Screening Organic Components and Toxicogenic Structures from Regional Fine Particulate Matters Responsible for Myocardial Fibrosis in Male Mice.

Numerous studies indicate that fine particulate matters (PM2.5) and its organic components are urgent risk factors for cardiovascular diseases (CVDs). Combining toxicological experiments, effect-directed analyses, and nontarget identification, this study aims to explore whether PM2.5 exposure in coal-combustion areas induces myocardial fibrosis and how to identify the effective organic components and their toxic structures to support regional risk control. First, we constructed an animal model of real-world PM2.5 exposure during the heating season and found that the exposure impaired cardiac systolic function and caused myocardial fibrosis, with chemokine Ccl2-mediated inflammatory response being the key cause of collagen deposition. Then, using the molecular event as target coupled with two-stage chromatographic isolation and mass spectrometry analyses, we identified a total of 171 suspect organic compounds in the PM2.5 samples. Finally, using hierarchical characteristic fragment analysis, we predicted that 40 of them belonged to active compounds with 6 alert structures, including neopentane, butyldimethylamine, 4-ethylphenol, hexanal, decane, and dimethylaniline. These findings provide evidence for risk management and prevention of CVDs in polluted areas.

High-Efficiency Effect-Directed Analysis Leveraging Five High Level Advancements: A Critical Review.

Organic contaminants are ubiquitous in the environment, with mounting evidence unequivocally connecting them to aquatic toxicity, illness, and increased mortality, underscoring their substantial impacts on ecological security and environmental health. The intricate composition of sample mixtures and uncertain physicochemical features of potential toxic substances pose challenges to identify key toxicants in environmental samples. Effect-directed analysis (EDA), establishing a connection between key toxicants found in environmental samples and associated hazards, enables the identification of toxicants that can streamline research efforts and inform management action. Nevertheless, the advancement of EDA is constrained by the following factors: inadequate extraction and fractionation of environmental samples, limited bioassay endpoints and unknown linkage to higher order impacts, limited coverage of chemical analysis (i.e., high-resolution mass spectrometry, HRMS), and lacking effective linkage between bioassays and chemical analysis. This review proposes five key advancements to enhance the efficiency of EDA in addressing these challenges: (1) multiple adsorbents for comprehensive coverage of chemical extraction, (2) high-resolution microfractionation and multidimensional fractionation for refined fractionation, (3) robust in vivo/vitro bioassays and omics, (4) high-performance configurations for HRMS analysis, and (5) chemical-, data-, and knowledge-driven approaches for streamlined toxicant identification and validation. We envision that future EDA will integrate big data and artificial intelligence based on the development of quantitative omics, cutting-edge multidimensional microfractionation, and ultraperformance MS to identify environmental hazard factors, serving for broader environmental governance.

Deep Learning Bridged Bioactivity, Structure, and GC-HRMS-Readable Evidence to Decipher Nontarget Toxicants in Sediments.

Identifying causative toxicants in mixtures is critical, but this task is challenging when mixtures contain multiple chemical classes. Effect-based methods are used to complement chemical analyses to identify toxicants, yet conventional bioassays typically rely on an apical and/or single endpoint, providing limited diagnostic potential to guide chemical prioritization. We proposed an event-driven taxonomy framework for mixture risk assessment that relied on high-throughput screening bioassays and toxicant identification integrated by deep learning. In this work, the framework was evaluated using chemical mixtures in sediments eliciting aryl-hydrocarbon receptor activation and oxidative stress response. Mixture prediction using target analysis explained <10% of observed sediment bioactivity. To identify additional contaminants, two deep learning models were developed to predict fingerprints of a pool of bioactive substances (event driver fingerprint, EDFP) and convert these candidates to MS-readable information (event driver ion, EDION) for nontarget analysis. Two libraries with 121 and 118 fingerprints were established, and 247 bioactive compounds were identified at confidence level 2 or 3 in sediment extract using GC-qToF-MS. Among them, 12 toxicants were analytically confirmed using reference standards. Collectively, we present a "bioactivity-signature-toxicant" strategy to deconvolute mixtures and to connect patchy data sets and guide nontarget analysis for diverse chemicals that elicit the same bioactivity.

