A GWAS approach identifies Dapp1 as a determinant of air pollution-induced airway hyperreactivity


Autoři: Hadi Maazi aff001;  Jaana A. Hartiala aff002;  Yuzo Suzuki aff001;  Amanda L. Crow aff002;  Pedram Shafiei Jahani aff001;  Jonathan Lam aff001;  Nisheel Patel aff001;  Diamanda Rigas aff001;  Yi Han aff002;  Pin Huang aff002;  Eleazar Eskin aff004;  Aldons. J. Lusis aff005;  Frank D. Gilliland aff002;  Omid Akbari aff001;  Hooman Allayee aff002
Působiště autorů: Departments of Molecular Microbiology and Immunology, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America aff001;  Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America aff002;  Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America aff003;  Department of Computer Science and Inter-Departmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, California, United States of America aff004;  Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America aff005;  Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America aff006;  Department of Microbiology, Immunology, and Molecular Genetics, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America aff007
Vyšlo v časopise: A GWAS approach identifies Dapp1 as a determinant of air pollution-induced airway hyperreactivity. PLoS Genet 15(12): e32767. doi:10.1371/journal.pgen.1008528
Kategorie: Research Article
doi: 10.1371/journal.pgen.1008528

Souhrn

Asthma is a chronic inflammatory disease of the airways with contributions from genes, environmental exposures, and their interactions. While genome-wide association studies (GWAS) in humans have identified ~200 susceptibility loci, the genetic factors that modulate risk of asthma through gene-environment (GxE) interactions remain poorly understood. Using the Hybrid Mouse Diversity Panel (HMDP), we sought to identify the genetic determinants of airway hyperreactivity (AHR) in response to diesel exhaust particles (DEP), a model traffic-related air pollutant. As measured by invasive plethysmography, AHR under control and DEP-exposed conditions varied 3-4-fold in over 100 inbred strains from the HMDP. A GWAS with linear mixed models mapped two loci significantly associated with lung resistance under control exposure to chromosomes 2 (p = 3.0x10-6) and 19 (p = 5.6x10-7). The chromosome 19 locus harbors Il33 and is syntenic to asthma association signals observed at the IL33 locus in humans. A GxE GWAS for post-DEP exposure lung resistance identified a significantly associated locus on chromosome 3 (p = 2.5x10-6). Among the genes at this locus is Dapp1, an adaptor molecule expressed in immune-related and mucosal tissues, including the lung. Dapp1-deficient mice exhibited significantly lower AHR than control mice but only after DEP exposure, thus functionally validating Dapp1 as one of the genes underlying the GxE association at this locus. In summary, our results indicate that some of the genetic determinants for asthma-related phenotypes may be shared between mice and humans, as well as the existence of GxE interactions in mice that modulate lung function in response to air pollution exposures relevant to humans.

Klíčová slova:

Air pollution – Asthma – Genetic loci – Genetic predisposition – Genome-wide association studies – Human genetics – Inbred strains – Inhalation


Zdroje

1. Network GA. Global Asthma Report (http://www.globalasthmareport.org/). 2018.

2. Romieu I, Moreno-Macias H, London SJ. Gene by environment interaction and ambient air pollution. Proc Am Thorac Soc. 2010;7(2):116–22. doi: 10.1513/pats.200909-097RM 20427582; PubMed Central PMCID: PMC3266017.

3. London SJ, Romieu I. Gene by environment interaction in asthma. Annu Rev Public Health. 2009;30:55–80. doi: 10.1146/annurev.publhealth.031308.100151 18980546.

4. McDonnell WF. Intersubject variability in human acute ozone responsiveness. Pharmacogenetics. 1991;1(2):110–3. doi: 10.1097/00008571-199111000-00010 1844868.

5. Khreis H, Kelly C, Tate J, Parslow R, Lucas K, Nieuwenhuijsen M. Exposure to traffic-related air pollution and risk of development of childhood asthma: A systematic review and meta-analysis. Environ Int. 2016. doi: 10.1016/j.envint.2016.11.012 27881237.

6. Jung CR, Young LH, Hsu HT, Lin MY, Chen YC, Hwang BF, et al. PM2.5 components and outpatient visits for asthma: A time-stratified case-crossover study in a suburban area. Environ Pollut. 2017;231(Pt 1):1085–92. doi: 10.1016/j.envpol.2017.08.102 28922715.

