Metabolite Profiling of Fish Skin Mucus: A Novel Approach for Minimally-Invasive Environmental Exposure Monitoring and Surveillance

Abstract

The application of 'omics tools to biologically based monitoring and surveillance of aquatic environments shows considerable promise for complementing chemical monitoring in ecological risk assessments. However, few of the current approaches offer the ability to sample ecologically relevant species (e.g., fish) in a way that produces minimal impact on the health of the organism(s) under study. In the current study we employ liquid chromatography tandem mass spectrometry (LC-MS/MS) to assess the potential for skin mucus-based metabolomics for minimally invasive sampling of the fathead minnow (FHM; Pimephales promelas). Using this approach we were able to detect 204 distinct metabolites in the FHM skin mucus metabolome representing a large number of metabolite classes. An analysis of the sex specificity of the skin mucus metabolome showed it to be highly sexually dimorphic with 72 of the detected metabolites showing a statistically significant bias with regard to sex. Finally, in a proof-of-concept fashion we report on the use of skin mucus-based metabolomics to assess exposures in male and female fathead minnows to an environmentally relevant concentration of bisphenol A, a nearly ubiquitous environmental contaminant and an established endocrine active chemical.

Showcasing metabolomic applications in aquaculture: a review

“…); and developing non‐invasive methods for pollution assessment by profiling metabolites in the excreted mucus of fish skin (Ekman et al . ). The results of these studies clearly demonstrate promising applications.…”

“…Using fish and shellfish, metabolomics is progressively being used to characterize mechanisms of toxicity and to develop novel methods to assess environmental contamination. For example, metabolomics-based studies have proven useful for: identifying physiological responses of various molluscs to agricultural runoff, heavy metals and endocrine disruptors Hanana et al 2014;Ji et al 2014Ji et al , 2015aJi et al ,b, 2016Leonard et al 2014;Zhou et al 2014;Campillo et al 2015;Song et al 2016); characterizing the consequence of pesticide exposure in carp (Kokushi et al 2015); assessing the effects of petrochemical contamination at industrialized sites harbouring caged mussels (Fasulo et al 2012;Cappello et al 2013Cappello et al , 2015; detecting freshwater locations which have multicontaminant sediment loadings (Watanabe et al 2015); identifying new and highly sensitive bioindicator species of clams for monitoring environmental contamination (Ji et al 2015c); and developing noninvasive methods for pollution assessment by profiling metabolites in the excreted mucus of fish skin (Ekman et al 2015). The results of these studies clearly demonstrate promising applications.…”

Regional feature extraction of various fishes based on chemical and microbial variable selection using machine learning

“…For example, to evaluate the inuence of environmental chemicals in sh habitats, exposure experiments were performed using laboratory animals, such as Japanese killish, fathead minnow, [11][12][13] and other sh species. [13][14][15] In these analyses, many analytical techniques were crucial for evaluating the relationships between sh metabolism/physiology/ecology and their environments, e.g., gene expression analysis by transcriptome sequencing, 16,17 phylogenetic analysis, [18][19][20] microbiota analysis in the gut 6,21,22 and sediment, 9,23 and metabolomics. [24][25][26][27][28] In particular, the nuclear magnetic resonance (NMR)-based metabolomic technique offers a high throughput, easy sample preparation, and inter-institution convertibility.…”

Is the Nutritional Value of Fish Fillet Related to Fish Maturation or Fish Age? Integrated Analysis of Transcriptomics and Metabolomics in Blunt Snout Bream (Megalobrama amblycephala)

“…Julia et al elucidated the underlying biochemical processes of meat quality traits, and the potential key molecules related to meat quality traits-drip loss were identified [10]. Regarding fish species, a comprehensive metabolomics analysis has been widely applied to detect the metabolite differences in fish embryo [11], plasma [12,13], liver [13,14], gonad [14][15][16] and skin mucus [17] under specific conditions or treatments. However, no metabolomics approach has been used to identify metabolites related to muscle composition, as well as those related to fish age and maturation.…”