Skip to main content
  • Research Note
  • Open access
  • Published:

Acute exposure to mercury drives changes in gene expression in Drosophila melanogaster

Abstract

Objective

We quantified the effect of acute exposure to a high dosage of inorganic mercury on gene expression in Drosophila melanogaster using RNA-sequencing of whole adult females.

Results

We found 119 genes with higher gene expression following treatment (including all 5 Drosophila metallothionine genes and a number of heat shock protein genes), and 31 with lower expression (several of which are involved in egg formation). Our results highlight biological processes and genetic pathways impacted by exposure to this toxic metal, and provide motivation for future studies to understand the genetic basis of response to mercury.

Peer Review reports

Introduction

Mercury exposure can occur through contamination of water, air and food, as well as through some household items, and such exposure can lead to a variety of pathologies [1]. The effects of mercury exposure can vary between life history stages, and because mercury exists in both organic and inorganic forms, its toxic effects are also dependent on the form encountered [2]. To help understand the impact of mercury on cellular function, past studies have characterized the impacts of mercury exposure on gene expression in several animal species, including organic methylmercury exposure on mouse pups [3, 4], inorganic mercury chloride in zebrafish [5, 6], and both organic and inorganic mercury in C. elegans [2, 7].

Drosophila melanogaster is a powerful model for understanding the impact of toxicant exposure [8], and because sets of genotyped inbred strains are available [9, 10], the Drosophila model can ultimately enable characterization of the genetic basis of variation in toxicant response [11,12,13]. Describing the regulatory changes driven by toxicant exposure is key to understanding the physiological response to stress, and work in Drosophila has shown that larval exposure to methylmercury [14, 15] and adult exposure to inorganic mercury [16] impact gene expression. Here, we sought to expand this understanding of the impact of mercury exposure on gene expression in Drosophila by employing a transcriptome-wide RNA sequencing approach (versus employing qRT-PCR on a focused set of genes [14, 16] or expression arrays [15]), examining flies following a high dosage, acute exposure to inorganic mercury. By employing several different strains from a large panel of inbred lines—the Drosophila Synthetic Population Resource [DSPR; 10]—we aimed to gain some insight into the mercury-induced expression response in this panel, and facilitate future genetic dissection of variation in the response to mercury toxicity via QTL (Quantitative Trait Locus) mapping, as has been executed for other traits [e.g. 11].

We exposed adult females from 7 DSPR strains to media containing either inorganic mercury(II) chloride (HgCl2) or a water control for six hours, extracted RNA from flash-frozen whole animals, and prepared and sequenced short-read mRNA sequencing libraries. An analysis that pooled together all strains identified 119 genes that were up-regulated and 31 genes that were down-regulated in response to this acute inorganic mercury exposure, and collectively these gene sets were enriched for a number of biological processes.

Materials and methods

Drosophila strains

The 7 strains tested—22033, 22112, 22114, 22117, 22127, 22217, 22238—are part of the DSPR “population B” collection of Recombinant Inbred Lines (RILs). Briefly, the DSPR was founded by intercrossing 8 highly-inbred founder lines, allowing the synthetic population to mix/recombine for 50 generations, and then several hundred RILs were derived by 25 generations of sibling mating [10]. The target strains for this study were chosen arbitrarily to represent a fraction of the variation existing in the panel.

Rearing experimental animals

Adult flies from each strain were allowed to lay eggs in vials for 2 days before being cleared. Nine days after vial set up any emerged adults were removed, and two days later all 0–2 day old animals were moved to fresh media vials. Mixed sex groups of flies were aged for 2 additional days, and we collected 2–4 day old females via CO2 anesthesia, generating 2 vials of 10 females per RIL. These females are likely mated given they were held with males for 48 h, but mating status was not confirmed, as is common practice in the Drosophila community. Test female flies were given 1 day to recover from any effects of anesthesia prior to mercury exposure.

