Analysis of the application of a gene chip method for detecting Mycobacterium tuberculosis drug resistance in clinical specimens: a retrospective study.
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SUBMITTER: Feng G
PROVIDER: S-EPMC8429459 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Objective: A Tag Array chip was used to detect plasmids of different template concentration, and then analyzed for sensitivity and specificity. Drug resistance genes from tuberculosis clinical specimens were detected, giving comparative phenotypic resistance results to explore the feasibility and value of clinical applications. Methods: Twenty-four strains of Mycobacterium Tuberculosis (MTB) having sequence differences in extracted plasmids of mutant strains. The plasmid was diluted into different concentrations, and then was performed to analyze the sensitivity and specificity of the chip system. A total of 427 clinical specimens (including spinal tuberculosis and pulmonary tuberculosis) were collected from patients who came from seven hospitals. Design, optimization and preparation of the chip detection system, sequencing and phenotypic drug susceptibility results were analyzed to evaluate the sensitivity and specificity of the gene chip. Results: In the template, concentrations of 1 × 103 copies/?L and above were consistent with sequencing results in the mutant. The sensitivity and specificity in spine Tuberculosis specimen of rifampicin (RFP) were 94.40 and 92.86%; isoniazide (INH) were 92.37 and 87.50%; ethambutol (EMB) were 61.36 and 89.29%; fluoroquinolones (FQS) were 79.41 and 92.86%; streptomycin (SM) were 90.18 and 89.29%; second line drugs (SLD) were 77.61 and 83.93%. In Pulmonary Tuberculosis specimen, the sensitivity and specificity respectively were RFP: 92.74%; 93.75%; INH: 91.26%; 87.50%; EMB: 54.17%; 89.58%; FQS: 84.87%; 93.75%; SM: 86.73%; 85.42%; SLD: 80.9%; 91.67%. The RFP, INH, FQs and SM resistance genes was highly sensitive and specific: however, for detection of amikacin (AMK), capreomycin (CPM), kanamycin (KM), specificity was higher, but sensitivity was lower. Sensitivity for the detection of a mutation in the eis promoter region could be improved. Conclusion: Tag Array chip can achieve rapid, accurate detection of tuberculosis resistance, which has important clinical significance and feasibility.
| S-EPMC6143819 | biostudies-literature
Project description:BACKGROUND:The prevalence of drug-resistant tuberculosis (DR-TB) has brought severe challenges to the prevention and control of tuberculosis. Studies have explored the status of antituberculosis drug (ATD) resistance in different regions of China. However, few studies have focused on DR-TB in Sichuan to date. Due to the large population in Sichuan, detailed investigations of the DR-TB burden in Sichuan are needed. The objective of this study was to investigate the drug resistance characteristics of TB isolates from tuberculosis patients with and without HIV (TB-HIV patients and TBw/oHIV patients) in Chengdu, Sichuan, China. METHODS:Isolates from respiratory samples of TBw/oHIV patients and TB-HIV patients hospitalized between January 2014 and December 2017 were collected. Nontuberculosis mycobacteria (NTM) were excluded. Drug sensitivity testing (DST) was performed according to the dilution method in microplates with 4 first-line ATDs and 8 second-line ATDs. TB strains were separated according to patient treatment history, patient age, calendar year and GeneXpert MTB/RIF (GeneXpert) assay results for further analysis. RESULTS:For the 7470 patients recruited, the multidrug-resistant tuberculosis (MDR-TB) rate was 2.1-fold (14.6% vs. 6.8%) higher than the national baseline level. The repeatedly admitted patients were more likely to have a resistance profile than the first-time-admitted cases in both the TB-only group (P<0.05) and the TB-HIV corresponding group (P<0.05). Among the 7273 TBw/oHIV cases and 197 TB-HIV cases, the positivity rates of acid-fast bacilli (AFB) in the TB-HIV group were significantly lower than those in the TBw/oHIV group (P<0.05). The repeatedly admitted TB-HIV patients had lower resistance rates to INH than the repeatedly admitted TBw/oHIV patients (24.4% vs. 41.5%, P<0.05). The Rifampicin-resistant TB strains in the TBw/oHIV group were more likely to be resistant to INH in the repeatedly