Non-target screening of surface water samples to identify exposome-related pollutants: a case study from Luxembourg

peer reviewedBackground Non-target screening of surface water samples collected over an extended period can reveal interesting temporal patterns in exposome-related pollutants. Additionally, geographical data on pollution sources close to the sampling sites, chemical classification data and the consideration of flow paths can provide valuable information on the origins and potential threat of tentatively identified chemical compounds. In this study, 271 surface water samples from 20 sampling sites across Luxembourg were analysed using high-resolution mass spectrometry, complementing routine target monitoring efforts in 2019–2022. Data analysis was performed using the open source R-package patRoon, which offers a customizable non-target workflow. By employing open source workflows featuring scoring terms, like spectral match and applying identification levels, tentative identifications can be prioritized, e.g. based on spectral similarity. Furthermore, by utilizing supplementary database information such as PubChemLite annotation categories and classification software such as classyFire, an overall assessment of the potential threats posed by the tentatively identified chemicals was conducted, enabling the prioritization of chemicals for future confirmation through targeted approaches. Results The study tentatively identified 378 compounds associated with the exposome including benzenoids, organoheterocyclic compounds, and organic phosphoric acids and derivatives (11 classyFire superclasses, 50 subclasses). The classification analysis not only revealed temporal variations in agrochemicals, with the majority of identifications occurring in May to July, but also highlighted the prevalence of pharmaceuticals such as venlafaxine in surface waters. Furthermore, potential sources of pollutants, like metallurgic industry or household products were explored by considering common uses and geographical information, as commercial uses of almost 100% of the identified chemicals are known. 41 chemicals were suggested for potential inclusion to governmental monitoring lists for further investigation. Conclusions The findings of this study complement existing knowledge on the pollution status of surface water in Luxembourg and highlight the usefulness of non-target screening for identifying temporal and spatial trends in pollutant levels. This approach, performed in a complementary manner to routine monitoring, can help to tentatively identify chemicals of concern for potential inclusion in target monitoring methods following additional confirmation and quantification efforts.R-AGR-3703 - IAS - LuxTIME (01/06/2020 - 15/01/2025) - FICKERS Andrea

High Resolution Mass Spectrometry of Polyfluorinated Polyether-Based Formulation

High resolution mass spectrometry (HRMS) was successfully applied to elucidate the structure of a polyfluorinated polyether (PFPE)-based formulation. The mass spectrum generated from direct injection into the MS was examined by identifying the different repeating units manually and with the aid of an instrument data processor. Highly accurate mass spectral data enabled the calculation of higher-order mass defects. The different plots of MW and the nth-order mass defects (up to n = 3) could aid in assessing the structure of the different repeating units and estimating their absolute and relative number per molecule. The three major repeating units were -C2H4O-, -C2F4O-, and -CF2O-. Tandem MS was used to identify the end groups that appeared to be phosphates, as well as the possible distribution of the repeating units. Reversed-phase HPLC separated of the polymer molecules on the basis of number of nonpolar repeating units. The elucidated structure resembles the structure in the published manufacturer technical data. This analytical approach to the characterization of a PFPE-based formulation can serve as a guide in analyzing not just other PFPE-based formulations but also other fluorinated and non-fluorinated polymers. The information from MS is essential in studying the physico-chemical properties of PFPEs and can help in assessing the risks they pose to the environment and to human health

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub

Dioxin2023 Plenary: Exploring Millions of PFAS with FAIR and Open Science

editorial reviewedPlenary presentation for Dioxin 2023 in Maastricht - Tuesday 12 September Exploring Millions of PFAS with FAIR and Open Science This presentation features a sound track created by Jamie Perera (slide 27) on "Our Chemical Past, Present and Future", which can be downloaded on Vimeo (video) or Soundcloud (sound only). Please leave feedback there if you enjoy it

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub

patRoon: open source software platform for environmental mass spectrometry based non-target screening

Abstract Mass spectrometry based non-target analysis is increasingly adopted in environmental sciences to screen and identify numerous chemicals simultaneously in highly complex samples. However, current data processing software either lack functionality for environmental sciences, solve only part of the workflow, are not openly available and/or are restricted in input data formats. In this paper we present patRoon , a new R based open-source software platform, which provides comprehensive, fully tailored and straightforward non-target analysis workflows. This platform makes the use, evaluation and mixing of well-tested algorithms seamless by harmonizing various common (primarily open) software tools under a consistent interface. In addition patRoon offers various functionality and strategies to simplify and perform automated processing of complex (environmental) data effectively. patRoon implements several effective optimization strategies to significantly reduce computational times. The ability of patRoon to perform time-efficient and automated non-target data annotation of environmental samples is demonstrated with a simple and reproducible workflow using open-access data of spiked samples from a drinking water treatment plant study. In addition, the ability to easily use, combine and evaluate different algorithms was demonstrated for three commonly used feature finding algorithms. This article, combined with already published works, demonstrate that patRoon helps make comprehensive (environmental) non-target analysis readily accessible to a wider community of researchers

