Skip to main content

The genomic basis of copper tolerance in Drosophila is shaped by a complex interplay of regulatory and environmental factors

Abstract

Background

Escalation 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.

Results

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.

Conclusions

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.

Background

Rapid industrialization and urbanization have had adverse impacts on biodiversity across ecosystems. Of the contaminants released into the environment due to an increase in human activity, heavy metals are particularly concerning due to their ability to bio-accumulate in soils. Specifically with regard to copper, anthropogenic sources are thought to have a greater influence on topsoil concentrations than either lithological or geographic factors [1]. Human sources of copper are characterized by many point sources of contamination, which has resulted in a highly heterogeneous environmental distribution [2], even across relatively short geographic distances [3]. Due to its highly heterogeneous distribution, and its dual nature as both an essential micronutrient and toxic element, the genetic basis of copper tolerance has the potential to be shaped by a complex interplay of environmental and regulatory factors.

As a commensal species, Drosophila melanogaster has a well-documented history as a sentinel of environmental toxins and can be readily sampled from a wide range of geographic locations, making it a prime choice species for the study of copper stress response [4]. D. melanogaster has also served as an important tool in the characterization of copper homeostasis and copper-related diseases [5, 6]. As copper acts as an essential micronutrient at low doses but can produce free radicals and damage DNA in excess, the mediation of copper often involves a complex system of regulators, chaperones, and transporters that are commonly found conserved across a wide range of species. Genetic manipulation of D. melanogaster has been used to successfully characterize the roles of the common metal-responsive transcription factor-1 (MTF-1) [7], Malvolio and the Ctr1 family of transporters which mediate copper uptake [8, 9]; the ATP7 transporter, which regulates copper efflux [10]; and the cysteine-rich metallothioneins, which serve to sequester metal ions [11, 12]. Excess copper accumulates in the midgut as the fly ages, which is thought to alter gut physiology [13]. Once copper crosses the gut endothelium, it is sequestered by the metallothioneins in the morphologically distinct copper cells and deposited in insoluble granules in the lysozymes [14]. Despite the name, copper cells are considered ‘cuprophobic’ and are inhibited by excess copper [15]. They are also responsible for stomach acid secretion, a function that is lost with age or gut damage, leading to an increase in pH [13].

While many of the aforementioned genes have had their roles in copper homeostasis validated in laboratory conditions, it is not known whether these same genes have an effect on the phenotype in natural populations. To date, there have been several studies exploring the nature of copper tolerance in natural strains of D. melanogaster, both with regard to individual genes [16, 17] and to broader developmental and learning and memory processes [18, 19]. Recently, Everman et al. [20] took benefit of a combination of high-throughput genomic and transcriptomic approaches to uncover several new copper gene candidates, using recombinant inbred lines. They found that copper resistance is genetically complex and impacted by variation in copper avoidance behaviour. In addition to identifying natural variants involved in response to copper, their pairing of genomic data with transcriptomic data also provided a greater opportunity to identify factors that regulate copper-induced changes in expression, beyond the well-known MTF-1 transcription factor [1120]. Prior expression analyses on metal exposure also suggest that there are a number of co-regulated gene clusters linked to broader stress and metabolism-related pathways in response to heavy metal exposure, independent of MTF-1 [20,21,22]. However, the factors responsible for these coordinated changes in expression have not yet been identified.

To date, genome-wide studies investigating the genetic basis of tolerance to copper and other heavy metals in D. melanogaster have focused on SNP variants or were naïve to the nature of the causal variant [20, 23]. The recent availability of new whole-genome assemblies based on long-read sequencing gives us the unprecedented opportunity to characterize complex forms of sequence variation that may have previously been overlooked [24, 25]. This is of particular importance with regard to transposable element insertions, which are often associated with changes in gene expression under stressful conditions (e.g. [26,27,28,29,30,31]). Indeed, a natural transposable element insertion in the MTF-1 targeted gene kuzbanian has been associated with increased tolerance to zinc in adult flies, with the effect of the insertion being background dependent [32].

In this study, we set out to assess variation in copper tolerance between natural populations of European D. melanogaster and investigate whether the phenotype is influenced by either geographic factors, the concentration of copper in soils, atmospheric pollution, or degree of urbanization. To better elucidate the genetic basis of copper tolerance in natural populations, we compared the transcriptomes of three copper-tolerant and three copper-sensitive strains from before and after copper treatment, using a combination of tissue enrichment analysis, gene ontology, and modular clustering, to examine patterns of gene co-regulation. Finally, we also investigated the physiological traits relevant for copper tolerance. We found that while copper tolerance is highly variable across much of Western Europe, the external factors involved in shaping these phenotypes are complex, likely controlled by multiple regulatory factors, and that tolerance is linked to gut physiology.

Results

Copper tolerance is a variable trait across European D. melanogaster associated with latitude and degree of urbanization

To assess the degree of copper tolerance in natural populations of D. melanogaster in Europe, we scored a total of 71 inbred strains, collected from nine locations by the DrosEU consortium, for copper mortality on a single dose until full mortality was achieved (Fig. 1A and Additional file 1: Table S1A). LT50 values ranged from 26.4 to 81.2 h, with a median value of 49.8 h (Additional file 2: Fig. S1A, Additional file 3: Table S2A). We observed very little zero-dose control mortality over the course of the assay (Additional file 3: Table S2B). Although we observed a high degree of within-population variance in copper tolerance (Fig. 1B), a linear regression between fly collection locations and LT50 values was not significant (p-value = 0.0744; Additional file 3: Table S2D).

Fig. 1
figure 1

Sampling locations and variation of copper tolerance across Europe. A Distribution of the nine European locations from the 2015 DrosEU collection (black border) and three additional collection locations (white border). Each population is labelled with the first three letters of the collection location (Additional file 1: Table S1). Label fill corresponds with the copper concentration legend. The map shows the spatial variation in copper topsoil concentrations, as obtained from the Land Use/Land Cover Area Frame Survey (LUCAS) topsoil database, whose samples were taken from 2009 onwards. The grey line marks the 45th parallel. B Boxplots of LT50 values by location. Note that the distance between boxplots is not linear. Locations from the 2015 DrosEU collection are outlined in black, while the three additional locations are outlined in grey. The full list of strains used is provided in Additional file 1: Table S1A-B. C Boxplots of LT50 values of strains split into northern (NE) and southern (SE) European locations by the 45th parallel. This point was chosen because the nine original collection sites could be clearly divided by this line (A). Significance assessed with a one-sided Wilcoxon test: p-value < 0.05 (*). D Boxplots of LT50 values of strains classified by degree of urbanization: U, urban; SU, semi-urban; and R, rural. Significance assessed with a linear model: p-value < 0.01 (**)

As stress tolerance is frequently clinal in Drosophila [33,34,35], we compared the differences in tolerance between northern and southern populations divided by the 45th parallel, as our nine collection sites could be clearly divided by this feature (Fig. 1A). Although the differences in copper tolerance were significant (one-sided Wilcoxon’s rank-sum test, p-value = 0.00529), because all southern populations were collected in Spain, we broadened the analysis by phenotyping an additional 19 strains from Portugal and Italy in the south of Europe, along with another 7 strains from Austria (Additional file 2: Fig. S1B). As the Portuguese and Austrian strains were caught in 2018 and the Italian strains in 2011, these strains have experienced different degrees of prior laboratory adaptation compared with the previously analysed nine locations that were all collected in 2015. We found that the association between tolerance and geography was still significant after the inclusion of these new data (one-sided Wilcoxon’s rank-sum test, p-value = 0.0189, Fig. 1C).

We further examined the relationship between copper tolerance and geography, copper soil concentrations, atmospheric pollution, and degree of urbanization by fitting a generalized linear model between these potential explanatory variables and the LT50 values across all twelve locations. Longitude was considered in addition to latitude, because European D. melanogaster have been reported to exhibit population structure along this axis [36]. As our initial interest in copper was spurred in part by its role as an environmental contaminant, we also tested the relationship between copper tolerance and metal pollution by considering copper concentration in topsoils (mgkg−1) and atmospheric pollution (PM10 and PM2.5; general and metal specific), obtained from publicly available data (see the ‘Methods’ section; Additional file 4: Table S3A). As copper contamination is often the result of a complex group of contamination sources, especially around urban areas [2], we also considered a more indirect measure of pollution: degree of urbanization. We classified each of the fly collection locations into urban, semi-urban, and rural classes, based on distance from high-, semi-, and low-population density areas ([37]; Additional file 4: Table S3A). The final model after performing a backward stepwise regression to eliminate the least significant variables only kept latitude and degree of urbanization (R2 = 12%, p-value = 0.0079): we found a positive correlation between latitude and LT50 (p-value = 0.015), and we found that urban populations have a higher LT50 compared with rural populations (p-value = 0.0086; Fig. 1D and Additional file 4: Table S3B). Note that although statistically significant, this model explains a small percentage of the variation in copper tolerance.

Tolerant and sensitive strains demonstrate differential expression profiles after copper exposure mostly concentrated in the midgut

To examine the gene regulatory changes that occur in D. melanogaster in response to copper exposure, we compared mated female whole-body transcriptomic profiles of three tolerant (GIM-012, MUN-020, MUN-008) and three sensitive strains (AKA-018, JUT-008, and COR-018), chosen primarily on the basis of their position at the tails of the phenotypic distribution (Additional file 2: Fig. S1A; see the ‘Methods’ section). Carrying out this analysis with strains from the ends of the distribution should be informative about the genes with the greater effects on the phenotypic response. Because we are interested in defining the genes that differentiate the copper tolerant from the copper-sensitive strains, we performed DGE analyses for tolerant and sensitive strains separately. Across the three tolerant strains, 239 genes were significantly differentially expressed (> 1.5 fold change and adjusted p-value < 0.05) between copper treatment and control conditions, while 984 genes were differentially expressed across the three sensitive strains, with an overlap of 152 genes (Fig. 2A and Additional file 5: Table S4A). Of these 152 genes, the direction of the change was discordant in six genes, being all up-regulated in tolerant strains and down-regulated in sensitive (Table 1). The proportion of down-regulated genes was higher in the sensitive strains, with most of these down-regulated genes unique to sensitive strains (Fig. 2A and Additional file 5: Table S4A). Note that we also performed a joint analysis considering all the strains together and the interaction term between phenotypic class (tolerant and sensitive) and treatment (control conditions and copper treatment). Analysing the data using this model leads to very similar results as the ones obtained when analysing tolerant and sensitive strains separately: 97% of the DEGs in response to stress in the sensitive strains, and 96% in the tolerant strains overlapped between the two analyses (Additional file 5: Table S4C). Moreover, 13 genes were identified when analysing the interaction between phenotypic class and treatment (Additional file 5: Table S4C).

