39
Pathogenomics Using bioinformatics to focus studies of bacterial pathogenicity

Pathogenomics Using bioinformatics to focus studies of bacterial pathogenicity

  • Upload
    aulii

  • View
    38

  • Download
    0

Embed Size (px)

DESCRIPTION

Pathogenomics Using bioinformatics to focus studies of bacterial pathogenicity. Explosion of data 23 of the 34 publicly available microbial genome sequences are for bacterial pathogens Approximately 21,000 pathogen genes with no known function! - PowerPoint PPT Presentation

Citation preview

Page 1: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

Pathogenomics

Using bioinformatics to focus studies of bacterial

pathogenicity

Page 2: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

Explosion of data

23 of the 34 publicly available microbial genome sequences are for bacterial pathogens

Approximately 21,000 pathogen genes with no known function!

>95 bacterial pathogen genome projects in progress …

Page 3: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

Pathogenomics

Opportunistic pathogen Pseudomonas aeruginosa

- Genome analysis and membrane protein bioinformatics

UBC Pathogenomics Project

- Identifying eukaryote:pathogen gene homologs

- Detecting pathogenicity islands

Page 4: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

Pseudomonas aeruginosa

• Found in soil, water, plants, animals• Common cause of hospital acquired infection: ICU

patients, Burn victims, cancer patients• Almost all cystic fibrosis (CF) patients infected by

age 10• Intrinsically resistant to many antibiotics• No vaccine

Page 5: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity
Page 6: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

OprM homology (3 previously known, now 18 predicted)

OprD homology (2 previously known, now 19)

TonB-dependent domain (8 previously known, now 34)

P. aeruginosa Genome Sequence Analysis: Outer Membrane Proteins (OMPs)

Approximately 150 OMPs predicted including three large paralogous families:

Page 7: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

AprFOpmM

OpmH

OpmFOpmKOpmL

OpmN

OpmQ

OpmD

OprN

OpmE

OpmJOpmA

OprM OprJ

OpmB

OpmGOpmI

OprMFamily

(MultidrugEfflux?)

ProteinSecretion? TolC

Page 8: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

POREPORIN

Peptidoglycan

LPS Mg++

Outermembrane

Cytoplasmicmembrane

Gram Negative Cell Envelope

Periplasm

Page 9: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

P. aeruginosa OprM structural model based on E. coli TolC

Outer membrane

Periplasm

Page 10: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

Residues implicated in blocking channel formation in OmpA are not conserved in OprF

Page 11: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

BathingSolution

PlanarBilayer

Membrane

VoltageSource

CurrentAmplifier

Protein

Planar Lipid Bilayer Apparatus

Page 12: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

The N-terminus of OprF forms channels in a lipid bilayer membrane 

0

5

10

15

20

25

30

35

40

0.2

0.4

0.6

0.8 1

1.2

1.4

1.6

1.8 2

2.2

2.4

2.6

2.8 3

Single channel conductance (nS)

No

. o

f ev

ents

Page 13: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

Improve computational prediction of…

- membrane and secreted proteins

- surface exposed regions of membrane proteins

Current and Future Research

Page 14: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

Omp85 membrane protein family studies

- Antigenic, conserved, vaccine candidate

- Two copies in most pathogenic bacteria genomes – why?

- Structure unknown, may have conformational epitopes

Current and Future Research

Page 15: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

Opportunistic pathogen Pseudomonas aeruginosa

- Genome analysis and membrane protein bioinformatics

UBC Pathogenomics Project

- Identifying eukaryote:pathogen gene homologs

- Detecting pathogenicity islands

Pathogenomics

Page 16: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

Genome data for…

Anthrax Necrotizing fasciitis Cat scratch disease Paratyphoid/enteric feverChancroid Peptic ulcers and gastritisChlamydia Periodontal diseaseCholera PlagueDental caries PneumoniaDiarrhea (E. coli etc.) SalmonellosisDiphtheria Scarlet feverEpidemic typhus ShigellosisMediterranean fever Strep throatGastroenteritis SyphilisGonorrhea Toxic shock syndromeLegionnaires' disease Tuberculosis Leprosy TularemiaLeptospirosis Typhoid feverListeriosis UrethritisLyme disease Urinary Tract InfectionsMeliodosis Whooping cough Meningitis +Hospital-acquired infections

Page 17: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

Bacterial Pathogenicity

Processes of microbial pathogenicity at the molecular level are still minimally understood

Pathogen proteins identified that manipulate host cells by interacting with, or mimicking, host proteins

Page 18: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

Yersinia Type III secretion system

Page 19: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

Approach

Idea: Could we identify novel virulence factors by identifying pathogen genes more similar to host genes than you would expect based on phylogeny?

