTCCCCGGCGTGGCACATTTCCACACCGTCCGCGTGAACCAGCCTGCAGGTAAGTTCTACACCTCCGAA
NZ_ATHI010 : TACGGCGGCGGCGTCATCGGCCGTTACTGTGACGTTCCCAAGCAGTTCCCGGGCGTGGCCCACTTCCACACCGTCCGCGTCAACCAGCCCGGCGGCAAGTACTACACCACCGAG
_AB061536 : ------------------------------------------ATGTTCCCCGGCGTCGCGCACTTCCACACCGTGCGTCTGGCCCAGCCCTCCGGCAAGTACTACACCGCCGAA
_AY626026 : ------------------------------------------ATGTTCCCCGGCGTCGCGCACTTCCACACCGTGCGTCTGGCCCAGCCCTCCGGCAAGTACTACACCGCCGAT
____DQ0926 : ------------------------------------------ATGTTCCCCGGCGTGGCGCACTTCCACACCATGCGTGTGGCCCAGCCTTCCGGCAAGTACTACCACAGCAAG
AF273034 : ------------------------------------------ATGTTCCCCGGCGTGGCGCACTTCCACACCATGCGTGTGGCCCAGCCTTCCGGCAAGTACTACCACAGCAAG
AY015495 : ------------------------------------------ATGTTCCCCGGCGTGGCGCACTTCCACACCATGCGTGTGGCCCAGCCTTCCGGCAAGTACTACCACAGCAAG
_DQ450464 : TACGGCGGCGGCGTTATCGGCCGTTACTGCGACCAGCCCGAAATGTTCCCCGGCGTGGCGCACTTCCACACCATGCGTGTGGCCCAGCCTTCCGGCAAGTACTACCACAGCAAG
CP014229 : TACGGCGGCGGCGTTATCGGCCGTTACTGCGACCAGCCCGAACAGTTCCCCGGCGTGGCCCACTTCCACACCATGCGTGTGGCCCAGCCCGCCGGCAAATACTATGACACCAAG
APPENDICES
270
_CP006585 : TACGGTGGCGGCGTCATTGGTCGTTACTGCGACCAGCCCGAAATGTTCCCCGGCGTGGCGCACTTCCACACTGTCCGTCTGGCCCAGCCTGCAGCCAAGTATTACACGGCTGAG
AF482461.1 : ------------------------------------------ATGTTCCCCGGCGTGGCGCACTTCCATACCGTTCGCGTGGCTCAGCCNGCCGGTAAGTTCTACACCACCAAG
AB061539 : ------------------------------------------ATGTTCCCCGGCGTGGCTCACTTCCACACCATGCGTGTGGCCCAGCCTGCGGGCAAGTACTACAACAGCAAG
AF418183 : ------------------------------------------ATGTTCCCCGGCGTGGCTCACTTCCACACCATGCGTGTGGCCCAGCCTGCGGGCAAGTTCTACAACAGCAAG
AP010904 : TACGGCGGCGGCGTCATCGGCCGTTACTGCGACCAGCCCGAAATGTTCCCCGGCGTCGCGCACTTCCACACCGTGCGTCTGGCCCAGCCCTCCGGCAAGTACTACACCGCCGAT
AY626033.1 : ------------------------------------------ATGTTCCCCGGCGTGGCGCACTTCCATACCGTTCGCGTGGCTCAGCCCGCCGGTAAGTTCTACACCACCAAG
FO203427 : TACGGCGGCGGCGTTATTGGCCGTTACTGTGACCAGCCCGAAATGTTCCCCGGCGTGGCTCACTTCCACACTGTTCGCGTGGCTCAGCCCATGGGCATGTGGTACAAGACCGAC
AF482462 : ------------------------------------------------------------------------ATGCGTGTGGCCCAGCCCGCTGCCAAATACTACCACACCAAA
_CP001649 : TACGGCGGCGGCGTTATCGGTCGTTACTGTGACCAGCCCGAAATGTTCCCCGGCGTTGCACACTTTCACACCGTTCGTGTTGCACAGCCCACCGCTAAGTACTACACCACCGAG
DSU58116 : ------------------------------------------ATGTTCCCCGGCGTGGCGCACTTCCATACCGTTCGCATGGCTCAGCCCGCCGGTAAGTTCTACACCACCAAG
HM800739_1 : ------------------------------------------ATGTTCCCCGGCGTGGCCCACTTCCACACCGTGCGTCTGGCCCAGCCCTCCGGCAAGTACTACACCGCCGAA
CP014206 : TACGGCGGCGGCGTTATCGGCCGTTACTGCGACCAGCCGCAGATGTTCCCCGGCGTTGCTCACTTCCACACCGTCCGCGTGGCTCAGCCCAACGGCAAGTGGTACAACACCAAG
AM494494 : ------------------------------------------ATGTTCCCCGGTGTGGCCCACTTCCACACCATGCGTGTGGCTCAGCCTTCGGGCAAGTACTACCACAGCAAG
AM494495 : -ATGGCGGCGGCGTTATCGGCCGCTACTGTGACCAGCCCGAAATGTTCCCCGGCGTCGCCCACTTCCATACGGTTCGTCTGGCCCAGCCCTCCGGCAAGTACTACACCACCGAG
AM494497 : ------------------------------------------ATGTTCCCCGGCGTGGCTCACTTCCACACCATGCGTGTGGCCCAGCCTTCCGGCAAGTACTACCACAGCAAG
