Understanding allergic multimorbidity within the non-eosinophilic interactome
Autoři:
Daniel Aguilar aff001; Nathanael Lemonnier aff004; Gerard H. Koppelman aff005; Erik Melén aff007; Baldo Oliva aff008; Mariona Pinart aff002; Stefano Guerra aff002; Jean Bousquet aff010; Josep M. Antó aff002
Působiště autorů:
Biomedical Research Networking Center in Hepatic and Digestive Diseases (CIBEREHD), Instituto de Salud Carlos III, Barcelona, Spain
aff001; ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
aff002; 6AM Data Mining, Barcelona, Spain
aff003; Institute for Advanced Biosciences, Inserm U 1209 CNRS UMR 5309 Université Grenoble Alpes, Site Santé, Allée des Alpes, La Tronche, France
aff004; University of Groningen, University Medical Center Groningen, Beatrix Children’s Hospital, Department of Pediatric Pulmonology and Pediatric Allergology, Groningen, Netherlands
aff005; University of Groningen, University Medical Center Groningen, GRIAC Research Institute
aff006; Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
aff007; Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
aff008; Asthma and Airway Disease Research Center, University of Arizona, Tucson, Arizona, United States of America
aff009; Hopital Arnaud de Villeneuve University Hospital, Montpellier, France
aff010; Charité, Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Comprehensive Allergy Center, Department of Dermatology and Allergy, Berlin, Germany
aff011
Vyšlo v časopise:
PLoS ONE 14(11)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0224448
Souhrn
Background
The mechanisms explaining multimorbidity between asthma, dermatitis and rhinitis (allergic multimorbidity) are not well known. We investigated these mechanisms and their specificity in distinct cell types by means of an interactome-based analysis of expression data.
Methods
Genes associated to the diseases were identified using data mining approaches, and their multimorbidity mechanisms in distinct cell types were characterized by means of an in silico analysis of the topology of the human interactome.
Results
We characterized specific pathomechanisms for multimorbidities between asthma, dermatitis and rhinitis for distinct emergent non-eosinophilic cell types. We observed differential roles for cytokine signaling, TLR-mediated signaling and metabolic pathways for multimorbidities across distinct cell types. Furthermore, we also identified individual genes potentially associated to multimorbidity mechanisms.
Conclusions
Our results support the existence of differentiated multimorbidity mechanisms between asthma, dermatitis and rhinitis at cell type level, as well as mechanisms common to distinct cell types. These results will help understanding the biology underlying allergic multimorbidity, assisting in the design of new clinical studies.
Klíčová slova:
Blood – Immune receptor signaling – Interaction networks – Interleukins – Monocytes – Signal processing – T cells – Toll-like receptors
Zdroje
1. Barabási AL, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011 Jan;12(1): 56–68. doi: 10.1038/nrg2918 21164525
2. Ghiassian SD, Menche J, Chasman DI, Giulianini F, Wang R, Ricchiuto P et al. Endophenotype Network Models: Common Core of Complex Diseases. Sci Rep. 2016 Jun 9;6: 27414. doi: 10.1038/srep27414 27278246
3. Gandhi TK, Zhong J, Mathivanan S, Karthick L, Chandrika KN, Mohan SS et al. Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets. Nat Genet. 2006 Mar;38(3): 285–93. doi: 10.1038/ng1747 16501559
4. Sharma A, Menche J, Huang CC, Ort T, Zhou X, Kitsak M et al. A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma. Hum Mol Genet. 2015 Jun 1;24(11): 3005–20. doi: 10.1093/hmg/ddv001 25586491
