PhyResSE: a Web Tool Delineating Mycobacterium tuberculosis Antibiotic Resistance and Lineage from Whole-Genome Sequencing Data

Evaluation of WHO catalog of mutations and five WGS analysis tools for drug resistance prediction of Mycobacterium tuberculosis isolates from China

The continuous advancement of molecular diagnostic techniques, particularly whole-genome sequencing (WGS), has greatly facilitated the early diagnosis of drug-resistant tuberculosis patients. Nonetheless, the interpretation of results from various types of mutations in drug-resistant-associated genes has become the primary challenge in the field of molecular drug-resistance diagnostics. In this study, our primary objective is to evaluate the diagnosis accuracy of the World Health Organization (WHO) catalog of mutations and five WGS analysis tools (PhyResSE, Mykrobe, TB Profiler, Gen-TB, and SAM-TB) in drug resistance to 10 anti-Mycobacterium tuberculosis (MTB) drugs. We utilized the data of WGS collected between 2014 and 2017 in Zhejiang Province, consisting of 110 MTB isolates as detailed in our previous study. Based on phenotypic drug susceptibility testing (DST) results using the proportion method on Löwenstein-Jensen medium with antibiotics, we evaluated the predictive accuracy of genotypic DST obtained by these tools. The results revealed that the WHO catalog of mutations and five WGS analysis tools exhibit robust predictive capabilities concerning resistance to isoniazid, rifampicin, ethambutol, streptomycin, amikacin, kanamycin, and capreomycin. Notably, Mykrobe, SAM-TB, and TB Profiler demonstrate the most accurate predictions for resistance to pyrazinamide, prothionamide, and para-aminosalicylic acid, respectively. These findings are poised to significantly guide and influence future clinical treatment strategies and resistance monitoring protocols.IMPORTANCEWhole-genome sequencing (WGS) has the potential for the early diagnosis of drug-resistant tuberculosis. However, the interpretation of mutations of drug-resistant-associated genes represents a significant challenge as the amount and complexity of WGS data. We evaluated the accuracy of the World Health Organization catalog of mutations and five WGS analysis tools in predicting drug resistance to first-line and second-line anti-TB drugs. Our results offer clinicians guidance on selecting appropriate WGS analysis tools for predicting resistance to specific anti-TB drugs.

Pangenome-wide association study reveals the selective absence of CRISPR genes (Rv2816c-19c) in drug-resistant Mycobacterium tuberculosis

The presence of intermittently dispersed insertion sequences and transposases in the Mycobacterium tuberculosis (Mtb) genome makes intra-genome recombination events inevitable. Understanding their effect on the gene repertoires (GR), which may contribute to the development of drug-resistant Mtb, is critical. In this study, publicly available WGS data of clinical Mtb isolates (endemic region n = 2,601; non-endemic region n = 1,130) were de novo assembled, filtered, scaffolded into assemblies, and functionally annotated. Out of 2,601 Mtb WGS data sets from endemic regions, 2,184 (drug resistant/sensitive: 1,386/798) qualified as high quality. We identified 3,784 core genes, 123 softcore genes, 224 shell genes, and 762 cloud genes in the pangenome of Mtb clinical isolates from endemic regions. Sets of 33 and 39 genes showed positive and negative associations (P < 0.01) with drug resistance status, respectively. Gene ontology clustering showed compromised immunity to phages and impaired DNA repair in drug-resistant Mtb clinical isolates compared to the sensitive ones. Multidrug efflux pump repressor genes (Rv3830c and Rv3855c) and CRISPR genes (Rv2816c-19c) were absent in the drug-resistant Mtb. A separate WGS data analysis of drug-resistant Mtb clinical isolates from the Netherlands (n = 1130) also showed the absence of CRISPR genes (Rv2816c-17c). This study highlights the role of CRISPR genes in drug resistance development in Mtb clinical isolates and helps in understanding its evolutionary trajectory and as useful targets for diagnostics development.IMPORTANCEThe results from the present Pan-GWAS study comparing gene sets in drug-resistant and drug-sensitive Mtb clinical isolates revealed intricate presence-absence patterns of genes encoding DNA-binding proteins having gene regulatory as well as DNA modification and DNA repair roles. Apart from the genes with known functions, some uncharacterized and hypothetical genes that seem to have a potential role in drug resistance development in Mtb were identified. We have been able to extrapolate many findings of the present study with the existing literature on the molecular aspects of drug-resistant Mtb, further strengthening the relevance of the results presented in this study.

Scalable de novo classification of antibiotic resistance of Mycobacterium tuberculosis

MOTIVATION

World Health Organization estimates that there were over 10 million cases of tuberculosis (TB) worldwide in 2019, resulting in over 1.4 million deaths, with a worrisome increasing trend yearly. The disease is caused by Mycobacterium tuberculosis (MTB) through airborne transmission. Treatment of TB is estimated to be 85% successful, however, this drops to 57% if MTB exhibits multiple antimicrobial resistance (AMR), for which fewer treatment options are available.

RESULTS

We develop a robust machine-learning classifier using both linear and nonlinear models (i.e. LASSO logistic regression (LR) and random forests (RF)) to predict the phenotypic resistance of Mycobacterium tuberculosis (MTB) for a broad range of antibiotic drugs. We use data from the CRyPTIC consortium to train our classifier, which consists of whole genome sequencing and antibiotic susceptibility testing (AST) phenotypic data for 13 different antibiotics. To train our model, we assemble the sequence data into genomic contigs, identify all unique 31-mers in the set of contigs, and build a feature matrix M, where M[i, j] is equal to the number of times the ith 31-mer occurs in the jth genome. Due to the size of this feature matrix (over 350 million unique 31-mers), we build and use a sparse matrix representation. Our method, which we refer to as MTB++, leverages compact data structures and iterative methods to allow for the screening of all the 31-mers in the development of both LASSO LR and RF. MTB++ is able to achieve high discrimination (F-1 >80%) for the first-line antibiotics. Moreover, MTB++ had the highest F-1 score in all but three classes and was the most comprehensive since it had an F-1 score >75% in all but four (rare) antibiotic drugs. We use our feature selection to contextualize the 31-mers that are used for the prediction of phenotypic resistance, leading to some insights about sequence similarity to genes in MEGARes. Lastly, we give an estimate of the amount of data that is needed in order to provide accurate predictions.

AVAILABILITY

The models and source code are publicly available on Github at https://github.com/M-Serajian/MTB-Pipeline.

SNPs in genes related to the repair of damage to DNA in clinical isolates of M. tuberculosis: A transversal and longitudinal approach

The presence of SNPs in genes related to DNA damage repair in M. tuberculosis can trigger hypermutagenic phenotypes with a higher probability of generating drug resistance. The aim of this research was to compare the presence of SNPs in genes related to DNA damage repair between sensitive and DR isolates, as well as to describe the dynamics in the presence of SNPs in M. tuberculosis isolated from recently diagnosed TB patients of the state of Veracruz, Mexico. The presence of SNPs in the coding regions of 65 genes related to DNA damage repair was analyzed. Eighty-six isolates from 67 patients from central Veracruz state, Mexico, were sequenced. The results showed several SNPs in 14 genes that were only present in drug-resistant genomes. In addition, by following of 15 patients, it was possible to describe three different dynamics of appearance and evolution of non-synonymous SNPs in genes related to DNA damage repair: 1) constant fixed SNPs, 2) population substitution, and 3) gain of fixed SNPs. Further research is required to discern the biological significance of each of these pathways and their utility as markers of DR or for treatment prognosis.

Improving the diagnosis of tuberculosis: old and new laboratory tools

INTRODUCTION

Despite recent advances in diagnostic technologies and new drugs becoming available, tuberculosis (TB) remains a major global health burden. If detected early, screened for drug resistance, and fully treated, TB could be easily controlled.

AREAS COVERED

Here the authors discuss M. tuberculosis culture methods which are considered the definitive confirmation of M. tuberculosis infection, and limited advances made to build on these core elements of TB laboratory diagnosis. Literature searches showed that molecular techniques provide enhanced speed of turnaround, sensitivity, and richness of data. Sequencing of the whole genome, is becoming well established for identification and inference of drug resistance. PubMed® literature searches were conducted (November 2022-March 2024).

EXPERT OPINION

This section highlights future advances in diagnosis and infection control. Prevention of prolonged hospital admissions and rapid TAT are of the most benefit to the overall patient experience. Host transcriptional blood markers have been used in treatment monitoring studies and, with appropriate evaluation, could be rolled out in a diagnostic setting. Additionally, the MBLA is being incorporated into latest clinical trial designs. Whole genome sequencing has enhanced epidemiological evidence. Artificial intelligence, along with machine learning, have the ability to revolutionize TB diagnosis and susceptibility testing within the next decade.

Exploring diagnostic methods for drug-resistant tuberculosis: A comprehensive overview

Despite available global efforts and funding, Tuberculosis (TB) continues to affect a considerable number of patients worldwide. Policy makers and stakeholders set clear goals to reduce TB incidence and mortality, but the emergence of multidrug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) complicate the reach of these goals. Drug-resistance TB needs to be diagnosed rapidly and accurately to effectively treat patients, prevent the transmission of MDR-TB, minimise mortality, reduce treatment costs and avoid unnecessary hospitalisations. In this narrative review, we provide a comprehensive overview of laboratory methods for detecting drug resistance in MTB, focusing on phenotypic, molecular and other drug susceptibility testing (DST) techniques. We found a large variety of methods used, with the BACTEC MGIT 960 being the most common phenotypic DST and the Xpert MTB/RIF being the most common molecular DST. We emphasise the importance of integrating phenotypic and molecular DST to address issues like resistance to new drugs, heteroresistance, mixed infections and low-level resistance mutations. Notably, most of the analysed studies adhered to the outdated definition of XDR-TB and did not consider the pre-XDR definition, thus posing challenges in aligning diagnostic methods with the current landscape of TB resistance.