Identification of hot spots and co-occurrence patterns of activities on thyroid hormone receptor and transthyretin binding in passive samplers from Czech surface waters.

One of the less studied in vitro biological activities in the aquatic environment are thyroid hormone receptor beta (TRß)-mediated agonistic and antagonistic activities and transthyretin (TTR) binding activity. They were measured mostly using active sampling methods, but rarely found. It is unclear if these activities co-occur, and the drivers of the (anti-)TRß activity are mostly unknown. Therefore, the main aim of the study was to determine (anti-)TRß activities as well as transthyretin (TTR) binding activity in passive samplers from Czech surface waters in combination with the search for the effect drivers based on liquid chromatography-high resolution mass spectrometry (LC-HRMS) analysis by applying suspect screening. Passive samplers (polar organic chemical integrative samplers, POCIS) were deployed at twenty-one sites (all ends of watersheds and other important sites in Elbe River) in the Czech rivers. The (anti-)TRß and TTR binding activity were measured using (anti-)TRß-CALUX and TTR-TRß-CALUX bioassays. Anti-TRß activity was found at eight sites, and TTR binding activity co-occurred there at six of these sites. The co-occurrence of TRß-mediated antagonistic activity and TTR binding indicate that they may have common effect drivers. No sample exhibited TRß agonistic activity. The extract from the site Bílina River, the most burdened with anti-TRß activity, was further successfully fractionated, and this activity was revealed in the fraction, where mid-polar compounds prevailed. However, the suspect LC-HRMS analysis did not reveal the chemical effect drivers. Our results showed that anti-TRß activity can be found in surface waters by employing passive sampling and frequently co-occurs with TTR binding activity. Overall, the fractionation procedure and non-target data acquisition used in this study can serve as a basis for searching the effect drivers in future research.

Toxicity Evaluation and Effect-Based Identification of Chlorine Disinfection Products of the Anti-COVID-19 Drug Chloroquine Phosphate.

Antiviral transformation products (TPs) generated during wastewater treatment are an environmental concern, as their discharge, in considerable amounts, into natural waters during a pandemic can pose possible risks to the aquatic environment. Identification of the hazardous TPs generated from antivirals during wastewater treatment is important. Herein, chloroquine phosphate (CQP), which was widely used during the coronavirus disease-19 (COVID-19) pandemic, was selected for research. We investigated the TPs generated from CQP during water chlorination. Zebrafish (Danio rerio) embryos were used to assess the developmental toxicity of CQP after water chlorination, and hazardous TPs were estimated using effect-directed analysis (EDA). Principal component analysis revealed that the developmental toxicity induced by chlorinated samples could be relevant to the formation of some halogenated TPs. Fractionation of the hazardous chlorinated sample, along with the bioassay and chemical analysis, identified halogenated TP387 as the main hazardous TP contributing to the developmental toxicity induced by chlorinated samples. TP387 could also be formed in real wastewater during chlorination in environmentally relevant conditions. This study provides a scientific basis for the further assessment of environmental risks of CQP after water chlorination and describes a method for identifying unknown hazardous TPs generated from pharmaceuticals during wastewater treatment.

Methodology for Effect-Based Identification of Bioconcentratable Endocrine Disrupting Chemicals (EDCs) in Water: Establishment, Validation, and Application.

