The Use of Artificial Neural Networks in Analysis Cationic Trypsinogen Gene and Hepatitis B Surface Antigen

Ou Qishui,Luo Xiu,Fu Lengxi,Liu Qicai,Zeng Kai,Zhuang Zehao

doi:10.3844/ajisp.2009.50.55

Abstract

Problem statement: More and more severe hepatitis cases reported in China are infected with HBV and the immune response of HBV will be reduced if mutations occur in the TCR, therefore it is very important to investigate the relation of T-cellular function and clinical effect by studying the function of T-cell receptor. Approach: Artificial Neural Networks (ANN) was applied to analyze basic data (the three structural of HBsAg, the ligand of HBsAg and the clinical immunological characterizations, the laboratory data, the genetypes of cationic trypsinogen gene (PRSS1) derived from 78 patients with pancreatitis and 60 normal controls were also collected. What is more, we used T-cell culture with HBV and flow cytometry to check the result of ANN predict. To examine the characteristics of T-cells capable of coexisting with the secreted HBsAg, T-cell receptor from A121T, C139S, silent mutation and normal PRSS1 gene in the patients with pancreatitis were put into research. To ensure that PRSS1 gene would affect HBsAg-specific T-cells receptor, we compared the rate of multiplication and CD4/CD8 of T-cell after culture with HBV at 0H, 12H, 24H, 36H, 48H and 72H time point. Results: The protein’s structural predicted by the ANN could specifically explain the phenomenon that the turbulence and different of anti-HBs lever of the patients with pancreatitis. The three-dimensional of the protein that consist with PRSS1 gene would accord with HBsAg. It may be one of the HBsAg-specific T-cell receptor. Result of T-cell culture also showed that different genetypes of PRSS1 had different results. In the T-cell proliferation, the groups of PRSS1 mutation (A121T and C139S) were significant lower than the group of silent mutation and normal controls and it was the same to the result of CD4/CD8. Conclusion: The ANN had been integrated into a previously published comprehensive web server to support immunology analysis and the PRSS1 gene may be the unit for HBsAg immune response.

Highlights

The T-Cell Receptor (TCR) plays a central role in the human immune system and more than 90% of human T-cells present a receptor that consists of TCRα and TCRβ, especially CD4 and CD8 T-cells
T7), the polymorphisms or mutations of trypsinogen genes and Vβ can be associated with loss of autoimmune tendencies[6-8] and it was report that high express of TCR β 6 and TCR β 7.2 will be found in the patients with effect of HBV[7-10], but it is not clear why the loss of the functional trypsinogen genes would confer a selective advantage
We found that the level of anti-HBs and CD4+T/ CD8+T is very different in the patients with pancreatitis in the process of pancreatitis

Summary

INTRODUCTION

The T-Cell Receptor (TCR) plays a central role in the human immune system and more than 90% of human T-cells present a receptor that consists of TCRα and TCRβ, especially CD4 and CD8 T-cells. The characteristics of the mutation of PRSS1 affect TCR in the idiopathic chronic pancreatitis or hereditary pancreatitis can be difficult to accurately characterize from complex clinical data and an Artificial Neural Network (ANN) is a machine learning technique that can effectively process complex and high noise data. In this study we applied Artificial Neural Networks (ANNs) to analyze the clinical data and the genetypes of PRSS1 in order to identify key immunological characteristics that the mutations of PRSS1 affect the function of TCR response to HBsAg. As we known, ANNs are adaptive, non-linear forms of Artificial Intelligence inspired by the way the human brain learns and processes information in order to solve specific problems, such as pattern recognition and classification problems

MATERIALS AND METHODS

RESULTS

Findings

CONCLUSION