7. Liu H, Fan X, Wang N, Zhang Y, Yu J. Exacerbating effects of PM2.5 in OVA-sensitized and challenged mice and the expression of TRPA1 and TRPV1 proteins in lungs. J Asthma. 2017;54(8):807–17. doi: 10.1080/02770903.2016.1266495 28102732.

8. Pennington AF, Strickland MJ, Klein M, Zhai X, Bates JT, Drews-Botsch C, et al. Exposure to Mobile Source Air Pollution in Early-life and Childhood Asthma Incidence: The Kaiser Air Pollution and Pediatric Asthma Study. Epidemiology. 2018;29(1):22–30. doi: 10.1097/EDE.0000000000000754 28926373; PubMed Central PMCID: PMC5718963.

9. He M, Ichinose T, Yoshida Y, Arashidani K, Yoshida S, Takano H, et al. Urban PM2.5 exacerbates allergic inflammation in the murine lung via a TLR2/TLR4/MyD88-signaling pathway. Sci Rep. 2017;7(1):11027. doi: 10.1038/s41598-017-11471-y 28887522; PubMed Central PMCID: PMC5591243.

10. Falcon-Rodriguez CI, De Vizcaya-Ruiz A, Rosas-Perez IA, Osornio-Vargas AR, Segura-Medina P. Inhalation of concentrated PM2.5 from Mexico City acts as an adjuvant in a guinea pig model of allergic asthma. Environ Pollut. 2017;228:474–83. doi: 10.1016/j.envpol.2017.05.050 28570992.

11. Mazenq J, Dubus JC, Gaudart J, Charpin D, Nougairede A, Viudes G, et al. Air pollution and children's asthma-related emergency hospital visits in southeastern France. Eur J Pediatr. 2017;176(6):705–11. doi: 10.1007/s00431-017-2900-5 28382539.

12. Zhang Y, Salam MT, Berhane K, Eckel SP, Rappaport EB, Linn WS, et al. Genetic and epigenetic susceptibility of airway inflammation to PM2.5 in school children: new insights from quantile regression. Environ Health. 2017;16(1):88. doi: 10.1186/s12940-017-0285-6 28821285; PubMed Central PMCID: PMC5563051.

13. Weichenthal S, Bai L, Hatzopoulou M, Van Ryswyk K, Kwong JC, Jerrett M, et al. Long-term exposure to ambient ultrafine particles and respiratory disease incidence in in Toronto, Canada: a cohort study. Environ Health. 2017;16(1):64. doi: 10.1186/s12940-017-0276-7 28629362; PubMed Central PMCID: PMC5477122.

14. Ober C, Yao TC. The genetics of asthma and allergic disease: a 21st century perspective. Immunol Rev. 2011;242(1):10–30. doi: 10.1111/j.1600-065X.2011.01029.x 21682736; PubMed Central PMCID: PMC3151648.

15. Moffatt MF, Kabesch M, Liang L, Dixon AL, Strachan D, Heath S, et al. Genetic variants regulating ORMDL3 expression contribute to the risk of childhood asthma. Nature. 2007;448(7152):470–3. doi: 10.1038/nature06014 17611496.

16. Moffatt MF, Gut IG, Demenais F, Strachan DP, Bouzigon E, Heath S, et al. A large-scale, consortium-based genomewide association study of asthma. N Engl J Med. 2010;363(13):1211–21. doi: 10.1056/NEJMoa0906312 20860503.

17. Sleiman PM, Flory J, Imielinski M, Bradfield JP, Annaiah K, Willis-Owen SA, et al. Variants of DENND1B associated with asthma in children. N Engl J Med. 2010;362(1):36–44. doi: 10.1056/NEJMoa0901867 20032318.

18. Ferreira MA, Matheson MC, Duffy DL, Marks GB, Hui J, Le Souef P, et al. Identification of IL6R and chromosome 11q13.5 as risk loci for asthma. Lancet. 2011;378(9795):1006–14. doi: 10.1016/S0140-6736(11)60874-X 21907864; PubMed Central PMCID: PMC3517659.

19. Hirota T, Takahashi A, Kubo M, Tsunoda T, Tomita K, Doi S, et al. Genome-wide association study identifies three new susceptibility loci for adult asthma in the Japanese population. Nat Genet. 2011;43(9):893–6. doi: 10.1038/ng.887 21804548; PubMed Central PMCID: PMC4310726.

