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Protein Quantitative Trait Loci Identify Novel Candidates Modulating Cellular Response to Chemotherapy


The central dogma of biology explains that DNA is transcribed to mRNA that is further translated into protein. Many genome-wide studies have implicated genetic variation that influences gene expression and that ultimately affect downstream complex traits including response to drugs. However, because of technical limitations, few studies have evaluated the contribution of genetic variation on protein expression and ensuing effects on downstream phenotypes. To overcome this challenge, we used a novel technology to simultaneously measure the baseline expression of 441 proteins in lymphoblastoid cell lines and compared them with publicly available genetic data. To further illustrate the utility of this approach, we compared protein-level measurements with chemotherapeutic induced apoptosis and cell-growth inhibition data. This study demonstrates the importance of using protein information to understand the functional consequences of genetic variants identified in genome-wide association studies. This protein data set will also have broad utility for understanding the relationship between other genome-wide studies of complex traits.


Vyšlo v časopise: Protein Quantitative Trait Loci Identify Novel Candidates Modulating Cellular Response to Chemotherapy. PLoS Genet 10(4): e32767. doi:10.1371/journal.pgen.1004192
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1004192

Souhrn

The central dogma of biology explains that DNA is transcribed to mRNA that is further translated into protein. Many genome-wide studies have implicated genetic variation that influences gene expression and that ultimately affect downstream complex traits including response to drugs. However, because of technical limitations, few studies have evaluated the contribution of genetic variation on protein expression and ensuing effects on downstream phenotypes. To overcome this challenge, we used a novel technology to simultaneously measure the baseline expression of 441 proteins in lymphoblastoid cell lines and compared them with publicly available genetic data. To further illustrate the utility of this approach, we compared protein-level measurements with chemotherapeutic induced apoptosis and cell-growth inhibition data. This study demonstrates the importance of using protein information to understand the functional consequences of genetic variants identified in genome-wide association studies. This protein data set will also have broad utility for understanding the relationship between other genome-wide studies of complex traits.


Zdroje

1. WheelerHE, DolanME (2012) Lymphoblastoid cell lines in pharmacogenomic discovery and clinical translation. Pharmacogenomics 13: 55–70.

2. WheelerHE, GamazonER, WingC, NjiajuUO, NjokuC, et al. (2013) Integration of cell line and clinical trial genome-wide analyses supports a polygenic architecture of Paclitaxel-induced sensory peripheral neuropathy. Clin Cancer Res 19: 491–499.

3. MitraAK, CrewsKR, PoundsS, CaoX, FeldbergT, et al. (2011) Genetic variants in cytosolic 5′-nucleotidase II are associated with its expression and cytarabine sensitivity in HapMap cell lines and in patients with acute myeloid leukemia. J Pharmacol Exp Ther 339: 9–23.

4. ZiliakD, O'DonnellPH, ImHK, GamazonER, ChenP, et al. (2011) Germline polymorphisms discovered via a cell-based, genome-wide approach predict platinum response in head and neck cancers. Transl Res 157: 265–272.

5. HuangRS, JohnattySE, GamazonER, ImHK, ZiliakD, et al. (2011) Platinum sensitivity-related germline polymorphism discovered via a cell-based approach and analysis of its association with outcome in ovarian cancer patients. Clin Cancer Res 17: 5490–5500.

6. NiuN, SchaidDJ, AboRP, KalariK, FridleyBL, et al. (2012) Genetic association with overall survival of taxane-treated lung cancer patients - a genome-wide association study in human lymphoblastoid cell lines followed by a clinical association study. BMC Cancer 12: 422.

7. BrownCC, HavenerTM, MedinaMW, AumanJT, MangraviteLM, et al. (2012) A genome-wide association analysis of temozolomide response using lymphoblastoid cell lines shows a clinically relevant association with MGMT. Pharmacogenet Genomics 22: 796–802.

8. O'DonnellPH, StarkAL, GamazonER, WheelerHE, McIlweeBE, et al. (2012) Identification of novel germline polymorphisms governing capecitabine sensitivity. Cancer 118: 4063–4073.

9. WheelerHE, GamazonER, StarkAL, O'DonnellPH, GorsicLK, et al. (2013) Genome-wide meta-analysis identifies variants associated with platinating agent susceptibility across populations. Pharmacogenomics J 13: 35–43.

