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.

Two connected histopathological hallmarks of Alzheimer’s disease (AD) are chronic neuroinflammation and synaptic dysfunction. The accumulation of the most prevalent posttranslationally modified form of Aβ1–42, pyroglutamylated amyloid-β (Aβ3(pE)-42) in astrocytes is directly linked to glial activation and the release of proinflammatory cytokines that in turn contribute to early synaptic dysfunction in AD. At present the mechanisms of Aβ3(pE)-42 uptake to astrocytes are unknown and pharmacological interventions that interfere with this process are not available. Here we developed a simple screening assay to identify substances from a plant extract library that prevent astroglial Aβ3(pE)-42 uptake. We first show that this approach yields valid and reproducible results. Second, we show endocytosis of Aβ3(pE)-42 oligomers by astrocytes and that quercetin, a plant flavonol, is effective to specifically block astrocytic buildup of oligomeric Aβ3(pE)-42. Importantly, quercetin does not induce a general impairment of endocytosis. However, it efficiently protects against early synaptic dysfunction following exogenous Aβ3(pE)-42 application.

Introduction: Climate change poses various threats to marine life, particularly in shallow tropical waters. Objective: The impact of increased temperature and ultraviolet (UV) exposure on two photosymbiotic cnidarians, a common bubble-tip anemone and an upside-down jellyfish, was investigated. Methods: To illustrate the response of aquatic organisms, the metabolomes of unstressed Entacmaea quadricolor and Cassiopea andromeda were compared for detailed metabolite profiling. UHPLC-MS cou-pled with chemometrics and GNPS molecular networking was employed for sample classification and identification of markers unique to stress responses in each organism. Results: Several compounds with bioactive functions, including peptides and terpenoids, were reported for the first time in both organisms, viz. cyclic tetraglutamate, campestriene, and ceramide aminoethyl phosphonate (CEAP d18:2/16:0). Both anemone and jellyfish were subjected to either elevated UV-B light intensity up to 6.6 KJ m-2 or increased temperatures (28°C, 30°C, 32°C, and 34°C) over 4 days. Phospholipids, steroids, and ceramides emerged as chief markers of both types of stress, as revealed by the multivariate data analysis. Lysophosphatidylcholine (LPC 16:0), LPC (18:0/0:0), and echinoclasterol sulfate appeared as markers in both UV and thermal stress models of the anemone, whereas methyl/pro-pyl cholestane-hexa-ol were discriminatory in the UV stress model only. In the case of jellyfish, nonpolar glycosyl ceramide GlcCer (d14:1/28:6) served as a marker for UV stress, whereas polar peptides were ele-vated in the thermal stress model. Interestingly, both models of jellyfish share a phospholipid, lysophos-phatidylethanolamine (LPE 20:4), as a distinctive marker for stress, reported to be associated indirectly with the activity of innate immune response within other photosymbiotic Cnidaria such as corals and appears to be a fundamental stress response in marine organisms. Conclusion: This study presents several bioinformatic tools for the first time in two cnidarian organisms to provide not only a broader coverage of their metabolome but also broader insights into cnidarian bleaching in response to different stressors, i.e., heat and UV light, by comparing their effects in anemone versus jellyfish.

Two connected histopathological hallmarks of Alzheimer’s disease (AD) are chronic neuroinflammation and synaptic dysfunction. The accumulation of the most prevalent posttranslationally modified form of Aβ1–42, pyroglutamylated amyloid-β (Aβ3(pE)-42) in astrocytes is directly linked to glial activation and the release of proinflammatory cytokines that in turn contribute to early synaptic dysfunction in AD. At present, the mechanisms of Aβ3(pE)-42 uptake to astrocytes are unknown and pharmacological interventions that interfere with this process are not available. Here we developed a simple screening assay to identify substances from a plant extract library that prevent astroglial Aβ3(pE)-42 uptake. We first show that this approach yields valid and reproducible results. Second, we show endocytosis of Aβ3(pE)-42 oligomers by astrocytes and that quercetin, a plant flavonol, is effective to specifically block astrocytic buildup of oligomeric Aβ3(pE)-42. Importantly, quercetin does not induce a general impairment of endocytosis. However, it efficiently protects against early synaptic dysfunction following exogenous Aβ3(pE)-42 application.

