Jasmonates (JAs) are a family of oxylipin phytohormones regulating plant development and growth and mediating ‘defense versus growth’ responses. The upstream JA biosynthetic precursor cis-(+)-12-oxo-phytodienoic acid (cis-OPDA) acts independently of CORONATIVE INSENSITIVE 1 (COI1)-mediated JA signaling in several stress-induced and developmental processes. However, its perception and metabolism are only partially understood. A few years ago, a low abundant isoleucine analog of the biologically active JA-Ile, OPDA-Ile, was detected years ago in wounded leaves of flowering plants, opening up the possibility that conjugation of cis-OPDA to amino acids might be a relevant mechanism for cis-OPDA regulation. Here, we extended the analysis of amino acid conjugates of cis-OPDA and identified naturally occurring OPDA-Val, OPDA-Phe, OPDA-Ala, OPDA-Glu, and OPDA-Asp accumulating in response to biotic and abiotic stress in Arabidopsis (Arabidopsis thaliana). The OPDA-amino acid conjugates displayed cis-OPDA-related plant responses in a JA-Ile-dependent manner. We also showed that the synthesis and hydrolysis of cis-OPDA amino acid conjugates are mediated by members of the amidosynthetase GRETCHEN HAGEN 3 (GH3) and the amidohydrolase INDOLE-3-ACETYL-LEUCINE RESISTANT 1 (ILR1)/ILR1-like (ILL) families. Thus, OPDA amino acid conjugates function in the catabolism or temporary storage of cis-OPDA in stress responses instead of acting as chemical signals per se.

SUMMARYWHIRLY1 belongs to a family of plant‐specific transcription factors capable of binding DNA or RNA in all three plant cell compartments that contain genetic materials. In Arabidopsis thaliana, WHIRLY1 has been studied at the later stages of plant development, including flowering and leaf senescence, as well as in biotic and abiotic stress responses. In this study, WHIRLY1 knockout mutants of A. thaliana were prepared by CRISPR/Cas9‐mediated genome editing to investigate the role of WHIRLY1 during early seedling development. The loss‐of‐function of WHIRLY1 in 5‐day‐old seedlings did not cause differences in the phenotype and the photosynthetic performance of the emerging cotyledons compared with the wild type. Nevertheless, comparative RNA sequencing analysis revealed that the knockout of WHIRLY1 affected the expression of a small but specific set of genes during this critical phase of development. About 110 genes were found to be significantly deregulated in the knockout mutant, wherein several genes involved in the early steps of aliphatic glucosinolate (GSL) biosynthesis were suppressed compared with wild‐type plants. The downregulation of these genes in WHIRLY1 knockout lines led to decreased GSL contents in seedlings and in seeds. Since GSL catabolism mediated by myrosinases was not altered during seed‐to‐seedling transition, the results suggest that AtWHIRLY1 plays a major role in modulation of aliphatic GSL biosynthesis during early seedling development. In addition, phylogenetic analysis revealed a coincidence between the evolution of methionine‐derived aliphatic GSLs and the addition of a new WHIRLY in core families of the plant order Brassicales.

Protein engineering through directed evolution and (semi)rational design has become a powerful approach for optimizing and enhancing proteins with desired properties. The integration of artificial intelligence methods has further accelerated protein engineering process by enabling the development of predictive models based on datadriven strategies. However, the lack of interpretability and transparency in these models limits their trustworthiness and applicability in real-world scenarios. Explainable Artificial Intelligence addresses these challenges by providing insights into the decision-making processes of machine learning models, enhancing their reliability and interpretability. Explainable strategies has been successfully applied in various biotechnology fields, including drug discovery, genomics, and medicine, yet its application in protein engineering remains underexplored. The incorporation of explainable strategies in protein engineering holds significant potential, as it can guide protein design by revealing how predictive models function, benefiting approaches such as machine learning-assisted directed evolution. This perspective work explores the principles and methodologies of explainable artificial intelligence, highlighting its relevance in biotechnology and its potential to enhance protein design. Additionally, three theoretical pipelines integrating predictive models with explainable strategies are proposed, focusing on their advantages, disadvantages, and technical requirements. Finally, the remaining challenges of explainable artificial intelligence in protein engineering and future directions for its development as a support tool for traditional protein engineering methodologies are discussed.