Fig. 2
figure 2

Copper differential gene expression and tissue analysis. A Venn diagrams showing the degree of overlap between the differentially expressed genes in response to copper across the three tolerant and three sensitive strains. The numbers represented in red are up-regulated genes, those in blue down-regulated genes, and those in orange are the genes with discordant changes in expression between tolerant and sensitive strains (Table 1). Expression data obtained from mated female whole-body RNA-seq (3 biological replicates of 20 females each for treated and control conditions). B Volcano plots of gene expression in tolerant (left) and sensitive strains (right). The horizontal dashed line represents the minimum adjusted p-value threshold (0.05), while the vertical dashed lines represent the fold change thresholds (log2(1.5) = 0.58). C Classification of the tolerant DEGs and sensitive DEGs according to the levels of expression that these genes have in the carcass, digestive system, head, and ovary according to the DGET expression database

Table 1 Differentially expressed genes up-regulated in the tolerant and down-regulated in the sensitive strains. ND is no data

As expected for metal treatment, the metallothioneins MtnA-MtnE were the most significantly differentially expressed genes by a large margin, both in tolerant and sensitive strains (Fig. 2B). While there was no relationship between their degree of induction and tolerance, all six strains were found to carry the 3′ indel polymorphism in MtnA that had previously been linked to oxidative stress resistance [17]. Other genes previously documented to play a role in copper homeostasis were notably absent from the differential expression lists, including the Ctr1 family of transporters, ATP7, Ccs, and Malvolio, suggesting that increased tolerance goes beyond metal chelation and homeostasis. Note that tolerant and sensitive strains did not differ in the expression of any of these genes in basal (nonstress) conditions either (Additional file 5: Table S4B).

In order to find the tissues displaying the greatest levels of transcriptomic change after copper exposure in tolerant and sensitive strains, we used the Drosophila Gene Expression Tool (DGET [38]). We classified our DEGs—taken from whole-body samples—according to their degree of expression in four of the available DGET tissue databases: head, carcass, digestive system, and ovaries of 4-day-old females (Fig. 2C). We focused on the overlap between our DEGs and those from DGET found to have higher levels of expression in these tissues (those categorized as having either high or extremely high expression: with RPKM values greater than 100). We found that the greatest level of overlap between DEGs and highly expressed genes according to DGET was seen for transcripts from the digestive system (hypergeometric test: tolerant p-value = 2.55×10−19; sensitive p-value = 9.59×10−36). The genes from our analysis that were highly expressed in the gut included the five metallothioneins MtnA-E and multiple serine peptidases, where many more peptidases were found significantly more down-regulated after copper exposure in sensitive strains. DEGs were also enriched to a lesser degree in the carcass (hypergeometric test: tolerant p-value = 4.58×10−7; sensitive p-value = 7.44×10−14). No DEG enrichment was seen for either the head or ovaries.

Regarding gut subsections, the most notable overlap between our DEGs and highly expressed genes according to DGET was found in the posterior gut regions in both tolerant and sensitive strains (Additional file 6: Fig. S2). Copper cells are responsible for copper storage [39, 40] and changes in gut acidity [41]. One such marker of gut acidity—vacuolar-type H+ATPase (Vha100-4) [42]was found down-regulated by 0.6 (p-value = 0.01) and 2.0 (p-value = 1.66×10−8) across tolerant and sensitive strains, respectively, suggesting that gut acidity may be playing an important physiological role.

Metabolism, reproduction, and peptidase inhibition contribute to copper response in tolerant and sensitive strains to different degrees

To determine what biological and physiological processes might differ between the tolerant and sensitive strains after the same period of copper exposure, we performed gene ontology (GO) enrichment analysis on tolerant and sensitive DEGs (Additional file 7: Table S5A). Metabolism-related terms were commonly seen as the largest and most significantly overrepresented terms in both tolerant and sensitive strains, although the exact processes often varied between the two groups (Fig. 3A). Chitin metabolic process (GO:0006030, adjusted p-value < 0.0001 both in tolerant and sensitive strains) and Chitin binding (GO:0008061, adjusted p-value < 0.0001 both in tolerant and sensitive strains) were also common to both analyses (Additional file 7: Table S5A). As expected, response to metal ion (GO:0010038, adjusted p-value = 8.34×10−4) was strongly overrepresented in the copper-tolerant strains (Fig. 3A). Additionally, several GO terms linked to reproduction and vitellogenesis—including vitelline membrane formation involved in chorion-containing eggshell formation (GO:0007305) and loss of vitellogen (encompassed by GO:0030704)—were found to be overrepresented in both analyses, but more so in sensitive strains (Fig. 3A and Additional file 7: Table S5A). Note that the shutdown of egg production is often a consequence of heavy metal toxicity [43, 44]. The majority of these terms were also found to be overrepresented in Gene Set Enrichment Analysis (GSEA) (Additional file 7: Table S6). Further KEGG analyses also emphasized the role of protein metabolism processes in copper stress response and lysosome activity both in tolerant and sensitive strains (Additional file 7: Table S7). Overall, these results suggest that under our assay conditions, the more tolerant strains could be undergoing metabolic stress response after 24 h of copper treatment, while the more sensitive strains could be progressing to shutting down non-essential biological processes—such as egg laying—at the same stage, as has been previously described for other stress responses [45, 46].

Fig. 3
figure 3

GO enrichment and correlational clustering analysis. A Top 15 enriched GO terms associated with the DEGs in tolerant and sensitive strains. The Y-axis indicates gene functions, and the X-axis indicates the percentage of total DEGs in a given GO category (gene ratio). B String interaction network of candidate genes taken from highly correlated modules (modules 2 to 5, Additional file 8: Fig. S4) of the MCC analysis for the treated samples of the tolerant strains (PPI enrichment p-value < 1×10−16). Up-regulated genes are shown in red, and down-regulated in blue. The genes with yellow labels are part of the serine peptidase cluster, while those with borders in bold were selected for further validation using RNAi knockdown or gene disruption lines

Tolerant and sensitive strains also differed in basal gene expression (Additional file 5: Table S4B), with the most significantly enriched molecular functions being enzyme inhibitor activity and endopeptidase inhibitor activity (Additional file 8: Fig. S3 and Additional file 7: Table S5B). Thus, similar to other stress responses, differences in basal gene expression between tolerant and sensitive strains contribute to differences in copper stress responses between these strains (e.g. [47]).

Finally, we also investigated the level of gene co-regulation in response to copper of tolerant and sensitive strains using modulated modularity clustering (MMC) analysis, which in contrast to previous analyses does not rely on any prior gene functional annotations [48]. Tolerant strains show a high level of expression coordination after copper exposure while sensitive strains showed the opposite pattern (Additional file 8: Fig. S4 and Additional file 7: Table S8). Briefly, across the tolerant strains, we identified 24 modules with an average positive correlation, |r| , of 0.72 in treated samples and 17 modules with a |r| = 0.65 in controls. The higher correlation values and greater degree of partitioning observed in the treated samples indicated that there are coordinated changes happening after copper exposure (Additional file 8: Fig. S4). For sensitive strains, 21 modules were identified in the treated samples, with an average |r| = 0.77, and 40 in the controls with an |r| = 0.71, with a less pronounced degree of partitioning, indicating a less coordinated response after 24 h of copper exposure (Additional file 8: Fig. S4). Heatmaps of the tolerant treated strains suggested that genes in modules 2–5 were very closely linked (Additional file 8: Fig. S4, Additional file 7: Table S8). Analysis of the 25 genes represented by these modules in STRING [49] revealed a group of seven tightly interacting serine peptidases (Fig. 3B) that are found highly expressed in the digestive system. While a number of these genes were from the Jonah family of serine peptidases, the discordantly expressed gene in tolerant vs. sensitive strains, Jon99Ci, was not included amongst them (Table 1). Of these seven serine proteases, four have previously been shown to be regulated by the histone and protein deacetylase Sirtin 2 (Sir2 [50]). On further inspection, 58 candidate DEGs from our tolerant strains and 187 from our sensitive strains were previously shown to display differential expression after Sir2 knockdown, a significant overlap (hypergeometric test: tolerant p-value = 1.30×10−20; sensitive p-value = 6.87×10−45; Additional file 8: Fig. S5A) [50]. Along with its role in heterochromatin formation, Sir2 is thought to have many additional protein targets that alter gene regulation. Amongst the targets of Sir2, gene expression datasets are available for DHR96, dfoxo, and HNF4 knockouts [51,52,53]. While a small degree of overlap was seen between our differential expression lists and that of dfoxo (hypergeometric test: tolerant p-value = 3.10×10−7; sensitive p-value = 1.50×10−20) and DHR96 knockout analyses (hypergeometric test: tolerant p-value = 3.04×10−7; sensitive p-value = 3.58×10−12; Additional file 8: Fig. S5), the greatest overlap was seen with HNF4 knockout analyses (hypergeometric test: tolerant p-value = 1.45×10−19; sensitive p-value = 8.94×10−66; Additional file 8: Fig. S5B) [51,52,53]. While little is known about the precise role HNF4 plays in the midgut, its inferred link to the serine peptidases suggests a potential role in gut function.

Eight out of 10 copper candidate genes were confirmed to play a role in copper tolerance

Ten of the candidate genes associated with copper response based on our transcriptomic analysis were chosen for further characterization. Three of these genes—CG5966, CG5773, and Cyp4e3—were chosen on the basis of their differential expression data alone. Three other candidates—Sodh-1, CG6910, and CG32444—have been linked to copper homeostasis previously in the literature, but their exact functions have not been well characterized [12, 54, 55]. The remaining four candidate genes CG11594, Cyp6w1, Cyp6a8, and Jon65Aiv were all found to be associated with TE insertions (see below). In addition, four of the ten candidates were part of the MMC cluster containing the serine peptidases (Sodh-1, CG6910, Jon65Aiv, and CG32444; Fig. 3B).

Seven of the genes tested showed changes in phenotype (copper survival) when knocked-down or disrupted in the direction that could be expected based on our RNA-seq data, i.e. if the gene was found to be up-regulated in response to copper, the knockdown of the gene was associated with decreased survival (Fig. 4, Additional file 9: Fig. S6, Table 2, Additional file 10: Table S9). Survival curves were significantly different for six of these seven genes when comparing the gene disruption or knockdown lines with their genetic background controls (Additional file 9: Fig. S6 and Table 2), with four of them also showing significant differences in LT100 (Fig. 4 and Table 2). On the other hand, CG6910 only showed differences in LT100 (Fig. 4 and Table 2). Of these seven confirmed genes, CG11594, Cyp6w1, Cyp6a8, and Jon65Aiv are novel candidates, whose full role in copper biology is not yet understood, while the other three genes, Sodh-1, CG6910, and CG32444, have prior links to the phenotype [12, 20, 55]. On the other hand, CG5966 displayed decreased mortality when knocked-down, which was not predicted by its induction on copper (Fig. 4, Additional file 9: Fig. S6, Table 2).

