dc.creatorShanks, Carly M.
dc.creatorHuang, Ji
dc.creatorCheng, Chia-Yi
dc.creatorShih, Hung-Jui S.
dc.creatorBrooks, Matthew D.
dc.creatorAlvarez, José M.
dc.creatorAraus, Viviana
dc.creatorSwift, Joseph
dc.creatorHenry, Amelia
dc.creatorCoruzzi, Gloria M.
dc.date.accessioned2023-05-02T18:29:12Z
dc.date.accessioned2024-05-02T14:50:16Z
dc.date.available2023-05-02T18:29:12Z
dc.date.available2024-05-02T14:50:16Z
dc.date.created2023-05-02T18:29:12Z
dc.date.issued2022-11-25
dc.identifierFrontiers in Plant Science, Volume 1325, November 2022, Article number 1006044
dc.identifier1664-462X
dc.identifierhttps://repositorio.unab.cl/xmlui/handle/ria/49178
dc.identifier10.3389/fpls.2022.1006044
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9259137
dc.description.abstractNitrogen (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. Copyright © 2022 Shanks, Huang, Cheng, Shih, Brooks, Alvarez, Araus, Swift, Henry and Coruzzi.
dc.languageen
dc.publisherFrontiers Media S.A.
dc.rightshttps://creativecommons.org/licenses/by/4.0/deed.es
dc.rightsAtribución 4.0 Internacional (CC BY 4.0)
dc.subjectDrought
dc.subjectGene regulatory network
dc.subjectGENIE3
dc.subjectNetwork validation
dc.subjectNitrogen
dc.subjectNUE
dc.subjectRice
dc.subjectWGCNA
dc.titleValidation of a high-confidence regulatory network for gene-to-NUE phenotype in field-grown rice
dc.typeArtículo


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