_KJ801800 : ------------------------------------------ATGTTCCCCGGCGTGGCCCACTTCCACACCGTCCGCCTGGACCAGCCCTCGGGCAAGTACTACACTTCCGAG
KJ801801 : ------------------------------------------ATGTTCCCCGGCGTGGCTCACTTCCACACCATGCGTGTGGCCCAGCCTTCCGGTAAGTACTACCACAGCAAG
CP002297 : TACGGCGGCGGCGTCATCGGCCGTTACTGTGACCAGCCCGAAAAGTTCCCCGGCGTGGCGCACTTCCATACCGTGCGCGTGGCCCAGCCTTCCGGCAAGTACTACTCTGCCGAC
CP000527 : TACGGCGGCGGCGTCATCGGCCGTTACTGTGACCAGCCCGAAAAGTTCCCCGGCGTGGCGCACTTCCATACCGTGCGCGTGGCCCAGCCTTCCGGCAAGTACTACTCTGCCGAC
AE017285 : TACGGCGGCGGCGTCATCGGCCGTTACTGTGACCAGCCCGAAAAGTTCCCCGGCGTGGCGCACTTCCATACCGTGCGCGTGGCCCAGCCTTCCGGCAAGTACTACTCTGCCGAC
DVU16723 : TACGGCGGCGGCGTCATCGGCCGTTACTGTGACCAGCCCGAAAAGTTCCCCGGCGTGGCGCACTTCCATACCGTGCGCGTGGCCCAGCCTTCCGGCAAGTACTACTCTGCCGAC
AB061541 : TACGGCGGCGGCGTTATCGGCCGTTACTGCGACCAGCCCGAAATGTTCCCCGGCGTTGCTCACTTCCACACCATGCGTGTGGCCCAGCCCGCGGGCAAGTTCTACCACACCAAG
AB061538 : TATGGCGGCGGCGTTATCGGCCGCTACTGTGACCAGCCCGAGATGTTCCCCGGCGTGGCCCATTTTCATACGGTTCGTGTGGCCCAGCCCTCCGGCAAGTACTACACCACCGAG
AY749039 : TACGGCGGCGGCGTCATCGGACGTTACTGCGACCAGCCCGAAATGTTCCCGGGTGTGGCCCACTTCCACACCGTTCGTCTGGCCCAGCCCTCCGGCAAGTACTACACCGCCGAT
_AY626029 : TACGGCGGCAGCGTCATCGGCCGTTACTGCGACCAGCCCCAGATGTTCCCCGGCGTGGCCCACTTCCACACCGTGCGCGTGAACCAGCCCTCCGGCCTGTACTACAAGACCGAC
_U78738 : TACGGCGGCGGCGTTATCGGCCGTTACTGCGACCAGCCCGAAATGTTCCCCGGCGTTGCTCACTTCCACACCATGCGTGTGGCCCAGCCCGCGGGCAAGTTCTACCACACCAAG
CP002431 : TACGGCGGCGGCGTTATCGGTCGTTACTGTGACCAGCCCCAGATGTTCCCCGGCGTGGCTCACTTCCACACCGTGCGTGTTGCCCAGCCCATGGGCATGTGGTACAACACCGAG
AB061540 : TACGGCGGCGGCGTCATCGGTCGTTACTGCGACCAGCCTGAGATGTTCCCCGGCGTGGCCCACTTCCACACCGTGCGCGTGAACCAGCCCTCCGGCAAATTCTACACCACCGAA
CP002085 : TATGGCGGAGGCGTCATCGGCCGTTATTCCGACGTCCCCAACCTTTTCCCTGGCGTCGAACACTTCCACACCGTGCGCGTCAACCAGCCCGCGTCCAAGTTCTACAAGACCGAG
FO203503 : TATGGCGGTGGCGTTATCGGTAGATATTGTGATCAGCCTCAGGCATTTCCAGGTGTTGAGCATTTTCATACAATGCGTGTAAACCAGCCTGCAGGTAAGTATTATACAACTGAT
CP003360 : TACGGCGGAGGGATCATCGGAAGATACTGCGATCAGCCGCAGCAGTTCCCTGGTGTTGCTCATTTCCATACTGTTCGTGTGAACCAGCCCTCAAGCAAGTATTACACCACCAAG
F4 : ----GCNGCGGCGTTNTCGGCCGTTACTGCGACCAGCCCCAGCAGTTCCCCGGCGTGGCGCATTTCCATACCGTGCGCGTCAACCAGCCCTCGGGCAAGTACTACACCAGCGAA
F6 : ----GCGGCGGCGTTATCGGCCGTTACTGCGACCAGCCCCAGCAGTTCCCCGGCGTGGCGCATTTCCATACCGTGCGCGTCAACCAGCCCTCGGGCAAGTACTACACCAGCGAA
E5 : -------GCGGCGTTATCGGCCGTTACTGCGACCAGCCCCAGCAGTTCCCCGGCGTGGCGCATTTCCATACCGTGCGCGTCAACCAGCCCTCGAGCAAGTACTACACCAGCGAA
C4 : ----GNGGCGGCGTTNTCGGCCGTTACTGTGACCAGCCCGAAATGTTCCCCGGCGTGGCACATTTCCACACCGTCCGCGTGAACCAGCCTGCAGGTAAGTTCTACACCTCCGAA
C11 : ----GNGGCGGCGTTA-CGGCCGTTACTGTGACCAGCCCGGAATGTTCCCCGGCGTGGCACATTTCCACACCGTCCGCGTGAGCCAGCCTGCAGGTAAGTTCTACACCTCCGAA
D4 : ----GCGGCNGCGTTNTCGGCCGTTACTGTGACCAGCCCGAAATGTTCCCCGGCGTGGCACATTTCCACACCGTCCGCGTGAACCAGCCTGCAGGTAAGTTCTACACCTCCGAA
D12 : ----GCGGCGGCGTTNTCGGCCGTTACTGTGACCAGCCCGAAATGTTCCCCGGCGTGGCACATTTCCACACCGTCCGCGTGAACCAGCCTGCAGGTAAGTTCTACACCTCCGAA
