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      The sequenced genomes of nonflowering land plants reveal the innovative evolutionary history of peptide signaling

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          Abstract

          An understanding of land plant evolution is a prerequisite for in-depth knowledge of plant biology. Here we extract and explore information hidden in the increasing number of sequenced plant genomes, from bryophytes to angiosperms, to elucidate a specific biological question—how peptide signaling evolved. To conquer land and cope with changing environmental conditions, plants have gone through transformations that must have required innovations in cell-to-cell communication. We discuss peptides mediating endogenous and exogenous changes by interaction with receptors activating intracellular molecular signaling. Signaling peptides were discovered in angiosperms and operate in tissues and organs such as flowers, seeds, vasculature, and 3D meristems that are not universally conserved across land plants. Nevertheless, orthologs of angiosperm peptides and receptors have been identified in nonangiosperms. These discoveries provoke questions regarding coevolution of ligands and their receptors, and whether de novo interactions in peptide signaling pathways may have contributed to generate novel traits in land plants. The answers to such questions will have profound implications for the understanding of the evolution of cell-to-cell communication and the wealth of diversified terrestrial plants. Under this perspective, we have generated, analyzed, and reviewed phylogenetic, genomic, structural, and functional data to elucidate the evolution of peptide signaling.

          Abstract

          The identification of orthologs of Arabidopsis signaling peptides and their receptors in nonflowering plants suggest their importance in cell-to-cell communication in all land plants.

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          Most cited references124

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          Basic local alignment search tool.

          A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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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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              MUSCLE: multiple sequence alignment with high accuracy and high throughput.

              We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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                Author and article information

                Journal
                Plant Cell
                Plant Cell
                plcell
                The Plant Cell
                Oxford University Press
                1040-4651
                1532-298X
                September 2021
                07 July 2021
                07 July 2021
                : 33
                : 9
                : 2915-2934
                Affiliations
                [1 ] Graduate School of Science and Technology, Kumamoto University , 860-8555 Kumamoto, Japan
                [2 ] Section for Evolutionary Biology and Genetics, Department of Biosciences, University of Oslo , 0316 Oslo, Norway
                [3 ] Section for Biochemistry and Molecular Biology, Department of Biosciences, University of Oslo , 0316 Oslo, Norway
                Author notes
                Author for correspondence: reidunn.aalen@ 123456ibv.uio.no
                [†]

                Present address: Department of Immunology, Graduate School of Medicine, Akita University, 010-8543 Akita, Japan.

                [‡]

                Senior author.

                Author information
                https://orcid.org/0000-0001-8503-5971
                https://orcid.org/0000-0001-9481-8396
                https://orcid.org/0000-0003-0872-3441
                https://orcid.org/0000-0001-8901-9274
                https://orcid.org/0000-0003-2243-9498
                https://orcid.org/0000-0002-9309-9104
                https://orcid.org/0000-0001-8174-1342
                Article
                koab173
                10.1093/plcell/koab173
                8462819
                34240188
                44b5b602-4568-4a77-8b90-b085248b3b35
                © The Author(s) 2021. Published by Oxford University Press on behalf of American Society of Plant Biologists.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence ( https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 20 January 2021
                : 08 June 2021
                Page count
                Pages: 20
                Funding
                Funded by: Japan Society for the Promotion of Science London (KAKENHI;
                Award ID: 18H05487
                Award ID: 20H00422
                Award ID: 20KK0135
                Award ID: 20K06770
                Funded by: Nakatsuji Foresight Foundation and the Sumitomo Foundation;
                Funded by: RBA;
                Award ID: RCN312785
                Categories
                Perspective
                AcademicSubjects/SCI02286
                AcademicSubjects/SCI02287
                AcademicSubjects/SCI01270
                AcademicSubjects/SCI01280
                AcademicSubjects/SCI02288

                Plant science & Botany
                Plant science & Botany

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