Mechanisms and biomarkers of inflammatory endotypes in chronic rhinosinusitis without nasal polyps

Abstract

Background: Chronic rhinosinusitis (CRS) without nasal polyps (CRSsNP) is a common disease that is characterized by multiple inflammatory endotypes. However, the molecular mechanisms in CRSsNP are poorly understood compared with those of polypoid CRS.

Objective: Our aim was to identify mechanisms and biomarkers associated with inflammatory endotypes underpinning CRSsNP.

Methods: Ethmoid tissues and nasal lavage fluids (NLFs) were obtained from control patients and patients with CRS. The gene expression profiles were determined by microarray analysis and quantitative RT-PCR, and expression of proteins was measured by ELISA and Luminex analysis.

Results: Microarray found that compared with their levels of expression in control tissue, the levels of expression of 126, 241, and 545 genes were more than 3-fold and significantly elevated in CRSsNP with type 1 (T1) endotype, type 2 (T2) endotype, and type 3 (T3) endotype, respectively. Selected identified genes were confirmed by RT-PCR. Gene set enrichment analysis suggested that T1 CRSsNP was associated with IFN-γ signaling and antiviral immunity controlled by T cells (T_H1 and CD8⁺), natural killer cells, and antigen-presenting cells; T2 CRSsNP was associated with STAT6 signaling and IgE-mediated activation controlled by eosinophils, mast cells, T_H2 cells, group 2 innate lymphoid cells, and antigen-presenting cells; and T3 CRSsNP was associated with IL-17 signaling, acute inflammatory response, complement-mediated inflammation, and infection controlled by neutrophils, T_H17 cells, B cells, and antigen-presenting cells. The results suggest that T1 (CXCL9 and CXCL10), T2 (eosinophilic proteins and CCL26), and T3 (CSF3) endotypic biomarkers in NLF may be able to distinguish tissue endotypes in CRSsNP.

Conclusions: Inflammatory endotypes in CRSsNP were controlled by different molecular mechanisms. NLF biomarker assays may allow for more precise and personalized medical treatments in CRS.

Keywords: Chronic rhinosinusitis without nasal polyps; biomarker; endotype; transcriptome.

Figure 1.. Endotype specific GO biological processes and KEGG pathways in CRSsNP.

Venn diagrams showed the overlaps and endotype specific GO biological processes (A) and KEGG pathways (C) in CRSsNP. Eight specific GO biological processes for each endotype are shown in B. The complete list of endotype specific GO biological processes is shown in Supplemental Tables E6–E8. Endotype specific KEGG pathways are shown in D.

Figure 2.. T1 CRSsNP specific genes in tissue and biomarkers of T1 endotype in NLF.

A Venn diagram identified that 12 genes were >3-fold significantly elevated in T1 endotype compared to control and all other CRSsNP endotypes (A). Hierarchical clustering was performed using the 12 T1 CRSsNP specific genes in the microarray data (B). Expression of mRNAs in control ET (n=42), CRSsNP ET (n=126), CRSwNP ET (n=56) and CRSwNP NP (n=63) is shown in C. CRSsNP ET was divided into two groups by the presence (T1, n=46) or absence (Non-T1, n=80) of T1 endotype based on the 90th percentile expression of IFNG in control ET (shown by red dotted line). The protein concentrations in NLF and tissue extracts are shown (E-G). Protein expression in NLF compared across the groups is shown in E. The Spearman rank correlations were assessed by matched RNA samples (D, n=126), matched NLF and RNA samples (F. n=68) and matched NLF and tissue extract samples (G. n=71) in CRSsNP. Results are shown as mean ± SEM. ** p<0.01, **** p<0.0001 by one-way ANOVA (C, E).

Figure 3.. T2 CRSsNP specific genes in tissue and biomarkers of T2 endotype in NLF.

A Venn diagram identified that 47 genes were >3-fold significantly elevated in T2 endotype compared to control and all other CRSsNP endotypes (A). The hierarchical clustering was performed using 47 T2 CRSsNP specific genes in the microarray data (B). Expression of mRNAs in control ET (n=42), CRSsNP ET (n=126), CRSwNP ET (n=56) and CRSwNP NP (n=63) was performed by qRT-PCR (C, D). CRSsNP was divided into two groups by the presence (T2, n=60) or absence (Non-T2, n=66) of T2 endotype based on the 90th percentile expression of CLC in control ET (shown by red dotted line in C). The protein concentrations in control NLF (n=42), CRSsNP NLF and CRSsNP ET extracts were determined by ELISA and Luminex (E-G). Protein expression in NLF compared across the groups is shown in E. The Spearman rank correlations were assessed by matched RNA samples (D, n=126), matched NLF and RNA samples (F. n=68) and matched NLF and tissue extract samples (G. n=71) in CRSsNP. Results are shown as mean ± SEM. * p<0.05, ** p<0.01, **** p<0.0001 by one-way ANOVA (C, E). UDL; under the detection limit.

Figure 4.. T3 CRSsNP specific genes in tissue and biomarkers of T3 endotype in NLF.

A Venn diagram identified that 84 genes were >3-fold significantly elevated in T3 endotype compared to control and all other CRSsNP endotypes (A). The hierarchical clustering was performed using 84 T3 CRSsNP specific genes in the microarray data (B). Expression of mRNAs in control ET (n=42), CRSsNP ET (n=126), CRSwNP ET (n=56) and CRSwNP NP (n=63) was performed by qRT-PCR (C, D). CRSsNP was divided into two groups by the presence (T3, n=31) or absence (Non-T3, n=95) of T3 endotype based on the 90th percentile expression of IL-17A in control ET (shown by red dotted line in C). The protein concentrations in control NLF (n=42), CRSsNP NLF and CRSsNP ET extracts were determined by Luminex (E-G). Protein expression in NLF compared across the groups is shown in E. The Spearman rank correlations were assessed by matched RNA samples (D, n=126), matched NLF and RNA samples (F. n=68) and matched NLF and tissue extract samples (G. n=71) in CRSsNP. Results are shown as mean ± SEM. * p<0.05, ** p<0.01, *** p<0.001 and **** p<0.0001 by one-way ANOVA (C, E). UDL; under the detection limit.