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Using iRNA-seq analysis to predict gene expression regulatory level and activity in Zea mays tissues

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 differe...

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Autores principales: Schulte, Lauren M, Koirtyohann, Kathryn M, McGinnis, Karen M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9157096/
https://www.ncbi.nlm.nih.gov/pubmed/35404453
http://dx.doi.org/10.1093/g3journal/jkac086
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author Schulte, Lauren M
Koirtyohann, Kathryn M
McGinnis, Karen M
author_facet Schulte, Lauren M
Koirtyohann, Kathryn M
McGinnis, Karen M
author_sort Schulte, Lauren M
collection PubMed
description 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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spelling pubmed-91570962022-06-04 Using iRNA-seq analysis to predict gene expression regulatory level and activity in Zea mays tissues Schulte, Lauren M Koirtyohann, Kathryn M McGinnis, Karen M G3 (Bethesda) Investigation 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. Oxford University Press 2022-04-11 /pmc/articles/PMC9157096/ /pubmed/35404453 http://dx.doi.org/10.1093/g3journal/jkac086 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Genetics Society of America. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Investigation
Schulte, Lauren M
Koirtyohann, Kathryn M
McGinnis, Karen M
Using iRNA-seq analysis to predict gene expression regulatory level and activity in Zea mays tissues
title Using iRNA-seq analysis to predict gene expression regulatory level and activity in Zea mays tissues
title_full Using iRNA-seq analysis to predict gene expression regulatory level and activity in Zea mays tissues
title_fullStr Using iRNA-seq analysis to predict gene expression regulatory level and activity in Zea mays tissues
title_full_unstemmed Using iRNA-seq analysis to predict gene expression regulatory level and activity in Zea mays tissues
title_short Using iRNA-seq analysis to predict gene expression regulatory level and activity in Zea mays tissues
title_sort using irna-seq analysis to predict gene expression regulatory level and activity in zea mays tissues
topic Investigation
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9157096/
https://www.ncbi.nlm.nih.gov/pubmed/35404453
http://dx.doi.org/10.1093/g3journal/jkac086
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