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Identifying Loci Contributing to Natural Variation in Xenobiotic Resistance in


A striking feature of biology is that within populations there is substantial interindividual phenotypic variation for traits of biomedical significance. Some fraction of this variation is due to environmental factors, but for many traits segregating genetic differences contribute significantly to phenotypic variation. Elucidating those causative sequence changes that result in complex trait variation is of central importance to biology, and requires the coordinated use of multiple approaches. Here we employ a multi-level strategy to dissect genetic variation in caffeine resistance in Drosophila melanogaster, leveraging powerful genetic screening in a multiparental mapping panel, a genomewide association study, high-throughput RNA sequencing, and gene knockdowns using RNA interference. We identify several short genomic regions that collectively explain a substantial portion of the heritable variation for caffeine resistance, and find that several of these regions harbor members of known detoxification enzyme families. One such gene—Cyp12d1—shows increased expression on exposure to caffeine, and experimentally reducing gene expression leads to a reduction in caffeine resistance. We additionally show that variation in the number of copies of Cyp12d1 is positively associated with resistance. These compelling lines of evidence imply that structural variation at this gene causally contributes to xenobiotic resistance in Drosophila.


Vyšlo v časopise: Identifying Loci Contributing to Natural Variation in Xenobiotic Resistance in. PLoS Genet 11(11): e32767. doi:10.1371/journal.pgen.1005663
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1005663

Souhrn

A striking feature of biology is that within populations there is substantial interindividual phenotypic variation for traits of biomedical significance. Some fraction of this variation is due to environmental factors, but for many traits segregating genetic differences contribute significantly to phenotypic variation. Elucidating those causative sequence changes that result in complex trait variation is of central importance to biology, and requires the coordinated use of multiple approaches. Here we employ a multi-level strategy to dissect genetic variation in caffeine resistance in Drosophila melanogaster, leveraging powerful genetic screening in a multiparental mapping panel, a genomewide association study, high-throughput RNA sequencing, and gene knockdowns using RNA interference. We identify several short genomic regions that collectively explain a substantial portion of the heritable variation for caffeine resistance, and find that several of these regions harbor members of known detoxification enzyme families. One such gene—Cyp12d1—shows increased expression on exposure to caffeine, and experimentally reducing gene expression leads to a reduction in caffeine resistance. We additionally show that variation in the number of copies of Cyp12d1 is positively associated with resistance. These compelling lines of evidence imply that structural variation at this gene causally contributes to xenobiotic resistance in Drosophila.


Zdroje

1. Glendinning JI. How do predators cope with chemically defended foods? Biol Bull. 2007;213(3):252–66. 18083965

2. Mithofer A, Boland W. Plant defense against herbivores: chemical aspects. Annu Rev Plant Biol. 2012;63:431–50. doi: 10.1146/annurev-arplant-042110-103854 22404468

3. Zhong W, Maradit-Kremers H, St Sauver JL, Yawn BP, Ebbert JO, Roger VL, et al. Age and sex patterns of drug prescribing in a defined American population. Mayo Clin Proc. 2013;88(7):697–707. doi: 10.1016/j.mayocp.2013.04.021 23790544

4. Holzinger F, Frick C, Wink M. Molecular basis for the insensitivity of the Monarch (Danaus plexippus) to cardiac glycosides. FEBS Lett. 1992;314(3):477–80. 1334851

5. Zhen Y, Aardema ML, Medina EM, Schumer M, Andolfatto P. Parallel molecular evolution in an herbivore community. Science. 2012;337(6102):1634–7. 23019645

6. Li X, Schuler MA, Berenbaum MR. Molecular mechanisms of metabolic resistance to synthetic and natural xenobiotics. Annu Rev Entomol. 2007;52:231–53. 16925478

7. Xu C, Li CY, Kong AN. Induction of phase I, II and III drug metabolism/transport by xenobiotics. Arch Pharm Res. 2005;28(3):249–68. 15832810

8. Snyder MJ, Glendinning JI. Causal connection between detoxification enzyme activity and consumption of a toxic plant compound. J Comp Physiol A. 1996;179(2):255–61. 8765561

9. Wink M, Theile V. Alkaloid tolerance in Manduca sexta and phylogenetically related sphingids (Lepidoptera: Sphingidae). Chemoecology. 2002;12:29–46.

