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.

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.

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.

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.

Cis-(+)-12-oxophytodienoic acid (cis-(+)-OPDA) is a bioactive jasmonate, a precursor of jasmonic acid, which also displays signaling activity on its own. Modulation of cis-(+)-OPDA actions may be carried out via biotransformation leading to metabolites of various functions, similar to other phytohormones. This work introduces a methodology for the synthesis of racemic cis-OPDA conjugates with amino acids (OPDA-aa) and their deuterium-labeled analogs, which enables the identification and accurate quantification of these compounds in plants. We have developed a highly sensitive liquid chromatography-tandem mass spectrometry-based method for the reliable determination of seven OPDA-aa (OPDA-Alanine, OPDA-Aspartate, OPDA-Glutamate, OPDA-Glycine, OPDA-Isoleucine, OPDA-Phenylalanine, and OPDA-Valine) from minute amount of plant material. The extraction from 10 mg of fresh plant tissue by 10% aqueous methanol followed by single-step sample clean-up on hydrophilic–lipophilic balanced columns prior to final analysis was optimized. The method was validated in terms of accuracy and precision, and the method parameters such as process efficiency, recovery and matrix effects were evaluated. In mechanically wounded 30-day-old Arabidopsis thaliana leaves, five endogenous (+)-OPDA-aa were identified and their endogenous levels reached a maximum of pmol/g. The time-course accumulation revealed a peak 60 min after the wounding, roughly corresponding to the accumulation of cis-(+)-OPDA. Current synthetic and analytical methodologies support studies on cis-(+)-OPDA conjugation with amino acids and research into the biological significance of these metabolites in plants.

Cis-(+)-12-oxophytodienoic acid (cis-(+)-OPDA) is a bioactive jasmonate, a precursor of jasmonic acid, which also displays signaling activity on its own. Modulation of cis-(+)-OPDA actions may be carried out via biotransformation leading to metabolites of various functions, similar to other phytohormones. This work introduces a methodology for the synthesis of racemic cis-OPDA conjugates with amino acids (OPDA-aa) and their deuterium-labeled analogs, which enables the identification and accurate quantification of these compounds in plants. We have developed a highly sensitive liquid chromatography-tandem mass spectrometry-based method for the reliable determination of seven OPDA-aa (OPDA-Alanine, OPDA-Aspartate, OPDA-Glutamate, OPDA-Glycine, OPDA-Isoleucine, OPDA-Phenylalanine, and OPDA-Valine) from minute amount of plant material. The extraction from 10 mg of fresh plant tissue by 10% aqueous methanol followed by single-step sample clean-up on hydrophilic–lipophilic balanced columns prior to final analysis was optimized. The method was validated in terms of accuracy and precision, and the method parameters such as process efficiency, recovery and matrix effects were evaluated. In mechanically wounded 30-day-old Arabidopsis thaliana leaves, five endogenous (+)-OPDA-aa were identified and their endogenous levels reached a maximum of pmol/g. The time-course accumulation revealed a peak 60 min after the wounding, roughly corresponding to the accumulation of cis-(+)-OPDA. Current synthetic and analytical methodologies support studies on cis-(+)-OPDA conjugation with amino acids and research into the biological significance of these metabolites in plants.

Cis-(+)-12-oxophytodienoic acid (cis-(+)-OPDA) is a bioactive jasmonate, a precursor of jasmonic acid, which also displays signaling activity on its own. Modulation of cis-(+)-OPDA actions may be carried out via biotransformation leading to metabolites of various functions, similar to other phytohormones. This work introduces a methodology for the synthesis of racemic cis-OPDA conjugates with amino acids (OPDA-aa) and their deuterium-labeled analogs, which enables the identification and accurate quantification of these compounds in plants. We have developed a highly sensitive liquid chromatography-tandem mass spectrometry-based method for the reliable determination of seven OPDA-aa (OPDA-Alanine, OPDA-Aspartate, OPDA-Glutamate, OPDA-Glycine, OPDA-Isoleucine, OPDA-Phenylalanine, and OPDA-Valine) from minute amount of plant material. The extraction from 10 mg of fresh plant tissue by 10% aqueous methanol followed by single-step sample clean-up on hydrophilic–lipophilic balanced columns prior to final analysis was optimized. The method was validated in terms of accuracy and precision, and the method parameters such as process efficiency, recovery and matrix effects were evaluated. In mechanically wounded 30-day-old Arabidopsis thaliana leaves, five endogenous (+)-OPDA-aa were identified and their endogenous levels reached a maximum of pmol/g. The time-course accumulation revealed a peak 60 min after the wounding, roughly corresponding to the accumulation of cis-(+)-OPDA. Current synthetic and analytical methodologies support studies on cis-(+)-OPDA conjugation with amino acids and research into the biological significance of these metabolites in plants.

Pathogenic Xanthomonas bacteria cause disease on more than 400 plant species. These Gram-negative bacteria utilize the type III secretion system to inject type III effector proteins (T3Es) directly into the plant cell cytosol where they can manipulate plant pathways to promote virulence. The host range of a given Xanthomonas species is limited, and T3E repertoires are specialized during interactions with specific plant species. Some effectors, however, are retained across most strains, such as Xanthomonas Outer Protein L (XopL). As an ‘ancestral’ effector, XopL contributes to the virulence of multiple xanthomonads, infecting diverse plant species. XopL homologs harbor a combination of a leucine-rich-repeat (LRR) domain and an XL-box which has E3 ligase activity. Despite similar domain structure there is evidence to suggest that XopL function has diverged, exemplified by the finding that XopLs expressed in plants often display bacterial species-dependent differences in their sub-cellular localization and plant cell death reactions. We found that XopL from X. euvesicatoria (XopLXe) directly associates with plant microtubules (MTs) and causes strong cell death in agroinfection assays in N. benthamiana. Localization of XopLXe homologs from three additional Xanthomonas species, of diverse infection strategy and plant host, revealed that the distantly related X. campestris pv. campestris harbors a XopL (XopLXcc) that fails to localize to MTs and to cause plant cell death. Comparative sequence analyses of MT-binding XopLs and XopLXcc identified a proline-rich-region (PRR)/α-helical region important for MT localization. Functional analyses of XopLXe truncations and amino acid exchanges within the PRR suggest that MT-localized XopL activity is required for plant cell death reactions. This study exemplifies how the study of a T3E within the context of a genus rather than a single species can shed light on how effector localization is linked to biochemical activity.