bioRxivEvolutionary Biology
The genomic basis of copper tolerance in Drosophila is shaped by a complex interplay of regulatory and environmental factors
L. Green, M. Coronado-Zamora, S. E. Radio, G. Rech, J. Salces-Ortiz, J. Gonzalez
Abstract
Increases in industrialization and anthropogenic activity have resulted in an increase of pollutants released into the environment. Of these pollutants, heavy metals such as copper are particularly concerning due to their bio-accumulative nature. Due to its highly heterogeneous distribution and its dual nature as an essential micronutrient and toxic element, the genetic basis of copper tolerance is likely shaped by a complex interplay of genetic and environmental factors. In this study, we utilized the natural variation present in multiple populations of Drosophila melanogaster collected across Europe to screen for variation in copper tolerance. We found that latitude and the degree of urbanization at the collection sites, rather than any other combination of environmental factors, were linked to copper tolerance. While previously identified copper-related genes were not differentially expressed in tolerant vs. sensitive strains, genes involved in metabolism, reproduction, and protease induction contributed to the differential stress response. Additionally, the greatest transcriptomic and physiological responses to copper toxicity were seen in the midgut; where we found that preservation of gut acidity is strongly linked to greater tolerance. Finally, we identified transposable element insertions likely to play a role in copper stress response. Overall, by combining genome-wide approaches with environmental association analysis, and functional analysis of candidate genes, our study provides a unique perspective on the genetic and environmental factors that shape copper tolerance in natural D. melanogaster populations, and identifies new genes, transposable elements and physiological traits involved in this complex phenotype.
- organism: Drosophila
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
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