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      Genome-Wide Identification, Characterization, and Expression Analyses of P-Type ATPase Superfamily Genes in Soybean

      , , , , , , ,
      Agronomy
      MDPI AG

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          Abstract

          P-type ATPases are transmembrane pumps of cations and phospholipids. They are energized by hydrolysis of ATP and play important roles in a wide range of fundamental cellular and physiological processes during plant growth and development. However, the P-type ATPase superfamily genes have not been characterized in soybean. Here, we performed genome-wide bioinformatic and expression analyses of the P-type ATPase superfamily genes in order to explore the potential functions of P-type ATPases in soybean. A total of 105 putative P-type ATPase genes were identified in the soybean genome. Phylogenetic relationship analysis of the P-type ATPase genes indicated that they can be divided into five subfamilies including P1B, P2A/B, P3A, P4 and P5. Proteins belonging to the same subfamily shared conserved domains. Forty-seven gene pairs were related to segmental duplication, which contributed to the expansion of the P-type ATPase genes during the evolution of soybean. Most of the P-type ATPase genes contained hormonal- and/or stress-related cis-elements in their promoter regions. Expression analysis by retrieving RNA-sequencing datasets suggested that almost all of the P-type ATPase genes could be detected in soybean tissues, and some genes showed tissue-specific expression patterns. Nearly half of the P-type ATPase genes were found to be significantly induced or repressed under stresses like salt, drought, cold, flooding, and/or phosphate starvation. Four genes were significantly affected by rhizobia inoculation in root hairs. The induction of two P2B-ATPase genes, GmACA1 and GmACA2, by phosphate starvation was confirmed by quantitative RT-PCR. This study provides information for understanding the evolution and biological functions of the P-type ATPase superfamily genes in soybean.

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          MEGA6: Molecular Evolutionary Genetics Analysis version 6.0.

          We announce the release of an advanced version of the Molecular Evolutionary Genetics Analysis (MEGA) software, which currently contains facilities for building sequence alignments, inferring phylogenetic histories, and conducting molecular evolutionary analysis. In version 6.0, MEGA now enables the inference of timetrees, as it implements the RelTime method for estimating divergence times for all branching points in a phylogeny. A new Timetree Wizard in MEGA6 facilitates this timetree inference by providing a graphical user interface (GUI) to specify the phylogeny and calibration constraints step-by-step. This version also contains enhanced algorithms to search for the optimal trees under evolutionary criteria and implements a more advanced memory management that can double the size of sequence data sets to which MEGA can be applied. Both GUI and command-line versions of MEGA6 can be downloaded from www.megasoftware.net free of charge.
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            TBtools - an integrative toolkit developed for interactive analyses of big biological data

            The rapid development of high-throughput sequencing techniques has led biology into the big-data era. Data analyses using various bioinformatics tools rely on programming and command-line environments, which are challenging and time-consuming for most wet-lab biologists. Here, we present TBtools (a Toolkit for Biologists integrating various biological data-handling tools), a stand-alone software with a user-friendly interface. The toolkit incorporates over 130 functions, which are designed to meet the increasing demand for big-data analyses, ranging from bulk sequence processing to interactive data visualization. A wide variety of graphs can be prepared in TBtools using a new plotting engine ("JIGplot") developed to maximize their interactive ability; this engine allows quick point-and-click modification of almost every graphic feature. TBtools is platform-independent software that can be run under all operating systems with Java Runtime Environment 1.6 or newer. It is freely available to non-commercial users at https://github.com/CJ-Chen/TBtools/releases.
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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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                Author and article information

                Contributors
                Journal
                ABSGGL
                Agronomy
                Agronomy
                MDPI AG
                2073-4395
                January 2021
                December 31 2020
                : 11
                : 1
                : 71
                Article
                10.3390/agronomy11010071
                eb6676f3-3d54-4d63-9803-bbb67e89e2d1
                © 2020

                https://creativecommons.org/licenses/by/4.0/

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