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Motivation Resistance co-occurrence within first-line anti-tuberculosis (TB) drugs is a common phenomenon. Existing methods based on genetic data analysis of Mycobacterium tuberculosis (MTB) have been able to predict resistance of MTB to individual drugs, but have not considered the resistance co-occurrence and cannot capture latent structure of genomic data that corresponds to lineages. Results We used a large cohort of TB patients from 16 countries across six continents where whole-genome sequences for each isolate and associated phenotype to anti-TB drugs were obtained using drug susceptibility testing recommended by the World Health Organization. We then proposed an end-to-end multi-task model with deep denoising auto-encoder (DeepAMR) for multiple drug classification and developed DeepAMR_cluster, a clustering variant based on DeepAMR, for learning clusters in latent space of the data. The results showed that DeepAMR outperformed baseline model and four machine learning models with mean AUROC from 94.4% to 98.7% for predicting resistance to four first-line drugs [i.e. isoniazid (INH), ethambutol (EMB), rifampicin (RIF), pyrazinamide (PZA)], multi-drug resistant TB (MDR-TB) and pan-susceptible TB (PANS-TB: MTB that is susceptible to all four first-line anti-TB drugs). In the case of INH, EMB, PZA and MDR-TB, DeepAMR achieved its best mean sensitivity of 94.3%, 91.5%, 87.3% and 96.3%, respectively. While in the case of RIF and PANS-TB, it generated 94.2% and 92.2% sensitivity, which were lower than baseline model by 0.7% and 1.9%, respectively. t-SNE visualization shows that DeepAMR_cluster captures lineage-related clusters in the latent space. Availability and implementation The details of source code are provided at http://www.robots.ox.ac.uk/∼davidc/code.php. Supplementary information Supplementary data are available at Bioinformatics online.

The dN/dS ratio provides evidence of adaptation or functional constraint in protein-coding genes by quantifying the relative excess or deficit of amino acid-replacing versus silent nucleotide variation. Inexpensive sequencing promises a better understanding of parameters, such as dN/dS, but analyzing very large data sets poses a major statistical challenge. Here, I introduce genomegaMap for estimating within-species genome-wide variation in dN/dS, and I apply it to 3,979 genes across 10,209 tuberculosis genomes to characterize the selection pressures shaping this global pathogen. GenomegaMap is a phylogeny-free method that addresses two major problems with existing approaches: 1) It is fast no matter how large the sample size and 2) it is robust to recombination, which causes phylogenetic methods to report artefactual signals of adaptation. GenomegaMap uses population genetics theory to approximate the distribution of allele frequencies under general, parent-dependent mutation models. Coalescent simulations show that substitution parameters are well estimated even when genomegaMap’s simplifying assumption of independence among sites is violated. I demonstrate the ability of genomegaMap to detect genuine signatures of selection at antimicrobial resistance-conferring substitutions in Mycobacterium tuberculosis and describe a novel signature of selection in the cold-shock DEAD-box protein A gene deaD/csdA. The genomegaMap approach helps accelerate the exploitation of big data for gaining new insights into evolution within species.

IntroductionThe Indian Tuberculosis (TB) Programme currently faces the dual challenges of tackling increasing numbers of drug resistant (DR) TB cases and regulating practices of a pluralistic private sector catering to TB patients. A study of health seeking behaviour of DR-TB patients in such a situation, offers an opportunity to understand the problems patients face while interacting with health systems.MethodologyForty-six DR-TB patients drawn from 15 high TB burden wards in Mumbai were interviewed using an open ended interview tool. Interviews were audio recorded and transcribed. Pathway schematics developed from analysis of patient records, were linked to transcripts. Open coding was used to analyse these units and themes were derived after collating the codes.Results and discussionThe paper presents themes interwoven with narratives in the discussions. These include awareness-action gap among patients, role of neighbourhood providers, responsiveness of health systems, the not-such a ‘merry go round’ that patients go/are made to go on while seeking care, costs of diagnostics and treatment, and how DR-TB is viewed as the ‘big TB’.ConclusionThe recommendations are based on a preventative ethos which is sustainable, compared to interventions with top-down approaches, which get piloted, but fail to sustain impact when scaled up.

