Tuberculosis drug resistance mutation database.
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ABSTRACT:
SUBMITTER: Sandgren A
PROVIDER: S-EPMC2637921 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Tuberculosis remains a major global public health challenge. Although incidence is decreasing, the proportion of drug-resistant cases is increasing. Technical and operational complexities prevent Mycobacterium tuberculosis drug susceptibility phenotyping in the vast majority of new and retreatment cases. The advent of molecular technologies provides an opportunity to obtain results rapidly as compared to phenotypic culture. However, correlations between genetic mutations and resistance to multiple drugs have not been systematically evaluated. Molecular testing of M. tuberculosis sampled from a typical patient continues to provide a partial picture of drug resistance. A database of phenotypic and genotypic testing results, especially where prospectively collected, could document statistically significant associations and may reveal new, predictive molecular patterns. We examine the feasibility of integrating existing molecular and phenotypic drug susceptibility data to identify associations observed across multiple studies and demonstrate potential for well-integrated M. tuberculosis mutation data to reveal actionable findings.
| S-EPMC4366577 | biostudies-literature
Project description:Bacterial factors may contribute to the global emergence and spread of drug-resistant tuberculosis (TB). Only a few studies have reported on the interactions between different bacterial factors. We studied drug-resistant Mycobacterium tuberculosis isolates from a nationwide study conducted from 2000 to 2008 in Switzerland. We determined quantitative drug resistance levels of first-line drugs by using Bactec MGIT-960 and drug resistance genotypes by sequencing the hot-spot regions of the relevant genes. We determined recent transmission by molecular methods and collected clinical data. Overall, we analyzed 158 isolates that were resistant to isoniazid, rifampin, or ethambutol, 48 (30.4%) of which were multidrug resistant. Among 154 isoniazid-resistant strains, katG mutations were associated with high-level and inhA promoter mutations with low-level drug resistance. Only katG(S315T) (65.6% of all isoniazid-resistant strains) and inhA promoter -15C/T (22.7%) were found in molecular clusters. M. tuberculosis lineage 2 (includes Beijing genotype) was associated with any drug resistance (adjusted odds ratio [OR], 3.0; 95% confidence interval [CI], 1.7 to 5.6; P < 0.0001). Lineage 1 was associated with inhA promoter -15C/T mutations (OR, 6.4; 95% CI, 2.0 to 20.7; P = 0.002). We found that the genetic strain background influences the level of isoniazid resistance conveyed by particular mutations (interaction tests of drug resistance mutations across all lineages; P < 0.0001). In conclusion, M. tuberculosis drug resistance mutations were associated with various levels of drug resistance and transmission, and M. tuberculosis lineages were associated with particular drug resistance-conferring mutations and phenotypic drug resistance. Our study also supports a role for epistatic interactions between different drug resistance mutations and strain genetic backgrounds in M. tuberculosis drug resistance.
| S-EPMC3370767 | biostudies-literature
Project description:Cancer therapies are limited by the development of drug resistance, and mutations in drug targets is one of the main reasons for developing acquired resistance. The adequate knowledge of these mutations in drug targets would help to design effective personalized therapies. Keeping this in mind, we have developed a database "CancerDR", which provides information of 148 anti-cancer drugs, and their pharmacological profiling across 952 cancer cell lines. CancerDR provides comprehensive information about each drug target that includes; (i) sequence of natural variants, (ii) mutations, (iii) tertiary structure, and (iv) alignment profile of mutants/variants. A number of web-based tools have been integrated in CancerDR. This database will be very useful for identification of genetic alterations in genes encoding drug targets, and in turn the residues responsible for drug resistance. CancerDR allows user to identify promiscuous drug molecules that can kill wide range of cancer cells. CancerDR is freely accessible at http://crdd.osdd.net/raghava/cancerdr/
| S-EPMC3595698 | biostudies-literature
Project description:Resistance prediction and mutation ranking are important tasks in the analysis of Tuberculosis sequence data. Due to standard regimens for the use of first-line antibiotics, resistance co-occurrence, in which samples are resistant to multiple drugs, is common. Analysing all drugs simultaneously should therefore enable patterns reflecting resistance co-occurrence to be exploited for resistance prediction. Here, multi-label random forest (MLRF) models are compared with single-label random forest (SLRF) for both predicting phenotypic resistance from whole genome sequences and identifying important mutations for better prediction of four first-line drugs in a dataset of 13402 Mycobacterium tuberculosis isolates. Results confirmed that MLRFs can improve performance compared to conventional clinical methods (by 18.10%) and SLRFs (by 0.91%). In addition, we identified a list of candidate mutations that are important for resistance prediction or that are related to resistance co-occurrence. Moreover, we found that retraining our analysis to a subset of top-ranked mutations was sufficient to achieve satisfactory performance. The source code can be found at http://www.robots.ox.ac.uk/~davidc/code.php.
