Whole genome sequencing of drug resistance Mycobacterium tuberculosis from extra-pulmonary sites.
Ontology highlight
ABSTRACT:
SUBMITTER: Shi T
PROVIDER: S-EPMC10435967 | biostudies-literature | 2023 Nov
REPOSITORIES: biostudies-literature
Publications
Whole genome sequencing of drug resistance <i>Mycobacterium tuberculosis</i> from extra-pulmonary sites.
Shi Tao T Shou Fenyong F He Ying Y Zhou Kan K Gao Wenwan W Nie Xiaoping X Han Mei M Liao Chuanyu C Li Tongxin T
Life science alliance 20230817 11
This study aimed to determinate characteristics of drug resistance <i>Mycobacterium tuberculosis</i> from patients with extra-pulmonary tuberculosis (EPTB). Patients were retrospectively studied from January 2020 to December 2021. All the isolates were cultured, tested drug susceptibility, and detected the gene mutation using whole genome sequencing. The correlations of whole genome sequencing, pattern of DR, patients' distribution, and transmission were analyzed. 111 DR-EPTB isolates included p ...[more]
Similar Datasets
Project description:Whole-genome sequencing allows rapid detection of drug-resistant Mycobacterium tuberculosis isolates. However, the availability of high-quality data linking quantitative phenotypic drug susceptibility testing (DST) and genomic data have thus far been limited. We determined drug resistance profiles of 176 genetically diverse clinical M. tuberculosis isolates from the Democratic Republic of the Congo, Ivory Coast, Peru, Thailand, and Switzerland by quantitative phenotypic DST for 11 antituberculous drugs using the BD Bactec MGIT 960 system and 7H10 agar dilution to generate a cross-validated phenotypic DST readout. We compared DST results with predicted drug resistance profiles inferred by whole-genome sequencing. Classification of strains by the two phenotypic DST methods into resistotype/wild-type populations was concordant in 73 to 99% of cases, depending on the drug. Our data suggest that the established critical concentration (5 mg/liter) for ethambutol resistance (MGIT 960 system) is too high and misclassifies strains as susceptible, unlike 7H10 agar dilution. Increased minimal inhibitory concentrations were explained by mutations identified by whole-genome sequencing. Using whole-genome sequences, we were able to predict quantitative drug resistance levels for the majority of drug resistance mutations. Predicting quantitative levels of drug resistance by whole-genome sequencing was partially limited due to incompletely understood drug resistance mechanisms. The overall sensitivity and specificity of whole-genome-based DST were 86.8% and 94.5%, respectively. Despite some limitations, whole-genome sequencing has the potential to infer resistance profiles without the need for time-consuming phenotypic methods.
| S-EPMC6496161 | biostudies-literature
Project description:Combating the spread of drug resistant tuberculosis is a global health priority. Whole genome association studies are being applied to identify genetic determinants of resistance to anti-tuberculosis drugs. Protein structure and interaction modelling are used to understand the functional effects of putative mutations and provide insight into the molecular mechanisms leading to resistance.To investigate the potential utility of these approaches, we analysed the genomes of 144 Mycobacterium tuberculosis clinical isolates from The Special Programme for Research and Training in Tropical Diseases (TDR) collection sourced from 20 countries in four continents. A genome-wide approach was applied to 127 isolates to identify polymorphisms associated with minimum inhibitory concentrations for first-line anti-tuberculosis drugs. In addition, the effect of identified candidate mutations on protein stability and interactions was assessed quantitatively with well-established computational methods.The analysis revealed that mutations in the genes rpoB (rifampicin), katG (isoniazid), inhA-promoter (isoniazid), rpsL (streptomycin) and embB (ethambutol) were responsible for the majority of resistance observed. A subset of the mutations identified in rpoB and katG were predicted to affect protein stability. Further, a strong direct correlation was observed between the minimum inhibitory concentration values and the distance of the mutated residues in the three-dimensional structures of rpoB and katG to their respective drugs binding sites.Using the TDR resource, we demonstrate the usefulness of whole genome association and convergent evolution approaches to detect known and potentially novel mutations associated with drug resistance. Further, protein structural modelling could provide a means of predicting the impact of polymorphisms on drug efficacy in the absence of phenotypic data. These approaches could ultimately lead to novel resistance mutations to improve the design of tuberculosis control measures, such as diagnostics, and inform patient management.
