Klebsiella pneumoniae and Acinetobacter baumannii

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Faculty of Medicine and Health Sciences Genomic insights into the emergence and spread of ‘high-risk’ Klebsiella pneumoniae and Acinetobacter baumannii clones Thesis submitted for the degree of doctor in Medical Sciences at the University of Antwerp to be defended by Mattia PALMIERI Supervisors: Prof. Herman Goossens Prof. Alex van Belkum Dr. Pieter Moons Antwerp, 2020

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Transcript of Klebsiella pneumoniae and Acinetobacter baumannii

Genomic insights into the emergence and spread of ‘high-risk’
Klebsiella pneumoniae and Acinetobacter baumannii clones
Thesis submitted for the degree of doctor in Medical Sciences at the
University of Antwerp to be defended by
Klebsiella pneumoniae and Acinetobacter baumannii clones
Genomische inzichten in het ontstaan en de verspreiding van
“hoog-risico” Klebsiella pneumoniae en Acinetobacter baumannii
Thesis submitted for the degree of doctor in Medical Sciences at the
University of Antwerp to be defended by
Doctoral committee:
Index of contents
1.1 The antimicrobial resistance crisis .............................................................................................. 10
1.2 The ESKAPE pathogens ................................................................................................................ 12
1.3 Whole Genome Sequencing (WGS): a disruptive diagnostic tool ............................................... 21
1.4 Aims ............................................................................................................................................. 26
1.5 References ................................................................................................................................... 27
CHAPTER 2 : Genomic epidemiology of carbapenem- and colistin-resistant Klebsiella pneumoniae
isolates from Serbia: predominance of ST101 strains carrying a novel OXA-48 plasmid ..................... 36
2.1 Abstract ....................................................................................................................................... 37
2.2 Introduction ................................................................................................................................. 37
2.4 Results ......................................................................................................................................... 41
2.5 Discussion .................................................................................................................................... 47
2.6 References ................................................................................................................................... 48
3.1 Abstract ....................................................................................................................................... 56
3.2 Introduction ................................................................................................................................. 56
3.4 Results ......................................................................................................................................... 59
3.5 Discussion .................................................................................................................................... 63
3.6 References ................................................................................................................................... 65
CHAPTER 4 : Genomic evolution and local epidemiology of Klebsiella pneumoniae from the Beijing
Hospital 301 over a fifteen-year period: dissemination of known and novel high-risk clones ............. 72
4.1 Introduction ................................................................................................................................. 73
4.4 Conclusions .................................................................................................................................. 86
4.5 References ................................................................................................................................... 87
5.1 Abstract ....................................................................................................................................... 94
5.2 Introduction ................................................................................................................................. 94
5.3 Methods ...................................................................................................................................... 96
5.4 Results ....................................................................................................................................... 102
5.5 Discussion .................................................................................................................................. 111
5.6 References ................................................................................................................................. 114
CHAPTER 6 : PFM-like, a novel family of subclass B2 metallo β-lactamase from Pseudomonas
synxantha belonging to the Pseudomonas fluorescens complex ........................................................ 119
6.1 Abstract ..................................................................................................................................... 120
7.1 Summary.................................................................................................................................... 130
7.3 References ................................................................................................................................. 136
While antibiotics still represent the major antibacterial agents for the treatment of bacterial
infections, an increasing number of bacteria is becoming (multi-drug) resistant (MDR), complicating
the treatment of infections. Carbapenems are highly effective antibiotics commonly used for the
treatment of severe bacterial infections of MDR bacteria, which are resistant to first-line antibiotics.
Of major concern, carbapenem resistance is on the rise, and in some countries it is so high that other
drugs, usually reserved as last options, are widely used. As an example, colistin, an old drug that was
essentially unused due to its toxicity, it’s now commonly adopted in some countries, and resistance
toward this antibiotic is on the rise.
Of the several pathogens associated with MDR, carbapenem-resistant K. pneumoniae and A.
baumannii represent major concerns. Both pathogens frequently cause outbreaks of infections, while
strains which are resistant to all available antibiotics are emerging. Concerning K. pneumoniae, a
novel kind of superbug has been emerging recently. While MDR K. pneumoniae clones causing
hospital outbreaks and hypervirulent, drug susceptible clones causing severe community-acquired
infections were two separate concerns, strains that showed convergence of the two traits are
emerging. Acquisition of hypervirulence and resistance genes have been observed in MDR and
hypervirulent clones, respectively, especially in Asia. Tracking the emergence and evolution of such
novel clones, which cause severe infections with limited treatment options, is fundamental.
The decreasing cost of Whole Genome Sequencing (WGS) is allowing its increase implementation in
bacterial diagnosis. However, there is still a lack of surveillance investigations for last-line resistance
mechanisms and for convergence of resistance and hypervirulence traits. Moreover, while the
phenotype prediction from the genomic data showed encouraging results, the understanding of the
genetic resistance mechanisms of some drugs, such as colistin, is still limited, and novel in silico tools
for the phenotype prediction are needed.
We employed WGS and bioinformatics, together with phenotypic techniques, to address different
problems: i) to decipher the colistin resistance mechanisms and the genomic epidemiology of clinical
isolates of K. pneumonia and A. baumannii from countries where carbapenem resistance is sky-high,
and colistin represent a life-saving agent. ii) to explore the longitudinal population dynamics of K.
pneumonia in a major Chinese hospital, focusing on the simultaneous carriage of resistance and
hypervirulence genes. iii) to predict the phenotype of K. pneumonia strains from their genomes. iv) to
study a novel carbapenemase-encoding gene obtained from environmental bacteria.
van infecties bemoeilijkt. Carbapenems zijn zeer effectieve antibiotica die vaak worden gebruikt voor
behandeling van ernstige MDR bacteriële infecties, die resistent bleken tegen eerstelijns antibiotica.
Zorgwekkend is dat de carbapenem-resistentie toeneemt en in sommige landen zo hoog is dat
andere geneesmiddelen, die meestal alleen als laatste optie worden gebruikt, op grote schaal
worden gebruikt. Colistine, een oud medicijn dat meestal niet werd gebruikt vanwege toxiciteit,
wordt nu in sommige landen algemeen gebruikt en de resistentie tegen dit antibioticum neemt toe.
Van de verschillende MDR pathogenen vormen carbapenem-resistente Klebsiella pneumoniae en
Acinetobacter baumannii klinisch belangrijke voorbeelden. Beide ziekteverwekkers veroorzaken vaak
uitbraken van infecties, terwijl er stammen ontstaan die resistent zijn tegen alle beschikbare
antibiotica. In het geval van K. pneumoniae is onlangs een nieuw soort superbacterie waargenomen.
Terwijl normaalgesproken MDR en hypervirulentie in K. pneumoniae klonen apart werden
waargenomen zij er nu klonen geïdentificeerd die convergentie van deze twee eigenschappen laten
zien. Acquisitie van hypervirulentie- en resistentiegenen is vooral in Azië gezien. Het volgen van de
opkomst en evolutie van dergelijke nieuwe klonen, die ernstige infecties veroorzaken met beperkte
behandelingsmogelijkheden, is van fundamenteel belang.