Fig. 4
figure 4

Copper survival experiments for all ten copper candidate genes. LT100 values comparing candidate gene disruption and knockdown lines with their genetic background controls (3 to 5 biological replicates of 15 females). Significant LT100 assessed with a two-sided t-test, with p-values < 0.05 are shown with *, p-values < 0.01 with **, and p-values < 0.001 with ***. Del is deletion and GDP is gene disruption. For RNAi knockdowns, genes thought to act in the ‘detox’ tissues—including the gut fat body and Malphigian tubules—were targeted with the 6g1HR-Gal4 driver, while those genes whose expression was more gut specific were targeted with MexG-Gal4. A ubiquitous Actin5C-Gal4 knockdown was used for all other crosses. Driver abbreviations are as follows: MexG-Gal4 w1118 is the introgressed w1118 version of MexG-Gal4 driver, HR is the HikoneR driver, and Act is the Actin5C-Gal4 driver (see Additional file 1: Table S1C)

Table 2 Eight out of 10 copper candidate genes play a role in copper tolerance. Fold change (FC) expression after copper treatment in tolerant and sensitive strains. No DE: gene was not differentially expressed. Kaplan-Meier survival curves results and average mortalities. GDP gene disruption line, Del gene deletion line, ⬆ increased mortality, ⬇ decreased mortality, – no data, NS not significant. Crosses with different backgrounds and reciprocal crosses are separated with “;”

CG5773 and Cyp4e3 did not display any changes in survivorship after knockdown (Fig. 4 and Additional file 10: Table S9, Table 2). As Cyp4e3 was initially tested with the 6g1HR-Gal4 driver, based on prior expression data [56], we repeated the crosses with the Actin5C-Gal4 ubiquitous driver. However, these additional assays did not show any significant changes in copper survival. While it is possible that the effects of these genes on copper tolerance are background sensitive, as per the example of Cyp6g1 [57] and Cyp12d1 [58], it is also possible that these genes have little to no true impact on the phenotype at all and are only present due to co-regulation with other genes that do directly affect copper tolerance—a phenomenon that has been observed with regard to the Cnc/Keap1 pathway [59] (Fig. 4, Additional file 9: Fig. S6, Additional file 10: Table S9).

Copper tolerance is correlated with gut acidity and CG11594 activity and not mitigated by changes in feeding behaviour

Both our DGET analysis and our GO analysis have lent evidence to the idea that there is a relationship between the gut and copper tolerance (Figs. 2C and 3A). As copper accumulation in D. melanogaster has previously been linked to changes in gut physiology [13], we assayed the changes in gut pH after copper exposure. Adults from the six RNA-sequenced strains were subject to copper assay conditions and then allowed to recover for 2 h on regular media supplemented with a mixture of Bromophenol Blue and yeast. If the acidic copper cell region of the gut remains un-inhibited by copper, this region should remain yellow under Bromophenol Blue (pH < 2.3). After 2 h, most recovering individuals had consumed enough media for the dye to be detected in the gut. Only AKA-018 had more than 10% of flies failing to feed on recovery—a phenomenon that was not seen in the controls (Fig. 5A). While all six strains showed decreased acidity across the copper cell region after copper treatment, the three sensitive strains showed a much higher loss of acidity than the three tolerant (chi-square test p-value < 0.05 for all comparisons, Fig. 5A and Additional file 11: Table S10A). Thirty-three per cent of individuals across all tolerant strains maintained a low pH under treated conditions compared to only 6% of the sensitive strains (chi-square test p-value < 0.001, Fig. 5A, Additional file 11: Table S10A). Differences were less pronounced under control conditions, with only JUT-008 showing an appreciable loss of acidity in the absence of copper compared with MUN-020 and GIM-012 (chi-square test p-value = 0.0003 and 0.0014, respectively, Additional file 11: Table S10A). From these results, we can infer a link between the loss of acidity in the copper cells and a decreased ability to tolerate copper.

Fig. 5
figure 5

Gut acidity after copper exposure is correlated with copper tolerance and is not linked to feeding behaviour. A Gut acidity results on the six RNA-sequenced strains after 24 h of copper treatment and 2 h of recovery (left) and after 24 h of control conditions and 2 h of recovery (right). Lower pH indicates that the dye turned yellow within the region of the gut containing copper cells; intermediate pH indicates that the dye turned green-brown, but a discrete acidic region could still be detected; higher pH indicates that the entire midgut was blue and the copper cell region could not be detected; and no feeding (clear or pale blue). For readability, chi-square test p-values comparing tolerant vs. sensitive strains are given in Additional file 11: Table S10. B Feeding avoidance in the presence of copper measured as a fold difference in consumption between treatment and control at 24 h (left) and 40 h (right) (see Additional file 11: Table S10). C Gut pH of the control strain w1118 (in brown) and the CG11594 deletion strain (in blue) after 16 h of copper treatment (left) and control conditions (right). An asterisk (*) indicates a difference across the treatment groups at p-value < 0.05 (*) and p-value < 0.01 (**). For all the plots, error bars represent the standard error of the mean of three biological replicates containing 28–44 females each (A and C) and the coefficient of variation of three control biological replicates containing 25–30 females each (B)

D. melanogaster often avoid food sources with high concentrations of heavy metals [60, 61]. To determine if the changes in gut acidity are influenced by changes in feeding behaviour, we repeated the copper tolerance assays on the six RNA-sequenced strains, this time with the addition of a 1% erioglaucine disodium salt to both the treatment and control solutions to act as a dye. We measured the level of dye consumed in both treatment and control conditions at two separate timepoints to determine the level of feeding avoidance. At 24 h, the level of feeding avoidance on copper across most of the lines was quite low when compared to their control counterparts, with no significant differences between strains (chi-square test p-value > 0.05; Fig. 5B and Additional file 11: Table S10B). Feeding avoidance was generally stronger at the 40-h mark, with no significant differences between tolerant and sensitive strains (two-sided t-test p-value > 0.05; Fig. 5B and Additional file 11: Table S10B). These results suggest no relationship between feeding behaviour and whether or not the line showed high or low copper tolerance or changes in gut acidity.

If the observed changes in gut acidity are not based in behaviour, they are likely physiological in nature. While metallothioneins could be good candidates [14], as mentioned above, we found MtnA-MtnE to be up-regulated in response to copper both in tolerant and sensitive strains (Fig. 2B), and no differences in MtnA-MtnE expression between tolerant and sensitive strains were found in basal conditions (Additional file 5: Table S4B). Thus, overall, changes in MtnA-MtnE expression are not likely to explain the identified differences in gut acidity (Fig. 5A). We thus decided to focus on CG11594 one of the seven candidate genes that we confirmed as having a role in copper tolerance (Fig. 4 and Table 2), as this is the most poorly characterized of the candidate genes, which although being associated with a number of stress phenotypes it had no prior links to copper biology before this work [62, 63]. To determine if CG11594 expression alters gut acidity, similar exposure and gut staining assays were carried out over a 16-h time period on a CG11594 deletion strain (w1118; CG11594), using w1118 as the background control strain. While both lines displayed a high degree of gut de-acidification after treatment than any of the six natural lines, the effects seen on the CG11594 deletion line were significantly greater than those on the background control line (one-sided t-test, p-value < 0.05, Fig. 5C and Additional file 11: Table S10C).

Curiously, the clearest differences between the two lines were seen not in the copper treatment, but in the control, where less than half of the CG11594 deletion individuals displayed a clearly defined acidic region. This is in stark comparison to the six sequenced strains, which displayed healthy guts under control conditions. These results suggest that physiology, not behaviour, is the main driver behind midgut de-acidification after copper exposure and that GG11594 plays a role in this change.

Transposable element insertions may influence copper tolerance

TE insertions are often associated with changes in gene expression under stressful conditions (e.g. [64]), and in D. melanogaster, several specific insertions have been linked to stress response including zinc stress (e.g. [27,28,29,30,31,32]). However, until recently, only the subset of TEs annotated in the reference genome could be analysed, thus limiting the power of genome-wide analysis to investigate this type of structural variant. We took advantage of the availability of de novo whole-genome assemblies and de novo TE annotations for the three tolerant and three sensitive strains analysed in this work [24], to investigate the association between proximal cis TE insertions and gene expression levels in both treated and control conditions (within 1kb of the insertion, see the ‘Methods’ section). Using QTLtools [65], we identified three TE insertions that were significantly associated with changes of expression in nearby genes: two in response to copper (FBti0061509 and FBti0063217) and one both in control and in response to copper (FBti0060314; Additional file 12: Table S11A). Although the number of significant associations is small, this is most probably due to the small number of genomes analysed (six)—suggesting that this approach should provide more insight with larger datasets.

As an alternative approach, we also investigated whether previously identified DEGs in tolerant and sensitive strains were located within 1kb of a TE insertion (Additional file 12: Table S11B). There were no significant differences between the percentage of differentially expressed genes located within 1kb of a TE in tolerant compared to sensitive strains (14.28% across the three tolerant strains and 11.29% in the three sensitive; Fisher’s exact test p-value = 0.2193). While 73.5% of the TE insertions were associated with gene up-regulation in tolerant strains, only 28% of the TEs were associated with up-regulation in sensitive strains (Fisher’s exact test p-value = 0.0014; Additional file 12: Table S11B). Because the effect of transposable elements, and other genetic variants, is often background dependent (e.g. [66]), we also investigate whether TEs were associated with DEGs identified at the strain level. None of the strains showed a significant enrichment of TEs nearby DEGs (test of proportions p-value > 0.05, Additional file 12: Table S11C).

Finally, we tested three TE insertions for their effects on copper tolerance. For each of the TE insertions, we constructed two outbred populations: one with the insertion and one without the insertion (see the ‘Methods’ section). This strategy limited testing to those TE insertions that have been found segregating in populations at a high enough level that we could obtain enough strains to construct the outbred populations. We chose two insertions that besides being located nearby DEGs showed signatures of positive selection in their flanking regions suggesting that they might be adaptive: FBti0020036 and FBti0020057 [26]. The third TE candidate, FBti0020195, is not present in any of our six sequenced strains but garnered special interest due to its location within CG32444, a candidate gene identified in this study and further confirmed with the use of gene disruption (Additional file 9: Fig. S6). For each of these three TE insertions, we constructed two outbred populations: one with the insertion and one without the insertion (see the ‘Methods’ section). For each of the paired outbred populations, those containing TE insertions demonstrated greater survivorship on copper than their negative counterparts, both on LT100 (one-sided t-test, p-value = 0.0055, p-value < 0.001 and p-value < 0.001 for FBti0020036, FBti0020057, and FBti0020195, respectively, Fig. 6 and Additional file 12: Table S11D), and across the entire survival curve (log-rank tests p-value < 0.001 for all three comparisons, Additional file 13: Fig. S7 and Additional file 12: Table S11D).

Fig. 6
figure 6

Copper survival experiments for the three candidate transposable element insertions. A Gene structure showing where each of the candidate TEs (in green) is inserted. For RhoGEF64C, only the 3′ region of the gene is depicted. B Relative change in average mortality at the end of the assay comparing outbred populations with and without the candidate TE (9 to 10 biological replicates of 10–15 females in treatment and of 5–10 females in control conditions). Significant LT100 p-values < 0.01 with **, and p-values < 0.001 with *** (one-sided t-test)

Discussion

The environmental determinants of copper tolerance in D. melanogaster are complex

In this study, we undertook a survey of multiple European D. melanogaster populations to determine how copper tolerance varies across the continent (Fig. 1), and whether this variation could be linked to the presence of copper or other environmental factors. To achieve this, we compared our phenotypic values with geographic factors, copper soil levels, atmospheric pollution levels, and degree of urbanization. We found a positive correlation between latitude and LT50 (p-value = 0.015, Additional file 4: Table S3B). While we also found evidence of a link between urban build-up and greater tolerance, no clear relationship could be drawn between tolerance and any of the direct measures of pollution available to us (Additional file 4: Table S3B). As Romic and Romic [2] noted, human sources of environmental copper are characterized by many point sources of contamination, and while we are aware that some well-known sources—such as atmospheric copper—are missing from our dataset, it is possible that there are others missing as well. Moreover, it is also unknown whether the greatest effect will be from an accumulation of multiple sources of the metal or a small number that are the most bio-available. As these point sources can be difficult to characterize, performing environmental sampling, e.g. soil sampling, alongside fly collections, may be a viable alternative [67]. The diversity of vegetation may also be worthy of record as copper uptake and storage varies across plant tissues and species [68]. Although we cannot discard that more extensive sampling could further help discern the relationships between phenotype and environment, our results indicate that the finer details of the surrounding environment should be receiving as much attention as the finer details of the genome when making sense of phenotypic differences.

