Integrative Analysis of a Cross-Loci Regulation Network Identifies as a Gene Regulating Insulin Secretion from Pancreatic Islets

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Complex diseases result from molecular changes induced by multiple genetic factors and the environment. To derive a systems view of how genetic loci interact in the context of tissue-specific molecular networks, we constructed an F2 intercross comprised of >500 mice from diabetes-resistant (B6) and diabetes-susceptible (BTBR) mouse strains made genetically obese by the Leptin^ob/ob mutation (Lep^ob). High-density genotypes, diabetes-related clinical traits, and whole-transcriptome expression profiling in five tissues (white adipose, liver, pancreatic islets, hypothalamus, and gastrocnemius muscle) were determined for all mice. We performed an integrative analysis to investigate the inter-relationship among genetic factors, expression traits, and plasma insulin, a hallmark diabetes trait. Among five tissues under study, there are extensive protein–protein interactions between genes responding to different loci in adipose and pancreatic islets that potentially jointly participated in the regulation of plasma insulin. We developed a novel ranking scheme based on cross-loci protein-protein network topology and gene expression to assess each gene's potential to regulate plasma insulin. Unique candidate genes were identified in adipose tissue and islets. In islets, the Alzheimer's gene App was identified as a top candidate regulator. Islets from 17-week-old, but not 10-week-old, App knockout mice showed increased insulin secretion in response to glucose or a membrane-permeant cAMP analog, in agreement with the predictions of the network model. Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes.

Vyšlo v časopise: Integrative Analysis of a Cross-Loci Regulation Network Identifies as a Gene Regulating Insulin Secretion from Pancreatic Islets. PLoS Genet 8(12): e32767. doi:10.1371/journal.pgen.1003107
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1003107

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Zdroje

1. EmilssonV, ThorleifssonG, ZhangB, LeonardsonAS, ZinkF, et al. (2008) Genetics of gene expression and its effect on disease. Nature 452: 423–428.

2. ManolioTA (2010) Genomewide association studies and assessment of the risk of disease. N Engl J Med 363: 166–176.

3. ManolioTA, CollinsFS, CoxNJ, GoldsteinDB, HindorffLA, et al. (2009) Finding the missing heritability of complex diseases. Nature 461: 747–753.

4. HindorffL, SethupathyP, JunkinsH, RamosE, MehtaJ, et al. (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proceedings of the National Academy of Sciences of the United States of America 106: 9362–9369 (Available at: www.genome.gov/gwastudies. Accessed on Feb 1st, 2012).

5. EppigJT, BlakeJA, BultCJ, KadinJA, RichardsonJE, et al. (2007) The mouse genome database (MGD): new features facilitating a model system. Nucl Acids Res 35: D630–637.

6. GhazalpourA, DossS, ZhangB, WangS, PlaisierC, et al. (2006) Integrating genetic and network analysis to characterize genes related to mouse weight. PLoS Genet 2: e130 doi:10.1371/journal.pgen.0020130.

7. SiebertsS, SchadtE (2007) Moving toward a system genetics view of disease. Mammalian Genome 18: 389–401.

8. SchadtEE, LumPY (2006) Thematic review series: Systems Biology Approaches to Metabolic and Cardiovascular Disorders. Reverse engineering gene networks to identify key drivers of complex disease phenotypes. J Lipid Res 47: 2601–2613.

9. BassoK, MargolinAA, StolovitzkyG, KleinU, Dalla-FaveraR, et al. (2005) Reverse engineering of regulatory networks in human B cells. Nat Genet 37: 382–390.

10. LeeSI, DudleyAM, DrubinD, SilverPA, KroganNJ, et al. (2009) Learning a prior on regulatory potential from eQTL data. PLoS Genet 5: e1000358 doi:10.1371/journal.pgen.1000358.

11. BandyopadhyayS, MehtaM, KuoD, SungMK, ChuangR, et al. (2010) Rewiring of genetic networks in response to DNA damage. Science 330: 1385–1389.

12. LeeSI, Pe'erD, DudleyAM, ChurchGM, KollerD (2006) Identifying regulatory mechanisms using individual variation reveals key role for chromatin modification. Proc Natl Acad Sci U S A 103: 14062–14067.

13. SchadtEE, LambJ, YangX, ZhuJ, EdwardsS, et al. (2005) An integrative genomics approach to infer causal associations between gene expression and disease. Nat Genet 37: 710–717.

14. ChenY, ZhuJ, LumPY, YangX, PintoS, et al. (2008) Variations in DNA elucidate molecular networks that cause disease. Nature 452: 429–435.

