The genetic basis of adaptation to copper pollution in Drosophila melanogaster

Abstract

Introduction: Heavy metal pollutants can have long lasting negative impacts on ecosystem health and can shape the evolution of species. The persistent and ubiquitous nature of heavy metal pollution provides an opportunity to characterize the genetic mechanisms that contribute to metal resistance in natural populations. Methods: We examined variation in resistance to copper, a common heavy metal contaminant, using wild collections of the model organism Drosophila melanogaster. Flies were collected from multiple sites that varied in copper contamination risk. We characterized phenotypic variation in copper resistance within and among populations using bulked segregant analysis to identify regions of the genome that contribute to copper resistance. Results and Discussion: Copper resistance varied among wild populations with a clear correspondence between resistance level and historical exposure to copper. We identified 288 SNPs distributed across the genome associated with copper resistance. Many SNPs had population-specific effects, but some had consistent effects on copper resistance in all populations. Significant SNPs map to several novel candidate genes involved in refolding disrupted proteins, energy production, and mitochondrial function. We also identified one SNP with consistent effects on copper resistance in all populations near CG11825, a gene involved in copper homeostasis and copper resistance. We compared the genetic signatures of copper resistance in the wild-derived populations to genetic control of copper resistance in the Drosophila Synthetic Population Resource (DSPR) and the Drosophila Genetic Reference Panel (DGRP), two copper-naïve laboratory populations. In addition to CG11825, which was identified as a candidate gene in the wild-derived populations and previously in the DSPR, there was modest overlap of copper-associated SNPs between the wild-derived populations and laboratory populations. Thirty-one SNPs associated with copper resistance in wild-derived populations fell within regions of the genome that were associated with copper resistance in the DSPR in a prior study. Collectively, our results demonstrate that the genetic control of copper resistance is highly polygenic, and that several loci can be clearly linked to genes involved in heavy metal toxicity response. The mixture of parallel and population-specific SNPs points to a complex interplay between genetic background and the selection regime that modifies the effects of genetic variation on copper resistance.

Keywords: Drosophila; genome wide association; heavy metals; stress resistance; toxicity.

FIGURE 1

Sites and traps used to collect Drosophila melanogaster. (A) Flies were collected from two orchards (DBF = Duncan’s Berry Farm; RFF = Rees’ Fruit Farm), two mine sites (AMM = Anschutz Mine; BBM = Burra Burra Mine), and one open-air market (GPO = Gilliland Orchard). (B) A representation of 16oz plastic bottles baited with banana, yeast, and water that were used to collect flies (created with BioRender.com). Small holes (4 mm diameter) were melted into the sides of bottles in a grid pattern, and a 15 cm wooden craft stick was provided to prevent flies from sticking in the bait or the side of the bottle.

FIGURE 2

Conceptual representation of LRT models for significant SNPs. SNPs that shift in allele frequency between control and copper-resistant pools in the same way will give the lowest p-values using LRT1. LRT2 should most effectively identify parallel shifts in allele frequency between the two high copper resistance populations (BBM and GPO) versus the low copper resistance populations (DBF and RFF). Shifts in allele frequency that are population-specific will yield the lowest p-values using LRT4.

FIGURE 3

Copper and starvation resistance vary within and among populations. (A) Copper resistance significantly varied among populations (F_6,1685 = 921.8, p < 0.0001), following a trend where resistance was higher in populations with closer proximity to sources of heavy metal contamination. Tukey’s HSD post hoc comparisons revealed that copper resistance was significantly different in all populations (adj p < 0.0001) except for the DGRP and DBF populations (adj p = 0.9) and the BBM and GPO populations (adj p = 0.9). (B) Starvation resistance also varied within and among populations. Tukey’s HSD post hoc comparisons revealed that starvation resistance was not different between the two laboratory populations (DSPR vs. DGRP, adj p = 0.94) or RFF, BBM, and GPO (all pairwise comparisons adj p > 0.64). Starvation resistance in the DBF population was significantly higher than the DSPR and DGRP and significantly lower than the other wild-derived populations (all pairwise comparisons adj p < 0.0001). In A and B, each point indicates either the strain-level mean (DSPR and DGRP, Table 1) or vial mean (N ≈ 20 females) resistance. (C) Population-level mean copper and starvation resistance were correlated with mean starvation resistance (F_1,4 = 17.6, p = 0.014). (D) Starvation and copper resistance were correlated among DSPR strains (F_1,191 = 31.3, p < 0.0001, adj R ² = 13.6%) as well among DGRP strains (E. F_1,122 = 23.2, p < 0.0001, adj R ² = 15.3%). In (D, E), the black line shows the best fit line and shading indicates the 95% CI of the regression model.

FIGURE 4

Copper resistance is influenced by multiple loci in wild-derived populations. The top panel shows 288 SNPs which shifted in allele frequency in a manner consistent with LRT1 (parallel shift, 70 SNPs), LRT2 (similar shift in high vs. low copper resistance populations, 23 SNPs), or LRT4 (population-specific shift, 195 SNPs). Points are partially transparent to show overlap of multiple SNPs. The bottom panel shows estimated F_ST values for each SNP. In both panels, SNPs are shaded according to which LRT model best fit their shift in allele frequency between the average and high copper resistance pools. 39 SNPs with population-specific effects on copper resistance (LRT4) also had elevated F_ST values, suggesting that these SNPs also contribute to population differentiation. In both plots, red horizontal dashed lines indicate thresholds; in the top panel the threshold indicates the 10% FDR cutoff used to identify significant SNPs. In the bottom panel the threshold is set at F_ST = 0.15 to differentiate SNPs with elevated F_ST estimates (Frankham et al., 2002).

FIGURE 5

Genome wide association mapping of copper resistance in the DGRP identified multiple significantly associated SNPs. The red dashed line indicates p < 10⁻⁵ and variants surpassing this value are highlighted in red. Sites that were significantly associated with copper resistance at a 5% FDR are highlighted in blue.

FIGURE 6

Shift in allele frequency between copper-resistant and control pools of flies from the four wild-derived populations at SNPs that fell within or near genes related to protein folding and mitochondrial function. Positive dz values indicate that the reference base is more common in the copper-resistant pool relative to the control pool. Open symbols indicate populations with high copper exposure risk; closed symbols indicate populations with low exposure risk. Note that some symbols perfectly overlap.