Transcript 1/3 of world infected

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
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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