Table 2.

Spotlight on whole-genome sequencing studies of drug-resistant M. tuberculosis

Reference	Description	Advances
Identifying M. tuberculosis drug-resistance determinants
Farhat et al. 2013 [7]	Large-scale WGS project: sequencing of 116 genomes from around the globe	Developed a phylogenetic convergence test, PhyC, to identify resistance associations; validated ponA1 mutations that increase MIC for rifampicin
Zhang et al. 2013 [57]	Large-scale WGS project: sequencing of 161 genomes from China	Identified genes that are under positive selection and have increased mutation frequencies in drug-resistant isolates
Walker et al. 2015 [58]	Analysis of 23 candidate resistance genes from 3651 clinical isolates	Demonstrated that drug-resistance in M. tuberculosis can be predicted with high sensitivity and specificity
Desjardins et al. 2016 [25]	Use of a combination of the correlated evolution test and a GWAS framework to identify drug-resistance-associated mutations in 498 genomes from China and South Africa	Identified ald loss-of-function as a novel mechanism of D-cycloserine resistance
Coll et al. 2018 [59]	GWAS study of 6465 M. tuberculosis clinical isolates from more than 30 countries	Identified new resistance-associated mutations in ethA and the thyX promoter
The Cryptic Consortium and the 100,000 Genomes Project [60]	Prediction of first-line-drug susceptibility in a dataset of 10,209 clinical isolates from 16 countries	Predicted drug-susceptibility phenotypes with high sensitivity and specificity using WGS in a large global dataset
Within-patient evolution of resistance
Eldholm et al. 2014 [61]	WGS of nine serial isolates cultured from a single patient over a 42-month period	First documented case of the evolution of susceptible TB into XDR-TB in a single patient in response to selective drug pressure
Trauner et al. 2017 [62]	Very deep WGS of serial sputum specimens from patients receiving treatment for TB	Demonstrated that the combination of multiple active drugs prevented fixing and dominance of transient mutants. The fewer drugs used, the more likely it was that resistance would develop and become fixed
Transmission versus de novo evolution of resistance
Nikolayevskyy et al. 2016 [63]	Literature review including meta-analysis of 12 studies published between 2005 and 2014	Showed that WGS studies have higher discriminatory power than fingerprinting techniques and can more sensitively detect transmission events
Ioerger et al. 2010 [64]	WGS of 14 phenotypically diverse strains within the Beijing lineage in South Africa	Showed that resistance mutations arose independently multiple times, and that XDR-TB isolates may be less fit and less able to transmit
Shah et al. 2017 [65]	Sequencing of more than 400 strains from South Africa	The majority of cases of XDR-TB in KwaZulu-Natal were due to transmission rather than de novo evolution
Manson et al. 2017 [66]	WGS of a set of 5310 isolates, with diverse geographical origin, genetic background, and drug-resistance profiles	Demonstrated that both de novo evolution and transmission contribute to drug-resistance worldwide
Geographic spread of multidrug-resistance
Cohen et al. 2019 [67]	Further analysis of geographic trends in MDR strains within the set of 5310 strains from Manson et al. [66]	Revealed extensive worldwide spread of MDR-TB clades between countries of varying TB burden
Nelson et al. 2018 [68]	Sequencing of 344 patients with XDR-TB, combined with global positioning system coordinates	Identified many cases of probable person-to-person transmission (≤ 5 SNPs) between people living a median of 108 km apart, suggesting that drivers of XDR-TB transmission include migration between urban and rural areas
Order of acquisition of resistance mutations
Cohen et al. 2015 [69]	WGS and drug-susceptibility testing on 337 clinical isolates collected in Kwazulu-Natal, South Africa	Showed that stepwise accumulation of mutations leading to XDR-TB in Kwazulu-Natal occurred over decades. Established the order of acquisition of drug-resistance mutations leading to XDR-TB, showing that isoniazid resistance almost always evolved prior to rifampicin resistance
Eldholm et al. 2015 [70]	WGS of all 252 available clinical isolates from an outbreak in Argentina	Showed stepwise accumulation of mutations leading to the development of MDR-TB in Argentina