Mercury exposure assay

We exposed sets of 10 female flies to a 625 µM solution of inorganic mercury(II) chloride, HgCl2 (Millipore Sigma, 215465, CAS number 7487-94-7), or a water control, in exposure chambers. Each chamber consisted of a standard, narrow polystyrene fly vial (Fisher Scientific, AS515), and a polyethylene flanged plastic cap (MOCAP, FCS13/16NA1) taped to it via narrow masking tape (ULINE, S-3049). The cap contained 1/8th of a teaspoon of Formula 4–24 Instant Drosophila Medium (Carolina, 173200) that was finely-ground by using a food processor, and reconstituted with 0.9-ml of either mercury solution or water. Adult females (3–5 days old) from all 7 RILs were enclosed in chambers for approximately 6 h, starting at 1-h after lights on.

Rearing and testing environment

All rearing/testing was executed in an incubator held at 25C, 50% relative humidity, with a 12 h light: 12 h dark light cycle. Flies were reared, and maintained prior to exposure, on a cornmeal-yeast-molasses medium.

RNA isolation, library preparation and sequencing

Following the exposure period animals were directly transferred, without anesthesia, to labeled, sterile, 2-ml screw-top tubes (Sarstedt, 72.693.005) containing 4–6 glass beads (BioSpec, 11079127, 2.7-mm diameter), and flash frozen in liquid nitrogen. Nearly all animals were alive following exposure, but those rare dead animals were removed from the exposure chambers prior to live animal collection. We immediately added 600-μl of ice-cold TRIzol Reagent (ThermoFisher, 15596018), homogenized the flies in a Mini-BeadBeater-96 (BioSpec) for 45-s, and froze the homogenate at -80C. The next day we isolated total RNA from all samples using a Direct-zol RNA Miniprep Kit (Zymo, R2050), and quantified RNA via a Qubit RNA High Sensitivity assay (Invitrogen, Q32852). Subsequently 100-ng of total RNA from each sample was used in the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina, along with unique dual indexing (NEB, E7760L and E6440S), to prepare poly-A selected mRNA-sequencing libraries. Each completed library was checked on D1000 ScreenTapes (Agilent TapeStation 4150) where we observed DNA fragment distributions averaging ~ 300-bp, library concentrations were quantified using the Qubit dsDNA Broad Range kit (Invitrogen, Q32850), and finally equal amounts of each library were pooled. The 14-plex was run on a mid-output flowcell on an Illumina NextSeq 550 instrument generating 75 bp paired-end reads. Raw sequence reads are available on the NCBI Sequence Read Archive (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1030387).

Data processing and analysis

Raw reads were processed with the nf-core RNA-seq pipeline v. 3.12.0 with Nextflow v. 23.04.3 [17,18,19]. Reads were trimmed of adapter sequences with TrimGalore v. 0.6.7 [20; Table S1], assessed for batch effects (Figure S1), and aligned to the Drosophila melanogaster genome assembly version 6.46 (GCF_000001215.4) with the Ensembl version 81 annotation using STAR v 2.7.10a [21] and read count quantification was performed with RSEM v. 1.3.1 [22; Table S2]. Patterns of differential gene expression between HgCl2 and control treatments were estimated using DESeq2 v. 1.40.2 [23] and log2-fold change values were adjusted with the lfcShrink function specifying the apeglm method [24; Table S3]. We classified those genes as differentially expressed that had Benjamini–Hochberg FDR-adjusted P-value (PFDR) < 0.01 and absolute log2 fold-change values > 2 (Fig. 1; Figure S2). Finally, we performed a gene ontology (GO) enrichment analysis with the function enrichGO in the R package clusterProfiler v 4.8.2 [25] and the D. melanogaster annotation provided in the R package org.Dm.eg.db v. 3.17.0 [26], focusing on the Biological Processes ontologies and specifying a PFDR threshold of 0.05 (Figure S3; Table S4; Table S5). All differential expression analyses were performed in R v. 4.3.1 [27]. Plots were generated with EnhancedVolcano [28; Fig. 1], plotMDS [29; Figure S1], pheatmap [30; Figure S2], and barplot [25; Figures S3-S4].

