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      Modified base-binding EVE and DCD domains: striking diversity of genomic contexts in prokaryotes and predicted involvement in a variety of cellular processes

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          Abstract

          Background

          DNA and RNA of all cellular life forms and many viruses contain an expansive repertoire of modified bases. The modified bases play diverse biological roles that include both regulation of transcription and translation, and protection against restriction endonucleases and antibiotics. Modified bases are often recognized by dedicated protein domains. However, the elaborate networks of interactions and processes mediated by modified bases are far from being completely understood.

          Results

          We present a comprehensive census and classification of EVE domains that belong to the PUA/ASCH domain superfamily and bind various modified bases in DNA and RNA. We employ the “guilt by association” approach to make functional inferences from comparative analysis of bacterial and archaeal genomes, based on the distribution and associations of EVE domains in (predicted) operons and functional networks of genes. Prokaryotes encode two classes of EVE domain proteins, slow-evolving and fast-evolving ones. Slow-evolving EVE domains in α-proteobacteria are embedded in conserved operons, potentially involved in coupling between translation and respiration, cytochrome c biogenesis in particular, via binding 5-methylcytosine in tRNAs. In β- and γ-proteobacteria, the conserved associations implicate the EVE domains in the coordination of cell division, biofilm formation, and global transcriptional regulation by non-coding 6S small RNAs, which are potentially modified and bound by the EVE domains. In eukaryotes, the EVE domain-containing THYN1-like proteins have been reported to inhibit PCD and regulate the cell cycle, potentially, via binding 5-methylcytosine and its derivatives in DNA and/or RNA. We hypothesize that the link between PCD and cytochrome c was inherited from the α-proteobacterial and proto-mitochondrial endosymbiont and, unexpectedly, could involve modified base recognition by EVE domains. Fast-evolving EVE domains are typically embedded in defense contexts, including toxin-antitoxin modules and type IV restriction systems, suggesting roles in the recognition of modified bases in invading DNA molecules and targeting them for restriction. We additionally identified EVE-like prokaryotic Development and Cell Death (DCD) domains that are also implicated in defense functions including PCD. This function was inherited by eukaryotes, but in animals, the DCD proteins apparently were displaced by the extended Tudor family proteins, whose partnership with Piwi-related Argonautes became the centerpiece of the Piwi-interacting RNA (piRNA) system.

          Conclusions

          Recognition of modified bases in DNA and RNA by EVE-like domains appears to be an important, but until now, under-appreciated, common denominator in a variety of processes including PCD, cell cycle control, antivirus immunity, stress response, and germline development in animals.

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          Most cited references149

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          Cytoscape: a software environment for integrated models of biomolecular interaction networks.

          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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            Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

            S Altschul (1997)
            The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSI-BLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.
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              FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments

              Background We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability. Methodology/Principal Findings Where FastTree 1 used nearest-neighbor interchanges (NNIs) and the minimum-evolution criterion to improve the tree, FastTree 2 adds minimum-evolution subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs. FastTree 2 uses heuristics to restrict the search for better trees and estimates a rate of evolution for each site (the “CAT” approximation). Nevertheless, for both simulated and genuine alignments, FastTree 2 is slightly more accurate than a standard implementation of maximum-likelihood NNIs (PhyML 3 with default settings). Although FastTree 2 is not quite as accurate as methods that use maximum-likelihood SPRs, most of the splits that disagree are poorly supported, and for large alignments, FastTree 2 is 100–1,000 times faster. FastTree 2 inferred a topology and likelihood-based local support values for 237,882 distinct 16S ribosomal RNAs on a desktop computer in 22 hours and 5.8 gigabytes of memory. Conclusions/Significance FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments. FastTree 2 is freely available at http://www.microbesonline.org/fasttree.
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                Author and article information

                Contributors
                koonin@ncbi.nlm.nih.gov
                Journal
                BMC Biol
                BMC Biol
                BMC Biology
                BioMed Central (London )
                1741-7007
                4 November 2020
                4 November 2020
                2020
                : 18
                : 159
                Affiliations
                GRID grid.419234.9, ISNI 0000 0004 0604 5429, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, ; Bethesda, MD 20894 USA
                Author information
                http://orcid.org/0000-0003-3943-8299
                Article
                885
                10.1186/s12915-020-00885-2
                7641849
                33148243
                186e45fc-1b1d-4ac9-afbe-1a15e9756e6d
                © The Author(s) 2020

                Open AccessThis 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
                : 14 July 2020
                : 1 October 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000016, U.S. Department of Health and Human Services;
                Award ID: Intramural funds
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2020

                Life sciences
                modified bases,dna and rna-binding domains,programmed cell death,cytochrome c,self versus non-self-discrimination,antivirus defense,restriction-modification,extended tudor family proteins, pirna pathway evolution

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