5. Santolini M, Barabási AL. Predicting perturbation patterns from the topology of biological networks. Proc Natl Acad Sci U S A. 2018 Jul 3;115(27): E6375–E6383. doi: 10.1073/pnas.1720589115 29925605
6. Kitsak M, Sharma A, Menche J, Guney E, Ghiassian SD, Loscalzo J, Barabási AL. Tissue Specificity of Human Disease Module. Sci Rep. 2016 Oct 17;6: 35241. doi: 10.1038/srep35241 27748412
7. Winter EE, Goodstadt L, Ponting CP. Elevated rates of protein secretion, evolution, and disease among tissue-specific genes. Genome Res. 2004 Jan;14(1): 54–61. doi: 10.1101/gr.1924004 14707169
8. Lage K, Hansen NT, Karlberg EO, Eklund AC, Roque FS, Donahoe PK et al. A large-scale analysis of tissue-specific pathology and gene expression of human disease genes and complexes. Proc Natl Acad Sci U S A. 2008 Dec 30;105(52): 20870–5. doi: 10.1073/pnas.0810772105 19104045
9. Barshir R, Shwartz O, Smoly IY, Yeger-Lotem E. Comparative analysis of human tissue interactomes reveals factors leading to tissue-specific manifestation of hereditary diseases. PLoS Comput Biol. 2014 Jun 12;10(6): e1003632. doi: 10.1371/journal.pcbi.1003632 24921629
10. Shen-Orr SS, Tibshirani R, Khatri P, Bodian DL, Staedtler F, Perry NM et al. Cell type-specific gene expression differences in complex tissues. Nat Methods. 2010 Apr;7(4): 287–9. doi: 10.1038/nmeth.1439 20208531
11. Pandey AK, Lu L, Wang X, Homayouni R, Williams RW. Functionally enigmatic genes: a case study of the brain ignorome. PLoS One. 2014 Feb 11;9(2): e88889. doi: 10.1371/journal.pone.0088889 24523945
12. Greene CS, Krishnan A, Wong AK, Ricciotti E, Zelaya RA, Himmelstein DS et al. Understanding multicellular function and disease with human tissue-specific networks. Nat Genet. 2015 Jun;47(6): 569–76. doi: 10.1038/ng.3259 25915600
13. Goh KI, Cusick ME, Valle D, Childs B, Vidal M, Barabási AL. The human disease network. Proc Natl Acad Sci U S A. 2007 May 22;104(21): 8685–90. doi: 10.1073/pnas.0701361104 17502601
14. Schuster-Böckler B, Bateman A. Protein interactions in human genetic diseases. Genome Biol. 2008 Jan 16;9(1): R9. doi: 10.1186/gb-2008-9-1-r9 18199329
15. Zhong Q, Simonis N, Li QR, Charloteaux B, Heuze F, Klitgord N et al. Edgetic perturbation models of human inherited disorders. Mol Syst Biol. 2009;5: 321. doi: 10.1038/msb.2009.80 19888216
16. Menche J, Sharma A, Kitsak M, Ghiassian SD, Vidal M, Loscalzo J et al. Disease networks. Uncovering disease-disease relationships through the incomplete interactome. Science. 2015 Feb 20;347(6224): 1257601. doi: 10.1126/science.1257601 25700523
17. Lee WI, Yao TC, Yeh KW, Chen LC, Ou LS, Huang JL; PATCH Study Group. Stronger Toll-like receptor 1/2, 4, and 7/8 but less 9 responses in peripheral blood mononuclear cells in non-infectious exacerbated asthmatic children. Immunobiology. 2013 Feb;218(2): 192–200. doi: 10.1016/j.imbio.2012.04.002 22727330
18. Park J, Lee DS, Christakis NA, Barabási AL. The impact of cellular networks on disease comorbidity. Mol Syst Biol. 2009;5: 262. doi: 10.1038/msb.2009.16 19357641
19. Gomez-Cabrero D, Menche J, Vargas C, Cano I, Maier D, Barabási AL et al. From comorbidities of chronic obstructive pulmonary disease to identification of shared molecular mechanisms by data integration. BMC Bioinformatics. 2016 Nov 22;17(Suppl 15): 441. doi: 10.1186/s12859-016-1291-3 28185567
20. Rubio-Perez C, Guney E, Aguilar D, Piñero J, Garcia-Garcia J, Iadarola B et al. Genetic and functional characterization of disease associations explains comorbidity. Sci Rep. 2017 Jul 24;7(1): 6207. doi: 10.1038/s41598-017-04939-4 28740175
