How Wastewater Reflects Human Metabolism─Suspect Screening of Pharmaceutical Metabolites in Wastewater Influent.

Pharmaceuticals and their human metabolites are contaminants of emerging concern in the aquatic environment. Most monitoring studies focus on a limited set of parent compounds and even fewer metabolites. However, more than 50% of the most consumed pharmaceuticals are excreted in higher amounts as metabolites than as parents, as confirmed by a literature analysis within this study. Hence, we applied a wide-scope suspect screening approach to identify human pharmaceutical metabolites in wastewater influent from three Swiss treatment plants. Based on consumption amounts and human metabolism data, a suspect list comprising 268 parent compounds and over 1500 metabolites was compiled. Online solid phase extraction combined with liquid chromatography coupled to high-resolution tandem mass spectrometry was used to analyze the samples. Data processing, annotation, and structure elucidation were achieved with various tools, including molecular networking as well as SIRIUS/CSI:FingerID and MetFrag for MS2 spectra rationalization. We confirmed 37 metabolites with reference standards and 16 by human liver S9 incubation experiments. More than 25 metabolites were detected for the first time in influent wastewater. Semiquantification with MS2Quant showed that metabolite to parent concentration ratios were generally lower compared to literature expectations, probably due to further metabolite transformation in the sewer system or limitations in the metabolite detection. Nonetheless, metabolites pose a large fraction to the total pharmaceutical contribution in wastewater, highlighting the need for metabolite inclusion in chemical risk assessment.

MSNovelist: de novo structure generation from mass spectra.

Current methods for structure elucidation of small molecules rely on finding similarity with spectra of known compounds, but do not predict structures de novo for unknown compound classes. We present MSNovelist, which combines fingerprint prediction with an encoder-decoder neural network to generate structures de novo solely from tandem mass spectrometry (MS2) spectra. In an evaluation with 3,863 MS2 spectra from the Global Natural Product Social Molecular Networking site, MSNovelist predicted 25% of structures correctly on first rank, retrieved 45% of structures overall and reproduced 61% of correct database annotations, without having ever seen the structure in the training phase. Similarly, for the CASMI 2016 challenge, MSNovelist correctly predicted 26% and retrieved 57% of structures, recovering 64% of correct database annotations. Finally, we illustrate the application of MSNovelist in a bryophyte MS2 dataset, in which de novo structure prediction substantially outscored the best database candidate for seven spectra. MSNovelist is ideally suited to complement library-based annotation in the case of poorly represented analyte classes and novel compounds.

A Modular and Expandable Ecosystem for Metabolomics Data Annotation in R.

Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics experiments have become increasingly popular because of the wide range of metabolites that can be analyzed and the possibility to measure novel compounds. LC-MS instrumentation and analysis conditions can differ substantially among laboratories and experiments, thus resulting in non-standardized datasets demanding customized annotation workflows. We present an ecosystem of R packages, centered around the MetaboCoreUtils, MetaboAnnotation and CompoundDb packages that together provide a modular infrastructure for the annotation of untargeted metabolomics data. Initial annotation can be performed based on MS1 properties such as m/z and retention times, followed by an MS2-based annotation in which experimental fragment spectra are compared against a reference library. Such reference databases can be created and managed with the CompoundDb package. The ecosystem supports data from a variety of formats, including, but not limited to, MSP, MGF, mzML, mzXML, netCDF as well as MassBank text files and SQL databases. Through its highly customizable functionality, the presented infrastructure allows to build reproducible annotation workflows tailored for and adapted to most untargeted LC-MS-based datasets. All core functionality, which supports base R data types, is exported, also facilitating its re-use in other R packages. Finally, all packages are thoroughly unit-tested and documented and are available on GitHub and through Bioconductor.

Inactivation and Site-specific Oxidation of Aquatic Extracellular Bacterial Leucine Aminopeptidase by Singlet Oxygen.

