Ranaweera Thilanka, Brown Brianna N I, Wang Peipei, Shiu Shin-Han
Department of Plant Biology, Michigan State University, East Lansing, MI, United States.
Department of Energy (DOE) Great Lakes Bioenergy Research Center, Michigan State University, East Lansing, MI, United States.
Front Plant Sci. 2022 Oct 10;13:998400. doi: 10.3389/fpls.2022.998400. eCollection 2022.
Switchgrass low-land ecotypes have significantly higher biomass but lower cold tolerance compared to up-land ecotypes. Understanding the molecular mechanisms underlying cold response, including the ones at transcriptional level, can contribute to improving tolerance of high-yield switchgrass under chilling and freezing environmental conditions. Here, by analyzing an existing switchgrass transcriptome dataset, the temporal regulatory basis of switchgrass transcriptional response to cold is dissected computationally. We found that the number of cold-responsive genes and enriched Gene Ontology terms increased as duration of cold treatment increased from 30 min to 24 hours, suggesting an amplified response/cascading effect in cold-responsive gene expression. To identify genomic sequences likely important for regulating cold response, machine learning models predictive of cold response were established using -mer sequences enriched in the genic and flanking regions of cold-responsive genes but not non-responsive genes. These -mers, referred to as putative -regulatory elements (pCREs) are likely regulatory sequences of cold response in switchgrass. There are in total 655 pCREs where 54 are important in all cold treatment time points. Consistent with this, eight of 35 known cold-responsive CREs were similar to top-ranked pCREs in the models and only these eight were important for predicting temporal cold response. More importantly, most of the top-ranked pCREs were novel sequences in cold regulation. Our findings suggest additional sequence elements important for cold-responsive regulation previously not known that warrant further studies.
与高地生态型相比，柳枝稷低地生态型具有显著更高的生物量，但耐寒性较低。了解冷响应的分子机制，包括转录水平的机制，有助于提高高产柳枝稷在低温和冷冻环境条件下的耐受性。在这里，通过分析现有的柳枝稷转录组数据集，我们通过计算剖析了柳枝稷对冷转录响应的时间调控基础。我们发现，随着冷处理时间从30分钟增加到24小时，冷响应基因的数量和富集的基因本体术语增加，这表明冷响应基因表达中存在放大的响应/级联效应。为了鉴定可能对调控冷响应重要的基因组序列，我们使用在冷响应基因的基因和侧翼区域中富集但在非响应基因中未富集的 - 聚体序列建立了预测冷响应的机器学习模型。这些 - 聚体，称为假定的 - 调控元件（pCREs），可能是柳枝稷冷响应的调控序列。总共有655个pCREs，其中54个在所有冷处理时间点都很重要。与此一致的是，35个已知的冷响应CREs中有8个与模型中排名靠前的pCREs相似，并且只有这8个对预测时间冷响应很重要。更重要 的是，大多数排名靠前的pCREs是冷调控中的新序列。我们的研究结果表明，以前未知的对冷响应调控重要的额外序列元件值得进一步研究。