admitted group than those in the first-time admitted patients (P<0.05). The proportions of XDR (3.6% vs. 1.3%, P<0.05) and XDR-TB/MDR-TB (7.3% vs. 2.2%, P<0.05) in all TB-HIV patients were significantly higher than those in all TBw/oHIV patients. The ratio of XDR-TB was significantly higher in the TB-HIV group than in the TBw/oHIV group (30.4% vs. 9.0%, P<0.05) and the all TB group (9.0% vs. 10.1%, P<0.05). Regarding age, the <25-year-old TB-HIV patients (9.1% vs. 0.7%, P<0.05) and 25~44-year-old TB-HIV patients (5.2% vs. 2.4%, P<0.05) were more likely to have a higher XDR proportion than their TBw/oHIV counterparts. The ATD-resistance profile in terms of different years from high to low was 2014>2015>2016?2017 for TBw/oHIV patients. The same trend was also observed for TB-HIV patients: 2014>2015>2016?2017. The GeneXpert TB-positive rate in the TBw/oHIV group was higher than that in the TB-HIV group [81%(639/792) vs. 65% (13/20), P<0.05]. In TBw/oHIV cases, the agreement was 92.3% and the Kappa value was 0.75. In TB-HIV cases, the agreement was 85.0% and the Kappa value was 0.32. CONCLUSION:In Sichuan, ATD resistance has improved since 2014, but to date, it remains severe. The different resistance profiles of TBw/oHIV patients and TB-HIV patients indicates the need for personalized treatment plans. Specifically, the GeneXpert assay might be more suitable for TBw/oHIV patients than for TB-HIV patients.
| S-EPMC6312223 | biostudies-other
Project description:ObjectiveWe designed and tested a Nanopore sequencing panel for direct tuberculosis drug resistance profiling. The panel targeted 10 resistance-associated loci. We assessed the feasibility of amplifying and sequencing these loci from 23 clinical specimens with low bacillary burden.ResultsAt least 8 loci were successfully amplified from the majority for predicting first- and second-line drug resistance (14/23, 60.87%), and the 12 specimens yielding all 10 targets were sequenced with Nanopore MinION and Illumina MiSeq. MinION sequencing data was corrected by Nanopolish and recurrent variants were filtered. A total of 67,082 bases across all consensus sequences were analyzed, with 67,019 bases called by both MinION and MiSeq as wildtype. For the 41 single nucleotide variants (SNVs) called by MiSeq with 100% variant allelic frequency (VAF), 39 (95.1%) were called by MinION. For the 22 mixed bases called by MiSeq, a SNV with the highest VAF (70%) was called by MinION. With short assay time, reasonable reagent cost as well as continuously improving sequencing chemistry and signal correction pipelines, this Nanopore method can be a viable option for direct tuberculosis drug resistance profiling in the near future.
| S-EPMC7501614 | 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
Project description:We have developed a software program that weights and integrates specific properties on the genes in a pathogen so that they may be ranked as drug targets. We applied this software to produce three prioritized drug target lists for Mycobacterium tuberculosis, the causative agent of tuberculosis, a disease for which a new drug is desperately needed. Each list is based on an individual criterion. The first list prioritizes metabolic drug targets by the uniqueness of their roles in the M. tuberculosis metabolome ("metabolic chokepoints") and their similarity to known "druggable" protein classes (i.e., classes whose activity has previously been shown to be modulated by binding a small molecule). The second list prioritizes targets that would specifically impair M. tuberculosis, by weighting heavily those that are closely conserved within the Actinobacteria class but lack close homology to the host and gut flora. M. tuberculosis can survive asymptomatically in its host for many years by adapting to a dormant state referred to as "persistence." The final list aims to prioritize potential targets involved in maintaining persistence in M. tuberculosis. The rankings of current, candidate, and proposed drug targets are highlighted with respect to these lists. Some features were found to be more accurate than others in prioritizing studied targets. It can also be shown that targets can be prioritized by using evolutionary programming to optimize the weights of each desired property. We demonstrate this approach in prioritizing persistence targets.
