SUMMARY The epigenome orchestrates genome accessibility, functionality and three-dimensional structure. Because epigenetic variation can impact transcription and thus phenotypes, it may contribute to adaptation. Here we report 1,107 high-quality single-base resolution methylomes and 1,203 transcriptomes from the 1001 Genomes collection of Arabidopsis thaliana. Although the genetic basis of methylation variation is highly complex, geographic origin is a major predictor of genome-wide DNA methylation levels and of altered gene expression caused by epialleles. Comparison to cistrome and epicistrome datasets identifies associations between transcription factor binding sites, methylation, nucleotide variation and co-expression modules. Physical maps for nine of the most diverse genomes reveals how transposons and other structural variants shape the epigenome, with dramatic effects on immunity genes. The 1001 Epigenomes Project provides a comprehensive resource for understanding how variation in DNA methylation contributes to molecular and non-molecular phenotypes in natural populations of the most studied model plant.
We analyzed global gene expression in Arabidopsis in response to various hormones and in related experiments as part of the AtGenExpress project. The experimental agents included seven basic phytohormones (auxin, cytokinin, gibberellin, brassinosteroid, abscisic acid, jasmonate and ethylene) and their inhibitors. In addition, gene expression was investigated in hormone-related mutants and during seed germination and sulfate starvation. Hormone-inducible genes were identified from the hormone response data. The effects of each hormone and the relevance of the gene lists were verified by comparing expression profiles for the hormone treatments and related experiments using Pearson's correlation coefficient. This approach was also used to analyze the relationships among expression profiles for hormone responses and those included in the AtGenExpress stress-response data set. The expected correlations were observed, indicating that this approach is useful to monitor the hormonal status in the stress-related samples. Global interactions among hormones-inducible genes were analyzed in a pairwise fashion, and several known and novel hormone interactions were detected. Genome-wide transcriptional gene-to-gene correlations, analyzed by hierarchical cluster analysis (HCA), indicated that our data set is useful for identification of clusters of co-expressed genes, and to predict the functions of unknown genes, even if a gene's function is not directly related to the experiments included in AtGenExpress. Our data are available online from AtGenExpressJapan; the results of genome-wide HCA are available from PRIMe. The data set presented here will be a versatile resource for future hormone studies, and constitutes a reference for genome-wide gene expression in Arabidopsis.
Strigolactones (SLs) have recently been found to regulate shoot branching, but the functions of SLs at other stages of development and the regulation of SL-related gene expression are mostly unknown in Arabidopsis. In this study, we performed real-time reverse transcription-PCR (RT-PCR) and microarray analysis using wild-type plants and SL-deficient/insensitive mutants to understand the molecular mechanisms underlying SL biosynthesis and signaling. We found that there is responsiveness to SL in the gene expression of Arabidopsis seedlings, which includes feedback regulation of two carotenoid cleavage dioxygenase genes. Microarray analysis revealed that exogenously applied SL regulated the expression of several genes, including light signaling-related genes and auxin-inducible genes. We also found that MORE AXILLARY GROWTH (MAX)2 plays an important role in the expression of SL-regulated genes. Our data support previous studies indicating that SL might function at the seedling stage. Analysis of SL-responsive and MAX2 downstream gene candidates provides new opportunities to broaden our understanding of SL signaling.
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