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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. 

Inferring phenotypic outcomes from genomic features is both a promise and challenge for systems biology. Using gene expression data to predict phenotypic outcomes, and functionally validating the genes with predictive powers are two challenges we address in this study. We applied an evolutionarily informed machine learning approach to predict phenotypes based on transcriptome responses shared both within and across species. Specifically, we exploited the phenotypic diversity in nitrogen use efficiency and evolutionarily conserved transcriptome responses to nitrogen treatments across Arabidopsis accessions and maize varieties. We demonstrate that using evolutionarily conserved nitrogen responsive genes is a biologically principled approach to reduce the feature dimensionality in machine learning that ultimately improved the predictive power of our gene-to-trait models. Further, we functionally validated seven candidate transcription factors with predictive power for NUE outcomes in Arabidopsis and one in maize. Moreover, application of our evolutionarily informed pipeline to other species including rice and mice models underscores its potential to uncover genes affecting any physiological or clinical traits of interest across biology, agriculture, or medicine.

 

The phytohormone cytokinin influences many aspects of plant growth and development, several of which also involve the cellular process of autophagy, including leaf senescence, nutrient remobilization, and developmental transitions. TheArabidopsistype-A response regulators (type-A ARR) are negative regulators of cytokinin signaling that are transcriptionally induced in response to cytokinin. Here, we describe a mechanistic link between cytokinin signaling and autophagy, demonstrating that plants modulate cytokinin sensitivity through autophagic regulation of type-A ARR proteins. Type-A ARR proteins were degraded by autophagy in an AUTOPHAGY-RELATED (ATG)5-dependent manner, and this degradation is promoted by phosphorylation on a conserved aspartate in the receiver domain of the type-A ARRs. EXO70D family members interacted with type-A ARR proteins, likely in a phosphorylation-dependent manner, and recruited them to autophagosomes via interaction of the EXO70D AIM with the core autophagy protein, ATG8. Consistently, loss-of-functionexo70D1,2,3mutants exhibited compromised targeting of type-A ARRs to autophagic vesicles, have elevated levels of type-A ARR proteins, and are hyposensitive to cytokinin. Disruption of both type-AARRsandEXO70D1,2,3compromised survival in carbon-deficient conditions, suggesting interaction between autophagy and cytokinin responsiveness in response to stress. These results indicate that the EXO70D proteins act as selective autophagy receptors to target type-A ARR cargos for autophagic degradation, demonstrating modulation of cytokinin signaling by selective autophagy.

 

The flowering plantArabidopsis thalianais a dicot model organism for research in many aspects of plant biology. A comprehensive annotation of its genome paves the way for understanding the functions and activities of all types of transcripts, includingmRNA, the various classes of non‐codingRNA, and smallRNA. TheTAIR10 annotation update had a profound impact on Arabidopsis research but was released more than 5 years ago. Maintaining the accuracy of the annotation continues to be a prerequisite for future progress. Using an integrative annotation pipeline, we assembled tissue‐specificRNA‐Seq libraries from 113 datasets and constructed 48 359 transcript models of protein‐coding genes in eleven tissues. In addition, we annotated various classes of non‐codingRNAincluding microRNA, long intergenicRNA, small nucleolarRNA, natural antisense transcript, small nuclearRNA, and smallRNAusing published datasets and in‐house analytic results. Altogether, we identified 635 novel protein‐coding genes, 508 novel transcribed regions, 5178 non‐codingRNAs, and 35 846 smallRNAloci that were formerly unannotated. Analysis of the splicing events andRNA‐Seq based expression profiles revealed the landscapes of gene structures, untranslated regions, and splicing activities to be more intricate than previously appreciated. Furthermore, we present 692 uniformly expressed housekeeping genes, 43% of whose human orthologs are also housekeeping genes. This updated Arabidopsis genome annotation with a substantially increased resolution of gene models will not only further our understanding of the biological processes of this plant model but also of other species.