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Summary Plants activate induced defenses through the recognition of molecular patterns. Like pathogen-associated molecular patterns (PAMPs), herbivore-associated molecular patterns (HAMPs) can be recognized by cell surface pattern recognition receptors leading to defensive transcriptional changes in host plants. Herbivore-induced defensive outputs are regulated by the circadian clock, but the underlying molecular mechanisms remain unknown. To investigate how the plant circadian clock regulates transcriptional reprogramming of a specific HAMP-induced pathway, we characterized the daytime and nighttime transcriptional response to caterpillar-derived In11 peptide, in the legume crop cowpea (Vigna unguiculata). Using diurnal and free-running conditions, we found that daytime In11 elicitation resulted in stronger late-induced gene expression than nighttime. Plants with a conditional arrhythmic phenotype in constant light (LL) conditions lost time-of-day dependent responses to In11 treatment, and this was associated with arrhythmic expression of circadian clock core transcription factorLate Elongated Hypocotyl VuLHY1andVuLHY2. Reporter assays with VuLHY homologs indicated that they interact with the promoter of daytime In11-inducedKunitz Trypsin Inhibitor(VuKTI) via a canonical and a polymorphic CCA1/LHY Binding Site (CBS), consistent with a mechanism of direct regulation by circadian clock transcription factors. This study improves our understanding of the time-dependent mechanisms that regulate herbivore-induced gene expression. 

Plants and animals detect biomolecules termed microbe-associated molecular patterns (MAMPs) and induce immunity. Agricultural production is severely impacted by pathogens which can be controlled by transferring immune receptors. However, most studies use a single MAMP epitope and the impact of diverse multicopy MAMPs on immune induction is unknown. Here, we characterized the epitope landscape from five proteinaceous MAMPs across 4,228 plant-associated bacterial genomes. Despite the diversity sampled, natural variation was constrained and experimentally testable. Immune perception in bothArabidopsisand tomato depended on both epitope sequence and copy number variation. For example, Elongation Factor Tu is predominantly single copy, and 92% of its epitopes are immunogenic. Conversely, 99.9% of bacterial genomes contain multiple cold shock proteins, and 46% carry a nonimmunogenic form. We uncovered a mechanism for immune evasion, intrabacterial antagonism, where a nonimmunogenic cold shock protein blocks perception of immunogenic forms encoded in the same genome. These data will lay the foundation for immune receptor deployment and engineering based on natural variation. 

Plants rely on innate immune systems to defend against a wide variety of biotic attackers. Key components of innate immunity include cell-surface pattern-recognition receptors (PRRs), which recognize pest- and pathogen-associated molecular patterns (PAMPs). Unlike other classes of receptors that often have visible cell-death immune outputs upon activation, PRRs generally lack rapid methods for assessing function. Here, we describe a genetically encoded bioluminescent reporter of immune activation by heterologously expressed PRRs in the model organism Nicotiana benthamiana. We characterized N. benthamiana transcriptome changes in response to Agrobacterium tumefaciens and subsequent PAMP treatment to identify pattern-triggered immunity (PTI)-associated marker genes, which were then used to generate promoter-luciferase fusion fungal bioluminescence pathway (FBP) constructs. A reporter construct termed pFBP_2xNbLYS1::LUZ allows for robust detection of PTI activation by heterologously expressed PRRs. Consistent with known PTI signaling pathways, reporter activation by receptor-like protein (RLP) PRRs is dependent on the known adaptor of RLP PRRs, i.e., SOBIR1. The FBP reporter minimizes the amount of labor, reagents, and time needed to assay function of PRRs and displays robust sensitivity at biologically relevant PAMP concentrations, making it ideal for high throughput screens. The tools described in this paper will be powerful for investigations of PRR function and characterization of the structure-function of plant cell-surface receptors. [Formula: see text] The author(s) have dedicated the work to the public domain under the Creative Commons CC0 “No Rights Reserved” license by waiving all of his or her rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law, 2023. 

Synopsis Many biological systems across scales of size and complexity exhibit a time-varying complex network structure that emerges and self-organizes as a result of interactions with the environment. Network interactions optimize some intrinsic cost functions that are unknown and involve for example energy efficiency, robustness, resilience, and frailty. A wide range of networks exist in biology, from gene regulatory networks important for organismal development, protein interaction networks that govern physiology and metabolism, and neural networks that store and convey information to networks of microbes that form microbiomes within hosts, animal contact networks that underlie social systems, and networks of populations on the landscape connected by migration. Increasing availability of extensive (big) data is amplifying our ability to quantify biological networks. Similarly, theoretical methods that describe network structure and dynamics are being developed. Beyond static networks representing snapshots of biological systems, collections of longitudinal data series can help either at defining and characterizing network dynamics over time or analyzing the dynamics constrained to networked architectures. Moreover, due to interactions with the environment and other biological systems, a biological network may not be fully observable. Also, subnetworks may emerge and disappear as a result of the need for the biological system to cope with for example invaders or new information flows. The confluence of these developments renders tractable the question of how the structure of biological networks predicts and controls network dynamics. In particular, there may be structural features that result in homeostatic networks with specific higher-order statistics (e.g., multifractal spectrum), which maintain stability over time through robustness and/or resilience to perturbation. Alternative, plastic networks may respond to perturbation by (adaptive to catastrophic) shifts in structure. Here, we explore the opportunity for discovering universal laws connecting the structure of biological networks with their function, positioning them on the spectrum of time-evolving network structure, i.e. dynamics of networks, from highly stable to exquisitely sensitive to perturbation. If such general laws exist, they could transform our ability to predict the response of biological systems to perturbations—an increasingly urgent priority in the face of anthropogenic changes to the environment that affect life across the gamut of organizational scales.