Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
The Global Problem of Extensively Drug Resistant TB Peter M. Small, MD Institute for Systems Biology Bill and Melinda Gates Foundation February 17, 2008 TB: A huge problem No estimate Very low levels Low levels High levels Very high levels Some quick facts 1/3 of world infected Most of the prevalent infections are in Asia Estimated TB Incidence rates 8.8 million new cases Most of the new cases are in Africa 1.6 million deaths Estimated Numbers of New TB Cases 750,000 in PLWA Sub-Saharan Africa has the most TB/HIV 450,000 MDR (Multi Drug Resistance) 25,000 XDR (Extreme Drug Resistance) HIV Prevalence in New TB Cases 2 What Is The Future of MDR / XDR-TB? • Public Health is important • What about Biology ? • Is drug resistance costly (to the bug) ? • Studies in E. coli suggest “fitness cost” • MDR / XDR-TB associated with HIV • Are XDR strains less “fit” ? Predictions from Mathematical Models • Assuming universal fitness cost: “MDR-TB will remain localized problem” • Assuming heterogeneous fitness: “MDR-TB could outcompete regular TB” • There is a lack of empirical data! • Molecular epidemiological studies inconclusive Our Hypothesis The relative fitness of drug-resistant MTB is heterogeneous: 1. Specific DR mutation(s) 2. Specific strain genetic background 3. Compensatory evolution Fitness: The Experimental Approach RIFS RIFR Conditioning Competition no RIF CFU measurements @ baseline & endpoint RIF 1st strain background: CDC1551 CDC1551 RIFR mutants 200ul wildtype RIF 2nd strain background: T85/Beijing 200ul wildtype RIF T85 RIFR mutants Clinical Isolates with Acquired RIFR 4 to 37 months RIFS RIFR Same DNA “fingerprint” Mechanism of Rifampicin Resistance • Rifampicin binds to RNA polymerase • Mutations in rpoB lead to resistance • >95% of clinical RIFR MTB strains have mutation in rpoB Fitness Cost of Rifampicin-Resistant MTB 1 0.9 Lab-derived mutants: Mean relative fitness 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 S531L H526Y H526D S531W H526R S522L Q513L H526P R529Q rpoB mutation 1.3 1.2 Clinical strains: Mean relative fitness 1.1 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1 2 3 S531L 4 5 6 Isolate pair 7 8 9 10 other rpoB Gagneux et al. Science 2006 Clinical Frequency of rpoB Mutations rpoB mutation S531L Mean fitness 1.02 Clinical frequency (%)* H526Y 0.82 11 H526D 0.78 7 S531W 0.82 4 H526R 0.82 3 R529Q 0.58 0 54 * based on 840 clinical isolates (O’Sullivan et al. 2005) Fitness: The Molecular Epidemiology Approach DNA “fingerprinting” (IS6110 RFLP) “reactivated” “transmitted” Population-based Molecular Epidemiological Study in San Francisco • INH resistance caused by different mutations • Different INHR mutations have different effects on bacterial virulence / fitness in animal models • katG activates INH and is a virulence factor • Hypothesis: – Mutants with high fitness cost will transmit less Mutations in 152 INHR Isolates from SF (1991-1999) Mutation N (%) KatG activity 1) Non-functional KatG 34 (22.4) -- 2) katG S315T 62 (40.8) -+ 3) inhA prom. -15 c→t 39 (25.7) ++ 17 (11.1) ++ No mutation Gagneux et al. PLoS Pathogens 2006 INHR Mutation and RFLP Clustering Mutation KatG activity % RFLP clustering p-value 1) Non-functional KatG -- 0.0 reference 2) katG S315T -+ 11.3 < 0.05 3) inhA -15 c→t ++ 17.8 < 0.01 The Biogeography of MTB M. canettii 12can Indo-Oceanic 239 TbD1 105 207 181 East-Asian 150 9 142 East-African-Indian 750 122 Middle East 115 182 183 193 pks 15/1 Δ7bp Americas Europe Euro-American 219 H37Rv-like 174 West Africa 726 761 South Africa 724 Central Africa West-African-1 711 702 7, 8, 10 West-African-2 M. bovis lineage Gagneux et al. PNAS 2006 Does Strain Lineage Impact Propensity Towards Low / High-Cost INHR Mutations ? Lineage / Mutation Odds Ratio P-value 5.6 < 0.001 2.0 0.052 3.8 < 0.001 Blue Lineage: 1) Non-functional katG mutations Red Lineage: 2) katG S315T Pink Lineage: 3) inhA prom. -15 c→t Blue Lineage Associated with MDR TheRussia Gambia The Vietnam Gambia The Gambia South Africa Conclusions • The future of MDR / XDR-TB is uncertain • Bacterial genetics plays a role… Magnitude? • Call for integrated approach: Mathematical Models Epidemiology Experiments The Vision: A Flood of Data 2007 2011 Frequency of Drug Resistance Mutations Phenotypic Drug Resistance Data Standard TB Diagnostics (still!) Primary culture Drug Resistance Testing Microscope Slides Direct Sequencing on Pooled Slides ~ 6 weeks + $$$ !!! Surveillance based on susceptibility test results from hundreds of patient specimens Surveillance based on DNA sequence results from hundreds of thousands of bacterial strains The Three Big Challenges: 1. Biology: Definitively determine the mutations associated with drug resistance 2. Engineering: Build a robotics, microfluidics and sequencing facility that can do 100,000 specimens per year 3. Politics: Ensure that TB programs submit specimens and respond to the results 20 Acknowledgments ISB • Sebastien Gagneux • Hadar Sheffer • Lee Rowen • Marta Janer Stanford • Brendan Bohannan • Alex Pym • Clara Davis Long • Gary Schoolnik • Tran Van • Kathy DeRiemer UCSF • Phil Hopewell • Midori KatoMaeda Funding: • National Institutes of Health • Wellcome Trust • Swiss National Science Foundation • Novartis Foundation