Dysregulation of complement system and CD4+ T cell activation pathways implicated in allergic response

Abstract

Allergy is a complex disease that is likely to involve dysregulated CD4+ T cell activation. Here we propose a novel methodology to gain insight into how coordinated behaviour emerges between disease-dysregulated pathways in response to pathophysiological stimuli. Using peripheral blood mononuclear cells of allergic rhinitis patients and controls cultured with and without pollen allergens, we integrate CD4+ T cell gene expression from microarray data and genetic markers of allergic sensitisation from GWAS data at the pathway level using enrichment analysis; implicating the complement system in both cellular and systemic response to pollen allergens. We delineate a novel disease network linking T cell activation to the complement system that is significantly enriched for genes exhibiting correlated gene expression and protein-protein interactions, suggesting a tight biological coordination that is dysregulated in the disease state in response to pollen allergen but not to diluent. This novel disease network has high predictive power for the gene and protein expression of the Th2 cytokine profile (IL-4, IL-5, IL-10, IL-13) and of the Th2 master regulator (GATA3), suggesting its involvement in the early stages of CD4+ T cell differentiation. Dissection of the complement system gene expression identifies 7 genes specifically associated with atopic response to pollen, including C1QR1, CFD, CFP, ITGB2, ITGAX and confirms the role of C3AR1 and C5AR1. Two of these genes (ITGB2 and C3AR1) are also implicated in the network linking complement system to T cell activation, which comprises 6 differentially expressed genes. C3AR1 is also significantly associated with allergic sensitisation in GWAS data.

Figure 1. Analysis strategy for identifying coordinated behaviour between disease dysregulated pathways.

Disease genes (e.g. FYN, SRC and LCK) that are targeted by anti-inflammatory drugs and associated with biomarkers of disease-relevant biological processes provide insight into the biological function resulting from the coordinated behaviour of both dysregulated pathways identified by integrating GWAS data and gene expression data (a) Co-enrichment analysis of Pareto-efficient pathways identify pathways that are involved in the systemic response to pollen sensitisation and involved in the cellular response to pollen allergen challenge; in this study, complement system was the top hit. (b) Coordination between disease dysregulated pathway (CD4+ T cell activation) and the pathway identified in the disease context (Complement system) is studied using inter-pathway interactions network analysis (INPAR-N). (C) Regression and correlation enrichment analysis is applied to test if the INPAR-N is associated with markers of the biological process involved in the disease onset, i.e. Th2 priming. (D) The genes of the INPAR-N are mapped to disease pathophysiology using drug target network analysis.

Figure 2. Pareto-efficient co-enrichment integration of gene expression and GWAS data at pathway level.

Panel (A) and (B) show the gene expression enrichment maps of the KEGG pathways and GO terms respectively. The degree of gene set overlapping is measured using the Jaccard index and depicted by line thickness. Complement system-related KEGG pathways and GO terms are less overlaped and less likely to be redundant. Panel (C) and (D) depict the KEGG and GO terms plot of objectives for the multicriteria enrichment of gene expression and GWAS data. Pathways are represented as circles with diameter proportional to the odds ratio of the gene expression enrichment and colour coded according to the co-enrichment p-value, pathways closer to the upper right corner are optimally associated at cellular and systemic level with pollen allergen response. Complement system-related pathways and GO terms are Pareto-efficient because they lie on the Pareto front within the significant bounds, suggesting that they may play a role on T cell response to allergen (pollen) and on the pathophysiology of allergic sensitisation with grass pollen.

Figure 3. Inter-pathway interactions analysis identifies disease network linking complement system to CD4+ T cell activation.

(A) INPAR network has 19 genes that link Complement system to CD4+ T cell activation. Blue nodes correspond to genes involved in T cell activation while red nodes correspond to genes involved in the complement system. (B) Drug target network analysis of the INPAR-N showing that several immunosuppressive drugs target the INPAR-N network genes, particularly the Src family of tyrosine kinases, including Src, Fyn, Lck. (C) Allergens trigger the innate immune system that in turn triggers the adaptive immune system. INPAR-N includes complement system proteins that interact with T cell membrane proteins.

Figure 4. Multivariate regression coefficients of INPAR-N on gene expression data of Th2 cytokine profile and master regulator.

The multivariate response vector (y-axis) consists of the Th2 master regulator (GATA3) and the genes involved in the Th2 cytokine profile. The multivariate predictor vector (x-axis) consists of the subset of the gene products linking complement system to CD4+ T cell activation (INPAR-N). Clustering of response and predictor variables is statistically significant (α = 0.05). There is one group of genes that contribute to increase the expression of the Th2 cytokine profile and another group that is down regulating the response. IL-6 behaves differently from all other cytokines, including GATA3. All complement system genes contribute to downregulate the response variables.