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      Construction and Analysis of the Complete Genome Sequence of Leprosy Agent Mycobacterium lepromatosis

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          ABSTRACT

          Leprosy is caused by Mycobacterium leprae and Mycobacterium lepromatosis. We report construction and analyses of the complete genome sequence of M. lepromatosis FJ924. The genome contained 3,271,694 nucleotides to encode 1,789 functional genes and 1,564 pseudogenes. It shared 1,420 genes and 885 pseudogenes (71.4%) with M. leprae but differed in 1,281 genes and pseudogenes (28.6%). In phylogeny, the leprosy bacilli started from a most recent common ancestor (MRCA) that diverged ~30 million years ago (Mya) from environmental organism Mycobacterium haemophilum. The MRCA then underwent reductive evolution with pseudogenization, gene loss, and chromosomal rearrangements. Analysis of the shared pseudogenes estimated the pseudogenization event ~14 Mya, shortly before species bifurcation. Afterwards, genomic changes occurred to lesser extent in each species. Like M. leprae, four major types of highly repetitive sequences were detected in M. lepromatosis, contributing to chromosomal rearrangements within and after MRCA. Variations in genes and copy numbers were noted, such as three copies of the gene encoding bifunctional diguanylate cyclase/phosphodiesterase in M. lepromatosis, but single copy in M. leprae; 6 genes encoding the TetR family transcriptional regulators in M. lepromatosis, but 11 such genes in M. leprae; presence of hemW gene in M. lepromatosis, but absence in M. leprae; and others. These variations likely aid unique pathogenesis, such as diffuse lepromatous leprosy associated with M. lepromatosis, while the shared genomic features should explain the common pathogenesis of dermatitis and neuritis in leprosy. Together, these findings and the genomic data of M. lepromatosis may facilitate future research and care for leprosy.

          IMPORTANCE Leprosy is a dreaded infection that still affects millions of people worldwide. Mycobacterium lepromatosis is a recently recognized cause in addition to the well-known Mycobacterium leprae. M. lepromatosis is likely specific for diffuse lepromatous leprosy, a severe form of the infection and endemic in Mexico. This study constructed and annotated the complete genome sequence of M. lepromatosis FJ924 and performed comparative genomic analyses with related mycobacteria. The results afford new and refined insights into the genome size, gene repertoire, pseudogenes, phylogenomic relationship, genome organization and plasticity, process and timing of reductive evolution, and genetic and proteomic basis for pathogenesis. The availability of the complete M. lepromatosis genome may prove to be useful for future research and care for the infection.

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          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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            A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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              MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.

              We present the latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, Mega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in Mega The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit Mega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line Mega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                Microbiol Spectr
                Microbiol Spectr
                spectrum
                Microbiology Spectrum
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2165-0497
                25 April 2022
                May-Jun 2022
                25 April 2022
                : 10
                : 3
                : e01692-21
                Affiliations
                [a ] Institute for Integrative Systems Biology (I2SysBio), University of Valenciagrid.5338.d, and CSIC, Paterna, Spain
                [b ] Genomics and Health Area, Foundation for the Promotion of Sanitary and Biomedical Research, Valencia, Spain
                [c ] Laboratory of Biometry and Evolutionary Biology UMR CNRS, University of Lyon, Villeurbanne, France
                [d ] Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
                [e ] Department of Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
                Institute of Bioinformatics and Applied Biotechnology
                Author notes
                [*]

                Present address: Li Zhang, Department of Environmental Health, The University of Cincinnati College of Medicine, Cincinnati, Ohio, USA.

                The authors declare no conflict of interest.

                Author information
                https://orcid.org/0000-0002-6911-6541
                https://orcid.org/0000-0002-3401-2662
                https://orcid.org/0000-0001-8472-0870
                Article
                01692-21 spectrum.01692-21
                10.1128/spectrum.01692-21
                9248898
                35467405
                5136f6f7-a431-47cc-b20e-79855c7b6017
                Copyright © 2022 Silva et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                : 27 September 2021
                : 7 April 2022
                Page count
                supplementary-material: 1, Figures: 7, Tables: 3, Equations: 2, References: 86, Pages: 17, Words: 11044
                Funding
                Funded by: HHS | NIH | National Cancer Institute (NCI), FundRef https://doi.org/10.13039/100000054;
                Award ID: CA016672
                Award Recipient :
                Funded by: HHS | NIH | National Cancer Institute (NCI), FundRef https://doi.org/10.13039/100000054;
                Award ID: CA143883
                Award Recipient :
                Funded by: Ministerio de Ciencia, Innovación y Universidades (Spain);
                Award ID: PGC2018-099344-B-I00
                Award Recipient :
                Funded by: Generalitat Valenciana;
                Award ID: Prometeo/2018/A/133
                Award Recipient :
                Categories
                Research Article
                computational-biology, Computational Biology
                Custom metadata
                May/June 2022

                mycobacterium leprae,mycobacterium lepromatosis,reductive evolution,genomics,leprosy

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