20. Noguchi E, Sakamoto H, Hirota T, Ochiai K, Imoto Y, Sakashita M, et al. Genome-wide association study identifies HLA-DP as a susceptibility gene for pediatric asthma in Asian populations. PLoS Genet. 2011;7(7):e1002170. doi: 10.1371/journal.pgen.1002170 21814517; PubMed Central PMCID: PMC3140987.

21. Torgerson DG, Ampleford EJ, Chiu GY, Gauderman WJ, Gignoux CR, Graves PE, et al. Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations. Nat Genet. 2011;43(9):887–92. doi: 10.1038/ng.888 21804549.

22. Forno E, Lasky-Su J, Himes B, Howrylak J, Ramsey C, Brehm J, et al. Genome-wide association study of the age of onset of childhood asthma. J Allergy Clin Immunol. 2012;130(1):83–90 e4. doi: 10.1016/j.jaci.2012.03.020 22560479; PubMed Central PMCID: PMC3387331.

23. Lasky-Su J, Himes BE, Raby BA, Klanderman BJ, Sylvia JS, Lange C, et al. HLA-DQ strikes again: genome-wide association study further confirms HLA-DQ in the diagnosis of asthma among adults. Clin Exp Allergy. 2012;42(12):1724–33. doi: 10.1111/cea.12000 23181788; PubMed Central PMCID: PMC6343489.

24. Ding L, Abebe T, Beyene J, Wilke RA, Goldberg A, Woo JG, et al. Rank-based genome-wide analysis reveals the association of ryanodine receptor-2 gene variants with childhood asthma among human populations. Human genomics. 2013;7:16. doi: 10.1186/1479-7364-7-16 23829686; PubMed Central PMCID: PMC3708719.

25. Galanter JM, Gignoux CR, Torgerson DG, Roth LA, Eng C, Oh SS, et al. Genome-wide association study and admixture mapping identify different asthma-associated loci in Latinos: the Genes-environments & Admixture in Latino Americans study. J Allergy Clin Immunol. 2014;134(2):295–305. doi: 10.1016/j.jaci.2013.08.055 24406073; PubMed Central PMCID: PMC4085159.

26. Pickrell JK, Berisa T, Liu JZ, Segurel L, Tung JY, Hinds DA. Detection and interpretation of shared genetic influences on 42 human traits. Nat Genet. 2016;48(7):709–17. doi: 10.1038/ng.3570 27182965; PubMed Central PMCID: PMC5207801.

27. White MJ, Risse-Adams O, Goddard P, Contreras MG, Adams J, Hu D, et al. Novel genetic risk factors for asthma in African American children: Precision Medicine and the SAGE II Study. Immunogenetics. 2016;68(6–7):391–400. doi: 10.1007/s00251-016-0914-1 27142222; PubMed Central PMCID: PMC4927336.

28. Almoguera B, Vazquez L, Mentch F, Connolly J, Pacheco JA, Sundaresan AS, et al. Identification of Four Novel Loci in Asthma in European American and African American Populations. Am J Respir Crit Care Med. 2017;195(4):456–63. doi: 10.1164/rccm.201604-0861OC 27611488; PubMed Central PMCID: PMC5378422.

29. Yan Q, Brehm J, Pino-Yanes M, Forno E, Lin J, Oh SS, et al. A meta-analysis of genome-wide association studies of asthma in Puerto Ricans. Eur Respir J. 2017;49(5). doi: 10.1183/13993003.01505–2016 28461288; PubMed Central PMCID: PMC5527708.

30. Ferreira MA, Vonk JM, Baurecht H, Marenholz I, Tian C, Hoffman JD, et al. Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology. Nat Genet. 2017;49(12):1752–7. doi: 10.1038/ng.3985 29083406; PubMed Central PMCID: PMC5989923.

31. Demenais F, Margaritte-Jeannin P, Barnes KC, Cookson WOC, Altmuller J, Ang W, et al. Multiancestry association study identifies new asthma risk loci that colocalize with immune-cell enhancer marks. Nat Genet. 2018;50(1):42–53. doi: 10.1038/s41588-017-0014-7 29273806; PubMed Central PMCID: PMC5901974.