Page 20: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

Prioritize for biological study

Search pathogen genes against databases. Identify those with eukaryotic similarity.

Modify screening method /algorithm

Approach

World Research Community

Study function in model host (C. elegans)

Study function in bacterium

Infection of mutant in model host

Collaborations with others

DATABASE

Rank candidates - evolutionary analysis.

C. elegans

Page 21: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

Informatics/Bioinformatics• BC Genome Sequence Centre• Centre for Molecular Medicine

and Therapeutics

Evolutionary Theory• Dept of Zoology

• Dept of Botany

• Canadian Institute for Advanced Research

Pathogen Functions• Dept. Microbiology

• Biotechnology Laboratory

• Dept. Medicine

• BC Centre for Disease Control

Host Functions• Dept. Medical Genetics

• C. elegans Reverse Genetics Facility

• Dept. Biological Sciences SFU

Interdisciplinary group

Coordinator

Page 22: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

Bacterium Eukaryote Horizontal Transfer

0.1

Bacillus subtilis

Escherichia coli

Salmonella typhimurium

Staphylococcua aureus

Clostridium perfringens

Clostridium difficile

Trichomonas vaginalis

Haemophilus influenzae

Acinetobacillus actinomycetemcomitans

Pasteurella multocida

N-acetylneuraminate lyase (NanA) of the protozoan Trichomonas vaginalis is 92-95% similar to NanA of Pasteurellaceae bacteria.

Page 23: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

N-acetylneuraminate lyase – role in pathogenicity?

Pasteurellaceae

•Mucosal pathogens of the respiratory tract

T. vaginalis

•Mucosal pathogen, causative agent of the STD Trichomonas

Page 24: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

N-acetylneuraminate lyase (sialic acid lyase, NanA)

Involved in sialic acid metabolism

Role in Bacteria: Proposed to parasitize the mucous membranes of animals for nutritional purposes

Role in Trichomonas: ?

Hydrolysis of glycosidic linkages of terminal sialic residues in glycoproteins, glycolipids SialidaseFree sialic acid

Transporter

Free sialic acid NanA

N-acetyl-D-mannosamine + pyruvate

Page 25: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

Sensor Histidine Kinase for 2-component Regulation System

Signal Transduction

Histidine kinases common in bacteria

Ser/Thr/Tyr kinases common in eukaryotes

However, a histidine kinase was recently identified in fungi, including pathogens Fusarium solani and Candida albicans

How did it get there?

Candida

Page 26: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

A Histidine Kinase in Streptomyces.The Missing Link?

0.1

Neurospora crassa NIK-1

Streptomyces coelicolor SC7C7

Fusarium solani FIK

Candida albicans CHIK1

Erwinia carotovora EXPS

Escherichia coli BARA

Pseudomonas aeruginosa LEMA

Pseudomonas syringae LEMA

Pseudomonas viridiflava LEMA

Pseudomonas tolaasii RTPA

Page 27: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

Universal role of this Histidine Kinase in pathogenicity?

Pathogenic Fungi•Senses change in osmolarity of the environment•Proposed role in pathogenicity

Pseudomonas species plant pathogens•Role in excretion of secondary metabolites that are virulence factors or antimicrobials

Virulence factor for human opportunistic pathogen Pseudomonas aeruginosa?

