Chemische Charakterisierung und Spurenanalytik
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The investigation of metabolic fluxes and metabolite distributions within cells by means of tracer molecules is a valuable tool to unravel the complexity of biological systems. Technological advances in mass spectrometry (MS) technology such as atmospheric pressure chemical ionization (APCI) coupled with high resolution (HR), not only allows for highly sensitive analyses but also broadens the usefulness of tracer‐based experiments, as interesting signals can be annotated de novo when not yet present in a compound library. However, several effects in the APCI ion source, i.e., fragmentation and rearrangement, lead to superimposed mass isotopologue distributions (MID) within the mass spectra, which need to be corrected during data evaluation as they will impair enrichment calculation otherwise. Here, we present and evaluate a novel software tool to automatically perform such corrections. We discuss the different effects, explain the implemented
algorithm, and show its application on several experimental datasets. This adjustable tool is available as an R package from CRAN.
The general understanding has been that PEGs are biologically inert, meaning they pass the human body without any relevant reaction with anything. But recent studies have shown that migth not be correct. Here a retrospective analysis of LC-MS data was performed to determine correlations between blood PEG levels in humans and different experimental factors.
Polyethylene glycols (PEGs) are widely used in everyday items such as food additives and in personal care products. In addition, they have multiple medical applications: as laxatives, excipients, and covalently coupled to drug molecules leading to improved pharmacokinetics (PEGylation). While generally regarded as biologically inert, the human body is known to produce antibodies against PEGylated molecules. In addition, PEGs have been shown to be part of a biomarker signature to predict colon cancer outcome, suggesting a more complex and yet unknown behavior of PEGs in the human body.
Here, we introduce PEGomics, a retrospective screening approach of publicly available LC-MS data. Using a custom R script to process entire studies, the presence of PEGs was reveled in most human plasma, serum and whole blood samples investigated. Several PEG species and adducts were identified and their correlation with different diseases and health conditions was investigated further.
Blood PEG levels significantly differed between patient groups in multiple clinical studies related to e.g. pregnancy duration, fasting and smoking. We discuss possible causes for these effects in the light of recent reports of allergies against PEGs and outline our further strategies to identify the source of PEGs in the human body as well as possible metabolic transformations.