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Transcript of Evolution of MDR-TB - APHL TB Conference... · PDF fileEvolution of MDR-TB ......

  • Evolution of MDR-TB

    Sbastien Gagneux, PhD10th National TB Conference

    Washington DC, 19th April 2017

    Dept. of Medical Parasitology & Infection BiologyTuberculosis Research Unit

  • Global TB Estimates (2016)

    Number of cases

    Number of deaths

    1.8 million10.4 million

    190,000480,000

    All forms of TB

    MDR-TB

    XDR-TB ~ 48,000 ~ 24,000

    >50% Case-fatality: Entering Post-Antibiotic Era !Only ~ 5% MDR / XDR

  • DrugResistantBacteriaAreLessFit

    DOGMA:

    DS

    DR

    Fitness

    Time

  • Proportion MDR among new TB (2015)

    WHO REPORT 2016

  • 0.01

    0.1

    1

    10

    100

    29 76 54 67 4 32 78 4 29 79 34 33 77 30 31

    Source reference

    Rel

    ativ

    e fit

    ness

    of r

    esis

    tant

    stra

    ins

    (log)

    The Relative Fitness of DR Mtb is Heterogeneous

    Borrell & Gagneux 2009 IJTLD 13: 1456-66

  • Exploring the Role of Epistasis

    Epistasis

    Drug-resistancemutation(s)

    Compensatorymutations

    Strain geneticbackground

    Successof MDR Mtb

    Borrell & Gagneux 2011 Clin Microbiol Infect 17: 815820

  • DR

    DS

    Fitn

    ess DR

    Compensation

    TimeBorrell & Gagneux 2009 IJTLD 13: 1456-66

    Evolution of Drug Resistance

  • Compensatory Mutations in rpoA/C

    Comas et al. 2012 Nature Genetics 44: 106110

  • Comas et al. 2012 Nature Genetics 44: 106110

    In clinico Fitness of rpoA/C Mutations

    High-confi. CMs

    All CMs

    *

    *

    * P < 0.05

  • Song et al. 2014 Mol Microbiol 91: 110619

    Genetics in M. smegmatis

    In vitro growth Transcription efficiency

  • Gygli et al. 2017 FEMS Microbiol Rev, in press.

    RIFR strains: N=1,488 + RpoA: N=73 + RpoC: N=729

  • Fenner et al. AAC 2012 56: 3047-53

    Mtb Lineage ImpactsINHResistanceLevelsP

    erce

    ntag

    e

    0

    20

    40

    60

    80

    100

    MIC

    3

    .0mg/L

    MIC

    3

    .0mg/L

    0.0110.004

    5

    5

    2

    2

    3

    1764

    24

    katG 315mutations

    inhA promotor-15mutations

    2

    2

    2

    3

    InhA pro15mutations

    KatG 315mutations

    MIC MIC 134clinical INHR isolates

    0.005

    Lineage4

    Lineage3

    Lineage2

    Lineage6

    Animalstrains

    Lineage5

    Lineage1

    Lineage7

    100

    100

    100

    100100

    100

    100

    99

    100100

  • Positive Sign Epistasis in DoubleR

    Borrell et al. 2013 Evol Med Publ Health eot003: 65-74

    rpoB / gyrA mutations

  • Epistasis

    Drug-resistancemutation(s)

    (e.g. rpoB/gyrA)

    Compensatorymutations

    (e.g. rpoA/C)

    Strain geneticbackground

    (e.g. L1 vs L2)

    Success of MDR Mtb MICs Patient outcome (?)

    Borrell & Gagneux 2011 Clin Microbiol Infect 17: 815820

    Conclusion (1): Epistasis matters!

  • A Web of Epistasis Mediates MDR in Mtb

    Trauner et al. 2014 Drugs 74: 1063-72

  • What about within-host evolution?

  • Simple population

  • Antibiotic treatment

    Time

  • Simple population + 1

  • Antibiotic treatmentTime

  • Case Study From Switzerland

    Tibetan refugee (HIV-neg.)

    Primary resistance to: isoniazid rifampicin (+ compensatory mutation in rpoC) pyrazinamid streptomycin ethionomide fluoroquinolones linezolid (!)