| S-EPMC4804620 | biostudies-other
Project description:Whole-genome sequencing (WGS) has shown tremendous potential in rapid diagnosis of drug-resistant tuberculosis (TB). In the current study, we performed WGS on drug-resistant Mycobacterium tuberculosis isolates obtained from Shanghai (n = 137) and Russia (n = 78). We aimed to characterise the underlying and high-frequency novel drug-resistance-conferring mutations, and also create valuable combinations of resistance mutations with high predictive sensitivity to predict multidrug- and extensively drug-resistant tuberculosis (MDR/XDR-TB) phenotype using a bootstrap method. Most strains belonged to L2.2, L4.2, L4.4, L4.5 and L4.8 lineages. We found that WGS could predict 82.07% of phenotypically drug-resistant domestic strains. The prediction sensitivity for rifampicin (RIF), isoniazid (INH), ethambutol (EMB), streptomycin (STR), ofloxacin (OFL), amikacin (AMK) and capreomycin (CAP) was 79.71%, 86.30%, 76.47%, 88.37%, 83.33%, 70.00% and 70.00%, respectively. The mutation combination with the highest sensitivity for MDR prediction was rpoB S450L + rpoB H445A/P + katG S315T + inhA I21T + inhA S94A, with a sensitivity of 92.17% (0.8615, 0.9646), and the mutation combination with highest sensitivity for XDR prediction was rpoB S450L + katG S315T + gyrA D94G + rrs A1401G, with a sensitivity of 92.86% (0.8158, 0.9796). The molecular information presented here will be of particular value for the rapid clinical detection of MDR- and XDR-TB isolates through laboratory diagnosis.
| S-EPMC8851352 | biostudies-literature
Project description:Background: Tuberculosis disease, caused by Mycobacterium tuberculosis, is a major public health problem. The emergence of M. tuberculosis strains resistant to existing treatments threatens to derail control efforts. Resistance is mainly conferred by mutations in genes coding for drug targets or converting enzymes, but our knowledge of these mutations is incomplete. Whole genome sequencing (WGS) is an increasingly common approach to rapidly characterize isolates and identify mutations predicting antimicrobial resistance and thereby providing a diagnostic tool to assist clinical decision making. Methods: We applied machine learning approaches to 16,688 M. tuberculosis isolates that have undergone WGS and laboratory drug-susceptibility testing (DST) across 14 antituberculosis drugs, with 22.5% of samples being multidrug resistant and 2.1% being extensively drug resistant. We used non-parametric classification-tree and gradient-boosted-tree models to predict drug resistance and uncover any associated novel putative mutations. We fitted separate models for each drug, with and without "co-occurrent resistance" markers known to be causing resistance to drugs other than the one of interest. Predictive performance was measured using sensitivity, specificity, and the area under the receiver operating characteristic curve, assuming DST results as the gold standard. Results: The predictive performance was highest for resistance to first-line drugs, amikacin, kanamycin, ciprofloxacin, moxifloxacin, and multidrug-resistant tuberculosis (area under the receiver operating characteristic curve above 96%), and lowest for third-line drugs such as D-cycloserine and Para-aminosalisylic acid (area under the curve below 85%). The inclusion of co-occurrent resistance markers led to improved performance for some drugs and superior results when compared to similar models in other large-scale studies, which had smaller sample sizes. Overall, the gradient-boosted-tree models performed better than the classification-tree models. The mutation-rank analysis detected no new single nucleotide polymorphisms linked to drug resistance. Discordance between DST and genotypically inferred resistance may be explained by DST errors, novel rare mutations, hetero-resistance, and nongenomic drivers such as efflux-pump upregulation. Conclusion: Our work demonstrates the utility of machine learning as a flexible approach to drug resistance prediction that is able to accommodate a much larger number of predictors and to summarize their predictive ability, thus assisting clinical decision making and single nucleotide polymorphism detection in an era of increasing WGS data generation.