21. Aguilar D, Pinart M, Koppelman GH, Saeys Y, Nawijn MC, Postma DS et al. Computational analysis of multimorbidity between asthma, eczema and rhinitis. PLoS One. 2017 Jun 9;12(6): e0179125. doi: 10.1371/journal.pone.0179125 28598986
22. 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 Dec;49(12): 1752–1757. doi: 10.1038/ng.3985 29083406
23. Demenais F, Margaritte-Jeannin P, Barnes KC, Cookson WOC, Altmüller J, Ang W et al. Multiancestry association study identifies new asthma risk loci that colocalize with immune-cell enhancer marks. Nat Genet. 2018 Jan;50(1): 42–53. doi: 10.1038/s41588-017-0014-7 29273806
24. Bousquet J, Anto J, Auffray C, Akdis M, Cambon-Thomsen A, Keil T, et al. MeDALL (Mechanisms of the Development of ALLergy): an integrated approach from phenotypes to systems medicine. Allergy 2011; 66: 596–604. doi: 10.1111/j.1398-9995.2010.02534.x 21261657
25. Pinart M, Benet M, Annesi-Maesano I, von Berg A, Berdel D, Carlsen KC et al. Comorbidity of eczema, rhinitis, and asthma in IgE-sensitised and non-IgE-sensitised children in MeDALL: a population-based cohort study. Lancet Respir Med. 2014; 2: 131–40. doi: 10.1016/S2213-2600(13)70277-7 24503268
26. Garcia-Aymerich J, Benet M, Saeys Y, Pinart M, Basagaña X, Smit HA et al. Phenotyping asthma, rhinitis, and eczema in MeDALL population-based birth cohorts: an allergic comorbidity cluster. Allergy 2015; 70: 973–84. doi: 10.1111/all.12640 25932997
27. Celestin J, Frieri M. Eosinophilic disorders in various diseases. Curr Allergy Asthma Rep. 2012 Feb;12(1): 18–24. doi: 10.1007/s11882-011-0240-5 22160831
28. Furuta GT, Atkins FD, Lee NA, Lee JJ. Changing roles of eosinophils in health and disease. Ann Allergy Asthma Immunol. 2014 Jul;113(1): 3–8. doi: 10.1016/j.anai.2014.04.002 24795292
29. Werfel T, Allam JP, Biedermann T, Eyerich K, Gilles S, Guttman-Yassky E et al. Cellular and molecular immunologic mechanisms in patients with atopic dermatitis. J Allergy Clin Immunol. 2016 Aug;138(2): 336–49. doi: 10.1016/j.jaci.2016.06.010 27497276
30. Lambrecht BN, Hammad H. The immunology of asthma. Nat Immunol. 2015 Jan;16(1): 45–56 doi: 10.1038/ni.3049 25521684
31. Olze H, Zuberbier T. Comorbidities between nose and skin allergy. Curr Opin Allergy Clin Immunol. 2011 Oct;11(5): 457–63. doi: 10.1097/ACI.0b013e32834a9764 21822129
32. Kita H. Eosinophils: multifunctional and distinctive properties. Int Arch Allergy Immunol. 2013;161 Suppl 2: 3–9.
33. Incorvaia C, Masieri S, Cavaliere C, Makri E, Sposato B, Frati F. Asthma associated to rhinitis. J Biol Regul Homeost Agents. 2018 Jan-Feb;32(1 Suppl. 1): 67–71. 29552876
34. Poddighe D, Brambilla I, Licari A, Marseglia GL. Pediatric rhinosinusitis and asthma. Respir Med. 2018 Aug;141: 94–99. doi: 10.1016/j.rmed.2018.06.016 30053979
35. Boulay ME, Boulet LP. The relationships between atopy, rhinitis and asthma: pathophysiological considerations. Curr Opin Allergy Clin Immunol. 2003 Feb;3(1): 51–5. doi: 10.1097/01.all.0000053268.39029.19 12582315
36. Yu S, Kim HY, Chang YJ, DeKruyff RH, Umetsu DT. Innate lymphoid cells and asthma. J Allergy Clin Immunol. 2014 Apr;133(4): 943–50. doi: 10.1016/j.jaci.2014.02.015 24679467
37. Gieseck RL 3rd, Wilson MS, Wynn TA. Type 2 immunity in tissue repair and fibrosis. Nat Rev Immunol. 2018 Jan;18(1): 62–76. doi: 10.1038/nri.2017.90 28853443
38. Davis AP, Grondin CJ, Johnson RJ, Sciaky D, McMorran R, Wiegers J et al. The Comparative Toxicogenomics Database: update 2019. Nucleic Acids Res. 2019 Jan 8;47(D1): D948–D954 doi: 10.1093/nar/gky868 30247620
39. Amberger JS, Hamosh A. Searching Online Mendelian Inheritance in Man (OMIM): A Knowledgebase of Human Genes and Genetic Phenotypes. Curr Protoc Bioinformatics. 2017 Jun 27;58: 1.2.1–1.2.12.
