High-quality data preprocessing is essential for untargeted metabolomics experiments, where increasing dataset scale and complexity demand adaptable, robust, and reproducible software solutions. Modern preprocessing tools must evolve to integrate seamlessly with downstream analysis platforms, ensuring efficient and streamlined workflows. Since its introduction in 2005, the xcms R package has become one of the most widely used tools for LC-MS data preprocessing. Developed through an open-source, community-driven approach, xcms has maintained long-term stability while continuously expanding its capabilities and accessibility. We present recent advancements that position xcms as a central component of a modular and interoperable software ecosystem for metabolomics data analysis. Key improvements include enhanced scalability, enabling the processing of large-scale experiments with thousands of samples on standard computing hardware. These developments empower users to build comprehensive, customizable, and reproducible workflows tailored to diverse experimental designs and analytical needs. An expanding collection of tutorials, documentation, and teaching materials further supports both new and experienced users in leveraging the broader R and Bioconductor ecosystems. These resources facilitate the integration of statistical modeling, visualization tools, and domain-specific packages, extending the reach and impact of xcms workflows. Together, these enhancements solidify xcms as a cornerstone of modern metabolomics research.

High-quality data preprocessing is essential for untargeted metabolomics experiments, where increasing dataset scale and complexity demand adaptable, robust, and reproducible software solutions. Modern preprocessing tools must evolve to integrate seamlessly with downstream analysis platforms, ensuring efficient and streamlined workflows. Since its introduction in 2005, the xcms R package has become one of the most widely used tools for LC-MS data preprocessing. Developed through an open-source, community-driven approach, xcms has maintained long-term stability while continuously expanding its capabilities and accessibility. We present recent advancements that position xcms as a central component of a modular and interoperable software ecosystem for metabolomics data analysis. Key improvements include enhanced scalability, enabling the processing of large-scale experiments with thousands of samples on standard computing hardware. These developments empower users to build comprehensive, customizable, and reproducible workflows tailored to diverse experimental designs and analytical needs. An expanding collection of tutorials, documentation, and teaching materials further supports both new and experienced users in leveraging the broader R and Bioconductor ecosystems. These resources facilitate the integration of statistical modeling, visualization tools, and domain-specific packages, extending the reach and impact of xcms workflows. Together, these enhancements solidify xcms as a cornerstone of modern metabolomics research.

High-quality data preprocessing is essential for untargeted metabolomics experiments, where increasing dataset scale and complexity demand adaptable, robust, and reproducible software solutions. Modern preprocessing tools must evolve to integrate seamlessly with downstream analysis platforms, ensuring efficient and streamlined workflows. Since its introduction in 2005, the xcms R package has become one of the most widely used tools for LC-MS data preprocessing. Developed through an open-source, community-driven approach, xcms has maintained long-term stability while continuously expanding its capabilities and accessibility. We present recent advancements that position xcms as a central component of a modular and interoperable software ecosystem for metabolomics data analysis. Key improvements include enhanced scalability, enabling the processing of large-scale experiments with thousands of samples on standard computing hardware. These developments empower users to build comprehensive, customizable, and reproducible workflows tailored to diverse experimental designs and analytical needs. An expanding collection of tutorials, documentation, and teaching materials further supports both new and experienced users in leveraging the broader R and Bioconductor ecosystems. These resources facilitate the integration of statistical modeling, visualization tools, and domain-specific packages, extending the reach and impact of xcms workflows. Together, these enhancements solidify xcms as a cornerstone of modern metabolomics research.

Processing by proteases irreversibly regulates the fate of plant proteins and hampers the production of recombinant protein in plants, yet only few processing events have been described in agroinfiltrated Nicotiana benthamiana, which has emerged as a favorite transient protein expression platform in plant science and molecular pharming. Here, we used in-gel digests and mass spectrometry to monitor the migration and topography of 5,040 plant proteins of agroinfiltrated N. benthamiana within a protein gel. By plotting the peptides over the gel slices, we generated peptographs that reveal where which part of each protein was detected within the protein gel. These data uncovered that 60% of the detected proteins have proteoforms that migrate at lower than predicted molecular weights, implicating extensive proteolytic processing. For instance, this analysis confirms the proteolytic removal and degradation of autoinhibitory prodomains of most but not all proteases, and revealed differential processing within pectinemethylesterase and lipase families. This analysis also uncovered intricate processing of glycosidases and uncovered that ectodomain shedding might be common for a diverse range of receptor-like kinases. Transient expression of double-tagged candidate proteins confirmed various processing events in vivo. This extensive proteomic dataset can be investigated further and demonstrates that most plant proteins are proteolytically processed and implicates an extensive proteolytic machinery shaping the proteome of agroinfiltrated N. benthamiana.

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.

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