| S-EPMC7188832 | biostudies-literature
Project description:Global Drug Resistance Tuberculosis
| PRJEB10385 | ENA
Project description:Background:Drug-resistant tuberculosis (TB) is a global public health issue. To monitor this in Canada, surveillance systems have been in place for the last 20 years. Objective:To describe drug resistance patterns among TB isolates in Canada in 2017 by type of resistance as well as geographic location, demographic data and origin and to compare current data to those of the previous 10 years. Methods:Data were derived and analyzed from two sources. The Canadian Tuberculosis Laboratory Surveillance System (CTBLSS) is an isolate-based laboratory surveillance system and was used to obtain information on the results of drug susceptibility testing (DST) as well as province or territory, sex and age of the individual from which the sample originated. The Canadian Tuberculosis Reporting System (CTBRS) is a case-based surveillance system with information on active and retreatment TB cases in Canada and was used to derive origin data, which is defined as either foreign-born, Canadian-born Indigenous or Canadian-born non-Indigenous. Analysis was descriptive and compared with data from these two sources for 2007-2016. Results:In 2017, 1,515 TB isolates were tested for resistance to anti-TB drugs, with 123 (8.1%) demonstrating resistance to any first-line anti-TB drug. Of these, 103 were monoresistant, six were polyresistant and 14 were multidrug-resistant tuberculosis (MDR-TB). No extensively drug-resistant tuberculosis (XDR-TB) isolates were reported. Drug resistance was reported in seven provinces/territories (British Columbia, Alberta, Saskatchewan, Manitoba, Ontario, Quebec and New Brunswick). There were 63 isolates from females with drug resistance (9.5%) and 60 isolates from males with drug resistance (7.0%). Drug resistance was found in a greater percentage of isolates among those aged 25-34 (n=29, 23.6%). By origin, 1,072 (11%) foreign-born TB cases reported between 2005 and 2015 were drug-resistant. Among the Canadian-born non-Indigenous and Canadian-born Indigenous TB cases, 143 (9%) and 54 (2%) were drug-resistant, respectively. Compared with previous years, the number of isolates tested increased slightly (from 1,267 to 1,515); however, there was a decrease in the percentage of isolates with reported drug resistance (from 10.5% in 2007 to 8.1% in 2017). Conclusion:In 2017, TB drug resistance rates remained low in Canada.
| S-EPMC6449088 | biostudies-literature
Project description:Background:Drug-resistant tuberculosis (TB) is a public health issue of global importance that poses a threat to TB control efforts. Canada conducts nationwide surveillance to monitor emerging drug resistance trends and document progress towards reaching the goal of TB elimination. Objective:To describe TB drug resistance trends across Canada from 2008-2018, with a focus on 2018, by drug resistance, geographic and demographic patterns. Methods:TB drug resistance data are captured through two independent surveillance systems managed by the Public Health Agency of Canada: Canadian Tuberculosis Laboratory Surveillance System (CTBLSS) and the Canadian Tuberculosis Reporting System (CTBRS). Data from these systems were analyzed and descriptive statistics were reported by resistance profile, place of residence (province), age groups, sex and country of birth. Results:In 2018, 1,459 TB isolates underwent drug susceptibility testing, a 4.3% decrease from 2017. Resistance to any first-line drug was reported in 148 isolates (10.1%), compared to 123 (8.1%) in 2017. Of these, 121 were monoresistant, five were polyresistant, 21 were multidrug-resistant tuberculosis (MDR-TB) and one was extensively drug-resistant TB (XDR-TB). Drug resistance was reported in all provinces and territories except Prince Edward Island, Northwest Territories and Yukon. Among individuals younger than 15 years, very little TB drug resistance was detected. Among individuals aged 15 years and older, the distribution of TB drug resistance varied with no discernable trends. The proportion of drug resistance was slightly higher in females than in males. By origin, 10.7% of foreign-born TB cases reported between 2006 and 2016 were drug-resistant. Among the Canadian-born non-Indigenous cases, 9.3% were drug resistant; among Canadian-born Indigenous, 2.4% were drug resistant. Conclusion:In 2018, the proportion of isolates with TB drug resistance in Canada remained low and below global averages, with stable drug resistance, both geographically and demographically.