A7 : ------GGCGGCGTTNTCGGCCGTTACTGTGACCAGCCCGAAATGTTCCCCGGCGTGGCACATTTCCACACCGTCCGCGTGAACCAGCCTGCAGGTAAGTTCTACACCTCCGAA
B8 : ----GCGGCGGCGTTNTCGGCCGTTACTGTGACCAGCCCGAAATGTTCCCCGGCGTGGCACATTTCCACACCGTCCGCGTGAACCAGCCTGCAGGTAAGTTCTACACCTCCGAA
C1 : ----GNGGCGGCGTTNTCGGCCGTTACTGTGACCAGCCCGAAATGTTCCCCGGCGTGGCACATTTCCACACCGTCCGCGTGAACCAGCCTGCAGGTAAGTTCTACACCTCCGAA
C_B6 : TACGGCGGCGGCGTTATCGGCCGTTACTGTGACCAGCCCGAAATGTTCCCCGGCGTGGCACATTTCCACACCGTCCGCGTGAACCAGCCTGCAGGTAAGTTCTACACCTCCGAA
C_D5 : TACGGCGGCGGCGTTATCGGCCGTTACTGTGACCAGCCCGAAATGTTCCCCGGCGTGGCACATTTCCACACCGTCCGCGTGAACCAGCCTGCAGGTAAGTTCTACACCTCCGAA
APPENDICES
271
F1 : ------GGCNGCGTTNTCGGCCGTTACTGCGACCAGCCCCAGCAGTTCCCCGGCGTGGCGCATTTCCATACCGTGCGCGTCAACCAGCCCTCGGGCAAGTACTACACCAGCGAA
gttccccggcgt gc ca tt ca acc T CG gT cCAGCC c gg aagT cTAca cc A
Figure A.2.6. Alignment showing the dsr sequences of known taxa from database belonging to Deltaproteobacteria and those of random clones from healthy control and UC patients.
APPENDICES
272
* 360 * 380 * 400 * 420 * 440 *
AB373130 : TACCTGCGTCACGTCTGCGACCTGTGGGAAATGCGCGGTTCCGGTCTGACCAACATGCACGGCGCCACCGGCGACATCGTTCTTCTTGGCACCACGACCCCGCAGCTGGAAGAA
AB373129 : TACCTGCGTCACGTCTGCGACCTGTGGGAAATGCGCGGTTCCGGTCTGACCAACATGCACGGCGCCACCGGCGACATCGTTCTTCTTGGCACCACGACCCCGCAGCTGGAAGAA
AB373128 : TACCTGCGTCACGTCTGCGACCTGTGGGAAATGCGCGGTTCCGGTCTGACCAACATGCACGGCGCCACCGGCGACATCGTTCTTCTTGGCACCACGACCCCGCAGCTGGAAGAA
AB373127 : TACCTGCGTCACGTCTGCGACCTGTGGGAAATGCGCGGTTCCGGTCTGACCAACATGCACGGCGCCACCGGCGACATCGTTCTTCTTGGCACCACGACCCCGCAGCTGGAAGAA
_AB373124| : TACCTGCGTCACGTCTGCGACCTGTGGGAAATGCGCGGTTCCGGTCTGACCAACATGCACGGCGCCACCGGCGACATCGTTCTTCTTGGCACCACGACCCCGCAGCTGGAAGAA
AB061531 : TTCCTGAAGCAGCTCTGCGATCTTTGGGAAATGCGCGGTTCCGGCCTGACCAACATGCACGGCGCCACCGGCGACATCGTGTTGTTGGGCACCACTACTCCCCAGCTGGAAGAG
AM493693.1 : TTCCTGAAGCAGCTCTGCGATCTGTGGGAAATGCGCGGTTCCGGCCTGACCAACATGCACGGCGCCACCGGCGACATCGTGTTGTTGGGCACCACTACTCCCCAGCTGGAAGAG
_AB061530 : TTCCTGAAGCAGCTCTGCGACCTGTGGGATATGCGCGGCTCCGGTTTGACCAACATGCATGGCGCCACCGGCGACATCGTGCTTCTGGGAACAACCACTCCTCAGCTGGAAGAA
CP001629 : TTTCTGAAGCAGCTCTGCGACCTGTGGGATATGCGCGGCTCCGGTTTGACCAACATGCATGGCGCCACCGGCGACATCGTGCTTCTGGGAACAACCACTCCTCAGCTGGAAGAA
AF418201 : TACCTCAATGACCTGTGCGACCTGTGGGAGTTCCGGGGCAGCGGCCTGACCAACATGCACGGCTCCACCGGCGACATCATCTTCCTGGGCACCACCACGCCCCAGCTGGAAGAG
_AF482464 : TACCTCAATGACCTGTGCGACCTGTGGGAGTTCCGGGGCAGCGGCTGCACCAACATGCACGGCTCCACCGGCGACATCATCTTCCTGGGCACCTCCACGCCCCAGCTGGAAGAG
CP000859 : TACCTCAATGACCTGTGCGACCTGTGGGAGTTCCGGGGCAGCGGCCTGACCAACATGCACGGCTCCACCGGCGACATCATCTTCCTGGGCACCACCACGCCCCAGCTGGAAGAG
_AY504426 : TGGCTCCGCAATCTTTGCGATCTGTGGGAATTCCGCGGCTCCGGCATCACCAACATGCACGGTTCCACGGGCGACATCGTCTTTTTGGGAACCGTCACCGAGCAGCTCGAGGAA
AY215506.1 : TGGCTCCGCAATCTTTGCGATCTGTGGGAATTCCGCGGCTCCGGCATCACCAACATGCACGGTTCCACGGGCGACATCGTCTTTTTGGGAACCGTCACCGAGCAGCTCGAGGAA
AY215505 : TGGCTCCGCAATCTTTGCGATCTGTGGGAATTCCGCGGCTCCGGCATCACCAACATGCACGGTTCCACGGGCGACATCGTCTTTTTGGGAACCGTCACCGAGCAGCTCGAGGAA
FJ416306 : TTTCTGAAAAAACTCTGCGATCTGTGGGAGTTCCGCGGTTCCGGCATCACCAACATGCACGGTTCTACCGGTGACATCATCTTTATCGGCACCTCCACTCCGCAGTTGGAAGAA