Since the wide occurrence of endocrine disrupting chemicals (EDCs) in water is associated with various adverse effects in aquatic organisms, it is urgent to identify key bioconcentratable EDCs. Currently, bioconcentration is generally ignored during the identification of key EDCs. Thus, a methodology for effect-based identification of bioconcentratable EDCs was established in Microcosm, validated in the field, and applied to typical surface water in Taihu Lake. In Microcosm, an inverted U-shaped relationship between logBCFs and logKows was observed for typical EDCs, with medium hydrophobic EDCs (3 ≤ logKow ≤ 7) exhibiting the greatest bioconcentration potentials. On this basis, enrichment methods for bioconcentratable EDCs were established using POM and LDPE, which better fitted the bioconcentration characteristics and enabled the enrichment of 71 ± 8% and 69 ± 6% bioconcentratable compounds. The enrichment methods were validated in the field, where LDPE exhibited a more significant correlation with the bioconcentration characteristics than POM, with mean correlation coefficients of 0.36 and 0.15, respectively, which was selected for further application. By application of the new methodology in Taihu Lake, 7 EDCs were prioritized from 79 identified EDCs as key bioconcentratable EDCs on consideration of their great abundance, bioconcentration potentials, and anti-androgenic potencies. The established methodology could support the evaluation and identification of bioconcentratable contaminants.

Identification of antimicrobial and glucocorticoid compounds in wastewater effluents with effect-directed analysis.

Pharmaceuticals, such as glucocorticoids and antibiotics, are inadequately removed from wastewater and may cause unwanted toxic effects in the receiving environment. This study aimed to identify contaminants of emerging concern in wastewater effluent with antimicrobial or glucocorticoid activity by applying effect-directed analysis (EDA). Effluent samples from six wastewater treatment plants (WWTPs) in the Netherlands were collected and analyzed with unfractionated and fractionated bioassay testing. Per sample, 80 fractions were collected and in parallel high-resolution mass spectrometry (HRMS) data were recorded for suspect and nontarget screening. The antimicrobial activity of the effluents was determined with an antibiotics assay and ranged from 298 to 711 ng azithromycin equivalents·L-1. Macrolide antibiotics were identified in each effluent and found to significantly contribute to the antimicrobial activity of each sample. Agonistic glucocorticoid activity determined with the GR-CALUX assay ranged from 98.1 to 286 ng dexamethasone equivalents·L-1. Bioassay testing of several tentatively identified compounds to confirm their activity revealed inactivity in the assay or the incorrect identification of a feature. Effluent concentrations of glucocorticoid active compounds were estimated from the fractionated GR-CALUX bioassay response. Subsequently, the biological and chemical detection limits were compared and a sensitivity gap between the two monitoring approaches was identified. Overall, these results emphasize that combining sensitive effect-based testing with chemical analysis can more accurately reflect environmental exposure and risk than chemical analysis alone.

Sample preparation for planar chromatography.

High-performance thin-layer chromatography has favorable properties for high-throughput separations with a high matrix tolerance. Sample preparation, however, is sometimes required to control specific matrix interferences and to enhance the detectability of target compounds. Trends in contemporary applications have shifted from absorbance and fluorescence detection to methods employing bioassays and mass spectrometry. Traditional methods (shake-flask, heat at reflux, Soxhlet, and hydrodistillation) are being challenged by automated instrumental approaches (ultrasound-assisted and microwave-assisted solvent extraction, pressurized liquid extraction, and supercritical fluid extraction) and the quick, easy cheap, efficient, rugged, and safe extraction method for faster and streamlined sample processing. Liquid-liquid extraction remains the most widely used approach for sample clean-up with increasing competition from solid-phase extraction. On-layer sample, clean-up by planar solid-phase extraction is increasingly used for complex samples and in combination with heart-cut multimodal systems. The automated spray-on sample applicator, the elution head interface, biological detection of target and non-target compounds, and straightforward mass spectrometric detection are highlighted as the main factors directing current interest toward faster and simpler sample workflows, analysis of more complex samples, and the determination of minor contaminants requiring high concentration factors.

Suspect Screening of Chemicals in Hospital Wastewaters Using Effect-Directed Analysis Approach as Prioritization Strategy.

