Dataset Information

Direct Whole-Genome Sequencing of Sputum Accurately Identifies Drug-Resistant Mycobacterium tuberculosis Faster than MGIT Culture Sequencing.

ABSTRACT: The current methods available to diagnose antimicrobial-resistant Mycobacterium tuberculosis infections require a positive culture or only test a limited number of resistance-associated mutations. A rapid accurate identification of antimicrobial resistance enables the prompt initiation of effective treatment. Here, we determine the utility of whole-genome sequencing (WGS) of M. tuberculosis directly from routinely obtained diagnostic sputum samples to provide a comprehensive resistance profile compared to that from mycobacterial growth indicator tube (MGIT) WGS. We sequenced M. tuberculosis from 43 sputum samples by targeted DNA enrichment using the Agilent SureSelectXT kit, and 43 MGIT positive samples from each participant. Thirty two (74%) sputum samples and 43 (100%) MGIT samples generated whole genomes. The times to antimicrobial resistance profiles and concordance were compared with Xpert MTB/RIF and phenotypic resistance testing from cultures of the same samples. Antibiotic susceptibility could be predicted from WGS of sputum within 5 days of sample receipt and up to 24 days earlier than WGS from MGIT culture and up to 31 days earlier than phenotypic testing. Direct sputum results could be reduced to 3 days with faster hybridization and if only regions encoding drug resistance are sequenced. We show that direct sputum sequencing has the potential to provide comprehensive resistance detection significantly faster than MGIT whole-genome sequencing or phenotypic testing of resistance from cultures in a clinical setting. This improved turnaround time enables prompt appropriate treatment with associated patient and health service benefits. Improvements in sample preparation are necessary to ensure comparable sensitivities and complete resistance profile predictions in all cases.

SUBMITTER: Doyle RM

PROVIDER: S-EPMC6062781 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Direct Whole-Genome Sequencing of Sputum Accurately Identifies Drug-Resistant Mycobacterium tuberculosis Faster than MGIT Culture Sequencing.

Doyle Ronan M RM Burgess Carrie C Williams Rachel R Gorton Rebecca R Booth Helen H Brown James J Bryant Josephine M JM Chan Jackie J Creer Dean D Holdstock Jolyon J Kunst Heinke H Lozewicz Stefan S Platt Gareth G Romero Erika Yara EY Speight Graham G Tiberi Simon S Abubakar Ibrahim I Lipman Marc M McHugh Timothy D TD Breuer Judith J

Journal of clinical microbiology 20180726 8

The current methods available to diagnose antimicrobial-resistant <i>Mycobacterium tuberculosis</i> infections require a positive culture or only test a limited number of resistance-associated mutations. A rapid accurate identification of antimicrobial resistance enables the prompt initiation of effective treatment. Here, we determine the utility of whole-genome sequencing (WGS) of <i>M. tuberculosis</i> directly from routinely obtained diagnostic sputum samples to provide a comprehensive resist ...[more]

PMID: 29848567

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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.

Project description:Next-Generation Sequencing (NGS) is widely used to investigate genomic variation. In several studies, the genetic variation of Mycobacterium tuberculosis has been analyzed in sputum samples without previous culture, using target enrichment methodologies for NGS. Alignments obtained by different programs generally map the sequences under default parameters, and from these results, it is assumed that only Mycobacterium reads will be obtained. However, variants of interest microorganism in clinical samples can be confused with a vast collection of reads from other bacteria, viruses, and human DNA. Currently, there are no standardized pipelines, and the cleaning success is never verified since there is a lack of rigorous controls to identify and remove reads from other sputum-microorganisms genetically similar to M. tuberculosis. Therefore, we designed a bioinformatic pipeline to process NGS data from sputum samples, including several filters and quality control points to identify and eliminate non-M. tuberculosis reads to obtain a reliable genetic variant report. Our proposal uses the SURPI software as a taxonomic classifier to filter input sequences and perform a mapping that provides the highest percentage of Mycobacterium reads, minimizing the reads from other microorganisms. We then use the filtered sequences to perform variant calling with the GATK software, ensuring the mapping quality, realignment, recalibration, hard-filtering, and post-filter to increase the reliability of the reported variants. Using default mapping parameters, we identified reads of contaminant bacteria, such as Streptococcus, Rhotia, Actinomyces, and Veillonella. Our final mapping strategy allowed a sequence identity of 97.8% between the input reads and the whole M. tuberculosis reference genome H37Rv using a genomic edit distance of three, thus removing 98.8% of the off-target sequences with a Mycobacterium reads loss of 1.7%. Finally, more than 200 unreliable genetic variants were removed during the variant calling, increasing the report's reliability.

Dataset Information

Direct Whole-Genome Sequencing of Sputum Accurately Identifies Drug-Resistant Mycobacterium tuberculosis Faster than MGIT Culture Sequencing.

Publications

Direct Whole-Genome Sequencing of Sputum Accurately Identifies Drug-Resistant Mycobacterium tuberculosis Faster than MGIT Culture Sequencing.

Similar Datasets

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