AF191907 : TTTCTGAAAAAACTGTGTGACCTGTGGGAGTTCCGCGGCTCCGGCATCACCAATATGCACGGCTCCACCGGTGACATCATTTTTATCGGTACCTCCACCCCGCAGTTGGAAGAA
AF360643 : TTTCTGAAAAAACTGTGTGACCTGTGGGAGTTCCGCGGCTCCGGCATCACCAATATGCACGGCTCCACCGGTGACATCATTTTTATCGGTACCTCCACCCCGCAGTTGGAAGAA
DLU58124 : TATCTCAGACAACTGACTGACCTGTGGGATTTCAGGGGTTCCGGCGTAACCAATATGCATGGCGCCACCGGGAATATCATCCTGCTGGGTACCACCACTCCCCAGTTGGAAGAA
_AJ250472 : TACTTGAGAAAACTGACGGATCTGTGGGACTTCAGAGGTTCCGGAGTTACCAATATGCATGGCGCCACCGGCGATATCATTCTTCTGGGTACCACCACTCCCCAGCTTGAAGAA
_AF418198 : TATCTTAGACAATTGACTGAGCTGTGGGACTTCAGGGGCTCCGGAATTACCAATATGCACGGCGCCACCGGCGACATCATTCTGCTGGGTACCACTACGCCCCAGCTGGAAGAG
_AF420282 : TTTCTGAAAAAGCTGTGCGACCTGTGGGAATTCCGCGGTTCCGGCGTCACCAACATGCACGGCTCCACCGGTGACATCATCTTCATCGGTACCTCCACGCCTCAGCTGGAAGAG
_CP001735. : TACCTCCGTCAGCTGTGCGACCTTTGGGATTTCCGCGGCAGCGGTCTGACCAACATGCATGGGTCCACCGGTGATATCGTTTTCCTCGGTACCCGGACCGAACAGTTGGAAGAA
AJ277293.1 : GTCCTCAGAAAGCTTTGCGACATCTGGGAAGAAAAGGGAAGCGGGCTTTTCAATTTCCATGGTTCCACCGGAGACATCATTCTTCTGGGAACCACGACGGATCAGCTGGAGCCG
AF269147 : TTCCTGCGCGGCCTGTGCGACATTTGGGAACTCCGTGGCTCCGGTCTGACCAACATGCACGGTTCCACCGGCGACATCGTGCTCATCGGCACCCAGACCCCGCAGCTTGAAGAG
_CP003221 : TTCCTGACAGACCTGTGCGACATCTGGGATCTGCGTGGTTCCGGCATGACCAACATGCACGGCTCCACCGGTGACATCGTGCTCCTGGGCACCTTCACCGAGCAGCTCGAAGAG
AF327308 : TTCCTGACAGACCTGTGCGACATCTGGGATCTGCGTGGTTCCGGCATGACCAACATGCACGGCTCCACCGGTGACATCGTGCTCCTGGGCACCTTCACCGAGCAGCTCGAAGAG
AB061535 : TTCCTGCGCGACCTGTGCGACATTTGGGACATGCGCGGTTCCGGTCTGACCAACATGCACGGCTCCACTGGCGACATCGTGCTTCTTGGCACCCAGACCGCCCAGCTCGAAGAA
AF271772 : TTCCTGACAGACCTGTGCGACATCTGGGATCTGCGTGGTTCCGGCATGACCAACATGCACGGCTCCACCGGTGACATCGTGCTCCTGGGCACCTTCACCGAGCAGCTCGAAGGA
EU716165 : TTCCTGACAGACCTGTGCGACATCTGGGATCTGCGTGGTTCCGGCATGACCAACATGCACGGCTCCACCGGTGACATCGTGCTCCTGGGCACCTTCACCGAGCAGCTCGAAGAG
____NC_016 : TTCCTGACAGACCTGTGCGACATCTGGGATCTGCGTGGTTCCGGCATGACCAACATGCACGGCTCCACCGGTGACATCGTGCTCCTGGGCACCTTCACCGAGCAGCTCGAAGAG
CP000112.1 : TACCTGCGCAAGCTCTGCGATATCTGGGACCTGCGCGGTTCCGGCCTGACCAACATGCACGGCTCCACCGGTGACATCGTTCTGCTGGGTACCACCACTCCCCAGCTGGAAGAG
NZ_ATHI010 : TACCTGCGCGGCCTGATCGACATCTGGGACATGCGCGGCTCCGGCCTGACCAACATGCACGGTTCCACCGGCGACATGGTGTGGCTGGGCACCTTCACCGAGCAGCTCGAAGAG
_AB061536 : TACCTGCGCGGCATCATGGACATCTGGGACCTGCGCGGTTCCGGCCTGACCAACATGCACGGCTCCACCGGTGACATCGTGCTGCTCGGCACCACCACCCCGCAGCTGGAAGAA
_AY626026 : TACCTGCGCGGCATCATGGACATCTGGGACCTGCGCGGTTCCGGCCTGACCAACATGCACGGCTCCACCGGTGACATCGTGCTGCTGGGCACCACCACCCCGCAGCTGGAAGAA
____DQ0926 : TTCCTGCGCGACCTGTGCGATATCTGGGACATGCGCGGCTCTGGCCTGACTAACATGCACGGTTCCACCGGCGATATCGTGCTGCTCGGTACCCAGACCCCCCAGCTGGAAGAA
AF273034 : TTCCTGCGCGACCTGTGCGACATCTGGGATCTGCGTGGTTCTGGTCTGACCAACATGCACGGCTCCACCGGCGACATCGTGCTGCTCGGTACCCAGACTGCCCAGCTGGAAGAA
AY015495 : TTCCTGCGCGACCTGTGCGACATTTGGGATCTGCGTGGTTCTGGTCTGACCAACATGCACGGCTCCACCGGCGACATCGTGCTGCTCGGTACCCAGACTGCCCAGCTGGAAGAA
_DQ450464 : TTCCTGCGCGACCTGTGCGACATTTGGGATCTGCGTGGTTCTGGTCTGACCAACATGCACGGCTCCACCGGCGACATCGTGCTGCTCGGTACCCAGACTGCCCAGCTGGAAGAA
