TBResist: A Global Consortium for Whole Genome Sequencing of Drug Resistant TB. IOM/IMCAS Meeting Beijing January 16-18, 2013

Transcription

1 TBResist: A Global Consortium for Whole Genome Sequencing of Drug Resistant TB IOM/IMCAS Meeting Beijing January 16-18, 2013

2 Past and Future of TB Research I. Comas and S. Gagneux PLoS Pathog October: 5(10):e

3 Reality: Control of DRTB will remain a formidable challenge for decades to come

4 High-Throughput Genomic Technologies Past Research has Contributed to Understanding of TB s: - epidemiology - comparative genomics - evolution - host-pathogen interaction

5 Contributions of TB Molecular Technology Dogmas Driving the Field in 1990 s: 1. Differences in clinical manifestation primarily driven by host variables and environment vs bacterial factors DRTB mostly acquired resulting from inappropriate treatment or lack of patient compliance 2. TB mainly consequence of reactivation of latent infection vs ongoing dx transmission 3. Mixed infections and exogenous with different strains unlikely 4. DRTB not likely in populations or countries where little TB treatment had taken place

6 Major Contribution of TB Genotyping: 1. Enabled pragmatic public health efforts, ie detecting dx outbreaks and ongoing TB transmission 2. Allowed differentiation between patients who relapsed due to tx failure and those reinfected with a different strain.first rigorous evidence that previous exposure to TB does not protect against reinfection and dx huge implications for vaccine development

7 Contributions of Whole Genome Sequences and Comparative Genomics Genome plasticity Insights into evolution and geographic distribution Possible association between strain genomic content and disease severity clinical genotypes difficult to standardize thus role of genotype in dx severity remains controversial

8 Elimination of Drug Resistant TB Will Require: Integration of epidemiology with systems biology----- systems epidemiology

9 TBResist: A Global Consortium for Whole Genome Sequencing of Drug Resistant TB To better address DRTB, co-morbidities with HIV and diabetes To better understand latency To better predict future disease trajectory To develop new tools to control disease better to reverse the global trends Only possible by developing huge databases of 1000s of whole genomes from well characterized patient isolates from diverse geographies and patient populations combined with clinical phenotypes and application of Comas and Gagneux systems epidemiology

10 TBResist Consortium Founding Members Laboratory Information for Public Health Excellence ( LIPHE ) National Institute of Allergy and Infectious Diseases (NIAID) of the U.S. National Institutes of Health Broad Institute of the Massachusetts Institute of Technology and Harvard (Broad Institute)* International Business Machines Corporation ( IBM )

11 TBResist Consortium Members Kwazulu-Natal Research Institute for Tuberculosis and HIV (K-RITH) Medical Research Council of South Africa Institute of Microbiology of the Chinese Academy of Sciences China Centers for Disease Control (China CDC) BGI Zunyi Medical College of China Foundation Merieux Karolinska Institute Harvard Medical School/Boston s Children Hospital Stanford University School of Medicine Academia Sinica (the National Academy of Taiwan) Russian Academy of Sciences (RAS) Burroughs Wellcome Fund

12 TBResist Members Under Discussion US CDC Emory University Eli Lilly Foundation

13 Interdisciplinary Team Critical for Success To make sense out of the data deluge and heterogeneity in omics data, the biologists will have to rely on a mathematically and statistically robust analytical framework: Analytical team: Dale Nordenberg, Novosana; IBM informatics team (EUResist for HIV); Atul Butte, Stanford;Gil Alterovitz; Bruce Berin and Ashlee Earl, Broad Institute; Alison Yao and PATRIC NIAID; Patrick Tao and colleagues BGI

14 TB Metabolomics Member Gary Schoolnik, Stanford University NIAID NIH TB omics Consortium

15 TBResist Sequencing Centers Broad Institute of the Massachusetts Institute of Technology and Harvard (Broad Institute) BGI Academia Sinica (the National Academy of Sciences of Taiwan) Russian Academy of Sciences (RAS)

16 Russian Academy of Sciences Vavilov Inst of General Genetics, Russian Academy of Sciences* Institute of Physical Chemical Medicine of Russian National Medical University Institute of Phthysiopulmonology, Ministry of Health and Social Dev, St. Petersberg 3 clinical labs led by Shipulin, Erokin RU Gov. Funding: 30 genomes completed; and up to 500 by 2017 plus 1000s SNP

17 IBM Contributions EUResist (HIV) Data Mining Tool 3 bioinformaticians Equipment to KRITH

18 Guiding Principle: Rapid Data Sharing Among Members and Public All raw sequence data, incl RNA-Seq data submitted as rapidly as possibe to apropriate public databases Clinical and other metadata: each participant must have a data sharing and release plan at for membership. Desire to release as soon as possible but can be delayed for up to 9mos or upon publication