De dalende kosten van Whole Genome Sequencing (WGS) maakt het mogelijk de implementatie
ervan in de bacteriële routinematige diagnostiek van infectieziekten te versnellen. Er is echter nog
steeds een gebrek aan surveillance van bestaande en nieuwe resistentiemechanismen en naar
convergentie van resistentie- en hypervirulentie-eigenschappen. Bovendien, alhoewel de fenotype-
voorspelling uit de genomische gegevens bemoedigende resultaten liet zien, is het begrip omtrent
resistentiemechanismen rond sommige geneesmiddelen, zoals colistine, nog steeds beperkt, en zijn
nieuw bio-informatische in silico instrumenten voor de fenotype-voorspelling nodig.
In mijn proefschrift gebruikte ik WGS en bio-informatica, samen met fenotypische technieken, om
verscheidene problemen aan te pakken. Ten eerste heb ik onderzoek uitgevoerd naar colistine-
resistentiemechanismen en de genomische
epidemiologie van klinische isolaten van K. pneumoniae en A. baumannii uit landen waar de
carbapenem-resistentie torenhoog is. Ten tweede bestudeerde ik de longitudinale
populatiedynamiek van K. pneumoniae in een groot Chinees ziekenhuis, met nadruk op de analyse
van lokale en internationale verspreiding van resistentie- en hypervirulentiegenen. Ik analyseerde en
ontwikkelde methoden om het fenotype van K. pneumoniae stammen uit hun genomen te
voorspellen. Tenslotte bestudeerde ik een nieuw carbapenemase-coderend gen dat was gevonden in
omgevingsbacteriën. Resultaten van deze onderzoekingen zijn samengevat in dit proefschrift.
bACC balanced accuracy
CC clonal complex
CG clonal group
CRKP/CR-Kp carbapenem-resistant K. pneumoniae
MBL metallo-β-lactamase
MDR multidrug-resistant
Figure 1. Antibiotic resistance strategies in bacteria. From Erik Gullberg, 2014.
Figure 2. Predicted global deaths due to antimicrobial-resistant infections every year, compared to
other major diseases. From O’Neill, 2014.
Figure 3. WHO priority pathogens list for R&D of new antibiotics. *Enterobacteriaceae include: K.
pneumoniae, E. coli, Enterobacter spp., Serratia spp., Proteus spp., Providencia spp. and
Morganella spp. From Tacconelli et al., 2018.
Table 1. β-lactamases types, including some examples of clinically relevant enzymes.
Figure 4. Regulation pathways of LPS modifications in Klebsiella pneumoniae. From Poirel et al.,
strains. From Paczosa and Mecsas, 2016.
Figure 6. Schematic representation of A. baumannii colistin resistance mechanisms. From Trebosc
et al., 2019.
Figure 7. A schematic representation of the hypothetical workflow after adoption of WGS, with low
complexity and an expected turnaround time within one day. Adapted from Didelot et al., 2012.
Figure 8. Overview of the three generations of sequencing technologies, with examples of the
major sequencing platforms. From Loman and Pallen, 2015.
In this preface, an overview of the contents of each chapter in this thesis is provided, the chapters
that are included as publications are listed, and the contribution to the chapters directly from the
author of this thesis are listed.
Chapter 1: General introduction and aims
This is an original overview of the background, key concepts and objectives of this thesis.
Chapter 2: Genomic epidemiology of carbapenem- and colistin-resistant Klebsiella pneumoniae
isolates from Serbia: predominance of ST101 strains carrying a novel OXA-48 plasmid
This chapter is an original work that resulted in a publication in Frontiers in Microbiology (DOI:
10.3389/fmicb.2020.00294). I was first author and the main contributor of the work presented in this
The nature and extent of the thesis author contributions to this chapter are detailed below:
• I contributed to the design of this published study and interpretation with Prof. Alex van Belkum,
Prof. Marco Maria D’Andrea and Prof. Gian Maria Rossolini.
• I performed all wet lab experiments, including antimicrobial susceptibility testing, MALDI-TOF MS
and DNA extraction.
assistance from Franck Tarendeau (bioMérieux Grenoble).
• I conducted all epidemiological, phylogenetic, and genomic analysis with Prof. Marco Maria
• I was responsible for the planning, drafting, editing, and submission of the manuscript, though all
co-authors also edited the manuscript.
Chapter 3: Abundance of colistin-resistant, OXA-23- and ArmA-producing Acinetobacter baumannii
belonging to International Clone 2 in Greece
This chapter is an original work that resulted in a publication in Frontiers in Microbiology (DOI:
10.3389/fmicb.2020.00668). I was first author and the main contributor of the work presented in this
The nature and extent of my contributions to this chapter are detailed below:
• I contributed to the design of this published study and interpretation with Prof. Alex van Belkum,
Prof. Marco Maria D’Andrea and Prof Gian Maria Rossolini. Dr. Nikos Legakis was responsible for the
collection, initial characterization and shipment of the strains. I verified some of the strain
characteristics for reasons of quality control.
• I performed MALDI-TOF MS under supervision by and assistance from Nadine Perrot.
• I performed all wet lab experiments, including antimicrobial susceptibility testing and DNA
• I conducted all epidemiological, phylogenetic, and genomic analysis with input from Prof. Marco
Maria D’Andrea.
• I was responsible for the planning, drafting, editing, and submission of the manuscript, though all
co-authors also edited the manuscript.
Chapter 4: Genomic evolution and local epidemiology of Klebsiella pneumoniae from the Beijing
Hospital 301 over a fifteen-year period: dissemination of known and novel high-risk clones
This chapter is an original work that resulted in an in-progress manuscript, soon to be submitted for
publication. I was first author and the main contributor of the work presented in this manuscript.
The nature and extent of my contributions to this chapter are detailed below:
• I conducted all epidemiological, phylogenetic, and genomic analysis together with Dr. Kelly L. Wyres.
• I wrote the first draft of the manuscript and consolidated the editing suggestions made by the co-
Chapter 5: Interpreting k-mer based signatures for antibiotic resistance prediction
This chapter is an original work that resulted in a submitted manuscript, under revision at the time of
submission of this thesis. I was second author.
The nature and extent of my contributions to this chapter are details below:
• I contributed to the design of this nearly published study and performed data interpretation with
Dr. Pierre Mahé, Dr. Magali Jaillard and Prof. Alex van Belkum.
• I built the K. pneumoniae database used to test the machine elarning algorithm.
• I contributed to the analysis of the data.
• I contributed to the initial writing and editing of the manuscript.
Chapter 6 : PFM-like, a novel family of subclass B2 metallo β-lactamase from Pseudomonas
synxantha belonging to the Pseudomonas fluorescens complex
This chapter is an original work that resulted in a publication in Antimicrobial Agents and
Chemotherapy (DOI: 10.1128/AAC.01700-19). I was second author and the main contributor of the
experimental work presented in this publication.
The nature and extent of my contributions to this chapter are detailed below:
• I performed most of the wet lab experiments, including antimicrobial susceptibility testing, gene
cloning, enzyme purification and kinetic analysis of hydrolysis.
• I conducted all bioinformatics analyses.
• I wrote the first draft of the manuscript.
Chapter 7 : Summary and future perspectives
This is an original summary of the implication and significance of the work presented in this thesis,
together with a brief general discussion and the future perspectives.
1.1 The antimicrobial resistance crisis
The discovery of antibiotics in the early phase of the previous century was one of the most important
developments in medicine and a milestone in the history of modern human society. Before the
introduction of antibiotics, infectious diseases were a major cause of mortality due to the systemic
infections, sepsis resulting from wound infections, pneumonia and also common infections
surrounding childbirth. In the absence of antibiotics, routine clinical practices such as organ
transplants, surgery and cancer chemotherapy would be impossible 1.
