Towards an understanding of global patterns of simple sequence repeat- mediated phase variation...
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Transcript of Towards an understanding of global patterns of simple sequence repeat- mediated phase variation...
Towards an understanding of global patterns of simple sequence repeat-
mediated phase variation during host persistence of Campylobacter jejuni
and Neisseria meningitidis
Edinburgh Workshop 29-30th September 2010
Chris BaylissRCUK Research FellowDepartment of GeneticsUniversity of Leicester
Outline
• Overview of my research areas
• Intro to SSRs and phase variation
• Measuring mutation rates/patterns
• Phase variation of C. jejuni genes in in vitro and in vivo models
• Models of SSR-phase variation
• Issues
My Research: Phase Variation
Campylobacter jejuni
Hb receptors/reversible selectionmodel
Mechanistic studies
Neisseria meningitidis
Haemophilus influenzae
In vitro models
Colonisation of chickens
R-M systems/Phage infection
Carriage samples
Disease samples
Impact of phase variation rate on
population structure
Selection of phase variants
Combinedmodel
Experimental models/Epidemiological samples
In silico models
Consequences of Localised Hypermutation:
Phase Variation
ONOFF
ON
Frequency = 10-2 to 10-4
SELECTION/MUTATION
SELECTION/MUTATIONMUTATION
Streisinger Model
Streisinger Model
Streisinger Model
Insertion
Streisinger Model
Streisinger Model
Deletion
ATG………..CAAT(30)…..//………….TAG ONATG………..CAAT(29)…..TAG OFFATG………..CAAT(28)……..TAG OFFATG………..CAAT(27)…..//………….TAG ON
ATTATA……..TA(10)…….ATTAAA…//…ATG ONATTATA……..TA(9)…..ATTAAA…//…ATG OFF
In-Frame Repeats
-35 -10
Promoter-Located Repeats
Functions of the Products of Repeat-Associated Genes
AdhesinsLOS/LPS Biosynthetic
Enzymes
IronAcquisition
Proteins
Capsule Biosynthetic
Enzymes
RestrictionEnzyme
Flagella Biosynthetic
Enzymes
Long Tracts of Simple Sequence Repeats in Bacterial Genomes
Repeat Type(min. no. rpts)
G/C
(8)
A/T
(10)
Di
(6)
Tetra
(5)
Penta
(3)
H. influenzae (Rd)
6 2 0 12 2
N. meningitidis (MC58)
26 11 4 2 5
C. Jejuni
(NCTC11168)29 2 0 0 0
E. coli
(K12)12 0 1 0 0
Length of PolyG/PolyC Repeat Tracts in C. jejuni Contingency Loci
7 8 9 10 11 12 >12
81116
122181-176
111680
2
4
6
8
10
12
14
16
Repeat Tract Length
Phase Variation of Simple Sequence Contingency Loci
ONOFF
ON
SELECTION/MUTATION
SELECTION/MUTATION
What are the mutation rates of SSRs?What are the determinants of SSR mutation rates?What are the fitness implications of differing switching rates?What are the roles of selective and non-selective bottlenecks?What are the implications of multiple SSCL?
