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Extracellular ATP (eATP) signaling inArabidopsis thalianais mediated by the purinoceptor P2K1. Previous studies have clarified that the downstream transcriptional responses to eATP involve jasmonate (JA)-based signaling components such as the JA receptor (COI1) and JA-responsive bHLH transcription factors (MYCs). However, the specific contributions of JA signaling itself on eATP signaling are unexplored. Here, we report that JA primes plant responses to eATP through P2K1. Our findings show that JA treatment significantly upregulatesP2K1transcription, corroborating our observation that JA facilitates eATP-induced cytosolic calcium elevation and transcriptional reprogramming in a JA signaling-dependent manner. Additionally, we find that salicylic acid pretreatment represses eATP-induced plant response. These results suggest that JA accumulation during biotic or abiotic stresses may potentiate eATP signaling, enabling plants to better cope with subsequent stress events. Plant hormone jasmonate (JA) enhances plant responses to extracellular ATP (eATP) inArabidopsis thalianathrough a mechanism dependent on the JA receptor COI1 and the eATP receptor P2K1. The reciprocal amplification of these signals provides a mechanistic explanation for how plants effectively respond to different stress events. 

This beginner’s guide is intended for plant biologists new to network analysis. Here, we introduce key concepts and resources for researchers interested in incorporating network analysis into research, either as a stand-alone component for generating hypotheses or as a framework for examining and visualizing experimental results. Network analysis provides a powerful tool to predict gene functions. Advances in and reduced costs for systems biology techniques, such as genomics, transcriptomics, and proteomics, have generated abundant -omics data for plants; however, the functional annotation of plant genes lags. Therefore, predictions from network analysis can be a starting point to annotate genes and ultimately elucidate genotype-phenotype relationships. In this paper, we introduce networks and compare network-building resources available for plant biologists, including databases and software for network analysis. We then compare four databases available for plant biologists in more detail: AraNet, GeneMANIA, ATTED-II, and STRING. AraNet, and GeneMANIA are functional association networks, ATTED-II is a gene coexpression database, and STRING is a protein-protein interaction database. AraNet, and ATTED-II are plant-specific databases that can analyze multiple plant species, whereas GeneMANIA builds networks for Arabidopsis thaliana and non-plant species, and STRING for multiple species. Finally, we compare the performance of the four databases in predicting known and probable gene functions of the A. thaliana Nuclear Factor-Y (NF-Y) genes. We conclude that plant biologists have an invaluable resource in these databases and discuss how users can decide which type of database to use depending on their research question.