Results of scientific work in chemistry can usually be obtained in the form of materials and data. A big step towards transparency and reproducibility of the scientific work can be gained if scientists publish their data in research data repositories in a FAIR manner. Nevertheless, in order to make chemistry a sustainable discipline, obtaining FAIR data is insufficient and a comprehensive concept that includes preservation of materials is needed. In order to offer a comprehensive infrastructure to find and access data and materials that were generated in chemistry projects, we combined the infrastructure Chemotion repository with an archive for chemical compounds. Samples play a key role in this concept: we describe how FAIR metadata of a virtual sample representation can be used to refer to a physically available sample in a materials’ archive and to link it with the FAIR research data gained using the said sample. We further describe the measures to make the physically available samples not only FAIR through their metadata but also findable, accessible and reusable.

Hornstedtia scyphifera (J.Koenig) Steud. represents a lesser-known member of the ginger family (Zingiberaceae) that is used in Malaysia as spice and traditional medicine. The phytochemical investigation of leaves from this species utilizing diverse analytical methods has provided comprehensive insights into its chemical profile for the first time. Headspace gas chromatography-mass spectrometry (HS-GCMS) and GCMS analyses of essential oil and nonpolar extracts verified α-pinene, camphene, p-cymene, and camphor as main volatile compounds. Metabolite profiling of the crude extract by ultra-high-performance-liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) unveiled terpenoids, flavonoids and other phenolics as major compound classes. Isolation and follow-up structure elucidation, involving 1D and 2D NMR, HRMS, UV and CD analysis, yielded two new sesquiterpenoids, (1R,5S,6S,7R,10R)-mustak-14-oic acid (1) and (1R,6S,7S,10R)-6-hydroxy-anhuienosol (2), along with 24 known compounds (seven terpenoids, seven flavonoids, ten phenolics), 21 of these never reported for H. scyphifera. Additionally, the crude extract and fractions from the purification process were screened for antibacterial and antifungal activity. This is supplemented by an extensive literature research for described bioactivities of all isolated compounds. Our results support and explain previously detected antimicrobial, antifungal and neuroprotective effects of H. scyphifera extracts and provide evidence for its potential pharmacological importance.

Hyaloperonospora arabidopsidis (Hpa) is an oomycete pathogen that causes downy mildew disease on Arabidopsis. This obligate biotroph manipulates the homeostasis of its host plant by secreting numerous effector proteins, among which are the RxLR effectors. Identifying the host targets of effectors and understanding how their manipulation facilitates colonization of plants are key to improve plant resistance to pathogens. Here we characterize the interaction between the RxLR effector HaRxL106 and BIM1, an Arabidopsis transcription factor (TF) involved in Brassinosteroid (BR) signaling. We report that HaRxL106 interacts with BIM1 in vitro and in planta. BIM1 is required by the effector to increase the host plant susceptibility to (hemi)biotrophic pathogens, and thus can be regarded as a susceptibility factor. Mechanistically, HaRxL106 requires BIM1 to induce the transcriptional activation of BR‐responsive genes and cause alterations in plant growth patterns that phenocopy the shade avoidance syndrome. Our results support previous observations of antagonistic interactions between activation of BR signaling and suppression of plant immune responses and reveal that BIM1, a new player in this crosstalk, is manipulated by the pathogenic effector HaRxL106.

Modular cloning systems that rely on type IIS enzymes for DNA assembly have many advantages for construct engineering for biological research and synthetic biology. These systems are simple to use, efficient, and allow users to assemble multigene constructs by performing a series of one-pot assembly steps, starting from libraries of cloned and sequenced parts. The efficiency of these systems also facilitates the generation of libraries of construct variants. We describe here a protocol for assembly of multigene constructs using the modular cloning system MoClo. Making constructs using the MoClo system requires to first define the structure of the final construct to identify all basic parts and vectors required for the construction strategy. The assembly strategy is then defined following a set of standard rules. Multigene constructs are then assembled using a series of one-pot assembly steps with the set of identified parts and vectors.

