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Nitrogen is an essential macronutrient for plant growth and basic metabolic processes. The application of nitrogen-containing fertilizer increases yield, which has been a substantial factor in the green revolution 1 . Ecologically, however, excessive application of fertilizer has disastrous effects such as eutrophication 2 . A better understanding of how plants regulate nitrogen metabolism is critical to increase plant yield and reduce fertilizer overuse. Here we present a transcriptional regulatory network and twenty-one transcription factors that regulate the architecture of root and shoot systems in response to changes in nitrogen availability. Genetic perturbation of a subset of these transcription factors revealed coordinate transcriptional regulation of enzymes involved in nitrogen metabolism. Transcriptional regulators in the network are transcriptionally modified by feedback via genetic perturbation of nitrogen metabolism. The network, genes and gene-regulatory modules identified here will prove critical to increasing agricultural productivity. The yeast one-hybrid network for nitrogen-associated metabolism in Arabidopsis reveals the transcription factors that regulate the architecture of root and shoot systems under conditions of changing nitrogen availability.

Background The discovery of genetic networks and cis -acting DNA motifs underlying their regulation is a major objective of transcriptome studies. The recent release of the maize genome ( Zea mays L.) has facilitated in silico searches for regulatory motifs. Several algorithms exist to predict cis -acting elements, but none have been adapted for maize. Results A benchmark data set was used to evaluate the accuracy of three motif discovery programs: BioProspector, Weeder and MEME. Analysis showed that each motif discovery tool had limited accuracy and appeared to retrieve a distinct set of motifs. Therefore, using the benchmark, statistical filters were optimized to reduce the false discovery ratio, and then remaining motifs from all programs were combined to improve motif prediction. These principles were integrated into a user-friendly pipeline for motif discovery in maize called Promzea, available at http://www.promzea.org and on the Discovery Environment of the iPlant Collaborative website. Promzea was subsequently expanded to include rice and Arabidopsis. Within Promzea, a user enters cDNA sequences or gene IDs; corresponding upstream sequences are retrieved from the maize genome. Predicted motifs are filtered, combined and ranked. Promzea searches the chosen plant genome for genes containing each candidate motif, providing the user with the gene list and corresponding gene annotations. Promzea was validated in silico using a benchmark data set: the Promzea pipeline showed a 22% increase in nucleotide sensitivity compared to the best standalone program tool, Weeder, with equivalent nucleotide specificity. Promzea was also validated by its ability to retrieve the experimentally defined binding sites of transcription factors that regulate the maize anthocyanin and phlobaphene biosynthetic pathways. Promzea predicted additional promoter motifs, and genome-wide motif searches by Promzea identified 127 non-anthocyanin/phlobaphene genes that each contained all five predicted promoter motifs in their promoters, perhaps uncovering a broader co-regulated gene network. Promzea was also tested against tissue-specific microarray data from maize. Conclusions An online tool customized for promoter motif discovery in plants has been generated called Promzea. Promzea was validated in silico by its ability to retrieve benchmark motifs and experimentally defined motifs and was tested using tissue-specific microarray data. Promzea predicted broader networks of gene regulation associated with the historic anthocyanin and phlobaphene biosynthetic pathways. Promzea is a new bioinformatics tool for understanding transcriptional gene regulation in maize and has been expanded to include rice and Arabidopsis.

The challenge of increasing crop yield while decreasing plants' susceptibility to various stresses can be lessened by understanding plant regulatory processes in a tissue-specific manner. Molecular network analysis techniques were developed to aid in understanding gene inter-regulation. However, few tools for molecular network mining are designed to extract the most relevant genes to act upon. In order to find and to rank these putative regulator genes, we generated NECorr, a computational pipeline based on multiple-criteria decision-making algorithms. With the objective of ranking genes and their interactions in a selected condition or tissue, NECorr uses the molecular network topology as well as global gene expression analysis to find hub genes and their condition-specific regulators. NECorr was applied to Arabidopsis thaliana flower tissue and identifies known regulators in the developmental processes of this tissue as well as new putative regulators. NECorr will accelerate translational research by ranking candidate genes within a molecular network of interest.

Annotating genes is essential to crop development and understanding gene functions sheds light on developing crop improvement strategies, such as marker-assisted breeding, genetic modification, or pest resistance. Through an extensive experimental effort and computational annotation projection, tens of thousands of genes have been annotated across plant species, with most of the gene annotations focusing on a well-studied species, Arabidopsis thaliana, but this represents a small fraction of the hundreds of thousands of genes across these different plant species. Phenotypes and their traits result from multiple processes and events involving multiscale information encoded from different omics, such as genomes, proteomes, or transcriptomes. This stresses a need for an efficient computational approach to capture and integrate information from biological networks and transfer this knowledge from well-studied species to unknown species to annotate and discover functional relationships between phenotypes and genes. Despite recent progress, existing methods only consider one or a few omics levels to perform reasoning on functional annotation-to-gene relations. The main objective of this study is to generate and explore a large-scale plant biological knowledge graph, the DasDB, and to enrich gene functional annotation linked to genes in different species using graph neural networks (GNNs). Integrating various data sources from different omics has resulted in a comprehensive graph database, facilitating researchers' in-depth understanding of complex biological networks at the highest level. In addition, applying GNNs on a large-scale knowledge graph database has shown promise in the ability of deep learning models to transfer this information from well-studied plant species to less-characterized plant species. This study benchmarks a new research direction in producing new functional annotation discovery in plant species with limited functional annotations. This pipeline was applied to a specific research problem: the mechanism involved in pea nodule nitrogen fixation. We managed to identify known gene markers of this process through a systematic analysis of the DasDB, showing the relevance of our approach. Furthermore, new potential targets to better understand and improve this process were identified.