SummaryBackgroundDiagnosing drug-resistance remains an obstacle to the elimination of tuberculosis. Phenotypic drug-susceptibility testing is slow and expensive, and commercial genotypic assays screen only common resistance-determining mutations. We used whole-genome sequencing to characterise common and rare mutations predicting drug resistance, or consistency with susceptibility, for all first-line and second-line drugs for tuberculosis.MethodsBetween Sept 1, 2010, and Dec 1, 2013, we sequenced a training set of 2099 Mycobacterium tuberculosis genomes. For 23 candidate genes identified from the drug-resistance scientific literature, we algorithmically characterised genetic mutations as not conferring resistance (benign), resistance determinants, or uncharacterised. We then assessed the ability of these characterisations to predict phenotypic drug-susceptibility testing for an independent validation set of 1552 genomes. We sought mutations under similar selection pressure to those characterised as resistance determinants outside candidate genes to account for residual phenotypic resistance.FindingsWe characterised 120 training-set mutations as resistance determining, and 772 as benign. With these mutations, we could predict 89·2% of the validation-set phenotypes with a mean 92·3% sensitivity (95% CI 90·7–93·7) and 98·4% specificity (98·1–98·7). 10·8% of validation-set phenotypes could not be predicted because uncharacterised mutations were present. With an in-silico comparison, characterised resistance determinants had higher sensitivity than the mutations from three line-probe assays (85·1% vs 81·6%). No additional resistance determinants were identified among mutations under selection pressure in non-candidate genes.InterpretationA broad catalogue of genetic mutations enable data from whole-genome sequencing to be used clinically to predict drug resistance, drug susceptibility, or to identify drug phenotypes that cannot yet be genetically predicted. This approach could be integrated into routine diagnostic workflows, phasing out phenotypic drug-susceptibility testing while reporting drug resistance early.

Mycobacterium tuberculosis strains of the Beijing lineage are globally distributed and are associated with the massive spread of multidrug-resistant (MDR) tuberculosis in Eurasia. Here we reconstructed the biogeographical structure and evolutionary history of this lineage by genetic analysis of 4,987 isolates from 99 countries and whole-genome sequencing of 110 representative isolates. We show that this lineage initially originated in the Far East, from where it radiated worldwide in several waves. We detected successive increases in population size for this pathogen over the last 200 years, practically coinciding with the Industrial Revolution, the First World War and HIV epidemics. Two MDR clones of this lineage started to spread throughout central Asia and Russia concomitantly with the collapse of the public health system in the former Soviet Union. Mutations identified in genes putatively under positive selection and associated with virulence might have favored the expansion of the most successful branches of the lineage.

The evolutionary timing and spread of the Mycobacterium tuberculosis complex (MTBC), one of the most successful groups of bacterial pathogens, remains largely unknown. Here, using mycobacterial tandem repeat sequences as genetic markers, we show that the MTBC consists of two independent clades, one composed exclusively of M. tuberculosis lineages from humans and the other composed of both animal and human isolates. The latter also likely derived from a human pathogenic lineage, supporting the hypothesis of an original human host. Using Bayesian statistics and experimental data on the variability of the mycobacterial markers in infected patients, we estimated the age of the MTBC at 40,000 years, coinciding with the expansion of “modern” human populations out of Africa. Furthermore, coalescence analysis revealed a strong and recent demographic expansion in almost all M. tuberculosis lineages, which coincides with the human population explosion over the last two centuries. These findings thus unveil the dynamic dimension of the association between human host and pathogen populations.