The increasing number of contaminants in the environment has pushed water monitoring programs to find out the most hazardous known and unknown chemicals in the environment. Sample treatment-simplification methods and non-target screening approaches can help researchers to not overlook potential chemicals present in complex aqueous samples. In this work, an effect-directed analysis (EDA) protocol using the sea urchin embryo test (SET) as a toxicological in vivo bioassay was used as simplified strategy to identify potential unknown chemicals present in a very complex aqueous matrix such as hospital effluent. The SET bioassay was used for the first time here to evaluate potential toxic fractions in hospital effluent, which were obtained after a two-step fractionation using C18 and aminopropyl chromatographic semi-preparative columns. The unknown compounds present in the toxic fractions were identified by means of liquid chromatography coupled to a Q Exactive Orbitrap high-resolution mass spectrometer (LC-HRMS) and using a suspect analysis approach. The results were complemented by gas chromatography-mass spectrometry analysis (GC-MS) in order to identify the widest range of chemical compounds present in the sample and the toxic fractions. Using EDA as sample treatment simplification method, the number of unknown chemicals (>446 features) detected in the raw sample was narrowed down to 94 potential toxic candidates identified in the significantly toxic fractions. Among them, the presence of 25 compounds was confirmed with available chemical standards including 14 pharmaceuticals, a personal care product, six pesticides and four industrial products. The observations found in this work emphasize the difficulties in identifying potential toxicity drivers in complex water samples, as in the case of hospital wastewater.

Bioassay-Guided Assessment of Antioxidative, Anti-Inflammatory and Antimicrobial Activities of Extracts from Medicinal Plants via High-Performance Thin-Layer Chromatography.

Natural products and their analogues have contributed significantly to treatment options, especially for anti-inflammatory and infectious diseases. Thus, the primary objective of this work was to compare the bioactivity profiles of selected medicinal plants that are historically used in folk medicine to treat inflammation and infections in the body. Chemical HPTLC fingerprinting was used to assess antioxidant, phenolic and flavonoid content, while bioassay-guided HPTLC was used to detect compounds with the highest antibacterial and anti-inflammatory activities. The results of this study showed that green tea leaf, walnut leaf, St. John's wort herb, wild thyme herb, European goldenrod herb, chamomile flower, and immortelle flower extracts were strong radical scavengers. Green tea and nettle extracts were the most active extracts against E. coli, while calendula flower extract showed significant potency against S. aureus. Furthermore, green tea, greater celandine, and fumitory extracts exhibited pronounced potential in suppressing COX-1 activity. The bioactive compounds from the green tea extract, as the most bioactive, were isolated by preparative thin-layer chromatography and characterized with their FTIR spectra. Although earlier studies have related green tea's anti-inflammatory properties to the presence of catechins, particularly epigallocatechin-3-gallate, the FTIR spectrum of the compound from the most intense bioactive zone showed the strongest anti-inflammatory activity can be attributed to amino acids and heterocyclic compounds. As expected, antibacterial activity in extracts was related to fatty acids and monoglycerides.

Bioactivity Profiles on 15 Different Effect Mechanisms for 15 Golden Root Products via High-Performance Thin-Layer Chromatography, Planar Assays, and High-Resolution Mass Spectrometry.