| S-EPMC6775242 | biostudies-literature
Project description:Tuberculosis (TB) remains one of the leading causes of morbidity and mortality worldwide. Extrapulmonary tuberculosis (EPTB) constitutes around 15-20% of TB cases in immunocompetent individuals. Extrapulmonary sites that are affected by TB include bones, lymph nodes, meningitis, pleura, and genitourinary tract. Whole genome sequencing has emerged as a powerful tool to map genetic diversity among Mycobacterium tuberculosis (MTB) isolates and identify the genomic signatures associated with drug resistance, pathogenesis, and disease transmission. Several pulmonary isolates of MTB have been sequenced over the years. However, availability of whole genome sequences of MTB isolates from extrapulmonary sites is limited. Some studies suggest that genetic variations in MTB might contribute to disease presentation in extrapulmonary sites. This can be addressed if whole genome sequence data from large number of extrapulmonary isolates becomes available. In this study, we have performed whole genome sequencing of five MTB clinical isolates derived from EPTB sites using next-generation sequencing platform. We identified 1434 nonsynonymous single nucleotide variations (SNVs), 143 insertions and 105 deletions. This includes 279 SNVs that were not reported before in publicly available datasets. We found several mutations that are known to confer resistance to drugs. All the five isolates belonged to East-African-Indian lineage (lineage 3). We identified 9 putative prophage DNA integrations and 14 predicted clustered regularly interspaced short palindromic repeats (CRISPR) in MTB genome. Our analysis indicates that more work is needed to map the genetic diversity of MTB. Whole genome sequencing in conjunction with comprehensive drug susceptibility testing can reveal clinically relevant mutations associated with drug resistance.
| S-EPMC6436032 | biostudies-literature
Project description:BackgroundTuberculosis is one of the deadliest disease caused by Mycobacterium tuberculosis. Its treatment still becomes a burden for many countries including Indonesia. Drug resistance is one of the problems in TB treatment. However, a development in the molecular field through Whole-genome sequencing (WGS) can be used as a solution in detecting mutations associated with TB- drugs. This investigation intended to implement this data for supporting the scientific community in deeply understanding any TB epidemiology and evolution in Papua along with detecting any mutations in genes associated with TB-Drugs.ResultA whole-genome sequencing was performed on the random samples from TB Referral Laboratory in Papua utilizing MiSeq 600 cycle Reagent Kit (V3). Furthermore, TBProfiler was used for genome analysis, RAST Server was employed for annotation, while Gview server was applied for BLAST genome mapping and a Microscope server was implemented for Regions of Genomic Plasticity (RGP). The largest genome of M. tuberculosis obtained was at the size of 4,396,040 bp with subsystems number at 309 and the number of coding sequences at 4326. One sample (TB751) contained one RGP. The drug resistance analysis revealed that several mutations associated with TB-drug resistance existed. In details, mutations of rpoB gene which were identified as S450L, D435Y, H445Y, L430P, and Q432K had caused the reduced effectiveness of rifampicin; while the mutases in katG (S315T), kasA (312S), inhA (I21V), and Rv1482c-fabG1 (C-15 T) genes had contributed to the resistance in isoniazid. In streptomycin, the resistance was triggered by the mutations in rpsL (K43R) and rrs (A514C, A514T) genes, and, in Amikacin, its resistance was led by mutations in rrs (A514C) gene. Additionally, in Ethambutol and Pyrazinamide, their reduced effectiveness was provoked by embB gene mutases (M306L, M306V, D1024N) and pncA (W119R).ConclusionsThe results from whole-genome sequencing of TB clinical sample in Papua, Indonesia could contribute to the surveillance of TB-drug resistance. In the drug resistance profile, there were 15 Multi Drugs Resistance (MDR) samples. However, Extensively Drug-resistant (XDR) samples have not been found, but samples were resistant to only Amikacin, a second-line drug.