| S-EPMC1475714 | biostudies-literature
Project description:Human immunodeficiency virus (HIV) is a chronic infection that can be managed by antiretroviral treatment (ART). However, periods of suboptimal viral suppression during lifelong ART can select for HIV drug resistant (DR) variants. Transmission of drug resistant virus can lessen or abrogate ART efficacy. Therefore, testing of individuals for drug resistance prior to initiation of treatment is recommended to ensure effective ART. Sensitive and inexpensive HIV genotyping methods are needed in low-resource settings where most HIV infections occur. The oligonucleotide ligation assay (OLA) is a sensitive point mutation assay for detection of drug resistance mutations in HIV pol. The current OLA involves four main steps from sample to analysis: (1) lysis and/or nucleic acid extraction, (2) amplification of HIV RNA or DNA, (3) ligation of oligonucleotide probes designed to detect single nucleotide mutations that confer HIV drug resistance, and (4) analysis via oligonucleotide surface capture, denaturation, and detection (CDD). The relative complexity of these steps has limited its adoption in resource-limited laboratories. Here we describe a simplification of the 2.5-hour plate-format CDD to a 45-minute paper-format CDD that eliminates the need for a plate reader. Analysis of mutations at four HIV-1 DR codons (K103N, Y181C, M184V, and G190A) in 26 blood specimens showed a strong correlation of the ratios of mutant signal to total signal between the paper CDD and the plate CDD. The assay described makes the OLA easier to perform in low resource laboratories.
| S-EPMC4713472 | biostudies-literature
Project description:Discovery of new anti-tuberculosis (TB) drugs is a time-consuming process due to the slow-growing nature of Mycobacterium tuberculosis (Mtb). A requirement of biosafety level 3 (BSL-3) facility for performing research associated with Mtb is another limitation for the development of TB drug discovery. In our screening of BSL-1 Mycobacterium spp. against a battery of TB drugs, M. smegmatis (ATCC607) exhibits good agreement with its drug susceptibility against the TB drugs under a low-nutrient culture medium (0.5% Tween 80 in Middlebrook 7H9 broth). M. smegmatis (ATCC607) enters its dormant form in 14 days under a nutrient-deficient condition (a PBS buffer), and shows resistance to a majority of TB drugs, but shows susceptibility to amikacin, capreomycin, ethambutol, and rifampicin (with high concentrations) whose activities against non-replicating (or dormant) Mtb were previously validated.
| S-EPMC7554168 | biostudies-literature
Project description:Motivation:Timely identification of Mycobacterium tuberculosis (MTB) resistance to existing drugs is vital to decrease mortality and prevent the amplification of existing antibiotic resistance. Machine learning methods have been widely applied for timely predicting resistance of MTB given a specific drug and identifying resistance markers. However, they have been not validated on a large cohort of MTB samples from multi-centers across the world in terms of resistance prediction and resistance marker identification. Summary:Several machine learning classifiers and linear dimension reduction techniques were developed and compared for a cohort of 13402 isolates collected from 16 countries across six continents and tested 11 drugs. Results:Compared to conventional molecular diagnostic test, area under curve (AUC) of the best machine learning classifier increased for all drugs especially by 23.11%, 15.22%, and 10.14% for pyrazinamide (PZA), ciprofloxacin (CIP), and ofloxacin (OFX) respectively (p < 0.01). Logistic regression (LR) and gradient tree boosting (GBT) found to perform better than other techniques. Moreover, LR/GBT with a sparse principal component analysis/non-negative matrix factorisation step compared with the classifier alone enhanced the best performance in terms of F1-score by 12.54%, 4.61%, 7.45%, and 9.58% for amikacin (AK), moxifloxacin (MOX), OFX, and capreomycin (CAP) respectively, as well increasing AUC for AK and CAP. Results provided a comprehensive comparison of various techniques and confirmed the application of machine learning for better prediction of the large diverse TB data. Furthermore, mutation ranking showed the possibility of finding new resistance/susceptible markers. Availability:The source code can be found at http://www.robots.ox.ac.uk/~davidc/code.php. Supplementary information:Supplementary data are available at Bioinformatics online.