Planar chromatography has recently been combined with six different effect-directed assays for three golden root (Rhodiola rosea L.) samples. However, the profiles obtained showed an intense tailing, making zone differentiation impossible. The profiling was therefore improved to allow for the detection of individual bioactive compounds, and the range of samples was extended to 15 commercial golden root products. Further effect-directed assays were studied providing information on 15 different effect mechanisms, i.e., (1) tyrosinase, (2) acetylcholinesterase, (3) butyrylcholinesterase, (4) ß-glucuronidase, and (5) α-amylase inhibition, as well as endocrine activity via the triplex planar yeast antagonist-verified (6-8) estrogen or (9-11) androgen screen, (12) genotoxicity via the planar SOS-Umu-C bioassay, antimicrobial activity against (13) Gram-negative Aliivibrio fischeri and (14) Gram-positive Bacillus subtilis bacteria, and (15) antioxidative activity (DPPH• radical scavengers). Most of the golden root profiles obtained were characteristic, but some samples differed substantially. The United States Pharmacopeia reference product showed medium activity in most of the assays. The six most active compound zones were further characterized using high-resolution mass spectrometry, and the mass signals obtained were tentatively assigned to molecular formulae. In addition to confirming the known activities, this study is the first to report that golden root constituents inhibit butyrylcholinesterase (rosin was tentatively assigned), ß-glucuronidase (rosavin, rosarin, rosiridin, viridoside, and salidroside were tentatively assigned), and α-amylase (stearic acid and palmitic acid were tentatively assigned) and that they are genotoxic (hydroquinone was tentatively assigned) and are both agonistic and antagonistic endocrine active.

Antimicrobial Diterpenes from Rough Goldenrod (Solidago rugosa Mill.).

Solidago rugosa is one of the goldenrod species native to North America but has sporadically naturalized as an alien plant in Europe. The investigation of the root and leaf ethanol extracts of the plant using a bioassay-guided process with an anti-Bacillus assay resulted in the isolation of two antimicrobial components. Structure elucidation was performed based on high-resolution tandem mass spectrometric and one- and two-dimensional NMR spectroscopic analyses that revealed (-)-hardwickiic acid (Compound 1) and (-)-abietic acid (Compound 2). The isolates were evaluated for their antimicrobial properties against several plant pathogenic bacterial and fungal strains. Both compounds demonstrated an antibacterial effect, especially against Gram-positive bacterial strains (Bacillus spizizenii, Clavibacter michiganensis subsp. michiganensis, and Curtobacterium flaccumfaciens pv. flaccumfaciens) with half maximal inhibitory concentration (IC50) between 1 and 5.1 µg/mL (5-20 times higher than that of the positive control gentamicin). In the used concentrations, minimal bactericidal concentration (MBC) was reached only against the non-pathogen B. spizizenii. Besides their activity against Fusarium avenaceum, the highest antifungal activity was observed for Compound 1 against Bipolaris sorokiniana with an IC50 of 3.8 µg/mL.

High-Performance Data Processing Workflow Incorporating Effect-Directed Analysis for Feature Prioritization in Suspect and Nontarget Screening.

Effect-directed analysis (EDA) aims at the detection of bioactive chemicals of emerging concern (CECs) by combining toxicity testing and high-resolution mass spectrometry (HRMS). However, consolidation of toxicological and chemical analysis techniques to identify bioactive CECs remains challenging and laborious. In this study, we incorporate state-of-the-art identification approaches in EDA and propose a robust workflow for the high-throughput screening of CECs in environmental and human samples. Three different sample types were extracted and chemically analyzed using a single high-performance liquid chromatography HRMS method. Chemical features were annotated by suspect screening with several reference databases. Annotation quality was assessed using an automated scoring system. In parallel, the extracts were fractionated into 80 micro-fractions each covering a couple of seconds from the chromatogram run and tested for bioactivity in two bioassays. The EDA workflow prioritized and identified chemical features related to bioactive fractions with varying levels of confidence. Confidence levels were improved with the in silico software tools MetFrag and the retention time indices platform. The toxicological and chemical data quality was comparable between the use of single and multiple technical replicates. The proposed workflow incorporating EDA for feature prioritization in suspect and nontarget screening paves the way for the routine identification of CECs in a high-throughput manner.

Identification of Glucocorticoid Receptor Antagonistic Activities and Responsible Compounds in House Dust: Bioaccessibility Should Not Be Ignored.

