Burkholderia pseudomallei Genome Analysis Project

The National University of Singapore Dept of Biochemistry in collaboration with the Singapore National Cancer Centre Omniarray Group, Singapore General Hospital, the Defence Medical Research Institute (DMRI) Singapore, and others, have since 1991 been applying molecular biology techniques to the study of the Gram-negative pathogen, Burkholderia pseudomallei (also previously known as Pseudomonas pseudomallei).

Owing to the lack of genome sequencing facilities in Singapore, we welcome the Sanger Institute's initiative to sequence the entire genome of B. pseudomallei. As the genome sequencing is finishing soon, the database of the contigs is housed at the Sanger Institute and gap closure is imminent as of early 2002.

To capitalize on the sequence information with regards to our understanding of virulence factors and pathogenicity islands, we are currently planning on a collaborative effort with Malaysia and Thailand to analyse the sequence and correlate the analysis with our research work.

In particular, a Type III secretion system (TTSS) which has been identified in Bpm (AF74878) although its function has not been demonstrated. It spans a region 29,814 bp and contains 25 ORFs. Eleven of these have homology to genes in TTSS of other pathogens, whilst 14 remain unknown.

This project aims to evaluate the feasibility of predicting putative protein function/properties from translated amino acids of these ORFs. This will enable us to make educated guesses and focus on the more likely candidates to mutate and study effect on virulence.

Secondly, the project aims to analyse

• codon usage differences
• GC content and G+C genome variations
• genomic signature divergences (dinucleotide bias)
• extremes of codon bias
• anomalies of amino acid usage (See Karlin, Trends in Microbiol 2001, 9:335)
• tRNA and tRNA-like loci as integration sites for foreign sequences (see Ochman et al, Nature)

Concurrently, as these pathogenicity islands are being predicted, Chua et al are carrying out site-specific transposon mediate mutagenesis studies that may identify pathogenicity attenuation in the organism as tested in C. elegans infection models.

By cross-correlating pathogenicity prediction methods with in vivo testing in worm models, we can have a gold standard of verifying the accuracy of prediction methods which may lead to the improvement of any such prediction methods.

Project Objectives

• Phase 1: Creation of a database of all known, putative and predicted Bpm genes
  • Search of all genomic, proteomic, pathway and other databases for submitted Bpm genes
  • Cross correlation of these genes to the Bpm sequenced genome
  • Provide the architecture for archival of analysed and predicted genes in Phase 2
  • Initiate basic annotation of Bpm genes, if possible cooperatively with other research groups carrying out wet lab research in B. pseudomallei.

• Phase 2: Analysis of the Bpm genomic data from Sanger Institute to identify putative new genes
  • To make use of known techniques in sequence analysis to identify putative homologs in Bpm based on known sequences in other related genomes
  • To automate the process of identifying new genes as databases get updated regularly
  • To organise the identified genes into metabolic, signal and other pathways

• Phase 3: To make use of the Karlin concepts to identify pathogenicity islands (PAIs) in the Bpm genome for future laboratory analysis
  Bacterial Bioinformatics Website on Pathogenicity Islands
  • To identify regions of G+C anomalies in the genome and search for putative PAIs
  • To identify regions of extremes of codon usage bias in the genome
  • To identify regions of anomalies of amino acid usage in the genome
  • To implement and use of other Karlin criteria to identify putative PAIs
  • To analyse the Type III Secretory System for PAI characteristics
  • See Edward JennerInstitute's Bacterial Bioinformatics Website

• Phase 4: To make use of BioGRID computational facilities and bioinformatics workflow integration systems to enhance the automation, speed and reproducibility in the achievement of the bioinformatics objectives
  • Carry out preliminary comparisons of Bpm genome with genomes of other organisms, including related species.
  • Other possible related genomes such as B. cepacia, B. mallei and B. fungorum.
    1. Sanger Institute B cepacia Genome Sequencing Project
    2. B cepacia sequences for FTP download
    3. Joint Genome Institute's B. fungorum Genome Project
    4. B fungorum sequences for FTP download

Project Participants

• Dr Chua Kim Lee (Project Leader, Dept of Biochemistry) (bchckl@nus.edu.sg)
• Dr Tan Tin Wee (Bioinformatics Centre, Dept of Biochemistry and BIC) (tinwee@bic.nus.edu.sg)
• Chen Kang (Biochemistry Honours Student, Dept of Biochemistry) (sci81486@nus.edu.sg)
• Huang Bo (Biochemistry Honours Student, Dept of Biochemistry) (sci80006@nus.edu.sg)
• Kwan Cai Nan (Biochemistry Honours Student, Dept of Biochemistry) (sci80505@nus.edu.sg)
• Dr Shoba Ranganathan (Bioinformatics Centre, Dept of Biochemistry and Dept of Biological Sciences) (shoba@bic.nus.edu.sg)
• Manisha B (Graduate Student, Bioinformatics Centre, Dept of Biochemistry) (manisha@bic.nus.edu.sg)