  • Clinical Cure Relaps

    Bloemberg et al. 2015 NEJM 373: 1986-8

    Resistancemutationsto 7 drugs

  • Clinical Cure Relaps

    bedaquilineRclofazimineR

    Bloemberg et al. 2015 NEJM 373: 1986-8

  • Clinical Cure Relaps

    capreomycinR

    bedaquilineRclofazimineR

    Bloemberg et al. 2015 NEJM 373: 1986-8

  • Clinical Cure Relaps

    capreomycinR

    bedaquilineRclofazimineR

    Bloemberg et al. 2015 NEJM 373: 1986-8

  • Clinical Cure Relaps

    delamanidR

    capreomycinR

    bedaquilineRclofazimineR

    Bloemberg et al. 2015 NEJM 373: 1986-8

  • Clinical Cure Relaps

    bedaquilineRclofazimineR

    capreomycinR

    delamanidR

    Bloemberg et al. 2015 NEJM 373: 1986-8

  • What about

    other

    patients? AndrejTrauner

  • Mtb in the host

    Rare(10)

    Minor(2)

    Major(1)

  • Antibiotic treatment

    Time

    How stable are individual clones?

  • time (weeks)0 2 4 6 8 16 24

    P01P02P03P04P05P06P07P08P09P10P11P12 D

    Non-efficacious treatmentEfficacious treatment

    Treatment

    PhenotypicDST

    INH

    RIF

    EMB

    STR

    GenotypicDST

    INJ

    FQ PZA

    DS DR

    Context of Treatment Efficacy

    Trauner et al. 2017 Genome Biol, in press.

  • 1 out of 12 patients.

    Emergence of FQ resistance

    Trauner et al. 2017 Genome Biol, in press.

  • Total variable SNPs = 492

    Site Frequency Spectrum

    Trauner et al. 2017 Genome Biol, in press.

  • Multiple isolatedpopulations

    Diversity driven by drift

    Purifying selectionon minor clones

    Predominant clone

    Trauner et al. 2017 Genome Biol, in press.

  • D: Detected ND: Not detectedpD : Stable pND : AbsentpDND: Loss pNDD: Gain

    Quantifying the Trajectory of Mutations

    Trauner et al. 2017 Genome Biol, in press.

  • Non-efficacious treatment: ALL SNPs NSY SNPs SYN SNPsEfficacious treatment: ALL SNPs NSY SNPs SYN SNPs

    Trauner et al. 2017 Genome Biol, in press.

  • Purifying Selection in Efficaciously Treated Patients

    Trauner et al. 2017 Genome Biol, in press.

  • Efficaciously treated Nonefficaciously treated

    Gene set Na Excessive mutationb Excess NSYc Excessive

    mutation Excess NSY

    DrugResistanced

    13 0/100 (0.501) 0/0 (1.000) 5/87 (0.001) 5/5 (0.177)

    DrugResistanceAssociatede,f

    166 10/100 (0.545) 4/10 (0.987) 6/87 (0.946) 6/6 (0.121)

    MycolateSuperpathwayg

    54 3/100 (0.881) 1/3 (0.964) 5/87 (0.229) 3/5 (0.876)

    MTBC T-CellAntigensh

    300 14/100 (0.153) 10/14 (0.426) 6/87 (0.550) 6/6 (0.121)

    a Number of genes in the gene set. b Proportion of mutations in gene set, p-value calculated with a one-sided binomial test. c Proportion of NSY mutations ingeneset, p-value calculated with a one-sided binomial test. d Walker et al., e Zhang et al., f Farhat et al., g ONeill et al., h Coscolla et al.

    Non-efficaciously Treated Patients Accumulate Mutations in DR Genes

    Trauner et al. 2017 Genome Biol, in press.

  • Most new mutants are unstable.

    Mutations with no (little) functional effect

    are more stable.

    This is particularly true in efficaciously

    treated patients.

    Non-efficaciously treated patients

    accumulate mutations in DR genes.

    Conclusions (2)

  • Thanks to CollaboratorsUniversity of Valencia

    Iaki Comas

    New York UniversityJoel Ernst

    University of Cape TownRob WilkinsonHelen Cox

    Stellenbosch UniversityRob Warren

    University of BernMatthias Egger

    University of ZurichErik Bttger

    UCSFMidori Kato-Maeda

    Sanger InstituteSimon Harris

    University of GhanaDorothy Yeboah-Manu

    Institute Trop. Med. AntwerpBouke de Jong

    Fudan UniversityQian Gao

    ETH ZurichRuedi AebersoldUwe SauerTanja StadlerJrg StellingChristian Beisel

    Forschunszentrum BorstelStefan Niemann

    Max-Planck Institute JenaJohannes Krause

  • Thanks to

    Sonia Borrell Mireia Coscolla Daniela Brites Andrej Trauner Julia Feldmann Miriam Reinhard Levan Jugheli Sebastian Gygli Liliana Ruthaiwa Rhastin Castro Chloe Loiseau Monica Ticlla Peter Major

    [email protected]