CP014229 : TTCCTGCGCGACCTGTGCGACATCTGGGATCTGCGCGGTTCCGGCCTGACCAACATGCACGGTTCCACGGGCGACATCGTGCTGCTGGGCACCCAGACCGTCCAGCTGGAAGAA
APPENDICES
273
_CP006585 : TACCTGGAAGCCATCTGCGACGTCTGGGACCTGCGCGGTTCCGGTCTGACCAACATGCACGGGTCCACCGGCGACATCGTGCTTCTGGGCACCCAGACCCCGCAGCTCGAAGAA
AF482461.1 : TTCCTGCGTGATCTCTGCGACCTGTGGGACCTCCGCGGTTCCGGTCTGACCAACATGCACGGCTCCACCGGTGACATCGTGCTTCTCGGTACCCAGACCCCGCAGCTCGAAGAA
AB061539 : TTCCTGCGCGACCTGTGCGACATTTGGGACATGCGCGGTTCCGGTCTGACCAACATGCACGGCTCCACTGGCGACATCGTGCTTCTTGGCACCCAGACCGCCCAGCTCGAAGAA
AF418183 : TTCCTGCGCGACCTGTGCGACATTTGGGACATGCGCGGTTCCGGTCTGACCAACATGCACGGCTCCACTGGCGACATCGTGCTTCTTGGCACCCAGACCGCCCAGCTCGAAGAA
AP010904 : TACCTGCGCGGCATCATGGACATCTGGGACCTGCGCGGTTCCGGCCTGACCAACATGCACGGCTCCACCGGTGACATCGTGCTGCTCGGCACCACCACCCCGCAGCTGGAAGAA
AY626033.1 : TTCCTGCGTGATCTCTGCGACCTGTGGGACCTCCGCGGTTCCGGTCTGACCAACATGCACGGCTCCACCGGTGACATCGTGCTTCTCGGTACTCAGACCCCGCAGCTCGAAGAA
FO203427 : TTCCTGCGCTCCTTGATGGACATCTGGGACCTGCGCGGTTCCGGTCTGACCAACATGCACGGCGCCACCGGTGACGTTGTCCTGCTTGGTACATCCACCCCGCAGCTCGAAGAA
AF482462 : TTCCTGCGTGACCTGTGCGACATCTGGGACCTGCGCGGTTCCGGTATGACCAACATGCACGGTTCCACCGGTGACATCGTGCTCCTGGGCACCCAGACCCCGCAGCTGGAAGAA
_CP001649 : TTCCTGAACAAGATCATCGACATTTGGGATATGCGCGGTTCCGGTCTGACCAACATGCACGGTTCTACCGGTGACATCGTCTTCCTCGGTACCACCACTCCTCAGCTCGAAGAA
DSU58116 : TTCCTGCGTGATCTCTGCNACCTGTGGGACCTGCGCNGTTCCGGTCTGACCAACATGCACGGCTCCACCGGTGACATCGTGCTTCTCGGTACTCAGACCCCGCAGCTCGAAGAA
HM800739_1 : TACCTGCGCGGCATCATGGACATCTGGGACCTGCGCGGTTCCGGCCTGACCAACATGCACGGCTCCACCGGTGACATCGTTCTGCTGGGCACCACCACCCCGCAGCTCGAAGAA
CP014206 : CTGCTCAACGGCCTGATGGACATCTGGGAACTCCGTGGTTCCGGTCTGACCAACATGCACGGCGCCACCGGCGACATCGTGTTCCTGGGCACCACCACTCCCCAGCTCGAAGAG
AM494494 : TTCCTGCGCGACCTGTGCGACATTTGGGACCTGCGTGGTTCCGGTCTGACCAACATGCACGGTTCCACCGGCGACATCGTGCTGCTTGGCACCCAGACCGCCCAGCTGGAAGAA
AM494495 : TATCTGCGCCAGATCATGGATATCTGGGATCTGCGCGGTTCCGGCCTGACCAACATGCACGGTTCCACCGGCGACATCGTGCTGCTCGGCACCACCACCCCGCAGCTCGAGGAA
AM494497 : TTCCTGCGCGACCTGTGCGACATTTGGGATCTGCGTGGTTCCGGTCTGACCAACATGCACGGTTCCACCGGTGACATCGTGCTGCTCGGTACCCAGACTGCCCAGCTGGAAGAA
_KJ801800 : TACCTGCGCGGCATCATGGACATCTGGGACCTGCGCGGCTCGGGTCTGACCAACATGCACGGCTCCACCGGCGACATCGTCCTGCTCGGCACCACCACGGCCCAATTGGAAGAG
KJ801801 : TTCCTGCGCGACCTGTGCGACATCTGGGATCTGCGTGGTTCCGGTCTGACCAACATGCACGGTTCCACCGGTGACATCGTGCTGCTCGGTACCCAGACTGCCCAGCTGGAAGAA
CP002297 : TACCTGCGCCAGCTGTGCGACATCTGGGACCTGCGCGGTTCCGGTCTGACCAACATGCACGGCTCCACGGGTGACATCGTTCTCCTCGGCACGCAGACCCCCCAGCTCGAAGAA
CP000527 : TACCTGCGCCAGCTGTGCGACATCTGGGACCTGCGCGGTTCCGGTCTGACCAACATGCACGGCTCCACGGGTGACATCGTTCTCCTCGGCACGCAGACCCCCCAGCTCGAAGAA
AE017285 : TACCTGCGCCAGCTGTGCGACATCTGGGACCTGCGCGGTTCCGGTCTGACCAACATGCACGGCTCCACGGGTGACATCGTTCTCCTCGGCACGCAGACCCCCCAGCTCGAAGAA
DVU16723 : TACCTGCGCCAGCTGTGCGACATCTGGGACCTGCGCGGTTCCGGTCTGACCAACATGCACGGCTCCACGGGTGACATCGTTCTCCTCGGCACGCAGACCCCCCAGCTCGAAGAA
AB061541 : TTCCTGCGCGACCTTTGCGACATTTGGGACATGCGTGGCTCCGGCCTGACCAACATGCACGGCTCCACCGGCGACATCGTGCTTCTTGGCACCCAGACCGCTCAGCTCGAAGAA