10. 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(3):1071–7. 17179088

11. Daborn PJ, Yen JL, Bogwitz MR, Le Goff G, Feil E, Jeffers S, et al. A single p450 allele associated with insecticide resistance in Drosophila. Science. 2002;297(5590):2253–6. 12351787

12. Schmidt JM, Good RT, Appleton B, Sherrard J, Raymant GC, Bogwitz MR, et al. Copy number variation and transposable elements feature in recent, ongoing adaptation at the Cyp6g1 locus. PLoS Genet. 2010;6(6):e1000998. doi: 10.1371/journal.pgen.1000998 20585622

13. Carrillo R, Gibson G. Unusual genetic architecture of natural variation affecting drug resistance in Drosophila melanogaster. Genet Res. 2002;80(3):205–13. 12688659

14. King EG, Kislukhin G, Walters KN, Long AD. Using Drosophila melanogaster to identify chemotherapy toxicity genes. Genetics. 2014;198(1):31–43. doi: 10.1534/genetics.114.161968 25236447

15. Marriage TN, King EG, Long AD, Macdonald SJ. Fine-mapping nicotine resistance loci in Drosophila using a multiparent advanced generation inter-cross population. Genetics. 2014;198(1):45–57. doi: 10.1534/genetics.114.162107 25236448

16. Ge D, Fellay J, Thompson AJ, Simon JS, Shianna KV, Urban TJ, et al. Genetic variation in IL28B predicts hepatitis C treatment-induced viral clearance. Nature. 2009;461(7262):399–401. doi: 10.1038/nature08309 19684573

17. Shuldiner AR, O'Connell JR, Bliden KP, Gandhi A, Ryan K, Horenstein RB, et al. Association of cytochrome P450 2C19 genotype with the antiplatelet effect and clinical efficacy of clopidogrel therapy. JAMA. 2009;302(8):849–57. doi: 10.1001/jama.2009.1232 19706858

18. Takeuchi F, McGinnis R, Bourgeois S, Barnes C, Eriksson N, Soranzo N, et al. A genome-wide association study confirms VKORC1, CYP2C9, and CYP4F2 as principal genetic determinants of warfarin dose. PLoS Genet. 2009;5(3):e1000433. doi: 10.1371/journal.pgen.1000433 19300499

19. Bhaskara S, Dean ED, Lam V, Ganguly R. Induction of two cytochrome P450 genes, Cyp6a2 and Cyp6a8, of Drosophila melanogaster by caffeine in adult flies and in cell culture. Gene. 2006;377:56–64. 16713132

20. Misra JR, Horner MA, Lam G, Thummel CS. Transcriptional regulation of xenobiotic detoxification in Drosophila. Genes Dev. 2011;25(17):1796–806. doi: 10.1101/gad.17280911 21896655

21. Morra R, Kuruganti S, Lam V, Lucchesi JC, Ganguly R. Functional analysis of the cis-acting elements responsible for the induction of the Cyp6a8 and Cyp6g1 genes of Drosophila melanogaster by DDT, phenobarbital and caffeine. Insect Mol Biol. 2010;19(1):121–30. doi: 10.1111/j.1365-2583.2009.00954.x 20002224

22. Willoughby L, Chung H, Lumb C, Robin C, Batterham P, Daborn PJ. A comparison of Drosophila melanogaster detoxification gene induction responses for six insecticides, caffeine and phenobarbital. Insect Biochem Mol Biol. 2006;36(12):934–42. 17098168

23. Churchill GA, Airey DC, Allayee H, Angel JM, Attie AD, Beatty J, et al. The Collaborative Cross, a community resource for the genetic analysis of complex traits. Nat Genet. 2004;36(11):1133–7. 15514660