As soon as antibiotics were introduced in clinical practice, clinically-relevant antibiotic resistant
bacterial strains were described. These strains emerged due to their ability to rapidly evolve via both
vertical and horizontal inheritance 2.
Moreover, antibiotics have been inappropriately used in particular outside healthcare settings and
especially in low-income countries. The misuse and overuse of antibiotics has not only been a
problem observed in human clinical settings, but also a frequent habit in agriculture, aquaculture and
animal farming. Alarmingly, these drugs are largely used as disease prophylaxis and growth factors 3.
This situation has led to selection and propagation of antibiotic resistant strains in many
environments, turning them into reservoirs that contribute to storage, transmission and selection of
new superbugs. Consequently, some infections previously easily manageable are now difficult or
impossible to treat 4. Infections caused by a pathogen resistant to the drug of treatment generally
have a poorer clinical outcome (possibly even death) and are also linked to a greater overall
consumption of healthcare resources, when compared to infections caused by antibiotic-susceptible
organisms 1.
Members of a bacterial species can all be naturally resistant to a specific drug (intrinsic resistance) or
(the) resistance trait(s) can be acquired by susceptible microorganisms (acquired resistance). On a
genetic level, resistance may arise i) endogenously, through random chromosomal point mutations,
often when sub-therapeutic concentrations of antibiotics increase mutability and specifically select
for resistant strains, or ii) exogenously, through horizontal gene transfer, when foreign DNA is
mobilized via conjugative plasmids (transformation), bacteriophages (transduction), transposons,
insertion sequences and naked DNA, eventually leading to the recombination of acquired DNA into
the chromosome 2. Concerning the endogenous mechanisms, the process toward high level
resistance is usually stepwise. The antibiotic selection pressure enriches for bacterial cells with an
initial mutation that allows its enhanced survival, followed by subsequent additional mutations that
confer increased resistance levels during further antibiotic therapy. Though mutation frequencies can
be as low as 10-8, this is offset by the huge numbers of cells in bacterial colonies 5. Concerning
exogenous mechanisms, the major genetic elements associated with resistance genes are plasmids.
These are nearly ideal carriers for acquisition and dissemination of resistance genes followed by
transposons, which can move genes between plasmids or chromosomes, and the integrons that can
ease the recruitment and expression of resistance determinants. These elements are widely present
among both Gram-negative and Gram-positive bacterial species and play a crucial role for
dissemination of resistance determinants 6.
From a biochemical point of view, four major mechanisms of resistance can occur in bacteria: i)
decreased antibiotic uptake associated with reduction of membrane permeability (e.g. resistance to
tetracyclines and quinolones); ii) enzymatic inhibition/inactivation of the antibiotic (e.g. resistance to
β-lactams by β-lactamases); iii) rapid efflux of the antibiotic from the cell (e.g. resistance to
tetracyclines and macrolides); iv) target alterations: mutation of the cellular structure (receptor) that
the antibiotics target (e.g. resistance to oxacillin and methicillin by mutating the mecA gene,
mutations in DNA gyrase resulting in resistance to several fluoroquinolones); and v) acquisition of
one or more alternative metabolic pathways to supplement those inhibited by antibiotics (e.g.
resistance to sulfonamides) 7(Figure1). These resistance mechanisms can be present together in
different combinations in a single bacterial cell, potentially allowing high level resistance to multiple
antibiotic compounds simultaneously 8.
Ever-growing levels of antimicrobial resistance (AMR) menace the health benefits facilitated by
antibiotics and this phenomenon is recognised as a global crisis 10. With an estimate of 50,000 deaths
across the US and Europe every year attributable to AMR, urgent international actions need to be
taken to preserve the efficacy of modern antibiotic treatments.
Without proactive solutions to prevent the continued escalation of antibiotic resistance, it is
estimated that by 2050 approximately 10 million people will die annually of antimicrobial-resistant
infections, which is more than the cumulative number of people dying today from any other type of
disease 1(Figure2).
1.2 The ESKAPE pathogens
Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter species), although not the only
worrisome pathogens, have been labelled as requiring special attention since they are responsible
for the majority of hospital acquired infections (HAIs), concurrently showing a high prevalence of
AMR 11. The World Health Organization (WHO) has also recently listed twelve bacterial species
against which new antibiotics are urgently needed 12. They describe three categories of pathogens
namely critical, high and medium priority, according to the urgency of need for new antibiotics
(Figure3). Carbapenem-resistant A. baumannii and P. aeruginosa along with extended spectrum β-
lactamase (ESBL) or carbapenem-resistant Enterobacteriaceae (including K. pneumoniae) were listed
in the critical priority list of pathogens.
Figure 3. WHO priority pathogens list for R&D of new antibiotics. *Enterobacteriaceae include: K. pneumoniae, E. coli, Enterobacter spp., Serratia spp., Proteus spp., Providencia spp. and Morganella spp.
1.2.1 Klebsiella pneumoniae
K. pneumoniae, belonging to the Enterobacteriaceae family, was first isolated in the late 19th century
and was initially known as Friedlaender’s bacterium 13. From a clinical point of view, the species K.
pneumoniae is the most important member of the genus Klebsiella spp., which also includes other
clinically relevant species such as K. oxytoca and, even if to a lesser extent, K. rhinoscleromatis and K.
ozaenae 14. Klebsiella spp. are Gram-negative, encapsulated, non-motile bacteria that are able to
readily colonize human mucosal surfaces, including the gastro-intestinal (GI) tract and oropharynx,
even if this colonization appears benign 15. From these sites, this opportunistic pathogen can gain
entry to other tissues where it can cause severe infections in humans. Major diseases include urinary
tract infections, lower respiratory tract infections, intraabdominal infections and bloodstream
infections. Other diseases, such as meningitis and wound infections, are less common 16.
As the best known genus member, K. pneumoniae is a common opportunistic mostly nosocomial
pathogen, accounting for about one third of all Gram-negative HAIs overall 17. It is also an important
cause of serious community onset infections such as necrotizing pneumonia, pyogenic liver abscesses
and endogenous endophthalmitis 14.
In healthcare settings, K. pneumoniae infections commonly occur among patients who already suffer
from serious underlying clinical conditions, often together with a state of general immunodeficiency.
Risk factors for K. pneumoniae infections include extremes of age, presence of malignancy, diabetes,
chronic liver disease, recent solid-organ transplantation, and chronic dialysis 18. Other risk factors for
nosocomial infections by K. pneumoniae are treatment with corticosteroids, chemotherapy, organ
transplantation, or other treatments or conditions resulting in neutropenia 19.
Over the last few decades, there has been a concerning rise in the acquisition of resistance to a wide
range of antibiotic classes by “classical” K. pneumoniae strains 20. Consequently, simple infections
such as UTIs have become hard to treat, while more serious infections such as pneumonia and
bacteremia have become increasingly life-threatening 21.
From the mid-1980s, a novel type of community-acquired invasive K. pneumoniae infection, primarily
in the form of pyogenic liver abscesses, has emerged in mostly Asian countries 22. K. pneumoniae
strains causing these invasive infections are defined as being hyper-virulent and express a distinct
hyper-mucoviscous phenotype when grown on agar plates 23.
