Chromatographic peak picking is among the first steps in data processing workflows of raw LC-HRMS datasets in untargeted metabolomics applications. Its performance is crucial for the holistic detection of all metabolic features as well as their relative quantification for statistical analysis and metabolite identification. Random noise, non-baseline separated compounds and unspecific background signals complicate this task. A machine-learning-based approach entitled PeakBot was developed for detecting chromatographic peaks in LC-HRMS profile-mode data. It first detects all local signal maxima in a chromatogram, which are then extracted as super-sampled standardized areas (retention-time versus m/z). These are subsequently inspected by a custom-trained convolutional neural network that forms the basis of PeakBot’s architecture. The model reports if the respective local maximum is the apex of a chromatographic peak or not as well as its peak center and bounding box. In training and independent validation datasets used for development, PeakBot achieved a high performance with respect to discriminating between chromatographic peaks and background signals (accuracy of 0.99). For training the machine-learning model a minimum of 100 reference features are needed to learn their characteristics to achieve high-quality peak-picking results for detecting such chromatographic peaks in an untargeted fashion. PeakBot is implemented in python (3.8) and uses the TensorFlow (2.5.0) package for machine-learning related tasks. It has been tested on Linux and Windows OSs. The package is available free of charge for non-commercial use (CC BY-NC-SA). It is available at https://github.com/christophuv/PeakBot. Supplementary data are available at Bioinformatics online.

Determining the mode of action of microbial biocontrol agents plays a key role in their development and registration as commercial biopesticides. The biocontrol rhizobacterium Lysobacter capsici AZ78 (AZ78) is able to inhibit a vast array of plant pathogenic oomycetes and Gram-positive bacteria due to the release of antimicrobial secondary metabolites. A combination of MALDI-qTOF-MSI and UHPLC-HRMS/M was applied to finely dissect the AZ78 metabolome and identify the main secondary metabolites involved in the inhibition of plant pathogenic microorganisms. Under nutritionally limited conditions, MALDI-qTOF-MSI revealed that AZ78 is able to release a relevant number of antimicrobial secondary metabolites belonging to the families of 2,5-diketopiperazines, cyclic lipodepsipeptides, macrolactones and macrolides. In vitro tests confirmed the presence of secondary metabolites toxic against Pythium ultimum and Rhodococcus fascians in AZ78 cell-free extracts. Subsequently, UHPLC-HRMS/MS was used to confirm the results achieved with MALDI-qTOF-MSI and investigate for further putative antimicrobial secondary metabolites known to be produced by Lysobacter spp. This technique confirmed the presence of several 2,5-diketopiperazines in AZ78 cell-free extracts and provided the first evidence of the production of the cyclic depsipeptide WAP-8294A2 in a member of L. capsici species. Moreover, UHPLC-HRMS/MS confirmed the presence of dihydromaltophilin/Heat Stable Antifungal Factor (HSAF) in AZ78 cell-free extracts. Due to the production of HSAF by AZ78, cell-free supernatants were effective in controlling Plasmopara viticola on grapevine leaf disks after exposure to high temperatures. Overall, our work determined the main secondary metabolites involved in the biocontrol activity of AZ78 against plant pathogenic oomycetes and Gram-positive bacteria. These results might be useful for the future development of this bacterial strain as the active ingredient of a microbial biopesticide that might contribute to a reduction in the chemical input in agriculture.

Lyophilization is a common method used for stabilizing biological samples prior to storage or to concentrate extracts. However, it is possible that this process may alter the metabolic composition or lead to the loss of metabolites. In this study, the performance of lyophilization is investigated in the example of wheat roots. To this end, native and ¹³C-labelled, fresh or already lyophilized root samples, and (diluted) extracts with dilution factors up to 32 and authentic reference standards were investigated. All samples were analyzed using RP-LC-HRMS. Results show that using lyophilization for the stabilization of plant material altered the metabolic sample composition. Overall, 7% of all wheat metabolites detected in non-lyophilized samples were not detected in dried samples anymore, and up to 43% of the remaining metabolites exhibited significantly increased or decreased abundances. With respect to extract concentration, less than 5% of the expected metabolites were completely lost by lyophilization and the recovery rates of the remaining metabolites were slightly reduced with increasing concentration factors to an average of 85% at an enrichment factor of 32. Compound annotation did not indicate specific classes of wheat metabolites to be affected.

Covalent or non-covalent heterogeneous multimerization of molecules associated with extracts from biological samples analyzed via LC-MS are quite difficult to recognize/annotate and therefore the prevalence of multimerization remains largely unknown. In this study, we utilized 13C labeled and unlabeled Pichia pastoris extracts to recognize heterogeneous multimers. More specifically, between 0.8% and 1.5% of the biologically-derived features detected in our experiments were confirmed to be heteromers, about half of which we could successfully annotate with monomeric partners. Interestingly, we found specific chemical classes such as nucleotides to disproportionately contribute to heteroadducts. Furthermore, we compiled these compounds into the first MS/MS library that included data from heteromultimers to provide a starting point for other labs to improve the annotation of such ions in other metabolomics data sets. Then, the detected heteromers were also searched in publicly accessible LC-MS datasets available in Metabolights, Metabolomics WB and GNPS/MassIVE to demonstrate that these newly annotated ions are also relevant to other public datasets. Furthermore, in additional datasets (Triticum aestivum, Fusarium graminearum, and Trichoderma reesei) our developed workflow also detected 0.5%-4.9% of metabolite features to originate from heterodimers, demonstrating heteroadducts to be present in metabolomics studies at a low percentage.

Laccases are oxidases that only require O₂ as a terminal oxidant. Thus, they provide an attractive green alternative to established alcohol oxidation protocols. However, laccases typically require catalytic amounts of mediator molecules to serve as electron shuttles between the enzyme and desired substrate. Consequently, laccase‐mediator systems are defined by a multitude of parameters such as, e. g., the choice of laccase and mediator, the respective concentrations, pH, and the oxygen source. This complexity and a perceived lack of comparable data throughout literature represent an entry burden into this field. To provide a solid starting point, particularly for organic chemists, we herein provide a time‐resolved, quantitative laccase and mediator screening based on the oxidation of anis alcohol as model reaction. We measured the redox potentials of mediators under the reaction conditions to relate them to their performance. Lastly, for particularly efficient laccase‐mediator pairs, we screened important reaction parameters, resulting in an optimized setup for mediator‐assisted laccase catalyzed oxidations.

Fusarium mangiferae causes the mango malformation disease (MMD) on young mango trees and seedlings resulting in economically significant crop losses. In addition, F. mangiferae produces a vast array of secondary metabolites (SMs), including mycotoxins that may contaminate the harvest. Their production is tightly regulated at the transcriptional level. Here, we show that lack of the H3 K9‐specific histone methyltransferase, FmKmt1, influences the expression of the F. mangiferae polyketide synthase (PKS) 8 (FmPKS8), a so far cryptic PKS. By a combination of reverse genetics, untargeted metabolomics, bioinformatics and chemical analyses including structural elucidation, we determined the FmPKS8 biosynthetic gene cluster (BGC) and linked its activity to the production of fusamarins (FMN), which can be structurally classified as dihydroisocoumarins. Functional characterization of the four FMN cluster genes shed light on the biosynthetic pathway. Cytotoxicity assays revealed moderate toxicities with IC₅₀ values between 1 and 50 μM depending on the compound.