Efficient DNA assembly methods are an essential prerequisite in the field of synthetic biology. Modular cloning systems, which rely on Golden Gate cloning for DNA assembly, are designed to facilitate assembly of multigene constructs from libraries of standard parts through a series of streamlined one-pot assembly reactions. Standard parts consist of the DNA sequence of a genetic element of interest such as a promoter, coding sequence, or terminator, cloned in a plasmid vector. Standard parts for the modular cloning system MoClo, also called level 0 modules, must be flanked by two BsaI restriction sites in opposite orientations and should not contain internal sequences for two type IIS restriction sites, BsaI and BpiI, and optionally for a third type IIS enzyme, BsmBI. We provide here a detailed protocol for cloning of level 0 modules. This protocol requires the following steps: (1) defining the type of part that needs to be cloned, (2) designing primers for amplification, (3) performing polymerase chain reaction (PCR) amplification, (4) cloning of the fragments using Golden Gate cloning, and finally (5) sequencing of the part. For large standard parts, it is preferable to first clone sub-parts as intermediate level-1 constructs. These sub-parts are sequenced individually and are then further assembled to make the final level 0 module.

Plant cells experience a variety of mechanical stresses from both internal and external sources, including turgor pressure, mechanical strains arising from heterogeneous growth between neighboring cells, and environmental factors like touch from soil, rain, or wind [1,2]. These stresses serve as signals at the cell-, tissue- and organismal level to coordinate plant growth during development and stress responses [3]. In plants, the physical cell wall-plasma membrane-microtubule continuum is proposed to be integral in transducing mechanical signals from the exterior to intracellular components [4–6]. Cortical microtubules (CMTs) rapidly reorient in response to mechanical stress to align with the maximal tensile stress direction [7,8]. Several studies proposed that CMTs themselves may act as stress sensors; the precise mechanisms involved in the regulation of CMTs and the modes of sensing, however, are still not clearly understood. Here, we show that IQD2 and KLCR1 are enriched at CMTs in proximity to the plasma membrane. IQD2, which is a bona fide microtubule-associated protein, promotes microtubule localization of KLCR1. By combining cross-linking mass spectrometry (XL-MS) and computational modeling with structure-function studies, we present first experimental insights into the composition and structure of IQD2-KLCR1 complexes. Further, we demonstrate that the IQD2-KLCR1 module is a positive regulator of microtubule mechano-responses in pavement cells. Collectively, our work identifies the IQD2-KLCR1 module as novel regulator of mechanostress-mediated CMT reorientation and provides a framework for future mechanistic studies aimed at a functional dissection of mechanotransduction at the plasma membrane-CMT interface during growth and plant morphogenesis.HighlightsIQD2 and KLCR1 localize to the plasma membrane-microtubule nexusIQD2 is required for efficient microtubule targeting of KLCR1in plantaIQD2 physically interacts with KLCR1 and microtubulesThe IQD2-KLCR1 module promotes mechano-stress induced microtubule reorganization

A bottleneck in the development of new anti-cancer drugs is the recognition of their mode of action (MoA). We combined metabolomics and machine learning to predict MoAs of novel anti-proliferative drug candidates, focusing on human prostate cancer cells (PC-3). As proof of concept, we studied 38 drugs with known effects on 16 key processes of cancer metabolism, profiling low molecular weight intermediates of the central carbon and cellular energy metabolism (CCEM) by LC-MS/MS. These metabolic patterns unveiled distinct MoAs, enabling accurate MoA predictions for novel agents by machine learning. We validate the transferability of MoA predictions from PC-3 to two other cancer cell models and show that correct predictions are still possible, but at the expense of prediction quality. Furthermore, metabolic profiles of treated cells yield insights into intracellular processes, exemplified for drugs inducing different types of mitochondrial dysfunction. Specifically, we predict that pentacyclic triterpenes inhibit oxidative phosphorylation and affect phospholipid biosynthesis, as supported by respiration parameters, lipidomics, and molecular docking. Using biochemical insights from individual drug treatments, our approach offers new opportunities, including the optimization of combinatorial drug applications.