Exploring of the molecular mechanism of rhinitis via bioinformatics methods

Song,Yufen; Yan,Zhaohui

doi:10.3892/mmr.2017.8213

Exploring of the molecular mechanism of rhinitis via bioinformatics methods

Authors:
- Yufen Song
- Zhaohui Yan
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Affiliations: Department of Otolaryngology, The Third Central Hospital of Tianjin, Tianjin 300170, P.R. China
Published online on: December 7, 2017 https://doi.org/10.3892/mmr.2017.8213
Pages: 3014-3020
Copyright: © Song et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The aim of this study was to analyze gene expression profiles for exploring the function and regulatory network of differentially expressed genes (DEGs) in pathogenesis of rhinitis by a bioinformatics method. The gene expression profile of GSE43523 was downloaded from the Gene Expression Omnibus database. The dataset contained 7 seasonal allergic rhinitis samples and 5 non‑allergic normal samples. DEGs between rhinitis samples and normal samples were identified via the limma package of R. The webGestal database was used to identify enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the DEGs. The differentially co‑expressed pairs of the DEGs were identified via the DCGL package in R, and the differential co‑expression network was constructed based on these pairs. A protein‑protein interaction (PPI) network of the DEGs was constructed based on the Search Tool for the Retrieval of Interacting Genes database. A total of 263 DEGs were identified in rhinitis samples compared with normal samples, including 125 downregulated ones and 138 upregulated ones. The DEGs were enriched in 7 KEGG pathways. 308 differential co‑expression gene pairs were obtained. A differential co‑expression network was constructed, containing 212 nodes. In total, 148 PPI pairs of the DEGs were identified, and a PPI network was constructed based on these pairs. Bioinformatics methods could help us identify significant genes and pathways related to the pathogenesis of rhinitis. Steroid biosynthesis pathway and metabolic pathways might play important roles in the development of allergic rhinitis (AR). Genes such as CDC42 effector protein 5, solute carrier family 39 member A11 and PR/SET domain 10 might be also associated with the pathogenesis of AR, which provided references for the molecular mechanisms of AR.

