The Role of OsWRKY Genes in Rice When Faced with Single and Multiple Abiotic Stresses

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Abstract

The WRKY genes are one of the largest families of transcription factors (TFs) and play a crucial role in certain processes in plants including stress signaling, regulation of transcriptional reprogramming associated with stress responses, and other regulatory networks. This study aims to investigate the WRKY gene family in the C3 model plant, Oryza sativa L., using a genome-wide in silico expression analysis. Firstly, 104 WRKY TF family members were identified, and then their molecular properties and expression signatures were analyzed systematically. In silico spatio-temporal and hormonal expression profiling revealed the roles of OsWRKY genes and their dynamism in diverse developmental tissues and hormones, respectively. Comparative mapping between OsWRKY genes and their synteny with C4 panicoid genomes showed the evolutionary insights of the WRKY TF family. Interactions of OsWRKY coding gene sequences represented the complexity of abiotic stress (AbS) and their molecular cross-talks. The expression signature of 26 novel candidate genes in response to stresses exhibited the putative involvement of individual and combined AbS (CAbS) responses. These novel findings unravel the in-depth insights into OsWRKY TF genes and delineate the plant developmental metabolisms and their functional regulations in individual and CAbS conditions.

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MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.

Sudhir Kumar, Glen Stecher, Koichiro Tamura (2016)

We present the latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, Mega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in Mega The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit Mega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line Mega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.

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Circos: an information aesthetic for comparative genomics.

Martin Krzywinski, Jacqueline Schein, Inanç Birol … (2009)

We created a visualization tool called Circos to facilitate the identification and analysis of similarities and differences arising from comparisons of genomes. Our tool is effective in displaying variation in genome structure and, generally, any other kind of positional relationships between genomic intervals. Such data are routinely produced by sequence alignments, hybridization arrays, genome mapping, and genotyping studies. Circos uses a circular ideogram layout to facilitate the display of relationships between pairs of positions by the use of ribbons, which encode the position, size, and orientation of related genomic elements. Circos is capable of displaying data as scatter, line, and histogram plots, heat maps, tiles, connectors, and text. Bitmap or vector images can be created from GFF-style data inputs and hierarchical configuration files, which can be easily generated by automated tools, making Circos suitable for rapid deployment in data analysis and reporting pipelines.

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The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible

Damian Szklarczyk, John Morris, Helen Cook … (2016)

A system-wide understanding of cellular function requires knowledge of all functional interactions between the expressed proteins. The STRING database aims to collect and integrate this information, by consolidating known and predicted protein–protein association data for a large number of organisms. The associations in STRING include direct (physical) interactions, as well as indirect (functional) interactions, as long as both are specific and biologically meaningful. Apart from collecting and reassessing available experimental data on protein–protein interactions, and importing known pathways and protein complexes from curated databases, interaction predictions are derived from the following sources: (i) systematic co-expression analysis, (ii) detection of shared selective signals across genomes, (iii) automated text-mining of the scientific literature and (iv) computational transfer of interaction knowledge between organisms based on gene orthology. In the latest version 10.5 of STRING, the biggest changes are concerned with data dissemination: the web frontend has been completely redesigned to reduce dependency on outdated browser technologies, and the database can now also be queried from inside the popular Cytoscape software framework. Further improvements include automated background analysis of user inputs for functional enrichments, and streamlined download options. The STRING resource is available online, at http://string-db.org/.