GeneticsGeneticsPeer-reviewed journal article
Characterizing the genetic basis of copper toxicity in Drosophila reveals a complex pattern of allelic, regulatory, and behavioral variation
Elizabeth R. EvermaniD, Kristen M. Cloud-Richardson, Stuart J. MacdonaldiD
- Citations
- 26
- FWCI
- 1.10
- Field percentile
- top 28%
Abstract
A range of heavy metals are required for normal cell function and homeostasis. Equally, the anthropogenic release of heavy metals into soil and water sources presents a pervasive health threat. Copper is one such metal; it functions as a critical enzymatic cofactor, but at high concentrations is toxic, and can lead to the production of reactive oxygen species. Using a combination of quantitative trait locus (QTL) mapping and RNA sequencing in the Drosophila Synthetic Population Resource (DSPR), we demonstrate that resistance to the toxic effects of ingested copper in D. melanogaster is genetically complex, and influenced by allelic and expression variation at multiple loci. Additionally, we find that copper resistance is impacted by variation in behavioral avoidance of copper and may be subject to life-stage specific regulation. Multiple genes with known copper-specific functions, as well as genes that are involved in the regulation of other heavy metals were identified as potential candidates to contribute to variation in adult copper resistance. We demonstrate that nine of 16 candidates tested by RNAi knockdown influence adult copper resistance, a number of which may have pleiotropic effects since they have previously been shown to impact the response to other metals. Our work provides new understanding of the genetic complexity of copper resistance, highlighting the diverse mechanisms through which copper pollution can negatively impact organisms. Additionally, we further support the similarities between copper metabolism and that of other essential and nonessential heavy metals.
- organism: Drosophila
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
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