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1	Min YG: The pathophysiology, diagnosis and treatment of allergic rhinitis. Allergy Asthma Immunol Res. 2:65–76. 2010. View Article : Google Scholar : PubMed/NCBI
2	Meltzer EO, Nathan R, Derebery J, Stang PE, Campbell UB, Yeh WS, Corrao M and Stanford R: Sleep, quality of life, and productivity impact of nasal symptoms in the United States: Findings from the Burden of Rhinitis in America survey. Allergy Asthma Proc. 30:pp. 244–254. 2009; View Article : Google Scholar : PubMed/NCBI
3	Makino Y, Noguchi E, Takahashi N, Matsumoto Y, Kubo S, Yamada T, Imoto Y, Ito Y, Osawa Y, Shibasaki M, et al: Apolipoprotein A-IV is a candidate target molecule for the treatment of seasonal allergic rhinitis. J Allergy Clin Immunol. 126:1163–1169.e5. 2010. View Article : Google Scholar : PubMed/NCBI
4	Nathan RA: The burden of allergic rhinitis. Allergy Asthma Proc. 28:pp. 3–9. 2007; View Article : Google Scholar : PubMed/NCBI
5	Passalacqua G and Durham SR; Global Allergy and Asthma European Network, : Allergic rhinitis and its impact on asthma update: Allergen immunotherapy. J Allergy Clin Immunol. 119:881–891. 2007. View Article : Google Scholar : PubMed/NCBI
6	Calderon MA, Alves B, Jacobson M, Hurwitz B, Sheikh A and Durham S: Allergen injection immunotherapy for seasonal allergic rhinitis. Cochrane Database Syst Rev. 24:CD0019362007.
7	Andiappan AK, Wang de Y, Anantharaman R, Parate PN, Suri BK, Low HQ, Li Y, Zhao W, Castagnoli P, Liu J and Chew FT: Genome-wide association study for atopy and allergic rhinitis in a singapore chinese population. PLoS One. 6:e197192011. View Article : Google Scholar : PubMed/NCBI
8	Dávila I, Mullol J, Ferrer M, Bartra J, del Cuvillo A, Montoro J, Jáuregui I, Sastre J and Valero A: Genetic aspects of allergic rhinitis. J Investig Allergol Clin Immunol. 19 Suppl 1:S25–S31. 2009.
9	Hanuskova E and Plevkova J: The role of histamine H4 receptors as a potential targets in allergic rhinitis and asthma. Open J Mol Int Physiol. 3:6–14. 2013. View Article : Google Scholar
10	Bunyavanich S, Melen E, Wilk JB, Granada M, Soto-Quiros ME, Avila L, Lasky-Su J, Hunninghake GM, Wickman M, Pershagen G, et al: Thymic stromal lymphopoietin (TSLP) is associated with allergic rhinitis in children with asthma. Clin Mol Allergy. 9:12011. View Article : Google Scholar : PubMed/NCBI
11	Thompson JA, Tan J and Greene CS: Cross-platform normalization of microarray and RNA-seq data for machine learning applications. Peerj. 4:e16212016. View Article : Google Scholar : PubMed/NCBI
12	Diboun I, Wernisch L, Orengo CA and Koltzenburg M: Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma. BMC Genomics. 7:2522006. View Article : Google Scholar : PubMed/NCBI
13	Zhao Z, Guo AY, van den Oord EJ, Aliev F, Jia P, Edenberg HJ, Riley BP, Dick DM, Bettinger JC, Davies AG, et al: Multi-species data integration and gene ranking enrich significant results in an alcoholism genome-wide association study. BMC Genomics. 13 Suppl 8:S162012. View Article : Google Scholar : PubMed/NCBI
14	Yang J, Yu H, Liu BH, Zhao Z, Liu L, Ma LX, Li YX and Li YY: DCGL v2.0: An R package for unveiling differential regulation from differential co-expression. PLoS One. 8:e797292013. View Article : Google Scholar : PubMed/NCBI
15	Yu H, Liu BH, Ye ZQ, Li C, Li YX and Li YY: Link-based quantitative methods to identify differentially coexpressed genes and gene pairs. BMC Bioinformatics. 12:3152011. View Article : Google Scholar : PubMed/NCBI
16	Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, Doerks T, Stark M, Muller J, Bork P, et al: The STRING database in 2011: Functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 39(Database Issue): D561–568. 2011. View Article : Google Scholar : PubMed/NCBI
17	Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P, von Mering C and Jensen LJ: STRING v9.1: Protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41(Database Issue): D808–D815. 2013.PubMed/NCBI
18	Brehmer D: Endonasal phototherapy with Rhinolight for the treatment of allergic rhinitis. Expert Rev Med Devices. 7:21–26. 2014. View Article : Google Scholar
19	Zhu XL, Ai ZH, Wang J, Xu YL and Teng YC: Weighted gene co-expression network analysis in identification of endometrial cancer prognosis markers. Asian Pac J Cancer Prev. 13:4607–4611. 2012. View Article : Google Scholar : PubMed/NCBI
20	Blood Pressure Lowering Treatment Trialists' Collaboration, ; Turnbull F, Neal B, Pfeffer M, Kostis J, Algert C, Woodward M, Chalmers J, Zanchetti A and MacMahon S: Blood pressure-dependent and independent effects of agents that inhibit the renin-angiotensin system. J Hypertens. 25:951–958. 2007. View Article : Google Scholar : PubMed/NCBI
21	Bernstein KE, Ong FS, Blackwell WL, Shah KH, Giani JF, Gonzalez-Villalobos RA, Shen XZ, Fuchs S and Touyz RM: A modern understanding of the traditional and nontraditional biological functions of angiotensin-converting enzyme. Pharmacol Rev. 65:1–46. 2012. View Article : Google Scholar : PubMed/NCBI
22	Okwan-Duodu D, Datta V, Shen XZ, Goodridge HS, Bernstein EA, Fuchs S, Liu GY and Bernstein KE: Angiotensin-converting enzyme overexpression in mouse myelomonocytic cells augments resistance to Listeria and methicillin-resistant Staphylococcus aureus. J Biol Chem. 285:39051–39060. 2010. View Article : Google Scholar : PubMed/NCBI