Page 28: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

Reduced virulence of a Pseudomonas aeruginosa transposon mutant disrupted in the

histidine kinase lemA

 

Cells challenged per mouse

Neutropenic mice

challenged per group

% Mortality

Wildtype LemA-

0.74x 106 7 100 100

0.74x 105 7 100 85.7

0.74x 104 7 100 50

0.74x 103 8 75 50

0.74x 102 8 62.5 50

0.74x 101 8 37.5 25 

Page 29: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

Trends in the Current Analysis

• Identifies the strongest cases of lateral gene transfer between bacteria and eukaryotes

• Most common “cross-kingdom” horizontal transfers:

Bacteria Unicellular Eukaryote

• A control: Method identifies all previously reported Chlamydia trachomatis eukaryotic-like genes.

Page 30: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

Horizontal Gene Transfer and Bacterial Pathogenicity

Transposons: ST enterotoxin genes in E. coli

Prophages:Shiga-like toxins in EHECDiptheria toxin gene, Cholera toxinBotulinum toxins

Plasmids:Shigella, Salmonella, Yersinia

Page 31: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

Horizontal Gene Transfer and Bacterial Pathogenicity

Pathogenicity Islands:

Uropathogenic and Enteropathogenic E. coliSalmonella typhimuriumYersinia spp.Helicobacter pyloriVibrio cholerae

Page 32: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

Pathogenicity Islands

Associated with

– Atypical %G+C– tRNA sequences– Transposases, Integrases and other mobility genes– Flanking repeats

Page 33: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

IslandPath: Identifying Pathogenicity Islands

Yellow circle = high %G+C

Pink circle = low %G+C

tRNA gene lies between the two dots

rRNA gene lies between the two dots

Both tRNA and rRNA lie between the two dots

Dot is named a transposase

Dot is named an integrase

Page 34: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

Neisseria meningitidis serogroup B strain MC58 Mean %G+C: 51.37 STD DEV: 7.57

%G+C SD Location Strand Product 39.95 -1 1834676..1835113 + virulence associated pro. homolog 51.96 1835110..1835211 - cryptic plasmid A-related 39.13 -1 1835357..1835701 + hypothetical 40.00 -1 1836009..1836203 + hypothetical 42.86 -1 1836558..1836788 + hypothetical 34.74 -2 1837037..1837249 + hypothetical 43.96 1837432..1838796 + conserved hypothetical 40.83 -1 1839157..1839663 + conserved hypothetical 42.34 -1 1839826..1841079 + conserved hypothetical 47.99 1841404..1843191 - put. hemolysin activ. HecB 45.32 1843246..1843704 - put. toxin-activating 37.14 -1 1843870..1844184 - hypothetical 31.67 -2 1844196..1844495 - hypothetical 37.57 -1 1844476..1845489 - hypothetical 20.38 -2 1845558..1845974 - hypothetical 45.69 1845978..1853522 - hemagglutinin/hemolysin-rel. 51.35 1854101..1855066 + transposase, IS30 family

Page 35: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

Variance of the Mean %G+C for all Genes in a Genome: Correlation with bacteria’s clonal nature

Page 36: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

Variance of the Mean %G+C for all Genes in a Genome

Is this a measure of clonality of a bacterium?

Are intracellular bacteria more clonal because they are ecologically isolated from other bacteria?

Page 37: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

Pathogenomics Project: Future Developments

• Identify eukaryotic motifs and domains in pathogen genes

• Identify further motifs associated with• Pathogenicity islands• Virulence determinants

• Functional tests for new predicted virulence factors

Page 38: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

Acknowledgements

• Pseudomonas Genome Project: PathoGenesis Corp. (Ken Stover) and University of Washington (Maynard Olsen)

• Membrane proteins: Manjeet Bains, Kendy Wong, Canadian Cystic Fibrosis Foundation

• Animal infection studies: Hong Yan

Page 39: Pathogenomics  Using bioinformatics to focus studies of bacterial pathogenicity

• Pathogenomics group– Ann M. Rose, Yossef Av-Gay, David L. Baillie, Fiona S. L. Brinkman,

Robert Brunham, Stefanie Butland, Rachel C. Fernandez, B. Brett Finlay, Hans Greberg, Robert E.W. Hancock, Steven J. Jones, Patrick Keeling, Audrey de Koning, Don G. Moerman, Sarah P. Otto, B. Francis Ouellette, Ivan Wan. Peter Wall Foundation

www.pathogenomics.bc.ca