CLN3 deficiency leads to neurological and metabolic perturbations during early development.

Juvenile neuronal ceroid lipofuscinosis (or Batten disease) is an autosomal recessive, rare neurodegenerative disorder that affects mainly children above the age of 5 yr and is most commonly caused by mutations in the highly conserved CLN3 gene. Here, we generated cln3 morphants and stable mutant lines in zebrafish. Although neither morphant nor mutant cln3 larvae showed any obvious developmental or morphological defects, behavioral phenotyping of the mutant larvae revealed hyposensitivity to abrupt light changes and hypersensitivity to pro-convulsive drugs. Importantly, in-depth metabolomics and lipidomics analyses revealed significant accumulation of several glycerophosphodiesters (GPDs) and cholesteryl esters, and a global decrease in bis(monoacylglycero)phosphate species, two of which (GPDs and bis(monoacylglycero)phosphates) were previously proposed as potential biomarkers for CLN3 disease based on independent studies in other organisms. We could also demonstrate GPD accumulation in human-induced pluripotent stem cell-derived cerebral organoids carrying a pathogenic variant for CLN3 Our models revealed that GPDs accumulate at very early stages of life in the absence of functional CLN3 and highlight glycerophosphoinositol and BMP as promising biomarker candidates for pre-symptomatic CLN3 disease.

Cracked and shucked: GC-APCI-IMS-HRMS facilitates identification of unknown halogenated organic chemicals in French marine bivalves.

High resolution mass spectrometry (HRMS)-based non-target analysis coupled with ion mobility spectrometry (IMS) is gaining momentum due to its ability to provide complementary information which can be useful in the identification of unknown organic chemicals in support of efforts in unraveling the complexity of the chemical exposome. The chemical exposome in the marine environment, though not as well studied as its freshwater counterparts, is not foreign to chemical diversity specially when it comes to potentially bioaccumulative and bioactive polyhalogenated organic contaminants and natural products. In this work we present in detail how we utilized IMS-HRMS coupled with gas chromatographic separation and atmospheric pressure chemical ionization (APCI) to annotate polyhalogenated organic chemicals in French bivalves collected from 25 sites along the French coasts. We describe how we used open cheminformatic tools to exploit isotopologue patterns, isotope ratios, Kendrick mass defect (Cl scale), and collisional cross section (CCS), in order to annotate 157 halogenated features (level 1: 54, level 2: 47, level 3: 50, and level 4: 6). Grouping the features into 11 compound classes was facilitated by a KMD vs CCS plot which showed co-clustering of potentially structurally-related compounds. The features were semi-quantified to gain insight into the distribution of these halogenated features along the French coast, ultimately allowing us to differentiate between sites that are more anthropologically impacted versus sites that are potentially biodiverse.

Adding open spectral data to MassBank and PubChem using open source tools to support non-targeted exposomics of mixtures.

The term "exposome" is defined as a comprehensive study of life-course environmental exposures and the associated biological responses. Humans are exposed to many different chemicals, which can pose a major threat to the well-being of humanity. Targeted or non-targeted mass spectrometry techniques are widely used to identify and characterize various environmental stressors when linking exposures to human health. However, identification remains challenging due to the huge chemical space applicable to exposomics, combined with the lack of sufficient relevant entries in spectral libraries. Addressing these challenges requires cheminformatics tools and database resources to share curated open spectral data on chemicals to improve the identification of chemicals in exposomics studies. This article describes efforts to contribute spectra relevant for exposomics to the open mass spectral library MassBank (https://www.massbank.eu) using various open source software efforts, including the R packages RMassBank and Shinyscreen. The experimental spectra were obtained from ten mixtures containing toxicologically relevant chemicals from the US Environmental Protection Agency (EPA) Non-Targeted Analysis Collaborative Trial (ENTACT). Following processing and curation, 5582 spectra from 783 of the 1268 ENTACT compounds were added to MassBank, and through this to other open spectral libraries (e.g., MoNA, GNPS) for community benefit. Additionally, an automated deposition and annotation workflow was developed with PubChem to enable the display of all MassBank mass spectra in PubChem, which is rerun with each MassBank release. The new spectral records have already been used in several studies to increase the confidence in identification in non-target small molecule identification workflows applied to environmental and exposomics research.

