The Global Problem of Extensively Drug Resistant TB
Peter M. Small, MDInstitute for Systems Biology
Bill and Melinda Gates Foundation
February 17, 2008
22
TB: A huge problem
Some quick facts
1/3 of world infectedMost 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 PLWASub-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
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
CFU measurements
@ baseline & endpointno RIF RIF
RIFS RIFR
Conditioning
Competition
CFU measurements@ baseline & endpointno RIFno RIF RIFRIF
RIFS RIFR
Conditioning
Competition
Fitness: The Experimental Approach
wildtype RIF
200ul
1st strain background: CDC1551CDC1551
RIFR mutants
wildtype RIF
200ul
2nd strain background: T85/BeijingT85
RIFR mutants
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
Lab-derivedmutants:
Gagneux et al. Science 2006
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
S531L H526Y H526D S531W H526R S522L Q513L H526P R529Q
rpoB mutation
Me
an
re
lati
ve
fit
ne
ss
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
S531L H526Y H526D S531W H526R S522L Q513L H526P R529Q
rpoB mutation
Me
an
re
lati
ve
fit
ne
ss
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
1 2 3 4 5 6 7 8 9 10
Isolate pair
Mean
rela
tive f
itn
ess
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
1 2 3 4 5 6 7 8 9 10
Isolate pair
Mean
rela
tive f
itn
ess
Clinicalstrains:
S531L other rpoB
Clinical Frequency of rpoB Mutations
rpoB
mutation
Mean
fitness
Clinical frequency (%)*
S531L 1.02 54
H526Y 0.82 11
H526D 0.78 7
S531W 0.82 4
H526R 0.82 3
R529Q 0.58 0
* 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) + +
No mutation 17 (11.1) + +
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
711
702
7, 8, 10
9
239
750
105
12can
115
182
183
193
174
724
726
761
pks15/1
Δ7bpH37Rv-like
122
TbD1207
181
150
142
219
M. canettii
Indo-Oceanic
East-African-Indian
East-Asian
Euro-American
West-African-1
M. bovis lineage
West-African-2
AmericasEurope
West Africa
Middle East
South Africa
Central Africa
711
702
7, 8, 10
9
239
750
105
12can
115
182
183
193
174
724
726
761
pks15/1
Δ7bpH37Rv-like
122
TbD1207
181
150
142
219
M. canettii
Indo-Oceanic
East-African-Indian
East-Asian
Euro-American
West-African-1
M. bovis lineage
West-African-2
AmericasEurope
West Africa
Middle East
South Africa
Central Africa
The Biogeography of MTB
Gagneux et al. PNAS 2006
Does Strain Lineage Impact Propensity Towards Low / High-Cost INHR Mutations ?
Lineage / MutationOdds Ratio
P-value
Blue Lineage:
1) Non-functional katG mutations 5.6 < 0.001
Red Lineage:
2) katG S315T 2.0 0.052
Pink Lineage:
3) inhA prom. -15 c→t 3.8 < 0.001
• The future of MDR / XDR-TB is uncertain
• Bacterial genetics plays a role… Magnitude?
• Call for integrated approach:
Conclusions
MathematicalModels
ExperimentsEpidemiology
2020
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
Primary culture Drug ResistanceTesting
PhenotypicDrug Resistance
Data
~ 6 weeks + $$$ !!!
Primary culture Drug ResistanceTesting
PhenotypicDrug Resistance
Data
~ 6 weeks + $$$ !!!
Frequency ofDrug Resistance
Mutations
Standard TBDiagnostics (still!)
MicroscopeSlides
Direct Sequencingon Pooled Slides
Frequency ofDrug Resistance
Mutations
Standard TBDiagnostics (still!)
MicroscopeSlides
Direct Sequencingon Pooled Slides
2007 2011
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:
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
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