The genetic basis to copper tolerance in D. melanogaster is complex and involves multiple regulatory factors

One of the most distinguishing features of our phenotypic dataset is the high degree of variation both within and between sampling locations (Fig. 1B and Additional file 2: Fig. S1). While high levels of phenotypic variation can sometimes result from an allele of large effect segregating within a population, as seen in Battlay et al. and Green et al. [58, 69], the gradual distribution of our LT50 values suggest that this is not the case and that the degree of phenotypic variation seen across our strains is likely an indication of the polygenic basis of the trait (Additional file 2: Fig. S1A [5, 20]). This was in turn backed by our RNA-sequencing analysis, which indicated that copper tolerance is a trait with a complex genetic architecture, involving multiple genes and regulatory factors, and with a large degree of expression change occurring in the gut (Fig. 2).

With regard to genes with prior links to metal response, variation in metallothionein expression was not found linked to phenotypic variation in the six strains sequenced (Fig. 2B; Additional file 5: Table S4C). However, as all six strains carry the 3′ indel that is believed to be linked to increased stress tolerance, and it is found to be close to fixation in northern Europe [17], thus, it is likely that metallothionein-tolerant variants have already been subject to selection. We also saw no significant differences in expression with regard to multiple genes previously linked to copper homeostasis. While this may initially come across as curious, many of the previous studies characterizing copper-related genes in genetically modified lines were carried out in a small number of strains with the aim of characterizing genes that play a role in human diseases [9, 55, 70], and not explicitly copper exposure in nature. While the lack of these genes in our DEG lists does not necessarily mean that they are not involved in copper tolerance, it does indicate that the genes contributing to the variation we see in tolerance in natural populations of D. melanogaster are much broader than previously characterized in these studies and that the biological basis behind copper tolerance may be constrained by the need to maintain copper levels in less extreme environments.

While it has been well documented that MTF-1 plays an important role in regulating gene expression in response to metal exposure, including metallothionein induction, it is unlikely to be the only regulatory factor affecting changes in expression, especially with regard to downstream metabolic processes affected by copper toxicity [22]. By using a combination of DGET and gene clustering, we were able to identify Sir2 and HNF4 as additional potential regulatory elements. Sir2 plays a multifaceted role in maintaining energy homeostasis, affecting fat mobilization [71], insulin signalling [72], and energy consumption [50]. HNF4—a direct target of Sir2 regulation—also influences a wide range of processes involved in cellular metabolism and systemic physiology [53]. These results are supported by our functional gene analysis. Of the eight confirmed candidate genes (Fig. 4), Sodh-1 and CG32444 have both been linked to the kind of metabolic processes modulated by HNF4 and Sir2, while also having found associated with copper toxicity previously [20, 55]. The two cytochrome P450s, Cyp6w1 and Cyp6a8, are both linked to oxidative stress [58, 73], a process that has been linked to metal tolerance previously [22]. CG6910 is down-regulated in MTF-1 knockout mutants [12]. The roles of the remaining three candidates in copper tolerance are more speculative. Jon65Aiv is known to be a serine protease with a likely role in digestion [74]. Serine proteases have also been shown to be down-regulated in clusters after exposure to another metal, manganese [75], and during ageing [76], although the reason for this perturbation remains unresolved. As copper inhibits larval midgut acidification [14], a phenotype also seen in ageing [13], it would be tempting to investigate the relationship between acidity and serine proteases directly. This also has interesting implications for cross-species comparisons: while serine protease function is well conserved across species [77], the degree of segmentation and the pH levels of the alimentary tracts of many other insect species (e.g. Lepidoptera) are not [78, 79].

While its role has not been well characterized, CG5966 is involved in triglyceride breakdown [80], and starvation response [81], a functional profile that fits with regulation by both Sir2 and HNF4. CG5966 has also been found to be highly up-regulated during mitochondria dysfunction [82], along with many other stress response genes. Finally, our pH assays give the greatest guidance to the role of CG11594, which may prove to play a role in gut integrity.

While our individual gene candidates may not be so well conserved outside of Drosophila, Sir2 and HNF4 do have well-conserved orthologs, much in the same manner as the metallothioneins. While there is no previous evidence for these genes playing a role in copper toxicity in arthropods, such evidence exists in mammalian cell culture: rat hepatocytes treated with copper sulphate display increased expression of Sir2 homologs Sirt1 and Sirt2 [83], while HNF4-α influences copper-responsive transcription changes in HepG2 cells [84]. Furthermore, while many of our putative candidates for Sir2 and HNF4 regulation were found highly expressed in the gut, both regulatory elements have been shown to play different roles in different tissues [53], presenting us with the possibility that not only might their roles in copper response be discordant in different tissues, but that this may apply to the general transcriptional signature post-metal exposure as well. Future assays using knockdown or disruption of these factors across multiple tissues in Drosophila would be able to confirm their specific roles in copper response.

Further changes in gene expression can potentially be traced back to transposable element insertions. TE insertions are often associated with the differential expression of nearby genes under stress conditions [27, 28, 64]. We identify several TE insertions located inside or nearby differentially expressed genes (Additional file 11: Table S10B). For three of these insertions, we further showed that their presence is associated with increased copper survival (Fig. 6). Further analysis, such as recombination mapping and CRISPR-based knockouts in these genetic backgrounds, could potentially assist in confirming the role of these specific TE insertions in altering gene expression and their effect on phenotype.

Gut acidity is linked to copper tolerance in D. melanogaster

Our analysis demonstrated that a large degree of the differential expression observed after copper exposure was occurring in the gut, a key tissue when it comes to copper physiology [13,14,15]. A role for the gut is also supported by the GO enrichment results: chitin binding and metabolic processes suggest a role for the peritrophic membrane [85], which is important for gut integrity (Fig. 3). A study on the effects of Lufenuron—a chitin disrupter—in Anthonomus grandis showed that gut disruption could lead to changes in metabolism and the down-regulation of vitellogen, also seen in our GO enrichment analysis [86]. In addition, chitin binding and metabolic processes also affect the cuticle, which may affect copper exposure via contact. Indeed, copper DEGs were also found to be enriched amongst extremely high and highly expressed genes from the carcass in DGET (Fig. 2C). A correlation between cuticle darkening and increased body copper content has also been reported in D. melanogaster [75].

Our gut pH assays clearly demonstrate that copper exposure results in a loss of acidity in the copper cell region—and that this effect is more sharply seen in the three sensitive strains (Fig. 5A). Our subsequent feeding response assays excluded differences in copper consumption as a potential explanation of varying losses in gut acidity, suggesting a more physiological process was responsible for the changes observed (Fig. 5B). While metallothioneins could be good candidates [14], our Mtn expression data do not sufficiently explain the differences we observed (Fig. 2B and Additional file 5: Table S4). This opens up the possibility that one or more of our gene candidates selected for further analysis may be affecting copper tolerance through changes in copper cells or gut acidity. While the function of CG11594 has mostly gone uncharacterized, its expression has been linked to both oxidative stress and ER stress in the DGRP strains [62, 63]. While disruption of CG11594 expression caused a strong loss in gut acidity after copper treatment, there was a notable loss under control conditions as well (Fig. 5C). These results imply that loss of gut acidity is a sub-phenotype to copper tolerance and that both share links to CG11594 activity—although the exact mechanism underpinning the relationship remains elusive. In light of previous studies, we can propose two tentative alternative hypotheses: regulation of CG11594 by both Sir2 and HNF4 suggests that the gene plays a general role in energy and metabolism [50], and it is differences in the allocation of energy and resources that affect survival. Alternatively, links to ER stress [63] could indicate a role linked to lysosome function or metal storage.

Conclusions

Our investigation across European natural populations of D. melanogaster proved copper tolerance to be a highly variable trait. We confirmed the involvement of multiple new candidate genes, identified two potential new regulatory factors that have previously only been seen to mediate metal responses in mammals, and described physiological changes linked to this trait. Unlike previous candidates, such as the metallothioneins, which are common across a wide phylogeny, it is unlikely that the exact genes shown to affect copper tolerance in D. melanogaster will be perturbed in other species vulnerable to metal toxicity. However, other, more general, molecular pathways and physiological changes in the gut we observed in D. melanogaster are likely to prove relevant in studying the effects of copper toxicity in other species.

Methods

Fly collections

Details of all the stocks used can be found in Additional file 1: Table S1. The nine original collections (73 strains) were carried out across the summer of 2015 by the DrosEU consortium (www.droseu.net). Each of the established isofemale strains (4 to 16 depending on the population, Additional file 1: Table S1) was repeatedly inbred for up to 20 generations. Of the additional 26 strains included for geographical and environmental analysis, the Mauternbach (Austria) and Recarei (Portugal) strains were caught in 2018 and the Bari (Italy) strains were caught in 2011 and have been kept as isofemale strains since then [87]. All fly collection sites are documented in Fig. 1A. All strains were maintained on semolina-yeast-agar media and were kept at 25°C on a 12:12-h light and dark cycle for at least one generation before use.

Copper tolerance assays

Copper sulphate (CuSO4) (CAT# 451657-10G) was obtained from Sigma-Aldrich. Copper assays were adapted from Bonilla-Ramirez et al. [61]. This particular method was chosen for two reasons: (i) Drosophila are known to show food avoidance with a high concentration of heavy metals [61]; however, this method allowed exposure via both contact and digestion; and (ii) the 4–5-day length of the assay gives sufficient time to differentiate between tolerant and sensitive strains without risking high control mortality.

Briefly, powdered CuSO4 was reconstituted to 20mM in a 5% sucrose solution. Brilliant blue food dye (E133) was added to aid visibility and even dispersal. An identical control solution without CuSO4 was prepared in the same manner. A total of 250μl of the CuSO4 sucrose solution was pipetted onto 70×17mm slips of filter paper (Whatman, CAT# 3030917), which were then placed into 15-ml Falcon tubes (Cultek, CAT# 352096), containing 1ml on 1% agar at the bottom. Papers were allowed to dry for 15 min before the flies were added. To assist respiration, holes were made in the lids of the falcon tubes. The number of dead flies was counted at different timepoints both in the control and treated conditions, until all flies were dead in the treated conditions.

For each isofemale strain, 4–7-day-old females were used in the copper survival assays both in control and treated (20mM copper) conditions. Three biological replicates of up to 15 flies each were performed for the treatment and for the control conditions (Additional file 1: Table S1). LT50 calculations were used to interpolate measures of survival for each of the strains. Linear models were fitted to timepoint-mortality data on a log-probit scale using the glm() function in the R statistical package, using a script adapted from Johnson et al. [88]. Of the 73 DrosEU strains screened, LT50 values were successfully calculated for 71, along with the 26 additional strains from Italy, Austria, and Portugal (Additional file 3: Table S2).