Multiple Functions of the Type II Thioesterase Associated with the Phoslactomycin Polyketide Synthase.

Polyketide synthases (PKSs) are molecular assembly lines that condense basic chemical building blocks for the production of structurally diverse polyketides. Many PKS biosynthetic gene clusters contain a gene encoding for a type II thioesterase (TEII). It is believed that TEIIs exert a proofreading function and restore or increase the productivity of PKSs by removing aberrant modifications on the acyl-carrier proteins (ACPs) of the PKS assembly line. Yet biochemical evidence is still sparse. Here, we investigated the function of PnG, the TEII of the phoslactomycin PKS (Pn PKS), in the context of its cognate assembly line in vitro. Biochemical analysis revealed that PnG preferentially hydrolyzes alkyl-ACPs over (alkyl)malonyl-ACPs by up to three orders of magnitude, supporting a proofreading role of the enzyme. We further demonstrate that PnG increases the in vitro production of different native and non-native tetra-, penta-, and hexaketide derivatives of phoslactomycin by more than one order of magnitude and show that these effects are caused by the initial clearing of the Pn PKS, as well as proofreading of the active assembly line. Finally, we demonstrate that PnG is able to release intermediate but notably also terminal polyketides from the Pn PKS. This allows PnG to functionally replace and overcome the terminal TEI activity of chimeric in vitro Pn PKS systems, as showcased with a phoslactomycin hexaketide system. Altogether, our experiments provide detailed insights into the molecular mechanisms and the multiple functions of PnG in its native context, as well as their potential use in future applications.

Studying the Parkinson's disease metabolome and exposome in biological samples through different analytical and cheminformatics approaches: a pilot study.

Parkinson's disease (PD) is the second most prevalent neurodegenerative disease, with an increasing incidence in recent years due to the aging population. Genetic mutations alone only explain <10% of PD cases, while environmental factors, including small molecules, may play a significant role in PD. In the present work, 22 plasma (11 PD, 11 control) and 19 feces samples (10 PD, 9 control) were analyzed by non-target high-resolution mass spectrometry (NT-HRMS) coupled to two liquid chromatography (LC) methods (reversed-phase (RP) and hydrophilic interaction liquid chromatography (HILIC)). A cheminformatics workflow was optimized using open software (MS-DIAL and patRoon) and open databases (all public MSP-formatted spectral libraries for MS-DIAL, PubChemLite for Exposomics, and the LITMINEDNEURO list for patRoon). Furthermore, five disease-specific databases and three suspect lists (on PD and related disorders) were developed, using PubChem functionality to identifying relevant unknown chemicals. The results showed that non-target screening with the larger databases generally provided better results compared with smaller suspect lists. However, two suspect screening approaches with patRoon were also good options to study specific chemicals in PD. The combination of chromatographic methods (RP and HILIC) as well as two ionization modes (positive and negative) enhanced the coverage of chemicals in the biological samples. While most metabolomics studies in PD have focused on blood and cerebrospinal fluid, we found a higher number of relevant features in feces, such as alanine betaine or nicotinamide, which can be directly metabolized by gut microbiota. This highlights the potential role of gut dysbiosis in PD development.

Discovering pesticides and their TPs in Luxembourg waters using open cheminformatics approaches.

