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