The Flow from Data to Information to Biological Knowledge in Gene Expression Microarray Research

<p>The image files are obtained from optical scanning of hybridized samples.</p

Data Acquisition to Summarize Steps 3–10 in Table 3

<p>In total, 21 studies (6 + 3 + 8 + 4) are included in the meta-analysis. The characteristics of the included studies are given in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0050184#pmed-0050184-t004" target="_blank">Table 4</a>.</p

Forest Plot of the Most Statistically Significant Up-Regulated and Down-Regulated Genes Identified from the Meta-Analysis

<p>Forest Plot of the Most Statistically Significant Up-Regulated and Down-Regulated Genes Identified from the Meta-Analysis</p

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

<p>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.</p

Analysis strategy for identifying coordinated behaviour between disease dysregulated pathways.

<p>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 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074821#pone.0074821-Ramasamy1" target="_blank">[6]</a> and gene expression data <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074821#pone.0074821-Benson1" target="_blank">[2]</a> (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.</p

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

<p>(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.</p

Complement system and INPAR-N genes differentially expressed in the atopic response to pollen.

<p>On GWAS data, genes were mapped to the most significant (best) SNP within a 50Kbp window of the gene. Only complement system genes that are specific markers of atopic response to pollen are shown.</p>*<p>Not available because only autosomes were analysed. Properdin (CFD) is on the sex chromosome X.</p

Geneset correlation enrichment analysis shows that INPAR-N is more enriched for genes exhibiting correlated gene expression and is more strongly associated with IL-13 protein levels, Th2 cytokines and Th2 master regulator than T cell activation or complement system.

<p>Geneset correlation enrichment analysis shows that INPAR-N is more enriched for genes exhibiting correlated gene expression and is more strongly associated with IL-13 protein levels, Th2 cytokines and Th2 master regulator than T cell activation or complement system.</p

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

<p>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.</p

Regression analysis shows that the disease network gene expression is predictive of the Th2 differentiation markers.

<p>Regression analysis shows that the disease network gene expression is predictive of the Th2 differentiation markers.</p