A data compendium of Mycobacterium tuberculosis antibiotic resistance

Abstract

The Comprehensive Resistance Prediction for Tuberculosis: an International Consortium (CRyPTIC) presents here a global collection of 15,211 Mycobacterium tuberculosis clinical isolates, all of which have undergone whole genome sequencing and have had their minimum inhibitory concentrations to 13 antitubercular drugs measured. The isolates represent five major M. tuberculosis lineages originating from 23 countries across four continents. 6,814 isolates were found resistant to at least one drug, including 2,129 samples fully satisfy the clinical definitions of RR/MDR, pre-XDR or XDR. Resistance status to eight antitubercular drugs can be accurately predicted using a genetic mutation catalogue for over 90% of the isolates. Furthermore, we show 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 mono-resistance is presented to showcase how this compendium could be used to advance our genetic understanding of rare resistance phenotypes and evaluate the likely performance of a widely used molecular diagnostic tool. It is hoped that this compendium, the largest M. tuberculosis matched phenotypic and genotypic dataset to date, will facilitate and inspire new research projects for years to come.

Genome-wide association studies of global Mycobacterium tuberculosis resistance to 13 antimicrobials in 10,228 genomes identify new resistance mechanisms

“…The MICs were positively correlated between many drugs, particularly among first-line drugs (see Figure 4A of [ 78 ]). Consequently, many of the 10,228 isolates we studied were MDR and XDR.…”

“…A total of 15,211 isolates were included in the initial CRyPTIC dataset with both genomes and phenotype measurements after passing genome quality control filters [ 31 , 78 ]; however, some plates were later removed due to problems identified at some laboratories with inoculating the plates [ 31 ]. Genomes were also excluded if they met any of the following criteria, determined by removing samples at the outliers of the distributions: (i) no high-quality phenotypes for any drugs; (ii) total number of contigs > 3,000; (iii) total bases in contigs < 3.5 × 10 6 or > 5 × 10 6 ; (iv) number of unique oligonucleotides < 3.5 × 10 6 or > 5 × 10 6 ; and (v) sequencing read length not 150/151 bases long.…”

“…A pairwise distance matrix was constructed for the full CRyPTIC dataset based on variant calls [ 78 ]. For visualisation of the dataset, a neighbour-joining tree was built from the distance matrix using the ape package in R and subset to the GWAS dataset.…”

“…This gave a GWAS dataset of 10,422 genomes used to create the variant presence/absence matrices. We used Mykrobe [78][79][80] to identify Mycobacterium genomes not belonging to lineages 1 to 4 or representing mixtures of lineages. This led to the exclusion of 193 genomes, which were removed from GWAS by setting the phenotypes to NA.…”

Nanopore sequencing for Mycobacterium tuberculosis drug susceptibility testing and outbreak investigation

“…6 Given perfect sequencing, the catalogue determines how well DST can be predicted, which is inexorably improving as the global community collects progressively more data. [20][21][22][23] In this study we take this as given, and ask whether Nanopore sequence data can provide as accurate genotyping of the resistance catalogue as Illumina data. If so, as catalogues improve, both Illumina and Nanopore data will provide concordant and progressively better results.…”