| S-EPMC6941922 | biostudies-literature
Project description:Tuberculosis is a significant global health threat, with one-third of the world's population infected with its causative agent Mycobacterium tuberculosis (Mtb). The emergence of multidrug-resistant (MDR) Mtb that is resistant to the frontline anti-tubercular drugs rifampicin and isoniazid forces treatment with toxic second-line drugs. Currently, ~4% of new and ~21% of previously treated tuberculosis cases are either rifampicin-drug-resistant or MDR Mtb infections1. The specific molecular host-pathogen interactions mediating the rapid worldwide spread of MDR Mtb strains remain poorly understood. W-Beijing Mtb strains are highly prevalent throughout the world and associated with increased drug resistance2. In the early 1990s, closely related MDR W-Beijing Mtb strains (W strains) were identified in large institutional outbreaks in New York City and caused high mortality rates3. The production of interleukin-1? (IL-1?) by macrophages coincides with the shift towards aerobic glycolysis, a metabolic process that mediates protection against drug-susceptible Mtb4. Here, using a collection of MDR W-Mtb strains, we demonstrate that the overexpression of Mtb cell wall lipids, phthiocerol dimycocerosates, bypasses the interleukin 1 receptor, type I (IL-1R1) signalling pathway, instead driving the induction of interferon-? (IFN-?) to reprogram macrophage metabolism. Importantly, Mtb carrying a drug resistance-conferring single nucleotide polymorphism in rpoB (H445Y)5 can modulate host macrophage metabolic reprogramming. These findings transform our mechanistic understanding of how emerging MDR Mtb strains may acquire drug resistance single nucleotide polymorphisms, thereby altering Mtb surface lipid expression and modulating host macrophage metabolic reprogramming.
| S-EPMC6158078 | biostudies-literature
Project description:Whole-genome sequencing of drug resistance isolates of Mycobacterium tuberculosis in Lima, Peru.
| PRJEB47846 | ENA
Project description:BackgroundDrug resistant Mycobacterium tuberculosis is complicating the effective treatment and control of tuberculosis disease (TB). With the adoption of whole genome sequencing as a diagnostic tool, machine learning approaches are being employed to predict M. tuberculosis resistance and identify underlying genetic mutations. However, machine learning approaches can overfit and fail to identify causal mutations if they are applied out of the box and not adapted to the disease-specific context. We introduce a machine learning approach that is customized to the TB setting, which extracts a library of genomic variants re-occurring across individual studies to improve genotypic profiling.ResultsWe developed a customized decision tree approach, called Treesist-TB, that performs TB drug resistance prediction by extracting and evaluating genomic variants across multiple studies. The application of Treesist-TB to rifampicin (RIF), isoniazid (INH) and ethambutol (EMB) drugs, for which resistance mutations are known, demonstrated a level of predictive accuracy similar to the widely used TB-Profiler tool (Treesist-TB vs. TB-Profiler tool: RIF 97.5% vs. 97.6%; INH 96.8% vs. 96.5%; EMB 96.8% vs. 95.8%). Application of Treesist-TB to less understood second-line drugs of interest, ethionamide (ETH), cycloserine (CYS) and para-aminosalisylic acid (PAS), led to the identification of new variants (52, 6 and 11, respectively), with a high number absent from the TB-Profiler library (45, 4, and 6, respectively). Thereby, Treesist-TB had improved predictive sensitivity (Treesist-TB vs. TB-Profiler tool: PAS 64.3% vs. 38.8%; CYS 45.3% vs. 30.7%; ETH 72.1% vs. 71.1%).ConclusionOur work reinforces the utility of machine learning for drug resistance prediction, while highlighting the need to customize approaches to the disease-specific context. Through applying a modified decision learning approach (Treesist-TB) across a range of anti-TB drugs, we identified plausible resistance-encoding genomic variants with high predictive ability, whilst potentially overcoming the overfitting challenges that can affect standard machine learning applications.
| S-EPMC8753810 | biostudies-literature