24. Kover PX, Valdar W, Trakalo J, Scarcelli N, Ehrenreich IM, Purugganan MD, et al. A Multiparent Advanced Generation Inter-Cross to fine-map quantitative traits in Arabidopsis thaliana. PLoS Genet. 2009;5(7):e1000551. doi: 10.1371/journal.pgen.1000551 19593375

25. Macdonald SJ, Long AD. Joint estimates of quantitative trait locus effect and frequency using synthetic recombinant populations of Drosophila melanogaster. Genetics. 2007;176(2):1261–81. 17435224

26. Rat Genome S, Mapping C, Baud A, Hermsen R, Guryev V, Stridh P, et al. Combined sequence-based and genetic mapping analysis of complex traits in outbred rats. Nat Genet. 2013;45(7):767–75. doi: 10.1038/ng.2644 23708188

27. Svenson KL, Gatti DM, Valdar W, Welsh CE, Cheng R, Chesler EJ, et al. High-resolution genetic mapping using the Mouse Diversity outbred population. Genetics. 2012;190(2):437–47. doi: 10.1534/genetics.111.132597 22345611

28. Threadgill DW, Churchill GA. Ten years of the collaborative cross. G3 (Bethesda). 2012;2(2):153–6.

29. Valdar W, Flint J, Mott R. Simulating the collaborative cross: power of quantitative trait loci detection and mapping resolution in large sets of recombinant inbred strains of mice. Genetics. 2006;172(3):1783–97. 16361245

30. King EG, Macdonald SJ, Long AD. Properties and power of the Drosophila Synthetic Population Resource for the routine dissection of complex traits. Genetics. 2012;191(3):935–49. doi: 10.1534/genetics.112.138537 22505626

31. 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(8):1558–66. doi: 10.1101/gr.134031.111 22496517

32. Kislukhin G, King EG, Walters KN, Macdonald SJ, Long AD. The genetic architecture of methotrexate toxicity is similar in Drosophila melanogaster and humans. G3 (Bethesda). 2013;3(8):1301–10.

33. Huang W, Massouras A, Inoue Y, Peiffer J, Ramia M, Tarone AM, et al. Natural variation in genome architecture among 205 Drosophila melanogaster Genetic Reference Panel lines. Genome Res. 2014;24(7):1193–208. doi: 10.1101/gr.171546.113 24714809

34. Mackay TF, Richards S, Stone EA, Barbadilla A, Ayroles JF, Zhu D, et al. The Drosophila melanogaster Genetic Reference Panel. Nature. 2012;482(7384):173–8. doi: 10.1038/nature10811 22318601

35. Ivanov DK, Escott-Price V, Ziehm M, Magwire MM, Mackay TF, Partridge L, et al. Longevity GWAS Using the Drosophila Genetic Reference Panel. J Gerontol A Biol Sci Med Sci. 2015.

36. King EG, Sanderson BJ, McNeil CL, Long AD, Macdonald SJ. Genetic dissection of the Drosophila melanogaster female head transcriptome reveals widespread allelic heterogeneity. PLoS Genet. 2014;10(5):e1004322. doi: 10.1371/journal.pgen.1004322 24810915

37. Giraud H, Lehermeier C, Bauer E, Falque M, Segura V, Bauland C, et al. Linkage disequilibrium with linkage analysis of multiline crosses reveals different multiallelic QTL for hybrid performance in the flint and dent heterotic groups of maize. Genetics. 2014;198(4):1717–34. doi: 10.1534/genetics.114.169367 25271305

38. Stankiewicz P, Lupski JR. Structural variation in the human genome and its role in disease. Annu Rev Med. 2010;61:437–55. doi: 10.1146/annurev-med-100708-204735 20059347

39. Weischenfeldt J, Symmons O, Spitz F, Korbel JO. Phenotypic impact of genomic structural variation: insights from and for human disease. Nat Rev Genet. 2013;14(2):125–38. doi: 10.1038/nrg3373 23329113