Correlation analysis with geographical and environmental variables

We first tested whether populations above (5 populations) and below (7 populations) the 45th parallel differed in copper tolerance. Because a Shapiro-Wilk test showed that data was not normally distributed (p-value = 0.0011), we performed a one-sided Wilcoxon test. We then tested whether copper tolerance correlated with geographical and environmental variables. Copper soil concentration data was taken from The European Soil Data Centre (ESDAC: https://esdac.jrc.ec.europa.eu/content/copper-distribution-topsoils) [3], with the exception of the Tenerife data, which was taken from Fernandez-Falcon et al. [89]. Air pollution data was taken from the European Environment Agency (EEA): https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm. The pollutants considered included PM10 (particulate matter 10μm or less in diameter) and PM2.5 (particulate matter 2.5μm or less in diameter), arsenic in PM10, cadmium in PM10, and lead in PM10 data. All measures were taken from the closest research station available for each catch site. General PM10 and PM2.5 data and atmospheric metal data for arsenic, cadmium, and lead were available for the majority of catch sites (Additional file 4: Table S3). Data for particulate copper taken from PM10 measures had to be excluded due to both insufficient geographical coverage and a lack of consistency in the measures made (PM10 and precipitation). All tests and linear regression models were performed in R (v.3.5.1) [90]. Regression models were fitted with LT50 values as the dependent variable, and with geographical and pollution measures as independent variables. The degree of urbanization of the fly collection locations was based on whether the closest population to a collection site was a city (> 50,000 inhabitants: urban), a town with a population > 5000 inhabitants (semi-urban), or less dense populations (<5000 inhabitants: rural; Additional file 4: Table S3). This degree of urbanization is based on the OECD/European Commission (2020), Cities in the World: A New Perspective on Urbanisation, OECD Urban Studies, OECD Publishing, Paris, available at: https://www.oecd.org/publications/cities-in-the-world-d0efcbda-en.htm [37]. We first tested whether any of the explanatory variables were correlated. We found that cadmium and arsenic were indeed highly correlated (Pearson correlation coefficient ρ=0.98). We thus performed multiple linear regression to test the association between copper tolerance (LT50) and the geographical and environmental variables considering only one of these two variables. We first created a linear model with all the measured variables (model: LT50 ~ longitude + latitude + copper + PM10 + PM2.5 + arsenic (or cadmium) + lead + DegreeUrbanization). We then carried out a backward stepwise regression to eliminate variables using the dropterm() function of the MASS package in R. At each step, we removed the least significant variable. Only variables with a p-value < 0.1 were retained in the minimal model [91], which considered latitude and degree of urbanization (R= 12%, p-value = 0.0079). Scripts to perform the analyses can be found in https://github.com/GonzalezLab/Dmelanogaster_Copper.

RNA-seq sample preparation

RNA-seq analysis for short-term copper exposure (24 h) was performed on six inbred strains, where those with the strain codes GIM-012, MUN-020, and MUN-008 were copper tolerant and JUT-008, COR-018, and AKA-018 were copper sensitive (Additional file 1: Table S1). To maximize odds of choosing mostly homozygous strains, we prioritized those strains with a high degree of inbreeding (minimum of F20), and a low degree of variation between biological replicates in the LT50 assays.

Four biological replicates of 25 mated female flies 4–7-day-old from each line—separated 24 h beforehand on CO2—were exposed to CuSO4 or the equivalent control conditions, as reported above, and removed after 24 h. This timeframe allowed low levels of death in the sensitive strains, but enough time to stress the tolerant strains, as measured by the induction of MtnB detected through RT-qPCR. Deceased individuals from strains COR-018 and JUT-008 were removed before whole-body RNA extraction. Twenty females from each biological replicate were flash frozen in liquid nitrogen and total RNA was isolated using the GenElute Mammalian Genomic RNA miniprep kit (Sigma-Aldrich, CAT# RTN350-1KT), following the manufacturer’s instructions. For each sample, the three repeats with the best RNA quality based on BioAnalyzer were retained for sequencing. One microgramme of total RNA from each sample (whole female body) was used for subsequent library preparation and sequencing using an Illumina Hiseq 2500. Libraries were prepared using the Truseq stranded mRNA library prep according to the manufacturer protocol. Only two control samples for both AKA-018 and MUN-020 showed high enough quality for further RNA-seq analysis. Thus overall, we used 34 samples.

Analysis of RNA-seq data

RNA-seq analysis was performed using the rnaseq pipeline (v.1.2) from the nf-core community, a nextflow collection of curated bioinformatic pipelines [92, 93]. The total number of raw reads obtained per sample range between 25.16M and 46.13M. Briefly, sequencing quality was assessed using FastQC (v.0.11.8, [94]). TrimGalore (v.0.5.0) was used for adapter removal [95], and Cutadapt (v.1.18) with default parameters was used for low-quality trimming [96]. Trimmed reads were mapped to the D. melanogaster genome r6.15 [97] using STAR (v.2.6, [98]). On average, 95.9% of the reads mapped to the reference genome. Technical duplications were explored using dupRadar [99]. Overall, we found no bias towards high number of duplicates at low read counts, so we did not remove duplicates from the alignments. We used featureCounts (v.1.6.2, [100]) for counting the number of reads mapping to genes (reverse-stranded parameter). Multi-mapping reads and reads overlapping with more than one feature were discarded. The matrix of counting data was then imported into DESeq2 [101] for differential expression (DE) analysis following the standard workflow and applying the design formula: Strain + Treatment in the analysis of the tolerant and sensitive strains. To compare resistant vs. tolerant strains in basal conditions, we used the design formula ~ Resistance. Finally, to perform the joint analysis with all the strains together and considering the interaction term between the phenotype and the treatment, we used the design formula ~ Strain + Treatment + Strain:Treatment. Normalization was performed using the standard DESeq2 normalization method, which accounts for sequencing depth and RNA composition [101, 102]. Differentially expressed genes were chosen based on both log2 fold change (> 1.5) and adjusted p-values (< 0.05). Gene counts and scripts to perform the DE analyses can be found at https://github.com/GonzalezLab/Dmelanogaster_Copper.

Functional profile analyses of the differentially expressed genes (GO, GSEA and KEGG) were performed using the R package clusterProfiler [103]. Breakdown of differentially expressed genes by tissue was performed using the Drosophila Gene Expression Tool (DGET: https://www.flyrnai.org/tools/dget/web/ [38]), with gut subsampling data taken from similar aged flies from Marianes and Spradling (2013) [41].

Modulated modularity clustering (MMC) was used to group differentially expressed genes into subsets of genetically correlated genes in both treated and control samples. All analyses were carried out as outlined in Stone et al. [48], except the variance filtering, which was performed in R (v.3.5.1) beforehand. The variance filter removed genes where no variance across repeats and samples was found, which basically removes genes with no expression. Additional network-based analysis was performed using STRING (v.10, [49]) with a minimum interaction score of 0.7. Subsequent visualizations were performed using Cytoscape (v.3.7.1, [104]).

RNAi and gene disruption assays

Candidate genes were functionally validated using RNAi knockdown lines from the KK library of the Vienna Drosophila Resource Centre ([105]; obtained from the VDRC) and the Transgenic RNAi Project (TRiP) developed from the Harvard Medical School ([106]; obtained from the Bloomington Drosophila Stock Centre). Additional gene disruptions were performed using either Drosophila Gene Disruption Project (GDP) lines ([107]; obtained from the Bloomington Drosophila Stock Centre) or one independent deletion mutant (w1118; CG11594; obtained from the Bloomington Drosophila Stock Centre). All stock numbers are provided in Additional file 1: Table S1.

The choice of Gal4 driver was based on data obtained for each gene from FlyAtlas 2 (http://flyatlas.gla.ac.uk/FlyAtlas2/index.html [56]). Three of the drivers were homozygous: the 6g1HR-Gal4 driver, described by Chung et al. [108], and two different background versions of the MexG-Gal4 driver, originally described by Phillips and Thomas [109]. All three were provided by Shane Denecke. The heterozygous Actin5C-Gal4/CyO driver was obtained from Bloomington Drosophila Stock Centre (BDSC ID 4144).

For all assays using homozygous Gal4 drivers, the mortality of all Gal4-RNAi crosses was compared to matching control crosses using the appropriate RNAi background strain. For the KK RNAi lines, comparisons were made to crosses using the KK construct-free control strain (VDSC ID 60100). For all assays containing TRiP RNAi lines, for those lines with the RNAi construct inserted into the attP2 site, comparisons were made to the y, v; attP2, y+ construct-free control strain (BDSC ID 36303) and/or those lines with the RNAi construct inserted into the attP40 site, the y, v; attP40, y+ construct-free control strain (BDSC ID 36304). Due to difficulties maintaining the strain, for all assays using crosses with the Actin5C-Gal4/CyO driver, the offspring that inherited the Gal4 construct were compared to their CyO inheriting siblings. All GDP lines and the w1118; CG11594 strain were compared to w1118.

Copper survival experiments were performed as described above using 4–7-day-old flies. Three to five biological replicates of up to 15 flies for the treatment and four to five biological replicates of up to 10 flies were performed for control conditions. Kaplan-Meier survival analysis was chosen as the best statistical comparison for comparing disrupted and control samples, and all analyses were performed using the R package Survminer (v.0.4.8). The significance of the survival curve was assessed with the log-rank test. Additionally, a relative change in average mortality is also provided as a proxy of the size of the effect of these genes on copper tolerance, and significance was tested using a two-sided t-test.

Gut pH assays

Four- to 5-day-old flies from the six strains taken from the RNA-seq analysis were subject to the same assay conditions used for the copper tolerance assays for 24 h. Assays were performed in triplicate, with each biological replicate consisting of 30–50 female individuals. Higher numbers were required for COR-018 and JUT-008 to account for the level of mortality expected during this timeframe. Flies were then transferred to regular Drosophila media, on which 200μl of a mixture of 1% Bromophenol Blue, dried yeast, and water (at a 1:1:3 ratio) had been added 20 min prior. Flies were permitted to feed for 2 h before having their midguts dissected in PBS and accessed for loss of acidity. Twenty-eight to 44 samples were dissected from each replicate (numbers varied as guts were often very fragile). Any individuals who proceeded to die after transfer to recovery media were discarded. Samples were determined to have experienced minimal loss in acidity if the cells in the acidic region of the midgut remained yellow (pH < 2.3), an intermediate loss if they had faded to green or brown, and full loss if they could not be distinguished from the surrounding sections (pH > 4). No feeding was recorded if no media was present in the gut. To test for differences in gut acidity between tolerant and sensitive strains, we used the chi-square test. To perform a post hoc analysis, we used the function chisq.posthoc.test() from the R package chisq.posthoc.test (v.0.1.2). The p-values of the chi-square and post hoc tests were corrected for multiple testing using the Bonferroni method.

Similar assays were carried out on lines w1118 and the CG11594 deletion line (w1118; CG11594) (Additional file 1: Table S1) over a shorter 16-h time period, to account for the greater sensitivity of these lines to copper.

