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      Rhodococcus comparative genomics reveals a phylogenomic-dependent non-ribosomal peptide synthetase distribution: insights into biosynthetic gene cluster connection to an orphan metabolite

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          Abstract

          Natural products (NPs) are synthesized by biosynthetic gene clusters (BGCs), whose genes are involved in producing one or a family of chemically related metabolites. Advances in comparative genomics have been favourable for exploiting huge amounts of data and discovering previously unknown BGCs. Nonetheless, studying distribution patterns of novel BGCs and elucidating the biosynthesis of orphan metabolites remains a challenge. To fill this knowledge gap, our study developed a pipeline for high-quality comparative genomics for the actinomycete genus Rhodococcus , which is metabolically versatile, yet understudied in terms of NPs, leading to a total of 110 genomes, 1891 BGCs and 717 non-ribosomal peptide synthetases (NRPSs). Phylogenomic inferences showed four major clades retrieved from strains of several ecological habitats. BiG-SCAPE sequence similarity BGC networking revealed 44 unidentified gene cluster families (GCFs) for NRPS, which presented a phylogenomic-dependent evolution pattern, supporting the hypothesis of vertical gene transfer. As a proof of concept, we analysed in-depth one of our marine strains, Rhodococcus sp. H-CA8f, which revealed a unique BGC distribution within its phylogenomic clade, involved in producing a chloramphenicol-related compound. While this BGC is part of the most abundant and widely distributed NRPS GCF, corason analysis unveiled major differences regarding its genetic context, co-occurrence patterns and modularity. This BGC is composed of three sections, two well-conserved right/left arms flanking a very variable middle section, composed of nrps genes. The presence of two non-canonical domains in H-CA8f’s BGC may contribute to adding chemical diversity to this family of NPs. Liquid chromatography-high resolution MS and dereplication efforts retrieved a set of related orphan metabolites, the corynecins, which to our knowledge are reported here for the first time in Rhodococcus . Overall, our data provide insights to connect BGC uniqueness with orphan metabolites, by revealing key comparative genomic features supported by models of BGC distribution along phylogeny.

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          Most cited references117

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          MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

          We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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            MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

            The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.
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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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                Author and article information

                Journal
                Microb Genom
                Microb Genom
                mgen
                mgen
                Microbial Genomics
                Microbiology Society
                2057-5858
                2021
                9 July 2021
                9 July 2021
                : 7
                : 7
                : 000621
                Affiliations
                [ 1]departmentLaboratorio de Microbiología Molecular y Biotecnología Ambiental , Departamento de Química y Centro de Biotecnología Daniel Alkalay Lowitt, Universidad Técnica Federico Santa María , Valparaíso 2340000, Chile
                [ 2]departmentCenter for Bioinformatics and Integrative Biology , Facultad de Ciencias de la Vida, Universidad Andres Bello , Santiago, Chile
                [ 3]departmentEvolution of Metabolic Diversity Laboratory , Unidad de Genómica Avanzada (Langebio), Cinvestav, Irapuato , Guanajuato, Mexico
                [ ]Present address: Institute of Quantitative Biology, Biochemistry and Biotechnology, School of Biological Sciences, University of Edinburgh, King’s Buildings, Edinburgh, UK
                Author notes
                *Correspondence: Beatriz Cámara, beatriz.camara@ 123456usm.cl
                *Correspondence: Agustina Undabarrena, agustina.undabarrena@ 123456usm.cl
                Author information
                https://orcid.org/0000-0001-9273-0665
                https://orcid.org/0000-0003-0306-1530
                https://orcid.org/0000-0003-2087-7435
                https://orcid.org/0000-0003-4384-8661
                https://orcid.org/0000-0003-1492-9497
                https://orcid.org/0000-0001-5227-7499
                Article
                000621
                10.1099/mgen.0.000621
                8477407
                34241590
                24dd3013-60c9-4724-acdf-73c214baecdb
                © 2021 The Authors

                This is an open-access article distributed under the terms of the Creative Commons Attribution NonCommercial License.

                History
                : 16 February 2021
                : 04 June 2021
                Funding
                Funded by: Comisión Nacional de Investigación Científica y Tecnológica
                Award ID: 21180908
                Award Recipient : LeonardoZamora-Leiva
                Funded by: Comisión Nacional de Investigación Científica y Tecnológica
                Award ID: 21191625
                Award Recipient : AndrésCumsille
                Funded by: Comisión Nacional de Investigación Científica y Tecnológica
                Award ID: ACT192057
                Award Recipient : EduardoCastro-Nallar
                Funded by: Fondo Nacional de Desarrollo Científico y Tecnológico
                Award ID: 1200834
                Award Recipient : EduardoCastro-Nallar
                Funded by: Fondo Nacional de Desarrollo Científico y Tecnológico
                Award ID: 3180399
                Award Recipient : AgustinaUndabarrena
                Funded by: Comisión Nacional de Investigación Científica y Tecnológica
                Award ID: ACT172128
                Award Recipient : BeatrizCamara
                Funded by: Fondo Nacional de Desarrollo Científico y Tecnológico
                Award ID: 1171555
                Award Recipient : BeatrizCamara
                Categories
                Research Articles
                Genomic Methodologies
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                biosynthetic gene clusters,comparative genomics,non-ribosomal peptide synthetase evolution,orphan metabolites,rhodococcus

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