AB061538 : TATCTCCGCCAGATCATGGACATCTGGGATCTGCGCGGCTCGGGCCTGACCAACATGCACGGTTCCACCGGCGACATCGTGCTGCTCGGCACCACCACCCCGCAGCTCGAGGAA
AY749039 : TACCTGCGCGGCATCATGGACATCTGGGATCTGCGCGGTTCCGGCCTGACCAACATGCACGGCTCCACGGGCGACATCGTGCTGCTCGGCACCACCACGCCGCAGCTGGAAGAA
_AY626029 : TTCCTCCGTCAGCTCTGCGACCTGTGGGAACTCCGCGGTTCGGGCCTGACCAACATGCACGGCTCCACGGGCGACATCGTGCTCCTGGGCACGACCACCCCGCAGCTGGAAGAG
_U78738 : TTCCTGCGCGACCTTTGCGACATTTGGGACATGCGTGGCTCCGGCCTGACCAACATGCACGGCTCCACCGGCGACATCGTGCTTCTTGGCACCCAGACCGCTCAGCTCGAAGAA
CP002431 : TTCCTGAACAGCCTCATGGACATCTGGGAACTGCGCGGCTCCGGCCTTACCAACATGCACGGCGCCACCGGCGACATCGTGCTGCTTGGCACGTCCACCCCGCAGCTTGAGGAG
AB061540 : TTCCTCCGTCAGCTCTGCGACCTCTGGGAGTTCCGCGGCTCCGGTCTGACCAACATGCATGGCGCCACCGGCGACATCGTGTTCATCGGCACCACCACCCCGCAGCTCGAAGAG
CP002085 : CGTCTGCGCGAGATCATGGACATCTGGAACCGCCGTGGCTCCAGCATGCTCAACATGCACGGCTCCACCGGCGACATCATTCTGCTGGGCGCCTTCACCGAGCAGCTCGAGCCC
FO203503 : TATCTTAGAAAATTGACCGACCTTTGGGACTTCAGGGGTTCCGGTGTTACCAATATGCATGGCGCAACCGGTGATATCATTCTTCTGGGAACAACTACTCCTCAGTTAGAAGAA
CP003360 : TTCCTGCGCGAACTCTGTGATCTGTGGGACAGACACGGAAGCGGTTTGACGAACATGCACGGATCGACCGGTGATATCGTCTTCTTGGGAACTACCACCGATCATCTCGAGCCC
F4 : TACCTCCGCCAGATCTGCGATCTGTGGGACCTTCGCGGCTCCGGTCTGACCAACCTG---------------------------------------------------------
F6 : TACCTCCGCCAGATCTGCGATCTGTGGGACCTTCGCGGCTCCGGTCTGACC---------------------------------------------------------------
E5 : TACCTCCGCCAGATCTGCGATCTGTGGGACCTTCGCGGCTCCGGTCTGACCAACCTGCACGGCNCCNCA---------------------------------------------
C4 : TACCTGCGCAAGCTCTGCGATATCTGGGACCTGCGCGGTTCCGGCCTGACCAACCTGC--------------------------------------------------------
C11 : TACCTGCGCAAGCTCTGCGATATCTGGGACCTGCGCGGTTCCGGCCTGACCAACCTGCNNGG----------------------------------------------------
D4 : TACCTGCGCAAGCTCTGCGATATCTGGGACCTGCGCGGTTCCGGCCTGACCAACCTGC--------------------------------------------------------
D12 : TACCTGCGCAAGCTCTGCGATATCTGGGACCTGCGCGGTTCCGGCCTGACCAACCTGC--------------------------------------------------------
A7 : TACCTGCGCAAGCTCTGCGATATCTGGGACCTGCGCGGTTCCGGCCTGACCAACCTGC--------------------------------------------------------
B8 : TACCTGCGCAAGCTCTGCGATATCTGGGACCTGCGCGGTTCCGGCCTGACCAACCTGC--------------------------------------------------------
C1 : TACCTGCGCAAGCTCTGCGATATCTGGGACCTGCGCGGTTCCGGCCTGACCAACCTGCNC------------------------------------------------------
C_B6 : TACCTGCGCNAGNTCTGCGA----------------------------------------------------------------------------------------------
C_D5 : TACCTGCGCAAGCTNTGCGA----------------------------------------------------------------------------------------------
APPENDICES
274
F1 : TACCTCCGCCAGATCTGCGATCTGTGGGACCTTCGCGGCTCCGGTCTGACCAACCTGCNCGG----------------------------------------------------
t ccT a T gA t tggga t cg gg tccgg tgaccaacatgca gg ccac gg ga atc t t t gg ac ac cag t ga ga
Figure A.2.6. Cont Alignment showing the dsr sequences of known taxa from database belonging to Deltaproteobacteria and those of random clones from healthy control and UC patients.