10. HartfordCM, DuanS, DelaneySM, MiS, KistnerEO, et al. (2009) Population-specific genetic variants important in susceptibility to cytarabine arabinoside cytotoxicity. Blood 113: 2145–2153.

11. HuangRS, DuanS, BleibelWK, KistnerEO, ZhangW, et al. (2007) A genome-wide approach to identify genetic variants that contribute to etoposide-induced cytotoxicity. Proc Natl Acad Sci U S A 104: 9758–9763.

12. DuanS, HuangRS, ZhangW, BleibelWK, RoeCA, et al. (2008) Genetic architecture of transcript-level variation in humans. Am J Hum Genet 82: 1101–1113.

13. StrangerBE, MontgomerySB, DimasAS, PartsL, StegleO, et al. (2012) Patterns of cis regulatory variation in diverse human populations. PLoS Genet 8: e1002639.

14. StrangerBE, NicaAC, ForrestMS, DimasA, BirdCP, et al. (2007) Population genomics of human gene expression. Nat Genet 39: 1217–1224.

15. VeyrierasJB, KudaravalliS, KimSY, DermitzakisET, GiladY, et al. (2008) High-resolution mapping of expression-QTLs yields insight into human gene regulation. PLoS Genet 4: e1000214.

16. PickrellJK, MarioniJC, PaiAA, DegnerJF, EngelhardtBE, et al. (2010) Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 464: 768–772.

17. BellJT, PaiAA, PickrellJK, GaffneyDJ, Pique-RegiR, et al. (2011) DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines. Genome Biol 12: R10.

18. PaiAA, CainCE, Mizrahi-ManO, De LeonS, LewellenN, et al. (2012) The contribution of RNA decay quantitative trait loci to inter-individual variation in steady-state gene expression levels. PLoS Genet 8: e1003000.

19. DegnerJF, PaiAA, Pique-RegiR, VeyrierasJB, GaffneyDJ, et al. (2012) DNase I sensitivity QTLs are a major determinant of human expression variation. Nature 482: 390–394.

20. GamazonER, ZiliakD, ImHK, LaCroixB, ParkDS, et al. (2012) Genetic architecture of microRNA expression: implications for the transcriptome and complex traits. Am J Hum Genet 90: 1046–1063.

21. KoDC, GamazonER, ShuklaKP, PfuetznerRA, WhittingtonD, et al. (2012) Functional genetic screen of human diversity reveals that a methionine salvage enzyme regulates inflammatory cell death. Proc Natl Acad Sci U S A 109: E2343–2352.

22. KatoT, Hayashi-TakagiA, ToyotaT, YoshikawaT, IwamotoK (2011) Gene expression analysis in lymphoblastoid cells as a potential biomarker of bipolar disorder. J Hum Genet 56: 779–783.

23. OvedK, MoragA, Pasmanik-ChorM, Oron-KarniV, ShomronN, et al. (2012) Genome-wide miRNA expression profiling of human lymphoblastoid cell lines identifies tentative SSRI antidepressant response biomarkers. Pharmacogenomics 13: 1129–1139.

24. MoragA, OvedK, GurwitzD (2013) Sex differences in human lymphoblastoid cells sensitivities to antipsychotic drugs. J Mol Neurosci 49: 554–558.

25. GamazonER, HuangRS, CoxNJ, DolanME (2010) Chemotherapeutic drug susceptibility associated SNPs are enriched in expression quantitative trait loci. Proc Natl Acad Sci U S A 107: 9287–9292.

26. ChenG, GharibTG, HuangCC, TaylorJM, MisekDE, et al. (2002) Discordant protein and mRNA expression in lung adenocarcinomas. Mol Cell Proteomics 1: 304–313.

27. GygiSP, RochonY, FranzaBR, AebersoldR (1999) Correlation between protein and mRNA abundance in yeast. Mol Cell Biol 19: 1720–1730.

28. NishizukaS, CharboneauL, YoungL, MajorS, ReinholdWC, et al. (2003) Proteomic profiling of the NCI-60 cancer cell lines using new high-density reverse-phase lysate microarrays. Proc Natl Acad Sci U S A 100: 14229–14234.