The diversity of hundreds of thousands of potential organic pollutants and the lack of (publicly available) information about many of them is a huge challenge for environmental sciences, engineering, and regulation. Suspect screening based on high-resolution liquid chromatography-mass spectrometry (LC-HRMS) has enormous potential to help characterize the presence of these chemicals in our environment, enabling the detection of known and newly emerging pollutants, as well as their potential transformation products (TPs). Here, suspect list creation (focusing on pesticides relevant for Luxembourg, incorporating data sources in 4 languages) was coupled to an automated retrieval of related TPs from PubChem based on high confidence suspect hits, to screen for pesticides and their TPs in Luxembourgish river samples. A computational workflow was established to combine LC-HRMS analysis and pre-screening of the suspects (including automated quality control steps), with spectral annotation to determine which pesticides and, in a second step, their related TPs may be present in the samples. The data analysis with Shinyscreen (https://gitlab.lcsb.uni.lu/eci/shinyscreen/), an open source software developed in house, coupled with custom-made scripts, revealed the presence of 162 potential pesticide masses and 96 potential TP masses in the samples. Further identification of these mass matches was performed using the open source approach MetFrag (https://msbi.ipb-halle.de/MetFrag/). Eventual target analysis of 36 suspects resulted in 31 pesticides and TPs confirmed at Level-1 (highest confidence), and five pesticides and TPs not confirmed due to different retention times. Spatio-temporal analysis of the results showed that TPs and pesticides followed similar trends, with a maximum number of potential detections in July. The highest detections were in the rivers Alzette and Mess and the lowest in the Sûre and Eisch. This study (a) added pesticides, classification information and related TPs into the open domain, (b) developed automated open source retrieval methods - both enhancing FAIRness (Findability, Accessibility, Interoperability and Reusability) of the data and methods; and (c) will directly support "L'Administration de la Gestion de l'Eau" on further monitoring steps in Luxembourg.

Increased coverage and high confidence in suspect screening of emerging contaminants in global environmental samples.

Suspect screening using liquid chromatography with high resolution mass spectrometry provides an opportunity for expanding the detection coverage of emerging contaminants in the environment. Screening workflows may suffer from high frequency of false positives or insufficient confidence in the identification of compounds; however, stringent criteria could lead to high false negatives. A workflow must have a balanced criteria, both selective and sensitive, to be able to identify real features without missing low abundant features traceable to analytes of interest. A highly selective (87%) and sensitive (97%) workflow was developed by characterizing the occurrence of contaminants in wastewater and surface water from Hong Kong, India, Philippines, Sweden, Switzerland, and the U.S. Sixty-eight contaminants were identified and confirmed with reference standards, including pharmaceuticals, pesticides, and industrial chemicals. The antimicrobials metronidazole, clindamycin, linezolid, and rifaximin were detected. Notably, antifungal compounds were detected in samples from six countries, with levels up to 1380 ng/L. Amoxicillin transformation products, penilloic acid (285-8047 ng/L) and penicilloic acid (107 ng/L), were confirmed for the first time with reference standards in wastewater samples from India, Sweden, and U.S. This workflow provides an efficient approach to broad-scale identification of emerging contaminants using publicly-available databases for suspect screening and prioritization.

Occurrence and Distribution of Pharmaceuticals and Their Transformation Products in Luxembourgish Surface Waters.

Pharmaceuticals and their transformation products (TPs) are continuously released into the aquatic environment via anthropogenic activity. To expand knowledge on the presence of pharmaceuticals and their known TPs in Luxembourgish rivers, 92 samples collected during routine monitoring events between 2019 and 2020 were investigated using nontarget analysis. Water samples were concentrated using solid-phase extraction and then analyzed using liquid chromatography coupled to a high-resolution mass spectrometer. Suspect screening was performed using several open source computational tools and resources including Shinyscreen (https://git-r3lab.uni.lu/eci/shinyscreen/), MetFrag (https://msbi.ipb-halle.de/MetFrag/), PubChemLite (https://zenodo.org/record/4432124), and MassBank (https://massbank.eu/MassBank/). A total of 94 pharmaceuticals, 88 confirmed at a level 1 confidence (86 of which could be quantified, two compounds too low to be quantified) and six identified at level 2a, were found to be present in Luxembourg rivers. Pharmaceutical TPs (12) were also found at a level 2a confidence. The pharmaceuticals were present at median concentrations up to 214 ng/L, with caffeine having a median concentration of 1424 ng/L. Antihypertensive drugs (15), psychoactive drugs (15), and antimicrobials (eight) were the most detected groups of pharmaceuticals. A spatiotemporal analysis of the data revealed areas with higher concentrations of the pharmaceuticals, as well as differences in pharmaceutical concentrations between 2019 and 2020. The results of this work will help guide activities for improving water management in the country and set baseline data for continuous monitoring and screening efforts, as well as for further open data and software developments.