15. CheungVG, SpielmanRS, EwensKG, WeberTM, MorleyM, et al. (2005) Mapping determinants of human gene expression by regional and genome-wide association. Nature 437: 1365–1369.

16. BremRB, KruglyakL (2005) The landscape of genetic complexity across 5,700 gene expression traits in yeast. Proc Natl Acad Sci USA 102: 1572–1577.

17. ZhuJ, SovaP, XuQ, DombekK, XuE, et al. (2012) Stitching together Multiple Data Dimensions Reveals Interacting Metabolomic and Transcriptomic Networks That Modulate Cell Regulation. PLoS Biol 10: e1001301 doi:10.1371/journal.pbio.1001301.

18. SchadtEE, MonksSA, DrakeTA, LusisAJ, CheN, et al. (2003) Genetics of gene expression surveyed in maize, mouse and man. Nature 422: 297–302.

19. SuthramS, BeyerA, KarpRM, EldarY, IdekerT (2008) eQED: an efficient method for interpreting eQTL associations using protein networks. Mol Syst Biol 4.

20. TuZ, ArgmannC, WongKK, MitnaulLJ, EdwardsS, et al. (2009) Integrating siRNA and protein-protein interaction data to identify an expanded insulin signaling network. Genome Research 19: 1057–1067.

21. LefebvreC, RajbhandariP, AlvarezMJ, BandaruP, LimWK, et al. (2010) A human B-cell interactome identifies MYB and FOXM1 as master regulators of proliferation in germinal centers. Mol Syst Biol 6: 377.

22. ZhuJ, ZhangB, SmithEN, DreesB, BremRB, et al. (2008) Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nat Genet 40: 854–861.

23. ZhuJ, LumPY, LambJ, GuhaThakurtaD, EdwardsSW, et al. (2004) An integrative genomics approach to the reconstruction of gene networks in segregating populations. Cytogenet Genome Res 105: 363–374.

24. KellerMP, ChoiY, WangP, DavisDB, RabagliaME, et al. (2008) A gene expression network model of type 2 diabetes links cell cycle regulation in islets with diabetes susceptibility. Genome Res 18: 706–716.

25. StoehrJP, NadlerST, SchuelerKL, RabagliaME, YandellBS, et al. (2000) Genetic obesity unmasks nonlinear interactions between murine type 2 diabetes susceptibility loci. Diabetes 49: 1946–1954.

26. DobrinR, GreenawaltD, HuG, KempD, KaplanL, et al. (2011) Dissecting cis regulation of gene expression in human metabolic tissues. PLoS ONE 6: e23480 doi:10.1371/journal.pone.0023480.

27. KilpeläinenT, ZillikensM, StančákovaA, FinucaneF, RiedJ, et al. (2011) Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile. Nature genetics 43: 753–813.

28. BremRB, StoreyJD, WhittleJ, KruglyakL (2005) Genetic interactions between polymorphisms that affect gene expression in yeast. Nature 436: 701–703.

29. DengM, TuZ, SunF, ChenT (2004) Mapping gene ontology to proteins based on protein-protein interaction data. Bioinformatics 20: 895–902.

30. SharanR, UlitskyI, ShamirR (2007) Network-based prediction of protein function. Mol Syst Biol 3.

31. WuX, JiangR, ZhangMQ, LiS (2008) Network-based global inference of human disease genes. Mol Syst Biol 4.

32. GartelAL, RadhakrishnanSK (2005) Lost in Transcription: p21 Repression, Mechanisms, and Consequences. Cancer Research 65: 3980–3985.

33. MinaminoT, OrimoM, ShimizuI, KuniedaT, YokoyamaM, et al. (2009) A crucial role for adipose tissue p53 in the regulation of insulin resistance. Nature medicine 15: 1082–1089.

34. NaazA, HolsbergerD, IwamotoG, NelsonA, KiyokawaH, et al. (2004) Loss of cyclin-dependent kinase inhibitors produces adipocyte hyperplasia and obesity. The FASEB journal : official publication of the Federation of American Societies for Experimental Biology 18: 1925–1932.

35. PalopJ, MuckeL (2010) Amyloid-beta-induced neuronal dysfunction in Alzheimer's disease: from synapses toward neural networks. Nature neuroscience 13: 812–820.

36. WangL, BalasB, Christ-RobertsC, KimR, RamosF, et al. (2007) Peripheral disruption of the Grb10 gene enhances insulin signaling and sensitivity in vivo. Molecular and cellular biology 27: 6497–7002.

37. RamosF, LanglaisP, HuD, DongL, LiuF (2006) Grb10 mediates insulin-stimulated degradation of the insulin receptor: a mechanism of negative regulation. American journal of physiology Endocrinology and metabolism 290: 6.

