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Towards modeling epigenetic phase variation of virulence factors Marjan van der Woude Centre for Immunology and Infection D EPARTMENT OF B IOLOGY.

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Presentation on theme: "Towards modeling epigenetic phase variation of virulence factors Marjan van der Woude Centre for Immunology and Infection D EPARTMENT OF B IOLOGY."— Presentation transcript:

1 Towards modeling epigenetic phase variation of virulence factors Marjan van der Woude Centre for Immunology and Infection D EPARTMENT OF B IOLOGY

2 Haraga 2008 Infectious dose Bottlenecks Expression of Virulence factors

3 Cell division Cell divisions Phase variation: Heritable yet reversible gene expression restreak 1 in 10-10 4 cells switch per generation lacZ reporter

4 1.Variable level of response within population 2.Phase variation Results in heterogeneous clonal population with cells expressing (ON) and not expressing gene(OFF). Cells in a clonal population may never have identical phenotype 1. 2.

5 Why study population heterogeneity? Host- pathogen /commensal interactions, interaction with (abiotic) environment, biofilms, resistance Interesting biology we may be missing: Wider implications: Combating Infectious Disease Diagnostics, Epidemiology, Vaccine development

6 Biological significance of phase variation? PV of adhesins: -?Facilitates bacterial dispersal? (from biofilms or colonized host tissue) - Evade the immune system -?Alters host pathogen interactions?

7 Cell division Cell divisions Phase variation: Heritable yet reversible gene expression restreak 1 in 10-10 4 cells switch per generation lacZ reporter

8 Single cells: overlay of phase contrast (all cells) and fluorescent image (ON cells, GFP+) Reporter fusions to analyze PV: gfp: Green Fluorescent Protein Alternatives: lux lacZ Native protein Also for Flow cytometry

9 Microbial Challenge #1 Getting the data: Analyze and Visualize an infrequent event Suitability -population, individual cells -lab, in vitro or infection model Sensitivity (need single copy for PV) Reporter relation to native protein?

10 promoter OFF ON Protein CDS Phase variation controlled by DNA methylation (epigenetic) Dam OxyR GATCs Protein CDS -10-35 Example: OxyR is a repressor but can only bind if 3 Dam target sequences (GATC) are unmethylated. Once OxyR is bound, Dam can not access GATC.

11 HM OFF UM ON METH Competition DNA binding protein and processive enzyme Actual DNA and protein concentration (at site) [Kaminska et al 2010] Role passage DNA replication fork(s) [Kaminska et al 2010] Other growth related variables

12 Significance OxyR binding affinity Role of each GATC GATC mutants x (NA locked Off) x x x Altered switch frequency OFFON WT K12 xxx x WT RS218 Altered switch frequency

13 Microbial Challenge #2 Getting the data: Acquiring relevant numerical data (low concentration proteins and enzyme) Microbial Challenge #3 Reduce complexity w/o oversimplifying (include DNA replication, growth?)

14 OxyR and Dam-dependent PV: variation on a theme agn family ON OFF gtrSalmonella enterica sp. E. coli Sarah Broadbent ON OFF

15 Molecular Rules Dam-dependent PV? agn family ONOFF gtr family Agn- outer membrane protein family in E. coli Gtr- LPS modification operons in Salmonella Both with evidence of past horizontal (phage) transfer

16 Expression of gtr can affect Salmonellae serotyping >2500 serovars Salmonella Bongori (V) Enterica Genus Species Subspecies Serotypes Enterica (I) Salamae (II) Arizonae (IIIa) Diarizonae (IIIb) Houtenae (IV) Indica (VI) Typhimurium Typhi Choleraesuis Paratyphi Enteriditis … … … … … … LT2 14028 DT104 SL1344 TR7095 … Serotypes (Kauffmann-White scheme) -Based on immunoreactivity of two surface antigens i) O Antigen (LPS) ii) H Antigen (Flagellar) Strain >98% of human clinical isolates

17 0.1 Enteritidus PT4_II Gallinarum_II Dublin_III Typhi CT18_II Typhi TY2_I Paratyphi A_I Typhimurium D23580_BTP1 Cholerasuis_II Infantis_II Cholerasuis_III Infantis_I Cholerasuis_I Hadar_1 Phage ST104 Phage ST64T Typhimurium DT104_III Paratyphi A_III * Phage P22 Hadar_II Typhimurium SL1344_II Typhimurium D23580_II Typhimurium DT10_I Typhimurium DT2_I Typhimurium LT2_II Choleraesuis_IV * Infantis_III Typhi CT18_I Typhi TY2_II Paratyphi A_II * Enteritidus PT4_I Gallinarum_I Typhimurium DT2_II Typhimurium LT2_I Typhimurium SL1344_I Typhimurium DT104_II Paratyphi B Typhimurium D23580_I Group 3 SPI16-like Group 4 Group 1 Group 2 gtrA gtrB gtrC ? gtrABC Lt2_I Glc:O12 2 1 4 oafA OAc:O5 gtr P22 Glc:O1 1 6 S. Typhimurium LT2 O-antigen (Pseudo gtrB) Gal Rha Man Abe O4, O12 L C + increasing #O repeats oafA Lt2_I Lt2_II WT ptac Lt2_1 Lipid-core LPS gel gtr operons modify the O-antigen Which gtr cluster conveys which O-serotype?

18 Model for gtr phase variation;Dam and OxyR gtrA OxyR BOxyR C -10-35 +1 OxyR A RNApol CH 3 ON gtrA OxyR C -10-35 +1 OxyR A OxyR B OFF CH 3 OxyR Broadbent et al 2010

19 gtrABC: modifies the O-antigen and phase varies 0-4 copies of gtr-family operons per Salmonella genome (phage remnants) Also on phage genomes If 3 of 4 copies PV then one can have 8 phenotypic variants in a population just from the gtr family! Combine with PV of possibly as many as 11 adhesins …..

20 WebLogo of 33 gtr regulatory regions identifies putative important elements OxyR half b.s.motif : ATAG/T.T…A.CTAT Predict PV rates / regulation based on DNA sequence and paramters?

21 Salmonella LPS modification project SEROTYPING -Improve ? Complete, Molecular diagnostics SEROTYPING -Improve ? Complete, Molecular diagnostics BIOCHEMISTRY Relate genes to chemical modification BIOCHEMISTRY Relate genes to chemical modification MOLECULAR -Genome sequencing MOLECULAR -Genome sequencing EXPRESSION -Phase variation /Regulated? EXPRESSION -Phase variation /Regulated? ROLE OF MODIFICATION Host-Pathogen interactions ROLE OF MODIFICATION Host-Pathogen interactions

22 van der Woude and Baumler, 2004 Can we predict Dam-dependent PV from DNA sequence? Any methylation dependent PV? OxyR and DamLrp and Dam from LPS modification DNA methyl * * * * *

23 Molecular Rules Dam-dependent PV? Pap family Lrp, needs PapI agn family ONOFF gtr family

24 Microbial Challenge #4 Testing relevance Choosing the strain and conditions that represent a natural situation of relevance Microbial Challenge #5 Devising and executing experiments within adhering to those wishes

25 Challenge(s) #6 What is enough data to make modeling feasible? How to decide if modeling is a worthwhile endeavor for the system? If the system is the best for the modeling?

26 Support from Renata Kaminska Sarah Broadbent Mark Davies Matt Lakins previous lab members With previous support from NSF Centre for Immunology and Infection D EPARTMENT OF B IOLOGY

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