Efflux pump gene single-nucleotide variants associated with resistance in Mycobacterium tuberculosis isolates with discrepant drug genotypes

INTRODUCTION

Single-nucleotide variants (SNVs) in Mycobacterium tuberculosis (M. tuberculosis) genomes can predict multidrug resistance (MDR) but not all phenotype-genotype correlations can be explained. We investigated SNVs in efflux pumps (EPs) in the context of M. tuberculosis drug resistance.

METHODS

We analysed 2221 M. tuberculosis genomes from 1432 susceptible and 200 MDR, 172 pre-extensively drug resistant (XDR) and 417 XDR isolates. Analysis of 47 EP genes was conducted using MTB-VCF, an in-house bioinformatics pipeline. SNVs were categorized according to their SIFT/Polyphen scores. Resistance genotypes were also called using the TB-Profiler tool.

RESULTS

Genome comparisons between susceptible and drug resistant (DR) isolates identified 418 unique SNVs in EP of which; 53.5% were in MDR, 68.9% in pre-XDR and 61.3% in XDR isolates. Twenty EPs had unique SNVs with a high SIFT/PolyPhen score, comprising 38 unique SNVs. Sixteen SNVs across 12 EP genes were significantly associated with drug resistance and enriched in pre-XDR and XDR strains. These comprised 12 previously reported SNVs (in Rv0191, Rv0507, Rv0676, Rv1217, Rv1218, Rv1273, Rv1458, Rv1819, and Rv2688) and 4 novel SNVs (in Rv1877 and Rv2333). We investigated their presence in genomes of 52 MDR isolates with phenotype-genotype discrepancies to rifampicin (RIF), isoniazid (INH), or fluoroquinolones. SNVs associated with RIF and INH (Rv1217_1218, Rv1819, Rv0450, Rv1458, Rv3827, Rv0507, Rv0676, Rv1273, and Rv2333), and with fluoroquinolone (Rv2688) resistance were present in these discrepant strains.

CONCLUSIONS

Considering SNVs in EPs as part of M. tuberculosis genome-based resistance interpretation may add value, especially in evaluation of XDR resistance in strains with phenotype-genotype discrepancies.

Specific Mycobacterium tuberculosis Strain Circulating in Prison Revealed by Cost-Effective Amplicon Sequencing

This scientific study focuses on tuberculosis (TB) within prison settings, where persons deprived of liberty (PDL) face significantly higher rates of the disease compared to the general population. The research employs the low-cost amplicon sequencing of Mycobacterium tuberculosis strains, aiming first to identify specific lineages and also to detect mutations associated with drug resistance. The method involves multiplex amplification, DNA extraction, and sequencing, providing valuable insights into TB dynamics and resistance-mutation profiles within the prison system at an affordable cost. The study identifies a characteristic lineage (X) circulating among PDLs in the penitentiary system in Uruguay, absent in the general population, and notes its prevalence at prison entry. No high-confidence mutations associated with drug resistance were found. The findings underscore the importance of molecular epidemiology in TB control, emphasizing the potential for intra-prison transmissions and the need for broader studies to understand strain dynamics.

GenoMycAnalyzer: a web-based tool for species and drug resistance prediction for Mycobacterium genomes

BACKGROUND

Drug-resistant tuberculosis (TB) is a major threat to global public health. Whole-genome sequencing (WGS) is a useful tool for species identification and drug resistance prediction, and many clinical laboratories are transitioning to WGS as a routine diagnostic tool. However, user-friendly and high-confidence automated bioinformatics tools are needed to rapidly identify M. tuberculosis complex (MTBC) and non-tuberculous mycobacteria (NTM), detect drug resistance, and further guide treatment options.

RESULTS

We developed GenoMycAnalyzer, a web-based software that integrates functions for identifying MTBC and NTM species, lineage and spoligotype prediction, variant calling, annotation, drug-resistance determination, and data visualization. The accuracy of GenoMycAnalyzer for genotypic drug susceptibility testing (gDST) was evaluated using 5,473 MTBC isolates that underwent phenotypic DST (pDST). The GenoMycAnalyzer database was built to predict the gDST for 15 antituberculosis drugs using the World Health Organization mutational catalogue. Compared to pDST, the sensitivity of drug susceptibilities by the GenoMycAnalyzer for first-line drugs ranged from 95.9% for rifampicin (95% CI 94.8-96.7%) to 79.6% for pyrazinamide (95% CI 76.9-82.2%), whereas those for second-line drugs ranged from 98.2% for levofloxacin (95% CI 90.1-100.0%) to 74.9% for capreomycin (95% CI 69.3-80.0%). Notably, the integration of large deletions of the four resistance-conferring genes increased gDST sensitivity. The specificity of drug susceptibilities by the GenoMycAnalyzer ranged from 98.7% for amikacin (95% CI 97.8-99.3%) to 79.5% for ethionamide (95% CI 76.4-82.3%). The incorporated Kraken2 software identified 1,284 mycobacterial species with an accuracy of 98.8%. GenoMycAnalyzer also perfectly predicted lineages for 1,935 MTBC and spoligotypes for 54 MTBC.

CONCLUSIONS

GenoMycAnalyzer offers both web-based and graphical user interfaces, which can help biologists with limited access to high-performance computing systems or limited bioinformatics skills. By streamlining the interpretation of WGS data, the GenoMycAnalyzer has the potential to significantly impact TB management and contribute to global efforts to combat this infectious disease. GenoMycAnalyzer is available at http://www.mycochase.org .

Proteomic Analysis of the Mycobacterium tuberculosis Outer Membrane for Potential Implications in Uptake of Small Molecules

Increased resistance to current antimycobacterial agents and a potential bias toward relatively hydrophobic chemical entities highlight an urgent need to understand how current anti-TB drugs enter the tubercle bacilli. While inner membrane proteins are well-studied, how small molecules cross the impenetrable outer membrane remains unknown. Here, we employed mass spectrometry-based proteomics to show that octyl-β-d-glucopyranoside selectively extracts the outer membrane proteins of Mycobacterium tuberculosis. Differentially expressed proteins between nutrient-replete and nutrient-depleted conditions were enriched to identify proteins involved in nutrient uptake. We demonstrate cell surface localization of seven new proteins using immunofluorescence and show that overexpression of the proteins LpqY and ProX leads to hypersensitivity toward streptomycin, while overexpression of SubI, SpmT, and Rv2041 exhibited higher membrane permeability, assessed through an EtBr accumulation assay. Further, proton NMR metabolomics suggests the role of six outer membrane proteins in glycerol uptake. This study identifies several outer membrane proteins that are involved in the permeation of small hydrophilic molecules and are potential targets for enhancing the uptake and efficacy of anti-TB drugs.

Whole genome sequencing for the prediction of resistant tuberculosis strains from northern India

PURPOSE

Tuberculosis is an important public health problem among infectious diseases. The problem becomes more concerning with the emergence of MDR-TB and pre-XDR-TB. Whole genome sequencing (WGS) detection of resistance has recently gained popularity as it has advantages over other commercial techniques.

METHODS

We performed in-house WGS followed by detailed analysis by an in-house pipeline to identify the resistance markers. This was accompanied by Phenotypic DST, and Sanger sequencing on all the 12 XDR, 06 pre-XDR, and 06 susceptible M. tb isolates. These results were collated with online M. tb WGS pipelines (TB profiler, PhyResSE, Mykrobe predictor) for comparative analysis.

RESULTS

Following our in-house analysis, we observed 64 non-synonymous SNPs, fifteen synonymous SNPs, and five INDELs in 25 drug resistance-associated genes/intergenic regions (IGRs) in M. tb isolates. Sensitivity for detecting XDR is 33%, 58%, 83%, and 83%, respectively, using Mykrobe predictor, PhyResSE, TB-profiler, and in-house pipeline for WGS analysis, respectively. TB-profiler detected a rare mutation H70R in the gyrA gene in one pre-XDR isolate. Lineage 2.2.1 East-Asian (Beijing sublineage type) predominated (60%) in WGS data analysis of the XDR isolates.

CONCLUSIONS

Our findings suggest that in-house analysis of WGS data and TB-profiler sensitivity was better for the detection of second-line resistance as compared to other automated tested tools. Frequent upgradation of newer mutations associated with resistance needs to be updated, as it potentiates tailored treatment for patients.

TB-DROP: deep learning-based drug resistance prediction of Mycobacterium tuberculosis utilizing whole genome mutations

The most widely practiced strategy for constructing the deep learning (DL) prediction model for drug resistance of Mycobacterium tuberculosis (MTB) involves the adoption of ready-made and state-of-the-art architectures usually proposed for non-biological problems. However, the ultimate goal is to construct a customized model for predicting the drug resistance of MTB and eventually for the biological phenotypes based on genotypes. Here, we constructed a DL training framework to standardize and modularize each step during the training process using the latest tensorflow 2 API. A systematic and comprehensive evaluation of each module in the three currently representative models, including Convolutional Neural Network, Denoising Autoencoder, and Wide & Deep, which were adopted by CNNGWP, DeepAMR, and WDNN, respectively, was performed in this framework regarding module contributions in order to assemble a novel model with proper dedicated modules. Based on the whole-genome level mutations, a de novo learning method was developed to overcome the intrinsic limitations of previous models that rely on known drug resistance-associated loci. A customized DL model with the multilayer perceptron architecture was constructed and achieved a competitive performance (the mean sensitivity and specificity were 0.90 and 0.87, respectively) compared to previous ones. The new model developed was applied in an end-to-end user-friendly graphical tool named TB-DROP (TuBerculosis Drug Resistance Optimal Prediction: https://github.com/nottwy/TB-DROP ), in which users only provide sequencing data and TB-DROP will complete analysis within several minutes for one sample. Our study contributes to both a new strategy of model construction and clinical application of deep learning-based drug-resistance prediction methods.