Very recently, strains with a hyper-virulent phenotype have been found to carry antimicrobial
resistance genes including carbapenemases 24 but also mechanisms of resistance against last resort
antibiotics such as colistin 25, thus leading to a terrific scenario in lacking of novel approach to treat
this kind of superbugs. Antimicrobial resistance in K. pneumoniae: the β-lactamases
K. pneumoniae can produce various enzymes that hydrolyze the four-membered ring of β-lactams
and inactivate them. These enzymes include ESBLs, oxacillinases, carbapenemases (including metallo-
and serine-β-lactamases), among others (Table 1). Genes encoding such enzymes are generally
present on plasmids which K. pneumoniae seems to readily acquire. Such plasmids often carry other
genes conferring resistance to other antibiotic classes including aminoglycosides, chloramphenicol,
sulfonamides, trimethoprim, and tetracyclines. Thus, bacteria containing these plasmids are often
multidrug-resistant (MDR) 26.
Narrow-spectrum β- lactamases
Extended-spectrum β- lactamases
SHV-2, CTX-M-15, VEB- 1, PER-1
Serine carbapenemases
Metallo β-lactamases B Hydrolyze carbapenems NDM-1, VIM-1, IMP-1 Cephalosporinases C Hydrolyze cephamycins and
some oxymino β-lactams AmpC, CMY-2, FOX-1
OXA-type enzymes D Hydrolyze carbapenems OXA-48, OXA-232 Table 1. β-lactamases types, including some examples of clinically relevant enzymes.
Two major types of antibiotic resistance have been commonly described in K. pneumoniae, both
involving the production of β-lactamases. The first mechanism, initially described in the late 1980’s
concomitantly in Europe 27 and in the US 28, is the production of variants of the SHV-1 or TEM-1 β-
lactamases, in which the substitution of only one or two amino acids led to the appearance of
variants that have been termed ESBLs. ESBLs are chromosomally or plasmid-encoded enzymes that
mediate resistance to penicillins, extended-spectrum (third generation) cephalosporins (e. g.
ceftazidime, cefotaxime, and ceftriaxone) and monobactams (e. g. aztreonam), but do not affect
cephamycins (e. g. cefoxitin and cefotetan) or carbapenems (e. g. meropenem and imipenem) 29. The
early SHV and TEM variants have been largely replaced by the CTX-M family of ESBLs, identified in
the early 1990s in Western Europe and South America and that are currently the most common type
of ESBL in enteric bacteria 30.
The second major mechanism of resistance is the expression of carbapenemases, which renders K.
pneumoniae resistant to all β-lactams, including the carbapenems. Carbapenemases can be classified
on the basis of their aminoacid sequence in different molecular classes: class A (e.g. IMI-, SME-, KPC-
type enzymes), class B (of which the main representatives in clinical isolates are the NDM-, IMP- and
VIM-types) and class D β-lactamases (e.g. OXA-48-types, OXA-232-types) 31.
Klebsiella pneumoniae carbapenemases (KPCs) represent the clinically most relevant mechanism of
acquired antimicrobial resistance observed in K. pneumoniae during recent years. This is due to their
very wide range of activity against several β-lactam families, including penicillins, older and newer
cephalosporins, aztreonam and carbapenems 32.
Several different KPC variants (KPC-2 to KPC-22) have been described, even if KPC-2 and KPC-3 are
the most widely diffused. KPCs are mostly plasmid-encoded enzymes and bacteria carrying these
plasmids are often susceptible to only a few antibiotics such as colistin, aminoglycosides, and
tigecycline. Antimicrobial resistance in K. pneumoniae: colistin resistance
Polymyxins represent the major antimicrobial therapeutic option against carbapenem-resistant K.
pneumoniae infections over the last decades. Indeed, polymyxin E (colistin) is considered as a “last
resort” antimicrobial for the treatment of MDR K. pneumoniae infections, essentially the only drug
that will reach adequate serum levels and that will pass the minimum inhibitory concentration (MIC)
of the infecting strain 33.
Consequently, the increasing prevalence of colistin-resistant K. pneumoniae is a major concern,
considering the scarcity of the alternative treatment options and the high mortality rate associated
with carbapenem- and colistin-resistant K. pneumoniae infections 34.
The target of colistin is the outer membrane of Gram-negative bacteria. An electrostatic interaction
occurs between the positively charged colistin molecule on the one side and the phosphate groups of
the negatively charged lipid A on the other side. Divalent cations (Ca2+ and Mg2+) are consequently
displaced from the negatively charged phosphate groups of membrane lipids 35. Then, the
lipopolysaccharide (LPS) is destabilized, the permeability of the bacterial membrane is increased, and
cytoplasmic leakage ultimately causes cell death 36. Even though LPS is the initial target, the exact
colistin mode of action is still uncertain 37.
Similar to what is observed in bacteria that are naturally resistant to colistin, LPS modifications via
addition of cationic groups, i.e. L-aminoarabinose (L-Ara4N) and phosphoethanolamine (pEtN), is
responsible for colistin resistance in K. pneumoniae. A large panel of genes and operons is involved in
qualitative modification of the LPS (Figure4). The pmrCAB operon encodes the pEtN
phosphotransferase PmrC, the response regulator PmrA, and the sensor kinase protein PmrB. The
pEtN phosphotransferase PmrC adds a pEtN group to the LPS. Environmental stimuli such as ferric
(Fe3+) iron, aluminium (Al3+), and low pH (e.g., pH 5.5) activate PmrB through its periplasmic domain.
The tyrosine kinase PmrB in turn activates PmrA by phosphorylation. Finally, PmrA activates the
transcription of the pmrCAB operon itself, and also of the pmrHFIJKLM operon and the pmrE gene
which are also involved in LPS modifications. Specific PmrA/B mutations are responsible for
constitutive activation of the PmrAB two-component system, and have been described as being
responsible for colistin resistance in K. pneumoniae 38.
The pmrHFIJKLM operon encodes for seven proteins, and together with the pmrE gene they are
responsible for the synthesis of the L-Ara4N and its coupling to lipid A. The phoPQ operon encodes
the regulator protein PhoP and the sensor protein kinase PhoQ. In a similar way to PmrB, PhoQ
senses environmental stimuli such as low magnesium (Mg2+) and low pH (e.g., pH 5.5), which mediate
PhoQ activation through its periplasmic domain. PhoQ in turn activates PhoP by phosphorylation.
Finally, PhoP activates the transcription of the pmrHFIJKLM operon, mediating the addition of L-
Ara4N to the LPS. PhoP can also activate the PmrA protein, both directly or indirectly via the PmrD
connector protein, causing the LPS modification via pEtN addition. Several mutations in the phoP/Q
genes are responsible for constitutive activation of the PhoPQ two component system and
consequently colistin resistance in K. pneumoniae 38.
MgrB is a small transmembrane protein that acts as a negative regulator of the PhoPQ two-
component system. Inactivation of the mgrB gene leads to overexpression of the phoPQ operon and
consequently colistin resistance. Several missense mutations resulting in amino acid substitutions
and nonsense mutations leading to a truncated MgrB protein have been observed. Insertional
inactivation caused by different insertion sequences (IS), belonging to several families and inserted at
different locations within the mgrB gene, is often responsible for colistin resistance in K. pneumoniae
The crrAB operon encodes the regulatory protein CrrA and the sensor protein kinase CrrB, which
regulate the pmrAB expression. Inactivation of the crrB gene leads to overexpression of the pmrAB
operon, finally resulting in colistin resistance 41.