Campylobacter jejuni:- Phase Variation
Frequencies
Campylobacter jejuni* Gram –ve commensal of gasterointestinal tract of birds and widespread environmental contaminant* Major agent of foodborne gasteroenteritis* Implicated in autoimmmune diseases such as Guillain-Barre syndrome
cj1139clacZ cat
G8
Reporter Constructs for Detecting Phase Variationin Campylobacter jejuni
lacZ
G8
G11
capA (cj0628/cj0629)
CapA -CapA antibodies
(surface-located autotransporter)
T6-G11Strain NCTC11168
ON
‘off’ variant
‘on’ variant
On-to-off
Off-to-on
Colony Blots of C. jejuni strain 11168 probed with anti-CapA
ON-to-OFFFreq. -ve = 0.03
(filter 1, 9/8/07)
OFF-to-ONFreq. +ve = 0.03
(filter 4, 23/7/07)
-2 -3 -4 -5
Total number of cellsNumber of variant cells
Frequency of variants in
‘start colony’ =
MHA-VT plates
MHA-VT-XGal plates
H. influenzae N. meningitidis C. jejuni
%G+C of Genome
38 51 31
MMR GenesMutS/MutL/
MutHMutS/MutL None
SSR Mutation Frequencies
1x10-3 (AGTC30) 4x10-5 (G12) 4x10-3(G11)
Mutational
Pattern90% +1/-1
Deletions>InsertionsUnknown
>95% +1/-1Short: ins>del
Long: del>ins
Cis-Acting Factors
Repeat
NumberRepeat Number
Repeat
Number
Trans-Acting Factors
PolI, RNaseH MMR, PolIV Unknown
No
en
viro
nm
enta
l fa
cto
rs
Campylobacter jejuni:- In vitro/In vivo Passage
PCR-Based Measurement of Repeat Tract Length
GGGGGGGGGG
FAM
Multiple Passages of Growth in MHB Broth
Plate Plate DilutionsDilutions
Colony Colony BlottingBlotting
Pick 30Pick 30coloniescolonies
PCR PCR ArrayArray
Pick 30Pick 30coloniescolonies
Colony Colony BlottingBlotting
PCR PCR ArrayArray
Day 0Day 0 Day 1Day 1 Day 2Day 2 Day 3Day 3 Day 4Day 4
Suspend Suspend inoculuminoculum
InoculateInoculate5mL MHB5mL MHB InoculateInoculate
5mL MHB5mL MHB
Plate Plate DilutionsDilutions
InoculateInoculate5mL MHB5mL MHB
InoculateInoculate5mL MHB5mL MHB
InoculateInoculate5mL MHB5mL MHB
Pallet Pallet the cellsthe cells
Analysis of Phase Variable Genes and Repeat Tracts
ConstantInoculum(3.5x108cfu;
6 tubes)
VariableInoculum
(from 3.5 x108
to 3.5x103cfu;6 tubes)
Inoculum Output
0.29 0.24-0.36
0.29 0.27-0.36
CapAFrequency -ve
Drift, Bottlenecks, Selection and Hitch-Hiking
6 Genes = 64 Genotypes
RandomDrift
Bottleneck
Mutation/Bottleneck
Mutation/Bottleneck
Selection
Mutation/Selection
Mutation/Selection
1139-off
0031-on
0685-on
0685-on1139-off
Neisseria meningitidis
PorA Phase Variation, Immune Evasion and Variant-Specific Immune Responses During
Carriage
Escape Assay
• Modified serum bactericidal assay using large inoculum (1x104-1x107 cfu) and multiple passages
• LPS phase variants with switches in expression of lgtG mediate escape of mAb B5 (translational switching)
• Escape dependent on size of inoculum, amount of antibody and rate of phase variation
Bayliss et al. 2008 Infect. Immun. 76:5038
PV of porA mediates immune escape in vitro
*Variants examined had 10C residues in the porA repeat tract*Escape is due to pre-existing variants
11C
10C
+/- mAb 1.2 10% human serum
+/- mAb 1.210% human serum
+/- mAb 1.210% human serum
Correlation of porA PV Expression to Escape
*Level of PorA expression is highest when 11C repeat units is present in 8047*~ 3 fold of reduction in expression of porA
• Repeat tract changes to expression
• Whole cell ELISA and lysate western blotting
11C 10C 9C
Week 0 Week 4 Week 12 Week 24Week -4
Phase Variation of NadA
Volunteer 1st 2nd 3rd 4thV43 12 - 12 -V51 12 12 12 12V52 12 12 12 -V54 14 14 12 -V58 12 12 - 12V59 13 12 12 12V88 11 9 9 9V138 12 12 12 -
Number of tetranucleotide repeatsAll volunteers colonised with Y:P1.21,16:CC174
OFF9 and 12 rpts
Computer Models
Multiple simple sequence contingency loci
• Multiple loci = multiple potential genotypes• Haemophilus influenzae strain Rd has 12
genes containing tetranucleotide repeat tracts, a potential 4096 genotypes (if two genotypes per locus, i.e. ON and OFF)
• Lic2 locus has three genotypes :- ON-Strong, ON-Weak and OFF (if all 12 loci had 3 genotypes then there is 531 441 potential genotypes)
Computer Model 1
• Population founded by single organism which divides by binary fission
• Three phase variable loci• Switching occurs in both directions at