Other Related Projects

• Transposon mediated mutagenesis and systematic screening of B.pseudomallei Virulence factors, Dept of Biochemistry, NUS
• B. pseudomallei infection model in C elegans Omniarray group, National Cancer Centre, Singapore
• T Cell responses in murine B.pseudomallei models, Dept of Biochemistry, NUS

Project URLs

• in progress

Software Tools

• Unix Shell Programming
• Apache Webserver
• Perl CGI programming
• MySQL relational database system

• AceDB (Phase 2)
  A genome database system developed since 1989 which provides a custom database kernel, with a non-standard data model designed specifically for handling scientific data flexibly, and a graphical user interface with many specific displays and tools for genomic data. AceDB is used both for managing data within genome projects, and for making genomic data available to the wider scientific community.

• EMBOSS suite (ftp) EMBOSS is a package of high-quality FREE Open Source software for sequence analysis

• EMBASSY suite : EMBASSY packages are those which have been integrated with the rest of the EMBOSS package

• PHYLIP suite a free package of programs for inferring phylogenies

• HMMER suite Profile hidden Markov models (profile HMMs) can be used to do sensitive database searching using statistical descriptions of a sequence family's consensus. HMMER is a freely distributable implementation of profile HMM software for protein sequence analysis.

• Artemis: Artemis is a free DNA sequence viewer and annotation tool that allows visualization of sequence features and the results of analyses within the context of the sequence, and its six-frame translation.

• Java programming
• BioGrid Computing
• KooP Workflow Integration

Useful Sites

• Sanger Institute Microbial Genome Sequencing initiative
• Sanger Institute : Burkholderia pseudomallei Genome Sequencing Project
• Burkholderia pseudomallei genome ongoing sequencing data FTP site
• Wellcome Trust Beowulf Genomics project
  Sequencing Info from http://www.wellcome.ac.uk/en/1/biovengensanseqbac.html#11
  

  		    Burkholderia pseudomallei
  		    Genome size: ~6 Mb
                      Sequencing funded by: Beowulf Genomics
                      Status: In progress (as of 20 Feb 2002)
  
                           Burkholderia pseudomallei (previously
                           called Pseudomonas pseudomallei)
                           causes melioidosis, a serious disease
                           that affects populations in
                           South-East Asia and Australia. 
                           Symptoms vary from localized
                           abscesses to pneumonia and
                           septicaemia. 
                           The sequencing project is being
                           carried out in collaboration with Dr
                           Rick Titball of the Defence Evaluation
                           Research Agency, Porton Down, and
                           Dr Ty Pitt of the Central Public
                           Health Laboratory.
  

• Contact Persons on B. pseudomallei sequencing: Bart Barrell (barrell@sanger.ac.uk) or Julian Parkhill (parkhill@sanger.ac.uk).

Useful References

• Karlin, S (2001) Detecting anomalous gene clusters and pathogenicity islands in diverse bacterial genomes. Trends in Microbiology 2001, 9:335ff.
• Cabello, F (1997) Pathogenicity islands: important but not unique factors contributing to Salmonella virulence [letter] Trends Microbiol 1997, 5:431-432.
• Hacker, J et al (1997) Pathogenicity islands of virulent bacteria: structure, function and impact on microbial evolution. Mol Microbiol 1997, 23:1089-1097.
• Ochman, H and Groisman, EA (1996) Distribution of pathogenicity islands in Salmonella spp. Infect Immun 1996, 64:5410-5412.
• Groisman, EA and Ochman, H (1996) Pathogenicity islands: bacterial evolution in quantum leaps. Cell 1996, 87:791-794.
• Lee, CA (1996) Pathogenicity islands and the evolution of bacterial pathogens. Agents Dis 1996, 5:1-7.
• Ochman, H, Lawrence, JG and Groisman, EA (2000) Lateral gene transfer and the nature of bacterial innovation (Review) Nature 2000, 405: 299-304. (18 May 2000)
• Sweigard JA and Ebbole DJ (2001) Functional analysis of pathogenicity genes in a genomics world. Curr Opin Microbiol 2001 4(4):387-392.