Plant and Cell PhysiologyPlant BiologyPeer-reviewed journal article
Modeling of transcriptomic variation among subgenomes in 25 accessions of common wheat reveals cis- and trans-regulation architectures
Yasuyuki Nomura, Moeko OkadaiD, Toshiaki TameshigeiD, Shotaro TakenakaiD, Kentaro K. ShimizuiD, Shuhei NasudaiD, Atsushi J. NaganoiD
Abstract
Common wheat is an allohexaploid plant, thus making it difficult to obtain homoeolog-distinguished transcriptome data. Lasy-Seq, a type of 3 RNA-seq, is efficient for obtaining homoeolog-distinguished transcriptomes and can thus overcome this measurement difficulty. This study obtained transcriptome data from the seedlings, second leaves, and root tips of 25 lines from mainly eastern transmitted area using Lasy-Seq. Roots and seedlings exhibited similar transcriptome profiles; however, they were different from those of the leaves. We determined the effects of subgenomes, lines and their interactions with leaves, roots, and seedlings on the expression levels of each homoeolog triad. Of the 19,805 homoeolog triads, 50.9-55.4%, 24.2-29.5%, and 7.7-9.0% showed significant effects on their expression levels from subgenome, line, and interaction, respectively. 51-55% and 24-30% have genetic variation in the cis- and trans-regulation. Hierarchical clustering and co-trans regulation network analysis of homoeolog triads revealed that the patterns of expression polymorphisms among the lines were shared in different genes. The triads in which the statistical model detected as line effects imply that expression variation between lines is caused by changes in a smaller number of common trans-factors. We assigned gene ontology (GO) terms of the Arabidopsis orthologs to wheat homoeolog triads via reciprocal BLAST between common wheat and Arabidopsis, thus improving the percentage of gene-assigned GO terms to all analyzed GO terms from 19.1% to 90.6%. GO term enrichment analysis revealed that GO terms related to each tissue type function were enriched in genes expressed in the leaves and roots. Our information provides fundamental knowledge for the future breeding of plants possessing complex gene regulatory networks such as common wheat.
- organism: plant (other)
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: deep learning
- Citations
- 5
- FWCI
- 2.33
- Field percentile
- top 14%
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