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      The colonization of land was a likely driving force for the evolution of mitochondrial retrograde signalling in plants

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          Abstract

          Most retrograde signalling research in plants was performed using Arabidopsis, so an evolutionary perspective on mitochondrial retrograde regulation (MRR) is largely missing. Here, we used phylogenetics to track the evolutionary origins of factors involved in plant MRR. In all cases, the gene families can be traced to ancestral green algae or earlier. However, the specific subfamilies containing factors involved in plant MRR in many cases arose during the transition to land. NAC transcription factors with C-terminal transmembrane domains, as observed in the key regulator ANAC017, can first be observed in non-vascular mosses, and close homologs to ANAC017 can be found in seed plants. Cyclin-dependent kinases (CDKs) are common to eukaryotes, but E-type CDKs that control MRR also diverged in conjunction with plant colonization of land. AtWRKY15 can be traced to the earliest land plants, while AtWRKY40 only arose in angiosperms and AtWRKY63 even more recently in Brassicaceae. Apetala 2 (AP2) transcription factors are traceable to algae, but the ABI4 type again only appeared in seed plants. This strongly suggests that the transition to land was a major driver for developing plant MRR pathways, while additional fine-tuning events have appeared in seed plants or later. Finally, we discuss how MRR may have contributed to meeting the specific challenges that early land plants faced during terrestrialization.

          Abstract

          Phylogenetic analysis shows that the transition to land was a major driving force for developing mitochondrial retrograde pathways in plants, while additional fine-tuning appeared in seed plants or even later.

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          MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization

          Abstract This article describes several features in the MAFFT online service for multiple sequence alignment (MSA). As a result of recent advances in sequencing technologies, huge numbers of biological sequences are available and the need for MSAs with large numbers of sequences is increasing. To extract biologically relevant information from such data, sophistication of algorithms is necessary but not sufficient. Intuitive and interactive tools for experimental biologists to semiautomatically handle large data are becoming important. We are working on development of MAFFT toward these two directions. Here, we explain (i) the Web interface for recently developed options for large data and (ii) interactive usage to refine sequence data sets and MSAs.
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            Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes.

            We describe and validate a new membrane protein topology prediction method, TMHMM, based on a hidden Markov model. We present a detailed analysis of TMHMM's performance, and show that it correctly predicts 97-98 % of the transmembrane helices. Additionally, TMHMM can discriminate between soluble and membrane proteins with both specificity and sensitivity better than 99 %, although the accuracy drops when signal peptides are present. This high degree of accuracy allowed us to predict reliably integral membrane proteins in a large collection of genomes. Based on these predictions, we estimate that 20-30 % of all genes in most genomes encode membrane proteins, which is in agreement with previous estimates. We further discovered that proteins with N(in)-C(in) topologies are strongly preferred in all examined organisms, except Caenorhabditis elegans, where the large number of 7TM receptors increases the counts for N(out)-C(in) topologies. We discuss the possible relevance of this finding for our understanding of membrane protein assembly mechanisms. A TMHMM prediction service is available at http://www.cbs.dtu.dk/services/TMHMM/. Copyright 2001 Academic Press.
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              W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis

              This article presents W-IQ-TREE, an intuitive and user-friendly web interface and server for IQ-TREE, an efficient phylogenetic software for maximum likelihood analysis. W-IQ-TREE supports multiple sequence types (DNA, protein, codon, binary and morphology) in common alignment formats and a wide range of evolutionary models including mixture and partition models. W-IQ-TREE performs fast model selection, partition scheme finding, efficient tree reconstruction, ultrafast bootstrapping, branch tests, and tree topology tests. All computations are conducted on a dedicated computer cluster and the users receive the results via URL or email. W-IQ-TREE is available at http://iqtree.cibiv.univie.ac.at. It is free and open to all users and there is no login requirement.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                J Exp Bot
                J Exp Bot
                exbotj
                Journal of Experimental Botany
                Oxford University Press (UK )
                0022-0957
                1460-2431
                19 November 2022
                03 September 2022
                03 September 2022
                : 73
                : 21 , Special Issue: Plant Organellar Signalling
                : 7182-7197
                Affiliations
                Department of Biology, Lund University , Lund, Sweden
                Department of Biology, Lund University , Lund, Sweden
                University of Glasgow , UK
                Author notes
                Author information
                https://orcid.org/0000-0001-7336-2764
                https://orcid.org/0000-0003-4024-968X
                Article
                erac351
                10.1093/jxb/erac351
                9675596
                36055768
                3f01207d-96b7-4c9e-a0a1-9a85d9455a7f
                © The Author(s) 2022. Published by Oxford University Press on behalf of the Society for Experimental Biology.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 15 February 2022
                : 25 August 2022
                : 29 August 2022
                : 03 October 2022
                Page count
                Pages: 16
                Funding
                Funded by: Swedish Research Council, DOI 10.13039/501100004359;
                Award ID: 2017-03854
                Award ID: 2021- 04358
                Funded by: Novo Nordisk Fonden, DOI 10.13039/501100009708;
                Award ID: NNF18OC0034822
                Funded by: Wenner-Gren Foundation, DOI 10.13039/100001388;
                Award ID: UPD2019-0211
                Award ID: N2022-0019
                Categories
                Research Paper
                AcademicSubjects/SCI01210

                Plant science & Botany
                chloroplasts,colonization of land,evolution,mitochondria,plants,retrograde signalling,stress response

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