40. Zichner T, Garfield DA, Rausch T, Stutz AM, Cannavo E, Braun M, et al. Impact of genomic structural variation in Drosophila melanogaster based on population-scale sequencing. Genome Res. 2013;23(3):568–79. doi: 10.1101/gr.142646.112 23222910

41. Good RT, Gramzow L, Battlay P, Sztal T, Batterham P, Robin C. The molecular evolution of cytochrome P450 genes within and between Drosophila species. Genome Biol Evol. 2014;6(5):1118–34. doi: 10.1093/gbe/evu083 24751979

42. McDonnell CM, King D, Comeron JM, Li H, Sun W, Berenbaum MR, et al. Evolutionary toxicogenomics: diversification of the Cyp12d1 and Cyp12d3 genes in Drosophila species. J Mol Evol. 2012;74(5–6):281–96. doi: 10.1007/s00239-012-9506-3 22811321

43. Schrider DR, Begun DJ, Hahn MW. Detecting highly differentiated copy-number variants from pooled population sequencing. Pac Symp Biocomput. 2013:344–55. 23424139

44. Adams MD, Celniker SE, Holt RA, Evans CA, Gocayne JD, Amanatides PG, et al. The genome sequence of Drosophila melanogaster. Science. 2000;287(5461):2185–95. 10731132

45. St Pierre SE, Ponting L, Stefancsik R, McQuilton P, FlyBase C. FlyBase 102—advanced approaches to interrogating FlyBase. Nucleic Acids Res. 2014;42(Database issue):D780–8. doi: 10.1093/nar/gkt1092 24234449

46. Mitchell CL, Saul MC, Lei L, Wei H, Werner T. The mechanisms underlying alpha-amanitin resistance in Drosophila melanogaster: a microarray analysis. PLoS One. 2014;9(4):e93489. doi: 10.1371/journal.pone.0093489 24695618

47. Coelho A, Fraichard S, Le Goff G, Faure P, Artur Y, Ferveur JF, et al. Cytochrome P450-dependent metabolism of caffeine in Drosophila melanogaster. PLoS One. 2015;10(2):e0117328. doi: 10.1371/journal.pone.0117328 25671424

48. Sztal T, Chung H, Berger S, Currie PD, Batterham P, Daborn PJ. A cytochrome p450 conserved in insects is involved in cuticle formation. PLoS One. 2012;7(5):e36544. doi: 10.1371/journal.pone.0036544 22574182

49. Osterwalder T, Yoon KS, White BH, Keshishian H. A conditional tissue-specific transgene expression system using inducible GAL4. Proc Natl Acad Sci U S A. 2001;98(22):12596–601. 11675495

50. Long AD, Macdonald SJ, King EG. Dissecting complex traits using the Drosophila Synthetic Population Resource. Trends Genet. 2014;30(11):488–95. doi: 10.1016/j.tig.2014.07.009 25175100

51. Turner TL, Miller PM, Cochrane VA. Combining genome-wide methods to investigate the genetic complexity of courtship song variation in Drosophila melanogaster. Mol Biol Evol. 2013;30(9):2113–20. doi: 10.1093/molbev/mst111 23777628

52. Beavis W. The power and deceit of QTL experiments: lessons from comparative QTL studies. Proceedings of the 49th Annual Corn and Sorghum Industry Research Conference. Washington, DC: American Seed Trade Association; 1994. p. 250–66.

53. Xu S. Theoretical basis of the Beavis effect. Genetics. 2003;165(4):2259–68. 14704201

54. Parker CC, Carbonetto P, Sokoloff G, Park YJ, Abney M, Palmer AA. High-resolution genetic mapping of complex traits from a combined analysis of F2 and advanced intercross mice. Genetics. 2014;198(1):103–16. doi: 10.1534/genetics.114.167056 25236452

55. Chung H, Sztal T, Pasricha S, Sridhar M, Batterham P, Daborn PJ. Characterization of Drosophila melanogaster cytochrome P450 genes. Proc Natl Acad Sci U S A. 2009;106(14):5731–6. doi: 10.1073/pnas.0812141106 19289821

56. Harrop TW, Pearce SL, Daborn PJ, Batterham P. Whole-genome expression analysis in the third instar larval midgut of Drosophila melanogaster. G3 (Bethesda). 2014;4(11):2197–205.

