Mixture effects of co-formulants and two plant protection products in a liver cell line.

Plant protection products (PPPs) consist of one or more active substances and several co-formulants. Active substances provide the functionality of the PPP and are consequently evaluated according to standard test methods set by legal data requirements before approval, whereas co-formulants' toxicity is not as comprehensively assessed. However, in some cases mixture effects of active substances and co-formulants might result in increased or different forms of toxicity. In a proof-of-concept study we hence built on previously published results of Zahn et al. (2018[38]) on the mixture toxicity of Priori Xtra® and Adexar® to specifically investigate the influence of co-formulants on the toxicity of these commonly used fungicides. Products, their respective active substances in combination as well as some co-formulants were applied to human hepatoma cell line (HepaRG) in several dilutions. Cell viability analysis, mRNA expression, abundance of xenobiotic metabolizing enzymes and intracellular concentrations of active substances determined by LC-MS/MS analyses demonstrated that the toxicity of the PPPs is influenced by the presence of co-formulants in vitro. PPPs were more cytotoxic than the mix of their active substances. Gene expression profiles of cells treated with the PPPs were similar to those treated with their respective mixture combinations with marked differences. Co-formulants can cause gene expression changes on their own. LC-MS/MS analyses revealed higher intracellular concentrations of active substances in cells treated with PPPs compared to those treated with the respective active substances' mix. Proteomic data showed co-formulants can induce ABC transporters and CYP enzymes. Co-formulants can contribute to the observed increased toxicity of PPPs compared to their active substances in combination due to kinetic interactions, necessitating a more comprehensive evaluation approach.

Corrigendum: Exercise blood-drop metabolic profiling links metabolism with perceived exertion.

[This corrects the article DOI: 10.3389/fmolb.2022.1042231.].

Exercise blood-drop metabolic profiling links metabolism with perceived exertion.

Background: Assessing detailed metabolism in exercising persons minute-to-minute has not been possible. We developed a "drop-of-blood" platform to fulfill that need. Our study aimed not only to demonstrate the utility of our methodology, but also to give insights into unknown mechanisms and new directions. Methods: We developed a platform, based on gas chromatography and mass spectrometry, to assess metabolism from a blood-drop. We first observed a single volunteer who ran 13 km in 60 min. We particularly monitored relative perceived exertion (RPE). We observed that 2,3-bisphosphoglycerate peaked at RPE in this subject. We next expanded these findings to women and men volunteers who performed an RPE-based exercise protocol to RPE at Fi O 2 20.9% or Fi O 2 14.5% in random order. Results: At 6 km, our subject reached his maximum relative perceived exertion (RPE); however, he continued running, felt better, and finished his run. Lactate levels had stably increased by 2 km, ketoacids increased gradually until the run's end, while the hypoxia marker, 2,3 bisphosphoglycerate, peaked at maximum relative perceived exertion. In our normal volunteers, the changes in lactate, pyruvate, ß hydroxybutyrate and a hydroxybutyrate were not identical, but similar to our model proband runner. Conclusion: Glucose availability was not the limiting factor, as glucose availability increased towards exercise end in highly exerted subjects. Instead, the tricarboxylic acid→oxphos pathway, lactate clearance, and thus and the oxidative capacity appeared to be the defining elements in confronting maximal exertion. These ideas must be tested further in more definitive studies. Our preliminary work suggests that our single-drop methodology could be of great utility in studying exercise physiology.

Optimized Workflow for On-Line Derivatization for Targeted Metabolomics Approach by Gas Chromatography-Mass Spectrometry.

Using manual derivatization in gas chromatography-mass spectrometry samples have varying equilibration times before analysis which increases technical variability and limits the number of potential samples analyzed. By contrast, automated derivatization methods can derivatize and inject each sample in an identical manner. We present a fully automated (on-line) derivatization method used for targeted analysis of different matrices. We describe method optimization and compare results from using off-line and on-line derivatization protocols, including the robustness and reproducibility of the methods. Our final parameters for the derivatization process were 20 µL of methoxyamine (MeOx) in pyridine for 60 min at 30 °C followed by 80 µL N-Methyl-N-trimethylsilyltrifluoracetamide (MSTFA) for 30 min at 30 °C combined with 4 h of equilibration time. The repeatability test in plasma and liver revealed a median relative standard deviation (RSD) of 16% and 10%, respectively. Serum samples showed a consistent intra-batch median RSD of 20% with an inter-batch variability of 27% across three batches. The direct comparison of on-line versus off-line demonstrated that on-line was fit for purpose and improves repeatability with a measured median RSD of 11% compared to 17% using the same method off-line. In summary, we recommend that optimized on-line methods may improve results for metabolomics and should be used where available.