38. ZhangJ, ZhangN, LiuM, LiX, ZhouL, et al. (2012) Disruption of Growth Factor Receptor-Binding Protein 10 in the Pancreas Enhances beta-Cell Proliferation and Protects Mice From Streptozotocin-Induced beta-Cell Apoptosis. Diabetes

39. NeedhamB, WlodekM, CiccotostoG, FamB, MastersC, et al. (2008) Identification of the Alzheimer's disease amyloid precursor protein (APP) and its homologue APLP2 as essential modulators of glucose and insulin homeostasis and growth. The Journal of Pathology 215: 155–218.

40. ZhouZ-d, ChanC, MaQ-h, XuX-h, XiaoZ-c, et al. (2011) The roles of amyloid precursor protein (APP) in neurogenesis: Implications to pathogenesis and therapy of Alzheimer disease. Cell adhesion & migration 5: 280–372.

41. ZhangB, HorvathS (2005) A general framework for weighted gene co-expression network analysis. Statistical applications in genetics and molecular biology 4.

42. SegalE, ShapiraM, RegevA, Pe'erD, BotsteinD, et al. (2003) Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nature genetics 34: 166–242.

43. Jiménez-PalomaresM, Ramos-RodríguezJ, López-AcostaJ, Pacheco-HerreroM, Lechuga-SanchoA, et al. (2012) Increased Aβ production prompts the onset of glucose intolerance and insulin resistance. American journal of physiology Endocrinology and metabolism 302: 80.

44. DuceJ, TsatsanisA, CaterM, JamesS, RobbE, et al. (2010) Iron-export ferroxidase activity of β-amyloid precursor protein is inhibited by zinc in Alzheimer's disease. Cell 142: 857–924.

45. HakonenE, UstinovJ, MathijsI, PalgiJ, BouwensL, et al. (2011) Epidermal growth factor (EGF)-receptor signalling is needed for murine beta cell mass expansion in response to high-fat diet and pregnancy but not after pancreatic duct ligation. Diabetologia 54: 1735–1778.

46. RankinM, KushnerJ (2010) Aging induces a distinct gene expression program in mouse islets. Islets 2: 345–397.

47. EizirikDL, SammethM, BouckenoogheT, BottuG, SisinoG, et al. (2012) The human pancreatic islet transcriptome: expression of candidate genes for type 1 diabetes and the impact of pro-inflammatory cytokines. PLoS Genet 8: e1002552 doi:10.1371/journal.pgen.1002552.

48. TiedgeM, LortzS, DrinkgernJ, LenzenS (1997) Relation between antioxidant enzyme gene expression and antioxidative defense status of insulin-producing cells. Diabetes 46: 1733–1775.

49. EizirikD, PipeleersD, LingZ, WelshN, HellerströmC, et al. (1994) Major species differences between humans and rodents in the susceptibility to pancreatic beta-cell injury. Proceedings of the National Academy of Sciences of the United States of America 91: 9253–9259.

50. WelshN, MargulisB, BorgL, WiklundH, SaldeenJ, et al. (1995) Differences in the expression of heat-shock proteins and antioxidant enzymes between human and rodent pancreatic islets: implications for the pathogenesis of insulin-dependent diabetes mellitus. Molecular medicine (Cambridge, Mass) 1: 806–826.

51. MiklossyJ, QingH, RadenovicA, KisA, VilenoB, et al. (2010) Beta amyloid and hyperphosphorylated tau deposits in the pancreas in type 2 diabetes. Neurobiol Aging 31: 1503–1515.

52. JansonJ, LaedtkeT, ParisiJE, O'BrienP, PetersenRC, et al. (2004) Increased Risk of Type 2 Diabetes in Alzheimer Disease. Diabetes 53: 474–481.

53. SwerdloffR, BattR, BrayG (1976) Reproductive hormonal function in the genetically obese (ob/ob) mouse. Endocrinology 98: 1359–1423.

54. LindströmP (2007) The physiology of obese-hyperglycemic mice [ob/ob mice]. TheScientificWorldJournal 7: 666–751.

55. RabagliaM, Gray-KellerM, FreyB, ShortreedM, SmithL, et al. (2005) Alpha-Ketoisocaproate-induced hypersecretion of insulin by islets from diabetes-susceptible mice. American journal of physiology Endocrinology and metabolism 289: 24.

56. BromanK, WuH, SenS, ChurchillG (2003) R/qtl: QTL mapping in experimental crosses. Bioinformatics (Oxford, England) 19: 889–979.

57. ClineMS, SmootM, CeramiE, KuchinskyA, LandysN, et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nat Protocols 2: 2366–2382.