In addition to jasmonoyl-isoleucine (JA-Ile), a well-established signaling molecule for plant growth and defense, its precursor, cis-12-oxo-phytodienoic acid (OPDA), is thought to possess independent signaling functions. Its perception in vascular plants is still uncharacterized. Several OPDA functions in Arabidopsis were inferred from a mutant that is affected in the function of the OPDA REDUCTASE3 (OPR3), catalyzing the conversion of OPDA within peroxisomes. Recently, opr3 plants were found to accumulate JA-Ile via a cytosolic OPR2-mediated bypass. Given the uncoupling of OPDA and JA biosynthesis in the JA-deficient mutant opr2opr3, potential OPDA signaling was investigated by a transcriptome approach comparing wild type, opr2opr3 and the JA- and OPDA-deficient mutantallene oxide synthase. Dissecting the wound response of seedlings revealed that OPDA lacked a transcriptional signature, and that previously characterized OPDA-response genes were wound-induced independently of OPDA. Exogenous application of OPDA to opr2opr3 seedlings led to JA-Ile formation and signaling even in absence of OPR2 and OPR3 and resulted in activation of sulfur assimilation. These divergent responses to endogenously synthesized and applied OPDA suggest a compartmentalization of endogenous OPDA which was investigated by a trans-organellar complementation approach. OPR3 complemented the opr2opr3 mutant in terms of fertility and wound-induced JA-Ile production irrespective of its subcellular localization. In vitro enzymatic activity of OPR3, however, showed conversion of OPDA and 4,5-didehydro-JA (4,5-ddh-JA), therefore not allowing to conclude which compound is translocated. Dissecting the conversion of either OPDA or 4,5-ddh-JA by OPR2 and OPR1 organelle variants pointed to a strong OPDA compartmentalization supporting its lacking signaling capacity.

Protein engineering using directed evolution and (semi)rational design has emerged as a powerful strategy for optimizing and enhancing enzymes or proteins with desired properties. Integrating artificial intelligence methods has further enhanced and accelerated protein engineering through predictive models developed in data-driven strategies. However, the lack of explainability and interpretability in these models poses challenges. Explainable Artificial Intelligence addresses the interpretability and explainability of machine learning models, providing transparency and insights into predictive processes. Nonetheless, there is a growing need to incorporate explainable techniques in predicting protein properties in machine learning-assisted protein engineering. This work explores incorporating explainable artificial intelligence in predicting protein properties, emphasizing its role in trustworthiness and interpretability. It assesses different machine learning approaches, introduces diverse explainable methodologies, and proposes strategies for seamless integration, improving trust-worthiness. Practical cases demonstrate the explainable model’s effectiveness in identifying DNA binding proteins and optimizing Green Fluorescent Protein brightness. The study highlights the utility of explainable artificial intelligence in advancing computationally assisted protein design, fostering confidence in model reliability.

Aphids are small insects that have developed specialized mouthparts and effector proteins to establish long-term relationships with plants. The peach-potato aphid, Myzus persicae, is a generalist, feeding on many plant species and capable of transmitting numerous pathogens. This study reveals how host-responsive cathepsins B (CathB) in the oral secretions of M. persicae facilitate aphid survival by modulating plant immune responses. Aphid CathB localize to processing bodies (p-bodies) and recruit key immune regulators EDS1, PAD4, and ADR1 to these bodies, suppressing plant defenses. A plant protein, Acd28.9 (Hsp20 family), counteracts this CathB activity and contributes to plant resistance to aphids. These findings highlight a novel role for p-bodies in plant immunity and uncover a plant resistance mechanism to aphid infestation.

Secreted immune proteases Rcr3 and Pip1 of tomato are both inhibited by Avr2 from the fungal plant pathogen Cladosporium fulvumbut only Rcr3 act as a decoy co-receptor that detects Avr2 in the presence of the Cf-2 immune receptor. Here, we identified crucial residues from tomato Rcr3 required for Cf-2-mediated signalling and bioengineered various proteases to trigger Avr2/Cf-2 dependent immunity. Despite substantial divergences in Rcr3 orthologs from eggplant and tobacco, only minimal alterations were sufficient to trigger Avr2/Cf-2-triggered immune signalling. Tomato Pip1, by contrast, was bioengineered with 16 Rcr3-specific residues to initiate Avr2/Cf-2-triggered immune signalling. These residues cluster on one side next to the substrate binding groove, indicating a potential Cf-2 interaction site. Our findings also revealed that Rcr3 and Pip1 have distinct substrate preferences determined by two variant residues and that both are suboptimal for binding Avr2. This study advances our understanding of Avr2 perception and opens avenues to bioengineer proteases to broaden pathogen recognition in other crops.

Flavor is the main factor driving consumers acceptance of food products. However, tracking the biochemistry of flavor is a formidable challenge due to the complexity of food composition. Current methodologies for linking individual molecules to flavor in foods and beverages are expensive and time-consuming. Predictive models based on machine learning (ML) are emerging as an alternative to speed up this process. Nonetheless, the optimal approach to predict flavor features of molecules remains elusive. In this work we present FlavorMiner, an ML-based multilabel flavor predictor. FlavorMiner seamlessly integrates different combinations of algorithms and mathematical representations, augmented with class balance strategies to address the inherent class of the input dataset. Notably, Random Forest and K-Nearest Neighbors combined with Extended Connectivity Fingerprint and RDKit molecular descriptors consistently outperform other combinations in most cases. Resampling strategies surpass weight balance methods in mitigating bias associated with class imbalance. FlavorMiner exhibits remarkable accuracy, with an average ROC AUC score of 0.88. This algorithm was used to analyze cocoa metabolomics data, unveiling its profound potential to help extract valuable insights from intricate food metabolomics data. FlavorMiner can be used for flavor mining in any food product, drawing from a diverse training dataset that spans over 934 distinct food products.