57. Stranger BE, Forrest MS, Dunning M, Ingle CE, Beazley C, Thorne N, et al. Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science. 2007;315(5813):848–53. 17289997

58. Li X, Zhuo R, Tiong S, Di Cara F, King-Jones K, Hughes SC, et al. The Smc5/Smc6/MAGE complex confers resistance to caffeine and genotoxic stress in Drosophila melanogaster. PLoS One. 2013;8(3):e59866. doi: 10.1371/journal.pone.0059866 23555814

59. King-Jones K, Horner MA, Lam G, Thummel CS. The DHR96 nuclear receptor regulates xenobiotic responses in Drosophila. Cell Metab. 2006;4(1):37–48. 16814731

60. Sun W, Valero MC, Seong KM, Steele LD, Huang IT, Lee CH, et al. A glycine insertion in the estrogen-related receptor (ERR) is associated with enhanced expression of three cytochrome P450 genes in transgenic Drosophila melanogaster. PLoS One. 2015;10(3):e0118779. doi: 10.1371/journal.pone.0118779 25761142

61. Lee Y, Moon SJ, Montell C. Multiple gustatory receptors required for the caffeine response in Drosophila. Proc Natl Acad Sci U S A. 2009;106(11):4495–500. doi: 10.1073/pnas.0811744106 19246397

62. Moon SJ, Kottgen M, Jiao Y, Xu H, Montell C. A taste receptor required for the caffeine response in vivo. Curr Biol. 2006;16(18):1812–7. 16979558

63. Lynch M, Walsh B. Genetics and Analysis of Quantitative Traits. Sunderland, Massachusetts: Sinauer Associates, Inc; 1998.

64. Pritchard JK. Are rare variants responsible for susceptibility to complex diseases? Am J Hum Genet. 2001;69(1):124–37. 11404818

65. Thornton KR, Foran AJ, Long AD. Properties and modeling of GWAS when complex disease risk is due to non-complementing, deleterious mutations in genes of large effect. PLoS Genet. 2013;9(2):e1003258. doi: 10.1371/journal.pgen.1003258 23437004

66. DIAbetes Genetics Replication Meta-analysis, et al. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet. 2014;46(3):234–44. doi: 10.1038/ng.2897 24509480

67. Stone EA. Joint genotyping on the fly: identifying variation among a sequenced panel of inbred lines. Genome Res. 2012;22(5):966–74. doi: 10.1101/gr.129122.111 22367192

68. Pinheiro J, Bates D, DebRoy S, Sarkar D, Team RDC. nlme: linear and nonlinear mixed effects models. R package version 31–101. 2011.

69. Churchill GA, Doerge RW. Empirical threshold values for quantitative trait mapping. Genetics. 1994;138(3):963–71. 7851788

70. Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 2013;14(4):R36. doi: 10.1186/gb-2013-14-4-r36 23618408

71. Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009;25(9):1105–11. doi: 10.1093/bioinformatics/btp120 19289445

72. Trapnell C, Hendrickson DG, Sauvageau M, Goff L, Rinn JL, Pachter L. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol. 2013;31(1):46–53. doi: 10.1038/nbt.2450 23222703

73. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010;28(5):511–5. doi: 10.1038/nbt.1621 20436464

74. 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(7150):151–6. 17625558

75. Green EW, Fedele G, Giorgini F, Kyriacou CP. A Drosophila RNAi collection is subject to dominant phenotypic effects. Nat Methods. 2014;11(3):222–3. doi: 10.1038/nmeth.2856 24577271

76. Broman K, Sen S. A Guide to QTL Mapping with R/qtl. New York: Springer Dordrecht; 2009.

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