Cortijo Sandra, Bhattarai Marcel, Locke James C W, Ahnert Sebastian E
The Sainsbury Laboratory, University of Cambridge, Cambridge, United Kingdom.
UMR5004 Biochimie et Physiologie Moléculaire des Plantes, Univ Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France.
Front Plant Sci. 2020 Dec 15;11:599464. doi: 10.3389/fpls.2020.599464. eCollection 2020.
Co-expression networks are a powerful tool to understand gene regulation. They have been used to identify new regulation and function of genes involved in plant development and their response to the environment. Up to now, co-expression networks have been inferred using transcriptomes generated on plants experiencing genetic or environmental perturbation, or from expression time series. We propose a new approach by showing that co-expression networks can be constructed in the absence of genetic and environmental perturbation, for plants at the same developmental stage. For this, we used transcriptomes that were generated from genetically identical individual plants that were grown under the same conditions and for the same amount of time. Twelve time points were used to cover the 24-h light/dark cycle. We used variability in gene expression between individual plants of the same time point to infer a co-expression network. We show that this network is biologically relevant and use it to suggest new gene functions and to identify new targets for the transcriptional regulators GI, PIF4, and PRR5. Moreover, we find different co-regulation in this network based on changes in expression between individual plants, compared to the usual approach requiring environmental perturbation. Our work shows that gene co-expression networks can be identified using variability in gene expression between individual plants, without the need for genetic or environmental perturbations. It will allow further exploration of gene regulation in contexts with subtle differences between plants, which could be closer to what individual plants in a population might face in the wild.
共表达网络是理解基因调控的有力工具。它们已被用于识别参与植物发育及其对环境响应的基因的新调控和功能。到目前为止，共表达网络是利用在经历遗传或环境扰动的植物上产生的转录组，或从表达时间序列推断出来的。我们提出了一种新方法，表明对于处于相同发育阶段的植物，在没有遗传和环境扰动的情况下也可以构建共表达网络。为此，我们使用了从在相同条件下生长相同时间的基因相同的个体植物产生的转录组。使用12个时间点来覆盖24小时的光/暗周期。我们利用同一时间点的个体植物之间基因表达的变异性来推断共表达网络。我们表明这个网络具有生物学相关性，并利用它来提出新的基因功能，以及识别转录调节因子GI、PIF4和PRR5的新靶点。此外，与需要环境扰动的常规方法相比，我们在这个网络中基于个体植物之间表达的变化发现了不同的共调控。我们的工作表明，可以利用个体植物之间基因表达的变异性来识别基因共表达网络，而无需遗传或环境扰动。这将允许在植物之间存在细微差异的背景下进一步探索基因调控，这可能更接近自然种群中个体植物可能面临的情况。
