Arabidopsis Coexpression Tool: a tool for gene coexpression analysis in Arabidopsis thaliana.
Ontology highlight
ABSTRACT:
SUBMITTER: Zogopoulos VL
PROVIDER: S-EPMC8334378 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Coexpressed genes tend to participate in related biological processes. Gene coexpression analysis allows the discovery of functional gene partners or the assignment of biological roles to genes of unknown function. In this protocol, we describe the steps necessary to create a gene coexpression tree for Arabidopsis thaliana, using publicly available Affymetrix CEL microarray data. Because the computational analysis described here is highly dependent on sample quality, we detail an automatic quality control approach. For complete details on the use and execution of this protocol, please refer to Zogopoulos et al. (2021).
| S-EPMC8885756 | biostudies-literature
Project description:Plant defense responses to pathogens involve massive transcriptional reprogramming. Recently, differential coexpression analysis has been developed to study the rewiring of gene networks through microarray data, which is becoming an important complement to traditional differential expression analysis. Using time-series microarray data of Arabidopsis thaliana infected with Pseudomonas syringae, we analyzed Arabidopsis defense responses to P. syringae through differential coexpression analysis. Overall, we found that differential coexpression was a common phenomenon of plant immunity. Genes that were frequently involved in differential coexpression tend to be related to plant immune responses. Importantly, many of those genes have similar average expression levels between normal plant growth and pathogen infection but have different coexpression partners. By integrating the Arabidopsis regulatory network into our analysis, we identified several transcription factors that may be regulators of differential coexpression during plant immune responses. We also observed extensive differential coexpression between genes within the same metabolic pathways. Several metabolic pathways, such as photosynthesis light reactions, exhibited significant changes in expression correlation between normal growth and pathogen infection. Taken together, differential coexpression analysis provides a new strategy for analyzing transcriptional data related to plant defense responses and new insights into the understanding of plant-pathogen interactions.
| S-EPMC5056366 | biostudies-literature
Project description:Analyses of gene set differential coexpression may shed light on molecular mechanisms underlying phenotypes and diseases. However, differential coexpression analyses of conceptually similar individual studies are often inconsistent and underpowered to provide definitive results. Researchers can greatly benefit from an open-source application facilitating the aggregation of evidence of differential coexpression across studies and the estimation of more robust common effects. We developed Meta Gene Set Coexpression Analysis (MetaGSCA), an analytical tool to systematically assess differential coexpression of an a priori defined gene set by aggregating evidence across studies to provide a definitive result. In the kernel, a nonparametric approach that accounts for the gene-gene correlation structure is used to test whether the gene set is differentially coexpressed between two comparative conditions, from which a permutation test p-statistic is computed for each individual study. A meta-analysis is then performed to combine individual study results with one of two options: a random-intercept logistic regression model or the inverse variance method. We demonstrated MetaGSCA in case studies investigating two human diseases and identified pathways highly relevant to each disease across studies. We further applied MetaGSCA in a pan-cancer analysis with hundreds of major cellular pathways in 11 cancer types. The results indicated that a majority of the pathways identified were dysregulated in the pan-cancer scenario, many of which have been previously reported in the cancer literature. Our analysis with randomly generated gene sets showed excellent specificity, indicating that the significant pathways/gene sets identified by MetaGSCA are unlikely false positives. MetaGSCA is a user-friendly tool implemented in both forms of a Web-based application and an R package "MetaGSCA". It enables comprehensive meta-analyses of gene set differential coexpression data, with an optional module of post hoc pathway crosstalk network analysis to identify and visualize pathways having similar coexpression profiles.
| S-EPMC8121311 | biostudies-literature
Project description:BackgroundSequencing of the first plant genomes has revealed that cytochromes P450 have evolved to become the largest family of enzymes in secondary metabolism. The proportion of P450 enzymes with characterized biochemical function(s) is however very small. If P450 diversification mirrors evolution of chemical diversity, this points to an unexpectedly poor understanding of plant metabolism. We assumed that extensive analysis of gene expression might guide towards the function of P450 enzymes, and highlight overlooked aspects of plant metabolism.ResultsWe have created a comprehensive database, 'CYPedia', describing P450 gene expression in four data sets: organs and tissues, stress response, hormone response, and mutants of Arabidopsis thaliana, based on public Affymetrix ATH1 microarray expression data. P450 expression was then combined with the expression of 4,130 re-annotated genes, predicted to act in plant metabolism, for co-expression analyses. Based on the annotation of co-expressed genes from diverse pathway annotation databases, co-expressed pathways were identified. Predictions were validated for most P450s with known functions. As examples, co-expression results for P450s related to plastidial functions/photosynthesis, and to phenylpropanoid, triterpenoid and jasmonate metabolism are highlighted here.ConclusionThe large scale hypothesis generation tools presented here provide leads to new pathways, unexpected functions, and regulatory networks for many P450s in plant metabolism. These can now be exploited by the community to validate the proposed functions experimentally using reverse genetics, biochemistry, and metabolic profiling.
| S-EPMC2383897 | biostudies-literature
Project description:Although plant development is highly reproducible, some stochasticity exists. This developmental stochasticity may be caused by noisy gene expression. Here we analyze the fluctuation of protein expression in Arabidopsis thaliana. Using the photoconvertible KikGR marker, we show that the protein expressions of individual cells fluctuate over time. A dual reporter system was used to study extrinsic and intrinsic noise of marker gene expression. We report that extrinsic noise is higher than intrinsic noise and that extrinsic noise in stomata is clearly lower in comparison to several other tissues/cell types. Finally, we show that cells are coupled with respect to stochastic protein expression in young leaves, hypocotyls and roots but not in mature leaves. Our data indicate that stochasticity of gene expression can vary between tissues/cell types and that it can be coupled in a non-cell-autonomous manner.