Implementation of Whole Genome Sequencing of Tuberculosis Isolates in a Referral Center in Rome: Six Years' Experience in Characterizing Drug-Resistant TB and Disease Transmission

Over the past years, Tuberculosis (TB) control strategies have been effective in reducing drug-resistant (DR) TB globally; however, a wider implementation of new diagnostic strategies, such as Whole genome sequencing (WGS), would be critical for further improvement. The aim of this study, based on WGS of Mycobacterium tuberculosis (MTB) strains isolated in a TB referral center over 6 years, was to evaluate the efficacy of this methodology in improving therapy guidance for clinicians and in improving the understanding of the epidemiology of TB transmission. WGS was performed in addition to pDST on 1001 strains consecutively isolated between January 2016 and December 2021; the results allowed us to improve the quality of data on resistance and to identify possible clusters of transmission. Prediction of rifampicin-resistant (RR) or multi-drug-resistant TB strains (MDR-TB, defined as resistance to at least rifampicin and isoniazid) was obtained for 50 strains (5%). Mutations predictive of an MDR isolate were further characterized, and Ser450Leu and Ser315Thr were found to be the most frequent mutations in rpoB and katG genes, respectively. Discordances between WGS and phenotypic drug susceptibility testing (pDST) were found in few strains, and their impact on clinical decisions and outcome was addressed. The introduction of WGS in our Institute improved our diagnostic routine, allowing accurate patient management, and was a valid instrument for epidemiological investigations and infection control.

A precision overview of genomic resistance screening in Ecuadorian isolates of Mycobacterium tuberculosis using web-based bioinformatics tools

INTRODUCTION

Tuberculosis (TB) is among the deadliest diseases worldwide, and its impact is mainly due to the continuous emergence of resistant isolates during treatment due to the laborious process of resistance diagnosis, nonadherence to treatment and circulation of previously resistant isolates of Mycobacterium tuberculosis. In this study, we evaluated the performance and functionalities of web-based tools, including Mykrobe, TB-profiler, PhyResSE, KvarQ, and SAM-TB, for detecting resistance in 88 Ecuadorian isolates of Mycobacterium tuberculosis drug susceptibility tested previously. Statistical analysis was used to determine the correlation between genomic and phenotypic analysis. Our results showed that with the exception of KvarQ, all tools had the highest correlation with the conventional drug susceptibility test (DST) for global resistance detection (98% agreement and 0.941 Cohen's kappa), while SAM-TB, PhyResSE, TB-profiler and Mykrobe had better correlations with DST for first-line drug analysis individually. We also identified that in our study, only 50% of mutations characterized by the web-based tools in the rpoB, katG, embB, pncA, gyrA and rrs regions were canonical and included in the World Health Organization (WHO) catalogue. Our findings suggest that SAM-TB, PhyResSE, TB-profiler and Mykrobe were efficient in determining canonical resistance-related mutations, but more analysis is needed to improve second-line detection. Improving surveillance programs using whole-genome sequencing tools for first-line drugs, MDR-TB and XDR-TB is essential to understand the molecular epidemiology of TB in Ecuador.

IMPORTANCE

Tuberculosis, an infectious disease caused by Mycobacterium tuberculosis, most commonly affects the lungs and is often spread through the air when infected people cough, sneeze, or spit. However, despite the existence of effective drug treatment, patient adherence, long duration of treatment, and late diagnosis have reduced the effectiveness of therapy and increased drug resistance. The increase in resistant cases, added to the impact of the COVID-19 pandemic, has highlighted the importance of implementing efficient and timely diagnostic methodologies worldwide. The significance of our research is in evaluating and identifying a more efficient and user-friendly web-based tool to characterize resistance in Mycobacterium tuberculosis by whole-genome sequencing, which will allow more routine application to improve TB strain surveillance programs locally.

Genomic Characterization of Drug-Resistant Mycobacterium tuberculosis L2/Beijing Isolates from Astana, Kazakhstan

Kazakhstan ranks among the countries with the highest number of MDR-TB patients per 100,000 population worldwide. The successful transmission of local MDR strains of Mycobacterium tuberculosis (Mtb) poses a significant threat to disease control. In this study, we employed whole-genome sequencing to examine drug resistance, compensatory mutations, population structure, and transmission patterns in a sample of 24 clinical isolates of L2/Beijing Mtb collected in Astana, Kazakhstan between 2021 and 2022. The genotypic prediction of Mtb susceptibility to anti-TB agents was consistent with the phenotypic susceptibility, except for bedaquiline. An analysis of resistance-associated genes characterized most of the isolates as pre-extensively drug-resistant tuberculosis (pre-XDR-TB) (n = 15; 62.5%). The phylogenetic analysis grouped the isolates into four transmission clusters; the dominant cluster was assigned to the "aggressive" Central Asia outbreak (CAO) clade of L2/Beijing (n = 15; 62.5%). Thirteen mutations with putative compensatory effects were observed exclusively in Mtb isolates containing the rpoB S450L mutation. The putative compensatory mutations had a stabilizing effect on RpoABC protein stability and dynamics. The high prevalence of the CAO clade in the population structure of Mtb may explain the rapid spread of MDR-TB in Kazakhstan.

Advancing tuberculosis management: the role of predictive, preventive, and personalized medicine

Tuberculosis is a major global health issue, with approximately 10 million people falling ill and 1.4 million dying yearly. One of the most significant challenges to public health is the emergence of drug-resistant tuberculosis. For the last half-century, treating tuberculosis has adhered to a uniform management strategy in most patients. However, treatment ineffectiveness in some individuals with pulmonary tuberculosis presents a major challenge to the global tuberculosis control initiative. Unfavorable outcomes of tuberculosis treatment (including mortality, treatment failure, loss of follow-up, and unevaluated cases) may result in increased transmission of tuberculosis and the emergence of drug-resistant strains. Treatment failure may occur due to drug-resistant strains, non-adherence to medication, inadequate absorption of drugs, or low-quality healthcare. Identifying the underlying cause and adjusting the treatment accordingly to address treatment failure is important. This is where approaches such as artificial intelligence, genetic screening, and whole genome sequencing can play a critical role. In this review, we suggest a set of particular clinical applications of these approaches, which might have the potential to influence decisions regarding the clinical management of tuberculosis patients.

simpiTB - a pipeline designed to extract meaningful information from whole genome sequencing data of Mycobacterium tuberculosis complex, allows to combine genomic, phylogenetic and clustering analyses in existing SITVIT databases

Data obtained from new sequencing technologies are evolving rapidly, leading to the development of specific bioinformatic tools, pipelines and softwares. Several algorithms and tools are today available allowing a better identification and description of Mycobacterium tuberculosis complex (MTBC) isolates worldwide. Our approach consists in applying existing methods to analyze DNA sequencing data (from FASTA or FASTQ files), and tentatively extract meaningful information that would facilitate identification as well as a better understanding and management of MTBC isolates (taking into account whole genome sequencing and classical genotyping data). The aim of this study is to propose a pipeline analysis allowing to potentially simplify MTBC data analysis by providing different ways to interpret genomic or genotyping information based on existing tools. Furthermore, we propose a "reconciledTB" list making a link with results directly obtained from whole genome sequencing (WGS) data and results obtained from classical genotyping analysis (data inferred from SpoTyping and MIRUReader). Data visualization graphics and trees generated provide additional elements to better understand and confer associations among information overlap analyses. Additionally, comparison between data entered in an international genotyping database (SITVITEXTEND) and ensuing data obtained from the pipeline not only provide meaningful information, but further suggest that simpiTB could also be suitable for new data integration in specific TB genotyping databases.

Drug resistance prediction for Mycobacterium tuberculosis with reference graphs

Tuberculosis is a global pandemic disease with a rising burden of antimicrobial resistance. As a result, the World Health Organization (WHO) has a goal of enabling universal access to drug susceptibility testing (DST). Given the slowness of and infrastructure requirements for phenotypic DST, whole-genome sequencing, followed by genotype-based prediction of DST, now provides a route to achieving this. Since a central component of genotypic DST is to detect the presence of any known resistance-causing mutations, a natural approach is to use a reference graph that allows encoding of known variation. We have developed DrPRG (Drug resistance Prediction with Reference Graphs) using the bacterial reference graph method Pandora. First, we outline the construction of a Mycobacterium tuberculosis drug resistance reference graph. The graph is built from a global dataset of isolates with varying drug susceptibility profiles, thus capturing common and rare resistance- and susceptible-associated haplotypes. We benchmark DrPRG against the existing graph-based tool Mykrobe and the haplotype-based approach of TBProfiler using 44 709 and 138 publicly available Illumina and Nanopore samples with associated phenotypes. We find that DrPRG has significantly improved sensitivity and specificity for some drugs compared to these tools, with no significant decreases. It uses significantly less computational memory than both tools, and provides significantly faster runtimes, except when runtime is compared to Mykrobe with Nanopore data. We discover and discuss novel insights into resistance-conferring variation for M. tuberculosis - including deletion of genes katG and pncA - and suggest mutations that may warrant reclassification as associated with resistance.