Fig. 1
figure 1

Effects of inorganic mercury exposure on gene expression. Exposure to HgCl2 in adult female Drosophila resulted in up-regulation of 119 genes and down-regulation of 31 genes compared with the control treatment. Values along the X-axis are log2 fold-change of the HgCl2/control contrast, and along the Y-axis are − log10 Benjamini–Hochberg FDR-adjusted P-values. Vertical dashed lines highlight absolute log2 fold-change (LFC) values > 2, horizontal dashed line indicates the significance threshold PFDR < 0.01. Genes that are significant and have LFC values > 2 are shown in red, those that are significant but with LFC values < 2 are in blue, genes that are not significant but have LFC values > 2 are in yellow, and genes that are not significant and with LFC values < 2 are in grey. The top 10 upregulated and top 10 downregulated genes (ranked by PFDR) are annotated with gene symbols, and include all 5 metallothionein genes in D. melanogaster (e.g. MtnB), and several vitelline membrane genes (e.g. Vm26Ab)

Results

Our libraries generated an average of 10,635,921 ± 2,254,415 (mean ± SD) 75-bp paired-end reads per sample, of which an average of 81.09% mapped to the reference genome as unique read pairs (Table S1). Initial assessment of raw gene expression data with multidimensional scaling (MDS) showed that samples clustered by treatment rather than strain genotype (Figure S1), suggesting some shifts in expression due to treatment are similar across strains. We treated the 7 strains as “replicates” in a differential expression analysis to examine such consistent changes, seeing 683 and 512 genes, respectively that are up- or downregulated at PFDR < 0.01 (Table S3). By additionally applying a fold-change threshold, we identify 119 genes with significantly higher expression in HgCl2-treated flies (log2 fold-change > 2, PFDR < 0.01), and 31 genes that showed significantly lower expression in HgCl2-treated flies (log2 fold-change < − 2, PFDR < 0.01; Fig. 1; Figure S2; Table S2). Here, recognizing that strains are likely to exhibit differences in the response to mercury that we are unable to assess with our design, we elected to focus solely on these genes with relatively larger changes in expression.

To understand the potential functional significance of the expression changes observed we performed gene ontology (GO) term enrichment, focusing on the Biological Processes category. The terms enriched among genes showing higher expression in HgCl2-treated flies included response to heat and abiotic stresses, and protein folding (Figure S3; Table S4), while the terms enriched among genes with lower expression in HgCl2-treated flies included those related to membrane formation and stability, and egg formation (Figure S4; Table S5).

Discussion

All five of the Drosophila metallothionine genes (MtnA–MtnE) showed increased expression following mercury exposure. These genes are expressed primarily in the digestive tract, provide essential roles in maintaining homeostasis of trace metals and heavy metal detoxification [31], and were previously shown to be up-regulated in response to copper exposure in flies from the same population we employed here [11]. Ten glutathione S-transferases (GSTs, Table S3) also showed higher expression due to HgCl2 treatment. Genes in this family are involved in the response to xenobiotic toxins in Drosophila [32], and knockdowns of GSTs—including GstE1 which is up-regulated in our study—have been shown to increase susceptibility to the toxic effects of methylmercury in flies [33]. We also found that many heat-shock proteins showed higher expression in HgCl2-exposed flies. This is consistent with a prior study finding Hsp83 was up-regulated in adults exposed to HgCl2 [16], although Hsp83 was not among the set of mercury-regulated genes we identified. Interestingly, while we found that Hsp23 increased in expression following inorganic mercury exposure in adults, Frat et al. [14] showed reduced Hsp23 expression in larvae exposed to methylmercury. This could suggest a difference in the physiological response between larval and adult life history stages, between forms/concentrations of mercury, or variation in the response by different strains.

