Pathogens being Studied(selected examples)

Pathogen Primary Disease

Bordetella pertussis Whooping cough

Borrelia burgdorferi Lyme disease

Campylobacter jejuni Gastroenteritis

Chlamydia pneumoniae Chlamydial pneumonia

Chlamydia trachomatis Chlamydia

Escherichia coli Diarrheal and urinary tract infections

Haemophilus influenzae Upper respiratory infections and Meningitis

Helicobacter pylori Peptic ulcers and gastritis

Leishmania major Leishmaniasis (kala azar)

Listeria monocytogenes Listeriosis

Mycoplasma pneumoniae Mycoplasmal pneumonia

Mycobacterium tuberculosis Tuberculosis

Neisseria gonorrhoeae Gonorrhea

Neisseria meningitidis Meningitis

Plasmodium falciparum Malaria

Pseudomonas aerguinosa Variety of mucosal infections (opportunistic)

Rickettsia prowazekii Epidemic typhus

Salmonella typhi Typhoid fever

Streptococcus pyogenes Strep throat, scarlet fever, necrotizing fasciitis

Treponema pallidum Syphillis

Ureaplasma urealyticum Urethritis

Vibrio cholerae Cholera

Yersinia pestis Plague

ExamplesSelected examples of pathogen proteins with higher than expected similarity to host/eukaryotic proteins:

Yop proteins of Yersinia species

The Yop virulon is an integrated system allowing extracellular Yersinia bacteria to disarm host cells involved in the immune response, to disrupt their communications (or even to induce their apoptosis) by the injection of bacterial effector proteins (for review, see Cornelis, 1998). YopH, a protein-tyrosine phosphatase, is a member of this system and it shares higher than expected similarity to eukaryotic protein-tyrosine phosphatases.

Isoleucyl-tRNA synthetase of Staphylococcus aureus and others

Resistance to mupirocin, a topical antimicrobial agent used against S. aureus, appears to be mediated by amino-acid substitutions in isoleucyl-tRNA synthetase (ITS) which mupirocin normally inactivates. The source of this mutant ITS is not recent random mutation of S. aureus ITS, but rather a plasmid containing an ITS gene that is more similar to eukaryotic ITS than organism phylogeny would predict (Brown et al., 1998). Other bacteria have been identified that contain this mutant ITS (that is similar to eukaryotic ITS), and all of these bacteria share resistance to mupirocin. Based on phylogenetic analysis, Brown et al., propose that a eukaryotic ITS gene was transferred to an unknown bacteria shortly after Eukarya and Archaea divergence, and that this gene was then recently transferred via a plasmid to S. aureus. Since Pseudomonas fluorescens naturally produces mupirocin (as pseudomonic acid), resistance to this compound may have conferred a competitive advantage to specific bacteria.

Enoyl-ACP reductase of Chlamydia trachomatis

C. trachomatis contains a number of “eukaryotic-like” genes involved in functions such as fatty acid biosynthesis. Most of these group phylogenetically with plant proteins (see tree below). Stephens et al. (1998) have proposed that the evolution of chlamydiae as intracellular parasites started with an opportunistic interaction with amoebal hosts, and the protochlamydiae became amoebal parasites or symbionts for a period long enough to acquire the "plant-like" genes, whose origin may actually be amoebal.

An Interdisciplinary Team

Our team comprises an unique group of Bioinformaticians, Evolutionary Theorists/Mathematical Modelers,

Microbiologists, Geneticists and an Ethicist (not all are shown above).

Pathogenomics: An interdisciplinary approach for the study of infectious disease

Fiona S. L. Brinkman 1,2, Yossef Av-Gay 3, David L. Baillie 4, Stefanie Butland 5, Rachel C. Fernandez2, B. Brett Finlay 2,6, Robert E.W. Hancock 2, Christy Haywood-Farmer 7, Steven J. Jones 8, Audrey de Koning 7, Don G. Moerman 7,9, Sarah P. Otto 7, B. Francis Ouellette 5, Iain E. P. Taylor 10, and

Ann M. Rose 1. 1 Dept of Medical Genetics, 2 Dept of Microbiology and Immunology, 3 Dept of Medicine, 6 Biotechnology Laboratory, 7 Dept of Zoology, 9 C. elegans Reverse Genetics Facility, 10 Dept of Botany, University of British Columbia, 4 Dept of Biological Sciences, Simon Fraser University, 5 Centre for Molecular Medicine and Therapeutics

and 8 BC Genome Sequence Centre, Centre for Integrated Genomics, Vancouver, British Columbia, Canada.

Goal

This project brings together a unique combination of UBC researchers and affiliates who, through exchange of new data and ideas, and capitalizing on new genomic and bioinformatic tools, will develop an automated approach to identify previously unrecognized mechanisms of pathogenicity.

Project Summary

We are utilizing bioinformatics tools to identify pathogen genes which interact with their host proteins and pathways. A unique combination of informatics, evolutionary biology, microbiology and eukaryotic genetics is being exploited to identify pathogen genes which are more similar to host genes than expected, and likely to interact with, or mimic, their host’s gene functions. We are building a database of the sequences of these proteins, based on the increasing number of pathogen genomes which have been, or are currently being, sequenced. Candidate functions identified by our informatics approach will be tested in the laboratory (see flow chart) to investigate their role in pathogen infection and host interaction. All information will be eventually made available in a public Pathogenomics Database.