32. Zhu Z, Lee PH, Chaffin MD, Chung W, Loh PR, Lu Q, et al. A genome-wide cross-trait analysis from UK Biobank highlights the shared genetic architecture of asthma and allergic diseases. Nat Genet. 2018;50(6):857–64. doi: 10.1038/s41588-018-0121-0 29785011; PubMed Central PMCID: PMC5980765.

33. Pividori M, Schoettler N, Nicolae DL, Ober C, Im HK. Shared and distinct genetic risk factors for childhood-onset and adult-onset asthma: genome-wide and transcriptome-wide studies. Lancet Respir Med. 2019. doi: 10.1016/S2213-2600(19)30055-4 31036433.

34. Shrine N, Portelli MA, John C, Soler Artigas M, Bennett N, Hall R, et al. Moderate-to-severe asthma in individuals of European ancestry: a genome-wide association study. Lancet Respir Med. 2019;7(1):20–34. doi: 10.1016/S2213-2600(18)30389-8 30552067; PubMed Central PMCID: PMC6314966.

35. Han Y, Jia Q, Jahani PS, Hurrell BP, Pan C, Huang P, et al. Large-scale genetic analysis identifies 66 novel loci for asthma. bioRxiv. 2019:749598. doi: 10.1101/749598

36. Vicente CT, Revez JA, Ferreira MAR. Lessons from ten years of genome-wide association studies of asthma. Clin Transl Immunology. 2017;6(12):e165. doi: 10.1038/cti.2017.54 29333270; PubMed Central PMCID: PMC5750453.

37. Kim KW, Ober C. Lessons Learned From GWAS of Asthma. Allergy Asthma Immunol Res. 2019;11(2):170–87. doi: 10.4168/aair.2019.11.2.170 30661310; PubMed Central PMCID: PMC6340805.

38. Khoury MJ. Editorial: Emergence of Gene-Environment Interaction Analysis in Epidemiologic Research. Am J Epidemiol. 2017;186(7):751–2. doi: 10.1093/aje/kwx226 28978194.

39. Leme AS, Berndt A, Williams LK, Tsaih SW, Szatkiewicz JP, Verdugo R, et al. A survey of airway responsiveness in 36 inbred mouse strains facilitates gene mapping studies and identification of quantitative trait loci. Mol Genet Genomics. 2010;283(4):317–26. doi: 10.1007/s00438-010-0515-x 20143096.

40. Bennett BJ, Farber CR, Orozco L, Kang HM, Ghazalpour A, Siemers N, et al. A high-resolution association mapping panel for the dissection of complex traits in mice. Genome Res. 2010;20(2):281–90. doi: 10.1101/gr.099234.109 20054062; PubMed Central PMCID: PMC2813484.

41. Farber CR, Bennett BJ, Orozco L, Zou W, Lira A, Kostem E, et al. Mouse genome-wide association and systems genetics identify Asxl2 as a regulator of bone mineral density and osteoclastogenesis. PLoS Genet. 2011;7(4):e1002038. doi: 10.1371/journal.pgen.1002038 21490954.

42. Davis RC, van Nas A, Bennett B, Orozco L, Pan C, Rau CD, et al. Genome-wide association mapping of blood cell traits in mice. Mamm Genome. 2013;24(3–4):105–18. doi: 10.1007/s00335-013-9448-0 23417284.

43. Hartiala J, Bennett BJ, Tang WH, Wang Z, Stewart AF, Roberts R, et al. Comparative genome-wide association studies in mice and humans for trimethylamine N-oxide, a proatherogenic metabolite of choline and L-carnitine. Arterioscler Thromb Vasc Biol. 2014;34(6):1307–13. Epub 2014/03/29. doi: 10.1161/ATVBAHA.114.303252 24675659; PubMed Central PMCID: PMC4035110.

44. Zhou X, Crow AL, Hartiala J, Spindler TJ, Ghazalpour A, Barsky LW, et al. The genetic landscape of hematopoietic stem cell frequency in mice. Stem Cell Reports. 2015;5(1):125–38. doi: 10.1016/j.stemcr.2015.05.008 26050929.

