N.E. Region, Indian Council of Medical Research (ICMR)-Regional Medical Research Centre, Post Box #105, Dibrugarh, Assam, 786 001, India.
National Tuberculosis Elimination Programme (NTEP), Gangtok, Sikkim, India.
Sci Rep. 2021 Apr 1;11(1):7365. doi: 10.1038/s41598-021-86626-z.
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.
在印度，结核病是一个巨大的公共卫生问题。本研究首次描述了来自印度锡金邦的结核分枝杆菌复合群（MTBC）的分子多样性。共培养了 399 份抗酸杆菌痰阳性样本洛氏培养基，并通过 spoligotyping 和 24 个位点 MIRU-VNTR 分型进行遗传特征分析。 spoligotyping 显示发生了 58 种不同 spoligotype。北京 spoligotype 是最主要的类型，占总分离株的 62.41%，并与多药耐药性有关。基于 24 个位点 MIRU-VNTR 分析对 249 株北京菌株进行最小生成树分析，确定了 12 个克隆复合体（单一位点变体）。主成分分析用于使用 24-MIRU VNTR 谱可视化来自锡金的 MTBC 分离株主要 spoligotype 可能的分组。基于人工智能的机器学习（ML）方法，如随机森林（RF）、支持向量机（SVM）和人工神经网络（ANN），用于使用 MIRU-VNTR 数据预测 MTBC 的优势 spoligotype。K 折交叉验证和使用未见测试数据集的验证显示，ANN、RF 和 SVM 对预测北京、CAS1_Delhi 和 T1 spoligotype 的准确率很高（93-99%）。然而，使用外部新验证数据集进行预测表明，RF 模型比 SVM 和 ANN 更准确。
