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Cryptic Microheteroresistance Explains Mycobacterium tuberculosis Phenotypic Resistance.

ABSTRACT: RATIONALE:Minority drug-resistant Mycobacterium tuberculosis subpopulations can be associated with phenotypic resistance but are poorly detected by Sanger sequencing or commercial molecular diagnostic assays. OBJECTIVES:To determine the role of targeted next-generation sequencing in resolving these minor variant subpopulations. METHODS:We used single molecule overlapping reads (SMOR), a targeted next-generation sequencing approach that dramatically reduces sequencing error, to analyze primary cultured isolates phenotypically resistant to rifampin, fluoroquinolones, or aminoglycosides, but for which Sanger sequencing found no resistance-associated variants (RAVs) within respective resistance-determining regions (study group). Isolates also underwent single-colony selection on antibiotic-containing agar, blinded to sequencing results. As a positive control, isolates with multiple colocalizing chromatogram peaks were also analyzed (control group). MEASUREMENTS AND MAIN RESULTS:Among 61 primary culture isolates (25 study group and 36 control group), SMOR described 66 (49%) and 45 (33%) of 135 total heteroresistant RAVs at frequencies less than 5% and less than 1% of the total mycobacterial population, respectively. In the study group, SMOR detected minor resistant variant subpopulations in 80% (n?=?20/25) of isolates with no Sanger-identified RAVs (median subpopulation size, 1.0%; interquartile range, 0.2-3.9%). Single-colony selection on drug-containing media corroborated SMOR results for 90% (n?=?18/20) of RAV-containing specimens, and the absence of RAVs in 60% (n?=?3/5) of isolates. Overall, Sanger sequencing was concordant with SMOR for 77% (n?=?53/69) of macroheteroresistant (5-95% total population), but only 5% of microheteroresistant (<5%) subpopulations (n?=?3/66) across both groups. CONCLUSIONS:Cryptic minor variant mycobacterial subpopulations exist below the resolving capability of current drug susceptibility testing methodologies, and may explain an important proportion of false-negative resistance determinations.

SUBMITTER: Metcalfe JZ

PROVIDER: S-EPMC5694839 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Cryptic Microheteroresistance Explains Mycobacterium tuberculosis Phenotypic Resistance.

Metcalfe John Z JZ Streicher Elizabeth E Theron Grant G Colman Rebecca E RE Allender Christopher C Lemmer Darrin D Warren Rob R Engelthaler David M DM

American journal of respiratory and critical care medicine 20171101 9

<h4>Rationale</h4>Minority drug-resistant Mycobacterium tuberculosis subpopulations can be associated with phenotypic resistance but are poorly detected by Sanger sequencing or commercial molecular diagnostic assays.<h4>Objectives</h4>To determine the role of targeted next-generation sequencing in resolving these minor variant subpopulations.<h4>Methods</h4>We used single molecule overlapping reads (SMOR), a targeted next-generation sequencing approach that dramatically reduces sequencing error, ...[more]

PMID: 28614668

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Project description:The ability to adapt to different conditions is key for Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), to successfully infect human hosts. Adaptations allow the organism to evade the host immune responses during acute infections and persist for an extended period of time during the latent infectious stage. In latently infected individuals, estimated to include one-third of the human population, the organism exists in a variety of metabolic states, which impedes the development of a simple strategy for controlling or eradicating this disease. Direct knowledge of the metabolic states of M. tuberculosis in patients would aid in the management of the disease as well as in forming the basis for developing new drugs and designing more efficacious drug cocktails. Here, we propose an in silico approach to create state-specific models based on readily available gene expression data. The coupling of differential gene expression data with a metabolic network model allowed us to characterize the metabolic adaptations of M. tuberculosis H37Rv to hypoxia. Given the microarray data for the alterations in gene expression, our model predicted reduced oxygen uptake, ATP production changes, and a global change from an oxidative to a reductive tricarboxylic acid (TCA) program. Alterations in the biomass composition indicated an increase in the cell wall metabolites required for cell-wall growth, as well as heightened accumulation of triacylglycerol in preparation for a low-nutrient, low metabolic activity life style. In contrast, the gene expression program in the deletion mutant of dosR, which encodes the immediate hypoxic response regulator, failed to adapt to low-oxygen stress. Our predictions were compatible with recent experimental observations of M. tuberculosis activity under hypoxic and anaerobic conditions. Importantly, alterations in the flow and accumulation of a particular metabolite were not necessarily directly linked to differential gene expression of the enzymes catalyzing the related metabolic reactions.

Dataset Information

Cryptic Microheteroresistance Explains Mycobacterium tuberculosis Phenotypic Resistance.

Publications

Cryptic Microheteroresistance Explains Mycobacterium tuberculosis Phenotypic Resistance.

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