Regulatory T Cells in Autoimmune Diabetes: Mechanisms of Action and Translational Potential

Potential Therapeutic Application of Regulatory T Cells in Diabetes Mellitus Type 1

The autoimmune reaction against the beta cells of the pancreatic islets in type 1 diabetes mellitus (T1DM) patients is active in prediabetes and during the development of the clinical manifestation of T1DM, but it decreases within a few years of the clinical manifestation of this disease. A key role in the pathogenesis of T1DM is played by regulatory T cell (Treg) deficiency or dysfunction. Immune interventions, such as potential therapeutic applications or the induction of the Treg-cell population in T1DM, will be important in the development of new types of treatment. The aim of this study was to evaluate innovative immune interventions as treatments for T1DM. After an evaluation of full-length papers from the PubMed database from 2010 to 2021, 20 trials were included for the final analysis. The analysis led to the following conclusions: Treg cells play an important role in the limitation of the development of T1DM, the activation or application of Tregs may be more effective in the early stages of T1DM development, and the therapeutic use of Treg cells in T1DM is promising but requires long-term observation in a large group of patients.

Comparative network stratification analysis for identifying functional interpretable network biomarkers

BACKGROUND

A major challenge of bioinformatics in the era of precision medicine is to identify the molecular biomarkers for complex diseases. It is a general expectation that these biomarkers or signatures have not only strong discrimination ability, but also readable interpretations in a biological sense. Generally, the conventional expression-based or network-based methods mainly capture differential genes or differential networks as biomarkers, however, such biomarkers only focus on phenotypic discrimination and usually have less biological or functional interpretation. Meanwhile, the conventional function-based methods could consider the biomarkers corresponding to certain biological functions or pathways, but ignore the differential information of genes, i.e., disregard the active degree of particular genes involved in particular functions, thereby resulting in less discriminative ability on phenotypes. Hence, it is strongly demanded to develop elaborate computational methods to directly identify functional network biomarkers with both discriminative power on disease states and readable interpretation on biological functions.

RESULTS

In this paper, we present a new computational framework based on an integer programming model, named as Comparative Network Stratification (CNS), to extract functional or interpretable network biomarkers, which are of strongly discriminative power on disease states and also readable interpretation on biological functions. In addition, CNS can not only recognize the pathogen biological functions disregarded by traditional Expression-based/Network-based methods, but also uncover the active network-structures underlying such dysregulated functions underestimated by traditional Function-based methods. To validate the effectiveness, we have compared CNS with five state-of-the-art methods, i.e. GSVA, Pathifier, stSVM, frSVM and AEP on four datasets of different complex diseases. The results show that CNS can enhance the discriminative power of network biomarkers, and further provide biologically interpretable information or disease pathogenic mechanism of these biomarkers. A case study on type 1 diabetes (T1D) demonstrates that CNS can identify many dysfunctional genes and networks previously disregarded by conventional approaches.

CONCLUSION

Therefore, CNS is actually a powerful bioinformatics tool, which can identify functional or interpretable network biomarkers with both discriminative power on disease states and readable interpretation on biological functions. CNS was implemented as a Matlab package, which is available at http://www.sysbio.ac.cn/cb/chenlab/images/CNSpackage_0.1.rar .