Expanded coverage of non-targeted LC-HRMS using atmospheric pressure chemical ionization: a case study with ENTACT mixtures.

Non-targeted analysis (NTA) is a rapidly evolving analytical technique with numerous opportunities to improve and expand instrumental and data analysis methods. In this work, NTA was performed on eight synthetic mixtures containing 1264 unique chemical substances from the U.S. Environmental Protection Agency's Non-Targeted Analysis Collaborative Trial (ENTACT). These mixtures were analyzed by atmospheric pressure chemical ionization (APCI) and electrospray ionization (ESI) using both positive and negative polarities for a total of four modes. Out of the 1264 ENTACT chemical substances, 1116 were detected in at least one ionization mode, 185 chemicals were detected using all four ionization modes, whereas 148 were not detected. Forty-four chemicals were detected only by APCI, and 181 were detected only by ESI. Molecular descriptors and physicochemical properties were used to assess which ionization type was preferred for a given compound. One ToxPrint substructure (naphthalene group) was found to be enriched in compounds only detected using APCI, and eight ToxPrints (e.g., several alcohol moieties) were enriched in compounds only detected using ESI. Examination of physicochemical parameters for ENTACT chemicals suggests that those with higher aqueous solubility preferentially ionized by ESI-. While ESI typically detects a larger number of compounds, APCI offers chromatograms with less background, fewer co-elutions, and additional chemical space coverage, suggesting both should be considered for broader coverage in future NTA research. Graphical abstract.

Applications of Machine Learning to In Silico Quantification of Chemicals without Analytical Standards.

Non-targeted analysis provides a comprehensive approach to analyze environmental and biological samples for nearly all chemicals present. One of the main shortcomings of current analytical methods and workflows is that they are unable to provide any quantitative information constituting an important obstacle in understanding environmental fate and human exposure. Herein, we present an in silico quantification method using mahine-learning for chemicals analyzed using electrospray ionization (ESI). We considered three data sets from different instrumental setups: (i) capillary electrophoresis electrospray ionization-mass spectrometry (CE-MS) in positive ionization mode (ESI+), (ii) liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF/MS) in ESI+ and (iii) LC-QTOF/MS in negative ionization mode (ESI-). We developed and applied two different machine-learning algorithms: a random forest (RF) and an artificial neural network (ANN) to predict the relative response factors (RRFs) of different chemicals based on their physicochemical properties. Chemical concentrations can then be calculated by dividing the measured abundance of a chemical, as peak area or peak height, by its corresponding RRF. We evaluated our models and tested their predictive power using 5-fold cross-validation (CV) and y randomization. Both the RF and the ANN models showed great promise in predicting RRFs. However, the accuracy of the predictions was dependent on the data set composition and the experimental setup. For the CE-MS ESI+ data set, the best model predicted measured RRFs with a mean absolute error (MAE) of 0.19 log units and a cross-validation coefficient of determination (Q2) of 0.84 for the testing set. For the LC-QTOF/MS ESI+ data set, the best model predicted measured RRFs with an MAE of 0.32 and a Q2 of 0.40. For the LC-QTOF/MS ESI- data set, the best model predicted measured RRFs with a MAE of 0.50 and a Q2 of 0.20. Our findings suggest that machine-learning algorithms can be used for predicting concentrations of nontargeted chemicals with reasonable uncertainties, especially in ESI+, while the application on ESI- remains a more challenging problem.

Examining NTA performance and potential using fortified and reference house dust as part of EPA's Non-Targeted Analysis Collaborative Trial (ENTACT).

