Towards an understanding of global
patterns of simple sequence repeatmediated phase variation during host
persistence of Campylobacter jejuni
and Neisseria meningitidis
Chris Bayliss
RCUK Research Fellow
Department of Genetics
University of Leicester
Edinburgh Workshop 29-30th September 2010
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
Experimental models/
Epidemiological samples
Mechanistic studies
Campylobacter jejuni
In vitro models
In silico models
Impact of phase
variation rate on
population
structure
Colonisation of chickens
Carriage samples
Combined
model
Neisseria meningitidis
Disease samples
Hb receptors/reversible selection
model
Haemophilus influenzae
R-M systems/Phage infection
Selection of
phase variants
Consequences of Localised Hypermutation:
Phase Variation
SELECTION
/MUTATION
SELECTION
/MUTATION
MUTATION
ON
Frequency = 10-2 to 10-4
OFF
ON
Streisinger Model
Streisinger Model
Streisinger Model
Insertion
Streisinger Model
Streisinger Model
Deletion
In-Frame Repeats
ATG………..CAAT(30)…..//………….TAG
ATG………..CAAT(29)…..TAG
ATG………..CAAT(28)……..TAG
ATG………..CAAT(27)…..//………….TAG
ON
OFF
OFF
ON
Promoter-Located Repeats
-35
-10
ATTATA……..TA(10)…….ATTAAA…//…ATG ON
ATTATA……..TA(9)…..ATTAAA…//…ATG
OFF
Functions of the Products of Repeat-Associated Genes
Flagella
Biosynthetic
Enzymes
Capsule
Biosynthetic
Enzymes
LOS/LPS
Biosynthetic
Enzymes
Iron
Acquisition
Proteins
Restriction
Enzyme
Adhesins
Long Tracts of Simple Sequence
Repeats in Bacterial Genomes
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
Repeat Type
(min. no. rpts)
Length of PolyG/PolyC Repeat Tracts
in C. jejuni Contingency Loci
16
14
12
10
8
6
4
2
11168
81-176
1221
81116
0
7
8
9
10
Repeat Tract Length
11
12
>12
Phase Variation of Simple Sequence Contingency Loci
SELECTION
/MUTATION
SELECTION
/MUTATION
ON
What
What
What
What
What
are
are
are
are
are
the
the
the
the
the
OFF
ON
mutation rates of SSRs?
determinants of SSR mutation rates?
fitness implications of differing switching rates?
roles of selective and non-selective bottlenecks?
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
Reporter Constructs for Detecting Phase Variation
in Campylobacter jejuni
cj1139c
lacZ
G8
cat
G8
lacZ
G11
capA (cj0628/cj0629)
T6-G11
Strain NCTC11168
ON
CapA
(surface-located autotransporter)
a-CapA antibodies
On-to-off
‘off’ variant
Off-to-on
‘on’
variant
Colony Blots of C. jejuni strain 11168 probed
with anti-CapA
ON-to-OFF
Freq. -ve = 0.03
(filter 1, 9/8/07)
OFF-to-ON
Freq. +ve = 0.03
(filter 4, 23/7/07)
MHA-VT plates
-2
-3
-4
-5
MHA-VT-XGal plates
Frequency of
variants in = Number of variant cells
‘start colony’
Total number of cells
N. meningitidis
C. jejuni
%G+C of
Genome
38
51
31
MMR Genes
MutS/MutL/
MutH
MutS/MutL
None
SSR Mutation
Frequencies
1x10-3 (AGTC30)
4x10-5 (G12)
4x10-3(G11)
>95% +1/-1
Mutational
Pattern
Deletions>Insertions
Cis-Acting
Factors
Repeat
Number
Repeat Number
Repeat
Number
Trans-Acting
Factors
PolI, RNaseH
MMR, PolIV
Unknown
90% +1/-1
Unknown
Short: ins>del
Long: del>ins
No environmental factors
H. influenzae
Campylobacter jejuni:In vitro/In vivo Passage
PCR-Based Measurement of Repeat Tract Length
GGGGGGGGGG
FAM
Multiple Passages of Growth in MHB Broth
Suspend
inoculum
Plate
Dilutions
Colony
Blotting
Inoculate
5mL MHB
Inoculate
5mL MHB
Day 0
Pick 30
colonies
PCR
Array
Inoculate Inoculate Inoculate
5mL MHB 5mL MHB 5mL MHB
Day 1
Day 2
Day 3
Pallet
the cells
Day 4
Colony
Blotting
Plate
Dilutions
Pick 30
colonies
PCR
Array
Analysis of Phase Variable Genes and
Repeat Tracts
CapA
Frequency -ve
Constant
Inoculum
Inoculum
Output
0.29
0.24-0.36
(3.5x108cfu;
6 tubes)
Variable
Inoculum
(from 3.5 x108
to 3.5x103cfu;
6 tubes)
0.29
0.27-0.36
Drift, Bottlenecks, Selection and HitchHiking
6 Genes = 64 Genotypes
Selection
Bottleneck
Random
Drift
0685-on
Mutation/Bottleneck
Mutation/Selection
1139-off
