Comparative analysis of inflammatory gene expression levels in metabolic syndrome & coronary artery disease

Background & objectives: Metabolic syndrome (MetS) increases the likelihood of developing coronary artery disease (CAD), and inflammation is involved in the pathogenesis of both these conditions. The present work was conducted to examine the relative expression of 18 key inflammatory genes associated with MetS and incident CAD in a representative group of patients. Methods: A total of 178 male patients, including 57 with CAD and 121 without CAD, were enrolled in the study. The participants without CAD were characterized for the presence of MetS using modified criteria specific for Asian Indians, which included a lower cut-off for waist circumference (≥90 cm for men). The expression of 18 inflammatory genes was evaluated in peripheral whole blood by quantitative polymerase chain reaction method. Results: Of the 121 participants without CAD, 53 (43.8%) had three or more risk factors (MetS group), 50 (41.3%) had one or two risk factors (non-MetS group), while 18 (14.8%) did not have any risk factors (control group). High nuclear factor-kappa B (NF-κB) expression levels and low interleukin-10 (IL-10) levels were observed in MetS patients. Linear association was seen between NF-κB and vascular endothelial growth factor A (VEGFA) expression and with increase in MetS components. Comparison of gene expression pattern between CAD and MetS revealed significantly higher expression of leukotriene genes - arachidonate 5-lipoxygenase (ALOX5), arachidonate 5-lipoxygenase activating protein (ALOX5 AP), leukotriene A4 hydrolase (LTA4H) and leukotriene C4 synthase (LTC4S), and lower expression of NF-κB, interleukin 1 beta (IL-1β), monocyte chemoattractant protein-1 (MCP-1/CCL2) and signal transducer and activator of transcription 3 (STAT3) genes in CAD. There was linear increase in expression of LTA4H, LTC4S, IL-8 and VEGFA genes across the four groups, namely from controls, non-MetS, MetS and CAD. Interpretation & conclusions: A distinct gene expression pattern was seen in MetS and CAD implying a well-orchestrated inflammatory and immune activity. Specifically, NF-κB might be playing an active role in MetS, allowing further expansion of the inflammatory process with resolution of inflammation in full-blown CAD, wherein other gene players such as leukotrienes may dominate

Network Analysis of Inflammatory Genes and Their Transcriptional Regulators in Coronary Artery Disease

<div><p>Network analysis is a novel method to understand the complex pathogenesis of inflammation-driven atherosclerosis. Using this approach, we attempted to identify key inflammatory genes and their core transcriptional regulators in coronary artery disease (CAD). Initially, we obtained 124 candidate genes associated with inflammation and CAD using Polysearch and CADgene database for which protein-protein interaction network was generated using STRING 9.0 (Search Tool for the Retrieval of Interacting Genes) and visualized using Cytoscape v 2.8.3. Based on betweenness centrality (BC) and node degree as key topological parameters, we identified interleukin-6 (IL-6), vascular endothelial growth factor A (VEGFA), interleukin-1 beta (IL-1B), tumor necrosis factor (TNF) and prostaglandin-endoperoxide synthase 2 (PTGS2) as hub nodes. The backbone network constructed with these five hub genes showed 111 nodes connected via 348 edges, with IL-6 having the largest degree and highest BC. Nuclear factor kappa B1 (NFKB1), signal transducer and activator of transcription 3 (STAT3) and JUN were identified as the three core transcription factors from the regulatory network derived using MatInspector. For the purpose of validation of the hub genes, 97 test networks were constructed, which revealed the accuracy of the backbone network to be 0.7763 while the frequency of the hub nodes remained largely unaltered. Pathway enrichment analysis with ClueGO, KEGG and REACTOME showed significant enrichment of six validated CAD pathways - smooth muscle cell proliferation, acute-phase response, calcidiol 1-monooxygenase activity, toll-like receptor signaling, NOD-like receptor signaling and adipocytokine signaling pathways. Experimental verification of the above findings in 64 cases and 64 controls showed increased expression of the five candidate genes and the three transcription factors in the cases relative to the controls (p<0.05). Thus, analysis of complex networks aid in the prioritization of genes and their transcriptional regulators in complex diseases.</p></div

Frequency of the hub genes and the accuracy of the backbone in 97 test network.

<p>Frequency of the hub genes and the accuracy of the backbone in 97 test network.</p

The inflammatory backbone network.

<p>The backbone network is derived from a master network consisting of 145 nodes connected via 1234 edges, constructed from 124 combined gene sets obtained from <i>Polysearch</i> and <i>CADgene</i> database. This backbone network consists of 111 nodes connected via 348 edges. Node color code: shades of red to green color depicts node with highest to lowest value of betweenness centrality (BC); Node size: from biggest to smallest circle map the node degree. Bigger and bright colored nodes represent genes with more links. IL-6 appears to be the super hub gene in the network with largest degree and highest BC.</p

An overview of the work flow.

<p>An overview of the work flow has been summarized and consists of the following steps- Step 1: Retrieval of inflammatory genes from literature using <i>PolySearch</i> text mining tool and from <i>CADgene</i> database; Step 2: Analysis of protein interactions using <i>STRING</i> database; Step 3: Topological analysis of network using <i>Cytoscape v2.8</i>, based on betweenness centrality and node degree, leading to the identification of hub genes and construction of the backbone network; Step 4: Construction of a regulatory network for the hub genes and identification of common transcription factors (TFs) regulating them; Step 5: Validation of the hub genes based on cross-validation (1 to 4 node deletion), functional enrichment analysis (ClueGo) and quantitative evaluation of key genes and their common transcription factors by real-time PCR.</p

Clinical characteristics of study participants included in the gene expression study.

<p>Continuous variables are expressed as mean±standard deviation. BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC-Total cholesterol; TG- Triglyceride; HDL-c, High Density Lipoprotein cholesterol; LDL-c, Low Density Lipoprotein cholesterol.</p

Grouping of network based on functionally enriched GO terms and pathways.

<p><b>A.</b> Functionally grouped network of enriched categories was generated for the hub genes and their regulators using <i>ClueGO</i>. GO terms are represented as nodes based on their kappa score level (≥0.3). Functionally grouped networks are linked to their biological function, where only the most significant term in the group is labeled. Functionally related groups partially overlap. Visualization has been carried out using <i>Cytoscape 2.8.3</i>. <b>B.</b> Table provides the results of <i>ClueGO</i> analysis. Nr: Number of genes from our list (8-genes) associated with the GO term. %: percentage of genes found from the total number of associated genes. p value: p value of the GO term after Benjamini-Hochberg correction. Associated genes are represented from among those associated with either GO term or specific pathway.</p

Accuracy of hub genes in the test network.

<p>Horizontal axis represents the number of genes removed in the test network. The vertical axis represents accuracy of the respective test network.</p

Relative expression of hub genes and their transcription factors.

<p>Gene expression of individual samples was normalized to GUSB mRNA levels. The data are expressed as mean ± S.E.M. for the affected (black bar) and unaffected (white bar) subjects.</p

Key genes selected based on topological parameters like BC and degree.

<p>Node degree and Betweenness Centrality (BC) are topological parameters used for gene prioritization in the network; A cut-off of BC >0.05 and/or node degree >50 were considered for gene prioritization. IL-6 constituted the super hub node having the largest degree and the highest BC.</p