Objectives In March 2020, the World Health Organization declared that an infectious respiratory disease caused by a new severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2, causing coronavirus disease 2019 (COVID-19)] became a pandemic. In our study, we have analyzed a large publicly available dataset, the Genome Aggregation Database (gnomAD), as well as a cohort of 37 Russian patients with COVID-19 to assess the influence of different classes of genetic variants in the angiotensin-converting enzyme-2 ( ACE2 ) gene on the susceptibility to COVID-19 and the severity of disease outcome. Results We demonstrate that the European populations slightly differ in alternative allele frequencies at the 2,754 variant sites in ACE2 identified in the gnomAD database. We find that the Southern European population has a lower frequency of missense variants and slightly higher frequency of regulatory variants. However, we found no statistical support for the significance of these differences. We also show that the Russian population is similar to other European populations when comparing the frequencies of the ACE2 variants. Evaluation of the effect of various classes of ACE2 variants on COVID-19 outcome in a cohort of Russian patients showed that common missense and regulatory variants do not explain the differences in disease severity. At the same time, we find several rare ACE2 variants (including rs146598386, rs73195521, rs755766792, and others) that are likely to affect the outcome of COVID-19. Our results demonstrate that the spectrum of genetic variants in ACE2 may partially explain the differences in severity of the COVID-19 outcome.

Type 2 diabetes mellitus (T2D) is a chronic metabolic disease resulting from insulin resistance and progressively reduced insulin secretion, which leads to impaired glucose utilization, dyslipidemia and hyperinsulinemia and progressive pancreatic beta cell dysfunction. The incidence of type 2 diabetes mellitus is increasing worldwide and nowadays T2D already became a global epidemic. The well-known interindividual variability of T2D drug actions such as biguanides, sulfonylureas/meglitinides, DPP-4 inhibitors/GLP1R agonists and SGLT-2 inhibitors may be caused, among other things, by genetic factors. Pharmacogenetic findings may aid in identifying new drug targets and obtaining in-depth knowledge of the causes of disease and its physiological processes, thereby, providing an opportunity to elaborate an algorithm for tailor or precision treatment. The aim of this article is to summarize recent progress and discoveries for T2D pharmacogenetics and to discuss the factors which limit the furthering accumulation of genetic variability knowledge in patient response to therapy that will allow improvement the personalized treatment of T2D.

To carry out research projects, clinical trials and other studies in the field of personalized medicine, it is necessary to have collections of high-quality biological samples of various types. With the development of biomedical technologies, the need for large collections of biological samples will grow every year, which necessitates the creation of various biobanks for standardized collection, storage and distribution of such samples. One of the goals of the National Association of Biobanks and Biobanking Specialists is the development of a network of Russian biobanks interacting with each other at various levels, as well as the development and implementation of organizational and legal tools for its regulation. It is required not only to standardize the access and exchange of biological samples and data, but also to create a unified terminology that will be used by biobanks throughout Russia. The main aim is to create an accurate, professional and legally correct tool containing information accessible and understandable to a wide range of researchers.

Type 2 diabetes (T2D) is a common chronic disease whose etiology is known to have a strong genetic component. Standard genetic approaches, although allowing for the detection of a number of gene variants associated with the disease as well as differentially expressed genes, cannot fully explain the hereditary factor in T2D. The explosive growth in the genomic sequencing technologies over the last decades provided an exceptional impetus for transcriptomic studies and new approaches to gene expression measurement, such as RNA-sequencing (RNA-seq) and single-cell technologies. The transcriptomic analysis has the potential to find new biomarkers to identify risk groups for developing T2D and its microvascular and macrovascular complications, which will significantly affect the strategies for early diagnosis, treatment, and preventing the development of complications. In this article, we focused on transcriptomic studies conducted using expression arrays, RNA-seq, and single-cell sequencing to highlight recent findings related to T2D and challenges associated with transcriptome experiments.

No abstract

Currently, one of the most promising areas of medicine is the development and implementation of new biomedical technologies in the field of human reproduction with the involvement of resources of biobanks and biocollections as well as modern genetic technologies. In this review, we considered the key dimensions of personalized medicine, such as biobanking and genomic medicine. We illustrated crucial aspects in the organization of human bioresource collections and the difficulties arising in the interaction of specialists in the field of biobanking. Problems in obtaining informed consent and collecting personal data are described. Furthermore, the need for creating and developing complex information systems for storing, processing, and analyzing data, creating genetic databases is emphasized. Foreign experience in consolidation of biobank data and the results of genomic studies is summarized. We also describe D.O. Ott Research Institute of Obstetrics, Gynecology and Reproductology’s experience in creating collections of human biomaterials (today it contains more than 60,000 samples, including samples of blood and its derivatives (plasma, serum, whole blood), urine samples, placental tissue, cell cultures, DNA, RNA, and others) and in quality management. The main results of genetic research are provided. Experience in these studies served as the basis for the creation of Biobank “Genofond” and the unique scientific facility “Human Reproductive Health”. The principle of creation of the collection, its purpose, and objectives for future research in the genetics of reproduction are described.

No abstract

Metformin is an oral hypoglycemic agent widely used in clinical practice for treatment of patients with type 2 diabetes mellitus (T2DM). The wide interindividual variability of response to metformin therapy was shown, and recently the impact of several genetic variants was reported. To assess the independent and combined effect of the genetic polymorphism on glycemic response to metformin, we performed an association analysis of the variants in ATM, SLC22A1, SLC47A1, and SLC2A2 genes with metformin response in 299 patients with T2DM. Likewise, the distribution of allele and genotype frequencies of the studied gene variants was analyzed in an extended group of patients with T2DM (n = 464) and a population group (n = 129). According to our results, one variant, rs12208357 in the SLC22A1 gene, had a significant impact on response to metformin in T2DM patients. Carriers of TT genotype and T allele had a lower response to metformin compared to carriers of CC/CT genotypes and C allele (p-value = 0.0246, p-value = 0.0059, respectively). To identify the parameters that had the greatest importance for the prediction of the therapy response to metformin, we next built a set of machine learning models, based on the various combinations of genetic and phenotypic characteristics. The model based on a set of four parameters, including gender, rs12208357 genotype, familial T2DM background, and waist–hip ratio (WHR) showed the highest prediction accuracy for the response to metformin therapy in patients with T2DM (AUC = 0.62 in cross-validation). Further pharmacogenetic studies may aid in the discovery of the fundamental mechanisms of type 2 diabetes, the identification of new drug targets, and finally, it could advance the development of personalized treatment.