Extracellular enzymes are master recyclers of organic matter, and to predict their functional lifetime, we need to understand their environmental transformation processes. In surface waters, direct and indirect photochemical transformation is a known driver of inactivation. We investigated molecular changes that occur along with inactivation in aminopeptidase, an abundant class of extracellular enzymes. We studied the inactivation kinetics and localized oxidation caused by singlet oxygen, 1O2, a major photochemically derived oxidant toward amino acids. Aminopeptidase showed second-order inactivation rate constants with 1O2 comparable to those of free amino acids. We then visualized site-specific oxidation kinetics within the three-dimensional protein and demonstrated that fastest oxidation occurred around the active site and at other reactive amino acids. However, second-order oxidation rate constants did not correlate strictly with the 1O2-accessible surface areas of those amino acids. We inspected site-specific processes by a comprehensive suspect screening for 723,288 possible transformation products. We concluded that histidine involved in zinc coordination at the active site reacted slower than what was expected by its accessibility, and we differentiated between two competing reaction pathways of 1O2 with tryptophan residues. This systematic analysis can be directly applied to other proteins and transformation reactions.

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.

Biodiversity Drives Micropollutant Biotransformation in Freshwater Phytoplankton Assemblages.

Biotransformation of chemical pollutants is an ecological process requiring multifunctionality (multiple metabolic pathways) and, potentially, high biodiversity. Phytoplankton communities are highly diverse functionally and taxonomically and co-occur with complex mixtures of organic pollutants in aquatic environments. Here, we investigated how phytoplankton species richness (SR) and class richness (CR) determine the biotransformation of a mixture of 37 structurally diverse pollutants using laboratory experiments and analysis of high-resolution mass spectrometry data. Laboratory phytoplankton communities were assembled from pure cultures by creating a gradient from one to five taxonomic groups, and 5 to 11 total species, in defined medium. The biotransformation of pollutants over 6 days and the total number of transformed chemicals increased with CR for 13 considerably transformed compounds. The total number of transformation products (TPs, up to 42) was positively affected by both CR and SR: CR had a positive effect on stable TPs found, and SR led to more transient TPs. Our data indicate that both taxonomic and functional diversity are important for biotransformation of anthropogenic chemicals in phytoplankton and suggest that plankton biodiversity could play a role in the remediation of pollutant loads in aquatic ecosystems.

Annotating Nontargeted LC-HRMS/MS Data with Two Complementary Tandem Mass Spectral Libraries.

Tandem mass spectral databases are indispensable for fast and reliable compound identification in nontargeted analysis with liquid chromatography⁻high resolution tandem mass spectrometry (LC-HRMS/MS), which is applied to a wide range of scientific fields. While many articles now review and compare spectral libraries, in this manuscript we investigate two high-quality and specialized collections from our respective institutes, recorded on different instruments (quadrupole time-of-flight or QqTOF vs. Orbitrap). The optimal range of collision energies for spectral comparison was evaluated using 233 overlapping compounds between the two libraries, revealing that spectra in the range of CE 20⁻50 eV on the QqTOF and 30⁻60 nominal collision energy units on the Orbitrap provided optimal matching results for these libraries. Applications to complex samples from the respective institutes revealed that the libraries, combined with a simple data mining approach to retrieve all spectra with precursor and fragment information, could confirm many validated target identifications and yield several new Level 2a (spectral match) identifications. While the results presented are not surprising in many ways, this article adds new results to the debate on the comparability of Orbitrap and QqTOF data and the application of spectral libraries to yield rapid and high-confidence tentative identifications in complex human and environmental samples.

Seasonal Dynamics of Glyphosate and AMPA in Lake Greifensee: Rapid Microbial Degradation in the Epilimnion During Summer.