Whole-Genome Sequencing of Mycobacterium tuberculosis Isolates from Diabetic and Non-Diabetic Patients with Pulmonary Tuberculosis

The association of tuberculosis and type 2 diabetes mellitus has been a recognized re-emerging challenge in management of the convergence of the two epidemics. Though much of the literature has studied this association, there is less knowledge in the field of genetic diversities that might occur in strains infecting tuberculosis patients with and without diabetes. Our study focused on determining the extent of diversity of genotypes of Mycobacterium tuberculosis in both these categories of patients. We subjected 55 M. tuberculosis isolates from patients diagnosed with pulmonary TB with and without type 2 diabetes mellitus to whole-genome sequencing on Illumina Hi Seq platform. The most common lineage identified was lineage 1, the Indo-Oceanic lineage (n = 22%), followed by lineage 4, the Euro-American lineage (n = 18, 33%); lineage 3, the East-African Indian lineage (n = 13, 24%); and lineage 2, the East-Asian lineage (n = 1, 2%). There were no significant differences in the distribution of lineages in both diabetics and non-diabetics in the South Indian population, and further studies involving computational analysis and comparative transcriptomics are needed to provide deeper insights.

The Role of Next-Generation Sequencing (NGS) in the Management of Tuberculosis: Practical Review for Implementation in Routine

Next-generation sequencing (NGS) has modernized the field of tuberculosis (TB) research by enabling high-throughput sequencing of the entire genome of Mycobacterium tuberculosis (MTB), which is the causative agent of TB. NGS has provided insights into the genetic diversity of MTB, which are crucial for understanding the evolution and transmission of the disease, and it has facilitated the identification of drug-resistant strains, enabling rapid and accurate tailoring of treatment. However, the high cost and the technical complexities of NGS currently limit its widespread use in clinical settings. International recommendations are thus necessary to facilitate the interpretation of polymorphisms, and an experimental approach is still necessary to correlate them to phenotypic data. This review aims to present a comparative, step-by-step, and up-to-date review of the techniques available for the implementation of this approach in routine laboratory workflow. Ongoing research on NGS for TB holds promise for improving our understanding of the disease and for developing more efficacious treatments.

Molecular epidemiology and transmission dynamics of multi-drug resistant tuberculosis strains using whole genome sequencing in the Amhara region, Ethiopia

BACKGROUND

Drug resistant Mycobacterium tuberculosis prevention and care is a major challenge in Ethiopia. The World health organization has designated Ethiopia as one of the 30 high burden multi-drug resistant tuberculosis (MDR-TB) countries. There is limited information regarding genetic diversity and transmission dynamics of MDR-TB in Ethiopia.

OBJECTIVE

To investigate the molecular epidemiology and transmission dynamics of MDR-TB strains using whole genome sequence (WGS) in the Amhara region.

METHODS

Forty-five MDR-TB clinical isolates from Amhara region were collected between 2016 and 2018, and characterized using WGS and 24-loci Mycobacterium Interspersed Repetitive Units Variable Number of Tandem Repeats (MIRU-VNTR) typing. Clusters were defined based on the maximum distance of 12 single nucleotide polymorphisms (SNPs) or alleles as the upper threshold of genomic relatedness. Five or less SNPs or alleles distance or identical 24-loci VNTR typing is denoted as surrogate marker for recent transmission.

RESULTS

Forty-one of the 45 isolates were analyzed by WGS and 44% (18/41) of the isolates were distributed into 4 clusters. Of the 41 MDR-TB isolates, 58.5% were classified as lineage 4, 36.5% lineage 3 and 5% lineage 1. Overall, TUR genotype (54%) was the predominant in MDR-TB strains. 41% (17/41) of the isolates were clustered into four WGS groups and the remaining isolates were unique strains. The predominant cluster (Cluster 1) was composed of nine isolates belonging to lineage 4 and of these, four isolates were in the recent transmission links.

CONCLUSIONS

Majority of MDR-TB strain cluster and predominance of TUR lineage in the Amhara region give rise to concerns for possible ongoing transmission. Efforts to strengthen TB laboratory to advance diagnosis, intensified active case finding, and expanded contact tracing activities are needed in order to improve rapid diagnosis and initiate early treatment. This would lead to the interruption of the transmission chain and stop the spread of MDR-TB in the Amhara region.

Operationalising targeted next-generation sequencing for routine diagnosis of drug-resistant TB

BACKGROUND

Phenotypic drug susceptibility testing (pDST) for Mycobacterium tuberculosis can take up to 8 weeks, while conventional molecular tests identify a limited set of resistance mutations. Targeted next-generation sequencing (tNGS) offers rapid results for predicting comprehensive drug resistance, and this study sought to explore its operational feasibility within a public health laboratory in Mumbai, India.

METHODS

Pulmonary samples from consenting patients testing Xpert MTB-positive were tested for drug resistance by conventional methods and using tNGS. Laboratory operational and logistical implementation experiences from study team members are shared below.

RESULTS

Of the total number of patients tested, 70% (113/161) had no history of previous TB or treatment; however, 88.2% (n = 142) had rifampicin-resistant/multidrug-resistant TB (RR/MDR-TB). There was a high concordance between resistance predictions of tNGS and pDST for most drugs, with tNGS more accurately identifying resistance overall. tNGS was integrated and adapted into the laboratory workflow; however, batching samples caused significantly longer result turnaround time, fastest at 24 days. Manual DNA extraction caused inefficiencies; thus protocol optimisations were performed. Technical expertise was required for analysis of uncharacterised mutations and interpretation of report templates. tNGS cost per sample was US$230, while for pDST this was US$119.

CONCLUSIONS

Implementation of tNGS is feasible in reference laboratories. It can rapidly identify drug resistance and should be considered as a potential alternative to pDST.

pncA Large Deletion is the Characteristic of Pyrazinamide-Resistant Mycobacterium tuberculosis belonging to the East Asian Lineage

BACKGROUND

Pyrazinamide (PZA) is often used as an add-on agent in the treatment of multidrug-resistant tuberculosis, regardless of phenotypic drug susceptibility testing (pDST) results. However, evaluating the effectiveness of PZA is challenging because of its low pH activity, which can result in unreliable pDST results. This study aimed to investigate the genomic characteristics associated with PZA resistance that can be used to develop genotypic DST.

MATERIALS AND METHODS

A publicly available whole genome sequencing (WGS) dataset of 10,725 Mycobacterium tuberculosis complex genomes (3,326 phenotypically PZA-resistant and 7,399 phenotypically PZA-susceptible isolates) were analyzed.

RESULTS

In total, 2,934 pncA non-silent mutations were identified in 2,880 isolates (26.9%). Detected mutations were found throughout the entire coding region of pncA in a scattered pattern, of which the most frequent mutation was p.Q10P (n = 278), followed by p.H57D (n = 167) and c.-11A>G (n = 122). The sensitivity and specificity of the group 1 or 2 mutations reported by the World Health Organization (WHO) mutational catalogue were 73.0% and 98.9%, respectively. We further identified 18 novel pncA mutations that were significantly associated with phenotypically PZA-resistant. In addition to these mutations, we identified 102 large deletions in the pncA gene, and all but two isolates were phenotypically resistant to PZA isolates. Notably, pncA deletions were mutually exclusive to pncA mutations, and more than half of the isolates with pncA large deletions belonged to the East Asian lineage (67.6%). The sensitivity, specificity, positive predictive value, and negative predictive value of the pooled variants (group 1 or 2 mutations, novel resistance-associated mutations, and large deletions of the pncA gene) were 79.0%, 98.9%, 97.0%, and 91.3%, respectively. The area under the curve (AUC) value for the pooled variants was significantly higher than the AUC value for the group 1 or 2 mutations (P <0.001), indicating that the pooled variants have a better discriminative ability for predicting PZA resistance.

CONCLUSION

Using WGS, we found that the pncA mutations are scattered without specific mutational hotspots, and large deletions associated with PZA resistance are more common in the East Asian lineage of M. tuberculosis isolates. Our data also demonstrated the reliability of group 1 or 2 mutations presented in the WHO mutation catalogue and the need for further investigation on group 3 mutations, contributing to the evaluation of the current knowledge base on mutations associated with the PZA-resistant M. tuberculosis complex.

Rapid Identification of Lineage and Drug Resistance in Clinical Samples of Mycobacterium tuberculosis

BACKGROUND

Mycobacterium tuberculosis is a slow-growing bacterium, which could delay its diagnosis and, therefore, promote the spread of the disease. Whole-genome sequencing allows us to obtain the complete drug-resistance profile of the strain; however, bacterial cultivation of clinical samples, along with complex processing, is required.

METHODS

In this work, we explore AmpliSeq, an amplicon-based enrichment method for preparing libraries for targeted next-generation sequencing, to identify lineage and drug resistance directly from clinical samples.

RESULTS

In our study, 111 clinical samples were tested. The lineage was identified in 100% of the culture-derived samples (52/52), in 95% of the smear (BK)-positive clinical samples (38/40) and in 42.1% of the BK-negative clinical samples (8/19). The drug-resistance profile was accurately identified in all but 11 samples, in which some phenotypic and genotypic discrepancies were found. In this respect, our panels were not exact in the detection of streptomycin resistance for isolates derived from clinical samples, as an extremely high number of SNPs in the rrs and rrl genes were detected due to cross-contamination.

CONCLUSION

This technique has demonstrated high sensitivity in obtaining the drug-resistance profile of the isolates, as even those samples with DNA concentrations below the detection limit of Qubit produced a result. AmpliSeq technology is cheaper than whole-genome sequencing, easy to perform by laboratory technicians and applicable to any microorganism using the Ion Torrent platform.