Fourteen of the 31 downregulated genes currently lack gene symbols (are “CG” numbers) and there is limited information available regarding their functional roles, but 7/31 are associated with oogenesis (Table S5). Notably, all individuals in our experiment were females collected at days 2–4 from mixed-sex groups, and so were likely mated. The results of our functional enrichment analysis are consistent with previous studies that showed HgCl2 exposure in adult D. melanogaster caused dose- and time-dependent atrophy of ovaries and a large reduction in egg-laying behaviors [34], and that nitrogenous toxins negatively impact egg-laying behavior in both D. melanogaster and D. suzukii [35].

In summary, we identified 119 genes that were up-regulated and 31 genes that were down-regulated in response to inorganic mercury exposure in adult female Drosophila. Our results recapitulate some past findings, and highlight some intriguing differences with prior studies. Given that the lines we assayed are part of a much larger set of genotyped inbred lines [10], future work can leverage the results we present here to assist in uncovering the genetic basis of variation in mercury toxicity, and help to shed light on the broader impact of exposure to this toxicant in this model system.

Limitations

First, our study did not employ within-strain replication, so we obtained information on only the “average” strain response, and were not able to characterize genotype-to-genotype variation in the expression change driven by mercury. Such variation is highly likely to exist [36,37,38], and can be evaluated in future studies. Second, although there is no accepted standard inorganic mercury dosage in Drosophila studies—a range of concentrations have been used, from up to 100 µM [16] to 400 µM [39, 40]—the dose we employed (625 µM) is relatively high. The dose was chosen to ensure we elicited an expression response in this small pilot study, and we anticipate that experiments testing difference dosages would find some dose-dependent variation in the response. Third, although a standardized dose of inorganic mercury was provided to flies in their media, the dose actually ingested by flies was not quantified, so it is plausible that unrecognized variation in the amount of mercury consumed may have impacted gene expression. Fourth, our study included only female flies; it is clear that there is sexual dimorphism for gene expression [41] and other quantitative traits [42], so the generality of our results for understanding the response of both sexes to mercury will require further study.

Availability of data and materials

The datasets generated and analyzed during the current study are available in the NCBI Sequence Read Archive, https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1030387.

References

  1. Bjørklund G, Dadar M, Mutter J, Aaseth J. The toxicology of mercury: current research and emerging trends. Environ Res. 2017;159:545–54.

    Article  PubMed  Google Scholar 

  2. McElwee MK, Ho LA, Chou JW, Smith MV, Freedman JH. Comparative toxicogenomic responses of mercuric and methyl-mercury. BMC Genomics. 2013;14:698.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Glover CN, Zheng D, Jayashankar S, Sales GD, Hogstrand C, Lundebye A-K. Methylmercury speciation influences brain gene expression and behavior in gestationally-exposed mice pups. Toxicol Sci. 2009;110:389–400.

    Article  CAS  PubMed  Google Scholar 

  4. Mellingen RM, Myrmel LS, Lie KK, Rasinger JD, Madsen L, Nøstbakken OJ. RNA sequencing and proteomic profiling reveal different alterations by dietary methylmercury in the hippocampal transcriptome and proteome in BALB/c mice. Metallomics. 2021;13: mfab022.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Lu X, Xiang Y, Yang G, Zhang L, Wang H, Zhong S. Transcriptomic characterization of zebrafish larvae in response to mercury exposure. Comp Biochem Physiol C Toxicol Pharmacol. 2017;192:40–9.

    Article  CAS  PubMed  Google Scholar 

  6. Zhang Q-L, Dong Z-X, Luo Z-W, Zhang M, Deng X-Y, Guo J, et al. The impact of mercury on the genome-wide transcription profile of zebrafish intestine. J Hazard Mater. 2020;389:121842.

