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Sulfur (S) is an essential macronutrient for plants and its availability in soils is an important determinant for growth and development. Current regulatory policies aimed at reducing industrial S emissions together with changes in agronomical practices have led to a decline in S contents in soils worldwide. Deficiency of sulfate-the primary form of S accessible to plants in soil-has adverse effects on both crop yield and nutritional quality. Hence, recent research has increasingly focused on unraveling the molecular mechanisms through which plants detect and adapt to a limiting supply of sulfate. A significant part of these studies involves the use of omics technologies and has generated comprehensive catalogs of sulfate deficiency-responsive genes and processes, principally in Arabidopsis together with a few studies centering on crop species such as wheat, rice, or members of the Brassica genus. Although we know that sulfate deficiency elicits an important reprogramming of the transcriptome, the transcriptional regulators orchestrating this response are not yet well understood. In this review, we summarize our current knowledge of gene expression responses to sulfate deficiency and recent efforts towards the identification of the transcription factors that are involved in controlling these responses. We further compare the transcriptional response and putative regulators between Arabidopsis and two important crop species, rice and tomato, to gain insights into common mechanisms of the response to sulfate deficiency., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Summary: GENIUS is a user-friendly web server that uses a novel machine learning algorithm to infer functional gene networks focused on specific genes and experimental conditions that are relevant to biological functions of interest. These functions may have different levels of complexity, from specific biological processes to complex traits that involve several interacting processes. GENIUS also enriches the network with new genes related to the biological function of interest, with accuracies comparable to highly discriminative Support Vector Machine methods., Availability and Implementation: GENIUS currently supports eight model organisms and is freely available for public use at http://networks.bio.puc.cl/genius ., Contact: genius.psbl@gmail.com., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2016. Published by Oxford University Press.)

Nitrogen is an essential macronutrient for plants and its availability is a key determinant of plant growth and development and crop yield. Besides their nutritional role, N nutrients and metabolites are signals that activate signaling pathways that modulate many plant processes. Because the most abundant inorganic N source for plants in agronomic soils is nitrate, much of the work to understand plant N-signaling has focused on this nutrient. Over the last years, several studies defined a comprehensive catalog of nitrate-responsive genes, involved in nitrate transport, metabolism and a variety of other processes. Despite significant progress in recent years, primarily using Arabidopsis thaliana as a model system, the molecular mechanisms by which nitrate elicits changes in transcript abundance are still not fully understood. Here we highlight recent advancements in identifying key transcription factors and transcriptional mechanisms that orchestrate the gene expression response to changes in nitrate availability in A. thaliana., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Technological advances in the last decade have enabled biologists to produce increasing amounts of information for the transcriptome, proteome, interactome, and other -omics data sets in many model organisms. A major challenge is integration and biological interpretation of these massive data sets in order to generate testable hypotheses about gene regulatory networks or molecular mechanisms that govern system behaviors. Constructing gene networks requires bioinformatics skills to adequately manage, integrate, analyze and productively use the data to generate biological insights. In this chapter, we provide detailed methods for users without prior knowledge of bioinformatics to construct gene networks and derive hypotheses that can be experimentally verified. Step-by-step instructions for acquiring, integrating, analyzing, and visualizing genome-wide data are provided for two widely used open source platforms, R and Cytoscape platforms. The examples provided are based on Arabidopsis data, but the protocols presented should be readily applicable to any organism for which similar data can be obtained.

Nitrogen (N) is an essential macronutrient for plants. In nature, N cycles between different inorganic and organic forms some of which can serve as nutrients for plants. The inorganic N forms nitrate and ammonium are the most important sources of N for plants. However, plants can also uptake and use organic N forms such as amino acids and urea. Besides their nutritional role, nitrate and other forms of N can also act as signals that regulate the expression of hundreds of genes causing modulation of plant metabolism, physiology, growth, and development. Although many genes and processes affected by changes in external or internal N have been identified, the molecular mechanisms involved in N sensing and signaling are still poorly understood. Classic reverse and forward genetics and more recently the advent of genomic and systems approaches have helped to characterize some of the components of the signaling pathways directing Arabidopsis responses to N. Here, we provide an update on recent advances to identify the components involved in N sensing and signaling in Arabidopsis and their importance for the plant response to N., (© 2010 John Wiley & Sons, Inc.)