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Systematic evaluation of factors affecting the characterization of wastewater effluents using gene expression
Citation:
Biales, A., D. Bencic, Mary Jean See, S. Glassmeyer, D. Kolpin, M. Mills, E. Furlong, R. Flick, W. Huang, AND Tom Purucker. Systematic evaluation of factors affecting the characterization of wastewater effluents using gene expression. SETAC, Pittsburgh, PA, November 13 - 17, 2022. https://doi.org/10.23645/epacomptox.21597687
Impact/Purpose:
Will provide guidance for the use of gene expression based tools in field applications.
Description:
Wastewater treatment effluent (WWTE) has been identified as a major source of diverse chemical contaminants with the potential to negatively affect the function of receiving ecosystems. Though the toxicity of effluent is dependent on the individual components of the mixture, it is difficult to predict toxicity due to incomplete characterization of the total chemical composition, the influence of the non-chemical background, and the potential for chemical components to interact and alter their respective bioactivity. For these reasons, there has been an increasing focus on the development of effect-based measures that can account for these complicating factors to characterize the total bioactivity of the effluent mixture. Several research efforts have employed gene expression-based approaches with the aim of characterizing effluent. Generally, these efforts identify genes that are differentially expressed relative to either an upstream reference site or laboratory exposed organisms and bioactivity is interpreted through mapping of these to annotated biological pathways. However, rarely do gene expression results corroborate the results of paired analytical chemical analysis. There are likely a myriad of reasons for this, but it is noted that there have been few, if any, systematic evaluations of the factors that can affect nature of gene expression in field applications. The current work aimed to address this knowledge gap by systematically evaluating the stability and information content of expression over repeated sampling events in the same location, using different control conditions for contrasts (laboratory water vs. upstream reference), and across different sampling strategies (field deployed vs grab sampling). To the best of our knowledge, this will be the most comprehensive evaluation of gene expression in field applications to date and has the potential to help guide future efforts to reduce background noise and to maximize the interpretability of gene expression-based measures.
URLs/Downloads:
DOI: Systematic evaluation of factors affecting the characterization of wastewater effluents using gene expression
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