Whole-genome sequencing reveals genotypic resistance in phenotypically susceptible Mycobacterium tuberculosis clinical isolates

Background

Whole-genome sequencing (WGS) data of Mycobacterium tuberculosis (MTB) complex strains have revealed insights about genetic variants associated with drug resistance (DR). Rapid genome-based diagnostics are being sought for specific and sensitive identification of DR; however, correct prediction of resistance genotypes requires both informatics tools and understanding of available evidence. We analyzed WGS datasets from phenotypically susceptible MTB strains using MTB resistance identification software.

Methods

WGS data for 1526 MTB isolates classified as phenotypically drug susceptible were downloaded from the ReSeqTB database. The TB-Profiler software was used to call Single Nucleotide Variants (SNV) associated with resistance to rifampicin (RIF), isoniazid (INH), ethambutol (EMB), pyrazinamide, fluoroquinolone (FLQ), streptomycin (STR), and aminoglycosides. The SNV were further matched against the 2021 World Health Organization (WHO) catalogue of resistance mutations.

Results

Genome analysis of 1526 MTB strains susceptible to first-line drugs revealed 39 SNV associated with DR to be present in across 14 genes in 5.9% (n = 90) isolates. Further interpretation of SNV based on the WHO catalog of mutations revealed resistance that 21 (1.4%) MTB isolates were resistant to first-line (4 to RIF, 14 to INH, 3 to EMB) drugs. While, 36 (2.6%) isolates were resistant to second-line (19 to STR, 14 to FLQ, and three to capreomycin) agents. The most frequent predictive SNV were; rpoB Ser450 Leu for RIF; katG Ser315Thr, inhA Ser94Ala, fabG1-15C >T (for INH); gyrA Asp94Gly for FLQ; embB Met306 Leu for EMB; rpsL Lys43Arg for STR; and tlyA Asn236 Lys for Capreomycin.

Conclusions

Our study highlights the value of WGS-based sequence data for identifying resistance in MTB. It also shows how MTB strains may be misclassified simply on phenotypic drug susceptibility testing, and that correct genome interpretation is key for correct interpretation of resistance genotypes that can be used to guide clinical treatment.

Latent tuberculosis and computational biology: A less-talked affair

Tuberculosis (TB) is a pervasive and devastating air-borne disease caused by the organisms belonging to the Mycobacterium tuberculosis (Mtb) complex. Currently, it is the global leader in infectious disease-related death in adults. The proclivity of TB to enter the latent state has become a significant impediment to the global effort to eradicate TB. Despite decades of research, latent tuberculosis (LTB) mechanisms remain poorly understood, making it difficult to develop efficient treatment methods. In this review, we seek to shed light on the current understanding of the mechanism of LTB, with an accentuation on the insights gained through computational biology. We have outlined various well-established computational biology components, such as omics, network-based techniques, mathematical modelling, artificial intelligence, and molecular docking, to disclose the crucial facets of LTB. Additionally, we highlighted important tools and software that may be used to conduct a variety of systems biology assessments. Finally, we conclude the article by addressing the possible future directions in this field, which might help a better understanding of LTB progression.

Performances of bioinformatics tools for the analysis of sequencing data of Mycobacterium tuberculosis complex strains

Whole-genome sequencing of Mycobacterium tuberculosis complex (MTBC) strains is a rapidly growing tool to obtain results regarding the resistance and phylogeny of the strains. We evaluated the performances of two bioinformatics tools for the analysis of whole-genome sequences of MTBC strains. Two hundred and twenty-seven MTBC strains were isolated and whole-genome sequenced at the laboratory of Avicenne Hospital between 2015 and 2021. We investigated the resistance and susceptibility status of strains using two online tools, Mykrobe and PhyResSE. We compared the genotypic and phenotypic resistance results obtained by drug susceptibility testing. Unlike with the Mykrobe tool, sequencing quality data were obtained using PhyResSE: average coverage of 98% and average depth of 119X. We found a similar concordance between phenotypic and genotypic results when determining susceptibility to first-line anti-tuberculosis drugs (95%) with both tools. The sensitivity and specificity of each tool compared to the phenotypic method were respectively 72% [52-87] and 98% [96-99] for Mykrobe and 76% [57-90] and 97% [94-99] for PhyResSE. Mykrobe and PhyResSE were easy to use and efficient. These platforms are accessible to people not trained in bioinformatics and constitute a complementary approach to phenotypic methods for the study of MTBC strains.

Experiences from 4 Years of Organization of an External Quality Assessment for Mycobacterium tuberculosis Whole-Genome Sequencing in the European Union/European Economic Area

Here, we report the development and key features of the first external quality assessment (EQA) scheme for Mycobacterium tuberculosis whole-genome sequencing (WGS). The results of four rounds (2017 to 2020) of implementation within the European tuberculosis reference laboratories network (ERLTB-Net-2) are presented and discussed. EQA panels comprising 10 genomic DNAs were distributed to ERLTB-Net 2 laboratories volunteering to participate in this exercise. Since 2018, five FASTQ files were added to better assess the dry WGS processes, and in 2020, three of the five files were replaced by synthetic files (providing additional flexibility for the mutations included in the panels). Ten National tuberculosis reference laboratories participated in all four EQA rounds, and seven participated in at least one. High-confidence resistance mutations were correctly identified by all laboratories, but challenges remained with respect to the identification of mixed loci and interpretation of rare mutations. M. tuberculosis genotyping and clustering analysis was >90% accurate for pure samples with the main challenges being related to the analysis of mixed genotypes and DNA FASTQ files. The development and implementation of this WGS EQA scheme has contributed to the continuous improvement in performance of participating laboratories in M. tuberculosis WGS and data analysis. This scheme can serve as a model of comprehensive quality assessment for M. tuberculosis WGS that can be replicated in different settings worldwide. IMPORTANCE The wider availability of whole-genome sequencing (WGS) coupled to new developments in bioinformatic tools and databases to interpret Mycobacterium tuberculosis complex WGS data has accelerated the adoption of this method for the routine prediction of antimycobacterial drug resistance and genotyping, thus necessitating the establishment of a comprehensive external quality control system. Here, we report 4 years of development and results from such a panel.

Whole-genome sequencing-based analyses of drug-resistant Mycobacterium tuberculosis from Taiwan

Drug-resistant tuberculosis (DR-TB) posed challenges to global TB control. Whole-genome sequencing (WGS) is recommended for predicting drug resistance to guide DR-TB treatment and management. Nevertheless, data are lacking in Taiwan. Phenotypic drug susceptibility testing (DST) of 12 anti-TB drugs was performed for 200 Mycobacterium tuberculosis isolates. WGS was performed using the Illumina platform. Drug resistance profiles and lineages were predicted in silico using the Total Genotyping Solution for TB (TGS-TB). Using the phenotypic DST results as a reference, WGS-based prediction demonstrated high concordance rates of isoniazid (95.0%), rifampicin (RIF) (98.0%), pyrazinamide (98.5%) and fluoroquinolones (FQs) (99.5%) and 96.0% to 99.5% for second-line injectable drugs (SLIDs); whereas, lower concordance rates of ethambutol (87.5%), streptomycin (88.0%) and ethionamide (84.0%). Furthermore, minimum inhibitory concentrations confirmed that RIF rpoB S450L, FQs gyrA D94G and SLIDs rrs a1401g conferred high resistance levels. Besides, we identified lineage-associated mutations in lineage 1 (rpoB H445Y and fabG1 c-15t) and predominant lineage 2 (rpoB S450L and rpsL K43R). The WGS-based prediction of drug resistance is highly concordant with phenotypic DST results and can provide comprehensive genetic information to guide DR-TB precision therapies in Taiwan.

Evaluation of Everolimus Activity against Mycobacterium tuberculosis Using In Vitro Models of Infection

Even though Everolimus has been investigated in a phase II randomized trial as a host-directed therapy (HDT) to treat tuberculosis (TB), an oncological patient treated with Everolimus for a neuroendocrine pancreatic neoplasia developed active TB twice and a non-tuberculous mycobacterial (NTM) infection in a year and a half time span. To investigate this interesting case, we isolated and genotypically characterized the Mycobacterium tuberculosis (Mtb) clinical strain from the patient and tested the effect of Everolimus on its viability in an axenic culture and in a peripheral blood mononuclear cell (PBMCs) infection model. To exclude strain-specific resistance, we tested the activity of Everolimus against Mtb strains of ancient and modern lineages. Furthermore, we investigated the Everolimus effect on ROS production and autophagy modulation during Mtb infection. Everolimus did not have a direct effect on mycobacteria viability and a negligible effect during Mtb infection in host cells, although it stimulated autophagy and ROS production. Despite being a biologically plausible HDT against TB, Everolimus does not exert a direct or indirect activity on Mtb. This case underlines the need for a careful approach to drug repurposing and implementation and the importance of pre-clinical experimental studies.

Clinical utility of target-based next-generation sequencing for drug-resistant TB

BACKGROUND: In high TB burden countries, access to drug susceptibility testing is a major bottleneck. Targeted next-generation sequencing (tNGS) is a promising technology for rapid resistance detection. This study assessed the role of tNGS for the diagnosis of drug-resistant TB (DR-TB).METHODS: A total of 161 samples from bacteriologically confirmed TB cases were subjected to tNGS using the Deeplex^® Myc-TB kit and sequenced using the MiSeq platform. These samples were also processed for conventional phenotypic DST (pDST) using 13 drugs on Mycobacteria Growth Indicator Tube and line-probe assays (MTBDRplus and MTBDRsl).RESULTS: There were 146 DR-TB and 15 drug-susceptible TB (DS-TB) samples. About 70% of patients with DR-TB had no previous TB treatment history. Overall, 88.2% had rifampicin-resistant/multidrug-resistant TB (RR/MDR-TB), 58.5% pre-extensively drug-resistant TB (pre-XDR-TB) and 9.2% had XDR-TB as defined by the WHO (2020). Around 8% (n = 13) of samples were non-culturable; however, identified 8 were resistant to first and second-line drugs using tNGS. Resistance frequency was similar across methods, with discordance in drugs less reliable using pDST or with limited mutational representation within databases. Sensitivities were aligned with literature reports for most drugs. We observed 10% heteroresistance, while 75% of strains were of Lineages 2 and 3.CONCLUSIONS: Programme data supported tNGS in the diagnosis of DR-TB for early treatment using individualised regimens.

