Data_Sheet_8_Using Network-Based Machine Learning to Predict Transcription Factors Involved in Drought Resistance.XLSX

Gene regulatory networks underpin stress response pathways in plants. However, parsing these networks to prioritize key genes underlying a particular trait is challenging. Here, we have built the Gene Regulation and Association Network (GRAiN) of rice (Oryza sativa). GRAiN is an interactive query-based web-platform that allows users to study functional relationships between transcription factors (TFs) and genetic modules underlying abiotic-stress responses. We built GRAiN by applying a combination of different network inference algorithms to publicly available gene expression data. We propose a supervised machine learning framework that complements GRAiN in prioritizing genes that regulate stress signal transduction and modulate gene expression under drought conditions. Our framework converts intricate network connectivity patterns of 2160 TFs into a single drought score. We observed that TFs with the highest drought scores define the functional, structural, and evolutionary characteristics of drought resistance in rice. Our approach accurately predicted the function of OsbHLH148 TF, which we validated using in vitro protein-DNA binding assays and mRNA sequencing loss-of-function mutants grown under control and drought stress conditions. Our network and the complementary machine learning strategy lends itself to predicting key regulatory genes underlying other agricultural traits and will assist in the genetic engineering of desirable rice varieties.基因调控网络（Gene regulatory networks）支撑着植物的胁迫响应通路。然而，解析此类网络以筛选特定性状背后的关键基因仍极具挑战。本研究构建了水稻（Oryza sativa）的基因调控与关联网络（Gene Regulation and Association Network，GRAiN）。GRAiN是一款基于查询的交互式网页平台，可支持用户研究转录因子（Transcription Factors，TFs）与非生物胁迫响应相关遗传模块间的功能关联。我们通过将多种网络推断算法整合应用于公开可用的基因表达数据，构建了GRAiN。此外，我们提出了一种监督机器学习框架，可辅助GRAiN筛选调控干旱条件下胁迫信号转导、调控基因表达的关键基因。该框架将2160个转录因子的复杂网络连接模式整合为单一干旱评分。研究发现，干旱评分最高的转录因子决定了水稻抗旱性的功能、结构与进化特征。我们准确预测了OsbHLH148转录因子的功能，并通过体外蛋白质-DNA结合实验，以及对对照和干旱胁迫条件下培养的功能丧失突变体开展mRNA测序，验证了该预测结果。本研究构建的网络及其配套的机器学习策略可用于预测其他农业性状背后的关键调控基因，将助力优良水稻品种的基因工程改造。