Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants.
Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants.
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DOI：
10.1073/pnas.1721487115
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发表时间：
2018-06-19
影响因子：
11.1
通讯作者：
Coruzzi GM
中科院分区：
文献类型：
--
作者：
Varala K;Marshall-Colón A;Cirrone J;Brooks MD;Pasquino AV;Léran S;Mittal S;Rock TM;Edwards MB;Kim GJ;Ruffel S;McCombie WR;Shasha D;Coruzzi GM
我们的研究利用时间-相对未开发的第四维基因调控网络（GRNs）-学习的时间转录逻辑的动态氮（N）信号在植物中。我们介绍了几个概念上的创新，在预测GRNs领域的时间序列数据的分析。我们由此产生的网络现在提供了“转录逻辑”的转录因子扰动，旨在提高氮的利用效率，全球粮食生产在边际土壤和可持续农业的一个重要问题。更广泛地说，我们开发和部署的基于时间的方法的组合可以应用于揭示生物学，农业或医学中任何反应系统的时间“转录逻辑”。本研究利用时间，相对未开发的第四维基因调控网络（GRNs），了解时间转录逻辑的动态氮（N）信号在植物中。我们的“即时”分析的时间序列转录组数据揭示了一个时间级联顺式元件的动态N信号。为了推断GRN中的转录因子（TF）靶边缘，我们将基于时间的机器学习方法应用于2，174个动态N响应基因。我们通过实验确定了一个网络精确度截止值，使用TF调节的三个TF中心（CRF 4，SNZ和CDF 1）的全基因组靶点，用于将网络“修剪”为155个TF和608个靶点。使用未用于网络修剪的另外四个TF（TGA 1、HHO 5/6和PHL 1）的全基因组TF靶调控数据再次证实了这种网络精度。通过独立的TF-靶标结合数据进一步过滤GRN中的这些较高置信度边缘，用于计算TF“N-特异性”指数。该改进的GRN识别了已知/验证的N信号调节因子（NLP 7/8、TGA 1/4、NAC 4、HRS 1和LBD 37/38/39）和146个其他调节因子的时间关系。本文验证的六种TF-CRF 4、SNZ、CDF 1、HHO 5/6和PHL 1调节动态N响应中的大量基因，靶向54%的N-吸收/同化途径基因。从表型上看，CRF 4在植物中的诱导性过表达调节基因，导致生物量、根发育和15 NO3 −吸收的改变，特别是在低氮条件下。这种动态N信号GRN现在为155个候选TF提供了时间“转录逻辑”，以提高具有潜在农业应用的氮利用效率。从广义上讲，这些基于时间的方法可以揭示生物学，农业或医学中任何生物反应系统的时间转录逻辑。
Our 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. We introduce several conceptual innovations to the analysis of time-series data in the area of predictive GRNs. Our resulting network now provides the “transcriptional logic” for transcription factor perturbations aimed at improving N-use efficiency, an important issue for global food production in marginal soils and for sustainable agriculture. More broadly, the combination of the time-based approaches we develop and deploy can be applied to uncover the temporal “transcriptional logic” for any response system in biology, agriculture, or medicine. 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 transcription factor (TF)-target edges in a GRN, we applied a time-based machine learning method to 2,174 dynamic N-responsive genes. We experimentally determined a network precision 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.
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影响因子：
4.4
作者：
Doidy J;Li Y;Neymotin B;Edwards MB;Varala K;Gresham D;Coruzzi GM
通讯作者：
Coruzzi GM
影响因子：
16.6
作者：
Medici, Anna;Marshall-Colon, Amy;Ronzier, Elsa;Szponarski, Wojciech;Wang, Rongchen;Gojon, Alain;Crawford, Nigel M.;Ruffel, Sandrine;Coruzzi, Gloria M.;Krouk, Gabriel
通讯作者：
Krouk, Gabriel
DOI：
10.1073/pnas.1404657111
发表时间：
2014-07-15
影响因子：
11.1
作者：
Para, Alessia;Li, Ying;Coruzzi, Gloria M.
通讯作者：
Coruzzi, Gloria M.
影响因子：
7.4
作者：
Katari, Manpreet S.;Nowicki, Steve D.;Gutierrez, Rodrigo A.
通讯作者：
Gutierrez, Rodrigo A.
影响因子：
16.6
作者：
Marchive, Chloe;Roudier, Francois;Krapp, Anne
通讯作者：
Krapp, Anne