Suppr超能文献

田间种植水稻中基因到氮利用效率(NUE)表型的高可信度调控网络的验证

Validation of a high-confidence regulatory network for gene-to-NUE phenotype in field-grown rice.

作者信息

Shanks Carly M, Huang Ji, Cheng Chia-Yi, Shih Hung-Jui S, Brooks Matthew D, Alvarez José M, Araus Viviana, Swift Joseph, Henry Amelia, Coruzzi Gloria M

机构信息

Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, United States.

Department of Life Science, College of Life Science, National Taiwan University, Taipei, Taiwan.

出版信息

Front Plant Sci. 2022 Nov 25;13:1006044. doi: 10.3389/fpls.2022.1006044. eCollection 2022.

Abstract

Nitrogen (N) and Water (W) - two resources critical for crop productivity - are becoming increasingly limited in soils globally. To address this issue, we aim to uncover the gene regulatory networks (GRNs) that regulate nitrogen use efficiency (NUE) - as a function of water availability - in Oryza sativa, a staple for 3.5 billion people. In this study, we infer and validate GRNs that correlate with rice NUE phenotypes affected by N-by-W availability in the field. We did this by exploiting RNA-seq and crop phenotype data from 19 rice varieties grown in a 2x2 N-by-W matrix in the field. First, to identify gene-to-NUE field phenotypes, we analyzed these datasets using weighted gene co-expression network analysis (WGCNA). This identified two network modules ("skyblue" & "grey60") highly correlated with NUE grain yield (NUEg). Next, we focused on 90 TFs contained in these two NUEg modules and predicted their genome-wide targets using the N-and/or-W response datasets using a random forest network inference approach (GENIE3). Next, to validate the GENIE3 TF→target gene predictions, we performed Precision/Recall Analysis (AUPR) using nine datasets for three TFs validated . This analysis sets a precision threshold of 0.31, used to "prune" the GENIE3 network for high-confidence TF→target gene edges, comprising 88 TFs and 5,716 N-and/or-W response genes. Next, we ranked these 88 TFs based on their significant influence on NUEg target genes responsive to N and/or W signaling. This resulted in a list of 18 prioritized TFs that regulate 551 NUEg target genes responsive to N and/or W signals. We validated the direct regulated targets of two of these candidate NUEg TFs in a plant cell-based TF assay called TARGET, for which we also had data for comparison. Gene ontology analysis revealed that 6/18 NUEg TFs - OsbZIP23 (LOC_Os02g52780), Oshox22 (LOC_Os04g45810), LOB39 (LOC_Os03g41330), Oshox13 (LOC_Os03g08960), LOC_Os11g38870, and LOC_Os06g14670 - regulate genes annotated for N and/or W signaling. Our results show that OsbZIP23 and Oshox22, known regulators of drought tolerance, also coordinate W-responses with NUEg. This validated network can aid in developing/breeding rice with improved yield on marginal, low N-input, drought-prone soils.

摘要

氮(N)和水(W)——对作物生产力至关重要的两种资源——在全球土壤中日益稀缺。为解决这一问题,我们旨在揭示调控水稻氮利用效率(NUE)(作为水分有效性的函数)的基因调控网络(GRN),水稻是35亿人的主食。在本研究中,我们推断并验证了与田间氮-水有效性影响的水稻NUE表型相关的GRN。我们通过利用在田间2×2氮-水矩阵中种植的19个水稻品种的RNA测序和作物表型数据来实现这一目标。首先,为了确定基因与NUE田间表型的关系,我们使用加权基因共表达网络分析(WGCNA)分析了这些数据集。这确定了与NUE籽粒产量(NUEg)高度相关的两个网络模块(“天蓝色”和“灰色60”)。接下来,我们聚焦于这两个NUEg模块中包含的90个转录因子(TF),并使用随机森林网络推理方法(GENIE3),利用氮和/或水响应数据集预测它们在全基因组范围内的靶标。接下来,为了验证GENIE3转录因子→靶基因的预测结果,我们使用九个数据集对三个经过验证的转录因子进行了精确率/召回率分析(AUPR)。该分析设定了0.31的精确率阈值,用于“修剪”GENIE3网络,以获得高可信度的转录因子→靶基因边,该网络包含88个转录因子和5716个氮和/或水响应基因。接下来,我们根据这88个转录因子对响应氮和/或水信号的NUEg靶基因的显著影响进行排序。这产生了一份18个优先转录因子的列表,它们调控551个响应氮和/或水信号的NUEg靶基因。我们在一种基于植物细胞的转录因子检测方法(称为TARGET)中验证了其中两个候选NUEg转录因子的直接调控靶标,我们也有用于比较的数据。基因本体分析表明,18个NUEg转录因子中的6个——OsbZIP23(LOC_Os02g52780)、Oshox22(LOC_Os04g45810)、LOB39(LOC_Os03g41330)、Oshox13(LOC_Os03g08960)、LOC_Osllg38870和LOC_Os06g14670——调控注释为氮和/或水信号的基因。我们的结果表明,已知的耐旱调节因子OsbZIP23和Oshox22也协调了水分响应与NUEg。这个经过验证的网络有助于在边际、低氮投入、易干旱土壤上培育/选育产量更高的水稻。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验