Changes in gene expression are important for response to abiotic stress. Transcriptome profiling performed on maize inbred and hybrid genotypes subjected to heat or cold stress identifies many transcript abundance changes in response to these environmental conditions. Motifs that are enriched near differentially expressed genes were used to develop machine learning models to predict gene expression responses to heat or cold. The best performing models utilize the sequences both upstream and downstream of the transcription start site. Prediction accuracies could be improved using models developed for specific co-expression clusters compared to using all up- or down-regulated genes or by only using motifs within unmethylated regions. Comparisons of expression responses in multiple genotypes were used to identify genes with variable response and to identify cis- or trans-regulatory variation. Models trained on B73 data have lower performance when applied to Mo17 or W22, this could be improved by using models trained on data from all genotypes. However, the models have low accuracy for correctly predicting genes with variable responses to abiotic stress. This study provides insights into cis-regulatory motifs for heat- and cold-responsive gene expression and provides a framework for developing models to predict expression response to abiotic stress across multiple genotypes.

中文翻译：

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应用顺式调控代码预测玉米的保守和可变热和冷胁迫响应

基因表达的变化对于应对非生物胁迫很重要。对遭受热或冷胁迫的玉米近交和杂种基因型进行的转录组分析表明，响应这些环境条件，许多转录本的丰度发生了变化。在差异表达基因附近富集的基元被用于开发机器学习模型，以预测基因对热或冷的反应。表现最好的模型利用转录起始位点上游和下游的序列。与使用所有上调或下调的基因或仅使用未甲基化区域内的基序相比，使用针对特定共表达簇开发的模型可以提高预测准确性。比较了多种基因型中的表达应答，以鉴定具有可变应答的基因并鉴定顺式或反式调节变异。当将B73数据训练的模型应用于Mo17或W22时，性能较低，这可以通过使用对所有基因型数据训练的模型来改善。但是，该模型的准确性较低，无法正确预测对非生物胁迫具有可变反应的基因。这项研究提供了对热和冷响应基因表达的顺式调控基元的见解，并为开发模型预测跨多种基因型对非生物胁迫的表达响应提供了框架。这些模型的准确率较低，无法正确预测对非生物胁迫具有可变反应的基因。这项研究提供了对热和冷响应基因表达的顺式调控基元的见解，并为开发模型预测跨多种基因型对非生物胁迫的表达响应提供了框架。这些模型的准确率较低，无法正确预测对非生物胁迫具有可变反应的基因。这项研究提供了对热和冷响应基因表达的顺式调控基元的见解，并为开发模型预测跨多种基因型对非生物胁迫的表达响应提供了框架。