Using informatics to focus bacterial pathogenicity studies
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Using informatics to focus bacterial pathogenicity studies. Using informatics to focus bacterial pathogenicity studies. Goal: Use informatic analyses to generate new testable hypotheses about pathogen protein function and pathogenicity mechanisms Test the hypotheses in the laboratory.

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Using informatics to focus bacterial pathogenicity studies

Using informatics to focusbacterial pathogenicity studies


Using informatics to focus bacterial pathogenicity studies

Using informatics to focusbacterial pathogenicity studies

Goal:

Use informatic analyses to generate new testable hypotheses about pathogen protein function and pathogenicity mechanisms

Test the hypotheses in the laboratory


Need for informatics in biology origins

Need for informatics in biology: origins

  • Gramicidine S (Consden et al., 1947), partial insulin sequence (Sanger and Tuppy, 1951)

  • First codon assignment UUU/phe (Nirenberg and Matthaei, 1961)

  • 3.5 kb RNA bacteriophage MS2 (Fiers et al., 1976) 5.4 kb bacteriophage X174 (Sanger et al., 1977)

  • Early databases: Dayhoff, 1972; Erdmann, 1978


Using informatics to focus bacterial pathogenicity studies

(from the National Centre for Biotechnology Information)


Using informatics to focus bacterial pathogenicity studies

  • Explosion of data

  • 22 of the 33 publicly available microbial genome sequences are for bacterial pathogens

  • Approximately 18,000 pathogen genes with no known function!

  • >95 bacterial pathogen genome projects in progress…


Pathogen informatics

Pathogen Informatics

  • Pseudomonas aeruginosa

    • Three dimensional comparative protein modeling

    • Phylogenetic analysis of gene families

    • Other analyses: Regulatory network complexity

  • Pathogenomics Project

    • Detecting eukaryote:pathogen homologs

    • Detecting pathogenicity islands


Pseudomonas aeruginosa

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


Outer membrane protein oprf

Outer membrane protein OprF

  • Nonspecific porin

  • Required for

    • Maintenance of cell shape

    • Growth in low-osmolarity environments

  • OprF- clinical mutant with multiple antimicrobial resistance being characterized

  • Adhesin in plant colonizing Pseudomonas species

  • Proposed vaccine component


Using informatics to focus bacterial pathogenicity studies

Gram Negative Cell Envelope

PORE

LPS

PORIN

+

+

Mg

Outer

membrane

Peptidoglycan

Periplasm

Cytoplasmic

membrane


Using informatics to focus bacterial pathogenicity studies

Structure of the outer membrane protein A transmembrane domain

Pautsch and Schulz (1998).

Nature Structural Biology 5:1013-1017

No channel formation detected


Using informatics to focus bacterial pathogenicity studies

OprF and OmpA share only 15% identity

OprF 1 -QGQNSVEIEAFGKRYFTDSVRNMKN-------ADLYGGSIGYFLTDDVELALSYGEYH

OmpA 1 APKDNTWYTGAKLGWSQYHDTGLINNNGPTHENKLGAGAFGGYQVNPYVGFEMGYDWLG

* * * * ** * *

OprF 52 DVRGTYETGNKKVHGNLTSLDAIYHFGTPGVGLRPYVSAGLA-HQNITNINSDSQGRQQ

OmpA 60 RMPYKGSVENGAYKAQGVQLTAKLGYPIT-DDLDIYTRLGGMVWRADTYSNVYGKNHDT

* * * * * * * *

OprF 110 MTMANIGAGLKYYFTENFFAKASLDGQYGLEKRDNGHQG--EWMAGLGVGFNFG

OmpA 118 GVSPVFAGGVEYAITPEIATRLEYQWTNNIGDAHTIGTRPDNGMLSLGVSYRFG

* * * * *** **


Using informatics to focus bacterial pathogenicity studies

Model of the

N-terminus of OprF based on OmpA

Brinkman, Bains and Hancock (2000). Journal of Bacteriology 182:5251-5255


Using informatics to focus bacterial pathogenicity studies

OprF model

(yellow and green) aligned with the crystal structure of OmpA (blue)