TBProfiler for automated calling of the association with drug resistance of variants in Mycobacterium tuberculosis

Following a huge global effort, the first World Health Organization (WHO)-endorsed catalogue of 17,356 variants in the Mycobacterium tuberculosis complex along with their classification as associated with resistance (interim), not associated with resistance (interim) or uncertain significance was made public In June 2021. This marks a critical step towards the application of next generation sequencing (NGS) data for clinical care. Unfortunately, the variant format used makes it difficult to look up variants when NGS data is generated by other bioinformatics pipelines. Furthermore, the large number of variants of uncertain significance in the catalogue hamper its useability in clinical practice. We successfully converted 98.3% of variants from the WHO catalogue format to the standardized HGVS format. We also created TBProfiler version 4.4.0 to automate the calling of all variants located in the tier 1 and 2 candidate resistance genes along with their classification when listed in the WHO catalogue. Using a representative sample of 339 clinical isolates from South Africa containing 691 variants in a tier 1 or 2 gene, TBProfiler classified 105 (15%) variants as conferring resistance, 72 (10%) as not conferring resistance and 514 (74%) as unclassified, with an average of 29 unclassified variants per isolate. Using a second cohort of 56 clinical isolates from a TB outbreak in Spain containing 21 variants in the tier 1 and 2 genes, TBProfiler classified 13 (61.9%) as unclassified, 7 (33.3%) as not conferring resistance, and a single variant (4.8%) classified as conferring resistance. Continued global efforts using standardized methods for genotyping, phenotyping and bioinformatic analyses will be essential to ensure that knowledge on genomic variants translates into improved patient care.

High Throughput Sequencing for Clinical Tuberculosis: An Overview

High throughput sequencing (HTS) can identify the presence of Mycobacterium tuberculosis DNA in a clinical sample while also providing information on drug susceptibility. Multiple studies have provided a context for exploring the clinical application of HTS for TB diagnosis. The workflow challenges, strengths and limitations of the various sequencing platforms, and tools used for analysis are presented to provide a framework for further innovations in the field.

The potential of genomics for infectious disease forecasting

Genomic technologies have led to tremendous gains in understanding how pathogens function, evolve and interact. Pathogen diversity is now measurable at high precision and resolution, in part because over the past decade, sequencing technologies have increased in speed and capacity, at decreased cost. Alongside this, the use of models that can forecast emergence and size of infectious disease outbreaks has risen, highlighted by the coronavirus disease 2019 pandemic but also due to modelling advances that allow for rapid estimates in emerging outbreaks to inform monitoring, coordination and resource deployment. However, genomics studies have remained largely retrospective. While they contain high-resolution views of pathogen diversification and evolution in the context of selection, they are often not aligned with designing interventions. This is a missed opportunity because pathogen diversification is at the core of the most pressing infectious public health challenges, and interventions need to take the mechanisms of virulence and understanding of pathogen diversification into account. In this Perspective, we assess these converging fields, discuss current challenges facing both surveillance specialists and modellers who want to harness genomic data, and propose next steps for integrating longitudinally sampled genomic data with statistical learning and interpretable modelling to make reliable predictions into the future.

Tuberculosis Treatment Monitoring and Outcome Measures: New Interest and New Strategies

Despite the advent of new diagnostics, drugs and regimens, tuberculosis (TB) remains a global public health threat. A significant challenge for TB control efforts has been the monitoring of TB therapy and determination of TB treatment success. Current recommendations for TB treatment monitoring rely on sputum and culture conversion, which have low sensitivity and long turnaround times, present biohazard risk, and are prone to contamination, undermining their usefulness as clinical treatment monitoring tools and for drug development. We review the pipeline of molecular technologies and assays that serve as suitable substitutes for current culture-based readouts for treatment response and outcome with the potential to change TB therapy monitoring and accelerate drug development.

Antimicrobial Resistance and Its Spread Is a Global Threat

Antimicrobial resistance (AMR) is a challenge to human wellbeing the world over and is one of the more serious public health concerns. AMR has the potential to emerge as a serious healthcare threat if left unchecked, and could put into motion another pandemic. This establishes the need for the establishment of global health solutions around AMR, taking into account microdata from different parts of the world. The positive influences in this regard could be establishing conducive social norms, charting individual and group behavior practices that favor global human health, and lastly, increasing collective awareness around the need for such action. Apart from being an emerging threat in the clinical space, AMR also increases treatment complexity, posing a real challenge to the existing guidelines around the management of antibiotic resistance. The attribute of resistance development has been linked to many genetic elements, some of which have complex transmission pathways between microbes. Beyond this, new mechanisms underlying the development of AMR are being discovered, making this field an important aspect of medical microbiology. Apart from the genetic aspects of AMR, other practices, including misdiagnosis, exposure to broad-spectrum antibiotics, and lack of rapid diagnosis, add to the creation of resistance. However, upgrades and innovations in DNA sequencing technologies with bioinformatics have revolutionized the diagnostic industry, aiding the real-time detection of causes of AMR and its elements, which are important to delineating control and prevention approaches to fight the threat.

Prediction of drug resistance by Sanger sequencing of Mycobacterium tuberculosis complex strains isolated from multidrug resistant tuberculosis suspect patients in Ethiopia

Background

Ethiopia is one of the high multidrug-resistant tuberculosis (MDR-TB) burden countries. However, phenotypic drug susceptibility testing can take several weeks due to the slow growth of Mycobacterium tuberculosis complex (MTBC) strains. In this study, we assessed the performance of a Sanger sequencing approach to predict resistance against five anti-tuberculosis drugs and the pattern of resistance mediating mutations.

Methods

We enrolled 226 MTBC culture-positive MDR-TB suspects and collected sputum specimens and socio-demographic and TB related data from each suspect between June 2015 and December 2016 in Addis Ababa, Ethiopia. Phenotypic drug susceptibility testing (pDST) for rifampicin, isoniazid, pyrazinamide, ethambutol, and streptomycin using BACTEC MGIT 960 was compared with the results of a Sanger sequencing analysis of seven resistance determining regions in the genes rpoB, katG, fabG-inhA, pncA, embB, rpsL, and rrs.

Result

DNA isolation for Sanger sequencing was successfully extracted from 92.5% (209/226) of the MTBC positive cultures, and the remaining 7.5% (17/226) strains were excluded from the final analysis. Based on pDST results, drug resistance proportions were as follows: isoniazid: 109/209 (52.2%), streptomycin: 93/209 (44.5%), rifampicin: 88/209 (42.1%), ethambutol: 74/209 (35.4%), and pyrazinamide: 69/209 (33.0%). Resistance against isoniazid was mainly mediated by the mutation katG S315T (97/209, 46.4%) and resistance against rifampicin by rpoB S531L (58/209, 27.8%). The dominating resistance-conferring mutations for ethambutol, streptomycin, and pyrazinamide affected codon 306 in embB (48/209, 21.1%), codon 88 in rpsL (43/209, 20.6%), and codon 65 in pncA (19/209, 9.1%), respectively. We observed a high agreement between phenotypic and genotypic DST, such as 89.9% (at 95% confidence interval [CI], 84.2%–95.8%) for isoniazid, 95.5% (95% CI, 91.2%–99.8%) for rifampicin, 98.6% (95% CI, 95.9–100%) for ethambutol, 91.3% (95% CI, 84.6–98.1%) for pyrazinamide and 57.0% (95% CI, 46.9%–67.1%) for streptomycin.

Conclusion

We detected canonical mutations implicated in resistance to rifampicin, isoniazid, pyrazinamide, ethambutol, and streptomycin. High agreement with phenotypic DST results for all drugs renders Sanger sequencing promising to be performed as a complementary measure to routine phenotypic DST in Ethiopia. Sanger sequencing directly from sputum may accelerate accurate clinical decision-making in the future.

Diversity and novel mutations in membrane transporters of Mycobacterium tuberculosis

Mycobacterium tuberculosis (MTB), the causative agent of tuberculosis (TB), encodes a family of membrane proteins belonging to Resistance-Nodulation-Cell Division (RND) permeases also called multidrug resistance pumps. Mycobacterial membrane protein Large (MmpL) transporters represent a subclass of RND transporters known to participate in exporting of lipid components across the cell envelope. These proteins perform an essential role in MTB survival; however, there are no data regarding mutations in MmpL, polyketide synthase (PKS) and acyl-CoA dehydrogenase FadE proteins from Khyber Pakhtunkhwa, Pakistan. This study aimed to screen mutations in transmembrane transporter proteins including MmpL, PKS and Fad through whole-genome sequencing (WGS) in local isolates of Khyber Pakhtunkhwa province, Pakistan. Fourteen samples were collected from TB patients and drug susceptibility testing was performed. However, only three samples were completely sequenced. Moreover, 209 whole-genome sequences of the same geography were also retrieved from NCBI GenBank to analyze the diversity of mutations in MmpL, PKS and Fad proteins. Among the 212 WGS (Accession ID: PRJNA629298, PRJNA629388, and ERR2510337-ERR2510345, ERR2510546-ERR2510645), numerous mutations in Fad (n = 756), PKS (n = 479), and MmpL (n = 306) have been detected. Some novel mutations were also detected in MmpL, PKS and acyl-CoA dehydrogenase Fad. Novel mutations including Asn576Ser in MmpL8, Val943Gly in MmpL9 and Asn145Asp have been detected in MmpL3. The presence of a large number of mutations in the MTB membrane may have functional consequences on proteins. However, further experimental studies are needed to elucidate the variants' effect on MmpL, PKS and FadE functions.