the same rates• Mutations occur during division giving
one genotype of the parental phenotype and one mutant
Number of genotypes
1 2 3 4 5 6 7 81 2 3 4 5 6 7 81 2 3 4 5 6 7 8
Nu
mb
er o
f p
opu
lati
ons
0
100
200
300
400
500
600
800
900
1000
700
1x10-6 (< 6) 3.6x10-5 (10) 1.24x10-4 (22)Mutation rate
(repeat number)
Effect of phase variation rate on the amount of genetic diversity produced in
20 generations
Effect of phase variation rate on the production of genotypes with multiple
switches
*Solution is when all three loci have switched from OFF to ON.*30 generations were used. *All cells of the parental genotype were removed at generation 20.*1000 replicates were performed
Number of populationscontaining solution
Mutation rate
3.6x10-5
1.24x10-4
21
370
Model 2
Effect of Interval Between Selective Environments
Environment ASelection for
ON Phenotype
Environment BSelection for
OFF Phenotype
Number of Generations
2,000-100,000 2,000-100,000
Mike Palmer and Marc LipsitchMike Palmer and Marc Lipsitch
Variable Repeat Number17 = ON = A 18 = OFF = B19 = OFF = B20 = ON = A
etc37 = OFF = B38 = ON = A
RepeatNumber
56789
10111213
Evolution of Repeat Tracts in the Absence of Selection
Environmental switch period:- 20 000 generationsFitness advantage:- 0.1
Evolution of Repeat Tracts with Selection and in a Fluctuating Environment
Environmental switch period:- 4 000 generationsFitness advantage:- 0.1
Environmental switch period:- 2 000 generationsFitness advantage:- 0.1
Environmental switch period:- 100 generationsFitness advantage:- 0.1
Summary Computer Simulation Model
• Selection is required to maintain large numbers of repeats in the repeat tracts
• Repeat number is determined by the frequency of the environmental switch
• Correlation between repeat number and environmental switch is also influenced by the conferred fitness advantage and mutational pattern
Model 3
• Model phase shifts in multiple loci using known mutation rates (excludes mutational patterns)
• Assumes each locus switches independently of other loci (can set PV rate for each gene, but not scalable with tract length changes)
• Simple deterministic model, average of multiple trees from a Monte Carlo simulation, performed in Excel (maximum of 100 generations)
Gene cj0045 cj0685 cj1326 capA cj1139 cj0032
Tract 9 9 10 12 9 9
Phenotype OFF ON OFF OFF OFF ON
Binary code
0 1 0 0 0 1
One Isolate B9.1
Sample from Chicken B9
Note:- genotype is not directly correlated with phenotype (i.e. cj0045 is OFF with 9 or 10 repeats
010001 010100 010101 110000 110001 110100 110101
10 2 2 3 5 1 7
Coded phenotypes of all 30 colonies for B9
Drift, Bottlenecks, Selection and Hitch-Hiking
6 Genes = 64 Genotypes
RandomDrift
Bottleneck
Mutation/Bottleneck
Mutation/Bottleneck
Selection
Mutation/Selection
Mutation/Selection
1139-off
0031-on
0685-on
0685-on1139-off
Modelling Changes in the Distribution of Phase Variants:- no selection
6 Phase variable genes = ON/OFF = 64 genotypes
Output 1 = all genes at G9 PV rate (0.0015)Output 2 = varied PV rates
0=off, 1=onOutput = 100 generations
000000
000100
001000
001100
010000
010100
011000
011100
100000
100100
101000
101100
110000
110100
111000
111100
Inoc
0
0.1
0.2
0.3
0.4
Fre
qu
en
cy
Genotypes
InocOutput1Output2B9
Scientific Issues
• What factors to include in a model – mutation rate, mutational pattern, population size, fitness, frequency of environmental switching, bottlenecks, number of loci, number of generations
• How to model – simulation of multiple populations or deterministic model of average solutions
Logistical Issues
• Data collection (sample bias)
• Computational power
• Biological and clinical relevance
• Simultaneous data collection and modelling (local collaborators)
• Relevance to systems biology
• Requirement for a modelling community
Jean-Philipe GautierJacques MarletFadil Bidmos
Nathalie IngoufRebecca Richards
Awais AnjumVladimir Manchev
Richard HaigJulian Ketley
(University of Leicester)
Neil OldfieldDel Ala’AldeenKarl Wooldridge
Michael JonesPaul Barrow
(University of Nottingham)
Michael TretyakovAlexander Gorban
(University of Leicester)
Michael PalmerMarc Lipsitch
Richard Moxon