Feeding avoidance assays

To measure the effect that the presence of copper has on feeding avoidance, the six strains from the RNA-seq analysis were assayed in similar conditions to that of the copper tolerance assay, but with the addition of erioglaucine disodium salt (1%, Sigma-Aldrich CAT#861146) to both the treated and control solutions. Erioglaucine disodium salt has been shown to be an effective tracer up to 48 h in Drosophila [110]. Assays were performed in triplicate for groups of 25–30 4–7-day-old females, with higher numbers used for COR-018 and JUT-008 to account for the degree of mortality expected at the end of this time period. All dead individuals were discarded. Flies were homogenized using a pestle, with each sample consisting of three flies in 620μl of distilled water. After crushing, samples were spun at 14,000rpm for 10 min and then frozen for 24 h. A total of 180μl of supernatant was loaded into each well of a 96-well Nunc-Immuno™ MicroWell™ plate (Sigma-Aldrich, CAT#M9410). Measurements were made using a Techan Infinite® 200 Microplate Reader, at 630nm, after 10 s of agitation at 9mm. Three technical replicates for six samples, for a total of 18 wells, were loaded for each treatment condition. Each of the three plates analysed contained four water blanks and five standards containing between 0.015 and 1.5×10−5 % of dye. All technical replicates per sample were averaged. The amount of dye consumed per strain (average of six samples per strain) was inferred from a linear model fitted from the points of the standard curve. All results are reported as the fold difference in feeding between treated and control samples for each timepoint. The coefficient of variation for each experiment was calculated as the standard deviation of the control sample concentrations divided by the mean of the control sample concentrations. We used a t-test (two-sided) to compare the concentrations between pairs of tolerant and sensitive strains. P-values were corrected with Bonferroni.

Transposable element analysis

eQTL analysis

The RNA-Seq data for tolerant and sensitive strains both in control and treated conditions were trimmed using the fastp package (v.0.20.0) [111] with default parameters. Expression levels were quantified with the salmon package (v.1.0.0) [112] against the ENSEMBL (Dm.BDGP6.22.9) transcripts. Obtained transcripts per million (TPM) were summed up to gene level and rlog normalized using DESeq2 (v.1.28.1) [101]. To test the association between gene expression and TE variants, we used the TE annotations for each one of the six genomes analysed available at https://github.com/sradiouy/Llewellyn-Green-et-al-2021. The genotype table with the information of the presence/absence of all the TEs present in each one of the strains was created using custom script (https://github.com/sradiouy/Llewellyn-Green-et-al-2021).

The eQTL analysis was performed using the QTLtools package (v.1.2) [65]. Putative cis-eQTL for the six strains were searched within a 1-kb window around each gene using the cis module in QTLtools in control and in treated conditions separately. No trans effects were considered. We used the nominal pass to evaluate the significance of the association of each gene expression level to all the TE insertions within the 1-kb window. This nominal pass involves the testing of all possible variant-phenotype pairs via linear regression. The variant-phenotype pair with the smallest nominal p-value is kept as the best QTL for that particular TE. In addition, we also performed a permutation pass (100,000 permutations) to adjust for multiple testing. We focused on the significant TE-gene associations with a nominal p-value < 0.05 and an adjusted p-value < 0.05.

Identification of TEs nearby DEGs

Reference gene annotation was lifted over to each of the six strain assemblies analysed using Liftoff (v.1.4.2, [113]), with default parameters, to produce gene annotations of each strain in GFF format. Liftoff annotation was transformed to BED format with a custom python script (https://github.com/sradiouy/Llewellyn-Green-et-al-2021). Then, bedtools closest (v.2.29.2, [114]) was used to define TE insertions within 1kb of each gene (parameters: -k 10, -D ref) using the TE annotations available at https://github.com/sradiouy/Llewellyn-Green-et-al-2021. We used the prop.test() function of R to assess whether there is an enrichment of TEs in DE genes compared to the whole genome for each strain.

Phenotypic validation

TE present and TE absent outbred populations were constructed for three candidate insertions: FBti0020195, FBti0020057, and FBti0020036. Each outbred population was developed to have a mixed genetic background, while remaining consistently homogenous for either the presence or absence of the selected element [115]. For each outbred population, ten females and ten males from each of the five nominated strains (four in the case of FBti0020195+) were pooled to establish each population (Additional file 1: Table S1). Each outbred was maintained for 8 generations in cages before being screened. Copper tolerance assays were carried out as per the prior experiments, using 4–7-day-old females. Nine to 10 biological replicates of up to 15 flies in treated and up to 10 flies in control were performed for each outbred population (Additional file 12: Table S11). The experiment was run until all flies were dead. Kaplan-Meier survival analysis was performed on present and absence pairs in the same manner as above. The significance of the survival curves was assessed with the log-rank test. Relative change in average mortality is also provided as a proxy of the size of the effect of these genes on copper tolerance, assessed with a one-sided t-test.

Availability of data and materials

Data is available in the additional files. Genome assemblies and the raw data (long and short read sequencing) have been deposited in NCBI under the BioProject accession PRJNA559813. RNA-sequence data is available under NCBI accession number: PRJNA646768; GEO: GSE154608. The six sequenced genomes are available together with gene annotations, TE annotations, and RNA-seq coverage profiles generated in this work for visualization and retrieval through the DrosOmics genome browser [116]. Scripts can be found at https://github.com/GonzalezLab/Dmelanogaster_Copper and https://github.com/sradiouy/Llewellyn-Green-et-al-2021.

References

  1. Panagos P, Ballabio C, Lugato E, Jones A, Borrelli P, Scarpa S, et al. Potential sources of anthropogenic copper inputs to European agricultural soils. Sustainability. 2018;10:2380.

    Article  CAS  Google Scholar 

  2. Romic M, Romic D. Heavy metals distribution in agricultural topsoils in urban area. Environ Geol. 2003;43:795–805.

    Article  CAS  Google Scholar 

  3. Orgiazzi A, Ballabio C, Panagos P, Jones A, Fernández-Ugalde O. LUCAS Soil, the largest expandable soil dataset for Europe: a review. Eur J Soil Sci. 2018;69:140–53.

    Article  Google Scholar 

  4. Wilson TG. Drosophila: sentinels of environmental toxicants. Integr Comp Biol. 2005;45:127–36.

    Article  CAS  Google Scholar 

  5. Navarro JA, Schneuwly S. Copper and zinc homeostasis: lessons from Drosophila melanogaster. Front Genet. 2017;8:223.

    Article  Google Scholar 

  6. Calap-Quintana P, González-Fernández J, Sebastiá-Ortega N, Llorens JV, Moltó MD. Drosophila melanogaster models of metal-related human diseases and metal toxicity. Int J Mol Sci. 2017;18:1456.

    Article  Google Scholar 

  7. Zhang B, Egli D, Georgiev O, Schaffner W. The Drosophila homolog of mammalian zinc finger factor MTF-1 activates transcription in response to heavy metals. Mol Cell Biol. 2001;21:4505–14.

    Article  CAS  Google Scholar 

  8. Turski ML, Thiele DJ. Drosophila Ctr1A functions as a copper transporter essential for development. J Biol Chem. 2007;282:24017–26.

    Article  CAS  Google Scholar 

  9. Southon A, Farlow A, Norgate M, Burke R, Camakaris J. Malvolio is a copper transporter in Drosophila melanogaster. J Exp Biol. 2008;211:709–16.

    Article  CAS  Google Scholar 

  10. Norgate M, Lee E, Southon A, Farlow A, Batterham P, Camakaris J, et al. Essential roles in development and pigmentation for the Drosophila copper transporter DmATP7. Mol Biol Cell. 2006;17:475–84.

    Article  CAS  Google Scholar 

  11. Egli D, Yepiskoposyan H, Selvaraj A, Balamurugan K, Rajaram R, Simons A, et al. A family knockout of all four Drosophila metallothioneins reveals a central role in copper homeostasis and detoxification. Mol Cell Biol. 2006;26:2286–96.

    Article  CAS  Google Scholar 

  12. Yepiskoposyan H, Egli D, Fergestad T, Selvaraj A, Treiber C, Multhaup G, et al. Transcriptome response to heavy metal stress in Drosophila reveals a new zinc transporter that confers resistance to zinc. Nucleic Acids Res. 2006;34:4866–77.

    Article  CAS  Google Scholar 

  13. Li H, Qi Y, Jasper H. Preventing age-related decline of gut compartmentalization limits microbiota dysbiosis and extends lifespan. Cell Host Microbe. 2016;19:240–53.

    Article  CAS  Google Scholar 

  14. McNulty M, Puljung M, Jefford G, Dubreuil RR. Evidence that a copper-metallothionein complex is responsible for fluorescence in acid-secreting cells of the Drosophila stomach. Cell Tissue Res. 2001;304:383–9.

    Article  CAS  Google Scholar 

  15. Dubreuil RR. Copper cells and stomach acid secretion in the Drosophila midgut. Int J Biochem Cell Biol. 2004;36:742–52.

    Article  Google Scholar 

  16. Maroni G, Wise J, Young JE, Otto E. Metallothionein gene duplications and metal tolerance in natural populations of Drosophila melanogaster. Genetics. 1987;117:739–44.

    Article  CAS  Google Scholar 

  17. Catalán A, Glaser-Schmitt A, Argyridou E, Duchen P, Parsch J. An indel polymorphism in the MtnA 3’ untranslated region is associated with gene expression variation and local adaptation in Drosophila melanogaster. PLoS Genet. 2016;12:e1005987.

    Article  Google Scholar 

  18. Pölkki M, Rantala MJ. Exposure to copper during larval development has intra- and trans-generational influence on fitness in later life. Ecotoxicol Environ Saf. 2021;207:111133.

    Article  Google Scholar 

  19. Zamberlan DC, Halmenschelager PT, Silva LFO, da Rocha JBT. Copper decreases associative learning and memory in Drosophila melanogaster. Sci Total Environ. 2020;710:135306.

    Article  CAS  Google Scholar 

  20. Everman ER, Cloud-Richardson KM, Macdonald SJ. Characterizing the genetic basis of copper toxicity in Drosophila reveals a complex pattern of allelic, regulatory, and behavioral variation. Genetics. 2021;217:1–20.

    Article  Google Scholar 

  21. Merritt TJS, Bewick AJ. Genetic diversity in insect metal tolerance. Front Genet. 2017;8:172.

    Article  Google Scholar 

  22. Roelofs D, Janssens TKS, Timmermans MJTN, Nota B, MariËn J, Bochdanovits Z, et al. Adaptive differences in gene expression associated with heavy metal tolerance in the soil arthropod Orchesella cincta. Mol Ecol. 2009;18:3227–39.

    Article  CAS  Google Scholar 

  23. Zhou S, Luoma SE, St. Armour GE, Thakkar E, Mackay TFC, Anholt RRH. A Drosophila model for toxicogenomics: genetic variation in susceptibility to heavy metal exposure. PLoS Genet. 2017;13:e1006907.

    Article  Google Scholar 

  24. Rech GE, Radío S, Guirao-Rico S, Aguilera L, Horvath V, Green L, et al. Population-scale long-read sequencing uncovers transposable elements associated with gene expression variation and adaptive signatures in Drosophila. Nat Commun. 2022;13:1–16.

    Article  Google Scholar 

  25. Chakraborty M, Emerson JJ, Macdonald SJ, Long AD. Structural variants exhibit widespread allelic heterogeneity and shape variation in complex traits. Nat Commun. 2019;10:1–11.

    Article  Google Scholar 

  26. Rech GE, Bogaerts-Márquez M, Barrón MG, Merenciano M, Villanueva-Cañas JL, Horváth V, et al. Stress response, behavior, and development are shaped by transposable element-induced mutations in Drosophila. PLoS Genet. 2019;15:e1007900.