How a PCR Sequencing Strategy Can Bring New Data to Improve the Diagnosis of Ethionamide Resistance

Ethionamide (ETH) is a second-line antituberculosis drug. ETH resistance (ETH-R) is mainly related to the mutations of the monooxygenase-activating ETH (EthA), the ETH target (InhA), and the inhA promoter. Nonetheless, diagnosing ETH-R is still challenging. We assessed the strategy used for detecting ETH-R at the French National Reference Center for Mycobacteria in 497 MDR-TB isolates received from 2008 to 2016. The genotypic ETH’s resistance detection was performed by sequencing ethA, ethR, the ethA-ethR intergenic region, and the inhA promoter in the 497 multidrug-resistant isolates, whereas the phenotypic ETH susceptibility testing (PST) was performed using the reference proportion method. Mutations were found in up to 76% of the 387 resistant isolates and in up to 28% of the 110 susceptible isolates. Our results do not support the role of ethR mutations in ETH resistance. Altogether, the positive predictive value of our genotypic strategy to diagnose ETH-R was improved when only considering the variants included in the WHO catalogue and in other databases, such as TB-Profiler. Therefore, our work will help to update the list of mutations that could be graded as being associated with resistance to improve ETH-R diagnosis.

Genetic Diversity of Mycobacterium tuberculosis Isolates From an Amerindian Population in Chiapas, México

This is the first report of the genetic diversity of the Mycobacterium tuberculosis complex isolates found in a Mexican-Amerindian setting. In this study, we analyzed isolates collected from the Highlands region of Chiapas, Mexico, by using spoligotyping and whole-genome sequencing analyses. Seventy-three M. tuberculosis isolates were analyzed initially by spoligotyping; no new spoligotypes were identified. Nineteen percent of the isolates were identified as SIT53 (T1) (n = 14), followed by SIT42 (14%, n = 10, LAM9) and SIT119 (11%; n = 8, X1). SIT53, SIT42, and orphan isolates (16.4%, n = 12) constituted about 50% of the isolates studied and were subjected to whole-genome sequencing (WGS) analysis. Most SIT53 (10/12) isolates belonged to the Euro-American sub-lineage 4.8. Most SIT42 isolates (4/7) as .well as most orphan isolates (5/8) belonged to the lineage 4.3.3 LAM group. By comparing the single-nucleotide polymorphism (SNP) patterns of the SIT53 isolates, we found one clone (&lt;7 SNPs) and four clustered isolates (&lt;15 SNPs). In isolates from the SIT42 and orphan groups, we did not find any clones or clusters. This work demonstrates the success of sub-lineage 4.8 to predominate in Mexico and confirms the dominion of sub-lineage 4.3.3 in Central and South America.

Consistency of Mycobacterium tuberculosis Complex Spoligotyping between the Membrane-Based Method and In Silico Approach

To tackle the spread of tuberculosis (TB), epidemiological studies are undertaken worldwide to investigate TB transmission chains. Clustered regulatory interspaced short palindromic repeats (CRISPR) locus diversity, also called spoligotyping, is a widely used genotyping assay for the characterization of Mycobacterium tuberculosis complex (MTBC). We compared herein the spoligotyping of MTBC clinical isolates using a membrane-based method (following an initial PCR step) and whole-genome sequencing (WGS)-based method (i.e., in silico spoligotyping). All MTBC strains isolated at the Lyon University Hospital, France, between November 2016 and December 2020 were included (n = 597). Spoligotyping profiles were also used for species identification among the MTBC. Outputs of both methods were analyzed, and discrepant results were investigated thanks to CRISPRbuilder-TB. The overall agreement was 85.7%. Spacer discrepancies observed between the methods were due to the insertion of IS6110 within the direct repeat (DR) sequence upstream or downstream of spacers, mutated DR sequences, or truncated spacers. Discrepancies did not impact species identification. Although spoligotyping-based species identification was inconclusive for 29 isolates, SNP-based phylogeny conducted after WGS allowed the identification of 23 M. tuberculosis (Mtb), 2 M. canettii, and 4 mixed MTBC infections. WGS yielded very few discrepancies compared to membrane-based spoligotyping. Overall agreement was significantly improved (92.4%) by the CRISPR locus reconstruction using CRISPRbuilder-TB for the MTBC isolates with the shared international type 53 in silico spoligotyping. A smooth transition from the membrane-based to the in silico-based genotyping of M. tuberculosis isolates is, therefore, possible for TB diagnosis and epidemiologic survey. IMPORTANCE Whole-genome sequencing (WGS) has profoundly transformed the perspectives of tuberculosis (TB) diagnosis, providing a better discriminatory power to determine relatedness between Mycobacterium tuberculosis complex (MTBC) isolates. Previous genotyping approaches, such as spoligotyping consisting of an initial PCR step followed by reverse dot hybridization, are currently being replaced by WGS. Several pipelines have been developed to extract a spoligotype from WGS data (in silico spoligotyping) allowing for the continuity of MTBC molecular surveys before and after WGS implementation. The present study found very good overall agreement between hybridization to membrane-based spoligotyping and in silico spoligotyping, indicating the possibility of a smooth transition from the traditional to the in silico-based genotyping of MTBC isolates for TB diagnosis and epidemiological survey.

Mycobacterium tuberculosis Lineages Associated with Mutations and Drug Resistance in Isolates from India

Current knowledge on resistance-conferring determinants in Mycobacterium tuberculosis is biased toward globally dominant lineages 2 and 4. In contrast, lineages 1 and 3 are predominant in India. In this study, we performed whole-genome sequencing of 498 MDR M. tuberculosis isolates from India to determine the prevalence of drug resistance mutations and to understand the genomic diversity. A retrospective collection of 498 M. tuberculosis isolates submitted to the National Institute for Research in Tuberculosis for phenotypic susceptibility testing between 2014 to 2016 were sequenced. Genotypic resistance prediction was performed using known resistance-conferring determinants. Genotypic and phenotypic results for 12 antituberculosis drugs were compared, and sequence data were explored to characterize lineages and their association with drug resistance. Four lineages were identified although lineage 1 predominated (43%). The sensitivity of prediction for isoniazid and rifampicin was 92% and 98%, respectively. We observed lineage-specific variations in the proportion of isolates with resistance-conferring mutations, with drug resistance more common in lineages 2 and 3. Disputed mutations (codons 430, 435, 445, and 452) in the rpoB gene were more common in isolates other than lineage 2. Phylogenetic analysis and pairwise SNP difference revealed high genetic relatedness of lineage 2 isolates. WGS based resistance prediction has huge potential, but knowledge of regional and national diversity is essential to achieve high accuracy for resistance prediction. IMPORTANCE Current knowledge on resistance-conferring determinants in Mycobacterium tuberculosis is biased toward globally dominant lineages 2 and 4. In contrast, lineages 1 and 3 are predominant in India. We performed whole-genome sequencing of 498 MDR M. tuberculosis isolates from India to determine the prevalence of drug resistance mutations and to understand genomic diversity. Four lineages were identified although lineage 1 predominated (43%). The sensitivity of prediction for isoniazid and rifampicin was 92% and 98%, respectively. We observed lineage-specific variations in the proportion of isolates with resistance-conferring mutations, with drug resistance more common in lineages 2 and 3. Disputed mutations (codons 430, 435, 445, and 452) in the rpoB gene were more common in isolates other than lineage 2. Phylogenetic analysis and pairwise SNP difference revealed high genetic relatedness of lineage 2 isolates. WGS based resistance prediction has huge potential, but knowledge of regional and national diversity is essential to achieve high accuracy for resistance prediction.

Whole genome sequencing analysis to evaluate the influence of T2DM on polymorphisms associated with drug resistance in M. tuberculosis

Background

Type 2 diabetes mellitus (T2DM) has been associated with treatment failure, and the development of drug resistance in tuberculosis (TB). Also, whole-genome sequencing has provided a better understanding and allowed the growth of knowledge about polymorphisms in genes associated with drug resistance. Considering the above, this study analyzes genome sequences to evaluate the influence of type 2 diabetes mellitus in the development of mutations related to tuberculosis drug resistance. M. tuberculosis isolates from individuals with (n = 74), and without (n = 74) type 2 diabetes mellitus was recovered from online repositories, and further analyzed.

Results

The results showed the presence of 431 SNPs with similar proportions between diabetics, and non-diabetics individuals (48% vs. 52%), but with no significant relationship. A greater number of mutations associated with rifampicin resistance was observed in the T2DM-TB individuals (23.2% vs. 16%), and the exclusive presence of rpoBQ432L, rpoBQ432P, rpoBS441L, and rpoBH445L variants. While these variants are not private to T2DM-TB cases they are globally rare highlighting a potential role of T2DM. The phylogenetic analysis showed 12 sublineages, being 4.1.1.3, and 4.1.2.1 the most prevalent in T2DM-TB individuals but not differing from those most prevalent in their geographic location. Four clonal complexes were found, however, no significant relationship with T2DM was observed. Samples size and potential sampling biases prevented us to look for significant associations.

Conclusions

The occurrence of globally rare rifampicin variants identified only in isolates from individuals with T2DM could be due to the hyperglycemic environment within the host. Therefore, further studies about the dynamics of SNPs’ generation associated with antibiotic resistance in patients with diabetes mellitus are necessary.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08709-z.

