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

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 s...

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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9732682/
https://www.ncbi.nlm.nih.gov/pubmed/36507422
http://dx.doi.org/10.3389/fpls.2022.1006044
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author 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.
author_facet 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.
author_sort Shanks, Carly M.
collection PubMed
description 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 in planta. 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 in planta 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.
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spelling pubmed-97326822022-12-10 Validation of a high-confidence regulatory network for gene-to-NUE phenotype in field-grown rice 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. Front Plant Sci Plant Science 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 in planta. 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 in planta 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. Frontiers Media S.A. 2022-11-25 /pmc/articles/PMC9732682/ /pubmed/36507422 http://dx.doi.org/10.3389/fpls.2022.1006044 Text en Copyright © 2022 Shanks, Huang, Cheng, Shih, Brooks, Alvarez, Araus, Swift, Henry and Coruzzi https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
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.
Validation of a high-confidence regulatory network for gene-to-NUE phenotype in field-grown rice
title Validation of a high-confidence regulatory network for gene-to-NUE phenotype in field-grown rice
title_full Validation of a high-confidence regulatory network for gene-to-NUE phenotype in field-grown rice
title_fullStr Validation of a high-confidence regulatory network for gene-to-NUE phenotype in field-grown rice
title_full_unstemmed Validation of a high-confidence regulatory network for gene-to-NUE phenotype in field-grown rice
title_short Validation of a high-confidence regulatory network for gene-to-NUE phenotype in field-grown rice
title_sort validation of a high-confidence regulatory network for gene-to-nue phenotype in field-grown rice
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9732682/
https://www.ncbi.nlm.nih.gov/pubmed/36507422
http://dx.doi.org/10.3389/fpls.2022.1006044
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