| S-EPMC8607662 | biostudies-literature
Project description:Delays in accurate diagnosis of drug resistant tuberculosis (DR-TB) can hinder treatment. Whole genome sequencing (WGS) provides more information than standard molecular and phenotypic testing, but commonly used platforms are expensive to implement, and data interpretation requires significant expertise. We aimed to optimise a TB WGS diagnostic pipeline balancing user-friendliness, cost-effectiveness and time to results, whilst ensuring accuracy. Growth conditions, DNA extraction protocols and Oxford Nanopore Technologies (ONT) library preparation kits were compared. ONT was compared with Illumina protocols. Software for basecalling and analysis were evaluated to find the most accurate resistance SNP and lineage predictor. Optimally, a spin-column CTAB DNA extraction method was combined with the RBK110.96 library preparation kit, high accuracy (HAC) basecalling and data analysis using TB-Profiler. Compared with Illumina, the pipeline was concordant for 16/17 (94%) isolates (lineage) and for 17/17 (100%) isolates (resistance SNPs). Our pipeline was 71% (12/17) concordant with phenotypic drug susceptibility test (DST) results. Time-to-diagnosis was around four weeks. This optimised TB sequencing pipeline requires less time and expertise to run and analyse than Illumina, takes less time than phenotypic DSTs and the results are comparable with Illumina. The cost per sample is comparable with other methods. These features make it an important tool for incorporating into routine DR-TB diagnostic pipelines and larger scale drug resistance surveillance in all settings.
| S-EPMC11809915 | biostudies-literature
Project description:Pulmonary tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (MTB). Whole-genome sequencing (WGS) holds great promise as an advanced technology for accurately predicting anti-TB drug resistance. The development of a reliable method for detecting drug resistance is crucial in order to standardize anti-TB treatments, enhance patient prognosis, and effectively reduce the risk of transmission. In this study, our primary objective was to explore and determine the potential of WGS for assessing drug resistance based on genetic variants recommended by the World Health Organization (WHO). A total of 1105 MTB strains were selected from samples collected from 2014–2018 in Zhejiang Province, China. Phenotypic drug sensitivity tests (DST) of the anti-TB drugs were conducted for isoniazid (INH), rifampicin (RFP), streptomycin, ethambutol, fluoroquinolones (levofloxacin and moxifloxacin), amikacin, kanamycin, and capreomycin, and the drug-resistance rates were calculated. The clean WGS data of the 1105 strains were acquired and analyzed. The predictive performance of WGS was evaluated by the comparison between genotypic and phenotypic DST results. For all anti-TB drugs, WGS achieved good specificity values (>90%). The sensitivity values for INH and RFP were 91.78% and 82.26%, respectively; however, they were ≤60% for other drugs. The positive predictive values for anti-TB drugs were >80%, except for ethambutol and moxifloxacin, and the negative predictive values were >90% for all drugs. In light of the findings from our study, we draw the conclusion that WGS is a valuable tool for identifying genome-wide variants. Leveraging the genetic variants recommended by the WHO, WGS proves to be effective in detecting resistance to RFP and INH, enabling the identification of multi-drug resistant TB patients. However, it is evident that the genetic variants recommended for predicting resistance to other anti-TB drugs require further optimization and improvement.