Biodegradation of metformin and its transformation product, guanylurea, by natural and exposed microbial communities

Metformin (MET) is a pharmaceutical product mostly biotransformed in the environment to a transformation product, guanylurea (GUA). In ready biodegradability tests (RBTs), however, contrasting results have been observed for metformin. The objective of this study was to measure the biodegradation of MET and GUA in RBTs, using activated sludge from the local wastewater treatment plant, either directly or after pre-exposure to MET, in a chemostat. The activated sludge community was cultivated in chemostats, in presence or absence of MET, for a period of nine months, and was used in RBT after one, three and nine months. The results of this study showed that the original activated sludge was able to completely remove MET (15 mg/l) and the newly produced GUA (50% of C0MET) under the test conditions. Inoculation of the chemostat led to a rapid shift in the community composition and abundance. The community exposed to 1.5 mg/l of MET was still able to completely consume MET in the RBTs after one-month exposure, but three- and nine-months exposure resulted in reduced removal of MET in the RBTs. The ability of the activated sludge community to degrade MET and GUA is the result of environmental exposure to these chemicals as well as of conditions that could not be reproduced in the laboratory system. A MET-degrading strain belonging to the genus Aminobacter has been isolated from the chemostat community. This strain was able to completely consume 15 mg/l of MET within three days in the test. However, community analysis revealed that the fluctuation in relative abundance of this genus (<1%) could not be correlated to the fluctuation in biodegradation capacity of the chemostat community

Effect-directed analysis and chemical identification of agonists of peroxisome proliferator-activated receptors in white button mushroom

Obesity has a serious effect on human health. It relates to metabolic syndrome, including the associated disorders such as type 2 diabetes, heart disease, stroke and hyperemia. The peroxisome proliferator-activated receptors (PPARs) are important receptors to control fat metabolism in the human body. Because of the safety concerns of synthetic drugs targeting PPARs, ligands from natural sources have drawn interest. Earlier, we have found high PPAR activities in extracts from Agaricus bisporus (white button mushroom, WBM). WBM contains a wide range of candidate compounds which could be agonists of PPARs. To identify which compounds are responsible for PPAR activation by WBM extracts, we used fractionation coupled to effect-directed analysis with reporter gene assays specific for all three PPARs for purification and LC/MS-TOF and NMR for compound identification in purified active fractions. Surprisingly, we identified the relatively common dietary fatty acid, linoleic acid, as the main ligand of PPARs in WBM. Possibly, the relatively low levels of linoleic acid in WBM are sufficient and instrumental in inducing its anti-obesogenic effects, avoiding high energy intake and negative health effects associated with high levels of linoleic acid consumption. However, it could not be excluded that a minor relatively potent compound contributes towards PPAR activation, while the anti-obesity effects of WBM may be further enhanced by receptor expression modulating compounds or compounds with completely PPAR unrelated modes of action

Evaluation of reverse osmosis drinking water treatment of riverbank filtrate using bioanalytical tools and non-target screening

Stand-alone reverse osmosis (RO) has been proposed to produce high-quality drinking water from raw riverbank filtrate impacted by anthropogenic activities. To evaluate RO efficacy in removing organic micropollutants, biological analyses were combined with non-target screening using high-resolution mass spectrometry and open cheminformatics tools. The bank filtrate induced xenobiotic metabolism mediated by the aryl hydrocarbon receptor AhR, adaptive stress response mediated by the transcription factor Nrf2 and genotoxicity in the Ames-fluctuation test. These effects were absent in the RO permeate (product water), indicating the removal of bioactive micropollutants by RO membranes. In the water samples, 49 potentially toxic compounds were tentatively identified with the in silico fragmentation tool MetFrag using the US Environmental Protection Agency CompTox Chemicals Dashboard database. 5 compounds were confirmed with reference standards and 16 were tentatively identified with high confidence based on similarities to accurate mass spectra in open libraries. The bioactivity data of the confirmed chemicals indicated that 2,6-dichlorobenzamide and bentazone in water samples can contribute to the activation of AhR and oxidative stress response, respectively. The bioactivity data of 7 compounds tentatively identified with high confidence indicated that these structures can contribute to the induction of such effects. This study showed that riverbank filtration followed by RO could produce drinking water free of the investigated toxic effects