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Translating transcription: proteomics in chronic rhinosinusitis with nasal polyps reveals significant discordance with messenger RNA expression
Author(s) -
Workman Alan D.,
Nocera Angela L.,
Mueller Sarina K.,
Otu Hasan H.,
Libermann Towia A.,
Bleier Benjamin S.
Publication year - 2019
Publication title -
international forum of allergy and rhinology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.503
H-Index - 46
eISSN - 2042-6984
pISSN - 2042-6976
DOI - 10.1002/alr.22315
Subject(s) - nasal polyps , periostin , transcriptome , messenger rna , proteomics , gene expression , medicine , immunology , gene , biology , bioinformatics , microbiology and biotechnology , genetics , extracellular matrix
Background Much of the literature examining chronic rhinosinusitis with nasal polyps (CRSwNP) immunopathology has been predicated on messenger RNA (mRNA) analysis with the assumption that transcriptional changes would reflect end‐effector protein expression. The purpose of this study was to test this hypothesis using matched transcriptomic and proteomic data sets. Methods Matched tissue proteomic and transcriptomic arrays were quantified in CRSwNP polyp tissue and control inferior turbinate tissue (n = 10/group). Mucus samples were additionally collected in 6 subjects from each group. Proteins were grouped into functional categories by bioinformatics and differential expression analyses. Log‐log regression and Pearson correlations were performed to determine the level of agreement between data sets. Results Of the 1310 proteins examined, 393 were significantly differentially expressed in CRSwNP. On regression analysis, differences in protein expression were poorly predicted by differences in mRNA expression ( R 2 = 0.020, p < 0.05). Several genes canonically thought to be overexpressed in CRSwNP, including IL‐5, IL‐13, TSLP, CCL13, and CCL26, showed substantial increases in mRNA transcription, but had minimally or unchanged protein expression. Others, including IgE, periostin, CCL18, and CST1/2, were increased at both the transcriptomic and proteomic levels. Among differentially regulated proteins, tissue and mucus protein levels showed weak correlation ( r = 0.26, p < 0.0001). Conclusion Proteomic analysis in CRSwNP has revealed novel disease‐associated proteins and pathways, yet correlates poorly with transcriptomic data. The increasing availability of proteomic arrays opens the door to new potential explanatory mechanisms in CRSwNP and suggests that mRNA based studies should be validated with protein analysis.