| S-EPMC5730595 | biostudies-literature
Project description:The transcriptomic datasets of the plant model organism Arabidopsis thaliana grown in the International Space Station provided by GeneLab have been mined to isolate the impact of spaceflight microgravity on gene expressions related to root growth. A set of computational tools is used to identify the hub genes that respond differently in spaceflight with controlled lighting compared to on the ground. These computational tools based on graph-theoretic approaches are used to infer gene regulatory networks from the transcriptomic datasets. The three main algorithms used for network analyses are LASSO, Pearson correlation, and the HITS algorithm. Graph-based spectral analyses reveal distinct properties of the spaceflight microgravity networks for the WS, Col-0, and mutant phyD ecotypes. The set of hub genes that are significantly altered in spaceflight microgravity are mainly involved in cell wall synthesis, protein transport, response to auxin, stress responses, and catabolic processes. Network analysis highlights five important root growth-regulating hub genes that have the highest outdegree distribution in spaceflight microgravity networks. These concerned genes coding for proteins are identified from the Gene Regulatory Networks (GRNs) corresponding to spaceflight total light environment. Furthermore, network analysis uncovers genes that encode nucleotide-diphospho-sugar interconversion enzymes that have higher transcriptional regulation in spaceflight microgravity and are involved in cell wall biosynthesis.
| S-EPMC7996555 | biostudies-literature
Project description:Polycomb group (PcG) proteins are conserved chromatin regulators involved in the control of key developmental programs in eukaryotes. They collectively provide the transcriptional memory unique to each cell identity by maintaining transcriptional states of developmental genes. PcG proteins form multi-protein complexes, known as Polycomb repressive complex 1 (PRC1) and Polycomb repressive complex 2 (PRC2). PRC1 and PRC2 contribute to the stable gene silencing in part through catalyzing covalent histone modifications. Components of PRC1 and PRC2 are well conserved from plants to animals. PcG-mediated gene silencing has been extensively investigated in efforts to understand molecular mechanisms underlying developmental programs in eukaryotes. Here, we describe our current knowledge on PcG-mediated gene repression which dictates developmental programs by dynamic layers of regulatory activities, with an emphasis given to the model plant Arabidopsis thaliana.
| S-EPMC4275700 | biostudies-literature
Project description:Gene co-expression analysis has been widely used in recent years for predicting unknown gene function and its regulatory mechanisms. The predictive accuracy depends on the quality and the diversity of data set used. In this report, we applied singular value decomposition (SVD) to array experiments in public databases to find that co-expression linkage could be estimated by a much smaller number of array data. Correlations of co-expressed gene were assessed using two regulatory mechanisms (feedback loop of the fundamental circadian clock and a global transcription factor Myb28), as well as metabolic pathways in the AraCyc database. Our conclusion is that a smaller number of informative arrays across tissues can suffice to reproduce comparable results with a state-of-the-art co-expression software tool. In our SVD analysis on Arabidopsis data set, array experiments that contributed most as the principal components included stamen development, germinating seed and stress responses on leaf.
| S-EPMC2608847 | biostudies-other
Project description:The three-dimensional packing of the genome plays an important role in regulating gene expression. We have used Hi-C, a genome-wide chromatin conformation capture (3C) method, to analyze Arabidopsis thaliana chromosomes dissected into subkilobase segments, which is required for gene-level resolution in this species with a gene-dense genome. We found that the repressive H3K27me3 histone mark is overrepresented in the promoter regions of genes that are in conformational linkage over long distances. In line with the globally dispersed distribution of RNA polymerase II in A. thaliana nuclear space, actively transcribed genes do not show a strong tendency to associate with each other. In general, there are often contacts between 5' and 3' ends of genes, forming local chromatin loops. Such self-loop structures of genes are more likely to occur in more highly expressed genes, although they can also be found in silent genes. Silent genes with local chromatin loops are highly enriched for the histone variant H3.3 at their 5' and 3' ends but depleted of repressive marks such as heterochromatic histone modifications and DNA methylation in flanking regions. Our results suggest that, different from animals, a major theme of genome folding in A. thaliana is the formation of structural units that correspond to gene bodies.
| S-EPMC4971768 | biostudies-literature
Project description:Recent discoveries have revealed that the genes for the biosynthesis of a variety of plant specialized metabolites are organized in operon-like clusters within plant genomes. Here we identify a regulatory process that is required for normal expression of metabolic gene clusters in Arabidopsis thaliana. Comparative gene expression analysis of a representative clustered gene was performed in a set of chromatin mutant lines. Subsequently, metabolite levels were analysed by GC-MS and the local chromatin structure was investigated by chromatin immunoprecipitation and nucleosome positioning. We show that the transcript levels of genes within two metabolic clusters are coordinately reduced in an arp6 and h2a.z background. We demonstrate that H2A.Z enrichment in the clusters is positively correlated with active cluster expression. We further show that nucleosome stability within the cluster regions is higher in the arp6 background compared with the wild-type. These results implicate ARP6 and H2A.Z in the regulation of metabolic clusters in Arabidopsis thaliana through localized chromatin modifications that enable the coordinate expression of groups of contiguous genes. These findings shed light on the complex process of cluster regulation, an area that could in the future open up new opportunities for the discovery and manipulation of specialized metabolic pathways in plants.
| S-EPMC4301183 | biostudies-literature