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      Metal tolerance gene family in barley: an in silico comprehensive analysis

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          Abstract

          Metal-tolerance proteins (MTPs) are divalent cation transporters that play critical roles in metal tolerance and ion homeostasis in plants. However, a comprehensive study of MTPs is still lacking in crop plants. The current study aimed to comprehensively identify and characterize the MTP gene family in barley ( Hordeum vulgare, Hv), an important crop. In total, 12 HvMTPs were identified in the barley genome in this study. They were divided into three phylogenetic groups (Zn-cation diffusion facilitator proteins [CDFs], Fe/Zn-CDFs, and Mn-CDFs) and further subdivided into seven groups (G1, G5, G6, G7, G8, G9, and G12). The majority of MTPs were hydrophobic proteins found in the vacuolar membrane. Gene duplication analysis of HvMTPs revealed one pair of segmental-like duplications in the barley genome. Evolutionary analysis suggested that barley MTPs underwent purifying natural selection. Additionally, the HvMTPs were analyzed in the pan-genome sequences of barley (20 accessions), which suggests that HvMTPs are highly conserved in barley evolution. Cis-acting regulatory elements, microRNA target sites, and protein–protein interaction analysis indicated the role of HvMTPs in a variety of biological processes. Expression profiling suggests that HvMTPs play an active role in maintaining barley nutrient homeostasis throughout its life cycle, and their expression levels were not significantly altered by abiotic stresses like cold, drought, or heat. The expression of barley HvMTP genes in the presence of heavy metals such as Zn 2+, Cu 2+, As 3+, and Cd 2+ revealed that these MTPs were induced by at least one metal ion, implying their involvement in metal tolerance or transportation. The identification and comprehensive investigation of MTP gene family members will provide important gene resources for the genetic improvement of crops for metal tolerance, bioremediation, or biofortification of staple crops.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s13353-022-00744-6.

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          MEGA11: Molecular Evolutionary Genetics Analysis Version 11

          Koichiro Tamura, Glen Stecher, Sudhir Kumar (2021)
          The Molecular Evolutionary Genetics Analysis (MEGA) software has matured to contain a large collection of methods and tools of computational molecular evolution. Here, we describe new additions that make MEGA a more comprehensive tool for building timetrees of species, pathogens, and gene families using rapid relaxed-clock methods. Methods for estimating divergence times and confidence intervals are implemented to use probability densities for calibration constraints for node-dating and sequence sampling dates for tip-dating analyses. They are supported by new options for tagging sequences with spatiotemporal sampling information, an expanded interactive Node Calibrations Editor , and an extended Tree Explorer to display timetrees. Also added is a Bayesian method for estimating neutral evolutionary probabilities of alleles in a species using multispecies sequence alignments and a machine learning method to test for the autocorrelation of evolutionary rates in phylogenies. The computer memory requirements for the maximum likelihood analysis are reduced significantly through reprogramming, and the graphical user interface has been made more responsive and interactive for very big data sets. These enhancements will improve the user experience, quality of results, and the pace of biological discovery. Natively compiled graphical user interface and command-line versions of MEGA11 are available for Microsoft Windows, Linux, and macOS from www.megasoftware.net .
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            The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets

            Damian Szklarczyk, Annika Gable, Katerina C. Nastou … (2020)
            Abstract Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein–protein interactions are particularly important due to their versatility, specificity and adaptability. The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations. To achieve this, STRING collects and scores evidence from a number of sources: (i) automated text mining of the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context and (iv) systematic transfers of interaction evidence from one organism to another. STRING aims for wide coverage; the upcoming version 11.5 of the resource will contain more than 14 000 organisms. In this update paper, we describe changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks. In addition, we describe how to query STRING with genome-wide, experimental data, including the automated detection of enriched functionalities and potential biases in the user's query data. The STRING resource is available online, at https://string-db.org/.
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              MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity

              Yupeng Wang, Haibao Tang, Jeremy D. DeBarry … (2012)
              MCScan is an algorithm able to scan multiple genomes or subgenomes in order to identify putative homologous chromosomal regions, and align these regions using genes as anchors. The MCScanX toolkit implements an adjusted MCScan algorithm for detection of synteny and collinearity that extends the original software by incorporating 14 utility programs for visualization of results and additional downstream analyses. Applications of MCScanX to several sequenced plant genomes and gene families are shown as examples. MCScanX can be used to effectively analyze chromosome structural changes, and reveal the history of gene family expansions that might contribute to the adaptation of lineages and taxa. An integrated view of various modes of gene duplication can supplement the traditional gene tree analysis in specific families. The source code and documentation of MCScanX are freely available at http://chibba.pgml.uga.edu/mcscan2/.
              Article 0 comments Cited 1863 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Umesh Kumar Tanwar: umetan@amu.edu.pl
                Ewa Sobieszczuk-Nowicka: ewa.sobieszczuk-nowicka@amu.edu.pl
                Journal
                Journal ID (nlm-ta): J Appl Genet
                Journal ID (iso-abbrev): J Appl Genet
                Title: Journal of Applied Genetics
                Publisher: Springer Berlin Heidelberg (Berlin/Heidelberg )
                ISSN (Print): 1234-1983
                ISSN (Electronic): 2190-3883
                Publication date (Electronic): 31 December 2022
                Publication date PMC-release: 31 December 2022
                Publication date (Print): 2023
                Volume: 64
                Issue: 2
                Pages: 197-215
                Affiliations
                [1 ]GRID grid.5633.3, ISNI 0000 0001 2097 3545, Department of Plant Physiology, Faculty of Biology, , Adam Mickiewicz University, ; ul. Uniwersytetu Poznańskiego 6, 61-614 Poznań, Poland
                [2 ]GRID grid.5633.3, ISNI 0000 0001 2097 3545, Department of Plant Ecophysiology, Faculty of Biology, , Adam Mickiewicz University, ; ul. Uniwersytetu Poznańskiego 6, 61-614 Poznań, Poland
                Author notes

                Communicated by: Izabela Pawłowicz

                Article
                Publisher ID: 744
                DOI: 10.1007/s13353-022-00744-6
                PMC ID: 10076399
                PubMed ID: 36586056
                SO-VID: 0635f719-e9c6-4919-80af-a4d8def56ea4
                Copyright © © The Author(s) 2022
                License:

                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/.

                History
                Date received : 28 October 2022
                Date revision received : 20 December 2022
                Date accepted : 21 December 2022
                Categories
                Subject: Plant Genetics • Original Paper
                Custom metadata
                issue-copyright-statement © The Author(s), under exclusive licence to Institute of Plant Genetics Polish Academy of Sciences 2023

                ScienceOpen disciplines: Genetics
                Keywords: metal-tolerance proteins,barley,metal stress,gene expression
                Data availability:
                ScienceOpen disciplines: Genetics
                Keywords: metal-tolerance proteins, barley, metal stress, gene expression

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