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Abstract Changes in gene expression are important for responses to abiotic stress. Transcriptome profiling of heat- or cold-stressed maize genotypes identifies many changes in transcript abundance. We used comparisons of expression responses in multiple genotypes to identify alleles with variable responses to heat or cold stress and to distinguish examples of cis- or trans-regulatory variation for stress-responsive expression changes. We used motifs enriched near the transcription start sites (TSSs) for thermal stress-responsive genes to develop predictive models of gene expression responses. Prediction accuracies can be improved by focusing only on motifs within unmethylated regions near the TSS and vary for genes with different dynamic responses to stress. Models trained on expression responses in a single genotype and promoter sequences provided lower performance when applied to other genotypes but this could be improved by using models trained on data from all three genotypes tested. The analysis of genes with cis-regulatory variation provides evidence for structural variants that result in presence/absence of transcription factor binding sites in creating variable responses. This study provides insights into cis-regulatory motifs for heat- and cold-responsive gene expression and defines a framework for developing models to predict expression responses across multiple genotypes.
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Genome-Wide TSS Identification in Maize
Regulation of gene expression is a fundamental biological process that relies on transcription factors (TF) recognizing specific cis motifs in the regulatory regions of the genes that they control. In most eukaryotic organisms, cis-regulatory elements are significantly enriched around the transcription start site (TSS). However, different from other genic features, TSSs need to be experimentally determined, becoming then important components of genome annotations. One of the methods for experimentally determining TSSs at the genome-wide level is CAGE (cap analysis of gene expression). This chapter describes how to prepare a CAGE library for sequencing, starting with RNA extraction, library construction, and quality controls before proceed to sequencing in the Illumina platform. We then describe how to use a computational pipeline to determine, from the alignment of CAGE tags, the genome-wide location of TSSs, followed with statistical approaches required to cluster TSSs that operate as transcriptional units, and to determine core promoter properties such as shape. The analyses described here focus on maize, since its large and yet deficiently annotated genome creates some unique challenges, but with some modifications can be easily adopted for other organisms as well.
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- Award ID(s):
- 1733633
- PAR ID:
- 10082081
- Date Published:
- Journal Name:
- Methods in molecular biology
- Volume:
- 1830
- ISSN:
- 1064-3745
- Page Range / eLocation ID:
- 239-256
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/978-1-4939-8657-6
