Laboratoire de Recherche en Sciences Végétales, Université de Toulouse, CNRS, UPS, 24 chemin de Borde Rouge, 31320 Auzeville-Tolosane, France.
PAPPSO, Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, 91190 Gif-sur-Yvette, France.
Cells. 2020 Oct 7;9(10):2249. doi: 10.3390/cells9102249.
In the global warming context, plant adaptation occurs, but the underlying molecular mechanisms are poorly described. Studying natural variation of the model plant adapted to various environments along an altitudinal gradient should contribute to the identification of new traits related to adaptation to contrasted growth conditions. The study was focused on the cell wall (CW) which plays major roles in the response to environmental changes. Rosettes and floral stems of four newly-described populations collected at different altitudinal levels in the Pyrenees Mountains were studied in laboratory conditions at two growth temperatures (22 vs. 15 °C) and compared to the well-described Col ecotype. Multi-omic analyses combining phenomics, metabolomics, CW proteomics, and transcriptomics were carried out to perform an integrative study to understand the mechanisms of plant adaptation to contrasted growth temperature. Different developmental responses of rosettes and floral stems were observed, especially at the CW level. In addition, specific population responses are shown in relation with their environment and their genetics. Candidate genes or proteins playing roles in the CW dynamics were identified and will deserve functional validation. Using a powerful framework of data integration has led to conclusions that could not have been reached using standard statistical approaches.
在全球变暖的背景下,植物会发生适应性进化,但其中的潜在分子机制还描述不清。研究适应不同环境的模式植物的自然变异,应该有助于发现与适应不同生长条件相关的新特征。本研究集中于细胞壁(CW),它在应对环境变化方面起着重要作用。在实验室条件下,对在比利牛斯山脉不同海拔高度采集的四个新描述种群的莲座丛和花茎,在两个生长温度(22 与 15°C)下进行了研究,并与描述良好的 Col 生态型进行了比较。结合表型组学、代谢组学、CW 蛋白质组学和转录组学的多组学分析,进行了综合研究,以了解植物对不同生长温度的适应机制。观察到莲座丛和花茎的不同发育反应,尤其是在 CW 水平上。此外,还显示了特定种群与其环境和遗传之间的关系。鉴定出了在 CW 动态中起作用的候选基因或蛋白质,它们将需要进行功能验证。使用强大的数据集成框架得出的结论,是使用标准统计方法无法得出的。