Non-targeted analysis (NTA) methods are being increasingly used to aid in the identification of unknown compounds in the environment, a problem that has challenged environmental chemists for decades. Despite its increased use, quality assurance practices for NTA have not been well established. Furthermore, capabilities and limitations of certain NTA methods have not been thoroughly evaluated. Standard reference material dust (SRM 2585) was used here to evaluate the ability of NTA to identify previously reported compounds, as well as a suite of 365 chemicals that were spiked at various stages of the analytical procedure. Analysis of the unaltered SRM 2585 extracts revealed that several previously reported compounds can be identified by NTA, and that correct identification was dependent on concentration. A manual inspection of unknown features in SRM 2585 revealed the presence of two chlorinated and fluorinated compounds in high abundance, likely precursors to perfluorooctane sulfonate (PFOS) and perfluorohexane sulfonate (PFHxS). A retrospective analysis of data from the American Healthy Homes Survey revealed that these compounds were present in 42% of sampled homes. Spiking the dust at various stages of sample preparation revealed losses from extraction, cleanup, and instrumental analysis; the log Kow for individual compounds influenced the overall recovery levels but no pattern could be discerned from the various degrees of interference that the matrix had on the ionization efficiency of the spiked chemicals. Analysis of the matrix-free chemical mixture at low, medium, and high concentrations led to more correct identifications than analysis at one, very high concentration. Varying the spiked amount and identifying reported compounds at known concentrations allowed an estimation of the lower limits of identification (LOIs) for NTA, analogous to limits of detection in targeted analysis. The LOIs were much lower than levels in dust that would be likely to cause bioactivity in humans, indicating that NTA is useful for identifying and monitoring compounds that may be of toxicological concern. Graphical abstract.

In silico MS/MS spectra for identifying unknowns: a critical examination using CFM-ID algorithms and ENTACT mixture samples.

High-resolution mass spectrometry (HRMS) enables rapid chemical annotation via accurate mass measurements and matching of experimentally derived spectra with reference spectra. Reference libraries are generated from chemical standards and are therefore limited in size relative to known chemical space. To address this limitation, in silico spectra (i.e., MS/MS or MS2 spectra), predicted via Competitive Fragmentation Modeling-ID (CFM-ID) algorithms, were generated for compounds within the U.S. Environmental Protection Agency's (EPA) Distributed Structure-Searchable Toxicity (DSSTox) database (totaling, at the time of analysis, ~ 765,000 substances). Experimental spectra from EPA's Non-Targeted Analysis Collaborative Trial (ENTACT) mixtures (n = 10) were then used to evaluate the performance of the in silico spectra. Overall, MS2 spectra were acquired for 377 unique compounds from the ENTACT mixtures. Approximately 53% of these compounds were correctly identified using a commercial reference library, whereas up to 50% were correctly identified as the top hit using the in silico library. Together, the reference and in silico libraries were able to correctly identify 73% of the 377 ENTACT substances. When using the in silico spectra for candidate filtering, an examination of binary classifiers showed a true positive rate (TPR) of 0.90 associated with false positive rates (FPRs) of 0.10 to 0.85, depending on the sample and method of candidate filtering. Taken together, these findings show the abilities of in silico spectra to correctly identify true positives in complex samples (at rates comparable to those observed with reference spectra), and efficiently filter large numbers of potential false positives from further consideration. Graphical abstract.

Binding of iodinated contrast media (ICM) and their transformation products with hormone receptors: Are ICM the new EDCs?

Iodinated contrast media (ICM) have been detected at high concentrations (as high as about 3 µg/L) in surface water systems, and recently in fish brains and gonad. The mismatch between the polarity of ICM and the high lipid content of brain raises questions on whether their bioaccumulation is receptor-mediated. Furthermore, the structural similarity of ICM to the natural thyroid hormones thyroxine and triiodothyronine suggest potential binding of ICM to nuclear receptors in the endocrine system. Therefore, an in silico approach based on Surflex-Dock module of SYBYL was used to investigate the molecular docking of selected ICM (diatrizoic acid, iohexol, iopamidol, and iopromide). These ICM showed interaction with nuclear receptors that play key roles in endocrine regulation, including the androgen and estrogen receptors. Furthermore, the results indicate peroxisome proliferator-activated receptor gamma (PPARg) as one of the viable targets in the endocrine disrupting potential of ICM with higher Cscores for the ICM and iopromide transformation products than the reference ligand for the receptor. The data obtained from in silico calculations showed stronger binding of iohexol to the transthyretin-binding pocket compared to the natural hormones, thyroxine and triiodothyronine, suggesting the potential of ICM to act as endocrine disrupting chemicals (EDCs) in the environment.

Connecting environmental exposure and neurodegeneration using cheminformatics and high resolution mass spectrometry: potential and challenges.

