Molecular diversity of Mycobacterium tuberculosis complex in Sikkim, India and prediction of dominant spoligotypes using artificial intelligence

Abstract In India, tuberculosis is an enormous public health problem. This study provides the first description of molecular diversity of the Mycobacterium tuberculosis complex (MTBC) from Sikkim, India. A total of 399 Acid Fast Bacilli sputum positive samples were cultured on Lőwenstein–Jensen medi...

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Autores principales:	Kangjam Rekha Devi, Jagat Pradhan, Rinchenla Bhutia, Peggy Dadul, Atanu Sarkar, Nitumoni Gohain, Kanwar Narain
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/390db4db447c4dc695c53457d3023caf
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spelling	oai:doaj.org-article:390db4db447c4dc695c53457d3023caf2021-12-02T14:25:32ZMolecular diversity of Mycobacterium tuberculosis complex in Sikkim, India and prediction of dominant spoligotypes using artificial intelligence10.1038/s41598-021-86626-z2045-2322https://doaj.org/article/390db4db447c4dc695c53457d3023caf2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86626-zhttps://doaj.org/toc/2045-2322Abstract In India, tuberculosis is an enormous public health problem. This study provides the first description of molecular diversity of the Mycobacterium tuberculosis complex (MTBC) from Sikkim, India. A total of 399 Acid Fast Bacilli sputum positive samples were cultured on Lőwenstein–Jensen media and genetic characterisation was done by spoligotyping and 24-loci MIRU-VNTR typing. Spoligotyping revealed the occurrence of 58 different spoligotypes. Beijing spoligotype was the most dominant type constituting 62.41% of the total isolates and was associated with Multiple Drug Resistance. Minimum Spanning tree analysis of 249 Beijing strains based on 24-loci MIRU-VNTR analysis identified 12 clonal complexes (Single Locus Variants). The principal component analysis was used to visualise possible grouping of MTBC isolates from Sikkim belonging to major spoligotypes using 24-MIRU VNTR profiles. Artificial intelligence-based machine learning (ML) methods such as Random Forests (RF), Support Vector Machines (SVM) and Artificial Neural Networks (ANN) were used to predict dominant spoligotypes of MTBC using MIRU-VNTR data. K-fold cross-validation and validation using unseen testing data set revealed high accuracy of ANN, RF, and SVM for predicting Beijing, CAS1_Delhi, and T1 Spoligotypes (93–99%). However, prediction using the external new validation data set revealed that the RF model was more accurate than SVM and ANN.Kangjam Rekha DeviJagat PradhanRinchenla BhutiaPeggy DadulAtanu SarkarNitumoni GohainKanwar NarainNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kangjam Rekha Devi Jagat Pradhan Rinchenla Bhutia Peggy Dadul Atanu Sarkar Nitumoni Gohain Kanwar Narain Molecular diversity of Mycobacterium tuberculosis complex in Sikkim, India and prediction of dominant spoligotypes using artificial intelligence
description	Abstract In India, tuberculosis is an enormous public health problem. This study provides the first description of molecular diversity of the Mycobacterium tuberculosis complex (MTBC) from Sikkim, India. A total of 399 Acid Fast Bacilli sputum positive samples were cultured on Lőwenstein–Jensen media and genetic characterisation was done by spoligotyping and 24-loci MIRU-VNTR typing. Spoligotyping revealed the occurrence of 58 different spoligotypes. Beijing spoligotype was the most dominant type constituting 62.41% of the total isolates and was associated with Multiple Drug Resistance. Minimum Spanning tree analysis of 249 Beijing strains based on 24-loci MIRU-VNTR analysis identified 12 clonal complexes (Single Locus Variants). The principal component analysis was used to visualise possible grouping of MTBC isolates from Sikkim belonging to major spoligotypes using 24-MIRU VNTR profiles. Artificial intelligence-based machine learning (ML) methods such as Random Forests (RF), Support Vector Machines (SVM) and Artificial Neural Networks (ANN) were used to predict dominant spoligotypes of MTBC using MIRU-VNTR data. K-fold cross-validation and validation using unseen testing data set revealed high accuracy of ANN, RF, and SVM for predicting Beijing, CAS1_Delhi, and T1 Spoligotypes (93–99%). However, prediction using the external new validation data set revealed that the RF model was more accurate than SVM and ANN.
format	article
author	Kangjam Rekha Devi Jagat Pradhan Rinchenla Bhutia Peggy Dadul Atanu Sarkar Nitumoni Gohain Kanwar Narain
author_facet	Kangjam Rekha Devi Jagat Pradhan Rinchenla Bhutia Peggy Dadul Atanu Sarkar Nitumoni Gohain Kanwar Narain
author_sort	Kangjam Rekha Devi
title	Molecular diversity of Mycobacterium tuberculosis complex in Sikkim, India and prediction of dominant spoligotypes using artificial intelligence
title_short	Molecular diversity of Mycobacterium tuberculosis complex in Sikkim, India and prediction of dominant spoligotypes using artificial intelligence
title_full	Molecular diversity of Mycobacterium tuberculosis complex in Sikkim, India and prediction of dominant spoligotypes using artificial intelligence
title_fullStr	Molecular diversity of Mycobacterium tuberculosis complex in Sikkim, India and prediction of dominant spoligotypes using artificial intelligence
title_full_unstemmed	Molecular diversity of Mycobacterium tuberculosis complex in Sikkim, India and prediction of dominant spoligotypes using artificial intelligence
title_sort	molecular diversity of mycobacterium tuberculosis complex in sikkim, india and prediction of dominant spoligotypes using artificial intelligence
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/390db4db447c4dc695c53457d3023caf
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Molecular diversity of Mycobacterium tuberculosis complex in Sikkim, India and prediction of dominant spoligotypes using artificial intelligence

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