    Article  CAS  PubMed  Google Scholar 

  7. Camacho J, de Conti A, Pogribny IP, Sprando RL, Hunt PR. Assessment of the effects of organic vs. inorganic arsenic and mercury in Caenorhabditis elegans. Curr Res Toxicol. 2022;3:100071.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Rand MD, Tennessen JM, Mackay TFC, Anholt RRH. Perspectives on the Drosophila melanogaster model for advances in toxicological science. Curr Protoc. 2023;3: e870.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Huang W, Massouras A, Inoue Y, Peiffer J, Ràmia M, Tarone AM, et al. Natural variation in genome architecture among 205 Drosophila melanogaster genetic reference panel lines. Genome Res. 2014;24:1193–208.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. King EG, Merkes CM, McNeil CL, Hoofer SR, Sen S, Broman KW, et al. Genetic dissection of a model complex trait using the Drosophila synthetic population resource. Genome Res. 2012;22:1558–66.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. 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  PubMed  Google Scholar 

  12. Montgomery SL, Vorojeikina D, Huang W, Mackay TFC, Anholt RRH, Rand MD. Genome-wide association analysis of tolerance to methylmercury toxicity in Drosophila implicates myogenic and neuromuscular developmental pathways. PLoS ONE. 2014;9: e110375.

    Article  PubMed  PubMed Central  Google Scholar 

  13. 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  PubMed  PubMed Central  Google Scholar 

  14. Frat L, Chertemps T, Pesce E, Bozzolan F, Dacher M, Planelló R, et al. Single and mixed exposure to cadmium and mercury in Drosophila melanogaster: molecular responses and impact on post-embryonic development. Ecotoxicol Environ Saf. 2021;220:112377.

    Article  CAS  PubMed  Google Scholar 

  15. Mahapatra CT, Bond J, Rand DM, Rand MD. Identification of methylmercury tolerance gene candidates in Drosophila. Toxicol Sci. 2010;116:225–38.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Paula MT, Zemolin AP, Vargas AP, Golombieski RM, Loreto ELS, Saidelles AP, et al. Effects of Hg(II) exposure on MAPK phosphorylation and antioxidant system in D. melanogaster. Environ Toxicol. 2014;29:621–30.

    Article  CAS  PubMed  Google Scholar 

  17. 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  PubMed  Google Scholar 

  18. 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  PubMed  Google Scholar 

  19. Harshil Patel, Ewels P, Peltzer A, Botvinnik O, Sturm G, Moreno D, et al. nf-core/rnaseq: nf-core/rnaseq v3.12.0-Osmium Octopus. 2023.

  20. Krueger F, James F, Ewels P, Afyounian E, Schuster-Boeckler B. FelixKrueger/TrimGalore: v0.6.7. 2021.

  21. 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  PubMed  Google Scholar 

  22. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform. 2011;12:323.

    Article  CAS  Google Scholar 

  23. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Zhu A, Ibrahim JG, Love MI. Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics. 2019;35:2084–92.

    Article  CAS  PubMed  Google Scholar 

  25. Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation. 2021;2:100141.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Carlson M. org.Dm.eg.db: genome wide annotation for fly. 2023; https://doi.org/10.18129/B9.bioc.org.Dm.eg.db.

  27. R Core Team. R: a language and environment for statistical computing. 2023.

  28. Blighe K, Rana S, Lewis M. EnhancedVolcano: Publication-ready volcano plots with enhanced colouring and labeling. 2023; https://doi.org/10.18129/B9.bioc.EnhancedVolcano.

  29. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43: e47.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Kolde R. pheatmap: Pretty Heatmaps. 2019. https://doi.org/10.32614/CRAN.package.pheatmap.

  31. Atanesyan L, Günther V, Celniker SE, Georgiev O, Schaffner W. Characterization of MtnE, the fifth metallothionein member in Drosophila. J Biol Inorg Chem. 2011;16:1047–56.

