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      Diversification of gene content in the Mycobacterium tuberculosis complex is determined by phylogenetic and ecological signatures

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          ABSTRACT

          We analyzed the pan-genome and gene content modulation of the most diverse genome data set of the Mycobacterium tuberculosis complex (MTBC) gathered to date. The closed pan-genome of the MTBC was characterized by reduced accessory and strain-specific genomes, compatible with its clonal nature. However, significantly fewer gene families were shared between MTBC genomes as their phylogenetic distance increased. This effect was only observed in inter-species comparisons, not within-species, which suggests that species-specific ecological characteristics are associated with changes in gene content. Gene loss, resulting from genomic deletions and pseudogenization, was found to drive the variation in gene content. This gene erosion differed among MTBC species and lineages, even within M. tuberculosis, where L2 showed more gene loss than L4. We also show that phylogenetic proximity is not always a good proxy for gene content relatedness in the MTBC, as the gene repertoire of Mycobacterium africanum L6 deviated from its expected phylogenetic niche conservatism. Gene disruptions of virulence factors, represented by pseudogene annotations, are mostly not conserved, being poor predictors of MTBC ecotypes. Each MTBC ecotype carries its own accessory genome, likely influenced by distinct selective pressures such as host and geography. It is important to investigate how gene loss confer new adaptive traits to MTBC strains; the detected heterogeneous gene loss poses a significant challenge in elucidating genetic factors responsible for the diverse phenotypes observed in the MTBC. By detailing specific gene losses, our study serves as a resource for researchers studying the MTBC phenotypes and their immune evasion strategies.

          IMPORTANCE

          In this study, we analyzed the gene content of different ecotypes of the Mycobacterium tuberculosis complex (MTBC), the pathogens of tuberculosis. We found that changes in their gene content are associated with their ecological features, such as host preference. Gene loss was identified as the primary driver of these changes, which can vary even among different strains of the same ecotype. Our study also revealed that the gene content relatedness of these bacteria does not always mirror their evolutionary relationships. In addition, some genes of virulence can be variably lost among strains of the same MTBC ecotype, likely helping them to evade the immune system. Overall, our study highlights the importance of understanding how gene loss can lead to new adaptations in these bacteria and how different selective pressures may influence their genetic makeup.

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          IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

          Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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            STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

            Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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              BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs.

              Genomics has revolutionized biological research, but quality assessment of the resulting assembled sequences is complicated and remains mostly limited to technical measures like N50.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review and editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review and editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review and editing
                Role: Editor
                Journal
                Microbiol Spectr
                Microbiol Spectr
                spectrum
                Microbiology Spectrum
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2165-0497
                February 2024
                17 January 2024
                17 January 2024
                : 12
                : 2
                : e02289-23
                Affiliations
                [1 ]Laboratory of Applied Research in Mycobacteria, Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo; , São Paulo, Brazil
                [2 ]Department of Preventive Veterinary Medicine and Animal Health, School of Veterinary Medicine and Animal Sciences, University of São Paulo; , São Paulo, Brazil
                University of Arkansas for Medical Sciences; , Little Rock, Arkansas, USA
                Author notes
                Address correspondence to Ana Marcia Sá Guimarães, anamarcia@ 123456usp.br

                The authors declare no conflict of interest.

                Author information
                https://orcid.org/0000-0002-8261-5863
                Article
                02289-23 spectrum.02289-23
                10.1128/spectrum.02289-23
                10871547
                38230932
                62f942d4-1807-446c-b019-92dac019bee8
                Copyright © 2024 Silva-Pereira et al.

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

                History
                : 12 June 2023
                : 19 December 2023
                Page count
                supplementary-material: 5, authors: 3, Figures: 7, Tables: 2, References: 105, Pages: 23, Words: 13401
                Funding
                Funded by: Morris Animal Foundation (MAF);
                Award ID: D17ZO-307
                Award Recipient :
                Funded by: Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP);
                Award ID: 2016/26108-0, 2019/20786-5
                Award Recipient :
                Funded by: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES);
                Award ID: 88887.508739/2020-00, 001
                Award Recipient :
                Funded by: Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq);
                Award ID: 140003/2019-3
                Award Recipient :
                Categories
                Research Article
                bacteriology, Bacteriology
                Custom metadata
                February 2024

                mycobacterium tuberculosis,genomics,pan-genome,mycobacterium bovis,mycobacterium africanum,evolution

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