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

RIFS RIFR

4 to 37 months

Same DNA “fingerprint”

Clinical Isolates with Acquired RIFR

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

Blue Lineage Associated with MDR

The GambiaSouth Africa

The Gambia Russia

The Gambia Vietnam

• 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