    Article  CAS  PubMed  Google Scholar 

  32. Saisawang C, Wongsantichon J, Ketterman AJ. A preliminary characterization of the cytosolic glutathione transferase proteome from Drosophila melanogaster. Biochem J. 2012;442:181–90.

    Article  CAS  PubMed  Google Scholar 

  33. Vorojeikina D, Broberg K, Love TM, Davidson PW, van Wijngaarden E, Rand MD. Glutathione S-transferase activity moderates methylmercury toxicity during development in Drosophila. Toxicol Sci. 2017;157:211–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Mojica-Vázquez LH, Madrigal-Zarraga D, García-Martínez R, Boube M, Calderón-Segura ME, Oyallon J. Mercury chloride exposure induces DNA damage, reduces fertility, and alters somatic and germline cells in Drosophila melanogaster ovaries. Environ Sci Pollut Res Int. 2019;26:32322–32.

    Article  PubMed  Google Scholar 

  35. Belloni V, Galeazzi A, Bernini G, Mandrioli M, Versace E, Haase A. Evolutionary compromises to metabolic toxins: ammonia and urea tolerance in Drosophila suzukii and Drosophila melanogaster. Physiol Behav. 2018;191:146–54.

    Article  CAS  PubMed  Google Scholar 

  36. Qu W, Gurdziel K, Pique-Regi R, Ruden DM. Lead modulates trans- and cis-expression quantitative trait loci (eQTLs) in Drosophila melanogaster Heads. Front Genet. 2018;9:395.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Knowles DA, Burrows CK, Blischak JD, Patterson KM, Serie DJ, Norton N, et al. Determining the genetic basis of anthracycline-cardiotoxicity by molecular response QTL mapping in induced cardiomyocytes. Elife. 2018;7: e33480.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Everman ER, Macdonald SJ. Gene expression variation underlying tissue-specific responses to copper stress in Drosophila melanogaster. G3. 2024;14: jkae015.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Chen Z, Wu X, Luo H, Zhao L, Ji X, Qiao X, et al. Acute exposure of mercury chloride stimulates the tissue regeneration program and reactive oxygen species production in the Drosophila midgut. Environ Toxicol Pharmacol. 2016;41:32–8.

    Article  CAS  PubMed  Google Scholar 

  40. Chen Z, Zhang W, Wang F, Mu R, Wen D. Sestrin protects Drosophila midgut from mercury chloride-induced damage by inhibiting oxidative stress and stimulating intestinal regeneration. Comp Biochem Physiol C Toxicol Pharmacol. 2021;248:109083.

    Article  CAS  PubMed  Google Scholar 

  41. Huang W, Carbone MA, Magwire MM, Peiffer JA, Lyman RF, Stone EA, et al. Genetic basis of transcriptome diversity in Drosophila melanogaster. Proc Natl Acad Sci USA. 2015;112:E6010-6019.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Mackay TFC, Huang W. Charting the genotype-phenotype map: lessons from the Drosophila melanogaster genetic reference panel. Wiley Interdiscip Rev Dev Biol. 2018;7: e289.

    Article  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

Data collection was supported by NIH R01 ES029922, BJS is supported by P20 GM103418, and the KU Genome Sequencing Core is supported by P30 GM145499.

Author information

Authors and Affiliations

Authors

Contributions

BJS: formal analysis, validation, visualization, software, data curation, writing—original draft, writing—review and editing. DJS-W: investigation, writing—review and editing. SJM: Supervision, project administration, resources, conceptualization, methodology, funding acquisition, investigation, writing—review and editing.

Corresponding author

Correspondence to Brian J. Sanderson.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare 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

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/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sanderson, B.J., Sims-West, D.J. & Macdonald, S.J. Acute exposure to mercury drives changes in gene expression in Drosophila melanogaster. BMC Res Notes 17, 279 (2024). https://doi.org/10.1186/s13104-024-06945-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13104-024-06945-y

Keywords