APPENDICES
275
Appendix 3: Taxonomic report
Table A.3.1 Accesion number of dsrB gene sequences used in this study Bacteria Accession number
Desulfovibrio gigas DSM 1382 = ATCC 19364 CP006585.1
Desulfatibacillum alkenivorans AK-01 CP001322.1
Desulfovibrio desulfuricans isolate SRDQC DQ450464.1
Desulfovibrio desulfuricans dsrA gene for sulfite
reductase and dsrB gene for sulfite reductase
AJ249777.1
Desulfovibrio vulgaris DP4 CP000527.1
Desulfovibrio piger ATCC 29098 Scfld439 NZ_DS996396.1
Desulfocapsa sulfexigens DSM 10523 CP003985.1
Desulfovibrio piezophilus C1TLV30 FO203427.1
Desulfovibrio vulgaris str. 'Miyazaki F' CP001197.1
Desulfovibrio salexigens DSM 2638 CP001649.1
Desulfovibrio vulgaris RCH1 CP002297.1
Desulfovibrio alaskensis G20 CP000112.1
Desulfobacter vibrioformis dissimilatory sulfite redutase AJ250472.1
Desulfovibrio aespoeensis Aspo-2 CP002431.1
Table A.3.2 Accesion number of sat gene sequences used in this study
Bacteria Accession number
Desulfobulbus japonicus DSM 18378 KE386997.1
Desulfobulbus propionicus DSM 2032 CP002364.1
Desulfobacter curvatus DSM 3379 KB893012.1
Desulfobacula sp. TS genomic scaffold TS-13370 KL662031.1
Desulfobulbus elongatus DSM 2908 JHZB01000003.1
Desulfobulbus mediterraneus DSM 13871 AUCW01000004.1
Desulfobulbus sp. Tol-SR contig_611 JROS01000122.1
Desulfocurvus vexinensis DSM 17965 JAEX01000001.1
Desulfonauticus sp. A7A AVAG01000038.1
Desulforegula conservatrix Mb1Pa
I997DRAFT_scaffold00017.17_C
AUEY01000017.1
Desulfosarcina sp. BuS5 AXAM01000009.1
Desulfovibrio alaskensis DSM 16109 AXWQ01000004.1
Desulfovibrio aminophilus DSM 12254 AUMA01000020.1
Desulfovibrio magneticus str. ALAO01000129.1
Desulfovibrio gigas strain ATCC 19364 KF113861.1
Desulfovibrio longus DSM 6739 ATVA01000004.1
Desulfatibacillum aliphaticivorans DSM 15576 AUCT01000002.1
APPENDICES
276
Table A.3.3 Accesion number of aprA gene sequences used in this study Bacteria Accession number
Thiobacillus denitrificans strain DSM 12475 NZ_AQWL00000000.1
Thiohalocapsa halophila strain DSM 6210 EF618603.1
Thiorhodovibrio winogradskyi strain DSM 6702 AprB
(aprB) and AprA (aprA) genes
EF641946.1
Desulfococcus multivorans strain DSM 2059 NZ_CP015381.1
Desulfobacter sp. DSM 2057 AprB (aprB) and AprA
(aprA) genes
EF442909.1
Desulfobacterium autotrophicum strain DSM 3382 AF418108.1
Desulfovibrio sulfodismutans strain DSM 3969 AprB
(aprB) and AprA (aprA) genes,
EF442897.1
Desulfomicrobium baculatum DSM 4028 NC_013173.1
Desulfovibrio piger ATCC 29098 Scfld442 genomic
scaffold
NZ_DS996397.1
Desulfovibrio ferrophilus strain DSM 15579 AprB
(aprB) and AprA (aprA) genes
EF442884.1
Desulfocaldus sp. Hobo AprB (aprB) and AprA (aprA)
genes
EF442898.1
Desulfohalobium retbaense DSM 5692 adenosine-5'-
phosphosulfate reductase alpha subunit (apsA) gene
AF418125.1
Desulfovibrio alaskansis G20 CP000112.1
Desulfovibrio desulfuricans subsp. desulfuricans AF226708.1
Table A.3.4 Accesion number of dsrA gene sequences used in this study Bacteria Accession number
Desulfovibrio gigas DSM 1382 = ATCC 19364 CP006585.1
Desulfomonile tiedjei DSM 6799 CP003360.1
Desulfovibrio desulfuricans dsrA gene for sulfite
reductase and dsrB gene for sulfite reductase
AJ249777.1
Desulfovibrio vulgaris DP4 CP000527.1
Desulfocapsa sulfexigens DSM 10523 CP003985.1
Desulfovibrio piezophilus C1TLV30 FO203427.1
Desulfovibrio vulgaris str. 'Miyazaki F' CP001197.1
Desulfurivibrio alkaliphilus AHT2 CP001940.1
Desulfovibrio salexigens DSM 2638 CP001649.1
Desulfobacter vibrioformis dissimilatory sulfite
redutase
AJ250472.1
Desulfovibrio hydrothermalis str. FO203519.1
Desulfohalobium retbaense DSM 5692 CP001734.1
Desulfococcus oleovorans Hxd3 CP000859.1
Desulfobacula toluolica Tol2 NC_018645.1
Bilophila wadsworthia dissimilatory sulfite reductase
A and dissimilatory sulfite reductase B genes
AF269147.2
Desulfobacter postgatei 2ac9 CM001488.1
Desulfohalobium retbaense DSM 5692 NC_013223.