Finally, the plasmid-mediated mcr-1 gene is responsible for horizontal transfer of colistin resistance.
It was initially described in E. coli and K. pneumoniae isolates from Chinese patients between 2011
and 2014 42. The encoded MCR-1 protein is a pEtN transferase, and its acquisition results in the
addition of pEtN to lipid A, similarly to the chromosomal mutations mentioned above. Following mcr-
1, several other variants, up to mcr-9, have been described 43–50.
Figure 4. Regulation pathways of LPS modifications in Klebsiella pneumoniae 37 Hyper-virulent K. pneumoniae
Despite rendering bacterial infections more difficult to treat, MDR does not enhance the virulence of
K. pneumoniae strains. However, starting from the 1980s, K. pneumoniae strains with the ability to
cause severe infections in apparently healthy individuals emerged. These strains are defined as
hyper-virulent K. pneumoniae (hvKp) compared to classical K. pneumoniae (cKp) strains as they are
able to infect both healthy and immunocompromised individuals, with resulting infections which are
generally invasive.
Infections were first described in Taiwan and are common on the Asian Pacific Rim. However, new
cases have recently been reported on a more global scale. In contrast to the infections caused by cKp,
most hvKp infections originate in the community 51. While pyogenic liver abscesses represents the
major disease, hvKp strains can also cause pneumonia and lung abscesses, among others 52.
Bacteremia is frequent among hvKP-infected patients and is correlated with a significantly poorer
prognosis 53.
Several virulence factors were reported and studied in hvKP strains. Capsule is a polysaccharide
matrix that overlays the cell and it is fundamental for K. pneumoniae virulence. hvKp strains are
characterized by hyper-capsulation which consists of an extensive mucoviscous exopolysaccharide
coating that is thicker and more robust than that of the typical capsule. This hyper-capsule
contributes significantly to the pathogenicity of hvKp 20.
Most hvKp are associated with only two of the 130 reported capsular serotypes, K1 and K2, that were
shown to be particularly anti-phagocytic and serum resistant 20,54. hvKp are also associated with
several other key virulence factors (Figure5); the rmpA and rmpA2 genes that upregulate capsule
expression thereby aiding the formation of a hyper-capsule which is linked to the hyper-mucoviscous
phenotype; the colibactin genotoxin that induces eukaryotic cell death and promotes bacterial
transfer from the intestines into the blood; the yersiniabactin, aerobactin and salmochelin
siderophores that enhance survival in the blood by promoting iron scavenging 20. Yersiniabactin
synthesis is encoded by the ybt locus that is generally mobilized by an integrative, conjugative
element termed ICEKp. Its prevalence is about 40% in K. pneumoniae and it is frequently acquired
and lost from MDR clones 55. Conversely, the salmochelin (iro), aerobactin (iuc) and rmpA/rmpA2 loci
are usually co-harbored by a virulence plasmid 56. The prevalence of that virulence plasmid is less
than 10% in the K. pneumoniae population, and until recently it was rarely reported among cKp
strains 57.
hvKp strains are generally susceptible to most antimicrobials. However, the last few years have seen
an increasing number of reports of ‘convergent’ K. pneumoniae strains that are both hyper-virulent
(carrying the iuc aerobactin locus, which is recognized as the single most important feature of hvKp
strains 58) and ESBL/carbapenemase producers. The majority of these reports represent sporadic
isolations, but in 2017 Gu and colleagues reported a fatal outbreak in a Chinese hospital caused by a
hyper-virulent carbapenemase-producing K. pneumoniae isolate 59.
1.2.2 Acinetobacter baumannii
Acinetobacter baumannii is a Gram-negative coccobacillus recognized as an important opportunistic
human pathogen causing infections of the urinary tract, skin, bloodstream, and soft tissues 60. The
majority of A. baumannii infections occur among critically ill patients in the intensive care unit (ICU)
setting, accounting for as much as 20% of infections in ICUs worldwide 61. MDR phenotypes due to
the acquisition of antibiotic resistance mechanisms represent a major factor of the success of A.
baumannii in hospital environments. Antibiotic modifying enzymes, decreased permeability to
antibiotic molecules, and active efflux pumps are among the major AMR mechanisms. Apart from its
multidrug resistance, the success of A. baumannii can also be attributed to its ability to survive in the
hospital environment 62. Examples of the challenges that A. baumannii faces as an opportunistic
human pathogen include the survival at low temperatures, the exposure to antiseptics and
desiccating agents and the rapid changes of environmental and nutritional conditions when
transferred into the human body from the hospital environment. Therefore, A. baumannii needs to
sense and adapt to these changes in an efficient and prompt manner. A. baumannii also has also the
ability to colonize the skin of patients or healthy individuals without causing any apparent illness.
However, transmission of such colonizing bacteria to a susceptible patient can result in immediate
infection. Multidrug-Resistant A. baumannii
The major mechanism of β-lactam resistance in A. baumannii is enzymatic degradation by β-
lactamases. A. baumannii strains are characterized by chromosomally encoded AmpC
cephalosporinases, which are also known as Acinetobacter-derived cephalosporinases (ADCs). The
overexpression of such enzymes in A. baumannii is regulated by the presence of an upstream
insertion sequence (IS) element, the major representative being ISAba1. The presence of this
production. Cefepime and carbapenems are not hydrolyzed by these enzymes.
ESBLs of the VEB-, PER-, TEM- and CTX-M-type have also been reported in A. baumannii. However,
the assessment of their prevalence is hindered by difficulties with laboratory detection in the
presence of ADCs 60.
The β-lactamases with carbapenemase activity are of major concern and include the serine
oxacillinases (Ambler class D OXA type) and the metallo-β-lactamases (MBLs) (Ambler class B).
The second intrinsic β-lactamase produced by A. baumannii is an oxacillinase, represented by the
OXA-51/69 variants. The OXA-51-like-encoding genes are chromosomally located in A. baumannii and
the carbapenemase activities of OXA-51/69 enzymes have been studied in detail 63,64. However, the
level of expression of the corresponding genes is quite low in most cases, resulting in a minor impact
on β-lactam susceptibility 65.
Identification of a carbapenem-hydrolyzing oxacillinase-encoding gene was first reported in A.
baumannii in 1995 and named blaOXA-23. This enzyme type now represents the major carbapenem
resistance determinant in A. baumannii on a global scale. Two other acquired OXA-type genes giving
rise to the production of proteins with carbapenemase activity have been reported, the blaOXA-24-like
and the blaOXA-58-like carbapenemase genes 65.
IS elements play an important role in oxacillinases-mediated carbapenem resistance in A. baumannii.
These elements provide two major functions. First, they encode a transposase, allowing the
mobilization of the carbapenemase-encoding gene. Second, they can contain promoter regions that
lead to overexpression of downstream genes. IS elements have been frequently described upstream
of blaOXA-23 and blaOXA-58 genes, but they may also promote carbapenem resistance in association with
intrinsic genes such as blaOXA-51. Some IS elements, in particular ISAba1, are relatively unique to A.
baumannii 60.
Aminoglycoside resistance in A. baumannii is encoded by acetyltransferases, nucleotidyltransferases,
and phosphotransferase-encoding genes. More alarmingly, 16S rRNA methylation is becoming
common in A. baumannii due to the expression of the armA gene. This resistance mechanism
protects the 30S ribosomal subunit from aminoglycoside binding conferring high-level resistance to
all clinically useful aminoglycosides, including gentamicin, tobramycin, and amikacin 66.