40. Piñero J, Bravo À, Queralt-Rosinach N, Gutiérrez-Sacristán A, Deu-Pons J, Centeno E et al. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res. 2017 Jan 4;45(D1): D833–D839. doi: 10.1093/nar/gkw943 27924018
41. The UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 2017 Jan 4;45(D1): D158–D169. doi: 10.1093/nar/gkw1099 27899622
42. Ramos EM, Hoffman D, Junkins HA, Maglott D, Phan L, Sherry ST et al. Phenotype-Genotype Integrator (PheGenI): synthesizing genome-wide association study (GWAS) data with existing genomic resources. Eur J Hum Genet. 2014 Jan;22(1): 144–7. doi: 10.1038/ejhg.2013.96 23695286
43. Lehne B, Lewis CM, Schlitt T. From SNPs to genes: disease association at the gene level. PLoS One. 2011;6(6): e20133. doi: 10.1371/journal.pone.0020133 21738570
44. Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P et al. The Reactome Pathway Knowledgebase. Nucleic Acids Res. 2018 Jan 4;46(D1): D649–D655. doi: 10.1093/nar/gkx1132 29145629
45. Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M et al. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 2017 Jan 4;45(D1): D362–D368. doi: 10.1093/nar/gkw937 27924014
46. Lukk M, Kapushesky M, Nikkilä J, Parkinson H, Goncalves A, Huber W et al. A global map of human gene expression. Nat Biotechnol. 2010 Apr;28(4): 322–4. doi: 10.1038/nbt0410-322 20379172
47. Bezginov A, Clark GW, Charlebois RL, Dar VU, Tillier ER. Coevolution reveals a network of human proteins originating with multicellularity. Mol Biol Evol. 2013 Feb;30(2): 332–46. doi: 10.1093/molbev/mss218 22977115
48. Faisal A, Peltonen J, Georgii E, Rung J, Kaski S. Toward computational cumulative biology by combining models of biological datasets. PLoS One. 2014 Nov 26;9(11): e113053. doi: 10.1371/journal.pone.0113053 25427176
49. Isik Z, Baldow C, Cannistraci CV, Schroeder M. Drug target prioritization by perturbed gene expression and network information. Sci Rep. 2015 Nov 30;5: 17417. doi: 10.1038/srep17417 26615774
50. Engreitz J, Daigle B Jr, Marshall J, Altman R (2010). “Independent component analysis: mining microarray data for fundamental human gene expression modules.” J Biomed Inform, 43(6): 932–944. doi: 10.1016/j.jbi.2010.07.001 20619355
51. Grapov D, Wanichthanarak K, Fiehn O. MetaMapR: pathway independent metabolomic network analysis incorporating unknowns. Bioinformatics. 2015 Aug 15;31(16): 2757–60. doi: 10.1093/bioinformatics/btv194 25847005
52. Nelms BD, Waldron L, Barrera LA, Weflen AW, Goettel JA, Guo G et al. CellMapper: rapid and accurate inference of gene expression in difficult-to-isolate cell types. Genome Biol. 2016 Sep 29;17(1): 201. doi: 10.1186/s13059-016-1062-5 27687735
53. Tomlins SA, Rhodes DR, Perner S, Dhanasekaran SM, Mehra R, Sun XW et al. Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science. 2005 Oct 28;310(5748): 644–8. doi: 10.1126/science.1117679 16254181
54. Rhodes DR, Kalyana-Sundaram S, Mahavisno V, Varambally R, Yu J, Briggs BB et al. Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles. Neoplasia. 2007 Feb;9(2): 166–80. doi: 10.1593/neo.07112 17356713
55. Kryuchkova-Mostacci N, Robinson-Rechavi M. A benchmark of gene expression tissue-specificity metrics. Brief Bioinform. 2017 Mar 1;18(2): 205–214. doi: 10.1093/bib/bbw008 26891983
56. Sonawane AR, Platig J, Fagny M, Chen CY, Paulson JN, Lopes-Ramos CM et al. Understanding Tissue-Specific Gene Regulation. Cell Rep. 2017 Oct 24;21(4): 1077–1088. doi: 10.1016/j.celrep.2017.10.001 29069589
57. Khatri P, Sirota M, Butte AJ. Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol. 2012;8(2): e1002375. doi: 10.1371/journal.pcbi.1002375 22383865
58. Stoney R, Robertson DL, Nenadic G, Schwartz JM. Mapping biological process relationships and disease perturbations within a pathway network. NPJ Syst Biol Appl. 2018 Jun 11;4: 22. doi: 10.1038/s41540-018-0055-2 29900005