Rationale and Power of the Approach

The processes of microbial pathogenicity at the molecular level are still minimally understood. Genomics and bioinformatics provide powerful new tools for the study of pathogenicity, hence the initiation at UBC by Dr. Julian Davies of a new field, Pathogenomics. The specific approach we are proposing is anchored in the fact that, as part of the infectivity process, many pathogens make use of host cellular processes. We hypothesize that some pathogen genes involved in such processes will be more similar to host genes than would be expected (based on phylogeny). We will identify such genes by applying specific bioinformatic and evolutionary analysis tools to sequenced genome datasets, and further examine such genes in the laboratory (both the pathogen gene and homolgous model host gene). We hypothesize that this approach will reveal new mechanisms of pathogen-host interaction, leading to a deeper understanding of the fundamentals of pathogenicity.

Power of the Approach

•Expression-independent method for identifying possible pathogenicity factors.

•Interdisciplinary team fosters unique ideas and collaborations.

•Automated approach can be continually updated.

•Enables better understanding of both the pathogen gene and homologous host/model host gene.

•Provides insight into horizontal gene transfer events and the evolution of pathogen-host interactions.

•Public database of findings, to be developed, will enable other researchers to capitalize on the findings and promote further collaboration.

Initial screen for candidate genes.Search pathogen proteins against sequence databases. Are the results inconsistent with the phylogeny (i.e. does the protein match more strongly the host, or its relatives, than you would expect?) Use low complexity filtering such as SEG.

Rank candidates.Rank pathogen protein in terms of how much more they resemble their host phyla than their own (e.g. the difference in BLAST score, through phylogenetic tree building, and by identifying unusual codon usage). Is the gene or gene's pathway a usual component of the pathogens phyla? Also rank based on other factors such whether the candidate gene encodes a probable surface-exposed or secreted protein.

Prioritize for further biological study. Has the candidate pathogen gene or a eukaryotic homolog been previously studied biologically? Can a putative function be inferred from its sequence? Is there a C. elegans homolog? Is the pathogen currently studied by UBC functional pathogenomics bacterial group? Has the genetic pathway of the host protein been dissected?

If C. elegans homolog exists: target gene for knockout by knockout facility.

Target for GFP fusion analysis to see when and where the gene is expressed in C. elegans

Analysis of knockout through expression chip, and susceptibility to infection by pathogen.

Database development. Create and maintain a database of pathogen-host interactions. Establish this as a platform for accelerating the study of pathogenicity and the identification of therapeutic drug targets.

Iteratively refine the initial screening methods and candidate ranking.

If pathogen being studied by UBC functional pathogenomics bacterial group: Examine subcellular localization and obtain a knockout of the gene.

If pathogen is not a focus of UBC group: Contact other groups regarding results – instigate collaboration for further study.

Analysis of knockout and gene through expression chip analysis and infectivity in an animal/tissue culture model, and C. elegans model if appropriate

Continually exchange C. elegans gene information: with microbiologists studying homologous pathogen gene

Continually exchange pathogen gene information with collaborators and with eukaryotic geneticists studying homologous gene in C. elegans

AcknowledgementsThis project is funded by the Peter Wall Institute for Advanced Studies, which supports “fundamental, interdisciplinary research and creative activities, which have the potential to result in significant advances to knowledge.”

Above: Small subunit rRNA tree for organisms whose genomes are completed (plus selected reference eukaryotes). Neighbor-joining tree constructed using Ribosomal

Database Project (www.cme.msu.edu/RDP/html) alignments.

Evolutionary significance.Manually inspect candidates. Are these valid cases of horizontal transfer, convergance and co-evolution or are they similar by chance? If horizontal transfer may be involved, when did this transfer occur?

References1. Felsenstein, J.. 1996.

Methods Enzymol. 266: 418-427.

2. Stephens, R.S., S. Kalman, C. Lammel, J. Fan, R. Marathe, L. Aravind, W. Mitchell, L. Olinger, R.L. Tatusov, Q. Zhao, E.V. Koonin, R.W. Davis. 1998. Science 282: 754 – 759.

3. Brown, J.R., J. Zhang, J.E. Hodgson. 1998. Current Bio. 8:R365-R367.

4. Cornelis, G.R. A. Boland, A.P. Boyd, C. Geuijen, M. Iriarte, C. Neyt, M.P. Sory, I. Stainier. 1998. Microbiol Mol Biol Rev 62:1315-1352.

0.1

Aquifex aeolicus

Haemophilus influenza

Escherichia coli

Anabaena

Synechocystis

Chlamydia trachomatis

Petunia x hybrida

Nicotiana tabacum

Brassica napus

Arabidopsis thaliana

Oryza sativa

BACTERIA

0.1

P. furiousis

A. fulgidus

M. jannaschiiM. thermoautotrophicum

A. pernix

Aquifex

T. maritima

M. tuberculosis

S. ynechocystis

B. subtilis

H .influenzae

E. coli

P. aeruginosa

R. prowazekii

H. pyloriT. pallidum B. burgdorferi

M. genitalium

M. pneumoniae

C. trachomatis

C. pneumoniae

P. falciparum

Leishmania

S. cerevisiae

H. sapienM. musculus

C. elegans

EUKARYA

ARCHAEA

Left: Phylogeny of chlamydial enoyl-acyl carrier protein reductase (a protein involved in lipid metabolism) using the neighbor-joining distance method (Felsenstein, 1996). Numbers at forks indicate the number of times out of 100 that the given node was observed.

100

100

100

96

63

64

52

83

99

Genomics and Bioinformatics

Evolutionary Theory

Host FunctionsPathogen Functions