Many residues are in the same three dimensional environment, though on different strands


Using informatics to focus bacterial pathogenicity studies

OprF and OmpA similarity

OprF 1 -QGQNSVEIEAFGKRYFTDSVRNMKN-------ADLYGGSIGYFLTDDVELALSYGEYH

OmpA 1 APKDNTWYTGAKLGWSQYHDTGLINNNGPTHENKLGAGAFGGYQVNPYVGFEMGYDWLG

* * * * ** * *

OprF 52 DVRGTYETGNKKVHGNLTSLDAIYHFGTPGVGLRPYVSAGLA-HQNITNINSDSQGRQQ

OmpA 60 RMPYKGSVENGAYKAQGVQLTAKLGYPIT-DDLDIYTRLGGMVWRADTYSNVYGKNHDT

* * * * * * * *

OprF 110 MTMANIGAGLKYYFTENFFAKASLDGQYGLEKRDNGHQG--EWMAGLGVGFNFG

OmpA 118 GVSPVFAGGVEYAITPEIATRLEYQWTNNIGDAHTIGTRPDNGMLSLGVSYRFG

* * * * *** **


Using informatics to focus bacterial pathogenicity studies

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


Using informatics to focus bacterial pathogenicity studies

Voltage

Current

Source

Amplifier

Protein

Planar

Bathing

Bilayer

Solution

Membrane

Planar Lipid Bilayer Apparatus


Using informatics to focus bacterial pathogenicity studies

The N-terminus of OprF forms channels

in a lipid bilayer membrane


Upstream of oprf is a probable sigma factor gene sigx

Upstream of OprF is a probable sigma factor gene, sigX

sigX

oprF

Promoter

Transcription terminator


Using informatics to focus bacterial pathogenicity studies

Disruption of sigX reduces expression of OprF

  • Marker

  • Wildtype

  • sigX- mutant

  • oprF- mutant

P. aeruginosa P. fluorescens


Using informatics to focus bacterial pathogenicity studies

No SigX expression:

sigX

oprF

SigX expression:

sigX

oprF


18 ecf sigma factors in the p aeruginosa genome

18 ECF sigma factors in the P. aeruginosa genome


Using informatics to focus bacterial pathogenicity studies

Percent Regulators as a Function of Genome Size

10

13

Specialized environments

Free-living

8

12

11

6

Regulators (%)

8

4

10

9

2 3

1

6

7

2

4 5

0

0

1000

2000

3000

4000

5000

6000

7000

Number of Genes

Genomes represented: 1, Mycoplasma genitalium; 2, Chlamydia trachomatis; 3, Treponema pallidum; 4, Borrelia burgdorferi; 5, Chlamydia pneumoniae; 6, Helicobacter pylori ---; 7, Helicobacter pylori---; 8, Haemophilus influenzae; 9, Neisseria meningitidis; 10, Mycobacterium tuberculosis; 11, Bacillus subtilis; 12, Escherichia coli; 13, Pseudomonas aeruginosa.


Using informatics to focus bacterial pathogenicity studies

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

Approximately 150 OMPs predicted including three large paralogous families:

  • OprM Familyof putative Efflux and Type I

    secretion proteins(18 members)

  • OprD Familyof putative Amino acid, Peptide and

    Aromatic compound transporters (19 members)

  • TonB Familyof putative iron-siderophore

    receptors (34 members)


Using informatics to focus bacterial pathogenicity studies

OprJ

OprM

OpmJ

OpmB

OpmA

OprM

Family

(Multidrug

Efflux?)

OpmG

OpmE

OpmI

OprN

OpmD

OpmQ

AprF

OpmM

Protein

Secretion?

