ArticleG3 (Bethesda, Md.)2022
Using iRNA-seq analysis to predict gene expression regulatory level and activity in Zea mays tissues.
Article in G3 (Bethesda, Md.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 authors at 1 institution in 1 country.
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Abstract
Plants regulate gene expression at the transcriptional and post-transcriptional levels to produce a variety of functionally diverse cells and tissues that ensure normal growth, development, and environmental response. Although distinct gene expression patterns have been characterized between different plant tissues, the specific role of transcriptional regulation of tissue-specific expression is not well-characterized in plants. RNA-seq, while widely used to assay for changes in transcript abundance, does not discriminate between differential expression caused by mRNA degradation and active transcription. Recently, the presence of intron sequences in RNA-seq analysis of libraries constructed with total RNA has been found to coincide with genes undergoing active transcription. We have adapted the intron RNA-sequencing analysis to determine genome-wide transcriptional activity in 2 different maize (Zea mays) tissues: husk and V2-inner stem tissue. A total of 5,341 genes were predicted to be transcriptionally differentially expressed between the 2 tissues, including many genes expected to have biological activity relevant to the functional and developmental identity of each tissue. Correlations with transcriptional enhancer and transcription factor activity support the validity of intron RNA-sequencing predictions of transcriptional regulation. A subset of transcription factors was further analyzed using gene regulatory network analysis to determine the possible impact of their activation. The predicted regulatory patterns between these genes were used to model a potential gene regulatory network of transcription factors and regulatory targets.
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