Identification of candidate disease genes in patients with common variable immunodeficiency

Background: Common variable immunodeficiency (CVID), the most prevalent form of primary immunodeficiency (PID), is characterized by hypogammaglobulinemia and recurrent infections. Understanding protein-protein interaction (PPI) networks of CVID genes and identifying candidate CVID genes are critical steps in facilitating the early diagnosis of CVID. Here, the aim was to investigate PPI networks of CVID genes and identify candidate CVID genes using computation techniques. Methods: Network density and biological distance were used to study PPI data for CVID and PID genes obtained from the STRING database. Gene expression data of patients with CVID were obtained from the Gene Expression Omnibus, and then Pearson’s correlation coefficient, a PPI database, and Kyoto Encyclopedia of Genes and Genomes were used to identify candidate CVID genes. We then evaluated our predictions and identified differentially expressed CVID genes. Results: The majority of CVID genes are characterized by a high network density and small biological distance, whereas most PID genes are characterized by a low network density and large biological distance, indicating that CVID genes are more functionally similar to each other and closely interact with one other compared with PID genes. Subsequently, we identified 172 CVID candidate genes that have similar biological functions to known CVID genes, and eight genes were recently reported as CVID-related genes. MYC, a candidate gene, was down-regulated in CVID duodenal biopsies, but up-regulated in blood samples compared with levels in healthy controls. Conclusion: Our findings will aid in a better understanding of the complex of CVID genes, possibly further facilitating the early diagnosis of CVID.[Figure not available: see fulltext.]. © 2019, Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature.This study was funded by the Act 211 Government of the Russian Federation (No. 02.A03.21.0006) and the IIP UB RAS project (No. AAAA-A18-118020590108-7)

Insulin-producing system at diabetes mellitus type 2

In T2D deficiency of insulin can be compensated for by the formation of new cells, producing insulin. Reprogramming of hepatocytes and cells of exocrine part of the pancreas into insulin+ cells is a potentially approach to generate new insulin-producing cells. An increase both the number of insulin+ hepatocytes and quantity in them of a transcription factor PDX1, which regulates expression of insulin gene, is a perspective way in in the development of new methods and approaches in the treatment of diabetes mellitus.The research is supported by the Russian Science Foundation (project № 16-15-00039 –П)

A co-expression network for differentially expressed genes in bladder cancer and a risk score model for predicting survival

Background: Urothelial bladder cancer (BLCA) is one of the most common internal malignancies worldwide with poor prognosis. This study aims to explore effective prognostic biomarkers and construct a prognostic risk score model for patients with BLCA. Methods: Weighted gene co-expression network analysis (WGCNA) was used for identifying the co-expression module related to the pathological stage of BLCA based on the RNA-Seq data retrieved from The Cancer Genome Atlas database. Prognostic biomarkers screened by Cox proportional hazard regression model and random forest were used to construct a risk score model that can predict the prognosis of patients with BLCA. The GSE13507 dataset was used as the independent testing dataset to test the performance of the risk score model in predicting the prognosis of patients with BLCA. Results: WGCNA identified seven co-expression modules, in which the brown module consisted of 77 genes was most significantly correlated with the pathological stage of BLCA. Cox proportional hazard regression model and random forest identified TPST1 and P3H4 as prognostic biomarkers. Elevated TPST1 and P3H4 expressions were associated with the high pathological stage and worse survival. The risk score model based on the expression level of TPST1 and P3H4 outperformed pathological stage indicators and previously proposed prognostic models. Conclusion: The gene co-expression network-based study could provide additional insight into the tumorigenesis and progression of BLCA, and our proposed risk score model may aid physicians in the assessment of the prognosis of patients with BLCA

The cytotoxic evaluation and regenerative potentials of isoflavones in diabetic animal models

The present study evaluated the antidiabetic effect of daidzein based on its ability to lower blood glucose, and influence the regenerative mechanism of pancreatic β-cell in type 1 diabetic animal models. Furthermore, the cytotoxic effect of this isoflavone at dose of 100mg/kg and 200mg/kg of animal body weight was evaluated in the kidney

The use of nanocluster polyoxometalates in the bioactive substance delivery systems

Nanoscale systems occupy the most important place among the vehicles intended for targeted drug delivery. Such vehicles are considered in this review. Attention is paid to the nanocluster polyoxometalate-based systems which are promising for transdermal iontophoretic transport. In this relation, and due to the characteristics of the skin as a transport medium, the problems of the transfer processes modeling are considered. © 2019, ITMO University. All rights reserved.Ministry of Education and Science of the Russian Federation, Minobrnauka: 4.6653.2017/8.9, AAAA-A18-118020590107-0; Ural Federal University, UrFUThe paper was prepared in the framework of implementation of the state assignment from the Ministry of Education and Science of the Russian Federation (Projects Nos. 4.6653.2017/8.9 and AAAA-A18-118020590107-0), and of the Program for Increasing Competitiveness of UrFU (Agreement No. 02.A03.21.0006)