With their diverse biological activities, peptides are promising candidates for therapeutic applications, showing antimicrobial, antitumour and hormonal signalling capabilities. Despite their advantages, therapeutic peptides face challenges such as short half-life, limited oral bioavailability and susceptibility to plasma degradation. The rise of computational tools and artificial intelligence (AI) in peptide research has spurred the development of advanced methodologies and databases that are pivotal in the exploration of these complex macromolecules. This perspective delves into integrating AI in peptide development, encompassing classifier methods, predictive systems and the avant-garde design facilitated by deep-generative models like generative adversarial networks and variational autoencoders. There are still challenges, such as the need for processing optimization and careful validation of predictive models. This work outlines traditional strategies for machine learning model construction and training techniques and proposes a comprehensive AI-assisted peptide design and validation pipeline. The evolving landscape of peptide design using AI is emphasized, showcasing the practicality of these methods in expediting the development and discovery of novel peptides within the context of peptide-based drug discovery.

Jasmonic acid (JA) signaling can be switched off by metabolism of JA. The master regulator MYC2, interacting with MED25, has been shown to be deactivated by the bHLH transcription factors MTB1, MTB2, and MTB3. An autoregulatory negative feedback loop has been proposed for this termination in JA signaling.

Electric signaling and Ca2+ waves were discussed to occur in systemic wound responses. Two new overlapping scenarios were identified: (i) membrane depolarization in two special cell types followed by an increase in systemic cytoplasmic Ca2+ concentration ([Ca2+]cyt), and (ii) glutamate sensed by GLUTAMATE RECEPTOR LIKE proteins and followed by Ca2+-based defense in distal leaves.

For the first time in 25 years, a new pathway for biosynthesis of jasmonic acid (JA) has been identified. JA production takes place via 12-oxo-phytodienoic acid (OPDA) including reduction by OPDA reductases (OPRs). A loss-of-function allele, opr3-3, revealed an OPR3-independent pathway converting OPDA to JA.

Research on mycorrhizal interactions has traditionally developed into separate disciplines addressing different organizational levels. This separation has led to an incomplete understanding of mycorrhizal functioning. Integration of mycorrhiza research at different scales is needed to understand the mechanisms underlying the context dependency of mycorrhizal associations, and to use mycorrhizae for solving environmental issues. Here, we provide a road map for the integration of mycorrhiza research into a unique framework that spans genes to ecosystems. Using two key topics, we identify parallels in mycorrhiza research at different organizational levels. Based on two current projects, we show how scientific integration creates synergies, and discuss future directions. Only by overcoming disciplinary boundaries, we will achieve a more comprehensive understanding of the functioning of mycorrhizal associations.

Plant glandular trichomes are able to secrete and store large amounts of volatile organic compounds (VOCs). VOCs typically accumulate in dedicated extracellular spaces, which can be either subcuticular, as in the Lamiaceae or Asteraceae, or intercellular, as in the Solanaceae. Volatiles are retained at high concentrations in these storage cavities with limited release into the atmosphere and without re-entering the secretory cells, where they would be toxic. This implies the existence of mechanisms allowing transport of VOCs to the cavity but preventing their diffusion out once they have been delivered. The cuticle and cell wall lining the cavity are likely to have key roles in retaining volatiles, but their exact composition and the potential molecular players involved are largely unknown.

Auxin coordinates plant development largely via hierarchical control of gene expression. During the past decades, the study of early auxin genes paired with the power of Arabidopsis genetics have unraveled key nuclear components and molecular interactions that perceive the hormone and activate primary response genes. Recent research in the realm of structural biology allowed unprecedented insight into: (i) the recognition of auxin-responsive DNA elements by auxin transcription factors; (ii) the inactivation of those auxin response factors by early auxin-inducible repressors; and (iii) the activation of target genes by auxin-triggered repressor degradation. The biophysical studies reviewed here provide an impetus for elucidating the molecular determinants of the intricate interactions between core components of the nuclear auxin response module.

The male gametophyte of higher plants appears as a solid box containing the essentials to transmit genetic material to the next generation. These consist of haploid generative cells that are required for reproduction, and an invasive vegetative cell producing the pollen tube, both mechanically protected by a rigid polymer, the pollen wall, and surrounded by a hydrophobic pollen coat. This coat mediates the direct contact to the biotic and abiotic environments. It contains a mixture of compounds required not only for fertilization but also for protection against biotic and abiotic stressors. Among its metabolites, the structural characteristics of two types of phenylpropanoids, hydroxycinnamic acid amides and flavonol glycosides, are highly conserved in Angiosperm pollen. Structural and functional aspects of these compounds will be discussed.

Natural accessions of many species harbor a wealth of genetic variation visible in a large array of phenotypes. Although expression level polymorphisms (ELPs) in several genes have been shown to contribute to variation in diverse traits, their general impact on adaptive variation has likely been underestimated. At present, ELPs have predominantly been correlated to quantitative trait loci (eQTLs) that occupy central hubs in signaling networks, which pleiotropically affect numerous traits. To increase the sensitivity of detecting minor effect eQTLs or those that act in a trait-specific manner, we emphasize the need for more systematic approaches. This requires, but is not limited to, refining experimental designs such as reduction of tissue complexity and combinatorial methods including a priori defined networks.

MAPK signal transduction modules play crucial roles in regulating many biological processes in plants, and their components are encoded by highly conserved genes. The recent availability of genome sequences for rice and poplar now makes it possible to examine how well the previously described Arabidopsis MAPK and MAPKK gene family structures represent the broader evolutionary situation in plants, and analysis of gene expression data for MPK and MKK genes in all three species allows further refinement of those families, based on functionality. The Arabidopsis MAPK nomenclature appears sufficiently robust to allow it to be usefully extended to other well-characterized plant systems.