1. The Global Problem of Extensively Drug Resistant TB Peter M. Small, MD Institute for Systems Biology Bill and Melinda Gates Foundation February 17, 2008

2. 22 TB: A huge problem Some quick facts 1/3 of world infected Most of the prevalent infections are in Asia 8.8 million new cases Most of the new cases are in Africa 1.6 million deaths 750,000 in PLWA Sub-Saharan Africa has the most TB/HIV 450,000 MDR (Multi Drug Resistance) 25,000 XDR (Extreme Drug Resistance) Estimated TB Incidence rates Estimated Numbers of New TB Cases HIV Prevalence in New TB Cases No estimate Very low levels High levels Very high levels Low levels

3. 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” ?

4. 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

5. Our Hypothesis The relative fitness of drug-resistant MTB is heterogeneous: 1.Specific DR mutation(s) 2.Specific strain genetic background 3.Compensatory evolution

6. Fitness: The Experimental Approach

7. wildtype RIF 200ul 1 st strain background: CDC1551 CDC1551 RIF R mutants wildtypeRIF 200ul 2 nd strain background: T85/Beijing T85 RIF R mutants

8. RIF S RIF R 4 to 37 months Same DNA “fingerprint” Clinical Isolates with Acquired RIF R

9. Mechanism of Rifampicin Resistance Rifampicin binds to RNA polymerase Mutations in rpoB lead to resistance >95% of clinical RIF R MTB strains have mutation in rpoB

10. Fitness Cost of Rifampicin-Resistant MTB Lab-derived mutants: Gagneux et al. Science 2006 Clinical strains: S531L other rpoB

11. Clinical Frequency of rpoB Mutations rpoB mutation Mean fitness Clinical frequency (%)* S531L1.0254 H526Y0.8211 H526D0.787 S531W0.824 H526R0.823 R529Q0.580 * based on 840 clinical isolates (O’Sullivan et al. 2005)

12. Fitness: The Molecular Epidemiology Approach DNA “fingerprinting” (IS6110 RFLP) “reactivated”“transmitted”

13. Population-based Molecular Epidemiological Study in San Francisco INH resistance caused by different mutations Different INH R 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

14. Mutations in 152 INH R Isolates from SF (1991-1999) MutationN(%)KatG activity 1) Non-functional KatG34(22.4)- 2) katG S315T62(40.8)- + 3) inhA prom. -15 c→t39(25.7)+ No mutation17(11.1)+ Gagneux et al. PLoS Pathogens 2006

15. INH R Mutation and RFLP Clustering MutationKatG activity % RFLP clustering p-value 1) Non-functional KatG- 0.0reference 2) katG S315T- +11.3< 0.05 3) inhA -15 c→t+ 17.8< 0.01

16. The Biogeography of MTB Gagneux et al. PNAS 2006

17. Does Strain Lineage Impact Propensity Towards Low / High-Cost INH R Mutations ? Lineage / Mutation Odds Ratio P-value Blue Lineage: 1) Non-functional katG mutations5.6< 0.001 Red Lineage: 2) katG S315T2.00.052 Pink Lineage: 3) inhA prom. -15 c→t3.8< 0.001

18. Blue Lineage Associated with MDR The GambiaSouth AfricaThe Gambia RussiaThe Gambia Vietnam

19. The future of MDR / XDR-TB is uncertain Bacterial genetics plays a role… Magnitude? Call for integrated approach: Conclusions Mathematical Models Experiments Epidemiology

20. 20 The Vision: A Flood of Data 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 20072011 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:

21. Acknowledgments Stanford Brendan Bohannan Alex Pym Clara Davis Long Gary Schoolnik Tran Van Kathy DeRiemer ISB Sebastien Gagneux Hadar Sheffer Lee Rowen Marta Janer UCSF Phil Hopewell Midori Kato- Maeda Funding: National Institutes of Health Wellcome Trust Swiss National Science Foundation Novartis Foundation