45. Lusis AJ, Seldin MM, Allayee H, Bennett BJ, Civelek M, Davis RC, et al. The Hybrid Mouse Diversity Panel: a resource for systems genetics analyses of metabolic and cardiovascular traits. J Lipid Res. 2016;57(6):925–42. doi: 10.1194/jlr.R066944 27099397; PubMed Central PMCID: PMC4878195.

46. Hiyari S, Green E, Pan C, Lari S, Davar M, Davis R, et al. Genome-Wide Association Study Identifies Cxcl Family Members as Partial Mediators of LPS-induced Periodontitis. J Bone Miner Res. 2018. Epub 2018/04/11. doi: 10.1002/jbmr.3440 29637625.

47. Orozco LD, Bennett BJ, Farber CR, Ghazalpour A, Pan C, Che N, et al. Unraveling inflammatory responses using systems genetics and gene-environment interactions in macrophages. Cell. 2012;151(3):658–70. doi: 10.1016/j.cell.2012.08.043 23101632.

48. Bennett BJ, Davis RC, Civelek M, Orozco L, Wu J, Qi H, et al. Genetic architecture of atherosclerosis in mice: A systems genetics analysis of common inbred strains. PLoS Genet. 2015;11(12):e1005711. doi: 10.1371/journal.pgen.1005711 26694027; PubMed Central PMCID: PMC4687930.

49. Parks BW, Sallam T, Mehrabian M, Psychogios N, Hui ST, Norheim F, et al. Genetic architecture of insulin resistance in the mouse. Cell Metab. 2015;21(2):334–47. Epub 2015/02/05. doi: 10.1016/j.cmet.2015.01.002 25651185; PubMed Central PMCID: PMC4349439.

50. Parks BW, Nam E, Org E, Kostem E, Norheim F, Hui ST, et al. Genetic control of obesity and gut microbiota composition in response to high-fat, high-sucrose diet in mice. Cell Metab. 2013;17(1):141–52. doi: 10.1016/j.cmet.2012.12.007 23312289.

51. Wang JJ, Rau C, Avetisyan R, Ren S, Romay MC, Stolin G, et al. Genetic Dissection of Cardiac Remodeling in an Isoproterenol-Induced Heart Failure Mouse Model. PLoS Genet. 2016;12(7):e1006038. Epub 2016/07/08. doi: 10.1371/journal.pgen.1006038 27385019; PubMed Central PMCID: PMC4934852.

52. Rau CD, Wang J, Avetisyan R, Romay MC, Martin L, Ren S, et al. Mapping genetic contributions to cardiac pathology induced by Beta-adrenergic stimulation in mice. Circ Cardiovasc Genet. 2015;8(1):40–9. Epub 2014/12/07. doi: 10.1161/CIRCGENETICS.113.000732 25480693; PubMed Central PMCID: PMC4334708.

53. Lavinsky J, Ge M, Crow AL, Pan C, Wang J, Salehi P, et al. The Genetic Architecture of Noise-Induced Hearing Loss: Evidence for a Gene-by-Environment Interaction. G3 (Bethesda). 2016;6(10):3219–28. Epub 2016/08/16. doi: 10.1534/g3.116.032516 27520957; PubMed Central PMCID: PMC5068943.

54. Lavinsky J, Crow AL, Pan C, Wang J, Aaron KA, Ho MK, et al. Genome-wide association study identifies nox3 as a critical gene for susceptibility to noise-induced hearing loss. PLoS Genet. 2015;11(4):e1005094. Epub 2015/04/17. doi: 10.1371/journal.pgen.1005094 25880434; PubMed Central PMCID: PMC4399881.

55. van Nas A, Pan C, Ingram-Drake LA, Ghazalpour A, Drake TA, Sobel EM, et al. The systems genetics resource: a web application to mine global data for complex disease traits. Front Genet. 2013;4:84. doi: 10.3389/fgene.2013.00084 23730305; PubMed Central PMCID: PMC3657633.

56. Awasthi S, Maity T, Oyler BL, Qi Y, Zhang X, Goodlett DR, et al. Quantitative targeted proteomic analysis of potential markers of tyrosine kinase inhibitor (TKI) sensitivity in EGFR mutated lung adenocarcinoma. J Proteomics. 2018. Epub 2018/04/17. doi: 10.1016/j.jprot.2018.04.005 29660496.