29. ShankavaramUT, ReinholdWC, NishizukaS, MajorS, MoritaD, et al. (2007) Transcript and protein expression profiles of the NCI-60 cancer cell panel: an integromic microarray study. Mol Cancer Ther 6: 820–832.

30. VogelC, Abreu RdeS, KoD, LeSY, ShapiroBA, et al. (2010) Sequence signatures and mRNA concentration can explain two-thirds of protein abundance variation in a human cell line. Mol Syst Biol 6: 400.

31. MwandaWO, OremJ, FuP, BanuraC, KakemboJ, et al. (2009) Dose-modified oral chemotherapy in the treatment of AIDS-related non-Hodgkin's lymphoma in East Africa. J Clin Oncol 27: 3480–3488.

32. CiaccioMF, WagnerJP, ChuuCP, LauffenburgerDA, JonesRB (2010) Systems analysis of EGF receptor signaling dynamics with microwestern arrays. Nat Methods 7: 148–155.

33. HauseRJ, KimHD, LeungKK, JonesRB (2011) Targeted protein-omic methods are bridging the gap between proteomic and hypothesis-driven protein analysis approaches. Expert Rev Proteomics 8: 565–575.

34. ZwellingLA, KohnKW (1979) Mechanism of action of cis-dichlorodiammineplatinum(II). Cancer Treat Rep 63: 1439–1444.

35. RowinskyEK, CazenaveLA, DonehowerRC (1990) Taxol: a novel investigational antimicrotubule agent. J Natl Cancer Inst 82: 1247–1259.

36. PazdurR, KudelkaAP, KavanaghJJ, CohenPR, RaberMN (1993) The taxoids: paclitaxel (Taxol) and docetaxel (Taxotere). Cancer Treat Rev 19: 351–386.

37. RicciMS, ZongWX (2006) Chemotherapeutic approaches for targeting cell death pathways. Oncologist 11: 342–357.

38. ImHK, GamazonER, StarkAL, HuangRS, CoxNJ, et al. (2012) Mixed effects modeling of proliferation rates in cell-based models: consequence for pharmacogenomics and cancer. PLoS Genet 8: e1002525.

39. ZhangW, DuanS, KistnerEO, BleibelWK, HuangRS, et al. (2008) Evaluation of genetic variation contributing to differences in gene expression between populations. Am J Hum Genet 82: 631–640.

40. Bergstrom LindS, ArtemenkoKA, ElfinehL, MayrhoferC, ZubarevRA, et al. (2011) Toward a comprehensive characterization of the phosphotyrosine proteome. Cell Signal 23: 1387–1395.

41. RojasAM, Sanchez-PulidoL, FuttererA, van WelyKH, MartinezAC, et al. (2005) Death inducer obliterator protein 1 in the context of DNA regulation. Sequence analyses of distant homologues point to a novel functional role. FEBS J 272: 3505–3511.

42. LeXF, BastRCJr (2011) Src family kinases and paclitaxel sensitivity. Cancer Biol Ther 12: 260–269.

43. ChenT, PengetnzeY, TaylorCC (2005) Src inhibition enhances paclitaxel cytotoxicity in ovarian cancer cells by caspase-9-independent activation of caspase-3. Mol Cancer Ther 4: 217–224.

44. Van WaardenburgRC, PrinsJ, MeijerC, UgesDR, De VriesEG, et al. (1996) Effects of c-myc oncogene modulation on drug resistance in human small cell lung carcinoma cell lines. Anticancer Res 16: 1963–1970.

45. WalkerTL, WhiteJD, EsdaleWJ, BurtonMA, DeCruzEE (1996) Tumour cells surviving in vivo cisplatin chemotherapy display elevated c-myc expression. Br J Cancer 73: 610–614.

46. GuF, MaY, ZhangZ, ZhaoJ, KobayashiH, et al. (2010) Expression of Stat3 and Notch1 is associated with cisplatin resistance in head and neck squamous cell carcinoma. Oncol Rep 23: 671–676.

47. IkutaK, TakemuraK, KiharaM, NishimuraM, UedaN, et al. (2005) Overexpression of constitutive signal transducer and activator of transcription 3 mRNA in cisplatin-resistant human non-small cell lung cancer cells. Oncol Rep 13: 217–222.

48. KatoK, NomotoM, IzumiH, IseT, NakanoS, et al. (2000) Structure and functional analysis of the human STAT3 gene promoter: alteration of chromatin structure as a possible mechanism for the upregulation in cisplatin-resistant cells. Biochim Biophys Acta 1493: 91–100.