Occurrence and fate of glyphosate, a widely used herbicide, and its main metabolite AMPA was investigated in Lake Greifensee, Switzerland. Monthly vertical concentration profiles in the lake showed an increase of glyphosate concentrations in the epilimnion from 15 ng/L in March to 145 ng/L in July, followed by a sharp decline to <5 ng/L in August. A similar pattern was observed for AMPA. Concentrations of glyphosate and AMPA in the two main tributaries generally were much higher than in the lake. Simulations using a numerical lake model indicated that a substantial amount of glyphosate and AMPA dissipated in the epilimnion, mainly in July and August, with half-lives of only ≈2-4 days which is ≫100 times faster than in the preceding months. Fast dissipation coincided with high water temperatures and phytoplankton densities, and low phosphate concentrations. This indicates that glyphosate might have been used as an alternative phosphorus source by bacterio- and phytoplankton. Metagenomic analysis of lake water revealed the presence of organisms known to be capable of degrading glyphosate and AMPA.

Similarity of High-Resolution Tandem Mass Spectrometry Spectra of Structurally Related Micropollutants and Transformation Products.

High-resolution tandem mass spectrometry (HRMS2) with electrospray ionization is frequently applied to study polar organic molecules such as micropollutants. Fragmentation provides structural information to confirm structures of known compounds or propose structures of unknown compounds. Similarity of HRMS2 spectra between structurally related compounds has been suggested to facilitate identification of unknown compounds. To test this hypothesis, the similarity of reference standard HRMS2 spectra was calculated for 243 pairs of micropollutants and their structurally related transformation products (TPs); for comparison, spectral similarity was also calculated for 219 pairs of unrelated compounds. Spectra were measured on Orbitrap and QTOF mass spectrometers and similarity was calculated with the dot product. The influence of different factors on spectral similarity [e.g., normalized collision energy (NCE), merging fragments from all NCEs, and shifting fragments by the mass difference of the pair] was considered. Spectral similarity increased at higher NCEs and highest similarity scores for related pairs were obtained with merged spectra including measured fragments and shifted fragments. Removal of the monoisotopic peak was critical to reduce false positives. Using a spectral similarity score threshold of 0.52, 40% of related pairs and 0% of unrelated pairs were above this value. Structural similarity was estimated with the Tanimoto coefficient and pairs with higher structural similarity generally had higher spectral similarity. Pairs where one or both compounds contained heteroatoms such as sulfur often resulted in dissimilar spectra. This work demonstrates that HRMS2 spectral similarity may indicate structural similarity and that spectral similarity can be used in the future to screen complex samples for related compounds such as micropollutants and TPs, assisting in the prioritization of non-target compounds. Graphical Abstract ᅟ.

Exploring micropollutant biotransformation in three freshwater phytoplankton species.

Phytoplankton constitute an important component of surface water ecosystems; however little is known about their contribution to biotransformation of organic micropollutants. To elucidate biotransformation processes, batch experiments with two cyanobacterial species (Microcystis aeruginosa and Synechococcus sp.) and one green algal species (Chlamydomonas reinhardtii) were conducted. Twenty-four micropollutants were studied, including 15 fungicides and 9 pharmaceuticals. Online solid phase extraction (SPE) coupled with liquid chromatography (LC)-high resolution tandem mass spectrometry (HRMS/MS) was used together with suspect and nontarget screening to identify transformation products (TPs). 14 TPs were identified for 9 micropollutants, formed by cytochrome P450-mediated oxidation, conjugation and methylation reactions. The observed transformation pathways included reactions likely mediated by promiscuous enzymes, such as glutamate conjugation to mefenamic acid and pterin conjugation of sulfamethoxazole. For 15 compounds, including all azole fungicides tested, no TPs were identified. Environmentally relevant concentrations of chemical stressors had no influence on the transformation types and rates.

Automated screening for small organic ligands using DNA-encoded chemical libraries.

DNA-encoded chemical libraries (DECLs) are collections of organic compounds that are individually linked to different oligonucleotides, serving as amplifiable identification barcodes. As all compounds in the library can be identified by their DNA tags, they can be mixed and used in affinity-capture experiments on target proteins of interest. In this protocol, we describe the screening process that allows the identification of the few binding molecules within the multiplicity of library members. First, the automated affinity selection process physically isolates binding library members. Second, the DNA codes of the isolated binders are PCR-amplified and subjected to high-throughput DNA sequencing. Third, the obtained sequencing data are evaluated using a C++ program and the results are displayed using MATLAB software. The resulting selection fingerprints facilitate the discrimination of binding from nonbinding library members. The described procedures allow the identification of small organic ligands to biological targets from a DECL within 10 d.

