Deep amplicon sequencing for culture-free prediction of susceptibility or resistance to 13 anti-tuberculous drugs

Copyright ©ERS 2021..

Conventional molecular tests for detecting Mycobacterium tuberculosis complex (MTBC) drug resistance on clinical samples cover a limited set of mutations. Whole-genome sequencing (WGS) typically requires culture.Here, we evaluated the Deeplex Myc-TB targeted deep-sequencing assay for prediction of resistance to 13 anti-tuberculous drugs/drug classes, directly applicable on sputum.With MTBC DNA tests, the limit of detection was 100-1000 genome copies for fixed resistance mutations. Deeplex Myc-TB captured in silico 97.1-99.3% of resistance phenotypes correctly predicted by WGS from 3651 MTBC genomes. On 429 isolates, the assay predicted 92.2% of 2369 first- and second-line phenotypes, with a sensitivity of 95.3% and a specificity of 97.4%. 56 out of 69 (81.2%) residual discrepancies with phenotypic results involved pyrazinamide, ethambutol and ethionamide, and low-level rifampicin or isoniazid resistance mutations, all notoriously prone to phenotypic testing variability. Only two out of 91 (2.2%) resistance phenotypes undetected by Deeplex Myc-TB had known resistance-associated mutations by WGS analysis outside Deeplex Myc-TB targets. Phenotype predictions from Deeplex Myc-TB analysis directly on 109 sputa from a Djibouti survey matched those of MTBSeq/PhyResSE/Mykrobe, fed with WGS data from subsequent cultures, with a sensitivity of 93.5/98.5/93.1% and a specificity of 98.5/97.2/95.3%, respectively. Most residual discordances involved gene deletions/indels and 3-12% heteroresistant calls undetected by WGS analysis or natural pyrazinamide resistance of globally rare "Mycobacterium canettii" strains then unreported by Deeplex Myc-TB. On 1494 arduous sputa from a Democratic Republic of the Congo survey, 14 902 out of 19 422 (76.7%) possible susceptible or resistance phenotypes could be predicted culture-free.Deeplex Myc-TB may enable fast, tailored tuberculosis treatment.

Errataetall:

CommentIn: Eur Respir J. 2021 Mar 18;57(3):. - PMID 33737379

Medienart:

E-Artikel

Erscheinungsjahr:

2021

Erschienen:

2021

Enthalten in:

Zur Gesamtaufnahme - volume:57

Enthalten in:

The European respiratory journal - 57(2021), 3 vom: 31. März

Sprache:

Englisch

Beteiligte Personen:

Jouet, Agathe [VerfasserIn]
Gaudin, Cyril [VerfasserIn]
Badalato, Nelly [VerfasserIn]
Allix-Béguec, Caroline [VerfasserIn]
Duthoy, Stéphanie [VerfasserIn]
Ferré, Alice [VerfasserIn]
Diels, Maren [VerfasserIn]
Laurent, Yannick [VerfasserIn]
Contreras, Sandy [VerfasserIn]
Feuerriegel, Silke [VerfasserIn]
Niemann, Stefan [VerfasserIn]
André, Emmanuel [VerfasserIn]
Kaswa, Michel K [VerfasserIn]
Tagliani, Elisa [VerfasserIn]
Cabibbe, Andrea [VerfasserIn]
Mathys, Vanessa [VerfasserIn]
Cirillo, Daniela [VerfasserIn]
de Jong, Bouke C [VerfasserIn]
Rigouts, Leen [VerfasserIn]
Supply, Philip [VerfasserIn]

Links:

Volltext

Themen:

Antitubercular Agents
Journal Article
Pharmaceutical Preparations
Research Support, Non-U.S. Gov't

Anmerkungen:

Date Completed 02.07.2021

Date Revised 10.11.2022

published: Electronic-Print

CommentIn: Eur Respir J. 2021 Mar 18;57(3):. - PMID 33737379

Citation Status MEDLINE

doi:

10.1183/13993003.02338-2020

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM315148888