A data compendium associating the genomes of 12,289 Mycobacterium tuberculosis isolates with quantitative resistance phenotypes to 13 antibiotics

Abstract

The Comprehensive Resistance Prediction for Tuberculosis: an International Consortium (CRyPTIC) presents here a data compendium of 12,289 Mycobacterium tuberculosis global clinical isolates, all of which have undergone whole-genome sequencing and have had their minimum inhibitory concentrations to 13 antitubercular drugs measured in a single assay. It is the largest matched phenotypic and genotypic dataset for M. tuberculosis to date. Here, we provide a summary detailing the breadth of data collected, along with a description of how the isolates were selected, collected, and uniformly processed in CRyPTIC partner laboratories across 23 countries. The compendium contains 6,814 isolates resistant to at least 1 drug, including 2,129 samples that fully satisfy the clinical definitions of rifampicin resistant (RR), multidrug resistant (MDR), pre-extensively drug resistant (pre-XDR), or extensively drug resistant (XDR). The data are enriched for rare resistance-associated variants, and the current limits of genotypic prediction of resistance status (sensitive/resistant) are presented by using a genetic mutation catalogue, along with the presence of suspected resistance-conferring mutations for isolates resistant to the newly introduced drugs bedaquiline, clofazimine, delamanid, and linezolid. Finally, a case study of rifampicin monoresistance demonstrates how this compendium could be used to advance our genetic understanding of rare resistance phenotypes. The data compendium is fully open source and it is hoped that it will facilitate and inspire future research for years to come.

The evolving biology of Mycobacterium tuberculosis drug resistance

“…Increasingly around the world, slow and labor-intensive microbiological drug susceptibility testing is being replaced by faster DNA-based methods, either PCR or next-generation sequencing (NGS) ( Dookie et al., 2022 ; Rowlinson and Musser, 2022 ). For example, a recent study reported the whole genome sequences of more than 12,000 Mtb clinical isolates, along with sensitivity data for 13 different drugs ( The CRyPTIC Consortium, 2022 ). DNA-based methods offer important advantages in speed, throughput, and safety, as well as altogether novel insights into drug resistance mechanisms ( Hicks et al., 2019 ; The CRyPTIC Consortium, 2022 ).…”

“…For example, a recent study reported the whole genome sequences of more than 12,000 Mtb clinical isolates, along with sensitivity data for 13 different drugs ( The CRyPTIC Consortium, 2022 ). DNA-based methods offer important advantages in speed, throughput, and safety, as well as altogether novel insights into drug resistance mechanisms ( Hicks et al., 2019 ; The CRyPTIC Consortium, 2022 ). Catalogues of sequencing results should be widely available and analyzed regularly for potential associations and emerging mutations of interest.…”

“…With thousands of Mtb genomes sequenced, the diversity of the Mtb pan genome is now evident. Many novel mutations have been linked with resistance to particular drugs, either alone or in association with known resistance loci ( Zhang et al., 2013 ; Zeng et al., 2018 ; The CRyPTIC Consortium, 2022 ). Each new mutation must then be studied to see if it truly confers resistance or compensates for fitness defects imposed by mutations at other sites.…”

Inclusion of minor alleles improves catalogue-based prediction of fluoroquinolone resistance inMycobacterium tuberculosis

“…Large matched WGS and phenotypic datasets, such as that compiled by the Comprehensive Resistance Prediction for Tuberculosis: an International Consortium (CRyPTIC) 11 , provide an opportunity to study the significance of minor alleles. In this paper we will investigate the extent to which catalogue-based fluoroquinolone resistance prediction is enhanced by including minor alleles containing known resistance-conferring mutations.…”

The antimicrobial resistance crisis needs action now

“…AMR surveillance is crucial to detect the emergence and spread of resistance in different ecological settings and could, if combined with phenotypic assays, point to new resistance mechanisms (we have recently published research along these lines [ 8 , 9 , 10 ]). We can learn lessons on surveillance from the COVID-19 pandemic response and should capitalize on the infrastructure that was developed to track SARS-CoV-2 by providing stable funding to maintain and increase global genomic capacitation, strengthen surveillance networks that can pivot to multiple pathogens, and maintain the rapid sharing of genomic data.…”