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

Kranthi Varala, Amy Marshall-Colón, Jacopo Cirrone, Matthew D. Brooks, Angelo V. Pasquino, Sophie Léran, Shipra Mittal, Tara M. Rock, Molly B. Edwards, Grace J. Kim, Sandrine Ruffel, W. Richard McCombie, Dennis Shasha, Gloria Coruzzi

Research output: Contribution to journalArticle

Abstract

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, and15NO3 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.

Original languageEnglish (US)
Pages (from-to)6494-6499
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume115
Issue number25
DOIs
StatePublished - Jun 19 2018

Fingerprint

Nitrogen
Gene Regulatory Networks
Transcription Factors
Genes
Genome
Systems Biology
Agriculture
Transcriptome
Biomass
Medicine

Keywords

  • Network inference
  • Nitrogen assimilation
  • Plant biology
  • Systems biology
  • Transcriptional dynamics

ASJC Scopus subject areas

  • General

Cite this

Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants. / Varala, Kranthi; Marshall-Colón, Amy; Cirrone, Jacopo; Brooks, Matthew D.; Pasquino, Angelo V.; Léran, Sophie; Mittal, Shipra; Rock, Tara M.; Edwards, Molly B.; Kim, Grace J.; Ruffel, Sandrine; Richard McCombie, W.; Shasha, Dennis; Coruzzi, Gloria.

In: Proceedings of the National Academy of Sciences of the United States of America, Vol. 115, No. 25, 19.06.2018, p. 6494-6499.

Research output: Contribution to journalArticle

Varala, K, Marshall-Colón, A, Cirrone, J, Brooks, MD, Pasquino, AV, Léran, S, Mittal, S, Rock, TM, Edwards, MB, Kim, GJ, Ruffel, S, Richard McCombie, W, Shasha, D & Coruzzi, G 2018, 'Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants', Proceedings of the National Academy of Sciences of the United States of America, vol. 115, no. 25, pp. 6494-6499. https://doi.org/10.1073/pnas.1721487115
Varala, Kranthi ; Marshall-Colón, Amy ; Cirrone, Jacopo ; Brooks, Matthew D. ; Pasquino, Angelo V. ; Léran, Sophie ; Mittal, Shipra ; Rock, Tara M. ; Edwards, Molly B. ; Kim, Grace J. ; Ruffel, Sandrine ; Richard McCombie, W. ; Shasha, Dennis ; Coruzzi, Gloria. / Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants. In: Proceedings of the National Academy of Sciences of the United States of America. 2018 ; Vol. 115, No. 25. pp. 6494-6499.
@article{81730193f2b14aea93365b40c1bd97a6,
title = "Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants",
abstract = "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, and15NO3 − 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.",
keywords = "Network inference, Nitrogen assimilation, Plant biology, Systems biology, Transcriptional dynamics",
author = "Kranthi Varala and Amy Marshall-Col{\'o}n and Jacopo Cirrone and Brooks, {Matthew D.} and Pasquino, {Angelo V.} and Sophie L{\'e}ran and Shipra Mittal and Rock, {Tara M.} and Edwards, {Molly B.} and Kim, {Grace J.} and Sandrine Ruffel and {Richard McCombie}, W. and Dennis Shasha and Gloria Coruzzi",
year = "2018",
month = "6",
day = "19",
doi = "10.1073/pnas.1721487115",
language = "English (US)",
volume = "115",
pages = "6494--6499",
journal = "Proceedings of the National Academy of Sciences of the United States of America",
issn = "0027-8424",
number = "25",

}

TY - JOUR

T1 - Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants

AU - Varala, Kranthi

AU - Marshall-Colón, Amy

AU - Cirrone, Jacopo

AU - Brooks, Matthew D.

AU - Pasquino, Angelo V.

AU - Léran, Sophie

AU - Mittal, Shipra

AU - Rock, Tara M.

AU - Edwards, Molly B.

AU - Kim, Grace J.

AU - Ruffel, Sandrine

AU - Richard McCombie, W.

AU - Shasha, Dennis

AU - Coruzzi, Gloria

PY - 2018/6/19

Y1 - 2018/6/19

N2 - 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, and15NO3 − 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.

AB - 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, and15NO3 − 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.

KW - Network inference

KW - Nitrogen assimilation

KW - Plant biology

KW - Systems biology

KW - Transcriptional dynamics

UR - http://www.scopus.com/inward/record.url?scp=85048748818&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85048748818&partnerID=8YFLogxK

U2 - 10.1073/pnas.1721487115

DO - 10.1073/pnas.1721487115

M3 - Article

VL - 115

SP - 6494

EP - 6499

JO - Proceedings of the National Academy of Sciences of the United States of America

JF - Proceedings of the National Academy of Sciences of the United States of America

SN - 0027-8424

IS - 25

ER -