Identification of novel single nucleotide variants in the drug resistance mechanism of <i>Mycobacterium tuberculosis</i> isolates by whole-genome analysis

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Background

Tuberculosis (TB) represents a major global health challenge. Drug resistance in Mycobacterium tuberculosis (MTB) poses a substantial obstacle to effective TB treatment. Identifying genomic mutations in MTB isolates holds promise for unraveling the underlying mechanisms of drug resistance in this bacterium.

Methods

In this study, we investigated the roles of single nucleotide variants (SNVs) in MTB isolates resistant to four antibiotics (moxifloxacin, ofloxacin, amikacin, and capreomycin) through whole-genome analysis. We identified the drug-resistance-associated SNVs by comparing the genomes of MTB isolates with reference genomes using the MuMmer4 tool.

Results

We observed a strikingly high proportion (94.2%) of MTB isolates resistant to ofloxacin, underscoring the current prevalence of drug resistance in MTB. An average of 3529 SNVs were detected in a single ofloxacin-resistant isolate, indicating a mutation rate of approximately 0.08% under the selective pressure of ofloxacin exposure. We identified a set of 60 SNVs associated with extensively drug-resistant tuberculosis (XDR-TB), among which 42 SNVs were non-synonymous mutations located in the coding regions of nine key genes (ctpI, desA3, mce1R, moeB1, ndhA, PE_PGRS4, PPE18, rpsA, secF). Protein structure modeling revealed that SNVs of three genes (PE_PGRS4, desA3, secF) are close to the critical catalytic active sites in the three-dimensional structure of the coding proteins.

Conclusion

This comprehensive study elucidates novel resistance mechanisms in MTB against antibiotics, paving the way for future design and development of anti-tuberculosis drugs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-024-10390-3.

Related collections

Most cited references 39

Record: found
Abstract: found
Article: found

Is Open Access

SWISS-MODEL: homology modelling of protein structures and complexes

Andrew Waterhouse, Martino Bertoni, Stefan Bienert … (2018)

Abstract Homology modelling has matured into an important technique in structural biology, significantly contributing to narrowing the gap between known protein sequences and experimentally determined structures. Fully automated workflows and servers simplify and streamline the homology modelling process, also allowing users without a specific computational expertise to generate reliable protein models and have easy access to modelling results, their visualization and interpretation. Here, we present an update to the SWISS-MODEL server, which pioneered the field of automated modelling 25 years ago and been continuously further developed. Recently, its functionality has been extended to the modelling of homo- and heteromeric complexes. Starting from the amino acid sequences of the interacting proteins, both the stoichiometry and the overall structure of the complex are inferred by homology modelling. Other major improvements include the implementation of a new modelling engine, ProMod3 and the introduction a new local model quality estimation method, QMEANDisCo. SWISS-MODEL is freely available at https://swissmodel.expasy.org.

0 comments Cited 3213 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

10 Years of GWAS Discovery: Biology, Function, and Translation.

Peter Visscher, Naomi R. Wray, Qian Zhang … (2017)

Application of the experimental design of genome-wide association studies (GWASs) is now 10 years old (young), and here we review the remarkable range of discoveries it has facilitated in population and complex-trait genetics, the biology of diseases, and translation toward new therapeutics. We predict the likely discoveries in the next 10 years, when GWASs will be based on millions of samples with array data imputed to a large fully sequenced reference panel and on hundreds of thousands of samples with whole-genome sequencing data.

0 comments Cited 855 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

MUMmer4: A fast and versatile genome alignment system

Guillaume Marçais, Arthur L. Delcher, Adam Phillippy … (2018)

The MUMmer system and the genome sequence aligner nucmer included within it are among the most widely used alignment packages in genomics. Since the last major release of MUMmer version 3 in 2004, it has been applied to many types of problems including aligning whole genome sequences, aligning reads to a reference genome, and comparing different assemblies of the same genome. Despite its broad utility, MUMmer3 has limitations that can make it difficult to use for large genomes and for the very large sequence data sets that are common today. In this paper we describe MUMmer4, a substantially improved version of MUMmer that addresses genome size constraints by changing the 32-bit suffix tree data structure at the core of MUMmer to a 48-bit suffix array, and that offers improved speed through parallel processing of input query sequences. With a theoretical limit on the input size of 141Tbp, MUMmer4 can now work with input sequences of any biologically realistic length. We show that as a result of these enhancements, the nucmer program in MUMmer4 is easily able to handle alignments of large genomes; we illustrate this with an alignment of the human and chimpanzee genomes, which allows us to compute that the two species are 98% identical across 96% of their length. With the enhancements described here, MUMmer4 can also be used to efficiently align reads to reference genomes, although it is less sensitive and accurate than the dedicated read aligners. The nucmer aligner in MUMmer4 can now be called from scripting languages such as Perl, Python and Ruby. These improvements make MUMer4 one the most versatile genome alignment packages available.

0 comments Cited 718 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Zhiyuan Yang: yangzhiyuan@link.cuhk.edu.hk

Stephen Kwok-Wing Tsui: kwtsui@cuhk.edu.hk

Journal

Journal ID (nlm-ta): BMC Genomics

Journal ID (iso-abbrev): BMC Genomics

Title: BMC Genomics

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2164

Publication date (Electronic): 14 May 2024

Publication date PMC-release: 14 May 2024

Publication date Collection: 2024

Volume: 25

Electronic Location Identifier: 478

Affiliations

[1 ]School of Artificial Intelligence, Hangzhou Dianzi University, ( https://ror.org/0576gt767) Hangzhou, 310018 China

[2 ]Agricultural Bioinformatics Key Laboratory of Hubei Province and 3D Genomics Research Centre, College of Informatics, Huazhong Agricultural University, ( https://ror.org/023b72294) Wuhan, 430070 China

[3 ]GRID grid.10784.3a, ISNI 0000 0004 1937 0482, School of Biomedical Sciences, , The Chinese University of Hong Kong, ; Hong Kong SAR, China

[4 ]GRID grid.168010.e, ISNI 0000000419368956, Stanford Cardiovascular Institute, , Stanford University School of Medicine, ; Stanford, CA 94305 USA

[5 ]Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, ( https://ror.org/00t33hh48) Hong Kong SAR, China

Article

Publisher ID: 10390

DOI: 10.1186/s12864-024-10390-3

PMC ID: 11094924

PubMed ID: 38745294

SO-VID: 0a15ad39-d753-4b39-8d83-f2e973467501

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 11 January 2024

Date accepted : 8 May 2024

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;

Award ID: 61903107

Custom metadata

ScienceOpen disciplines: Genetics

Keywords: mycobacterium tuberculosis,single nucleotide variant,whole-genome sequencing

Data availability:

ScienceOpen disciplines: Genetics

Keywords: mycobacterium tuberculosis, single nucleotide variant, whole-genome sequencing

Identification of novel single nucleotide variants in the drug resistance mechanism of Mycobacterium tuberculosis isolates by whole-genome analysis

Read this article at

Abstract

Background

Methods

Results

Conclusion

Supplementary Information

Related collections

Arabidopsis genomics

Most cited references 39

SWISS-MODEL: homology modelling of protein structures and complexes

10 Years of GWAS Discovery: Biology, Function, and Translation.

MUMmer4: A fast and versatile genome alignment system

Author and article information

Contributors

Journal

Affiliations

Article

History

Funding

Categories

Custom metadata

Comments

Comment on this article

Similar content 238

Most referenced authors 434