| S-EPMC10451829 | biostudies-literature
Project description:BackgroundZoonotic human tuberculosis (TB) caused by Mycobacterium bovis (M. bovis) is as vital as Mycobacterium tuberculosis, however with scarce available information. We aimed to use whole-genome sequencing (WGS) technology to take a deep insight into the circulating genotypes of human M. bovis and the genomic characteristics underlying virulence and drug resistance.MethodsThe study included smear positive Ziehl-Neelsen samples from patients with suspected tuberculosis. Samples were cultured on Lowenstein-Jensen media and suspected colonies of M. bovis were selected to undergo DNA extraction and WGS. Data was analysed using the Bacterial and Viral Bioinformatics Resource Center (BV-BRC), and online bioinformatics tools. A phylogenetic tree was constructed for our sequenced strains, in addition to a set of 59 previously sequenced M. bovis genomes from different hosts and countries.ResultsOut of total 112 mycobacterial positive cultures, five M. bovis were isolated and underwent WGS. All sequenced strains belonged to Mycobacterium tuberculosis var bovis, spoligotype BOV_1; BOV_11. Resistance gene mutations were determined in 100% of strains to pyrazinamide (pncA and rpsA), isoniazid (KatG and ahpC), ethambutol (embB, embC, embR and ubiA), streptomycin (rpsl) and fluoroquinolones (gyrA and gyrB). Rifampin (rpoB and rpoC) and delamanid (fbiC) resistance genes were found in 80% of strains. The major represented virulence classes were the secretion system, cell surface components and regulation system. The phylogenetic analysis revealed close genetic relatedness of three sequenced M. bovis strains to previous reported cow strains from Egypt and human strains from France, as well as relatedness of one M. bovis strain to four human Algerian strains. One sequenced strain was related to one cow strain from Egypt and a human strain from South Africa.ConclusionsAll sequenced M. bovis isolates showed the same spoligotype, but diverse phylogeny. Resistance gene mutations were detected for anti-TB drugs including pyrazinamide, isoniazid, streptomycin, ethambutol, fluoroquinolones, cycloserine, rifampin and delamanid. The virulence profile comprised genes assigned mainly to secretion system, cell surface components and regulation system. Phylogenetic analysis revealed genetic relatedness between our isolates and previously sequenced bovine strains from Egypt as well as human strains from other nearby countries in the region.
| S-EPMC11529264 | biostudies-literature
Project description:BackgroundDiagnosing drug-resistance remains an obstacle to the elimination of tuberculosis. Phenotypic drug-susceptibility testing is slow and expensive, and commercial genotypic assays screen only common resistance-determining mutations. We used whole-genome sequencing to characterise common and rare mutations predicting drug resistance, or consistency with susceptibility, for all first-line and second-line drugs for tuberculosis.MethodsBetween Sept 1, 2010, and Dec 1, 2013, we sequenced a training set of 2099 Mycobacterium tuberculosis genomes. For 23 candidate genes identified from the drug-resistance scientific literature, we algorithmically characterised genetic mutations as not conferring resistance (benign), resistance determinants, or uncharacterised. We then assessed the ability of these characterisations to predict phenotypic drug-susceptibility testing for an independent validation set of 1552 genomes. We sought mutations under similar selection pressure to those characterised as resistance determinants outside candidate genes to account for residual phenotypic resistance.FindingsWe characterised 120 training-set mutations as resistance determining, and 772 as benign. With these mutations, we could predict 89·2% of the validation-set phenotypes with a mean 92·3% sensitivity (95% CI 90·7-93·7) and 98·4% specificity (98·1-98·7). 10·8% of validation-set phenotypes could not be predicted because uncharacterised mutations were present. With an in-silico comparison, characterised resistance determinants had higher sensitivity than the mutations from three line-probe assays (85·1% vs 81·6%). No additional resistance determinants were identified among mutations under selection pressure in non-candidate genes.InterpretationA broad catalogue of genetic mutations enable data from whole-genome sequencing to be used clinically to predict drug resistance, drug susceptibility, or to identify drug phenotypes that cannot yet be genetically predicted. This approach could be integrated into routine diagnostic workflows, phasing out phenotypic drug-susceptibility testing while reporting drug resistance early.FundingWellcome Trust, National Institute of Health Research, Medical Research Council, and the European Union.
| S-EPMC4579482 | biostudies-literature