Understanding the expression of Toll-like receptors in Asian Indians predisposed to coronary artery disease

Overexpression of Toll-Like Receptors 2, 3, 4, and 8 Is Correlated to the Vascular Atherosclerotic Process in the Hyperlipidemic Rabbit Model: The Effect of Statin Treatment

BACKGROUND

Atherosclerosis is the major cause of cardiovascular disease; hypercholesterolemia is a major risk factor. We hypothesized that specific TLR members (TLR2, TLR3, TLR4, TLR8) may play a role in atherosclerosis progression and its accompanying inflammatory response. We determined the association of atherosclerotic lesions and TLR mRNA expression in different aortic sites. We also assessed the effects of fluvastatin (Flu) treatment on TLR expression and plaque characteristics.

METHODS

Male rabbits, fed with an atherogenic diet for a duration of 3 months, were screened for advanced atherosclerotic lesions in the aorta. Additional animals received normal diet or normal diet plus Flu for 1 additional month. TLR mRNA expression in various thoracic and abdominal aortic segments was assessed, together with atherosclerotic changes.

RESULTS

After high lipid diet, the atherosclerotic burden increased more in the abdominal than in the thoracic aorta; TLR2, 3, 4, and 8 also increased significantly. Flu decreased atherosclerotic plaque, calcium deposition, lipid cores, intraplaque hemorrhage, erythrocyte membranes, endothelial cells, and macrophage infiltration, while increasing smooth muscle cells in plaques of both aortic segments; it also lowered TLR2, 3, 4, and 8 expression in all aortic segments to a stronger degree than resumption of normal diet. There was a strong association between blood and tissue parameters during experimental period and finally a strong correlation found between these parameters with mRNA of TLR2, 3, 4, and 8 in various stages.

CONCLUSION

For the first time TLR2, 3, 4, and 8 mRNA expression is prospectively explored after hypercholesterolemic diet in the rabbit model. TLR2, 3, 4, and 8 mRNA expression is strongly upregulated and correlates with the progression of atherosclerosis in the aorta. Flu significantly inhibited this progress and reduced inflammation via TLR downregulation which was strongly associated with regression of plaque morphology and atherosclerosis promoting factors.

Toll-like receptor 4 gene polymorphisms and susceptibility to colorectal cancer: a meta-analysis and review

INTRODUCTION

Many case-control studies have investigated the association between toll-like receptor 4 (TLR4) Asp299Gly and Thr399Ile polymorphisms and risk of colorectal cancer (CRC). However, published data are still conflicting.

MATERIAL AND METHODS

A systematic search was conducted in the electronic databases of PubMed, MEDLINE, EMBASE, Web of Science and CNKI between 2000 and 2014. The associations between TLR4 polymorphisms and CRC susceptibility were assessed by pooled odds ratios (ORs) and 95% confidence intervals (95% CI) in fixed or random effects models.

RESULTS

In total nine case-control studies were identified in this meta-analysis. For TLR4 Asp299Gly polymorphism, 9 studies included 1198 cases and 1290 controls. The GG genotype carriers had higher risk for developing CRC than AA + GA genotype carriers (OR = 1.95, 95% CI: 1.00-3.77, p = 0.05). No association was found in other genetic models (p > 0.05). Analysis stratified by ethnicity showed no association in any genetic models among the Asian or Caucasian population. For TLR4 Thr399Ile polymorphism, 6 studies contained 619 cases and 632 controls. The overall analysis showed significantly increased risk in TT homozygote carriers compared to CC homozygote (OR = 4.99, 95% CI: 1.41-17.65, p = 0.01) and C carriers (TC + CC) (OR = 4.50, 95% CI: 1.27-15.87, p = 0.02). In terms of analyses stratified by race, a significant association was found in each genetic model among the Asian population, rather than the Caucasian group.

CONCLUSIONS

The GG homozygote carriers of TLR4 Asp299Gly and TT homozygote carriers of TLR4 Thr399Ile polymorphisms might be correlated with an increased risk of CRC, suggesting they may serve as genetic risk factors for CRC.

Network analysis of inflammatory genes and their transcriptional regulators in coronary artery disease

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.