    Article  CAS  Google Scholar 

  27. Schmidt JM, Robin C. An adaptive allelic series featuring complex gene rearrangements. PLoS Genet. 2011;7:e1002347.

    Article  CAS  Google Scholar 

  28. Guio L, Barrõn MG, González J. The transposable element Bari-Jheh mediates oxidative stress response in Drosophila. Mol Ecol. 2014;23:2020–30.

    Article  CAS  Google Scholar 

  29. Mateo L, Ullastres A, González J. A transposable element insertion confers xenobiotic resistance in Drosophila. PLoS Genet. 2014;10:e1004560.

    Article  Google Scholar 

  30. Merenciano M, Ullastres A, de Cara MAR, Barrón MG, González J. Multiple independent retroelement insertions in the promoter of a stress response gene have variable molecular and functional effects in Drosophila. PLoS Genet. 2016;12:e1006249.

    Article  Google Scholar 

  31. Ullastres A, Merenciano M, González J. Regulatory regions in natural transposable element insertions drive interindividual differences in response to immune challenges in Drosophila. Genome Biol. 2021;22:1–30.

    Article  Google Scholar 

  32. Le Manh H, Guio L, Merenciano M, Rovira Q, Barrón MG, González J. Natural and laboratory mutations in kuzbanian are associated with zinc stress phenotypes in Drosophila melanogaster. Sci Rep. 2017;7:1–12.

    Google Scholar 

  33. Hallas R, Schiffer M, Hoffmann AA. Clinal variation in Drosophila serrata for stress resistance and body size. Genet Res (Camb). 2002;79:141–8.

    Article  Google Scholar 

  34. Hoffmann AA, Weeks AR. Climatic selection on genes and traits after a 100 year-old invasion: a critical look at the temperate-tropical clines in Drosophila melanogaster from eastern Australia. Genetica. 2006;129:133–47.

    Article  Google Scholar 

  35. Arthur AL, Weeks AR, Sgrò CM. Investigating latitudinal clines for life history and stress resistance traits in Drosophila simulans from eastern Australia. J Evol Biol. 2008;21:1470–9.

    Article  CAS  Google Scholar 

  36. Kapun M, Barron MG, Staubach F, Obbard DJ, Axel R, Vieira J, et al. Genomic analysis of European Drosophila melanogaster populations reveals longitudinal structure, continent-wide selection, and previously unknown DNA viruses. Mol Biol Evol. 2020;37:2661–78.

    Article  CAS  Google Scholar 

  37. OECD/European Commission. Cities in the world: a new perspective on urbanisation, OECD Urban Studies. Paris. Available online at: https://www.oecd.org/publications/cities-in-the-world-d0efcbda-en.htm: OECD Publishing; 2020.

    Google Scholar 

  38. Hu Y, Comjean A, Perrimon N, Mohr SE. The Drosophila Gene Expression Tool (DGET) for expression analyses. BMC Bioinformatics. 2017;18:1–9.

    Article  CAS  Google Scholar 

  39. Filshie BK, Poulson DF, Waterhouse DF. Ultrastructure of the copper-accumulating region of the Drosophila larval midgut. Tissue Cell. 1971;3:77–102.

    Article  CAS  Google Scholar 

  40. Tapp RL, Hockaday A. Combined histochemical and x-ray microanalytical studies on the copper-accumulating granules in the mid-gut of larval Drosophila. J Cell Sci. 1977;26:201–15.

    Article  CAS  Google Scholar 

  41. Marianes A, Spradling AC. Physiological and stem cell compartmentalization within the Drosophila midgut. Elife. 2013;2013:e00886.

    Article  Google Scholar 

  42. Hung RJ, Hu Y, Kirchner R, Liu Y, Xu C, Comjean A, et al. A cell atlas of the adult Drosophila midgut. Proc Natl Acad Sci U S A. 2020;117:1514–23.

    Article  CAS  Google Scholar 

  43. Terashima J, Bownes M. A microarray analysis of genes involved in relating egg production to nutritional intake in Drosophila melanogaster. Cell Death Differ. 2005;12:429–40.

    Article  CAS  Google Scholar 

  44. Ojima N, Hara Y, Ito H, Yamamoto D. Genetic dissection of stress-induced reproductive arrest in Drosophila melanogaster females. PLoS Genet. 2018;14:e1007434.

    Article  Google Scholar 

  45. Marshall KE, Sinclair BJ. Repeated stress exposure results in a survival–reproduction trade-off in Drosophila melanogaster. Proc R Soc B Biol Sci. 2010;277:963–9.

    Article  Google Scholar 

  46. Klepsatel P, Gáliková M, Xu Y, Kühnlein RP. Thermal stress depletes energy reserves in Drosophila. Sci Rep. 2016;6:1–12.

    Article  Google Scholar 

  47. Horváth V, Guirao-Rico S, Salces-Ortiz J, Rech GE, Green L, Aprea E, et al. Basal and stress-induced expression changes consistent with water loss reduction explain desiccation tolerance of natural Drosophila melanogaster populations. bioRxiv. 2022; 2022.03.21.485105.

  48. Stone EA, Ayroles JF. Modulated modularity clustering as an exploratory tool for functional genomic inference. PLoS Genet. 2009;5:e1000479.

    Article  Google Scholar 

  49. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607–13.

    Article  CAS  Google Scholar 

  50. Palu RAS, Thummel CS. Sir2 acts through hepatocyte nuclear factor 4 to maintain insulin signaling and metabolic homeostasis in Drosophila. PLoS Genet. 2016;12:e1005978.

    Article  Google Scholar 

  51. King-Jones K, Horner MA, Lam G, Thummel CS. The DHR96 nuclear receptor regulates xenobiotic responses in Drosophila. Cell Metab. 2006;4:37–48.

    Article  CAS  Google Scholar 

  52. Alic N, Andrews TD, Giannakou ME, Papatheodorou I, Slack C, Hoddinott MP, et al. Genome-wide dFOXO targets and topology of the transcriptomic response to stress and insulin signalling. Mol Syst Biol. 2011;7:502.

    Article  CAS  Google Scholar 

  53. Barry WE, Thummel CS. The Drosophila HNF4 nuclear receptor promotes glucose-stimulated insulin secretion and mitochondrial function in adults. Elife. 2016;5:e11183.

    Article  Google Scholar 

  54. Fields M, Lewis CG, Beal T. Accumulation of sorbitol in copper deficiency: dependency on gender and type of dietary carbohydrate. Metabolism. 1989;38:371–5.

    Article  CAS  Google Scholar 

  55. Southon A, Burke R, Norgate M, Batterham P, Camakaris J. Copper homoeostasis in Drosophila melanogaster S2 cells. Biochem J. 2004;383:303–9.

    Article  CAS  Google Scholar 

  56. Leader DP, Krause SA, Pandit A, Davies SA, Dow JAT. FlyAtlas 2: a new version of the Drosophila melanogaster expression atlas with RNA-Seq, miRNA-Seq and sex-specific data. Nucleic Acids Res. 2018;46:D809–15.

    Article  CAS  Google Scholar 

  57. Denecke S, Fusetto R, Martelli F, Giang A, Battlay P, Fournier-Level A, et al. Multiple P450s and variation in neuronal genes underpins the response to the insecticide imidacloprid in a population of Drosophila melanogaster. Sci Rep. 2017;7:1–11.

    Article  CAS  Google Scholar 

  58. Green L, Battlay P, Fournier-Level A, Good RT, Robin C. Cis- And trans-acting variants contribute to survivorship in a naïve Drosophila melanogaster population exposed to ryanoid insecticides. Proc Natl Acad Sci U S A. 2019;116:10424–9.

    Article  CAS  Google Scholar 

  59. Kalsi M, Palli SR. Transcription factor cap n collar C regulates multiple cytochrome P450 genes conferring adaptation to potato plant allelochemicals and resistance to imidacloprid in Leptinotarsa decemlineata (Say). Insect Biochem Mol Biol. 2017;83:1–12.

    Article  CAS  Google Scholar 

  60. Balamurugan K, Egli D, Hua H, Rajaram R, Seisenbacher G, Georgiev O, et al. Copper homeostasis in Drosophila by complex interplay of import, storage and behavioral avoidance. EMBO J. 2007;26:1035–44.

    Article  CAS  Google Scholar 

  61. Bonilla-Ramirez L, Jimenez-Del-Rio M, Velez-Pardo C. Acute and chronic metal exposure impairs locomotion activity in Drosophila melanogaster: a model to study Parkinsonism. BioMetals. 2011;24:1045–57.

    Article  CAS  Google Scholar 

  62. Weber AL, Khan GF, Magwire MM, Tabor CL, Mackay TFC, Anholt RRH. Genome-wide association analysis of oxidative stress resistance in Drosophila melanogaster. PLoS One. 2012;7:e34745.

    Article  CAS  Google Scholar 

  63. Chow CY, Wolfner MF, Clark AG. Using natural variation in Drosophila to discover previously unknown endoplasmic reticulum stress genes. Proc Natl Acad Sci U S A. 2013;110:9013–8.

    Article  CAS  Google Scholar 

  64. Horváth V, Merenciano M, González J. Revisiting the relationship between transposable elements and the eukaryotic stress response. Trends Genet. 2017;33:832–41.

    Article  Google Scholar 

  65. Delaneau O, Ongen H, Brown AA, Fort A, Panousis NI, Dermitzakis ET. A complete tool set for molecular QTL discovery and analysis. Nat Commun. 2017;8:1–7.

    Article  Google Scholar 

  66. Mackay TFC. Epistasis and quantitative traits: using model organisms to study gene–gene interactions. Nat Rev Genet. 2014;15:22–33.

    Article  CAS  Google Scholar 

  67. Massadeh A, Al-Momani F, Elbetieha A. Assessment of heavy metals concentrations in soil samples from the vicinity of busy roads: influence on Drosophila melanogaster life cycle. Biol Trace Elem Res. 2008;122:292–9.

    Article  CAS  Google Scholar 

  68. Adriano DC. Trace elements in terrestrial environments. New York: Springer New York; 2001.

    Book  Google Scholar 

  69. Battlay P, Schmidt JM, Fournier-Level A, Robin C. Genomic and transcriptomic associations identify a new insecticide resistance phenotype for the selective sweep at the Cyp6g1 locus of Drosophila melanogaster. G3 Genes Genomes Genet. 2016;6:2573–81.

    CAS  Google Scholar 

  70. Norgate M, Southon A, Zou S, Zhan M, Sun Y, Batterham P, et al. Copper homeostasis gene discovery in Drosophila melanogaster. BioMetals. 2007;20:683–97.

    Article  CAS  Google Scholar 

  71. Banerjee KK, Ayyub C, Sengupta S, Kolthur-Seetharam U. dSir2 deficiency in the fatbody, but not muscles, affects systemic insulin signaling, fat mobilization and starvation survival in flies. Aging (Albany NY). 2012;4:206–23.

    Article  CAS  Google Scholar 

  72. Banerjee KK, Deshpande RS, Koppula P, Ayyub C, Kolthur-Seetharam U. Central metabolic sensing remotely controls nutrient-sensitive endocrine response in Drosophila via Sir2/Sirt1-upd2-IIS axis. J Exp Biol. 2017;220:1187–91.

    Google Scholar 

  73. Misra JR, Horner MA, Lam G, Thummel CS. Transcriptional regulation of xenobiotic detoxification in Drosophila. Genes Dev. 2011;25:1796–806.