Manson et al. 2017 [66]	WGS of 5310 isolates with diverse geographical origin, genetic background, and drug-resistance profiles	Established that a clear order of acquisition of resistance mutations holds globally: isoniazid resistance overwhelmingly evolves prior to rifampicin resistance across all geographies, lineages, and all time periods (including decades after rifampicin introduction)
Evolution of compensatory and stepping-stone mutations
Fonseca et al. 2015 [71]	Review paper	Discussed the evolution of compensatory mutations that can ease fitness effects caused by resistance
Comas et al. 2012 [72]	Comparison of the genome sequences of ten clinical rifampicin-resistant isolates to those of the corresponding rifampicin-susceptible isolates from the same individual at an earlier timepoint	Identified compensatory mutations in rpoB that conferred high competitive fitness in vitro and were also found frequently in clinical populations
Casali et al. 2014 [73]	Large-scale analysis of 1000 strains from Russia	Examined strains with primary rifampicin-resistance mutations in rpoB, and identified accompanying compensatory mutations in rpoA and rpoC
Cohen et al. 2015 [69]	WGS and drug-susceptibility testing of 337 clinical isolates collected in Kwazulu-Natal, South Africa	Identified putative rifampicin compensatory mutations in rpoA, rpoB, and rpoC
Merker et al. 2018 [74]	Sequencing of highly resistant TB strains from Central Asia	Showed that the presence of rifampicin compensatory mutations are associated with transmission success and higher drug-resistance rates
Coll et al. 2018 [59]	GWAS study of 6465 M. tuberculosis clinical isolates from more than 30 countries	Identified putative compensatory mutations for pyrazinamide and PAS resistance
Safi et al. 2018 [15]	Genetically and biochemically characterized strains selected in vitro for ethambutol resistance	Showed that multi-step selection is required to achieve the highest levels of ethambutol resistance
Understanding mixed infections and spatial heterogeneity within a patient
Köser et al. 2013 [75]	WGS for rapid drug-susceptibility testing of a patient with XDR-TB	Determined that the patient carried two different XDR-TB Beijing strains with differing resistance mutations
Liu et al. 2015 [76]	Deep WGS of serial sputum isolates within a patient	Identified three dominant subclones differing by 10–14 SNPs within a single patient, with different resistance patterns and probably different anatomical distributions
Lieberman et al. 2016 [77]	Sequencing of samples from post-mortem biopsies from different body sites	Observed sublineages evolving within a patient, as well as distinct strains from mixed infections that were differentially distributed across body sites
Dheda et al. 2018 [78]	Sequencing of samples biopsied from seven different body sites, as well as pre-treatment and serial sputum samples	Showed that drug concentrations at different sites were inversely correlated with bacterial MICs. Sequencing and comparison to sputum samples suggested ongoing acquired resistance
Sobkowiak et al. 2018 [79]	Assessed methods for detecting mixed infections using WGS data from in vitro and in silico artificially mixed M. tuberculosis samples	Frequency of mixed infections in the Karonga Study in Mali is approximately 10% and only associated with year of diagnosis, not with age, sex, HIV or prior TB infection. Computational methods can identify mixed infections using WGS data
Bench to bedside with WGS
Pankhurst et al. 2016 [80]	Prospective study evaluating the use of WGS for diagnosis	Compared WGS of positive liquid cultures to routine laboratory workflows. Illumina MiSeq-based bioinformatics classification of species and drug resistance was faster (by a median of 21 days) and cheaper (by 7%), yet offered similar accuracy to routine techniques
Doughty et al. 2014 [81]	Sequencing-based detection without culturing	Proof-of-concept culture-free metagenomics detection of M. tuberculosis from sputum samples using Illumina MiSeq
Votintseva et al. 2017 [82]	Evaluation of Oxford Nanopore sequencing for diagnostic or surveillance purposes	Proof-of-concept detection of M. tuberculosis DNA in sputum samples using a portable sequencer

Abbreviations: GWAS genome-wide association study, MDR multidrug-resistant, MIC minimum inhibitory concentration, PAS para-aminosalicylic acid, SNP single nucleotide polymorphism, TB tuberculosis, XDR extensively drug-resistant