The major fluoroquinolone resistance mechanism depends on modifications of DNA gyrase or
topoisomerase IV through mutations in the gyrA and parC genes. Such mutations modify the
fluoroquinolone’s target binding site 60.
21 Colistin resistance in A. baumannii
The main mechanism of colistin resistance in A. baumannii corresponds to the addition of cationic
groups to the LPS (Figure6). Colistin resistance may also be the consequence of a complete loss of
LPS production. However, LPS loss is associated to growth defects and decreased virulence, and for
these reasons very few clinical isolates are LPS deficient 67.
Colistin resistance has been linked to mutations in the two-component transcriptional regulator
genes pmrA/B and consequent pmrC overexpression in most instances. The pEtN phosphotransferase
PmrC adds a pEtN group to the lipid A of the lipopolysaccharide, lowering the net negative charge of
the cell membrane, thus impacting the binding of colistin and preventing the cell membrane leakage.
The complete loss of LPS is caused by alterations of the lipid A biosynthesis genes, namely the lpxA,
lpxC, and lpxD genes. Mutations identified in those genes were either substitutions, truncations,
frameshifts , or insertional inactivation by the insertion sequence ISAba11 37.
Colistin resistance may also result from the overexpression of etpA, a pmrC homolog. This is
mediated by insertional inactivation of a gene encoding an H-NS family transcriptional regulator 68 or
by integration of insertion sequence elements upstream of the eptA gene itself 69–71.
Figure 6. Schematic representation of A. baumannii colistin resistance mechanisms 69
The current methods of clinical microbiology diagnostics mainly consist on conventional culturing of
clinical samples on different agar plates, followed by antimicrobial susceptibility testing (AST) and
further characterization on a case-by-case basis. The major steps in processing a sample are isolating
a pathogen, determining its species, testing antimicrobial susceptibility and virulence and, in specific
settings, intra-species typing for epidemiological purposes. The first three steps are crucial for the
treatment and management of an infected patient, while the last step is valuable for identifying
outbreaks and improve the surveillance. Depending on the pathogen, this practice usually takes one
to two days for culturing, an additional one to two days for species identification and susceptibility
testing, and several days for typing 72. While the species identification and AST can be performed
significantly faster, for example by employing MALDI-TOF MS and rapid disk diffusion after 4-6 hours
of culture 73,74, the overall diagnostic process, including typing, remains complex, time-consuming
and difficult to automate 72.
Several methods for rapid diagnostic testing have been developed and evaluated. Molecular
methods, such as PCR, microarray, and nucleic acid sequencing, have been widely adopted in the
clinical laboratory. These methods are able to identify microorganisms, genes and genetic
polymorphisms with high sensitivity and specificity through detection of specific nucleic acid targets.
Regardless of methodology, molecular diagnostics have the capability to reduce the time to results
and provide more accurate diagnosis. Despite these clear advantages, molecular diagnostic methods
are still expensive, and AST is limited to the detection of few resistance markers 75.
WGS has all the essentials to dramatically revolutionize bacterial diagnosis and surveillance by
replacing current time-consuming and labour-intensive techniques with a single and rapid diagnostic
test (Figure 7). Over the past two decades, huge progress was made in the field of high-throughput
sequencing technologies, and nowadays sequencing the full genome of a bacterial pathogen is
considered neither challenging nor particularly expensive anymore. As a result, WGS is believed as
the obvious and inevitable future diagnostics in multiple reviews and opinion articles 72,75–79.
Figure 7. A schematic representation of the hypothetical workflow after adoption of WGS, with low complexity and an expected turnaround time within one day (Adapted from
72 ).
However, WGS diagnostics is still not widely adopted in clinical microbiology, which may seem in
contrast with the number of applications for which WGS has huge potential, and which are already
widely used in the academic research 80.
Some major applications of WGS in diagnosing infectious diseases include:
i) Strain identification and typing. WGS data can be exploited to obtain information concerning the
bacterial species and subtype. WGS can also allow the phylogenetic placement of a given sequence
relative to an existing set of isolates for which the complete genome sequence is also known. WGS-
based strain identification offers a greater resolution compared to current genetic marker-based
approaches such as multi-locus sequence typing (MLST) pulsed-field gel electrophoresis (PFGE),
variable-number tandem repeat (VNTR) profiling. The greater resolution offered by WGS is also of
major significance for bacteria with large accessory genomes. While the core genome contains the
essential housekeeping genes which are present in all members of a lineage, the accessory genome is
defined as the genome fraction containing nonessential genes. In K. pneumoniae and A. baumannii
most of the relevant genes, like those encoding for resistance or virulence, are located in the
accessory genome.
ii) Phenotype prediction. WGS data provide a rich resource that can be exploited to predict the
pathogen’s phenotype. The major bacterial traits of clinical relevance are AMR and virulence, but
may also include other traits such as the ability to form biofilms or survival in the environment.
Concerning AMR prediction, several databases and bioinformatics tools were developed to detect
known genes and mutations associated with a resistance phenotype 81. More recently, the use of
machine learning (ML) techniques was assessed for the antimicrobial susceptibility prediction
without any previous knowledge of the actual AMR determinants involved 82. In general, ML
algorithms work by finding the relevant features in a complex data set that enable strong and reliable
prediction 83. ML algorithms are used to select the genomic features that are relevant to a given
antibiotic susceptibility profile. These relevant genomic features are then used as a phenotype
“classifier” for unknown genomes and as a source for identifying important genomic regions. From a
practical point of view, the counts of overlapping K-mers (subsequences of length ‘k’ contained
within a biological sequence) are computed and combined with the clinical laboratory generated
phenotypic data for each antibiotic to form one large matrix containing both the k-mers and
antibiotics as features. Different algorithms (boosting algorithms, penalized regression models,
decision trees, random forest, neural networks or set cover machines) are then used to build a
predictive model 82.
iii) Tracking outbreaks and identifying sources of recurrent infections. WGS can identify isolates
which are part of an outbreak and, by combining epidemiological data with phylogenetic information,
detect putative transmission events between patients or between patients and the environment.
WGS was successfully employed to reconstruct outbreaks within hospitals and the community
caused by pathogens belonging to several species, including carbapenem-resistant K. pneumoniae 84–
86 and A. baumannii 87. A recent review summarizes the major bioinformatics tool for outbreak
investigations 88.
iv) Improved surveillance. Molecular surveillance and real-time tracking of bacterial disease are
among the major promises of WGS implementation. In order to achieve this, the genomes sequenced
each year together with their metadata (e.g. sampling date, geographic location, isolation host) need
to be shared and methodically archived in an exploitable form. With such data, surveillance
initiatives have the capability to identify the likely geographic origin of emerging bacteria and AMR
genes, to group seemingly unrelated cases into outbreaks, and to clearly identify the emergence of
new clones. In a hospital environment, surveillance can help to detect cross-transmission events
between the hospital and the community and to improve antimicrobial stewardship; on a wider scale,
it can anticipate worldwide emerging trends consequently enabling anticipatory policy decisions.
Despite the WGS potential, there are some major bottlenecks to its implementation as a routine
clinical microbiology diagnostic tool. Major limitations include: the cost of performing WGS, which is
still high but it keeps falling; a lack of clinical microbiologists with bioinformatics skills; a lack of the
necessary computational infrastructure in most medical settings; the incompleteness of reference
microbial genomics databases required for AMR and virulence determinants detection; and the lack
of standardized, effective and easy to use bioinformatics protocols 75,80.
