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      Targeted hypermutation of putative antigen sensors in multicellular bacteria

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          Significance

          To defend themselves against pathogens, bacteria employ a wide range of conflict systems, some of which are enriched in multicellular bacteria. Here, we show that numerous multicellular bacteria use related diversity-generating retroelements (DGRs) to diversify such putative conflict systems. Error-prone reverse transcription in DGRs introduces random, targeted mutations and rapid diversification. We used Thiohalocapsa PB-PSB1, a member of multicellular bacterial consortia, to study this association between conflict systems and DGRs. We characterized the natural diversity of PB-PSB1 DGRs and propose they function as hypervariable antigen sensors. If their role in pathogen defense is confirmed, accumulation of these DGR-diversified systems in multicellular bacteria would suggest that rapidly diversifying immune systems confer important fitness advantages for the evolution of multicellularity.

          Abstract

          Diversity-generating retroelements (DGRs) are used by bacteria, archaea, and viruses as a targeted mutagenesis tool. Through error-prone reverse transcription, DGRs introduce random mutations at specific genomic loci, enabling rapid evolution of these targeted genes. However, the function and benefits of DGR-diversified proteins in cellular hosts remain elusive. We find that 82% of DGRs from one of the major monophyletic lineages of DGR reverse transcriptases are encoded by multicellular bacteria, which often have two or more DGR loci in their genomes. Using the multicellular purple sulfur bacterium Thiohalocapsa sp. PB-PSB1 as an example, we characterized nine distinct DGR loci capable of generating 10 282 different combinations of target proteins. With environmental metagenomes from individual Thiohalocapsa aggregates, we show that most of PB-PSB1’s DGR target genes are diversified across its biogeographic range, with spatial heterogeneity in the diversity of each locus. In Thiohalocapsa PB-PSB1 and other bacteria hosting this lineage of cellular DGRs, the diversified target genes are associated with NACHT-domain anti-phage defenses and putative ternary conflict systems previously shown to be enriched in multicellular bacteria. We propose that these DGR-diversified targets act as antigen sensors that confer a form of adaptive immunity to their multicellular consortia, though this remains to be experimentally tested. These findings could have implications for understanding the evolution of multicellularity, as the NACHT-domain anti-phage systems and ternary systems share both domain homology and conceptual similarities with the innate immune and programmed cell death pathways of plants and metazoans.

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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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            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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              ModelFinder: Fast Model Selection for Accurate Phylogenetic Estimates

              Model-based molecular phylogenetics plays an important role in comparisons of genomic data, and model selection is a key step in all such analyses. We present ModelFinder, a fast model-selection method that greatly improves the accuracy of phylogenetic estimates. The improvement is achieved by incorporating a model of rate-heterogeneity across sites not previously considered in this context, and by allowing concurrent searches of model-space and tree-space.
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                Author and article information

                Contributors
                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                14 February 2024
                27 February 2024
                14 February 2024
                : 121
                : 9
                : e2316469121
                Affiliations
                [1] aDepartment of Ecology, Evolution and Marine Biology, University of California , Santa Barbara, CA 93106
                [2] bDepartment of Chemical Engineering, University of California , Santa Barbara, CA 93106
                [3] cDepartment of Civil and Environmental Engineering, Massachusetts Institute of Technology , Cambridge, MA 02139
                [4] dBay Paul Center, Marine Biological Laboratory , Woods Hole, MA 02543
                [5] eDepartment of Earth Science, University of California , Santa Barbara, CA 93106
                [6] fMarine Science Institute, University of California , Santa Barbara, CA 93106
                [7] gDepartment of Bioengineering, University of California , Santa Barbara, CA 93106
                [8] hDepartment of Bioengineering, University of California , Santa Barbara, CA 93106
                Author notes
                2To whom correspondence may be addressed. Email: ewilbanks@ 123456ucsb.edu .

                Edited by Marlene Belfort, University at Albany, State University of New York, Albany, NY; received September 29, 2023; accepted January 10, 2024

                1Present address: Université de Brest, Institut Français de Recherche pour l’Exploitation de la Mer, Biologie et Ecologie des Ecosystèmes marins Profonds, Plouzané F-29280, France.

                Author information
                https://orcid.org/0000-0003-4160-3679
                https://orcid.org/0000-0002-1805-0896
                https://orcid.org/0000-0002-4463-166X
                https://orcid.org/0000-0002-2695-270X
                https://orcid.org/0000-0002-5738-5568
                https://orcid.org/0000-0001-5914-9107
                https://orcid.org/0000-0002-6065-8491
                https://orcid.org/0000-0002-2387-2886
                Article
                202316469
                10.1073/pnas.2316469121
                10907252
                38354254
                928af09a-9999-4ca3-b11e-4a46dbcb0a9f
                Copyright © 2024 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).

                History
                : 29 September 2023
                : 10 January 2024
                Page count
                Pages: 12, Words: 8675
                Funding
                Funded by: DOD | USA | AFC | CCDC | Army Research Office (ARO), FundRef 100000183;
                Award ID: W911NF-19-2-0026
                Award Recipient : David L Valentine Award Recipient : Michelle A O'Malley Award Recipient : Elizabeth G Wilbanks
                Funded by: DOD | USA | AFC | CCDC | Army Research Office (ARO), FundRef 100000183;
                Award ID: W911NF-19-D-0001
                Award Recipient : David L Valentine Award Recipient : Michelle A O'Malley Award Recipient : Elizabeth G Wilbanks
                Funded by: Joint Genome Institute (JGI), FundRef 100015911;
                Award ID: 508543
                Award Recipient : Elizabeth G Wilbanks
                Categories
                dataset, Dataset
                research-article, Research Article
                microbio, Microbiology
                423
                Biological Sciences
                Microbiology

                microbial ecology,targeted mutation,diversity-generating retroelements,multicellularity,bacterial immune systems

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