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Cowpea [Vigna unguiculata (L.) Walp.] is one of the most tolerant legume crops to drought and salt stresses. WRKY transcription factor (TF) family members stand out among plant transcriptional regulators related to abiotic stress tolerance. However, little information is currently available on the expression of the cowpea WRKY gene family (VuWRKY) in response to water deficit. Thus, we analyzed genomic and transcriptomic data from cowpea to identify VuWRKY members and characterize their structure and transcriptional response under root dehydration stress. Ninety-two complete VuWRKY genes were found in the cowpea genome based on their domain characteristics. They were clustered into three groups: I (15 members), II (58), and III (16), while three genes were unclassified. Domain analysis of the encoded proteins identified four major variants of the conserved heptapeptide motif WRKYGQK. In silico analysis of VuWRKY gene promoters identified eight candidate binding motifs of cis-regulatory elements, regulated mainly by six TF families associated with abiotic stress responses. Ninety-seven VuWRKY modulated splicing variants associated with 55 VuWRKY genes were identified via RNA-Seq analysis available at the Cowpea Genomics Consortium (CpGC) database. qPCR analyses showed that 22 genes are induced under root dehydration, with VuWRKY18, 21, and 75 exhibiting the most significant induction levels. Given their central role in activating signal transduction cascades in abiotic stress response, the data provide a foundation for the targeted modification of specific VuWRKY family members to improve drought tolerance in this important climate-resilient legume in the developing world and beyond.

Almost 48 years after the gene-for-gene interaction model was proposed by Flor, NBS-LRR resistance (R) genes are still the focus of intense research interest. Also called NB-LRR, NB-ARC, NLR (Nucleotide Binding Domain and Leucine-rich Repeat), this group regards the largest R gene class, accounting for more than half of the plant R genes, with an impressing number and structural diversity, especially in angiosperms. With the omics advent, especially genomics and transcriptomics, an emergence of the so-called NLRomics came out, allowing the annotation and comparative analysis of NBS-LRR members at functional and structural level on a previously unfeasible scale. The present chapter provides a retrospective of studies involving NBS-LRR genes and proteins, including information on mechanisms of action, regulation, patterns of expression, subcellular localization, origin, diversification, and evolution in plants. Aspects of plant-pathogen coevolution are discussed, as well as resistance-breaking mechanisms reported in the literature. Main databases compiling NBS-LRR genes are discussed, and a routine for their annotation and structural analysis is presented. Practical implications of NBS-LRR molecules are considered, together with aspects about existing limitations and their potential biotechnological applications.

Drought stress induces the expression of a significant number of transcription factor (TF) genes that are involved in stress tolerance and response. Nowadays, novel “omics” and next-generation sequencing provide a global picture of drought-stress responses and a comprehensive understanding of TF regulatory networks that modulate this response. Several TFs have been shown to be involved in the regulation of stress-responsive genes under drought condition, including Arabidopsis thaliana, rice (Oryza sativa, Poaceae), soybean (Glycine max, Fabaceae), and other plants. Most data have been obtained from transcriptome analyses, studies using small RNA molecules, chromatin modulation, and genomic DNA modifications. Based on recent advances in genomic technologies, improvements in crop drought-stress tolerance have been attained from marker-assisted breeding and gene transfer. In this chapter, we summarize recent progress on the role of TFs in response to drought in plants. Studies on crops have shown that facing drought requires ET and ABA signaling pathways through selectively interacting with TFs, once again highlighting a close relationship between different signaling pathways referred to in this chapter.