Connecting chemical exposures over a lifetime to complex chronic diseases with multifactorial causes such as neurodegenerative diseases is an immense challenge requiring a long-term, interdisciplinary approach. Rapid developments in analytical and data technologies, such as non-target high resolution mass spectrometry (NT-HR-MS), have opened up new possibilities to accomplish this, inconceivable 20 years ago. While NT-HR-MS is being applied to increasingly complex research questions, there are still many unidentified chemicals and uncertainties in linking exposures to human health outcomes and environmental impacts. In this perspective, we explore the possibilities and challenges involved in using cheminformatics and NT-HR-MS to answer complex questions that cross many scientific disciplines, taking the identification of potential (small molecule) neurotoxicants in environmental or biological matrices as a case study. We explore capturing literature knowledge and patient exposure information in a form amenable to high-throughput data mining, and the related cheminformatic challenges. We then briefly cover which sample matrices are available, which method(s) could potentially be used to detect these chemicals in various matrices and what remains beyond the reach of NT-HR-MS. We touch on the potential for biological validation systems to contribute to mechanistic understanding of observations and explore which sampling and data archiving strategies may be required to form an accurate, sustained picture of small molecule signatures on extensive cohorts of patients with chronic neurodegenerative disorders. Finally, we reflect on how NT-HR-MS can support unravelling the contribution of the environment to complex diseases.

Towards a harmonized method for the global reconnaissance of multi-class antimicrobials and other pharmaceuticals in wastewater and receiving surface waters.

Antimicrobial resistance is a worldwide problem that is both pressing and challenging due to the rate at which it is spreading, and the lack of understanding of the mechanisms that link human, animal and environmental sources contributing to its proliferation. One knowledge gap that requires immediate attention is the significance of antimicrobial residues and other pharmaceuticals that are being discharged from wastewater treatment plants (WWTPs) on the dissemination of antimicrobial resistance in the environment. In this work we provide an approach to develop a harmonized analytical method for 8 classes of antimicrobials and other pharmaceuticals that can be used for global monitoring in wastewater and receiving waters. Analysis of these trace organic chemicals in the influent and effluent wastewater, and in the respective upstream and downstream receiving waters from different countries across the globe is not trivial. Here, we demonstrated that sample preparation using solid-phase extraction (SPE) not only provides a convenient and cost-effective shipping of samples, but also adds stability to the analytes during international shipping. It is important that SPE cartridges are maintained at cold temperature during shipment if the duration is longer than 7 days because a significant decrease in recoveries were observed after 7 days in the cartridges stored at room temperature, especially for sulfonamides and tetracyclines. To compensate for sample degradation during shipment, and matrix effects in liquid chromatography/mass spectrometry, the use of stable isotope labeled compounds should be employed when available and affordable. The importance of applying a defined tolerance for the ion ratios (Q/q) that have been optimized for wastewater and surface water is discussed. The tolerance range was set to be the mean Q/q of the analyte standard at various concentrations ±40% for the influent, and ±30% for the effluent, upstream, and downstream samples; for tetracyclines and quinolones, however, the tolerance range was ±80% in order to minimize false negative and false positive detection. The optimized procedures were employed to reveal differences in antimicrobial and pharmaceutical concentrations in influent, effluent, and surface water samples from Hong Kong, India, Philippines, Sweden, Switzerland, and United States. The antimicrobials with the highest concentrations in influent and effluent samples were ciprofloxacin (48,103 ng/L, Hong Kong WWTP 1) and clarithromycin (5178 ng/L, India WWTP 2), respectively. On the other hand, diclofenac (108,000 ng/L, Sweden WWTP 2), caffeine (67,000 ng/L, India WWTP 1), and acetaminophen (28,000 ng/L, India WWTP 1) were the highest detected pharmaceuticals in the receiving surface water samples. Hong Kong showed the highest total antimicrobial concentrations that included macrolides, quinolones, and sulfonamides with concentrations reaching 60,000 ng/L levels in the influent. Antidepressants were predominant in Sweden, Switzerland, and the United States.

Selective Uptake and Bioaccumulation of Antidepressants in Fish from Effluent-Impacted Niagara River.