More and more contaminants in dust have been found to be glucocorticoid receptor (GR) disrupting chemicals. However, little is known about the related potency and responsible toxicants, especially for the main bioaccessible ones in dust. An effect-directed analysis (EDA)-based workflow was developed, including solvent-based exhaustive extraction/tenax-assisted bioaccessible extraction (TBE), high-throughput bioassays, suspect and non-target analysis, as well as in silico candidate selection, for a more realistic identification of responsible contaminants in dust. None of the 39 dust samples from 23 cities in China exhibited GR agonistic activity, while GR antagonistic potencies were detected in 34.8% of samples, being significantly different from the high detection frequency of GR agonistic activities in other environmental media. The GR antagonistic potencies of the dust samples were all reduced after bioaccessible extraction. The mean bioaccessibility of GR antagonistic potency compared with the related exhaustive extracts was 36.8%, and the lowest value was 9%. By using in silico candidate selection, greater than 99% candidate chemical structures which were found by a non-target screening strategy were removed. Di-n-butyl phthalate (DnBP), diisobutyl phthalate (DiBP), and nicotine (NIC) were responsible for the activities of the exhaustive extracts of dust, contributing up to 91% potencies. DiBP and DnBP were also responsible for the bioaccessible activities, contributing up to 79% potencies. However, the contribution from NIC decreased significantly and can be ignored because of its low bioaccessibility. This study suggests that the improved workflow combining extraction, reporter gene bioassays, suspect and non-target analysis, as well as in silico candidate selection is useful for EDA analysis in dust samples. In addition, exhaustive extraction may overestimate the risk of contaminants, while bioaccessibility evaluation based on bioaccessible extraction is essential in both effect evaluation and toxicant identification.

Effect-Directed Analysis Based on the Reduced Human Transcriptome (RHT) to Identify Organic Contaminants in Source and Tap Waters along the Yangtze River.

Since a large number of contaminants are detected in source waters (SWs) and tap waters (TWs), it is important to perform a comprehensive effect evaluation and key contributor identification. A reduced human transcriptome (RHT)-based effect-directed analysis, which consisted of a concentration-dependent RHT to reveal the comprehensive effects and noteworthy pathways and systematic identification of key contributors based on the interactions between compounds and pathway effects, was developed and applied to typical SWs and TWs along the Yangtze River. By RHT, 42% more differentially expressed genes and 33% more pathways were identified in the middle and lower reaches, indicating heavier pollution. Hormone and immune pathways were prioritized based on the detection frequency, sensitivity, and removal efficiency, among which the estrogen receptor pathway was the most noteworthy. Consistent with RHT, estrogenic effects were widespread along the Yangtze River based on in vitro evaluations. Furthermore, 38 of 100 targets, 39 pathway-related suspects, and 16 estrogenic nontargets were systematically identified. Among them, diethylstilbestrol was the dominant contributor, with the estradiol equivalent (EEQ) significantly correlated with EEQwater. In addition, zearalenone and niclosamide explained up to 54% of the EEQwater. The RHT-based EDA method could support the effect evaluation, contributor identification, and risk management of micropolluted waters.

Effect-directed analysis of toxic organics in PM2.5 exposure to the cellular bioassays in vitro: Application in Shanxi of China.