1.3.1 Different WGS platforms
From 2005, novel sequencing technologies emerged under the name of second (or next) generation
sequencing platforms, as opposed to the automated Sanger method, which is a first-generation
technology (Figure 8). Three major technologies, Illumina, SOLiD and 454, were employed to
generate bacterial genomes. From 2011, Illumina displaced the other competitors, and nowadays it
represents the major sequencing platform 89.
Illumina sequencing is based on the sequencing-by-synthesis principle to elucidate the sequence of
DNA. Briefly, DNA polymerases catalyse the binding of fluorescently labelled deoxyribonucleotide
triphosphates (dNTPs) into a DNA template strand during subsequent cycles of DNA synthesis. During
each cycle, at the point of incorporation, the nucleotides are identified by fluorophore excitation.
This process takes place across millions of fragments in a massively parallel fashion. The size of the
Illumina reads (the fragments of DNA that are sequenced by the instrument) is up to 300 bases. With
appropriate multiplexing, the ordinary coverage for a bacterial genome sequence project is between
30 and 100 reads per base. Illumina reads accuracy rates are typically around 99.9%, although
systematic biases related to GC-rich regions and some specific DNA motifs exist 90. Illumina has
developed several instruments ranging from low-throughput benchtop machines (MiniSeq, MiSeq) to
ultra-high-throughput instruments (HiSeq, NovaSeq). Illumina sequencing is considered as short-read
sequencing. Such short reads are insufficiently large to cover repeat elements such as transposons
and insertion sequences, which usually mobilize resistance and virulence determinants.
Consequently, short-read genome assemblies are fragmented and can consist of up to hundreds of
DNA fragments, called contigs. Sequencing technologies producing longer reads can cover such
repeats allowing the complete assembly of bacterial genomes.
In 2011, the first single-molecule, third generation long-read sequencing technology was released by
Pacific Biosciences (PacBio), while in 2014 Oxford Nanopore Technologies (ONT) released the MinION
instrument. PacBio’s single-molecule real-time (SMRT) sequencing it’s also based on the sequencing-
by-synthesis principle, as it detects sequence information during the replication process of the target
DNA molecule. The method is based on the optical observation of the polymerase-mediated
synthesis in real time. A zero-mode waveguide (ZMW), a hole less than half the wavelength of light,
limits fluorescent excitation to only a single polymerase together with its template. Consequently,
only fluorescently labelled nucleotides integrated into the growing DNA chain emit signals of
sufficient duration to be read 91.
SMRT sequencers (RSII, Sequel and Sequel II) have fast run times, typically less than three hours, and
the long reads produced can be longer than 80 Kb. The raw base-called error rate is decreasing over
the last years, and is now reduced to < 1% 92. As a major drawback, the high cost per base compared
with Illumina technologies and the massive cost for a PacBio sequencer represent major obstacles for
the implementation of this technology in the clinical microbiology laboratory 93.
ONT sequencing principle is based on the passage of a single stranded DNA in a nanopore over which
a voltage is continuously applied. The current through the nanopore changes depending on which
base is passing through it. Such changes can be processed and translated to obtain the sequence of
the DNA molecule that passes through the pore 94. The MinION is the main ONT device, it’s a small
and portable sequencer that can be used outside of traditional laboratories. Its throughput is up to
30 Gb per run, and it can produce reads longer than 200 Kb. The raw base-called error rate is claimed
to have been reduced to < 5% for nanopore sequences 95. An important feature of the MinION
sequencer is that the output can be analysed during its generation. This allows strain identification
within 30 minutes and prediction of the antibiotic resistance profile within 10 hours after the start of
a run 89.
Figure 8. Overview of the three generations of sequencing technologies, with examples of the major sequencing platforms
96 .
1.4 Aims
Antimicrobial resistance is a severe threat to public health worldwide, leading to growing costs,
treatment failure, morbidity and mortality. Nowadays, the antibiotic resistance level of bacterial
strains can be assessed by simple, mostly culture-based clinical AST methods. Although the classic
tests are reliable, they require extensive manual laboratory work and results are normally obtained
after several days only. WGS is a high-throughput DNA sequencing strategy that can produce a large
amount of data in a single reaction. WGS could potentially reduce the turnaround time for laboratory
results and allow clinically actionable information to be obtained sooner than traditional laboratory
diagnostic tests. However, translating genomic information to AST results is challenging. Moreover,
WGS allows for high resolution epidemiologic investigations, fundamental to track the spread and
the evolution of novel ‘high-risk’ clones.
This research project focuses on the use of WGS in order to study collections of MDR strains obtained
from countries with high AMR rates. The general aim is to study the AMR mechanisms at the
genomic level, with particular focus on last line drugs, such as colistin, and to perform
and K. pneumoniae strains.
The research was part of an initiative to define new diagnostic routing in infectious disease under the
name of ND4ID (Novel Diagnostics for Infectious Diseases). This project received funding from the
European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie
grant agreement No 675412.
The specific aims of this thesis are:
1. To investigate the genetic mechanisms of colistin resistance in K. pneumoniae (CHAPTER2)
and A. baumannii (CHAPTER 3) from two countries facing high AMR levels. Resistance
mechanism analysis of other antimicrobials, plasmid analysis and genomic epidemiology
investigations were also performed.
2. To study the population of K. pneumoniae isolates collected over a 15-year period in the
Beijing hospital H301 (CHAPTER 4). WGS was employed to decipher the genomic
epidemiology, the AMR and virulence determinants, as well as the emergence of novel ‘high-
risk’ clones, characterized by hyper-virulence and MDR.
3. To build and evaluate a machine learning algorithm for the prediction of antimicrobial
susceptibilities from genomic data (CHAPTER 5). To test the algorithm performances for the
phenotype prediction of K. pneumoniae genomes.
4. To perform classical molecular and enzymology techniques for the cloning, expression and
enzymatic activity testing of a novel carbapenemase. WGS was employed to detect the
putative determinant of carbapenem resistance and its genetic environment and to perform
phylogenetic analysis (CHAPTER 6).
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ST101 strains carrying a novel OXA-48 plasmid
Mattia Palmieri1, Marco Maria D’Andrea2,3, Andreu Coello Pelegrin1, Caroline Mirande4, Snezana
Brkic5, Ivana Cirkovic6, Herman Goossens7, Gian Maria Rossolini8,9, Alex van Belkum1
1bioMérieux, Data Analytics Unit, La Balme Les Grottes, France.
2Department of Biology, University of “Tor Vergata”, Rome, Italy.
3Department of Medical Biotechnologies, University of Siena, Siena, Italy.
4bioMérieux, R&D Microbiology, La Balme Les Grottes, France.
5Institute for Laboratory Diagnostics Konzilijum, Belgrade, Serbia.
6Institute of Microbiology and Immunology, Faculty of Medicine, University of Belgrade, Serbia.