Mutation/Bottleneck
Mutation/Selection
0031-on
0685-on
1139-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
1.00E+07
11C
Size of Inoculum
1.00E+06
1.00E+05
10C
1.00E+04
1.00E+03
5.00E+05
1.00E+02
5.00E+03
(5.00E+3 No Antibody)
1.00E+01
1.00E+00
P0
P1
P2
P3
P4
Passage
+/- mAb 1.2
10% human serum
+/- mAb 1.2
10% human serum
+/- mAb 1.2
10% human serum
*Variants examined had 10C residues in the porA repeat tract
*Escape is due to pre-existing variants
Correlation of porA PV Expression to Escape
• Repeat tract changes to expression
• Whole cell ELISA and lysate western blotting
1
11C
10C
9C
0.8
OD 405
11C Repeat
0.6
10C Repeat
9C Repeat
0.4
0.2
0
0
0.0001
0.001
0.01
1
mAb Dilutions
*Level of PorA expression is highest when 11C repeat units is present in 8047
*~ 3 fold of reduction in expression of porA
Week -4
Week 0
Week 4
Week 12
Week 24
Phase Variation of NadA
Volunteer
V43
V51
V52
V54
V58
V59
V88
V138
1st
12
12
12
14
12
13
11
12
2nd
12
12
14
12
12
9
12
3rd
12
12
12
12
12
9
12
4th
12
12
12
9
OFF
9 and 12 rpts
Number of tetranucleotide repeats
All volunteers colonised with Y:P1.21,16:CC174
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 :- ONStrong, 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
Effect of phase variation rate on the
amount of genetic diversity produced in
20 generations
Number of populations
Mutation rate
1x10-6 (< 6)
(repeat number)
3.6x10-5 (10)
1.24x10-4 (22)
1000
900
800
700
600
500
400
300
200
100
0
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
Number of genotypes
1
2
3
4
5
6
7
8
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
Mutation rate
Number of populations
containing solution
3.6x10-5
21
1.24x10-4
370
Model 2
Effect of Interval Between Selective Environments
Environment A
Selection for
ON
Phenotype
Number of
Generations
2,000-100,000
Variable Repeat Number
17 = ON = A
18 = OFF = B
19 = OFF = B
20 = ON = A
etc
37 = OFF = B
38 = ON = A
2,000-100,000
Environment B
Selection for
OFF
Phenotype
Mike Palmer and Marc Lipsitch
Evolution of Repeat Tracts in the Absence of Selection
Repeat
Number
5
6
7
8
9
10
11
12
13
Evolution of Repeat Tracts with Selection
and in a Fluctuating Environment
Environmental switch period:- 20 000 generations
Fitness advantage:- 0.1
Environmental switch period:- 4 000 generations
Fitness advantage:- 0.1
Environmental switch period:- 2 000 generations
Fitness advantage:- 0.1
Environmental switch period:- 100 generations
Fitness 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)
Sample from Chicken B9
One Isolate B9.1
cj0045 cj0685 cj1326
Gene
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
Note:- genotype is not directly correlated with phenotype (i.e. cj0045 is OFF with 9 or 10 repeats
Coded phenotypes of all 30 colonies for B9
010001
010100
010101
110000
110001
110100
110101
10
2
2
3
5
1
7
Drift, Bottlenecks, Selection and HitchHiking
6 Genes = 64 Genotypes
Selection
Bottleneck
Random
Drift
0685-on
Mutation/Bottleneck
Mutation/Selection
1139-off
Mutation/Bottleneck
Mutation/Selection
0031-on
0685-on
1139-off
Modelling Changes in the Distribution
of Phase Variants:- no selection
6 Phase variable genes = ON/OFF = 64 genotypes
Inoc
Output1
Output2
B9
Frequency
0.4
0.3
0.2
0.1
0=off, 1=on
Output = 100 generations
Inoc
111100
111000
110100
110000
101100
100100
100000
101000
Genotypes
011100
011000
010100
010000
001100
001000
000100
000000
0
Output 1 = all genes at G9 PV rate (0.0015)
Output 2 = varied PV rates
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 Gautier
Jacques Marlet
Fadil Bidmos
Nathalie Ingouf
Rebecca Richards
Awais Anjum
Vladimir Manchev
Richard Haig
Julian Ketley
(University of Leicester)
Neil Oldfield
Del Ala’Aldeen
Karl Wooldridge
Michael Jones
Paul Barrow
(University of Nottingham)
Michael Tretyakov
Alexander Gorban
(University of Leicester)
Michael Palmer
Marc Lipsitch
Richard Moxon