To optimize the effect-directed analysis (EDA) approach to identify the fine particulate matter (PM2.5) bound organic toxicants, Jinzhong city, in the Shanxi Province of China, was selected as the object of our study. First, PM2.5 samples were collected and their organic extracts were separated out in 9 fractions (F1-F9) using reversed-phase high performance liquid chromatography after purification using gel permeation chromatography. Second, the toxicity effects of each fraction were measured by human bronchial epithelial cells (BEAS-2B) in vitro. And toxicity effects included antioxidant stress (ROS, LDH, and CAT) and an inflammatory response (IL-6, IL-1ß, and TNF-α). The results showed that the scores of the toxicity effects on multiple lines of evidence were the highest in the F3 and F4 fractions compared with those of the control. Subsequently, the main poisons, o-cymene, p-cymene, benzene, ethylbenzene, xylene, and styrene, were identified using GC×GC-TOF/MS. Finally, to confirm the above possible candidates, (1) the levels of o-cymene, p-cymene and BTEXS in daily PM2.5 were measured using GC-MS in November 2020, and the rates of detection of these pollutants were 100% in PM2.5. Among them, o-cymene and p-cymene were first reported as the key toxic substances of PM2.5, and their average concentration values were 0.16 ± 0.11 and 0.18 ± 0.15 ng‧m-3, respectively. (2) the toxicity of p-cymene may be no less than that of other benzene derivatives according to their LC50 in Daphnia magna. (3) based on canonical correlation analysis, the exposure to p-cymene, benzene, and styrene in PM2.5 was most likely associated with the toxicity effects (CAT, IL-6, and TNF-α), which in turn caused the observed toxicity. In conclusion, p-cymene, benzene, and styrene were found to be the key toxic organics in PM2.5 for cells in vitro. EDA technology avoids the limitations of chemical analysis and uncertainty of the biological testing and adds new toxicants to the control list of PM2.5, contributing to this study field. However, the application of EDA to PM2.5 still faces challenges such as the selection of biological effects, loss of toxicity with the separation process, influence of the dosing method, and identification of the unknown effects of pollutants.

SETApp: A machine learning and image analysis based application to automate the sea urchin embryo test.

Since countless xenobiotic compounds are being found in the environment, ecotoxicology faces an astounding challenge in identifying toxicants. The combination of high-throughput in vivo/in vitro bioassays with high-resolution chemical analysis is an effective way to elucidate the cause-effect relationship. However, these combined strategies imply an enormous workload that can hinder their implementation in routine analysis. The purpose of this study was to develop a new high throughput screening method that could be used as a predictive expert system that automatically quantifies the size increase and malformation of the larvae and, thus, eases the application of the sea urchin embryo test in complex toxicant identification pipelines such as effect-directed analysis. For this task, a training set of 242 images was used to calibrate the size-increase and malformation level of the larvae. Two classification models based on partial least squares discriminant analysis (PLS-DA) were built and compared. Moreover, Hierarchical PLS-DA shows a high proficiency in classifying the larvae, achieving a prediction accuracy of 84 % in validation. The scripts built along the work were compiled in a user-friendly standalone app (SETApp) freely accessible at https://github.com/UPV-EHU-IBeA/SETApp. The SETApp was tested in a real case scenario to fulfill the tedious requirements of a WWTP effect-directed analysis.

Quality Assessment of Apple and Grape Juices from Serbian and German Markets by Planar Chromatography-Chemometrics.

The high consumption of plant-based foods on a global scale has increased the number of adulterations in the food industry. Along with this, analytical approaches to fraud detection need to be further developed. A nontargeted effect-directed profiling by high-performance thin-layer chromatography hyphenated with five effect-directed assays (free radical scavenging assay, Aliivibrio fischeri bioassay, and acetylcholinesterase, butyrylcholinesterase, and tyrosinase inhibition assays) and multi-imaging provided additional information on the antioxidative, antimicrobial, and enzyme inhibition activities for 18 apple and 18 grape juices from markets in Serbia and Germany. Bioactive zones of interest were eluted using an elution head-based interface and further characterized by electrospray ionization high-resolution mass spectrometry. The different profiles were evaluated chemometrically, and several compounds, which were characteristic of samples from different markets located in Serbia and Germany, were identified in apple juice (such as chlorogenic acid, phloridzin, epicatechin, and caffeic acid) and grape juice (such as chlorogenic acid, epicatechin, and quercetin). The developed rapid and simple method for the quality assessment of fruit juices coming from different (geographic) markets showed clear quality differences. Thus, it could be used to learn more about quality differences, to detect fraud in fruit juice production, and to verify the authenticity of the origin.

High-performance thin-layer chromatography in combination with a yeast-based multi-effect bioassay to determine endocrine effects in environmental samples.

