Feature-based molecular networking for identification of organic micropollutants including metabolites by non-target analysis applied to riverbank filtration

Abstract

Due to growing concern about organic micropollutants and their transformation products (TP) in surface and drinking water, reliable identification of unknowns is required. Here, we demonstrate how non-target liquid chromatography (LC)-high-resolution tandem mass spectrometry (MS/MS) and the feature-based molecular networking (FBMN) workflow provide insight into water samples from four riverbank filtration sites with different redox conditions. First, FBMN prioritized and connected drinking water relevant and seasonally dependent compounds based on a modification-aware MS/MS cosine similarity. Within the resulting molecular networks, forty-three compounds were annotated. Here, carbamazepine, sartans, and their respective TP were investigated exemplarily. With chromatographic information and spectral similarity, four additional TP (dealkylated valsartan, dealkylated irbesartan, two oxygenated irbesartan isomers) and olmesartan were identified and partly verified with an authentic standard. In this study, sartans and TP were investigated and grouped regarding their removal behavior under different redox conditions and seasons for the first time. Antihypertensives were grouped into compounds being well removed during riverbank filtration, those primarily removed under anoxic conditions, and rather persistent compounds. Observed seasonal variations were mainly limited to varying river water concentrations. FBMN is a powerful tool for identifying previously unknown or unexpected compounds and their TP in water samples by non-target analysis. Graphical abstract

Non-target Analysis and Chemometric Evaluation of a Passive Sampler Monitoring of Small Streams

“…Unexpectedly, more features were prioritized for data in negative ionization mode compared to that in positive mode, which is surprising as many NTS monitoring studies include only measurements in a positive mode. 5,7,9,28 This emphasizes that measurements in a negative ionization mode should not be neglected in NTS studies.…”

“…Furthermore, Spring samples showed a higher number of prioritized features compared to Summer , which might be explained by higher pesticide applications in this period. Unexpectedly, more features were prioritized for data in negative ionization mode compared to that in positive mode, which is surprising as many NTS monitoring studies include only measurements in a positive mode. ,,, This emphasizes that measurements in a negative ionization mode should not be neglected in NTS studies.…”

Combining Feature-Based Molecular Networking and Contextual Mass Spectral Libraries to Decipher Nutrimetabolomics Profiles

“…), increasing the link between peak picking algorithms and in silico annotation tools [ 12 ]. Until now, FBMN has been successfully applied in various fields of metabolomics, allowing level II/level III identification of transformation products of organic micropollutants in water samples [ 13 ], native plant constituents [ 14 , 15 , 16 ], and endogenous urinary metabolites [ 17 ]. However, mass spectral library matching is generally performed by the comparison with mass spectral libraries containing MS/MS spectra acquired under a wide range of instrumental conditions (e.g., time-of-flight, orbitrap, hybrid ion traps, etc.)…”

“…However, mass spectral library matching is generally performed by the comparison with mass spectral libraries containing MS/MS spectra acquired under a wide range of instrumental conditions (e.g., time-of-flight, orbitrap, hybrid ion traps, etc.) and collision energies used, with different curation protocols providing different mass accuracy levels [ 13 , 16 ], thus suffering from limited reliability of the annotation due to differences in observed mass fragments and their intensity ratios. This issue can be managed by implementing better contextualized libraries containing reference spectra of study-related compounds and acquired under experimental conditions equal to or comparable to the experimental data being analyzed.…”

Boosting annotation in nutrimetabolomics by Feature-Based Molecular Networking: analytical and computational strategies applied to human urine samples from an untargeted LC-MS/MS based bilberry-blueberry intervention study

“…), increasing the link between peak picking algorithms and in silico annotation tools (Nothias, Petras, Schmid, Dührkop, Rainer, Sarvepalli, et al, 2020). Until now, FBMN has been successfully applied in various fields of metabolomics, such as the identification of transformation products of organic micropollutants in water samples (Oberleitner, Schmid, Schulz, Bergmann, & Achten, 2021), native plant constituents (Padilla-González, Sadgrove, Ccana-Ccapatinta, Leuner, & Fernandez-Cusimamani, 2020;Rivera-Mondragón, Tuenter, Ortiz, Sakavitsi, Nikou, Halabalaki, et al, 2020;Xie, Kong, Li, Nothias, Melnik, Zhang, et al, 2020) and endogenous urinary metabolites (Neto & Raftery, 2021;Quinn, Melnik, Vrbanac, Fu, Patras, Christy, et al, 2020), while no applications have been reported till now in nutrimetabolomics.…”