Classification of Mycobacterium tuberculosis DR, MDR,XDR Isolates and Identification of Signature MutationPattern of Drug Resistance

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Abstract

Mycobacterium tuberculosis - a global threat, the recent breakout in MDR-TB and XDR-TB has challenged researchers in diagnosis to provide effective treatment. The main objective to combat drug resistance is to provide rapid, reliable and sensitive diagnostic methods in health care centres. This study focuses on development of an effective pipeline to identify drug resistance mutations in whole genome data of Mycobacterium tuberculosis utilizing the Next Generation Sequencing approach and classification of drug resistance strains based on genetic markers obtained from TGS-TB, tbvar and TBDReamDB. 74 isolates are characterized into 20 DR-TB, 16 MDR-TB, 16 XDR-TB and 6 nonresistant strains based on known drug resistance genetic markers. Results provide mutation pattern for each of the classified strains and profiling of drug resistance to the group of anti-TB drugs. The presence of specific mutation causing resistance to a drug will help set the dosage levels which play an important role in the treatment. Findings on amino acid changes and its respective codon positions in candidate genes will provide insights in drug sensitivity and a way for discovery of potent drugs. The implementation of these approaches in clinical setting provides rapid and sensitive diagnostics to combat the emerging drug resistance.

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TGS-TB: Total Genotyping Solution for Mycobacterium tuberculosis Using Short-Read Whole-Genome Sequencing

Tsuyoshi Sekizuka, Akifumi Yamashita, Yoshiro Murase … (2015)

Whole-genome sequencing (WGS) with next-generation DNA sequencing (NGS) is an increasingly accessible and affordable method for genotyping hundreds of Mycobacterium tuberculosis (Mtb) isolates, leading to more effective epidemiological studies involving single nucleotide variations (SNVs) in core genomic sequences based on molecular evolution. We developed an all-in-one web-based tool for genotyping Mtb, referred to as the Total Genotyping Solution for TB (TGS-TB), to facilitate multiple genotyping platforms using NGS for spoligotyping and the detection of phylogenies with core genomic SNVs, IS6110 insertion sites, and 43 customized loci for variable number tandem repeat (VNTR) through a user-friendly, simple click interface. This methodology is implemented with a KvarQ script to predict MTBC lineages/sublineages and potential antimicrobial resistance. Seven Mtb isolates (JP01 to JP07) in this study showing the same VNTR profile were accurately discriminated through median-joining network analysis using SNVs unique to those isolates. An additional IS6110 insertion was detected in one of those isolates as supportive genetic information in addition to core genomic SNVs. The results of in silico analyses using TGS-TB are consistent with those obtained using conventional molecular genotyping methods, suggesting that NGS short reads could provide multiple genotypes to discriminate multiple strains of Mtb, although longer NGS reads (≥300-mer) will be required for full genotyping on the TGS-TB web site. Most available short reads (~100-mer) can be utilized to discriminate the isolates based on the core genome phylogeny. TGS-TB provides a more accurate and discriminative strain typing for clinical and epidemiological investigations; NGS strain typing offers a total genotyping solution for Mtb outbreak and surveillance. TGS-TB web site: https://gph.niid.go.jp/tgs-tb/.

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Phylogenetic analysis of modularity in protein interaction networks

Sinan Erten, Xin Li, Gurkan Bebek … (2009)

Background In systems biology, comparative analyses of molecular interactions across diverse species indicate that conservation and divergence of networks can be used to understand functional evolution from a systems perspective. A key characteristic of these networks is their modularity, which contributes significantly to their robustness, as well as adaptability. Consequently, analysis of modular network structures from a phylogenetic perspective may be useful in understanding the emergence, conservation, and diversification of functional modularity. Results In this paper, we propose a phylogenetic framework for analyzing network modules, with applications that extend well beyond network-based phylogeny reconstruction. Our approach is based on identification of modular network components from each network separately, followed by projection of these modules onto the networks of other species to compare different networks. Subsequently, we use the conservation of various modules in each network to assess the similarity between different networks. Compared to traditional methods that rely on topological comparisons, our approach has key advantages in (i) avoiding intractable graph comparison problems in comparative network analysis, (ii) accounting for noise and missing data through flexible treatment of network conservation, and (iii) providing insights on the evolution of biological systems through investigation of the evolutionary trajectories of network modules. We test our method, MOPHY, on synthetic data generated by simulation of network evolution, as well as existing protein-protein interaction data for seven diverse species. Comprehensive experimental results show that MOPHY is promising in reconstructing evolutionary histories of extant networks based on conservation of modularity, it is highly robust to noise, and outperforms existing methods that quantify network similarity in terms of conservation of network topology. Conclusion These results establish modularity and network proximity as useful features in comparative network analysis and motivate detailed studies of the evolutionary histories of network modules.

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Author and article information

Journal

Journal ID (nlm-ta): Bioinformation

Journal ID (iso-abbrev): Bioinformation

Journal ID (publisher-id): Bioinformation

Title: Bioinformation

Publisher: Biomedical Informatics

ISSN (Electronic): 0973-2063

Publication date Collection: 2019

Publication date (Electronic): 15 April 2019

Volume: 15

Issue: 4

Pages: 261-268

Affiliations

[1 ]Department of Biotechnology, Rashtreeya Vidyalaya College of Engineering,Mysuru road,Bangaluru,India

Author notes

[* ]Vidya Niranjan vidya.n@ 123456rvce.edu.in

Article

Publisher ID: 97320630015261

DOI: 10.6026/97320630015261

PMC ID: 6599436

PubMed ID: 31285643

SO-VID: b276aa26-ac67-46cc-b38b-38882d67eed2

License:

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.

History

Date received : 28 February 2019

Date revision received : 15 March 2019

Date accepted : 15 April 2019

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