59. Dice Lee R. "Measures of the Amount of Ecologic Association Between Species". Ecology. 1945; 26 (3): 297–302.
60. Sørensen T. "A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons". Kongelige Danske Videnskabernes Selskab. 1948; 5 (4): 1–34.
61. Fuxman Bass JI, Diallo A, Nelson J, Soto JM, Myers CL, Walhout AJ. Using networks to measure similarity between genes: association index selection. Nat Methods. 2013 Dec;10(12): 1169–76. doi: 10.1038/nmeth.2728 24296474
62. Nishimura D. Biocarta. Biotech Software & Internet Report. 2001 Vol. 2, No. 3.
63. Benjamini Yoav; Hochberg Yosef "Controlling the false discovery rate: a practical and powerful approach to multiple testing". Journal of the Royal Statistical Society, Series B. 1995; 57 (1): 289–300.
64. Riba M, Garcia Manteiga JM, Bošnjak B, Cittaro D, Mikolka P, Le C et al. Revealing the acute asthma ignorome: characterization and validation of uninvestigated gene networks. Sci Rep. 2016 Apr 21;6: 24647. doi: 10.1038/srep24647 27097888
65. Glaab E, Baudot A, Krasnogor N, Schneider R, Valencia A. EnrichNet: network-based gene set enrichment analysis. Bioinformatics. 2012 Sep 15;28(18): i451–i457. doi: 10.1093/bioinformatics/bts389 22962466
66. Ko Y, Cho M, Lee JS, Kim J. Identification of disease comorbidity through hidden molecular mechanisms. Sci Rep. 2016 Dec 19;6: 39433. doi: 10.1038/srep39433 27991583
67. Guney E, Oliva B. Exploiting protein-protein interaction networks for genome-wide disease-gene prioritization. PLoS One. 2012;7(9): e43557. doi: 10.1371/journal.pone.0043557 23028459
68. Guney E, Oliva B. Analysis of the robustness of network-based disease-gene prioritization methods reveals redundancy in the human interactome and functional diversity of disease-genes. PLoS One. 2014 Apr 14;9(4): e94686. doi: 10.1371/journal.pone.0094686 24733074
69. Lee DS, Park J, Kay KA, Christakis NA, Oltvai ZN, Barabási AL. The implications of human metabolic network topology for disease comorbidity. Proc Natl Acad Sci U S A. 2008 Jul 22;105(29): 9880–5. doi: 10.1073/pnas.0802208105 18599447
70. McCormack T, Frings O, Alexeyenko A, Sonnhammer EL. Statistical assessment of crosstalk enrichment between gene groups in biological networks. PLoS One. 2013;8(1): e54945. doi: 10.1371/journal.pone.0054945 23372799
71. Fajt ML, Wenzel SE. Asthma phenotypes and the use of biologic medications in asthma and allergic disease: the next steps toward personalized care. J Allergy Clin Immunol. 2015 Feb;135(2): 299–310. doi: 10.1016/j.jaci.2014.12.1871 25662302
72. Thomson NC. Novel approaches to the management of noneosinophilic asthma. Ther Adv Respir Dis. 2016 Jun;10(3): 211–34. doi: 10.1177/1753465816632638 26929306
73. Goh KI, Choi IG. Exploring the human diseasome: the human disease network. Brief Funct Genomics. 2012 Nov;11(6): 533–42. doi: 10.1093/bfgp/els032 23063808
74. Prussin C, Metcalfe DD. 5. IgE, mast cells, basophils, and eosinophils. J Allergy Clin Immunol. 2006 Feb;117(2 Suppl Mini-Primer): S450–6. doi: 10.1016/j.jaci.2005.11.016 16455345
75. Pan K-H, Lih C-J, Cohen SN (2005) Effects of threshold choice on biological conclusions reached during analysis of gene expression by DNA microarrays. Proceedings of the National Academy of Sciences of the United States of America 102: 8961–8965. doi: 10.1073/pnas.0502674102 15951424
76. Aguirre-Plans J, Piñero J, Menche J, Sanz F, Furlong LI, Schmidt HHHW, Oliva B, Guney E. Proximal Pathway Enrichment Analysis for Targeting Comorbid Diseases via Network Endopharmacology. Pharmaceuticals (Basel). 2018 Jun 22;11(3).