| S-EPMC6596891 | biostudies-other
Project description:Tuberculosis (TB) is the leading cause of death by an infectious disease, and global TB control efforts are increasingly threatened by drug resistance in Mycobacterium tuberculosis. Unlike most bacteria, where lateral gene transfer is an important mechanism of resistance acquisition, resistant M. tuberculosis arises solely by de novo chromosomal mutation. Using whole-genome sequencing data from two natural populations of M. tuberculosis, we characterized the population genetics of known drug resistance loci using measures of diversity, population differentiation, and convergent evolution. We found resistant subpopulations to be less diverse than susceptible subpopulations, consistent with ongoing transmission of resistant M. tuberculosis. A subset of resistance genes ("sloppy targets") were characterized by high diversity and multiple rare variants; we posit that a large genetic target for resistance and relaxation of purifying selection contribute to high diversity at these loci. For "tight targets" of selection, the path to resistance appeared narrower, evidenced by single favored mutations that arose numerous times in the phylogeny and segregated at markedly different frequencies in resistant and susceptible subpopulations. These results suggest that diverse genetic architectures underlie drug resistance in M. tuberculosis and that combined approaches are needed to identify causal mutations. Extrapolating from patterns observed for well-characterized genes, we identified novel candidate variants involved in resistance. The approach outlined here can be extended to identify resistance variants for new drugs, to investigate the genetic architecture of resistance, and when phenotypic data are available, to find candidate genetic loci underlying other positively selected traits in clonal bacteria. IMPORTANCEMycobacterium tuberculosis, the causative agent of tuberculosis (TB), is a significant burden on global health. Antibiotic treatment imposes strong selective pressure on M. tuberculosis populations. Identifying the mutations that cause drug resistance in M. tuberculosis is important for guiding TB treatment and halting the spread of drug resistance. Whole-genome sequencing (WGS) of M. tuberculosis isolates can be used to identify novel mutations mediating drug resistance and to predict resistance patterns faster than traditional methods of drug susceptibility testing. We have used WGS from natural populations of drug-resistant M. tuberculosis to characterize effects of selection for advantageous mutations on patterns of diversity at genes involved in drug resistance. The methods developed here can be used to identify novel advantageous mutations, including new resistance loci, in M. tuberculosis and other clonal pathogens.
| S-EPMC5790871 | biostudies-literature
Project description:BACKGROUND: Emergence of drug resistant varieties of tuberculosis is posing a major threat to global tuberculosis eradication programmes. Although several approaches have been explored to counter resistance, there has been limited success due to a lack of understanding of how resistance emerges in bacteria upon drug treatment. A systems level analysis of the proteins involved is essential to gain insights into the routes required for emergence of drug resistance. RESULTS: We derive a genome-scale protein-protein interaction network for Mycobacterium tuberculosis H37Rv from the STRING database, with proteins as nodes and interactions as edges. A set of proteins involved in both intrinsic and extrinsic drug resistance mechanisms are identified from literature. We then compute shortest paths from different drug targets to the set of resistance proteins in the protein-protein interactome, to derive a sub-network relevant to study emergence of drug resistance. The shortest paths are then scored and ranked based on a new scheme that considers (a) drug-induced gene upregulation data, from microarray experiments reported in literature, for the individual nodes and (b) edge-hubness, a network parameter which signifies centrality of a given edge in the network. High-scoring paths identified from this analysis indicate most plausible pathways for the emergence of drug resistance. Different targets appear to have different propensities for four drug resistance mechanisms. A new concept of 'co-targets' has been proposed to counter drug resistance, co-targets being defined as protein(s) that need to be simultaneously inhibited along with the intended target(s), to check emergence of resistance to a given drug. CONCLUSION: The study leads to the identification of possible pathways for drug resistance, providing novel insights into the problem of resistance. Knowledge of important proteins in such pathways enables identification of appropriate 'co-targets', best examples being RecA, Rv0823c, Rv0892 and DnaE1, for drugs targeting the mycolic acid pathway. Insights obtained about the propensity of a drug to trigger resistance will be useful both for more careful identification of drug targets as well as to identify target-co-target pairs, both implementable in early stages of drug discovery itself. This approach is also inherently generic, likely to significantly impact drug discovery.
| S-EPMC2649132 | biostudies-literature