    Article  CAS  Google Scholar 

  74. Ross J, Jiang H, Kanost MR, Wang Y. Serine proteases and their homologs in the Drosophila melanogaster genome: an initial analysis of sequence conservation and phylogenetic relationships. Gene. 2003;304:117–31.

    Article  CAS  Google Scholar 

  75. Vásquez-Procopio J, Rajpurohit S, Missirlis F. Cuticle darkening correlates with increased body copper content in Drosophila melanogaster. BioMetals. 2020;33:293–303.

    Article  Google Scholar 

  76. Carlson KA, Gardner K, Pashaj A, Carlson DJ, Yu F, Eudy JD, et al. Genome-wide gene expression in relation to age in large laboratory cohorts of drosophila melanogaster. Genet Res Int. 2015;2015:835624.

    Google Scholar 

  77. Chapman RF. The insects: structure and function. United Kingdom: Cambridge University Press; 1998.

    Book  Google Scholar 

  78. Dow J. pH gradients in lepidopteran midgut. J Exp Biol. 1992;172:355–75.

    Article  CAS  Google Scholar 

  79. Clark TM. Evolution and adaptive significance of larval midgut alkalinization in the insect superorder Mecopterida. J Chem Ecol. 1999;25:1945–60.

    Article  CAS  Google Scholar 

  80. Wat LW, Chao C, Bartlett R, Buchanan JL, Millington JW, Chih HJ, et al. A role for triglyceride lipase brummer in the regulation of sex differences in Drosophila fat storage and breakdown. PLoS Biol. 2020;18:e3000595.

    Article  Google Scholar 

  81. Hood SE, Kofler XV, Chen Q, Scott J, Ortega J, Lehmann M. Nuclear translocation ability of Lipin differentially affects gene expression and survival in fed and fasting Drosophila. J Lipid Res. 2020;61:1720.

    Article  CAS  Google Scholar 

  82. Fernández-Ayala DJM, Chen S, Kemppainen E, O’Dell KMC, Jacobs HT. Gene expression in a Drosophila model of mitochondrial disease. PLoS One. 2010;5:e8549.

    Article  Google Scholar 

  83. Sun Y, Liu C, Liu Y, Hosokawa T, Saito T, Kurasaki M. Changes in the expression of epigenetic factors during copper-induced apoptosis in PC12 cells. J Environ Sci Heal Part A. 2014;49:1023–8.

    Article  CAS  Google Scholar 

  84. Song MO, Freedman JH. Role of hepatocyte nuclear factor 4α in controlling copper-responsive transcription. Biochim Biophys Acta Mol Cell Res. 2011;1813:102–8.

    Article  CAS  Google Scholar 

  85. Kuraishi T, Binggeli O, Opota O, Buchon N, Lemaitre B. Genetic evidence for a protective role of the peritrophic matrix against intestinal bacterial infection in Drosophila melanogaster. Proc Natl Acad Sci U S A. 2011;108:15966–71.

    Article  CAS  Google Scholar 

  86. Cruz GS, Wanderley-Teixeira V, Antonino JD, Gonçalves GGA, Costa HN, Ferreira MCN, et al. Lufenuron indirectly downregulates Vitellogenin in the boll weevil females reducing egg viability. Physiol Entomol. 2021;46:24–33.

    Article  CAS  Google Scholar 

  87. Mateo L, Rech GE, González J. Genome-wide patterns of local adaptation in Western European Drosophila melanogaster natural populations. Sci Rep. 2018;8:1–14.

    Article  CAS  Google Scholar 

  88. Johnson RM, Dahlgren L, Siegfried BD, Ellis MD. Acaricide, fungicide and drug interactions in honey bees (Apis mellifera). PLoS One. 2013;8:e54092.

    Article  CAS  Google Scholar 

  89. Fernandez Falcon M, Perez Frances JF, López Carreño I, Borges-Perez A. Available micronutrients in agricultural soils of Tenerife (Canary Islands). I.: copper and zinc. Agrochimica. 1994;38:268–76.

    CAS  Google Scholar 

  90. R Core Team. R: a language and environment for statistical computing. 2022.

    Google Scholar 

  91. Kutner MH, Nachtsheim C, Neter J. Applied linear regression models. McGraw-Hill Irwin; 2005.

    Google Scholar 

  92. Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nat Biotechnol. 2017;35:316–9.

    Article  Google Scholar 

  93. Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, et al. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020;38:276–8.

    Article  CAS  Google Scholar 

  94. Andrews S. FASTQC. A quality control tool for high throughput sequence data. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. 2010.

    Google Scholar 

  95. Krueger F. Trim galore. A wrapper tool around Cutadapt and FastQC to consistently apply quality and adapter trimming to FastQ files. Available online at: https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/. 2015.

    Google Scholar 

  96. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10–2.

    Article  Google Scholar 

  97. Larkin A, Marygold SJ, Antonazzo G, Attrill H, dos Santos G, Garapati PV, et al. FlyBase: updates to the Drosophila melanogaster knowledge base. Nucleic Acids Res. 2021;49:D899–907.

    Article  CAS  Google Scholar 

  98. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21.

    Article  CAS  Google Scholar 

  99. Sayols S, Scherzinger D, Klein H. dupRadar: a Bioconductor package for the assessment of PCR artifacts in RNA-Seq data. BMC Bioinformatics. 2016;17:1–5.

    Article  Google Scholar 

  100. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30:923–30.

    Article  CAS  Google Scholar 

  101. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:1–21.

    Article  Google Scholar 

  102. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11:1–12.

    Article  Google Scholar 

  103. Yu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. Omi A J Integr Biol. 2012;16:284–7.

    Article  CAS  Google Scholar 

  104. Lopes CT, Franz M, Kazi F, Donaldson SL, Morris Q, Bader GD, et al. Cytoscape Web: an interactive web-based network browser. Bioinformatics. 2010;26:2347–8.

    Article  CAS  Google Scholar 

  105. Dietzl G, Chen D, Schnorrer F, Su KC, Barinova Y, Fellner M, et al. A genome-wide transgenic RNAi library for conditional gene inactivation in Drosophila. Nature. 2007;448:151–6.

    Article  CAS  Google Scholar 

  106. Perkins LA, Holderbaum L, Tao R, Hu Y, Sopko R, McCall K, et al. The transgenic RNAi project at Harvard medical school: resources and validation. Genetics. 2015;201:843–52.

    Article  CAS  Google Scholar 

  107. Bellen HJ, Levis RW, Liao G, He Y, Carlson JW, Tsang G, et al. The BDGP gene disruption project: single transposon insertions associated with 40% of Drosophila genes. Genetics. 2004;167:761–81.

    Article  CAS  Google Scholar 

  108. Chung H, Bogwitz MR, McCart C, Andrianopoulos A, Ffrench-Constant RH, Batterham P, et al. Cis-regulatory elements in the accord retrotransposon result in tissue-specific expression of the Drosophila melanogaster insecticide resistance gene Cyp6g1. Genetics. 2007;175:1071–7.

    Article  CAS  Google Scholar 

  109. Phillips MD, Thomas GH. Brush border spectrin is required for early endosome recycling in Drosophila. J Cell Sci. 2006;119:1361–70.

    Article  CAS  Google Scholar 

  110. Shell BC, Schmitt RE, Lee KM, Johnson JC, Chung BY, Pletcher SD, et al. Measurement of solid food intake in Drosophila via consumption-excretion of a dye tracer. Sci Rep. 2018;8:1–13.

    Article  CAS  Google Scholar 

  111. Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34:i884–90.

    Article  Google Scholar 

  112. Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017;14:417–9.

    Article  CAS  Google Scholar 

  113. Shumate A, Salzberg SL. Liftoff: accurate mapping of gene annotations. Bioinformatics. 2021;37:1639–43.

    Article  CAS  Google Scholar 

  114. Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010;26:841–2.

    Article  CAS  Google Scholar 

  115. Behrman EL, Howick VM, Kapun M, Staubach F, Bergland AO, Petrov DA, et al. Rapid seasonal evolution in innate immunity of wild Drosophila melanogaster. Proc R Soc B Biol Sci. 2018:285.

  116. Coronado-Zamora M, Salces-Ortiz J, González J. DrosOmics: the comparative genomics browser to explore omics data in natural strains of D. melanogaster. bioRxiv. 2022;2022.07.22.

    Google Scholar 

Download references

Acknowledgements

We thank DrosEU members for sharing the European D. melanogaster strains (Additional file 1: Table S1) and Shane Denecke and Trent Perry for sharing Gal4 driver lines. We thank Joshua Schmidt for scripts related to the Kaplan-Meier analysis. We thank Luciano Massetti for making us aware of the availability of the atmospheric pollution data. We thank Ewan Harney for comments on the manuscript.

Funding

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (H2020-ERC-2014-CoG-647900). S. R. was funded by the MICINN/FSE/AEI (PRE2018-084755). J.S-O was funded by a Juan de la Cierva-Formación fellowship (FJCI-2016-28380). The DrosEU consortium is funded by an ESEB Special Topic Network. The funding bodies had no role in the design of the study and collection, analysis, and interpretation of data or in writing the manuscript.

Author information

Authors and Affiliations

Authors

Contributions

L.G. contributed to the design of the work; to the acquisition, analysis, and interpretation of the data; and to the drafting of the manuscript. M.C-Z., S.R., and G.E.R. contributed to the data analysis and to the revision of the manuscript. J.S-O. contributed to the design of the work, to the acquisition of the data, and to the revision of the manuscript. J.G. conceived the study and contributed to the design of the work; to the acquisition, analysis, and interpretation of the data; and to the drafting of the manuscript. The authors read and approved the final manuscript.

Corresponding authors

Correspondence to Llewellyn Green or Josefa González.

Ethics declarations

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Table S1.

Data on the fly strains used in this work.

Additional file 2: Figure S1.

Copper tolerance phenotypes across all populations.

Additional file 3: Table S2.

Mortality data for all the strains analyzed in this work.

Additional file 4: Table S3.

Atmospheric metal pollution data, copper soil concentration and degree of urbanization classification.

Additional file 5: Table S4.

Differential gene expression (DGE) analysis performed with DESeq2.

Additional file 6: Figure S2.

DGET expression analysis for gut subsections.

Additional file 7: Table S5.

GO enrichment analysis. Table S6. Gene Set Enrichmnet analysis. Table S7. KEGG analysis. Table S8. Modulated Modularity Cluster analysis.

Additional file 8: Figures S3.

GO clustering analysis. Figure S4. Modulated Modularity Cluster analysis. Figures S5. Overlapping between DEGs and regulatory factors.

Additional file 9: Figure S6.

Kaplan-Meier survival curves for the survival assays performed on RNAi knockdowns and disruption mutants for all gene candidates.

Additional file 10: Table S9.

Mortality data for all mutant and RNAi lines analyzed in this work.

Additional file 11: Table S10.

Raw data for all pH and feeding analysis presented in this work.

Additional file 12: Table S11.

Transposable element analysis.

Additional file 13: Figure S7.

Kaplan-Meier survival curves for the survival assays performed on outbred populations with and without the three candidate TE insertions.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Green, L., Coronado-Zamora, M., Radío, S. et al. The genomic basis of copper tolerance in Drosophila is shaped by a complex interplay of regulatory and environmental factors. BMC Biol 20, 275 (2022). https://doi.org/10.1186/s12915-022-01479-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12915-022-01479-w

Keywords

  • Transcriptomics
  • Transposable elements
  • Gut physiology
  • Functional validation