77. Ogris C, Guala D, Helleday T, Sonnhammer EL. A novel method for crosstalk analysis of biological networks: improving accuracy of pathway annotation. Nucleic Acids Res. 2017 Jan 25;45(2): e8. doi: 10.1093/nar/gkw849 27664219
78. Spergel JM. From atopic dermatitis to asthma: the atopic march. Ann Allergy Asthma Immunol. 2010 Aug;105(2): 99–106. doi: 10.1016/j.anai.2009.10.002 20674819
79. Kraft P, Zeggini E, Ioannidis JP. Replication in genome-wide association studies. Stat Sci. 2009 Nov 1;24(4): 561–573. doi: 10.1214/09-STS290 20454541
80. Wang K, Bucan M, Grant SF, Schellenberg G, Hakonarson H. Strategies for genetic studies of complex diseases. Cell. 2010 Aug 6; 142(3): 351–3. doi: 10.1016/j.cell.2010.07.025 20691891
81. Schork AJ, Thompson WK, Pham P, Torkamani A, Roddey JC, Sullivan PF et al. All SNPs are not created equal: genome-wide association studies reveal a consistent pattern of enrichment among functionally annotated SNPs. PLoS Genet. 2013 Apr; 9(4): e1003449. doi: 10.1371/journal.pgen.1003449 23637621
82. Das J, Yu H. HINT: High-quality protein interactomes and their applications in understanding human disease. BMC Syst Biol. 2012 Jul 30;6: 92. doi: 10.1186/1752-0509-6-92 22846459
83. Gillis J, Pavlidis P. "Guilt by association" is the exception rather than the rule in gene networks. PLoS Comput Biol. 2012;8(3): e1002444. doi: 10.1371/journal.pcbi.1002444 22479173
84. Gillis J, Ballouz S, Pavlidis P. Bias tradeoffs in the creation and analysis of protein-protein interaction networks. J Proteomics. 2014 Apr 4;100:44–54. doi: 10.1016/j.jprot.2014.01.020 24480284
85. Vidal M. How much of the human protein interactome remains to be mapped? Sci Signal. 2016 May 10;9(427): eg7. doi: 10.1126/scisignal.aaf6030 27165778
86. Cornish AJ, David A, Sternberg MJE. PhenoRank: reducing study bias in gene prioritization through simulation. Bioinformatics. 2018 Jun 15;34(12): 2087–2095. doi: 10.1093/bioinformatics/bty028 29360927
87. Schaefer MH, Serrano L, Andrade-Navarro MA. Correcting for the study bias associated with protein-protein interaction measurements reveals differences between protein degree distributions from different cancer types. Front Genet. 2015 Aug 4;6:260. doi: 10.3389/fgene.2015.00260 26300911
88. Dudley AM, Janse DM, Tanay A, Shamir R, Church GM. A global view of pleiotropy and phenotypically derived gene function in yeast. Mol Syst Biol. 2005;1:2005.0001. doi: 10.1038/msb4100004 16729036
89. de Waal Malefyt R, Haanen J, Spits H, Roncarolo MG, te Velde A, Figdor C et al. Interleukin 10 (IL-10) and viral IL-10 strongly reduce antigen-specific human T cell proliferation by diminishing the antigen-presenting capacity of monocytes via downregulation of class II major histocompatibility complex expression. J Exp Med. 1991 Oct 1;174(4): 915–24. doi: 10.1084/jem.174.4.915 1655948
90. Krause K, Metz M, Makris M, Zuberbier T, Maurer M. The role of interleukin-1 in allergy-related disorders. Curr Opin Allergy Clin Immunol. 2012 Oct;12(5): 477–84. doi: 10.1097/ACI.0b013e3283574d0c 22885885
91. Cavalli G, Dinarello CA. Treating rheumatological diseases and co-morbidities with interleukin-1 blocking therapies. Rheumatology (Oxford). 2015 Dec;54(12): 2134–44.
