Significance of chemical affinity on metal subcellular distribution in yellow perch (Perca flavescens) livers from Lake Saint-Pierre (QUEBEC, Canada)
2022
Desjardins, Kimberley | Khadra, Mélissa | Caron, Antoine | Ponton, Dominic E. | Rosabal, Maikel | Amyot, Marc
The subcellular partitioning approach provides useful information on the location of metals within cells and is often used on organisms with high levels of bioaccumulation to establish relationships between the internal concentration and the potential toxicity of metals. Relatively little is known about the subcellular partitioning of metals in wild fish with low bioaccumulation levels in comparison with those from higher contaminated areas. This study aims to examine the subcellular partitioning of various metals considering their chemical affinity and essentiality at relatively low contamination levels. Class A (Y, Sr), class B (Cu, Cd, MeHg), and borderline (Fe, Mn) metal concentrations were measured in livers and subcellular fractions of yellow perch (n = 21) collected in Lake Saint-Pierre, QC, Canada. The results showed that all metals, apart from MeHg, were distributed among subcellular fractions according to their chemical affinity. More than 60% of Y, Sr, Fe, and Mn were found in the metal-sensitive fractions. Cd and Cu were largely associated with the metallothionein-like proteins and peptides (60% and 67% respectively) whereas MeHg was found mainly in the metal-sensitive fractions (86%). In addition, the difference between the subcellular distribution of Cu and other essential metals like Fe and Mn denotes that, although the essentiality of some metals is a determinant of their subcellular distribution, the chemical affinity of metals is also a key driver. The similarity of the subcellular partitioning results with previous studies on yellow perch and other fish species from higher contaminated areas supports the idea that metals are distributed in the cellular environment according to their chemical properties regardless of the bioaccumulation gradient.
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