49. SuWP, ChengFY, ShiehDB, YehCS, SuWC (2012) PLGA nanoparticles codeliver paclitaxel and Stat3 siRNA to overcome cellular resistance in lung cancer cells. Int J Nanomedicine 7: 4269–4283.

50. WalkerSR, ChaudhuryM, FrankDA (2011) STAT3 Inhibition by Microtubule-Targeted Drugs: Dual Molecular Effects of Chemotherapeutic Agents. Mol Cell Pharmacol 3: 13–19.

51. WuH, CaoY, WengD, XingH, SongX, et al. (2008) Effect of tumor suppressor gene PTEN on the resistance to cisplatin in human ovarian cancer cell lines and related mechanisms. Cancer Lett 271: 260–271.

52. KimST, XuB, KastanMB (2002) Involvement of the cohesin protein, Smc1, in Atm-dependent and independent responses to DNA damage. Genes Dev 16: 560–570.

53. ManniniL, LiuJ, KrantzID, MusioA (2010) Spectrum and consequences of SMC1A mutations: the unexpected involvement of a core component of cohesin in human disease. Hum Mutat 31: 5–10.

54. BarberTD, McManusK, YuenKW, ReisM, ParmigianiG, et al. (2008) Chromatid cohesion defects may underlie chromosome instability in human colorectal cancers. Proc Natl Acad Sci U S A 105: 3443–3448.

55. ManniniL, MengaS, TonelliA, ZanottiS, BassiMT, et al. (2012) SMC1A codon 496 mutations affect the cellular response to genotoxic treatments. Am J Med Genet A 158A: 224–228.

56. ChenS, BlankJL, PetersT, LiuXJ, RappoliDM, et al. (2010) Genome-wide siRNA screen for modulators of cell death induced by proteasome inhibitor bortezomib. Cancer Res 70: 4318–4326.

57. YamaguchiH, DurellSR, ChatterjeeDK, AndersonCW, AppellaE (2007) The Wip1 phosphatase PPM1D dephosphorylates SQ/TQ motifs in checkpoint substrates phosphorylated by PI3K-like kinases. Biochemistry 46: 12594–12603.

58. HuangX, YuanW, HuangW, BaiY, DengY, et al. (2006) ZNF569, a novel KRAB-containing zinc finger protein, suppresses MAPK signaling pathway. Biochem Biophys Res Commun 346: 621–628.

59. ZhuC, QiX, ChenY, SunB, DaiY, et al. (2011) PI3K/Akt and MAPK/ERK1/2 signaling pathways are involved in IGF-1-induced VEGF-C upregulation in breast cancer. J Cancer Res Clin Oncol 137: 1587–1594.

60. SlichenmyerWJ, Von HoffDD (1991) Taxol: a new and effective anti-cancer drug. Anticancer Drugs 2: 519–530.

61. WenY, GamazonER, BleibelWK, WingC, MiS, et al. (2012) An eQTL-based method identifies CTTN and ZMAT3 as pemetrexed susceptibility markers. Hum Mol Genet 21: 1470–1480.

62. NicolaeDL, GamazonE, ZhangW, DuanS, DolanME, et al. (2010) Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet 6: e1000888.

63. GaffneyDJ, VeyrierasJB, DegnerJF, Pique-RegiR, PaiAA, et al. (2012) Dissecting the regulatory architecture of gene expression QTLs. Genome Biol 13: R7.

64. FuJ, WolfsMG, DeelenP, WestraHJ, FehrmannRS, et al. (2012) Unraveling the regulatory mechanisms underlying tissue-dependent genetic variation of gene expression. PLoS Genet 8: e1002431.

65. HuangRS, DuanS, ShuklaSJ, KistnerEO, ClarkTA, et al. (2007) Identification of genetic variants contributing to cisplatin-induced cytotoxicity by use of a genomewide approach. Am J Hum Genet 81: 427–437.

66. NjiajuUO, GamazonER, GorsicLK, DelaneySM, WheelerHE, et al. (2012) Whole-genome studies identify solute carrier transporters in cellular susceptibility to paclitaxel. Pharmacogenet Genomics 22: 498–507.

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Genetika Reprodukčná medicína

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