57. Zhang X, Maity T, Kashyap MK, Bansal M, Venugopalan A, Singh S, et al. Quantitative Tyrosine Phosphoproteomics of Epidermal Growth Factor Receptor (EGFR) Tyrosine Kinase Inhibitor-treated Lung Adenocarcinoma Cells Reveals Potential Novel Biomarkers of Therapeutic Response. Mol Cell Proteomics. 2017;16(5):891–910. Epub 2017/03/24. doi: 10.1074/mcp.M117.067439 28331001; PubMed Central PMCID: PMC5417828.

58. Allam A, Marshall AJ. Role of the adaptor proteins Bam32, TAPP1 and TAPP2 in lymphocyte activation. Immunol Lett. 2005;97(1):7–17. Epub 2005/01/01. doi: 10.1016/j.imlet.2004.09.019 15626471.

59. Consortium GTEx. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45(6):580–5. doi: 10.1038/ng.2653 23715323; PubMed Central PMCID: PMC4010069.

60. Gauderman WJ, Urman R, Avol E, Berhane K, McConnell R, Rappaport E, et al. Association of improved air quality with lung development in children. N Engl J Med. 2015;372(10):905–13. Epub 2015/03/05. doi: 10.1056/NEJMoa1414123 25738666; PubMed Central PMCID: PMC4430551.

61. Gauderman WJ, Avol E, Gilliland F, Vora H, Thomas D, Berhane K, et al. The effect of air pollution on lung development from 10 to 18 years of age. N Engl J Med. 2004;351(11):1057–67. doi: 10.1056/NEJMoa040610 15356303.

62. Gauderman WJ, Vora H, McConnell R, Berhane K, Gilliland F, Thomas D, et al. Effect of exposure to traffic on lung development from 10 to 18 years of age: a cohort study. Lancet. 2007;369(9561):571–7. doi: 10.1016/S0140-6736(07)60037-3 17307103.

63. De Sanctis GT, Merchant M, Beier DR, Dredge RD, Grobholz JK, Martin TR, et al. Quantitative locus analysis of airway hyperresponsiveness in A/J and C57BL/6J mice. Nat Genet. 1995;11(2):150–4. doi: 10.1038/ng1095-150 7550342.

64. Smith D, Helgason H, Sulem P, Bjornsdottir US, Lim AC, Sveinbjornsson G, et al. A rare IL33 loss-of-function mutation reduces blood eosinophil counts and protects from asthma. PLoS Genet. 2017;13(3):e1006659. Epub 2017/03/09. doi: 10.1371/journal.pgen.1006659 28273074; PubMed Central PMCID: PMC5362243.

65. Marshall AJ, Niiro H, Lerner CG, Yun TJ, Thomas S, Disteche CM, et al. A novel B lymphocyte-associated adaptor protein, Bam32, regulates antigen receptor signaling downstream of phosphatidylinositol 3-kinase. J Exp Med. 2000;191(8):1319–32. doi: 10.1084/jem.191.8.1319 10770799; PubMed Central PMCID: PMC2193139.

66. Marshall AJ, Zhang T, Al-Alwan M. Regulation of B-lymphocyte activation by the PH domain adaptor protein Bam32/DAPP1. Biochem Soc Trans. 2007;35(Pt 2):181–2. doi: 10.1042/BST0350181 17371232.

67. Al-Alwan M, Hou S, Zhang TT, Makondo K, Marshall AJ. Bam32/DAPP1 promotes B cell adhesion and formation of polarized conjugates with T cells. J Immunol. 2010;184(12):6961–9. doi: 10.4049/jimmunol.0904176 20495066.

68. Hou S, Pauls SD, Liu P, Marshall AJ. The PH domain adaptor protein Bam32/DAPP1 functions in mast cells to restrain FcvarepsilonRI-induced calcium flux and granule release. Mol Immunol. 2010;48(1–3):89–97. doi: 10.1016/j.molimm.2010.09.007 20956018.

69. Ortner D, Grabher D, Hermann M, Kremmer E, Hofer S, Heufler C. The adaptor protein Bam32 in human dendritic cells participates in the regulation of MHC class I-induced CD8+ T cell activation. J Immunol. 2011;187(8):3972–8. doi: 10.4049/jimmunol.1003072 21930970.