92. Ruscitti P, Cipriani P, Liakouli V, Carubbi F, Berardicurti O, Di Benedetto P, Ciccia F et al. The Emerging Role of IL-1 Inhibition in Patients Affected by Rheumatoid Arthritis and Diabetes. Rev Recent Clin Trials. 2018;13(3):210–214. doi: 10.2174/1574887113666180314102651 29542422
93. Nombela I, Ortega-Villaizan MDM. Nucleated red blood cells: Immune cell mediators of the antiviral response. PLoS Pathog. 2018 Apr 26;14(4):e1006910. doi: 10.1371/journal.ppat.1006910 29698529
94. Takeda K, Akira S. Toll-like receptors. Curr Protoc Immunol. 2015 Apr 1;109:14.12.1–10
95. Aryan Z, Rezaei N. Toll-like receptors as targets for allergen immunotherapy. Curr Opin Allergy Clin Immunol. 2015 Dec;15(6): 568–74. doi: 10.1097/ACI.0000000000000212 26418475
96. Radman M, Golshiri A, Shamsizadeh A, Zainodini N, Bagheri V, Arababadi MK et al Toll-like receptor 4 plays significant roles during allergic rhinitis. Allergol Immunopathol (Madr). 2015 Jul-Aug;43(4): 416–20.
97. Renkonen J, Toppila-Salmi S, Joenväärä S, Mattila P, Parviainen V, Hagström J et al. Expression of Toll-like receptors in nasal epithelium in allergic rhinitis. APMIS. 2015 Aug;123(8): 716–25. doi: 10.1111/apm.12408 26061394
98. Compalati E, Ridolo E, Passalacqua G, Braido F, Villa E, Canonica GW. The link between allergic rhinitis and asthma: the united airways disease. Expert Rev Clin Immunol. 2010 May;6(3): 413–23. doi: 10.1586/eci.10.15 20441427
99. Yii ACA, Tay TR, Choo XN, Koh MSY, Tee AKH, Wang DY. Precision medicine in united airways disease: A "treatable traits" approach. Allergy. 2018 Oct;73(10): 1964–1978. doi: 10.1111/all.13496 29869791
100. Oka A, Hirano T, Yamaji Y, Ito K, Oishi K, Edakuni N et al. Determinants of Incomplete Asthma Control in Patients with Allergic Rhinitis and Asthma. J Allergy Clin Immunol Pract. 2017 Jan–Feb;5(1): 160–164. doi: 10.1016/j.jaip.2016.08.002 27707660
101. Wise SK, Lin SY, Toskala E, Orlandi RR, Akdis CA, Alt JA et al. International Consensus Statement on Allergy and Rhinology: Allergic Rhinitis. Int Forum Allergy Rhinol. 2018 Feb;8(2): 108–352. doi: 10.1002/alr.22073 29438602
102. Nilsson D, Henmyr V, Hallden C, Sall T, Kull I, Wickman M et al. Replication of genomewide associations with allergic sensitization and allergic rhinitis. Allergy 2014;69:1506–1514. doi: 10.1111/all.12495 25066275
103. Lun SW, Wong CK, Ko FW, Hui DS, Lam CW. Expression and functional analysis of toll-like receptors of peripheral blood cells in asthmatic patients: implication for immunopathological mechanism in asthma. J Clin Immunol. 2009 May;29(3): 330–42. doi: 10.1007/s10875-008-9269-1 19067129
104. Panzer R, Blobel C, Fölster-Holst R, Proksch E. TLR2 and TLR4 expression in atopic dermatitis, contact dermatitis and psoriasis. Exp Dermatol. 2014 May;23(5): 364–6. doi: 10.1111/exd.12383 24661005
105. Atkins D, Furuta GT, Liacouras CA, Spergel JM. Eosinophilic esophagitis phenotypes: Ready for prime time? Pediatr Allergy Immunol. 2017 Jun;28(4): 312–319. doi: 10.1111/pai.12715 28339136
106. Nhu QM, Aceves SS. Tissue Remodeling in Chronic Eosinophilic Esophageal Inflammation: Parallels in Asthma and Therapeutic Perspectives. Front Med (Lausanne). 2017 Aug 7;4:128.