7Laboratory of Medical Microbiology, Vaccine and Infectious Disease Institute, University of Antwerp,
Klebsiella pneumoniae is a major cause of severe healthcare-associated infections and often shows
MDR phenotypes. Carbapenem resistance is frequent, and colistin represents a key molecule to treat
infections caused by such isolates. Here we evaluated the antimicrobial resistance mechanisms and
the genomic epidemiology of clinical K. pneumoniae isolates from Serbia. Consecutive non-replicate
K. pneumoniae clinical isolates (n=2,298) were collected from seven hospitals located in five Serbian
cities and tested for carbapenem resistance by disk diffusion. Isolates resistant to at least one
carbapenem (n=426) were further tested for colistin resistance with Etest or Vitek2. Broth
microdilution (BMD) was performed to confirm the colistin resistance phenotype, and colistin-
resistant isolates (N=45, 10.6%) were characterized by Vitek2 and whole genome sequencing. Three
different clonal groups (CGs) were observed: CG101 (ST101, N=38), CG258 (ST437, N=4; ST340, N=1;
ST258, N=1) and CG17 (ST336, N=1). mcr genes, encoding for acquired colistin resistance, were not
observed, while all the genomes presented mutations previously associated with colistin resistance.
In particular, all strains had a mutated MgrB, with MgrBC28S being the prevalent mutation and
associated with ST101. Isolates belonging to ST101 harbored the carbapenemase OXA-48, which is
generally encoded by an IncL/M plasmid that was no detected in our isolates. MinION sequencing
was performed on a representative ST101 strain, and the obtained long reads were assembled
together with the Illumina high quality reads to decipher the blaOXA-48 genetic background. The blaOXA-
48 gene was located in a novel IncFIA-IncR hybrid plasmid, also containing the extended spectrum β-
lactamase-encoding gene blaCTX-M-15 and several other antimicrobial resistance genes. Non-ST101
isolates presented different MgrB alterations (C28S, C28Y, K2*, K3*, Q30*, adenine deletion leading
to frameshift and premature termination, IS5-mediated inactivation) and expressed different
carbapenemases: OXA-48 (ST437 and ST336), NDM-1 (ST437 and ST340) and KPC-2 (ST258). Our
study reports the clonal expansion of the newly emerging ST101 clone in Serbia. This high-risk clone
appears adept at acquiring resistance, and efforts should be made to contain the spread of such
Klebsiella pneumoniae has emerged as one of the most challenging antibiotic-resistant pathogens,
since it can cause a variety of infections, including pneumonia and bloodstream infections, and
exhibits a remarkable propensity to acquire antimicrobial resistance (AMR) traits. In particular,
carbapenem-resistant K. pneumoniae (CRKP) are challenging pathogens due to the limited treatment
options, high mortality rates, and potential for rapid dissemination in health care settings (Paczosa
and Mecsas, 2016).
Treatment options for CRKP infections are usually limited to aminoglycosides, tigecycline, fosfomycin
and colistin. Novel β-lactam-β-lactamase inhibitors combinations, such as ceftazidime-avibactam and
meropenem-vaborbactam, have represented a major breakthrough for treatment of some CRKP (e. g.
those producing KPC-type and OXA-48-like enzymes), but unfortunately they do not cover strains
producing metallo-carbapenemases (Bassetti et al., 2018). Colistin, despite its nephrotoxicity and
neurotoxicity, remains a key component of some anti-CRKP regimens (Karaiskos et al., 2017).
Colistin resistance (colR) is mainly mediated by modifications of the lipid A moiety of the bacterial
lipopolysaccharide (LPS) by addition of positively charged 4-amino-4-deoxy-L-arabinose (LAra4N)
and/or phosphoethanolamine (pEtN) residues. A large panel of genes and operons is involved in
modifications of the LPS, and mutations conferring colistin resistance have mainly been observed in
mgrB, phoP/phoQ, pmrA/pmrB, and crrB genes (Cheng et al., 2010; Cannatelli et al., 2013, 2014a;
Wright et al., 2015). Recently, several plasmid-mediated colistin resistance genes, named mcr,
encoding pEtN transferases, have also been reported in E. coli and other members of
Enterobacterales, including K. pneumoniae (Sun et al., 2018).
Global dissemination of CRKP is mainly caused by the spread of a few successful clones. Major
representatives of these high-risk clonal lineages include the Clonal Group (CG) 11, CG15, CG307,
CG17, CG37, CG101 and CG147 strains. CG258 strains, and in particular those of ST258, are major
players in the worldwide spread of KPC-type carbapenemases, and are responsible for 68% of the
CRKP outbreaks (Navon-Venezia et al., 2017). CG101 strains harbor different clinically-relevant
resistance determinants, such as carbapenemases of the KPC, OXA-48, VIM and NDM types, and
virulence genes, such as an integrative conjugative element carrying the yersiniabactin siderophore
(ICEKp3), the fimbriae cluster (mrkABCDFHIJ), the ferric uptake system (kfuABC), a capsular K type
K17, and an O antigen type of O1 (Roe et al., 2019). These features, together with the ability to
produce biofilm, are likely major factors in the ecological success of CG101 strains. Indeed, spreading
of this clone is on the rise (Navon-Venezia et al., 2017).
Multidrug resistance (MDR) prevalence in clinical isolates of K. pneumoniae, including resistance to
third-generation cephalosporins, fluoroquinolones and aminoglycosides, may be as high as 50% in
Southern Europe, and even higher proportions have been observed in Eastern Europe. In Serbia, in
2016, MDR K. pneumoniae accounted for 63% of all K. pneumoniae infections in humans, of which
35% were also carbapenem resistant (WHO Regional Office for Europe, 2017). Previous studies
reported that NDM-1 was the main K. pneumoniae-associated carbapenemase observed in Serbia in
the period 2013-2014 followed by OXA-48, while KPC was only sporadically reported (Grundmann et
al., 2017; Trudic et al., 2017). Novovi et al. performed a molecular epidemiology study of
carbapenem- and colistin-resistant strains from Serbia, showing prevalence of CG258 and CG101
strains, producing NDM-1 and OXA-48 carbapenemases, respectively. However, the proportion of
colistin resistance among those isolates was not reported, and the mechanisms of colistin resistance
of those isolates were not elucidated (Novovi et al., 2017).
In this study, we used whole genome sequencing (WGS) to study the genomic epidemiology and
antimicrobial resistance mechanisms of colR K. pneumoniae isolates from Serbia, including some
representative of the previously mentioned collection as reference to study the dynamic changes of
population structure (Novovi et al., 2017).
2.3 Materials and methods
Bacterial isolates and susceptibility testing. In the period between November 2013 and May 2017, K.
pneumoniae isolates were obtained from routine microbiological cultures of clinical samples (e.g.
urine, blood, skin, bronchial aspirate) from seven Serbian medical centers distributed in five Serbian
cities (Niš, Novi Sad, Belgrade, Kraljevo and Subotica). Bacteria were not isolated by the authors but
provided by the respective medical centers. Therefore, an ethics approval was not required as per
institutional and national guidelines and regulations. Information about patients antimicrobial
treatment were not available. Identification at the species level was performed by MALDI-TOF MS
(Vitek MS, bioMérieux, Marcy l’Etoile, France), and carbapenem susceptibility was determined by
disk diffusion and interpreted according to the EUCAST breakpoints (EUCAST, 2019). Isolates non-
susceptible to at least one carbapenem (ertapenem, meropenem and imipenem) were tested for
colistin resistance by Vitek2 or Etest (bioMérieux, Marcy l’Etoile, France) according to manufacturer’s
instructions (note that the warning by EUCAST about colistin susceptibility testing was only issued in
July 2016, and for this reason the above methods were used for colistin su