Because of its portable data, discriminatory power, and recently proposed standardization, mycobacterial interspersed repetitive-unit-variable-number tandem-repeat (MIRU-VNTR) typing has become a major method for the epidemiological tracking of Mycobacterium tuberculosis complex (MTBC) clones. However, no public MIRU-VNTR database based on well-characterized reference strains has been available hitherto for easy strain identification. Therefore, a collection of 186 reference strains representing the primary MTBC lineages was used to build a database, which is freely accessible at http://www.MIRU-VNTRplus.org. The geographical origin and the drug susceptibility profile of each strain were stored together with comprehensive genetic lineage information, including the 24-locus MIRU-VNTR profile, the spoligotyping pattern, the singlenucleotide-and large-sequence-polymorphism profiles, and the IS6110 restriction fragment length polymorphism fingerprint. Thanks to flexible import functions, a single or multiple user strains can be analyzed, e.g., for lineage identification with or without the use of reference strains, by best-match or tree-based analyses with single or combined marker data sets. The results can easily be exported. In the present study, we evaluated the database consistency and various analysis parameters both by testing the reference collection against itself and by using an external population-based data set comprising 629 different strains. Under the optimal conditions found, lineage predictions based on typing by 24-locus MIRU-VNTR analysis optionally combined with spoligotyping were verified in >99% of the cases. On the basis of this evaluation, a user strategy was defined, which consisted of best-match analysis followed, if necessary, by tree-based analysis. The MIRU-VNTRplus database is a powerful tool for high-resolution clonal identification and has little equivalent in terms of functionalities among the bacterial genotyping databases available so far.Mycobacterium tuberculosis is among the most successful human pathogens worldwide and is responsible for extensive morbidity and mortality, with approximately 2 million deaths each year (51). The importance of tuberculosis (TB) as a major public health problem has been dramatically reinforced due to the human immunodeficiency virus coepidemic and the emergence of (multi)drug-resistant M. tuberculosis strains.Methods for genotyping of clinical M. tuberculosis complex (MTBC) strains have proven to be valuable tools for TB control. At the individual clinical management level, the application of genotyping enables the detection (1) or exclusion (25) of laboratory errors and the follow-up of relapse cases to identify treatment failures, reactivations of latent disease, and exogenous reinfections (46). At the public health level, genotyping enables the detection of unsuspected outbreaks and the identification of transmission chains and secondary cases of infection (4, 46).

BackgroundThe World Health Organization recommends universal drug susceptibility testing for Mycobacterium tuberculosis complex to guide treatment decisions and improve outcomes. We assessed whether DNA sequencing can accurately predict antibiotic susceptibility profiles for first-line anti-tuberculosis drugs. MethodsWhole-genome sequences and associated phenotypes to isoniazid, rifampicin, ethambutol and pyrazinamide were obtained for isolates from 16 countries across six continents. For each isolate, mutations associated with drug-resistance and drug-susceptibility were identified across nine genes, and individual phenotypes were predicted unless mutations of unknown association were also present. To identify how whole-genome sequencing might direct first-line drug therapy, complete susceptibility profiles were predicted. These were predicted to be pan-susceptible if predicted susceptible to isoniazid and to other drugs, or contained mutations of unknown association in genes affecting these other drugs. We simulated how negative predictive value changed with drug-resistance prevalence.Results10,209 isolates were analysed. The greatest proportion of phenotypes were predicted for rifampicin (9,660/10,130; (95.4%)) and the lowest for ethambutol (8,794/9,794; (89.8%)). Isoniazid, rifampicin, ethambutol and pyrazinamide resistance was correctly predicted with 97.1%, 97.5% 94.6% and 91.3% sensitivity, and susceptibility with 99.0%, 98.8%, 93.6% and 96.8% specificity, respectively. 5,250 (89.5%) drug profiles were correctly predicted for 5,865/7,516 (78.0%) isolates with complete phenotypic profiles. Among these, 3,952/4,037 (97.9%) predictions of pan-susceptibility were correct. The negative predictive value for 97.5% of simulated drug profiles exceeded 95% where the prevalence of drug-resistance was below 47.0%. ConclusionsPhenotypic testing for first-line drugs can be phased down in favour of DNA sequencing to guide anti- tuberculosis drug therapy.