Transcript Powerpoint file - Centre for Microbial Diseases and Immunity

Pathogenomics: An interdisciplinary approach for the study of infectious disease
Fiona S. L. Brinkman 1,2, Steven J. Jones 3, Ivan Wan3, Yossef Av-Gay 4, David L. Baillie 5, Robert C. Brunham 6, Stefanie Butland 7, Rachel C.
Fernandez 2, B. Brett Finlay 2,8, Hans Greberg1, Robert E.W. Hancock 2, Christy Haywood-Farmer 9, Patrick Keeling 10, Audrey de Koning 9, Don G.
Moerman 9,11, Sarah P. Otto 9, B. Francis Ouellette 7, Iain E. P. Taylor 10, and Ann M. Rose 1.
1 Dept of Medical Genetics, 2 Dept of Microbiology and Immunology, 4 Dept of Medicine, 8 Biotechnology Laboratory, 9 Dept of Zoology, 11 C. elegans Reverse
Genetics Facility, 10 Dept of Botany, University of British Columbia, 5 Dept of Biological Sciences, Simon Fraser University, 7 Centre for Molecular Medicine and
Therapeutics, 6 UBC Centre for Disease Control and 3 BC Genome Sequence Centre, Centre for Integrated Genomics, Vancouver, British Columbia, Canada.
www.pathogenomics.bc.ca
Trends in the Bioinformatic/Evolutionary Analysis
Project Summary
A 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
Genomics and bioinformatics provide powerful new tools for the study of
pathogenicity, hence the initiation of a new field, Pathogenomics. Our
approach is anchored in the fact that, as part of the infection 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 or motifs). We are attempting to
identify such genes by applying specific bioinformatic and evolutionary
analysis tools to sequenced genome datasets, and further examining such
genes in the laboratory (both the pathogen gene and a homologous model
host gene). We hypothesize that this approach will reveal new mechanisms of
pathogen-host interaction.
Power of the Approach
•While our primary focus is to identify new genes or pathways involved in virulence, our approach has
also identified the strongest cases of lateral gene transfer between bacteria and eukaryotes identified to
date. We have also found that most cases of probable recent cross-domain gene transfer involve
movement of a bacterial gene to a unicellular eukaryote. It has previously been proposed that such
eukaryotes may obtain bacterial genes through ingestion of bacteria (the “you are what you eat”
hypothesis; 1).
•Provides insight into horizontal gene transfer events and the evolution of
pathogenicity and pathogen-host interactions.
Initial screen for candidate genes.
Search pathogen proteins against sequence and motif databases.
Are the results inconsistent with phylogeny (i.e. does the protein
match more strongly the host, or its relatives, than expected?). Are
there eukaryotic protein motifs in the pathogen protein? Filter out
closely related bacteria from the search to identify eukaryotic hits to
the pathogen proteins that may not have been previously detected.
An example of a eukaryotic-like bacterial gene in our database:
Iteratively refine the
initial screening
methods and
candidate ranking.
• BC Genome Sequence
Centre
• Centre for Molecular
Medicine and Therapeutics
• Dept of Zoology
• Dept of Botany
• Canadian Institute for
Advanced Research
Coordinator
Pathogen Functions
Host Functions
•
•
•
•
• Dept. Medical Genetics
• C. elegans Reverse Genetics
Facility
• Dept. Biological Sciences
SFU
Dept. Microbiology
Biotechnology Laboratory
Dept. Medicine
BC Centre for Disease
Control
Human
Escherichia coli
Caenorhabditis elegans
Methanococcus jannaschii
Methanobacterium thermoautotrophicum
Bacillus subtilis
Prioritize for further biological study.
Has the candidate pathogen gene or a eukaryotic homolog been
previously studied biologically? (Prioritize unstudied genes) Is there a
C. elegans homolog? (See below) Is the pathogen currently studied
by the UBC pathogenomics bacterial group?
Acinetobacter calcoaceticus
Haemophilus influenzae
Chlorobium vibrioforme
Neighbor-joining tree of GMP reductases and
related proteins. Blue=Bacteria, Red=Archaea,
and Green=Eukarya
An Interdisciplinary Team
Evolutionary Theory
0.1
Aquifex aeolicus
•Public database of findings, to be developed, will enable other researchers to
capitalize on the findings and promote further collaboration.
Bioinformatics
Rat
Streptococcus pyogenes
•Expression-independent method for identifying possible pathogenicity
factors.
If C. elegans homolog exists:
target gene for knockout by
knockout facility.
Analysis of knockout through
expression chip, and
susceptibility to infection by
pathogen.
Target for GFP fusion analysis
to see when and where the
gene is expressed in C.
elegans
Continually exchange C.
elegans gene information:
with microbiologists studying
homologous pathogen gene
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 pathogen gene
information with collaborators and with
eukaryotic geneticists studying
homologous gene in C. elegans
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.