MycoVarP: Mycobacterium Variant and Drug Resistance Prediction Pipeline for Whole-Genome Sequence Data Analysis

Whole-genome sequencing (WGS) provides a comprehensive tool to analyze the bacterial genomes for genotype–phenotype correlations, diversity of single-nucleotide variant (SNV), and their evolution and transmission. Several online pipelines and standalone tools are available for WGS analysis of Mycobacterium tuberculosis (Mtb) complex (MTBC). While they facilitate the processing of WGS data with minimal user expertise, they are either too general, providing little insights into bacterium-specific issues such as gene variations, INDEL/synonymous/PE-PPE (IDP family), and drug resistance from sample data, or are limited to specific objectives, such as drug resistance. It is understood that drug resistance and lineage-specific issues require an elaborate prioritization of identified variants to choose the best target for subsequent therapeutic intervention. Mycobacterium variant pipeline (MycoVarP) addresses these specific issues with a flexible battery of user-defined and default filters. It provides an end-to-end solution for WGS analysis of Mtb variants from the raw reads and performs two quality checks, viz, before trimming and after alignments of reads to the reference genome. MycoVarP maps the annotated variants to the drug-susceptible (DS) database and removes the false-positive variants, provides lineage identification, and predicts potential drug resistance. We have re-analyzed the WGS data reported by Advani et al. (2019) using MycoVarP and identified some additional variants not reported so far. We conclude that MycoVarP will help in identifying nonsynonymous, true-positive, drug resistance–associated variants more effectively and comprehensively, including those within the IDP of the PE-PPE/PGRS family, than possible from the currently available pipelines.

Novel Mutations in Putative Nicotinic Acid Phosphoribosyltransferases of Mycobacterium tuberculosis and Their Effect on Protein Thermodynamic Properties

pncB1 and pncB2 are two putative nicotinic acid phosphoribosyltransferases, playing a role in cofactor salvage and drug resistance in Mycobacterium tuberculosis. Mutations have been reported in first- and second-line drug targets, causing resistance. However, pncB1 and pncB2 mutational data are not available, and neither of their mutation effects have been investigated in protein structures. The current study has been designed to investigate mutations and also their effects on pncB1 and pncB2 structures. A total of 287 whole-genome sequenced data of drug-resistant Mycobacterium tuberculosis isolates from Khyber Pakhtunkhwa of Pakistan were retrieved (BioSample PRJEB32684, ERR2510337-ERR2510445, ERR2510546-ERR2510645) from NCBI. The genomic data were analyzed for pncB1 and pncB2 mutations using PhyResSE. All the samples harbored numerous synonymous and non-synonymous mutations in pncB1 and pncB2 except one. Mutations Pro447Ser, Arg286Arg, Gly127Ser, and delTCAGGCCG1499213>1499220 in pncB1 are novel and have not been reported in literature and TB databases. The most common non-synonymous mutations exhibited stabilizing effects on the pncB1 structure. Moreover, 36 out of 287 samples harbored two non-synonymous and 34 synonymous mutations in pncB2 among which the most common was Phe204Phe (TTT/TTC), present in 8 samples, which may have an important effect on the usage of specific codons that may increase the gene expression level or protein folding effect. Mutations Ser120Leu and Pro447Ser, which are present in the loop region, exhibited a gain in flexibility in the surrounding residues while Gly429Ala and Gly127Ser also demonstrated stabilizing effects on the protein structure. Inhibitors designed based on the most common pncB1 and pncB2 mutants may be a more useful strategy in high-burden countries. More studies are needed to elucidate the effect of synonymous mutations on organism phenotype.

Application of Next Generation Sequencing for Diagnosis and Clinical Management of Drug-Resistant Tuberculosis: Updates on Recent Developments in the Field

The World Health Organization’s End TB Strategy prioritizes universal access to an early diagnosis and comprehensive drug susceptibility testing (DST) for all individuals with tuberculosis (TB) as a key component of integrated, patient-centered TB care. Next generation whole genome sequencing (WGS) and its associated technology has demonstrated exceptional potential for reliable and comprehensive resistance prediction for Mycobacterium tuberculosis isolates, allowing for accurate clinical decisions. This review presents a descriptive analysis of research describing the potential of WGS to accelerate delivery of individualized care, recent advances in sputum-based WGS technology and the role of targeted sequencing for resistance detection. We provide an update on recent research describing the mechanisms of resistance to new and repurposed drugs and the dynamics of mixed infections and its potential implication on TB diagnosis and treatment. Whilst the studies reviewed here have greatly improved our understanding of recent advances in this arena, it highlights significant challenges that remain. The wide-spread introduction of new drugs in the absence of standardized DST has led to rapid emergence of drug resistance. This review highlights apparent gaps in our knowledge of the mechanisms contributing to resistance for these new drugs and challenges that limit the clinical utility of next generation sequencing techniques. It is recommended that a combination of genotypic and phenotypic techniques is warranted to monitor treatment response, curb emerging resistance and further dissemination of drug resistance.

Molecular Epidemiology of Mycobacterium tuberculosis Complex Strains in Urban and Slum Settings of Nairobi, Kenya

Kenya is a country with a high tuberculosis (TB) burden. However, knowledge on the genetic diversity of Mycobacterium tuberculosis complex (MTBC) strains and their transmission dynamics is sparsely available. Hence, we used whole-genome sequencing (WGS) to depict the genetic diversity, molecular markers of drug resistance, and possible transmission clusters among MTBC strains in urban and slum settings of Nairobi. We analyzed 385 clinical MTBC isolates collected between 2010 and 2015 in combination with patients’ demographics. We showed that the MTBC population mainly comprises strains of four lineages (L1–L4). The two dominating lineages were L4 with 55.8% (n = 215) and L3 with 25.7% (n = 99) of all strains, respectively. Genome-based cluster analysis showed that 30.4% (117/385) of the strains were clustered using a ≤5 single-nucleotide polymorphism (SNP) threshold as a surrogate marker for direct patient-to-patient MTBC transmission. Moreover, 5.2% (20/385) of the strains were multidrug-resistant (MDR), and 50.0% (n = 10) were part of a genome-based cluster (i.e., direct MDR MTBC transmission). Notably, 30.0% (6/20) of the MDR strains were resistant to all first-line drugs and are part of one molecular cluster. Moreover, TB patients in urban living setting had 3.8 times the odds of being infected with a drug-resistant strain as compared to patients from slums (p-value = 0.002). Our results show that L4 strains are the main causative agent of TB in Nairobi and MDR strain transmission is an emerging concern in urban settings. This emphasizes the need for more focused infection control measures and contact tracing of patients with MDR TB to break the transmission chains.

Antimicrobial Resistance in Rivers: A Review of the Genes Detected and New Challenges

River ecosystems are very important parts of the water cycle and an excellent habitat, food, and drinking water source for many organisms, including humans. Antibiotics are emerging contaminants which can enter rivers from various sources. Several antibiotics and their related antibiotic resistance genes (ARGs) have been detected in these ecosystems by various research programs and could constitute a substantial problem. The presence of antibiotics and other resistance cofactors can boost the development of ARGs in the chromosomes or mobile genetic elements of natural bacteria in rivers. The ARGs in environmental bacteria can also be transferred to clinically important pathogens. However, antibiotics and their resistance genes are both not currently monitored by national or international authorities responsible for controlling the quality of water bodies. For example, they are not included in the contaminant list in the European Water Framework Directive or in the US list of Water-Quality Benchmarks for Contaminants. Although ARGs are naturally present in the environment, very few studies have focused on non-impacted rivers to assess the background ARG levels in rivers, which could provide some useful indications for future environmental regulation and legislation. The present study reviews the antibiotics and associated ARGs most commonly measured and detected in rivers, including the primary analysis tools used for their assessment. In addition, other factors that could enhance antibiotic resistance, such as the effects of chemical mixtures, the effects of climate change, and the potential effects of the coronavirus disease 2019 pandemic, are discussed. Environ Toxicol Chem 2022;41:687-714. © 2022 SETAC.

SAM-TB: a whole genome sequencing data analysis website for detection of Mycobacterium tuberculosis drug resistance and transmission

Whole genome sequencing (WGS) can provide insight into drug-resistance, transmission chains and the identification of outbreaks, but data analysis remains an obstacle to its routine clinical use. Although several drug-resistance prediction tools have appeared, until now no website integrates drug-resistance prediction with strain genetic relationships and species identification of nontuberculous mycobacteria (NTM). We have established a free, function-rich, user-friendly online platform for MTB WGS data analysis (SAM-TB, http://samtb.szmbzx.com) that integrates drug-resistance prediction for 17 antituberculosis drugs, detection of variants, analysis of genetic relationships and NTM species identification. The accuracy of SAM-TB in predicting drug-resistance was assessed using 3177 sequenced clinical isolates with results of phenotypic drug-susceptibility tests (pDST). Compared to pDST, the sensitivity of SAM-TB for detecting multidrug-resistant tuberculosis was 93.9% [95% confidence interval (CI) 92.6–95.1%] with specificity of 96.2% (95% CI 95.2–97.1%). SAM-TB also analyzes the genetic relationships between multiple strains by reconstructing phylogenetic trees and calculating pairwise single nucleotide polymorphism (SNP) distances to identify genomic clusters. The incorporated mlstverse software identifies NTM species with an accuracy of 98.2% and Kraken2 software can detect mixed MTB and NTM samples. SAM-TB also has the capacity to share both sequence data and analysis between users. SAM-TB is a multifunctional integrated website that uses WGS raw data to accurately predict antituberculosis drug-resistance profiles, analyze genetic relationships between multiple strains and identify NTM species and mixed samples containing both NTM and MTB. SAM-TB is a useful tool for guiding both treatment and epidemiological investigation.