Exploring molecular relationships among human metabolites in wastewater influent using LC-TIMS-HRMS
Year: 2026
Type: article
Abstract: Wastewater treatment plant (WWTP) influent contains a wide range of human-derived compounds and metabolites. While non-target screening (NTS) based on liquid chromatography–high-resolution mass spectrometry combined with trapped ion mobility spectrometry (LC-TIMS-HRMS) enables their comprehensive detection, the resulting datasets often comprise thousands of chemical features that remain difficult to interpret. Molecular networking approaches have therefore emerged as promising tools for exploring molecular relationships among these features and supporting their interpretation. This exploratory study employed feature-based molecular networking (FBMN) to investigate molecular relationships among human-associated compounds in wastewater using a connected sampling design. Features detected in WWTP influent were compared with those detected close to the point of human excretion, represented by an exemplary subset of toilet flush samples from a German university building. Among the approximately 17,000 detected features, an overlap of 24% was observed between both sample types. FBMN further facilitated the interpretation of features by organizing structurally related compounds into molecular networks. Established wastewater-based epidemiology (WBE) biomarkers frequently clustered together with related phase I metabolites, whereas the 58 tentatively annotated phase II metabolites (24 glucuronides and 34 sulfates) formed distinct molecular networks largely independent of their parent compounds. The observed clustering patterns demonstrate how molecular relationship analysis can support the interpretation and prioritization of compounds beyond individual annotations. Overall, the results highlight distinct occurrence and molecular relationship patterns among human-derived metabolites in wastewater influent and illustrate the value of molecular relationship analysis for interpreting complex NTS datasets and its potential to support future applications, including WBE.
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $3,990
OpenAlex ID: W7225824885