Analysis of Expression and Localization of TLR-2 by Immunofluorescent Technique in Healthy and Inflammed Oral Tissues

A Hybrid Model for Predicting Pattern Recognition Receptors Using Evolutionary Information

This study describes a method developed for predicting pattern recognition receptors (PRRs), which are an integral part of the immune system. The models developed here were trained and evaluated on the largest possible non-redundant PRRs, obtained from PRRDB 2.0, and non-pattern recognition receptors (Non-PRRs), obtained from Swiss-Prot. Firstly, a similarity-based approach using BLAST was used to predict PRRs and got limited success due to a large number of no-hits. Secondly, machine learning-based models were developed using sequence composition and achieved a maximum MCC of 0.63. In addition to this, models were developed using evolutionary information in the form of PSSM composition and achieved maximum MCC value of 0.66. Finally, we developed hybrid models that combined a similarity-based approach using BLAST and machine learning-based models. Our best model, which combined BLAST and PSSM based model, achieved a maximum MCC value of 0.82 with an AUROC value of 0.95, utilizing the potential of both similarity-based search and machine learning techniques. In order to facilitate the scientific community, we also developed a web server "PRRpred" based on the best model developed in this study (http://webs.iiitd.edu.in/raghava/prrpred/).

Altered Toll-Like Receptor Signalling in Children with Down Syndrome

Toll-like receptors (TLRs) are the key in initiating innate immune responses. TLR2 is crucial in recognising lipopeptides from gram-positive bacteria and is implicated in chronic inflammation. Children with Down syndrome (DS) are prone to infections from these pathogens and have an increased risk of autoimmunity. Sparstolonin B (SsnB) is a TLR antagonist which attenuates cytokine production and improves outcomes in sepsis. We hypothesised that TLR signalling may be abnormal in children with DS and contribute to their clinical phenotype. We evaluated TLR pathways in 3 ways: determining the expression of TLR2 on the surface of neutrophils and monocytes by flow cytometry, examining the gene expression of key regulatory proteins involved in TLR signal propagation, MyD88, IRAK4, and TRIF, by quantitative PCR, and lastly determining the cytokine production by ELISA following immunomodulation with proinflammatory stimuli (lipopolysaccharide (LPS), Pam3Csk4) and the anti-inflammatory agent SsnB. We report TLR2 expression being significantly increased on neutrophils, total monocytes, and intermediate and nonclassical monocytes in children with DS (n = 20, mean age 8.8 ± SD 5.3 years, female n = 11) compared to controls (n = 15, mean age 6.2 ± 4.2 years, female n = 5). At baseline, the expression of MyD88 was significantly lower, and TRIF significantly raised in children with DS. The TLR antagonist SsnB was effective in reducing TLR2 and CD11b expression and abrogating cytokine production in both cohorts. We conclude that TLR signalling and the TLR2 pathway are dysregulated in DS, and this disparate innate immunity may contribute to chronic inflammation in DS. SsnB attenuates proinflammatory mediators and may be of therapeutic benefit.

Evaluation of TLR2 and 4 in Chronic Periodontitis

INTRODUCTION

Periodontal disease is the major cause of adult tooth loss and is commonly characterized by a chronic inflammation caused by infection due to oral bacteria. Members of Toll-Like Receptor (TLR) family recognize conserved microbial structures, such as bacterial lipopolysaccharides and activate signalling pathways that result in immune responses against microbial infections.

AIM

The aim of the present study was to assess the mRNA expression of Toll-Like Receptor 2 and 4 in tissues with or without chronic periodontitis.

MATERIALS AND METHODS

Gingival tissue samples were collected from controls (30 subjects with healthy periodontal tissues) and experimental group (30 subjects with chronic periodontitis). Total RNA was extracted and RT-PCR was done for evaluation of TLR-2 and TLR-4. Mann Whitney U-test, Pearson Chi-square Test was used for statistics.

RESULTS

The results showed that there is a significant (p-value= 0.004) association between TLR-4 and the experimental group comprising of chronic periodontitis patients in comparison to the insignificant (p-value= 0.085) TLR-2 expression.

CONCLUSION

This study concludes that TLR-2 and TLR-4 expressed in the gingival tissues recognize different bacterial cell wall components thus helping us to associate its potential in diagnosing periodontal disease. Hence, in the future, these scientific findings can pave the way in using TLR as a diagnostic biomarker for periodontal disease.