Microvolume trace environmental analysis using peak-focusing online solid-phase extraction-nano-liquid chromatography-high-resolution mass spectrometry.

Online solid-phase extraction was combined with nano-liquid chromatography coupled to high-resolution mass spectrometry (HRMS) for the analysis of micropollutants in environmental samples from small volumes. The method was validated in surface water, Microcystis aeruginosa cell lysate, and spent Microcystis growth medium. For 41 analytes, quantification limits of 0.1-28 ng/L (surface water) and 0.1-32 ng/L (growth medium) were obtained from only 88 µL of sample. In cell lysate, quantification limits ranged from 0.1-143 ng/L or 0.33-476 ng/g dry weight from a sample of 88 µL, or 26 µg dry weight, respectively. The method matches the sensitivity of established online and offline solid-phase extraction-liquid chromatography-mass spectrometry methods but requires only a fraction of the sample used by those techniques, and is among the first applications of nano-LC-MS for environmental analysis. The method was applied to the determination of bioconcentration in Microcystis aeruginosa in a laboratory experiment, and the benefit of coupling to HRMS was demonstrated in a transformation product screening.

Strategies to characterize polar organic contamination in wastewater: exploring the capability of high resolution mass spectrometry.

Wastewater effluents contain a multitude of organic contaminants and transformation products, which cannot be captured by target analysis alone. High accuracy, high resolution mass spectrometric data were explored with novel untargeted data processing approaches (enviMass, nontarget, and RMassBank) to complement an extensive target analysis in initial "all in one" measurements. On average 1.2% of the detected peaks from 10 Swiss wastewater treatment plant samples were assigned to target compounds, with 376 reference standards available. Corrosion inhibitors, artificial sweeteners, and pharmaceuticals exhibited the highest concentrations. After blank and noise subtraction, 70% of the peaks remained and were grouped into components; 20% of these components had adduct and/or isotope information available. An intensity-based prioritization revealed that only 4 targets were among the top 30 most intense peaks (negative mode), while 15 of these peaks contained sulfur. Of the 26 nontarget peaks, 7 were tentatively identified via suspect screening for sulfur-containing surfactants and one peak was identified and confirmed as 1,3-benzothiazole-2-sulfonate, an oxidation product of a vulcanization accelerator. High accuracy, high resolution data combined with tailor-made nontarget processing methods (all available online) provided vital information for the identification of a wider range of heteroatom-containing compounds in the environment.

Automatic recalibration and processing of tandem mass spectra using formula annotation.

High accuracy, high resolution tandem mass spectrometry (MS/MS) is becoming more common in analytical applications, yet databases of these spectra remain limited. Databases require good quality spectra with sufficient compound information, but processing, calibration, noise reduction and retrieval of compound information are time-consuming tasks that prevent many contributions. We present a comprehensive workflow for the automatic processing of MS/MS using formula annotation for recalibration and cleanup to generate high quality spectra of standard compounds for upload to MassBank (www.massbank.jp). Compound information is retrieved via Internet services. Reference standards of 70 pesticides were measured at various collision energies on an LTQ-Orbitrap XL to develop and evaluate the workflow. A total of 944 resulting spectra are now available on MassBank. Evidence of nitrogen adduct formation during MS/MS fragmentation processes was found, highlighting the benefits high accuracy MS/MS offers for spectral interpretation. A database of recalibrated, cleaned-up spectra resulted in the most correct spectra ranked in first place, regardless of whether the search spectra were recalibrated or not, whereas the average rank of the correct molecular formula was improved from 2.55 (uncalibrated) to 1.53 when using recalibrated MS/MS data. The workflow is available as an R package RMassBank capable of generating MassBank records from raw MS and MS/MS data and can be adjusted to process data acquired with different settings and instruments. This workflow is a vital step towards addressing the need for more high quality, high accuracy MS/MS spectra in spectral databases and provides important information for spectral interpretation.