STIFDB2: An Updated Version of Plant Stress-Responsive TranscrIption Factor DataBase with Additional Stress Signals, Stress-Responsive Transcription Factor Binding Sites and Stress-Responsive Genes in Arabidopsis and Rice (original) (raw)
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Plant Stress Gene Database: A collection of plant genes responding to stress condition
Plants are exposed to variety of stress factors (biotic and abiotic) in both natural and agricultural conditions, which have a serious impact on plant development and growth. Plants adapt to these stresses through a series of events which occurs at every level of plant organization i.e. cellular, biochemical and molecular level. This complex response requires an extensive molecular regulation of gene expression. Understanding the mechanisms by which plants perceive environmental signals and transmit the signals to cellular machinery to activate adaptive responses is of fundamental importance to biology and has been intensively investigated in recent years. We developed a database namely, Plant Stress Gene Database which provides information about the genes involved in stress conditions in plants. A total of 259 genes from 11 plant species (Arabidopsis thaliana, Arachis hypogaea, Glycine max, Hordeum vulgare, Oryza sativa, Pennisetum, Phaseolus vulgaris, Saccharum officinarum, Solanum lycopersicum, Triticum aestivum and Zea mays) are available in it. This database attempts to provide all the available information about these genes along with their ortholog and paralog. Database is publicly accessible at http://ccbb.jnu.ac.in/stressgenes/frontpage.html.
Bioinformation, 2008
The expressions of proteins in the cell are carefully regulated by transcription factors that interact with their downstream targets in specific signal transduction cascades. Our understanding of the regulation of functional genes responsive to stress signals is still nascent. Plants like Arabidopsis thaliana, are convenient model systems to study fundamental questions related to regulation of the stress transcriptome in response to stress challenges. Microarray results of the Arabidopsis transcriptome indicate that several genes could be upregulated during multiple stresses, such as cold, salinity, drought etc. Experimental biochemical validations have proved the involvement of several transcription factors could be involved in the upregulation of these stress responsive genes. In order to follow the intricate and complicated networks of transcription factors and genes that respond to stress situations in plants, we have developed a computer algorithm that can identify key transcription factor binding sites upstream of a gene of interest. Hidden Markov models of the transcription factor binding sites enable the identification of predicted sites upstream of plant stress genes. The search algorithm, STIF, performs very well, with more than 90% sensitivity, when tested on experimentally validated positions of transcription factor binding sites on a dataset of 60 stress upregulated genes.
Cis-regulatory code of stress-responsive transcription in Arabidopsis thaliana
Proceedings of the National Academy of Sciences, 2011
Environmental stress leads to dramatic transcriptional reprogramming, which is central to plant survival. Although substantial knowledge has accumulated on how a few plant cis-regulatory elements (CREs) function in stress regulation, many more CREs remain to be discovered. In addition, the plant stress cis-regulatory code, i.e., how CREs work independently and/or in concert to specify stress-responsive transcription, is mostly unknown. On the basis of gene expression patterns under multiple stresses, we identified a large number of putative CREs (pCREs) in Arabidopsis thaliana with characteristics of authentic cis-elements. Surprisingly, biotic and abiotic responses are mostly mediated by two distinct pCRE superfamilies. In addition, we uncovered cis-regulatory codes specifying how pCRE presence and absence, combinatorial relationships, location, and copy number can be used to predict stress-responsive expression. Expression prediction models based on pCRE combinations perform significantly better than those based on simply pCRE presence and absence, location, and copy number. Furthermore, instead of a few master combinatorial rules for each stress condition, many rules were discovered, and each appears to control only a small subset of stress-responsive genes. Given there are very few documented interactions between plant CREs, the combinatorial rules we have uncovered significantly contribute to a better understanding of the cis-regulatory logic underlying plant stress response and provide prioritized targets for experimentation. machine learning | motif discovery | transcription factor binding site
Transcriptional regulatory networks in Arabidopsis thaliana during single and combined stresses
Differentially evolved responses to various stress conditions in plants are controlled by complex regulatory circuits of transcriptional activators, and repressors, such as transcription factors (TFs). To understand the general and condition-specific activities of the TFs and their regulatory relationships with the target genes (TGs), we have used a homogeneous stress gene expression dataset generated on ten natural ecotypes of the model plant Arabidopsis thaliana, during five single and six combined stress conditions. Knowledge-based profiles of binding sites for 25 stress-responsive TF families (187 TFs) were generated and tested for their enrichment in the regulatory regions of the associated TGs. Condition-dependent regulatory sub-networks have shed light on the differential utilization of the underlying network topology, by stress-specific regulators and multifunctional regulators. The multifunctional regulators maintain the core stress response processes while the transient regulators confer the specificity to certain conditions. Clustering patterns of transcription factor binding sites (TFBS) have reflected the combinatorial nature of transcriptional regulation, and suggested the putative role of the homotypic clusters of TFBS towards maintaining transcriptional robustness against cis-regulatory mutations to facilitate the preservation of stress response processes. The Gene Ontology enrichment analysis of the TGs reflected sequential regulation of stress response mechanisms in plants.
