13
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Genome-Wide Identification and Analysis of MAPK and MAPKK Gene Families in Bread Wheat ( Triticum aestivum L.)

      brief-report

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The mitogen-activated protein kinase (MAPK) cascade is a universal signal transduction module that plays a vital role in regulating growth and development, as well as environmental stress responses in plants. Wheat is one of the most important crops worldwide. Although the MAPK kinase kinase (MAP3K) family in wheat has been investigated, the MAPK and MAPK kinase (MAP2K) gene families remain unknown at present. Here, 54 MAPK and 18 MAPKK genes were identified in wheat using recent genomic information. Phylogenetic analysis of Triticum aestivum L. MAPKs and MAPKKs ( TaMAPKs and TaMAPKKs) together with homologous genes from other species classified them into four groups, and the clustering was consistent with the genomic exon/intron structures. Conserved motif analysis found that MAPK proteins contained a typical TXY phosphorylation site and MAPKK proteins contained an S/T-X5-S/T motif. RNA-seq data mapping analysis showed that MAPK and MAPKK genes in group IV had tissue-specific expression profiles, whereas each group I member showed relatively high expression in all organs. Expression patterns of TaMAPK and TaMAPKK genes under stress conditions were also investigated and stress-responsive candidates were identified. Co-expression network analysis identified 11 TaMAPK genes and 6 TaMAPKK genes involved in the interaction network pathway. Overall, this study provided useful information for evolutionary and functional surveys of MAPK and MAPKK gene families in wheat and beyond.

          Related collections

          Most cited references23

          • Record: found
          • Abstract: found
          • Article: not found

          Prediction of protein subcellular localization.

          Because the protein's function is usually related to its subcellular localization, the ability to predict subcellular localization directly from protein sequences will be useful for inferring protein functions. Recent years have seen a surging interest in the development of novel computational tools to predict subcellular localization. At present, these approaches, based on a wide range of algorithms, have achieved varying degrees of success for specific organisms and for certain localization categories. A number of authors have noticed that sequence similarity is useful in predicting subcellular localization. For example, Nair and Rost (Protein Sci 2002;11:2836-2847) have carried out extensive analysis of the relation between sequence similarity and identity in subcellular localization, and have found a close relationship between them above a certain similarity threshold. However, many existing benchmark data sets used for the prediction accuracy assessment contain highly homologous sequences-some data sets comprising sequences up to 80-90% sequence identity. Using these benchmark test data will surely lead to overestimation of the performance of the methods considered. Here, we develop an approach based on a two-level support vector machine (SVM) system: the first level comprises a number of SVM classifiers, each based on a specific type of feature vectors derived from sequences; the second level SVM classifier functions as the jury machine to generate the probability distribution of decisions for possible localizations. We compare our approach with a global sequence alignment approach and other existing approaches for two benchmark data sets-one comprising prokaryotic sequences and the other eukaryotic sequences. Furthermore, we carried out all-against-all sequence alignment for several data sets to investigate the relationship between sequence homology and subcellular localization. Our results, which are consistent with previous studies, indicate that the homology search approach performs well down to 30% sequence identity, although its performance deteriorates considerably for sequences sharing lower sequence identity. A data set of high homology levels will undoubtedly lead to biased assessment of the performances of the predictive approaches-especially those relying on homology search or sequence annotations. Our two-level classification system based on SVM does not rely on homology search; therefore, its performance remains relatively unaffected by sequence homology. When compared with other approaches, our approach performed significantly better. Furthermore, we also develop a practical hybrid method, which combines the two-level SVM classifier and the homology search method, as a general tool for the sequence annotation of subcellular localization.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            nhmmer: DNA homology search with profile HMMs

            Summary: Sequence database searches are an essential part of molecular biology, providing information about the function and evolutionary history of proteins, RNA molecules and DNA sequence elements. We present a tool for DNA/DNA sequence comparison that is built on the HMMER framework, which applies probabilistic inference methods based on hidden Markov models to the problem of homology search. This tool, called nhmmer, enables improved detection of remote DNA homologs, and has been used in combination with Dfam and RepeatMasker to improve annotation of transposable elements in the human genome. Availability: nhmmer is a part of the new HMMER3.1 release. Source code and documentation can be downloaded from http://hmmer.org. HMMER3.1 is freely licensed under the GNU GPLv3 and should be portable to any POSIX-compliant operating system, including Linux and Mac OS/X. Contact: wheelert@janelia.hhmi.org
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              The ERF transcription factor TaERF3 promotes tolerance to salt and drought stresses in wheat.

              Salinity and drought are major limiting factors of wheat (Triticum aestivum) productivity worldwide. Here, we report the function of a wheat ERF transcription factor TaERF3 in salt and drought responses and the underlying mechanism of TaERF3 function. Upon treatment with 250 mM NaCl or 20% polyethylene glycol (PEG), transcript levels of TaERF3 were rapidly induced in wheat. Using wheat cultivar Yangmai 12 as the transformation recipient, four TaERF3-overexpressing transgenic lines were generated and functionally characterized. The seedlings of the TaERF3-overexpressing transgenic lines exhibited significantly enhanced tolerance to both salt and drought stresses as compared to untransformed wheat. In the leaves of TaERF3-overexpressing lines, accumulation levels of both proline and chlorophyll were significantly increased, whereas H₂O₂ content and stomatal conductance were significantly reduced. Conversely, TaERF3-silencing wheat plants that were generated through virus-induced gene silencing method displayed more sensitivity to salt and drought stresses compared with the control plants. Real-time quantitative RT-PCR analyses showed that transcript levels of ten stress-related genes were increased in TaERF3-overexpressing lines, but compromised in TaERF3-silencing wheat plants. Electrophoretic mobility shift assays showed that the TaERF3 protein could interact with the GCC-box cis-element present in the promoters of seven TaERF3-activated stress-related genes. These results indicate that TaERF3 positively regulates wheat adaptation responses to salt and drought stresses through the activation of stress-related genes and that TaERF3 is an attractive engineering target in applied efforts to improve abiotic stress tolerances in wheat and other cereals. © 2014 Society for Experimental Biology, Association of Applied Biologists and John Wiley & Sons Ltd.
                Bookmark

                Author and article information

                Journal
                Genes (Basel)
                Genes (Basel)
                genes
                Genes
                MDPI
                2073-4425
                20 October 2017
                October 2017
                : 8
                : 10
                : 284
                Affiliations
                [1 ]State Key Laboratory of Crop Stress Biology in Arid Areas, College of Agronomy and Yangling Branch of China Wheat Improvement Center, Northwest A&F University, Yangling 712100, China; zhanhaoshuang@ 123456nwsuaf.edu.cn (H.Z.); yuehongsx@ 123456163.com (H.Y.); jooxian568394@ 123456163.com (X.Z.); ours2010@ 123456163.com (M.W.); sweining2002@ 123456yahoo.com (W.S.)
                [2 ]Australia-China Joint Research Centre for Abiotic and Biotic Stress Management in Agriculture, Horticulture and Forestry, Yangling 712100, China
                Author notes
                [* ]Correspondence: small@ 123456nwsuaf.edu.cn ; Tel.: +86-29-87082984; Fax: +86-29-87082203
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-4787-7550
                Article
                genes-08-00284
                10.3390/genes8100284
                5664134
                29053643
                a41738cf-44a5-4785-8e47-4c7c4f10e612
                © 2017 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 20 August 2017
                : 18 October 2017
                Categories
                Brief Report

                abiotic stress,co-expression network,expression profiles,triticum aestivum

                Comments

                Comment on this article