Biology subjects

Flasch, M.

Publications and source records attributed to Flasch, M..

3 recordsLinked to original sources

bioRxiv2022

Homologue Series Detection and Management in LC-MS data with homologueDiscoverer

SummaryUntargeted metabolomics data analysis is highly labor intensive and can be severely frustrated by both experimental noise and redundant features. Homologous polymer series are a particular case of features that can either represent large numbers of noise features, or alternatively represent features of interest with large peak redundancy. Here we present homologueDiscoverer, an R package which allows for the targeted and untargeted detection of homologue series as well as their evaluation and management using interactive plots and simple local database functionalities. AvailabilityhomologueDiscoverer is freely available at github https://github.com/kevinmildau/homologueDiscoverer. Contactkevin.mildau@univie.ac.at, christoph.bueschl@boku.ac.at, juergen.zanghellini@univie.ac.at

bioinformatics↗

bioRxiv2022

A broad, exposome-type evaluation of xenobiotic phase II biotransformation in human biofluids by LC-MS/MS

Xenobiotics are chemicals foreign to a specific organism that humans are exposed to on a daily basis through their food, drugs and the environment. These molecules are frequently metabolized to increase polarity and subsequent excretion. During sample preparation, deconjugation of phase II metabolites is a critical step to capture the total exposure to chemicals in liquid chromatography mass spectrometry assays (LC-MS). Knowledge on deconjugation efficiencies of different enzymes and the extend of conjugation in human biofluids has primarily been investigated for single compounds or individual chemical classes. In this study, the performance of three {beta}-glucuronidase and arylsulfatase mixtures from H. pomatia, from recombinant sources (BGS), and from Escherichia coli combined with recombinant arylsulfatase (ASPC) was compared and the efficiency of phase II deconjugation was monitored in breast milk, urine and plasma. An innovative LC-MS/MS biomonitoring method encompassing more than 80 highly diverse xenobiotics (e.g., plasticizers, industrial chemicals, mycotoxins, phytoestrogens, pesticides) was utilized for the comprehensive investigation of phase II conjugation in experiments investigating levels in breast milk and urine obtained from breastfeeding women. Overall, it was confirmed that H. pomatia is the most efficient enzyme in hydrolyzing different classes of xenobiotics for future exposome-scale biomonitoring studies. The recombinant BGS formulation, however, provided better results for breast milk samples, primarily due to lower background contamination, a major issue when employing the typically applied crude H. pomatia extracts. A deeper understanding of the global xenobiotic conjugation patterns will be essential for capturing environmental and food-related exposures within the exposome framework more comprehensively. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/500202v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@4fbe48org.highwire.dtl.DTLVardef@15588edorg.highwire.dtl.DTLVardef@90b394org.highwire.dtl.DTLVardef@1fde3e5_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2021

PeakBot: Machine learning based chromatographic peak picking

MotivationChromatographic 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. ResultsA machine-learning framework 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 vs. m/z). These are subsequently inspected by a custom-trained convolutional neural network that forms the basis of PeakBots 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. AvailabilityThe package is available free of charge for non-commercial use (CC BY-NC-SA). It is available at https://github.com/christophuv/PeakBot. Contactchristoph.bueschl@univie.ac.at Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