Effect-directed analysis (EDA) that combines effect-based methods (EBMs) with high-performance thin-layer chromatography (HPTLC) is a useful technique for spatial, temporal, and process-related effect evaluation and may provide a link between effect testing and responsible substance identification. In this study, a yeast multi endocrine-effect screen (YMEES) for the detection of endocrine effects is combined with HPTLC. Simultaneous detection of estrogenic, androgenic, and gestagenic effects on the HPTLC plate is achieved by mixing different genetically modified Arxula adeninivorans yeast strains, which contain either the human estrogen, androgen, or progesterone receptor. Depending on the yeast strain, different fluorescent proteins are formed when an appropriate substance binds to the specific hormone receptor. This allows to measure hormonal effects at different wavelengths. Two yeast cell application approaches, immersion and spraying, are compared. The sensitivity and reproducibility of the method are shown by dose-response investigations for reference compounds. The spraying approach indicated similar sensitivities and higher precisions for the tested hormones compared to immersion. The EC10s for estrone (E1), 17ß-estradiol (E2), 17α-ethinylestradiol (EE2), 5α-dihydrotestosterone (DHT), and progesterone (P4) were 95, 1.4, 10, 7.4, and 15 pg/spot, respectively. Recovery rates of E1, E2, EE2, DHT, and P4 between 88 and 120% show the usability of the general method in combination with sample enrichment by solid phase extraction (SPE). The simultaneous detection of estrogenic, androgenic, and gestagenic effects in wastewater and surface water samples demonstrates the successful application of the YMEES in such matrices. This promising method allows us to identify more than one endocrine effect on the same HPTLC plate, which saves time and material. The method could be used for comparison, evaluation, and monitoring of different river sites and wastewater treatment steps and should be tested in further studies.

Coupling high-performance thin-layer chromatography with a battery of cell-based assays reveals bioactive components in wastewater and landfill leachates.

Over the last two decades, effect-directed analysis (EDA) gained importance as a seminal screening tool for tracking biological effects of environmental organic micro-pollutants (MPs). As EDA using high-performance liquid chromatography and bioassays is costly and time consuming, recent implementations of this approach have combined high-performance thin-layer chromatography (HPTLC) with effect-based methods (EBMs) using cell-based bioassays, enabling the detection of estrogenic, androgenic, genotoxic, photosystem II (PSII)- inhibiting, and dioxin-like sample components on a HPTLC plate. In the present study, the developed methodologies were applied as a HPTLC-based bioassay battery, to investigate toxicant elimination efficiency of wastewater treatment plants (WWTPs), and to characterize the toxic potential of landfill leachates. Activity levels detected in untreated landfill leachates, expressed as reference compound equivalence (EQ) concentration, were up to 16.8 µg ß-naphthoflavone-EQ L-1 (indicating the degree of dioxin-like activity), 1.9 µg estradiol-EQ L-1 (estrogenicity) and 8.3 µg diuron-EQ L­1 (PSII-inhibition), dropping to maximal concentrations of 47 ng ß-naphthoflavone-EQ L-1, 0.7 µg estradiol-EQ L-1 and 53.1 ng diuron-EQ L-1 following treatment. Bisphenol A (BPA) is suggested to be the main contributor to estrogenic activity, with concentrations determined by the planar yeast estrogen screen corresponding well to results from chemical analysis. In the investigated WWTP samples, a decrease of estrogenic activity of 6-100% was observed following treatment for most of the active fractions, except of a 20% increase in one fraction (Rf = 0.568). In contrast, androgenicity with concentrations up to 640 ng dihydrotestosterone-EQ L-1 was completely removed by treatment. Interestingly, genotoxic activity increased over the WWTP processes, releasing genotoxic fractions into receiving waters. We propose this combined HPTLC and EBM battery to contribute to an efficient, cheap, fast and robust screening of environmental samples; such an assay panel would allow to gain an estimate of potential biological effects for prioritization prior to substance identification, and its routine application will support an inexpensive identification of the toxicity drivers as a first tier in an EDA strategy.