Defining the role of omics in assessing ecosystem health: Perspectives from the Canadian environmental monitoring program

Abstract

Scientific reviews and studies continue to describe omics technologies as the next generation of tools for environmental monitoring, while cautioning that there are limitations and obstacles to overcome. However, omics has not yet transitioned into national environmental monitoring programs designed to assess ecosystem health. Using the example of the Canadian Environmental Effects Monitoring (EEM) program, the authors describe the steps that would be required for omics technologies to be included in such an established program. These steps include baseline collection of omics endpoints across different species and sites to generate a range of what is biologically normal within a particular ecosystem. Natural individual variability in the omes is not adequately characterized and is often not measured in the field, but is a key component to an environmental monitoring program, to determine the critical effect size or action threshold for management. Omics endpoints must develop a level of standardization, consistency, and rigor that will allow interpretation of the relevance of changes across broader scales. To date, population-level consequences of routinely measured endpoints such as reduced gonad size or intersex in fish is not entirely clear, and the significance of genome-wide molecular, proteome, or metabolic changes on organism or population health is further removed from the levels of ecological change traditionally managed. The present review is not intended to dismiss the idea that omics will play a future role in large-scale environmental monitoring studies, but rather outlines the necessary actions for its inclusion in regulatory monitoring programs focused on assessing ecosystem health. Environ Toxicol Chem 2016;35:20-35. # 2015 SETAC

Transcriptional and biochemical biomarker responses in a freshwater mussel (Anodonta anatina) under environmentally relevant Cu exposure

“…Ideally, molecular responses would be extrapolated to predict effects on higher biological levels. The link is however complicated by a lacking understanding of how baseline gene expression patterns vary with, for example, species, sexes, developmental stages, and seasons (Bahamonde et al 2016;Fent and Sumpter 2011). Although not always addressed, understanding the variation is thus necessary to discern actual responses from the background noise, for meaningful interpretation of transcriptional data and for successful integration into molecular biomarker panels (Bahamonde et al 2016).…”

Returning to normal? Assessing transcriptome recovery over time in male rainbow darter (Etheostoma caeruleum) liver in response to wastewater‐treatment plant upgrades

“…As pointed out by Bahamonde et al [79], environmental factors such as temperature, flows, microhabitat use, and/or nutrient availability may be important in altering gene expression across years and sites. However, the number of differentially expressed probes was not affected by the changes in effluent quality at either of the studied sites.…”

“…Our results indicated that factors characteristic of individual years are affecting the number of genes that are differently expressed. As pointed out by Bahamonde et al [79], environmental factors such as temperature, flows, microhabitat use, and/or nutrient availability may be important in altering gene expression across years and sites. However, gene set enrichment analysis and subnetwork enrichment analysis may be more sensitive types of analyses (compared with the number of differentially expressed probes) when it comes to interpreting gene patterns changes in response to MWWTP effluent exposure.…”

Evolutionary toxicology in an omics world

“…These investigations take a targeted approach to explore effects on pathways likely affected by adaptation (Clark & Di Giulio, 2012; Oziolor et al., 2016b). On the other hand, with top‐down ‐omics approaches, understanding of transcriptomic, metabolomic, and proteomic differences in responsiveness and basal levels in adapted populations may inform possible fitness costs in a much more thorough manner (Bahamonde, Feswick, Isaacs, Munkittrick, & Martyniuk, 2016; Bundy, Davey, & Viant, 2008; Monsinjon & Knigge, 2007). One example is a comparative transcriptomic approach that finds altered responsiveness in biocide resistant Salmonella enterica Typhimurium that further explain increased sensitivity of this strain to other antimicrobials (Curiao et al., 2016).…”