OpmN

OpmH

TolC

OpmK

OpmL

OpmF

0.1


Using informatics to focus bacterial pathogenicity studies

OprM structural model based on TolC


Using informatics to focus bacterial pathogenicity studies

OprM structural model based on TolC


Using informatics to focus bacterial pathogenicity studies

OprM structural model based on TolC


Future developments

Future Developments

  • Modeling of other outer membrane proteins in Neisseria species.

  • Developing a better algorithms for secondary structure prediction


Pathogenomics

Pathogenomics

Goal:

Identify previously unrecognized mechanisms of microbial pathogenicity using a unique combination of informatics, evolutionary biology, microbiology and genetics.


Pathogenicity

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.

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


Eukaryotic like pathogen genes

Eukaryotic-like pathogen genes

- YopH, a protein-tyrosine phosphatase, of Yersinia pestis

- Enoyl-acyl carrier protein reductase (involved in lipid metabolism) of Chlamydia trachomatis

Aquifex aeolicus

96

Haemophilus influenza

100

Escherichia coli

Anabaena

100

Synechocystis

100

Chlamydia trachomatis

63

Petunia x hybrida

64

Nicotiana tabacum

83

Brassica napus

99

Arabidopsis thaliana

0.1

52

Oryza sativa


Pathogens

Pathogens

AnthraxNecrotizing fasciitis

Cat scratch diseaseParatyphoid/enteric fever

Chancroid Peptic ulcers and gastritis

Chlamydia Periodontal disease

CholeraPlague

Dental cariesPneumonia

Diarrhea (E. coli etc.)Salmonellosis

DiphtheriaScarlet fever

Epidemic typhusShigellosis

Mediterranean feverStrep throat

Gastroenteritis Syphilis

GonorrheaToxic shock syndrome

Legionnaires' disease Tuberculosis

LeprosyTularemia

Leptospirosis Typhoid fever

Listeriosis Urethritis

Lyme disease Urinary Tract Infections

Meliodosis Whooping cough

Meningitis Hospital-acquired infections


Pathogens1

Pathogens

Chlamydophila psittaci Respiratory disease, primarily in birds

Mycoplasma mycoides Contagious bovine pleuropneumonia

Mycoplasma hyopneumoniae Pneumonia in pigs

Pasteurella haemolytica Cattle shipping fever

Pasteurella multicoda Cattle septicemia, pig rhinitis

Ralstonia solanacearum Plant bacterial wilt

Xanthomonas citri Citrus canker

Xylella fastidiosa Citrus variegated chlorosis

Bacterial wilt


Interdisciplinary group

Interdisciplinary group

  • 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


Approach

Approach

Screen for candidate genes.

Search pathogen genes against sequence databases. Identify those with eukaryotic similarity/motifs

  • Rank candidates.

  • how much like host protein?

  • info available about protein?

Modify screening method /algorithm

Evolutionary significance.

- Horizontal transfer?

- Similar by chance?

Prioritize for biological study.

- Previously studied biologically?

- Can UBC microbiologists study it?

- C. elegans homolog?


Bacterium eukaryote horizontal transfer

Bacillus subtilis

Escherichia coli

Salmonella typhimurium

Staphylococcua aureus

Clostridium perfringens

Clostridium difficile

Trichomonas vaginalis

Haemophilus influenzae

Acinetobacillus actinomycetemcomitans

0.1

Pasteurella multocida

Bacterium Eukaryote Horizontal Transfer

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


N acetylneuraminate lyase role in pathogenicity

N-acetylneuraminate lyase – role in pathogenicity?

  • Pasteurellaceae

  • Mucosal pathogens of the respiratory tract

  • T. vaginalis

  • Mucosal pathogen, causative agent of the STD Trichomonas


N acetylneuraminate lyase sialic acid lyase nana

N-acetylneuraminate lyase (sialic acid lyase, NanA)

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

Sialidase

Free sialic acid

Transporter

Free sialic acid

NanA

N-acetyl-D-mannosamine

+ pyruvate

Involved in sialic acid metabolism

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

Role in Trichomonas: ?


