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      Omics analysis of Mycobacterium tuberculosis isolates uncovers Rv3094c, an ethionamide metabolism-associated gene

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          Abstract

          Global control of the tuberculosis epidemic is threatened by increasing prevalence of drug resistant M. tuberculosis isolates. Many genome-wide studies focus on SNP-associated drug resistance mechanisms, but drug resistance in 5–30% of M. tuberculosis isolates (varying with antibiotic) appears unrelated to reported SNPs, and alternative drug resistance mechanisms involving variation in gene/protein expression are not well-studied. Here, using an omics approach, we identify 388 genes with lineage-related differential expression and 68 candidate drug resistance-associated gene pairs/clusters in 11 M. tuberculosis isolates (variable lineage/drug resistance profiles). Structural, mutagenesis, biochemical and bioinformatic studies on Rv3094c from the Rv3093c-Rv3095 gene cluster, a gene cluster selected for further investigation as it contains a putative monooxygenase/repressor pair and is associated with ethionamide resistance, provide insights on its involvement in ethionamide sulfoxidation, the initial step in its activation. Analysis of the structure of Rv3094c and its complex with ethionamide and flavin mononucleotide, to the best of our knowledge the first structures of an enzyme involved in ethionamide activation, identify key residues in the flavin mononucleotide and ethionamide binding pockets of Rv3094c, and F221, a gate between flavin mononucleotide and ethionamide allowing their interaction to complete the sulfoxidation reaction. Our work broadens understanding of both lineage- and drug resistance-associated gene/protein expression perturbations and identifies another player in mycobacterial ethionamide metabolism.

          Abstract

          A multi-omic analysis of 11 Mycobacterium tuberculosis (Mtb) isolates with different drug resistance profiles leads to the discovery of Rv3094c, an enzyme capable of activating the clinically relevant Mtb drug, ethionamide.

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          Most cited references56

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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                Author and article information

                Contributors
                wankanglin@icdc.cn
                blj@ibp.ac.cn
                ouyangsy@fjnu.edu.cn
                hongtaizhang@aliyun.com
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                7 February 2023
                7 February 2023
                2023
                : 6
                : 156
                Affiliations
                [1 ]GRID grid.9227.e, ISNI 0000000119573309, Key Laboratory of RNA Biology, Institute of Biophysics, , Chinese Academy of Sciences, ; Beijing, 100101 China
                [2 ]GRID grid.411503.2, ISNI 0000 0000 9271 2478, Provincial University Key Laboratory of Cellular Stress Response and Metabolic Regulation, the Key Laboratory of Innate Immune Biology of Fujian Province, Biomedical Research Center of South China, Key Laboratory of OptoElectronic Science and Technology for Medicine of the Ministry of Education, Fujian Key Laboratory of Special Marine Bio-resources Sustainable Utilization, College of Life Sciences, , Fujian Normal University, ; Fuzhou, 350117 Fujian Province China
                [3 ]GRID grid.198530.6, ISNI 0000 0000 8803 2373, State Key Laboratory for Infectious Diseases Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, , Chinese Center for Disease Control and Prevention, ; Beijing, 102206 China
                [4 ]GRID grid.216417.7, ISNI 0000 0001 0379 7164, Department of Physiology, Xiangya School of Medicine, , Central South University, ; Changsha, Hunan 410078 China
                [5 ]Hunan Chest Hospital, Changsha, 410013 Hunan Province China
                [6 ]GRID grid.410726.6, ISNI 0000 0004 1797 8419, University of Chinese Academy of Sciences, ; Beijing, 100049 China
                [7 ]GRID grid.9227.e, ISNI 0000000119573309, National Laboratory of Biomacromolecules, Institute of Biophysics, , Chinese Academy of Sciences, ; Beijing, 100101 China
                [8 ]Guangdong Province Key Laboratory of TB Systems Biology and Translational Medicine, Foshan, 528000 Guangdong Province China
                [9 ]GRID grid.418263.a, ISNI 0000 0004 1798 5707, Beijing Center for Disease Prevention and Control, ; Beijing, 100013 China
                Author information
                http://orcid.org/0000-0002-5550-9889
                http://orcid.org/0000-0002-8505-8724
                http://orcid.org/0000-0002-3997-0237
                http://orcid.org/0000-0002-6522-3745
                http://orcid.org/0000-0002-1120-1524
                http://orcid.org/0000-0001-9308-5872
                Article
                4433
                10.1038/s42003-023-04433-w
                9904262
                36750726
                22219079-2418-4b5b-bff2-b5325244a40d
                © The Author(s) 2023

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 9 June 2021
                : 5 January 2023
                Funding
                Funded by: the National Natural Science Foundation of China【grant numbers 31770148】
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                © The Author(s) 2023

                antimicrobial resistance,clinical microbiology,pathogens

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