Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants

First author: Kranthi Varala; Affiliations: Purdue University (普渡大学): West Lafayette, USA

Corresponding author: Gloria M. Coruzzi(New York University)

This study exploits time, the relatively unexplored fourth dimension of gene regulatory networks (GRNs), to learn the temporal transcriptional logic underlying dynamic nitrogen (N) signaling in plants. Our “just-in-time” analysis of time-series transcriptome data uncovered a temporal cascade of cis elements underlying dynamic N signaling. To infer (推断) transcription factor (TF)-target edges in a GRN, we applied a time-based machine learning method (机器学习) to 2,174 dynamic N-responsive genes. We experimentally determined a network precision (精度) cutoff, using TF-regulated genome-wide targets of three TF hubs (CRF4, SNZ, and CDF1), used to “prune” the network to 155 TFs and 608 targets. This network precision was reconfirmed using genome-wide TF-target regulation data for four additional TFs (TGA1, HHO5/6, and PHL1) not used in network pruning. These higher-confidence edges in the GRN were further filtered by independent TF-target binding data, used to calculate a TF “N-specificity” index. This refined GRN identifies the temporal relationship of known/validated regulators of N signaling (NLP7/8, TGA1/4, NAC4, HRS1, and LBD37/38/39) and 146 additional regulators. Six TFs—CRF4, SNZ, CDF1, HHO5/6, and PHL1—validated herein regulate a significant number of genes in the dynamic N response, targeting 54% of N-uptake/assimilation pathway genes. Phenotypically, inducible overexpression of CRF4 in planta regulates genes resulting in altered biomass, root development, and 15NO3− uptake, specifically under low-N conditions. This dynamic N-signaling GRN now provides the temporal “transcriptional logic” for 155 candidate TFs to improve nitrogen use efficiency with potential agricultural applications. Broadly, these time-based approaches can uncover the temporal transcriptional logic for any biological response system in biology, agriculture, or medicine.

本文研究了基因调控网络GRN很少被研究的影响因素，即时间对于植物中N信号的时序转录机制。作者通过时间序列的转录组数据分析发现了作用于植物N信号转导的顺式作用元件的时间级联。作者应用了基于时间序列数据的机器学习方法来对2174个N响应基因进行推断转录因子和靶基因互作关系。作者通过三个转录因子中心节点（CRF4、SNZ和CDF1）与受其调控的基因组靶基因作为网络构建的精度阈值，用来将构建的网络精简成155个转录因子和688个靶基因。作者又用了另外4个不曾在网络修建时用到的转录因子，即TGA1、HHO5/6和PHL1与其全基因组靶基因来再次验证之前用来网络修剪的精度阈值。作者通过另外单独的用来计算转录因子“N特异性”指数的转录因子与其靶基因结合数据进一步过滤了GRN中高置信度的互作关系。最终的GRN鉴定了N信号转导已知的调控子（NLP7/8、TGA1/4、NAC4、HRS1和LBD37/38/39）的时间关系和另外146个新的调控子。CRF4、SNZ、CDF1、HHO5/6和PHL1等6个转录因子在N响应过程中调控了许多的基因，并靶向大约54%的N吸收和同化通路基因。通过植株体内过表达CRF4调控了一些参与生物量、根系发育和15NO3− 吸收相关的基因，尤其是在低N的条件下。本文所构建的N信号GRN提供了155个候选转录因子的时间“转录逻辑”，具有潜在农艺运用，提升N使用效率。更广泛的讲，本文所应用的时间序列转录组分析方法能够揭示生物、农艺或者医药领域任何生物学响应的时间“转录逻辑”。

通讯：John W.S. Brown (http://coruzzilab.bio.nyu.edu/wordpress/?page_id=5)

个人简历：1972-1976年，美国福德汉姆大学，生物学学士；1976-1979年，纽约大学医学院，细胞和分子生物学博士；1979-1980年，哥伦比亚大学，博士后；1980-1983年，洛克菲勒大学，博士后；

研究方向：植物系统生物学。

doi: https://doi.org/10.1073/pnas.1721487115

Journal: PNAS

First Published date: 16 May, 2018

（P.S. 原文下载：链接:https://pan.baidu.com/s/1qFiSsqwlF_7JjmGMbedfhQ  密码:dmu6）