Desulfobulbus propionicus DSM 2032 CP002364.1
APPENDICES
277
Desulfarculus baarsii DSM 2075 CP002085.1
Desulfotalea psychrophila LSv54 CR522870.1
Table A.3.5 Accesion number of beta-gal gene sequences used in this study
Bacteria Accession number
Escherichia coli EBG repressor (ebgR), EBG
enzyme alpha subunit
M64441.1
E.coli beta-gal gene M38327.1
Escherichia coli strain 86-24 EU889485.1
Enterobacter cloacae gene for beta-gal D42077.1
Escherichia coli strain TW14359 EU889487.1
Escherichia coli strain ST540 CP007265.1
Enterobacter cloacae CHS 79 aeeaF-
supercont1.1.C16
JMUS01000016.1
Klebsiella pneumoniae beta-gal HQ324708.1
Klebsiella pneumoniae UCI 68 aeeav-
supercont1.3.C7
JMZU01000007.1
Citrobacter freundii MGH 56 aedZq-
supercont1.2.C34
JMUJ01000034.1
Lelliottia amnigena CHS 78 aedZL-
supercont1.2.C11
JMZV01000011.1
Salmonella enterica subsp. enterica serovar
Typhi str
CP002099.1
Shigella sonnei Ss046 CP000038.1
Enterobacter aerogenes MGH 61 aeeaS-
supercont1.1.C16
JMUL01000016.1
Enterobacter cloacae JMUU01000020.1
Raoultella sp. UIWRF1100 beta-gal KR424228.1
Table A.3.6 Accesion number of nan-H gene sequences used in this study Bacteria Accession number
Bacteroides fragilis neuraminidase (nanH)
gene
M31663.1
Bacteroides fragilis neuraminidase (nanH)
gene
AF031639.1
Bacteroides fragilis strain KLE1758 LTZK01000072.1
Bacteroides fragilis strain IQS-1
neuraminidase (nanH) gene
KU378646.1
Bacteroides fragilis NCTC 9343 CR626927.1
Bacteroides intestinalis strain KLE1704 LTDF01000084.1
APPENDICES
278
Table A.3.7 Accesion number of leuB gene sequences used in this study Bacteria Accession number
Bacteroides caecimuris strain I48 CP015401.1
Bacteroides cellulosilyticus strain WH2 NZ_CP012801.1
Bacteroides dorei CP007619.1
Bacteroides fragilis strain BOB25 CP011073.1
Bacteroides helcogenes P 36-108 NC_014933.1
Bacteroides intestinalis DSM 17393 NZ_ABJL02000008.1
Bacteroides mediterraneensis NZ_LT635783.1
Bacteroides nordii CL02T12C05 NZ_JH724315.1
Bacteroides ovatus strain ATCC 8483 NZ_CP012938.1
Bacteroides salyersiae strain
2789STDY5608871
NZ_CYXS01000012.1
Bacteroides sp. NZ_FNVX01000005.1
Bacteroides thetaiotaomicron strain 7330 NZ_CP012937.1
Table A.3.7.Cont
Table A.3.8 Accesion number of TcdC gene sequences used in this study Bacteria Accession number
C.difficile tcdA, tdcC genes and cdd1 gene Y10689.1
C.difficile tcdC gene X92982.1
Clostridium difficile tcdC gene for TcdC
strain M7
AJ428943.1
Clostridium difficile strain ATCC 43594
Cdd1 (cdd1) gene, partial cds; and TcdC
(tcdC) gene
DQ870674.1
Clostridium difficile strain 79A292 Cdd1
(cdd1) gene, partial cds; and TcdC (tcdC)
gene
DQ870676.1
Clostridium difficile strain CH6186 tcdC
(tcdC) gene, tcdC-mb1
FJ409548.
Bacteria Accession number
Bacteroides uniformis strain
2789STDY5834898
NZ_CZAO01000004.1
Bacteroides vulgatus ATCC 8482 CP000139.1
Bacteroides xylanisolvens XB1A FP929033.1
APPENDICES
279
Table A.3.9 Accesion number of hydA gene sequences used in this study
Bcteria Accession number
Clostridium stercorarium subsp. leptospartum DSM
9219
NZ_CP014673.1
Clostridium autoethanogenum DSM 10061, CP006763.1
Clostridium perfringens hydA gene for hydrogenase AB016820.1
Clostridium felsineum strain DSM794 FeFe-
hydrogenase gene
JF720844.1
Clostridium kluyveri DSM 555 CP000673.1
[Clostridium] celerecrescens strain 152B JPME01000007.1
Clostridium sp. hydA gene GQ180214.1
Table A.3.10 Accesion number of fadA gene sequences used in this study
Bcteria Accession number
Fusobacterium equinum strain CMW8396 LRPX01000104.1
Fusobacterium sp. GG657973.1
Fusobacterium nucleatum strain PK1594
adhesin A (fadA) gene
DQ012979.1
Fusobacterium gonidiaformans ATCC 25563 ACET02000016.1
Fusobacterium hwasookii ChDC F128 ALVD01000022.1
Fusobacterium massiliense strain Marseille-
P2749
NZ_LT608327.1
Fusobacterium necrophorum strain ATCC
25286
FMXX01000074.1
Fusobacterium sp. GL988028.1
Fusobacterium nucleatum strain 3349 adhesin
A (fadA) gene
DQ012978.1
Fusobacterium periodonticum strain 33693
adhesin A (fadA) gene
DQ012980.1
Fusobacterium ulcerans ATCC 49185 ACDH02000047.1
Fusobacterium varium ATCC 27725 ACIE02000038.1
APPENDICES
280
APPENDICES
281