23	Song GG, Kim JH and Lee YH: Associations between the insertion/deletion polymorphism of the angiotensin-converting enzyme and susceptibility to aortic aneurysms: A meta-analysis. J Renin Angiotensin Aldosterone Syst. 16:211–218. 2015. View Article : Google Scholar : PubMed/NCBI
24	Lin H, Lin D and Zheng CQ: Angiotensin-converting enzyme insertion/deletion polymorphism associated with allergic rhinitis susceptibility: Evidence from 1410 subjects. J Renin Angiotensin Aldosterone Syst. 15:593–600. 2013. View Article : Google Scholar : PubMed/NCBI
25	Evans RM: The steroid and thyroid hormone receptor superfamily. Science. 240:889–895. 1988. View Article : Google Scholar : PubMed/NCBI
26	LaForce C: Use of nasal steroids in managing allergic rhinitis. J Allergy Clin Immunol. 103:S388–S394. 1999. View Article : Google Scholar : PubMed/NCBI
27	Mygind N: Topical steroid treatment for allergic rhinitis and allied conditions. Clin Otolaryngol Allied Sci. 7:343–352. 1982. View Article : Google Scholar : PubMed/NCBI
28	Benson M, Strannegård IL, Strannegård O and Wennergren G: Topical steroid treatment of allergic rhinitis decreases nasal fluid TH2 cytokines, eosinophils, eosinophil cationic protein and IgE but has no significant effect on IFN-gamma, IL-1beta, TNF-alpha, or neutrophils. J Allergy Clin Immunol. 106:307–312. 2000. View Article : Google Scholar : PubMed/NCBI
29	Proud D, Baumgarten CR, Naclerio RM and Ward PE: Kinin metabolism in human nasal secretions during experimentally induced allergic rhinitis. J Immunol. 138:428–434. 1987.PubMed/NCBI
30	Ciprandi G, De Amici M, Tosca M and Fuchs D: Tryptophan metabolism in allergic rhinitis: The effect of pollen allergen exposure. Hum Immunol. 71:911–915. 2010. View Article : Google Scholar : PubMed/NCBI
31	Wjst M and Hyppönen E: Vitamin D serum levels and allergic rhinitis. Allergy. 62:1085–1086. 2007. View Article : Google Scholar : PubMed/NCBI
32	Shi J, Zhang Y, Qi S, Liu G, Dong X, Huang N, Li W, Chen H and Zhu B: Identification of potential crucial gene network related to seasonal allergic rhinitis using microarray data. Eur Arch Otorhinolaryngol. 274:231–237. 2017. View Article : Google Scholar : PubMed/NCBI
33	Hauswald B and Yarin YM: Acupuncture in allergic rhinitis: A mini-review. Allergo J Int. 23:115–119. 2014. View Article : Google Scholar : PubMed/NCBI
34	Yu Y, Wu A, Zhang Z, Yan G, Zhang F, Zhang L, Shen X, Hu R, Zhang Y, Zhang K and Wang F: Characterization of the GufA subfamily member SLC39A11/Zip11 as a zinc transporter. J Nutr Biochem. 24:1697–1708. 2013. View Article : Google Scholar : PubMed/NCBI
35	Bao S and Knoell DL: Zinc modulates airway epithelium susceptibility to death receptor-mediated apoptosis. Am J Physiol Lung Cell Mol Physiol. 290:L433–L441. 2006. View Article : Google Scholar : PubMed/NCBI
36	Besecker B, Bao S, Bohacova B, Papp A, Sadee W and Knoell DL: The human zinc transporter SLC39A8 (Zip8) is critical in zinc-mediated cytoprotection in lung epithelia. Am J Physiol Lung Cell Mol Physiol. 294:L1127–L1136. 2008. View Article : Google Scholar : PubMed/NCBI
37	Prasad AS: Clinical, immunological, anti-inflammatory and antioxidant roles of zinc. Exp Gerontol. 43:370–377. 2008. View Article : Google Scholar : PubMed/NCBI
38	AlKhyat THAT, Alturaihy SH and Ali ZA: The relationship between tumour necrosis factor -alpha and zinc/copper ratio in iraqi patients with allergic rhinitis. Glob J Sci Front Res. 13:2013.
39	Hirsch DS, Pirone DM and Burbelo PD: A new family of Cdc42 effector proteins, CEPs, function in fibroblast and epithelial cell shape changes. J Biol Chem. 276:875–883. 2001. View Article : Google Scholar : PubMed/NCBI
40	Hall A: Rho GTPases and the actin cytoskeleton. Science. 279:509–514. 1998. View Article : Google Scholar : PubMed/NCBI
41	Wesselborg S, Bauer MK, Vogt M, Schmitz ML and Schulze-Osthoff K: Activation of transcription factor NF-kappaB and p38 mitogen-activated protein kinase is mediated by distinct and separate stress effector pathways. J Biol Chem. 272:12422–12429. 1997. View Article : Google Scholar : PubMed/NCBI
42	Puls A, Eliopoulos AG, Nobes CD, Bridges T, Young LS and Hall A: Activation of the small GTPase Cdc42 by the inflammatory cytokines TNF(alpha) and IL-1, and by the Epstein-Barr virus transforming protein LMP1. J Cell Sci. 112:2983–2992. 1999.PubMed/NCBI
43	Ito TK, Yokoyama M, Yoshida Y, Nojima A, Kassai H, Oishi K, Okada S, Kinoshita D, Kobayashi Y, Fruttiger M, et al: A crucial role for CDC42 in senescence-associated inflammation and atherosclerosis. PLoS One. 9:e1021862014. View Article : Google Scholar : PubMed/NCBI
44	Kim KC and Huang S: Histone methyltransferases in tumor suppression. Cancer Biol Ther. 2:491–499. 2003. View Article : Google Scholar : PubMed/NCBI
45	Huang S: Histone methyltransferases, diet nutrients and tumour suppressors. Nat Rev Cancer. 2:469–476. 2002. View Article : Google Scholar : PubMed/NCBI
46	Park JA, Kim TH, Lee B, Kwon E and Kim KC: Expression of PRDM10 in arthritic synovial derived tissues. Genes Genomics. 35:685–691. 2013. View Article : Google Scholar
47	Center CM: Methods of using prdm1 genetic variants to prognose, diagnose and treat inflammatory bowel disease. 2011.
48	Horsch M, Aguilar-Pimentel JA, Bönisch C, Côme C, Kolster-Fog C, Jensen KT, Lund AH, Lee I, Grossman LI, Sinkler C, et al: Cox4i2, Ifit2, and Prdm11 Mutant Mice: Effective selection of genes predisposing to an altered airway inflammatory response from a large compendium of mutant mouse lines. PLoS One. 10:e01345032015. View Article : Google Scholar : PubMed/NCBI