Towards a More Reliable Identification of Isomeric Metabolites Using Pattern Guided Retention Validation.

Reliable analyte identification is critical in metabolomics experiments to ensure proper interpretation of data. Due to chemical similarity of metabolites (as isobars and isomers) identification by mass spectrometry or chromatography alone can be difficult. Here we show that isomeric compounds are quite common in the metabolic space as given in common metabolite databases. Further, we show that retention information can shift dramatically between different experiments decreasing the value of external or even in-house compound databases. As a consequence the retention information in compound databases should be updated regularly, to allow a reliable identification. To do so we present a feasible and budget conscious method to guarantee updates of retention information on a regular basis using well designed compound mixtures. For this we combine compounds in "Ident-Mixes", showing a way to distinctly identify chemically similar compounds through combinatorics and principle of exclusion. We illustrate the feasibility of this approach by comparing Gas chromatography (GC)-columns with identical properties from three different vendors and by creating a compound database from measuring these mixtures by Liquid chromatography-mass spectrometry (LC-MS). The results show the high influence of used materials on retention behavior and the ability of our approach to generate high quality identifications in a short time.

WiPP: Workflow for Improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) Data.

Lack of reliable peak detection impedes automated analysis of large-scale gas chromatography-mass spectrometry (GC-MS) metabolomics datasets. Performance and outcome of individual peak-picking algorithms can differ widely depending on both algorithmic approach and parameters, as well as data acquisition method. Therefore, comparing and contrasting between algorithms is difficult. Here we present a workflow for improved peak picking (WiPP), a parameter optimising, multi-algorithm peak detection for GC-MS metabolomics. WiPP evaluates the quality of detected peaks using a machine learning-based classification scheme based on seven peak classes. The quality information returned by the classifier for each individual peak is merged with results from different peak detection algorithms to create one final high-quality peak set for immediate down-stream analysis. Medium- and low-quality peaks are kept for further inspection. By applying WiPP to standard compound mixes and a complex biological dataset, we demonstrate that peak detection is improved through the novel way to assign peak quality, an automated parameter optimisation, and results in integration across different embedded peak picking algorithms. Furthermore, our approach can provide an impartial performance comparison of different peak picking algorithms. WiPP is freely available on GitHub (https://github.com/bihealth/WiPP) under MIT licence.

Determination of osteocalcin in meat and bone meal of bovine and porcine origin using matrix-assisted laser desorption ionization/time-of-flight mass spectrometry and high-resolution hybrid mass spectrometry.

A method has been developed for determining the origin of meat and bone meal (MBM) by detecting species-specific osteocalcin (OC) using matrix-assisted laser desorption ionization/time-of-flight (MALDI/TOF) and high-resolution hybrid mass spectrometry (HR-Q/TOF MS). The analysis is based on the detection of typical species-specific OC and its tryptic peptide fragments which differ in mass due to differences in the amino-acid sequences between species. After dissolving the MBM samples in EDTA buffer, purification after ultrafiltration was performed using two methods: solid-phase extraction using Zip-Tip C(18) or size exclusion coupled with reverse-phase chromatography. Fractions containing partially purified intact OC were analyzed using LC-Q/TOF and MALDI/TOF mass spectrometry. Species-specific OC was detected at the typical protonated and doubly protonated molecular ions. Furthermore, typical porcine- and bovine-derived tryptic fragments from MBM were detected after enzymatic digestion. In order to determine the underlying amino-acid sequences and to confirm the assignment to OC-derived peptides, MS/MS analysis was carried out. In conclusion, we were able to detect OC in bovine and porcine MBM with high sensitivity and the MS-based method described here by which total OC mass and marker peptides of digested OC are recorded can be used as an alternative approach to detect genus-specific differences in MBM and can be applied as a confirmatory method to mainly immunological osteocalcin screening methods.