NanA of the pathogenic Pasteurellaceae
bacteria is 92-95% similar to NanA of the
eukaryotic pathogenic protozoan
Trichomonas vaginalis.
Example: Relationship between GMP
reductase of E. coli and Metazoans
Evolutionary significance.
Manually inspect candidates. Are these valid cases of horizontal
transfer, co-evolution or are they similar by chance? If horizontal
transfer may be involved, when did this transfer occur?
•Automated approach can be continually updated.
N-acetylneuraminate lyase (NanA) is
involved in sialic acid metabolism and is
used by some bacteria to parasitize the
mucous membranes of animals for
nutritional purposes.
•A control: Our method identifies all previously reported Chlamydia trachomatis eukaryotic-like genes.
Pig roundworm
•Interdisciplinary team fosters unique ideas and collaborations.
The first gene identified was not a
eukaryotic-like bacterial gene, but rather
a bacterial-like eukaryotic gene.
However, its possible role in
pathogenicity make it of interest.
•G+C analysis of genome ORFs, used to identify pathogenicity islands, revealed the following trend:
Low variance of the mean G+C of ORFs for a given genome correlates with an intracellular lifestyle for
the bacterium and a clonal nature (Two-tailed P value of 0.004, for a nonparametric correlation). This
variance is similar within a given species. G+C variance may therefore be a useful marker for
investigating the clonality of bacteria. Its relationship with intracellular lifestyle may reflect the ecological
isolation of intracellular bacteria, as was previously proposed to explain the lack of chromosome
rearrangement for Chlamydia species (2).
Rank candidates.
Rank pathogen protein by how much more they resemble their host
phyla than their own (e.g. BLAST score, phylogenetic distance score,
tree building, unusual motifs, unusual codon usage).
•Enables better understanding of both the pathogen gene and homologous
host/model host gene.
First Gene Identified - Evidence of gene
transfer from bacterial to protozoan
pathogen
Guanosine monophosphate
reductase of E. coli is 81%
similar to the corresponding
enzyme studied in humans
and rats, and shares a
significant phylogenetic
relationship with metazoans
(left). A similar protein has
been identified in other
gamma subdivision
proteobacteria including
other enterobacteriaceae
and Vibrio cholerae (from
unfinished genome
projects; not shown),
suggesting a cross-domain
gene transfer may have
occurred before divergence
of these gamma
proteobacteria. Its role in
virulence has not been
investigated.
This represents the strongest evidence known to date of
horizontal gene transfer between a bacteria and eukaryote.
Neither nanA from the Pasteurellaceae Haemophilus
influenzae, Pasteurella multocida, and Actinobacillus
actinomycetemcomitans, nor nanA in T. vaginalis, have been
investigated for their role in pathogenicity though some are
well studied and are involved in a pathway implicated in
virulence (3,4, 5).
Bacillus subtilis
Escherichia coli
Neighbor-joining
distance matrix tree of
known and probable
N-acetylneuraminate
lyases, rooted by
Bacillus subtilus
dihydrodipicolinate
synthase.
Salmonella typhimurium
Staphylococcua aureus
Clostridium perfringens
Clostridium difficile
Trichomonas vaginalis
Haemophilus influenzae
PhyloBLAST – a program to aid analysis
PhyloBLAST compares your protein sequence to a SWISSPROT/
TREMBL database using BLAST2 and then allows you to perform
user-defined phylogenetic analyses based on user-selected proteins
listed in the BLAST output. PhyloBLAST was initially developed as a
tool specifically for this project, but is now available on the internet as
a beta version at: www.pathogenomics.bc.ca/phyloBLAST
Some Features
- Organism information and phylogenetic distance measures are
added to the BLAST output and subsequent phylogenetic trees
- You may select sequences (in the list of BLAST hits) for further
analysis, by simply clicking boxes next to each sequence of interest.
Analyses vary from obtaining a FASTA file of the sequences,
ClustalW alignment, or user-defined phylogenetic trees (currently
based on PHYLIP programs).
-All programs for tree construction are linked together, for ease of
use, but have full options for more expert use. Results may be
obtained by email or the webpage.
Acinetobacillus actinomycetemcomitans
0.1
Pasteurella multocida
Acknowledgements
This project is funded by the Peter
Wall Institute for Advanced Studies.
References
1.
Doolittle, WF. 1998. Trends Genet.
14:307-311.
2.
Read TD, Brunham RC, Shen C, Gill
SR. et al. 2000. Nucleic Acids Res.
28:1397-1406.
3.
Meysnick KC, Dimock K, Gerber GE.
1996. Mol. Biochem. Parasitol.
76:289-292.
4.
Lilley GG, Barbosa JA, Pearce LA.
1998. Protein Expr. Purif. 12:295-304.
5.
Muller HE, Mannheim W. 1995. Int. J.
Med. Microbiol. Virol. Parasitol.
Infect. Dis. 283:105-114.