The Generation Challenge Programme comparative plant stress-responsive gene catalogue
Nucleic Acids Research, 2007
The Generation Challenge Programme (GCP; www.generationcp.org) has developed an online resource documenting stress-responsive genes comparatively across plant species. This public resource is a compendium of protein families, phylogenetic trees, multiple sequence alignments (MSA) and associated experimental evidence. The central objective of this resource is to elucidate orthologous and paralogous relationships between plant genes that may be involved in response to environmental stress, mainly abiotic stresses such as water deficit ('drought'). The web-based graphical user interface (GUI) of the resource includes query and visualization tools that allow diverse searches and browsing of the underlying project database. The web interface can be accessed at
Plants always have to fight against various environmental stress conditions like cold, drought, salinity, submergence, etc. The prime target of recent research in plant biology is to unveil the intricate series of events in responses and adaptation to different stress conditions. Sufficient in-silico computational studies are yet to be done to distinguish the stress related genes from the non-stress related ones. As common mechanisms of stress responses exist among different plants, we sought to identify the general structural and functional features that may be hidden in stress related genes of different plant species. We assumed that these features in stress-related genes might be different from non stress related genes. One hundred and sixty stress-responsive genes from five different plant species were studied. Computational and bioinformatics studies were done to determine several structural properties like length of gene, exon, intron, UTRs as well as to identify overrepresented sequence motif and enrichment of gene ontology (GO) functions. The UTRs of stress related genes were found to be significantly different from non-stress related genes and a “G-C” rich small sequence motif was found to be associated significantly with stress genes. Key biological processes like small GTPase mediated signal transduction, cellular components like thylakoid and molecular functions like oxidoreductase activity are significantly enriched for stress related genes. Further studies are required to identify more stress specific features of plant stress genes which may help to establish a computational model for detecting stress related genes from various gene lists.
Transcriptomic Analysis of Multiple Enviornmental Stresses in Plants
Global losses in agricultural production due to abiotic stresses have been estimated to be $120 billion. In the wake of shrinking arable land and rampant changes in climate, a second green revolution is important to meet the food needs of the rapidly growing population. This warrants radical changes in research strategies. Genomics approaches such as transcriptome profiling have led to the identification of gene networks important for many different stresses. However, under natural conditions plants are challenged by simultaneous occurrence of two or more stresses. Several studies have recently analyzed the transcriptional responses to two stresses simultaneously and are discussed here. For plant biotechnologies to deliver the promise of a second green revolution, a systems biology approach of examining multiple stresses at various developmental stages is necessary.
Reduced representation sequencing of plant stress transcriptomes
Journal of Plant Biochemistry and Biotechnology, 2012
Plants, as any other organisms, possess evolutionary old mechanisms to cope with the various stresses they are exposed to day by day. The management of stresses and their consequences requires substantial energy, which is frequently subtracted from biomass (in crops: yield). Therefore, a deeper understanding of stress biology has been, is, and will be of paramount importance for plant breeding. One goal of plant stress research centers around the transcriptome, the entirety of transcripts from expressed genes, and aims at identifying major genes in the stress management of the inflicted plant. The development of appropriate technologies to quantitatively study the transcriptomes (indeed the various sub-transcriptomes) in stressed plants and to extract biological meaning from the massive data will be demonstrated here. In particular, reduced complexity sequencing techniques such as deepSuperSAGE and MACE (massive analysis of cDNA ends) and their potential in stress biology are portrayed.