107. Davis BP, Rothenberg ME. Mechanisms of Disease of Eosinophilic Esophagitis. Annu Rev Pathol. 2016 May 23;11:365–93. doi: 10.1146/annurev-pathol-012615-044241 26925500
108. Reeves SR, Kaber G, Sheih A, Cheng G, Aronica MA, Merrilees MJ et al. Subepithelial Accumulation of Versican in a Cockroach Antigen-Induced Murine Model of Allergic Asthma. J Histochem Cytochem. 2016 Jun;64(6): 364–80. doi: 10.1369/0022155416642989 27126823
109. Pasanen A, Karjalainen MK, Bont L, Piippo-Savolainen E, Ruotsalainen M, Goksör E et al. Genome-Wide Association Study of Polymorphisms Predisposing to Bronchiolitis. Sci Rep. 2017 Jan 31;7:41653. doi: 10.1038/srep41653 28139761
110. Arango Duque G, Descoteaux A. Macrophage cytokines: involvement in immunity and infectious diseases. Front Immunol. 2014 Oct 7;5:491. doi: 10.3389/fimmu.2014.00491 25339958
111. Demopoulos CA, Pinckard RN, Hanahan DJ. Platelet-activating factor. Evidence for 1-O-alkyl-2-acetyl-sn-glyceryl-3-phosphorylcholine as the active component (a new class of lipid chemical mediators). J Biol Chem. 1979 Oct 10;254(19):9355–8. 489536
112. Kald B, Smedh K, Olaison G, Sjödahl R, Tagesson C. Platelet-activating factor acetylhydrolase activity in intestinal mucosa and plasma of patients with Crohn’s disease. Digestion. 1996 Nov-Dec;57(6):472–7. doi: 10.1159/000201376 8913710
113. Travers J, Pei Y, Morin SM, Hood AF. Antiinflammatory activity of the platelet-activating factor receptor antagonist A-85783. Arch Dermatol Res. 1998 Oct;290(10):569–73. doi: 10.1007/s004030050353 9836508
114. Gill P, Jindal NL, Jagdis A, Vadas P. Platelets in the immune response: Revisiting platelet-activating factor in anaphylaxis. J Allergy Clin Immunol. 2015 Jun;135(6):1424–32. doi: 10.1016/j.jaci.2015.04.019 26051949
115. Zissler UM, Chaker AM, Effner R, Ulrich M, Guerth F, Piontek G et al. Interleukin-4 and interferon-γ orchestrate an epithelial polarization in the airways. Mucosal Immunol. 2016 Jul;9(4): 917–26. doi: 10.1038/mi.2015.110 26577568
116. Kingo K, Mössner R, Rätsep R, Raud K, Krüger U, Silm H et al. Association analysis of IL20RA and IL20RB genes in psoriasis. Genes Immun. 2008 Jul;9(5): 445–51. doi: 10.1038/gene.2008.36 18480827
117. Zivkovic AR, Schmidt K, Sigl A, Decker SO, Brenner T, Hofer S. Reduced serum butyrylcholinesterase activity indicates severe systemic inflammation in critically ill patients. Mediators Inflamm. 2015;2015:274607. doi: 10.1155/2015/274607 25762852
118. Delacour H, Dedome E, Courcelle S, Hary B, Ceppa F. Butyrylcholinesterase deficiency. Ann Biol Clin (Paris). 2016 Jun 1;74(3):279–85.
119. Kim TH, Lee JY, Lee HM, Lee SH, Cho WS, Ju YH et al. Remodelling of nasal mucosa in mild and severe persistent allergic rhinitis with special reference to the distribution of collagen, proteoglycans, and lymphaticvessels. Clin Exp Allergy. 2010 Dec;40(12):1742–54. doi: 10.1111/j.1365-2222.2010.03612.x 20860724
120. Stephenson EL, Mishra MK, Moussienko D, Laflamme N, Rivest S, Ling CC et al. Chondroitin sulfate proteoglycans as novel drivers of leucocyte infiltration in multiple sclerosis. Brain. 2018 Apr 1;141(4):1094–1110. doi: 10.1093/brain/awy033 29506186
Článek vyšel v časopise
PLOS One
2019 Číslo 11
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