The continuous release of pharmaceuticals and personal care products (PPCPs) into freshwater systems impacts the health of aquatic organisms. This study evaluates the concentrations and bioaccumulation of PPCPs and the selective uptake of antidepressants in fish from the Niagara River, which connects two of the North American Great lakes (Erie and Ontario). The Niagara River receives PPCPs from different wastewater treatment plants (WWTPs) situated along the river and Lake Erie. Of the 22 targeted PPCPs, 11 were found at part-per-billion levels in WWTP effluents and at part-per-trillion levels in river water samples. The major pollutants observed were the antidepressants (citalopram, paroxetine, sertraline, venlafaxine, and bupropion, and their metabolites norfluoxetine and norsertraline) and the antihistamine diphenhydramine. These PPCPs accumulate in various fish organs, with norsertraline exhibiting the highest bioaccumulation factor (up to about 3000) in the liver of rudd (Scardinius erythrophthalmus), which is an invasive species to the Great Lakes. The antidepressants were selectively taken up by various fish species at different trophic levels, and were further metabolized once inside the organism. The highest bioaccumulation was found in the brain, followed by liver, muscle, and gonads, and can be attributed to direct exposure to WWTP effluent.

Integrative Advanced Oxidation and Biofiltration for Treating Pharmaceuticals in Wastewater.

Advanced oxidation of active pharmaceutical ingredients (APIs) in wastewater produces transformation products (TPs) that are often more biodegradable than the parent compounds. Secondary effluent from a wastewater treatment plant was treated using UV-based advanced oxidation (LPUV/H2O2 and MPUV/NO3) followed by biological aerated filtration (BAF), and different APIs and their transformation products were monitored. The advanced oxidation processes degraded the APIs by 55-87% (LPUV/H2O2) and 58-95% (MPUV/NO3), while minor loss of APIs was achieved in the downstream BAF system. Eleven TPs were detected following oxidation of carbamazepine (5) and iopromide (6); three key TPs were biodegraded in the BAF system. The other TPs remained relatively constant in the BAF. The decrease in UV absorbance (UVA254) of the effluent in the BAF system was linearly correlated to the degradation of the APIs (for the MPUV/NO3-BAF), and can be applied to monitor the biotransformation of APIs in biological-based systems.

Application of metabolite profiling tools and time-of-flight mass spectrometry in the identification of transformation products of iopromide and iopamidol during advanced oxidation.

The efficiency of wastewater treatment systems in removing pharmaceuticals is often assessed on the basis of the decrease in the concentration of the parent compound. However, what is perceived as "removal" during treatment may not necessarily mean mineralization of the pharmaceutical compound but simply conversion into different transformation products (TPs). Using liquid chromatography coupled to a quadrupole time-of-flight mass spectrometer (LC-QToF-MS), we demonstrated conversion of iopromide in wastewater to at least 14 TPs after an advanced oxidation process (AOP) using UV (fluence = 1500 mJ/cm(2)) and H2O2 (10 mg/L). Due to the complexity of the wastewater matrix, the initial experiments were performed using a high concentration (10 mg/L) of iopromide in order to facilitate the identification of TPs. Despite the high concentration of iopromide used, cursory inspection of UV and mass spectra only revealed four TPs in the chromatograms of the post-AOP samples. However, the use of METLIN database and statistics-based profiling tools commonly used in metabolomics proved effective in discriminating between background signals and TPs derived from iopromide. High-resolution mass data allowed one to predict molecular formulas of putative TPs with errors below 5 ppm relative to the observed m/z. Tandem mass spectrometry (MS/MS) data and isotope pattern comparisons provided necessary information that allowed one to elucidate the structure of iopromide TPs. The presence of the proposed iopromide TPs was determined in unspiked wastewater from a municipal wastewater treatment plant, but no iopromide and TPs were detected. Using analogous structural modifications and oxidation that results from the AOP treatment of iopromide, the potential TPs of iopamidol (a structurally similar compound to iopromide) were predicted. The same mass fragmentation pattern observed in iopromide TPs was applied to the predicted iopamidol TPs. LC-QToF-MS revealed the presence of two iopamidol TPs in unspiked AOP-treated wastewater.