Detection and Quantification of Photosystem II Inhibitors Using the Freshwater Alga Desmodesmus subspicatus in Combination with High-Performance Thin-Layer Chromatography

Extrapolation of cytotoxic masked effects in planar in vitro assays

The masking of specific effects in in vitro assays by cytotoxicity is a commonly known phenomenon. This may result in a partial or complete loss of effect signals. For common in vitro assays, approaches for identifying and quantifying cytotoxic masking are partly available. However, a quantification of cytotoxicity-affected signals is not possible. As an alternative, planar bioassays that combine high-performance thin layer chromatography with in vitro assays, such as the planar yeast estrogen screen (p-YES), might allow for a quantification of cytotoxically affected signals. Affected signals form a typical ring structure with a supressed or completely lacking centre that results in a double peak chromatogram. This study investigates whether these double peaks can be used for fitting a peak function to extrapolate the theoretical, unaffected signals. The precision of the modelling was evaluated for four individual peak functions, using 42 ideal, undistorted peaks from estrogenic model compounds in the p-YES. Modelled ED50-values from bisphenol A (BPA) experiments with cytotoxically disturbed signals were 13 times higher than for the apparent data without compensation for cytotoxicity (320 ± 63 ng versus 24 ± 17 ng). This finding has a high relevance for the modelling of mixture effects according to concentration addition that requires unaffected, complete dose-response relationships. Finally, we applied the approach to results of a p-YES assay on leachate samples of an elastomer material used in water engineering. In summary, the fitting approach enables the quantitative evaluation of cytotoxically affected signals in planar in vitro assays and also has applications for other fields of chemical analysis like distorted chromatography signals.

Identification and Prioritization of Environmental Organic Pollutants: From an Analytical and Toxicological Perspective

Exposure to environmental organic pollutants has triggered significant ecological impacts and adverse health outcomes, which have been received substantial and increasing attention. The contribution of unidentified chemical components is considered as the most significant knowledge gap in understanding the combined effects of pollutant mixtures. To address this issue, remarkable analytical breakthroughs have recently been made. In this review, the basic principles on recognition of environmental organic pollutants are overviewed. Complementary analytical methodologies (i.e., quantitative structure-activity relationship prediction, mass spectrometric nontarget screening, and effect-directed analysis) and experimental platforms are briefly described. The stages of technique development and/or essential parts of the analytical workflow for each of the methodologies are then reviewed. Finally, plausible technique paths and applications of the future nontarget screening methods, interdisciplinary techniques for achieving toxicant identification, and burgeoning strategies on risk assessment of chemical cocktails are discussed.

Methodological advances and future directions of microalgal bioassays for evaluation of potential toxicity in environmental samples: A review

Microalgal bioassays are widely applied to evaluate the potential toxicity of various persistent toxic substances in environmental samples due to multiple advantages, including high sensitivity, short test duration, and cost-effectiveness. Microalgal bioassay is gradually developing in method, and the scope of application to environmental samples is also expanding. Here, we reviewed the published literature on microalgal bioassays for environmental assessments, focusing on types of samples, sample preparation methods, and endpoints, and highlighted key scientific advancements. Bibliographic analysis was performed with the keywords 'microalgae' and 'toxicity' or 'bioassay', and 'microalgal toxicity'; 89 research articles were selected and reviewed. Traditionally, most studies implementing microalgal bioassays focused on water samples (44%) with passive samplers (38%). Studies using the direct exposure method (41%) of injecting microalgae into sampled water mainly evaluated toxic effects by growth inhibition (63%). Recently, various automated sampling techniques, in situ bioanalytical methods with multiple endpoints, and targeted and non-targeted chemical analyses have been applied. More research is needed to identify causative toxicants affecting microalgae and to quantify the cause-effect relationships. This study provides the first comprehensive overview of recent advances in microalgal bioassays performed with environmental samples, suggesting future research directions based on current understanding and limitations.

Probing Herbicide Toxicity to Algae (Selenastrum capricornutum) by Lipid Profiling with Machine Learning and Microchip/MALDI-TOF Mass Spectrometry

Matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS)-based lipid profiling is a powerful method to study the cytotoxicity of chemical exposure to microorganisms at the single cell level. We report here a combined approach of machine learning (ML) and microchip-based MALDI-time of flight (TOF) mass spectrometry to investigate the cytotoxic effect of herbicides on algae through single cell lipid profiling. Algal species Selenastrum capricornutum was chosen as the target system, and its exposure to different doses of common chemical herbicides and the resulting cytotoxic behaviors under various stress conditions were characterized. A lipid library for S. capricornutum has been established with 63 identified lipids that include glycosyldiacylglycerols and triacylglycerols. We demonstrated that major alternations occurred for lipids with functional groups of digalactosyldiacylglycerol (DGDG), triacylglycerol (TAG), and monogalactosyldiacylglycerol (MGDG). DGDG was shown to decrease upon exposure to herbicides of norflurazon and atrazine, while some MGDG and TAG lipids would increase for norflurazon. Compared to other algae, S. capricornutum was more strongly impacted by norflurazon than atrazine while the latter was observed to have a greater effect on C. reinhardtii. Machine learning algorithms have been applied to improve the classification of herbicide impact and help identify lipid species affected by the chemical exposure. A total of 69 machine learning models were trained and tested for the identification of ideal algorithms in the classification process, in which flexible discriminant analysis and support vector machine model were found to be the most accurate and consistent. The ML algorithms accurately differentiated herbicide impact and have identified cytotoxic differences that were previously hidden. The results suggest that herbicides express toxicity among different algae likely on the basis of metabolic differences. The ML-assisted method proves to be highly effective and can provide an advanced technological platform for probing cytotoxicity for bacterial species and in metabolic pathway analysis.

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 β-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 L^‑1 (PSII-inhibition), dropping to maximal concentrations of 47 ng β-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.

Advancements in effect-based surface water quality assessment

Legally-prescribed chemical monitoring is unfit for determining the pollution status of surface waters, and there is a need for improved assessment methods that consider the aggregated risk of all bioavailable micropollutants present in the aquatic environment. Therefore, the present study aimed to advance effect-based water quality assessment by implementing methodological improvements and to gain insight into contamination source-specific bioanalytical responses. Passive sampling of non-polar and polar organic compounds and metals was applied at 14 surface water locations that were characterized by two major anthropogenic contamination sources, agriculture and wastewater treatment plant (WWTP) effluent, as well as reference locations with a low expected impact from micropollutants. Departing from the experience gained in previous studies, a battery of 20 in vivo and in vitro bioassays was composed and subsequently exposed to the passive sampler extracts. Next, the bioanalytical responses were divided by their respective effect-based trigger values to obtain effect-based risk quotients, which were summed per location. These cumulative ecotoxicological risks were lowest for reference locations (4.3-10.9), followed by agriculture locations (11.3-27.2) and the highest for WWTP locations (12.8-47.7), and were mainly driven by polar organic contaminants. The bioanalytical assessment of the joint risks of metals and (non-)polar organic compounds resulted in the successful identification of pollution source-specific ecotoxicological risk profiles: none of the bioassays were significantly associated with reference locations nor with multiple location types, while horticulture locations were significantly characterized by anti-AR and anti-PR activity and cytotoxicity, and WWTP sites by ERα activity and toxicity in the in vivo bioassays. It is concluded that the presently employed advanced effect-based methods can readily be applied in surface water quality assessment and that the integration of chemical- and effect-based monitoring approaches will foster future-proof water quality assessment strategies on the road to a non-toxic environment.

Estrogenic activity of food contact materials-evaluation of 20 chemicals using a yeast estrogen screen on HPTLC or 96-well plates

Food contact materials (FCM) may contain complex mixtures of estrogenic chemicals. A yeast estrogen screen performed on high performance thin-layer chromatography plates (planar-YES, P-YES) is promising for analysis of such mixtures, as it could allow for better elucidation of effects compared with established methods in microtiter plates. However, the P-YES has not been directly compared with established methods. We compared the performance of a microtiter plate YES (lyticase-YES, L-YES) to P-YES on silica gel HPTLC plates using 17β-estradiol (E2), 20 chemicals representative of migrants from plastic FCM, and three migrates of coated metal food cans. Effective doses (ED10, ED50) and estradiol equivalencies were calculated for each chemical. Thirteen chemicals had calculable EDs in the L-YES or P-YES, with average EDs 13-fold (range 0.63-36) more potent in P-YES than in the L-YES. Normalized to E2, the median estrogenicity was within 1.5-fold (0.43-8.8) between the assays. Therefore, P-YES was as or more sensitive than L-YES but potencies relative to E2 were comparable between assays. With chromatography, the P-YES detected estrogenicity in coated metal cans, effects that were unmeasurable in L-YES. With the sample preparation methods used in this study, both YES assays are sufficiently sensitive to detect bisphenol A below the specific migration limit for plastic packaging (0.05 mg/kg food). This study demonstrates that P-YES outperforms L-YES because it is more sensitive, provides comparable estradiol equivalents, and circumvents confounding mixture effects. The P-YES will be useful for routine monitoring of FCM and toxicant identification in problematic materials. Graphical abstract.