70. Jorgensen ED, Dozmorov I, Frank MB, Centola M, Albino AP. Global gene expression analysis of human bronchial epithelial cells treated with tobacco condensates. Cell Cycle. 2004;3(9):1154–68. 15326394.

71. Kelada SN, Carpenter DE, Aylor DL, Chines P, Rutledge H, Chesler EJ, et al. Integrative genetic analysis of allergic inflammation in the murine lung. Am J Respir Cell Mol Biol. 2014;51(3):436–45. Epub 2014/04/04. doi: 10.1165/rcmb.2013-0501OC 24693920; PubMed Central PMCID: PMC4189492.

72. Kelada SNP. Plethysmography Phenotype QTL in Mice Before and After Allergen Sensitization and Challenge. G3-Genes Genom Genet. 2016;6(9):2857–65. doi: 10.1534/g3.116.032912 WOS:000384021400018. 27449512

73. Kool M, Soullie T, van Nimwegen M, Willart MA, Muskens F, Jung S, et al. Alum adjuvant boosts adaptive immunity by inducing uric acid and activating inflammatory dendritic cells. J Exp Med. 2008;205(4):869–82. doi: 10.1084/jem.20071087 18362170; PubMed Central PMCID: PMC2292225.

74. Diaz-Sanchez D, Dotson AR, Takenaka H, Saxon A. Diesel exhaust particles induce local IgE production in vivo and alter the pattern of IgE messenger RNA isoforms. J Clin Invest. 1994;94(4):1417–25. doi: 10.1172/JCI117478 7523450; PubMed Central PMCID: PMC295270.

75. Acciani TH, Brandt EB, Khurana Hershey GK, Le Cras TD. Diesel exhaust particle exposure increases severity of allergic asthma in young mice. Clin Exp Allergy. 2013;43(12):1406–18. doi: 10.1111/cea.12200 24112543.

76. Brandt EB, Biagini Myers JM, Acciani TH, Ryan PH, Sivaprasad U, Ruff B, et al. Exposure to allergen and diesel exhaust particles potentiates secondary allergen-specific memory responses, promoting asthma susceptibility. J Allergy Clin Immunol. 2015;136(2):295–303 e7. doi: 10.1016/j.jaci.2014.11.043 25748065; PubMed Central PMCID: PMC4530081.

77. Hirota JA, Knight DA. Human airway epithelial cell innate immunity: relevance to asthma. Curr Opin Immunol. 2012;24(6):740–6. doi: 10.1016/j.coi.2012.08.012 23089231.

78. Brusselle GG, Provoost S, Bracke KR, Kuchmiy A, Lamkanfi M. Inflammasomes in respiratory disease: from bench to bedside. Chest. 2014;145(5):1121–33. doi: 10.1378/chest.13-1885 24798836.

79. Bracken SJ, Adami AJ, Szczepanek SM, Ehsan M, Natarajan P, Guernsey LA, et al. Long-term exposure to house dust mite leads to the suppression of allergic airway disease despite persistent lung inflammation. Int Arch Allergy Immunol. 2015;166(4):243–58. doi: 10.1159/000381058 25924733; PubMed Central PMCID: PMC4485530.

80. Maazi H, Patel N, Sankaranarayanan I, Suzuki Y, Rigas D, Soroosh P, et al. ICOS:ICOS-ligand interaction is required for type 2 innate lymphoid cell function, homeostasis, and induction of airway hyperreactivity. Immunity. 2015;42(3):538–51. doi: 10.1016/j.immuni.2015.02.007 25769613; PubMed Central PMCID: PMC4366271.

81. Rau CD, Parks B, Wang Y, Eskin E, Simecek P, Churchill GA, et al. High-density genotypes of inbred mouse strains: improved power and precision of association mapping. G3 (Bethesda). 2015;5(10):2021–6. doi: 10.1534/g3.115.020784 26224782; PubMed Central PMCID: PMC4592984.

82. Furlotte NA, Eskin E. Efficient multiple-trait association and estimation of genetic correlation using the matrix-variate linear mixed model. Genetics. 2015;200(1):59–68. Epub 2015/03/01. doi: 10.1534/genetics.114.171447 25724382; PubMed Central PMCID: PMC4423381.

83. R Development Core Team (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. http://www.R-project.org/.

Štítky
Genetika Reprodukční medicína

Článek vyšel v časopise

PLOS Genetics


2019 Číslo 12

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