Eukaryote bacteria horizontal transfer

Eukaryote Bacteria Horizontal Transfer?

Rat

0.1

GMP reductase of E. coli is 81% similar to the corresponding enzyme studied in humans and rats

Role in virulence not yet investigated

Human

Escherichia coli

Caenorhabditis elegans

Pig roundworm

Methanococcus jannaschii

Methanobacterium thermoautotrophicum

Bacillus subtilis

Streptococcus pyogenes

Aquifex aeolicus

Acinetobacter calcoaceticus

Haemophilus influenzae

Chlorobium vibrioforme


Eukaryote bacteria horizontal transfer1

Hypocrea jecorina EGLII

Trichoderma viride EGL2

Penicillium janthinellum EGL2

Macrophomina phaseolina EGL2

Cryptococcus flavus CMC1

Ralstonia solanacearum egl

Humicola insolens CMC3

Humicola grisea CMC3

Aspergillus aculeatus CMC2

Aspergillus nidulans EGLA

Macrophomina phaseolina egl1

Aspergillus aculeatus CEL1

Aspergillus niger EGLB

Vibrio species manA

Eukaryote Bacteria Horizontal Transfer?

Ralstonia solanacearum cellulase (ENDO-1,4-BETA-GLUCANASE) is 56% similar to endoglucanase present in a number of fungi.

Demonstrated virulence factor for plant bacterial wilt


Functional studies

Functional studies

Prioritized candidates

Study function of gene.

Investigate role of bacterial gene in disease: Infection study in model host

Study function of similar gene in model host, C. elegans.

Contact other groups for possible collaborations.

C. elegans

DATABASE

World Research Community


Pathogenicity islands

Pathogenicity Islands

  • Virulence genes commonly in clusters

  • Associated with

    • tRNA sequences

    • Transposases, Integrases and other mobility genes

    • Flanked by repeats


Using informatics to focus bacterial pathogenicity studies

G+C Analysis: 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


Using informatics to focus bacterial pathogenicity studies

Neisseria meningitidis serogroup B strain MC58

Mean %G+C: 51.37 STD DEV: 7.57

%G+C SD Location Strand Product

37.22 -1 1831577..1832527 + pilin gene inverting

39.95 -1 1834676..1835113 + VapD-related

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


G c of orfs analysis of variance

%G+C of ORFs: Analysis of Variance

  • %G+C variance is similar within a given species

  • Low %G+C variance correlates with an intracellular lifestyle for the bacterium and a clonal nature (P = 0.004)

    • Neisseria meningitidis +/- 7%

    • Chlamydia species+/- 2%

  • Intracellular bacteria ecologically isolated?


Future developments1

Future Developments

  • Identify eukaryotic motifs and domains in pathogen genes

  • Identify further motifs associated with

    • Pathogenicity islands

    • Virulence determinants

  • Functional tests for new potential virulence factors

  • www.pathogenomics.bc.ca


Informatics as a focus

Informatics as a focus

  • Outer membrane protein modeling: Focus mutational studies and studies of surface exposed sequences

  • Phylogenetic analyses: Focus study of gene mutants under certain environmental conditions

  • Other analyses - Regulatory network complexity: Change focus of regulation studies

  • Eukaryote:pathogen homologs: Focus identification of “mimics”

  • Pathogenicity islands: Focus identification of recently obtained virulence determinants


Acknowledgements

Acknowledgements

  • Pathogenomics group: Ann Rose, Steven Jones, Ivan Wan, Hans Greberg, Yossef Av-Gay, David Baillie, Bob Brunham, Stefanie Butland, Rachel Fernandez, Brett Finlay, Patrick Keeling, Audrey de Koning, Sarah Otto, Francis Ouellette, Peter Wall Institute

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

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

  • Bob Hancock


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