It is virtually impossible to reliably assess water quality with target chemical analyses only. Therefore, a complementary effect-based risk assessment by bioanalyses on mixtures of bioavailable micropollutants is proposed: the Smart Integrated Monitoring (SIMONI) strategy. The goal of this strategy is to obtain more reliable information on the water quality to select optimum measures for improvement. The SIMONI strategy is 2-tiered. Tier 1 is a bioanalytical hazard identification of sites. A tier 2 ecological risk assessment is carried out only at a limited number of sites where increased hazards are detected in tier 1. Tier 2 will be customized, based on tier 1 evaluation and additional knowledge of the aquatic system. The present study focuses on the tier 1 bioanalytical hazard identification to distinguish "hot spots" of chemical pollution. First, a selection was made of relevant and cost-effective bioanalytical endpoints to cover a wide spectrum of micropollutant modes of action. Specific endpoints may indicate which classes of chemicals might cause adverse effects. Second, effect-based trigger values (EBT) were derived for these bioassays to indicate potential ecological risks. Comparison of EBT with bioassay responses should discriminate sites exhibiting different chemical hazards. Third, a model was designed to estimate the overall risks for aquatic ecosystems. The associated follow-up for risk management is a "toxicity traffic light" system: green, low hazard (no action required); orange, potential risk (further research needed); and red, high risk (mitigation measures). Thanks to cost-effectiveness, flexibility, and relevance, the SIMONI strategy has the potential to become the first bioanalytical tool to be applied in regular water quality monitoring programs. Environ Toxicol Chem 2017;36:2385-2399. © 2017 SETAC.

The widespread occurrence of feminized male fish downstream of some UK Wastewater Treatment Works (WwTWs) has been associated with exposure to estrogenic and potentially antiandrogenic (AA) contaminants in the effluents. In this study, profiling of AA contaminants in WwTW effluents and fish was conducted using HPLC in combination with in vitro androgen receptor transcription screens. Analysis of extracts of wastewater effluents revealed complex profiles of AA activity comprising 21-53 HPLC fractions. Structures of bioavailable antiandrogens were identified by exposing rainbow trout to a WwTW effluent and profiling the bile for AA activity using yeast (anti-YAS) and mammalian-based (AR-CALUX) androgen receptor transcription screens. The predominant fractions with AA activity in both androgen receptor screens contained the germicides chlorophene and triclosan, and together these contaminants accounted for 51% of the total anti-YAS activity in the fish bile. Other AA compounds identified in bile included chloroxylenol, dichlorophene, resin acids, napthols, oxybenzone, 4-nonylphenol, and bisphenol A. Pure standards of these compounds were active in the androgen receptor screens at potencies relative to flutamide of between 0.1 and 13.0. Thus, we have identified, for the first time, a diverse range of AA chemicals in WwTWs that are bioavailable to fish and which need to be assessed for their risk to the reproductive health of these organisms and other aquatic biota.

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SummaryRecent studies indicated that the production of secondary metabolites by soil bacteria can be triggered by interspecific interactions. However, little is known to date about interspecific interactions between Gram‐positive and Gram‐negative bacteria. In this study, we aimed to understand how the interspecific interaction between the Gram‐positive Paenibacillus sp. AD87 and the Gram‐negative Burkholderia sp. AD24 affects the fitness, gene expression and the production of soluble and volatile secondary metabolites of both bacteria. To obtain better insight into this interaction, transcriptome and metabolome analyses were performed. Our results revealed that the interaction between the two bacteria affected their fitness, gene expression and the production of secondary metabolites. During interaction, the growth of Paenibacillus was not affected, whereas the growth of Burkholderia was inhibited at 48 and 72 h. Transcriptome analysis revealed that the interaction between Burkholderia and Paenibacillus caused significant transcriptional changes in both bacteria as compared to the monocultures. The metabolomic analysis revealed that the interaction increased the production of specific volatile and soluble antimicrobial compounds such as 2,5‐bis(1‐methylethyl)‐pyrazine and an unknown Pederin‐like compound. The pyrazine volatile compound produced by Paenibacillus was subjected to bioassays and showed strong inhibitory activity against Burkholderia and a range of plant and human pathogens. Moreover, strong additive antimicrobial effects were observed when soluble extracts from the interacting bacteria were combined with the pure 2,5‐bis(1‐methylethyl)‐pyrazine. The results obtained in this study highlight the importance to explore bacterial interspecific interactions to discover novel secondary metabolites and to perform simultaneously metabolomics of both, soluble and volatile compounds.