Background Multidrug-resistant (MDR) Mycobacterium tuberculosis complex (MTBC) strains are a serious health problem in India, also contributing to one-fourth of the global MDR tuberculosis (TB) burden. About 36% of the MDR MTBC strains are reported fluoroquinolone (FQ) resistant leading to high pre-extensively drug-resistant (pre-XDR) and XDR-TB (further resistance against bedaquiline and/or linezolid) rates. Still, factors driving the MDR/pre-XDR epidemic in India are not well defined. Methods In a retrospective study, we analyzed 1852 consecutive MTBC strains obtained from patients from a tertiary care hospital laboratory in Mumbai by whole genome sequencing (WGS). Univariate and multivariate statistics was used to investigate factors associated with pre-XDR. Core genome multi locus sequence typing, time scaled haplotypic density (THD) method and homoplasy analysis were used to analyze epidemiological success, and positive selection in different strain groups, respectively. Results In total, 1016 MTBC strains were MDR, out of which 703 (69.2%) were pre-XDR and 45 (4.4%) were XDR. Cluster rates were high among MDR (57.8%) and pre-XDR/XDR (79%) strains with three dominant L2 (Beijing) strain clusters (Cl 1–3) representing half of the pre-XDR and 40% of the XDR-TB cases. L2 strains were associated with pre-XDR/XDR-TB (P < 0.001) and, particularly Cl 1–3 strains, had high first-line and FQ resistance rates (81.6–90.6%). Epidemic success analysis using THD showed that L2 strains outperformed L1, L3, and L4 strains in short- and long-term time scales. More importantly, L2 MDR and MDR + strains had higher THD success indices than their not-MDR counterparts. Overall, compensatory mutation rates were highest in L2 strains and positive selection was detected in genes of L2 strains associated with drug tolerance (prpB and ppsA) and virulence (Rv2828c). Compensatory mutations in L2 strains were associated with a threefold increase of THD indices, suggesting improved transmissibility. Conclusions Our data indicate a drastic increase of FQ resistance, as well as emerging bedaquiline resistance which endangers the success of newly endorsed MDR-TB treatment regimens. Rapid changes in treatment and control strategies are required to contain transmission of highly successful pre-XDR L2 strains in the Mumbai Metropolitan region but presumably also India-wide.

Background: Public–private interface agency (PPIA) intervention models in Patna (E. India) and Mumbai (W. India) for pulmonary drug-sensitive (DS) tuberculosis (TB) patients were evaluated over 2 years after maturity to examine effect on reduction of patient pathways and retention.  The models engaged private providers, diagnostic facilities and pharmacies into an effective network providing free diagnostic tests and treatment. Methods: A population-based retrospective study was undertaken to assess effectiveness of the PPIA model in care pathways of 64 (Patna) and 86 (Mumbai) patients through in-depth interviews conducted within 6 months of initiation treatments to identify types and facilities accessed, duration to diagnosis and treatment. Median durations based on facilities accessed were statistically analysed.  Comparisons were made with baseline values and endline pathways of patients accessing PPIA engaged/non-engaged facilities in private and public sectors. Results: Compared to non-engaged facilities, persons accessing engaged facilities at first point-of-care had shorter pathways (Mumbai: 32 vs 43 days), (Patna: 15 vs 40 days).  Duration for first care-seeking was considerably shorter for patients accessing PPIA in Patna and for both engaged and non-engaged private facilities in Mumbai (4 days).  Whilst PPIA engaged facilities diagnosed more cases than others, the RNTCP in Mumbai provided diagnosis early.  There was good retention of patients by PPIA-engaged (1st) facilities (90% post-diagnosis in Patna) but this was affected by the hub-spoke referral system in Mumbai (13%). Second diagnosis is a common feature in Mumbai.  The spoke-hub model in Mumbai contributed considerably to treatment delay; PPIA-engaged providers were better at retaining patients post treatment initiation 11/25 (44%). Conclusion: PPIA-engaged facilities, accessed at onset, result in marked reduction in pathway durations.  Such initiatives should engage a critical mass of competent providers, proximal investigation facilities with enhanced disease awareness and literacy efforts amongst communities.  Patient movement should be minimized for early treatment and retention.

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