GenoMycAnalyzer: a web-based tool for species and drug resistance prediction for Mycobacterium genomes.

BACKGROUND: Drug-resistant tuberculosis (TB) is a major threat to global public health. Whole-genome sequencing (WGS) is a useful tool for species identification and drug resistance prediction, and many clinical laboratories are transitioning to WGS as a routine diagnostic tool. However, user-friendly and high-confidence automated bioinformatics tools are needed to rapidly identify M. tuberculosis complex (MTBC) and non-tuberculous mycobacteria (NTM), detect drug resistance, and further guide treatment options. RESULTS: We developed GenoMycAnalyzer, a web-based software that integrates functions for identifying MTBC and NTM species, lineage and spoligotype prediction, variant calling, annotation, drug-resistance determination, and data visualization. The accuracy of GenoMycAnalyzer for genotypic drug susceptibility testing (gDST) was evaluated using 5,473 MTBC isolates that underwent phenotypic DST (pDST). The GenoMycAnalyzer database was built to predict the gDST for 15 antituberculosis drugs using the World Health Organization mutational catalogue. Compared to pDST, the sensitivity of drug susceptibilities by the GenoMycAnalyzer for first-line drugs ranged from 95.9% for rifampicin (95% CI 94.8-96.7%) to 79.6% for pyrazinamide (95% CI 76.9-82.2%), whereas those for second-line drugs ranged from 98.2% for levofloxacin (95% CI 90.1-100.0%) to 74.9% for capreomycin (95% CI 69.3-80.0%). Notably, the integration of large deletions of the four resistance-conferring genes increased gDST sensitivity. The specificity of drug susceptibilities by the GenoMycAnalyzer ranged from 98.7% for amikacin (95% CI 97.8-99.3%) to 79.5% for ethionamide (95% CI 76.4-82.3%). The incorporated Kraken2 software identified 1,284 mycobacterial species with an accuracy of 98.8%. GenoMycAnalyzer also perfectly predicted lineages for 1,935 MTBC and spoligotypes for 54 MTBC. CONCLUSIONS: GenoMycAnalyzer offers both web-based and graphical user interfaces, which can help biologists with limited access to high-performance computing systems or limited bioinformatics skills. By streamlining the interpretation of WGS data, the GenoMycAnalyzer has the potential to significantly impact TB management and contribute to global efforts to combat this infectious disease. GenoMycAnalyzer is available at http://www.mycochase.org .

Investigating Bacterial Volatilome for the Classification and Identification of Mycobacterial Species by HS-SPME-GC-MS and Machine Learning.

Species of Mycobacteriaceae cause disease in animals and humans, including tuberculosis and leprosy. Individuals infected with organisms in the Mycobacterium tuberculosis complex (MTBC) or non-tuberculous mycobacteria (NTM) may present identical symptoms, however the treatment for each can be different. Although the NTM infection is considered less vital due to the chronicity of the disease and the infrequency of occurrence in healthy populations, diagnosis and differentiation among Mycobacterium species currently require culture isolation, which can take several weeks. The use of volatile organic compounds (VOCs) is a promising approach for species identification and in recent years has shown promise for use in the rapid analysis of both in vitro cultures as well as ex vivo diagnosis using breath or sputum. The aim of this contribution is to analyze VOCs in the culture headspace of seven different species of mycobacteria and to define the volatilome profiles that are discriminant for each species. For the pre-concentration of VOCs, solid-phase micro-extraction (SPME) was employed and samples were subsequently analyzed using gas chromatography-quadrupole mass spectrometry (GC-qMS). A machine learning approach was applied for the selection of the 13 discriminatory features, which might represent clinically translatable bacterial biomarkers.

Comparison of the Genedia MTB/NTM Detection Kit and Anyplex plus MTB/NTM Detection Kit for detection of Mycobacterium tuberculosis complex and nontuberculous mycobacteria in clinical specimens.

BACKGROUND: Real-time (RT) PCR is a rapid and accurate method that is widely used for the detection of Mycobacterium tuberculosis complex (MTB). The aim of this study was to evaluate and compare the performance of the Genedia MTB/NTM Detection Kit and the Anyplex plus MTB/NTM Detection kit in the detection of MTB and nontuberculous mycobacteria (NTM) in clinical specimens. METHODS: From October 2017 to February 2018, 236 respiratory specimens and 137 non-respiratory specimens, from patients with suspected tuberculosis, were examined. AFB smear, culture, and RT-PCR using the Genedia MTB/NTM Detection kit (Green Cross Medical Science Corp.) and the Anyplex plus MTB/NTM Detection kit (Seegene) were applied. PCR performance in the detection of MTB and NTM was evaluated in relation to culture results and between the two assays. RESULTS: Culture was positive for MTB in 30 (8.0%) of the 373 specimens and for NTM in 23 (6.2%). The sensitivity and specificity of MTB detection with the Genedia kit were 76.7% and 99.7%, respectively, whereas the Anyplex kit sensitivity and specificity for MTB detection were 86.7% and 97.5%, respectively. Both kits exhibited the same sensitivity (73.9%) for NTM detection, and the specificity was 100% and 99.4% for the Genedia and Anyplex kits, respectively. CONCLUSIONS: The Genedia and Anyplex kits demonstrated high sensitivity and specificity for the detection of MTB and NTM. Both kits have a high concordance rate and can be used more widely in clinical laboratories for the early detection of tuberculosis.

Implication of species change of Nontuberculous Mycobacteria during or after treatment.

BACKGROUND: Co-existence or subsequent isolation of multiple nontuberculous mycobacteria (NTM) species in same patient has been reported. However, clinical significance of these observations is unclear. The aim of this study was to determine clinical implications of changes of NTM species during or after treatment in patients with NTM lung disease. METHODS: Patients with NTM lung disease, who experienced changes of NTM species during treatment or within 2 years of treatment completion between January 1, 2009 and December 31, 2015, were included in the analysis. Demographic, clinical, microbiological, and radiographic data were reviewed and analyzed. RESULTS: During the study period, 473 patients were newly diagnosed with NTM lung disease. Treatment was started in 164 patients (34.6%). Among these 164 patients, 16 experienced changes of NTM species during or within 2 years of treatment completion. Seven showed changes from M. avium complex (MAC) to M. abscessus subspecies abscessus (MAA) and five patients displayed changes from M. abscessus subspecies massiliense (MAM) to MAC. With isolation of new NTM species, 6 out of 7 patients with change from MAC to MAA reported worsening of symptoms, whereas none of the five patients with change from MAM to MAC reported worsening of symptoms. All MAA isolated during or after treatment for MAC lung diseases showed inducible resistance to clarithromycin. CONCLUSIONS: Change of NTM species may occur during or after treatment for NTM lung disease. Especially, changes from MAC to MAA is accompanied by symptomatic and radiographic worsening as well as inducible resistance to clarithromycin.