The knowledge about specific mechanisms generating TP53 dysfunction in diffuse large B-cell lymphoma is limited. The aim of the current study was to comprehensively explore TP53 gene variability resulting from somatic mutations, promoter methylation, and allelic imbalance in tumorous tissue of diffuse large B-cell lymphoma (DLBCL). DNA samples from 74 patients with DLBCL were used. Genomic DNA was isolated from paraffin blocks of lymph nodes or from extranodal biopsies of tumors by the phenol–chloroform extraction method with guanidine. Analysis of coding sequences of the TP53 gene was based on Sanger’s direct sequencing method. The methylation status of the TP53 promoter was analyzed using by methylation-specific PCR on bisulfite-converted DNA. Assessment of the detected mutations was carried out in the IARC TP53 Database and the TP53 UMD mutation database of human cancer. The mutations in regions coding for the DNA-binding domain were prevalent (95%). In the analyzed sample of patients, codons 275, 155, 272, and 212 were hotspots of mutations in the TP53 gene. In addition, functionally significant intron mutations (IVS6-36G > C and IVS5 + 43G > T) were detected. Instances of TP53 promoter methylation were observed only in a few samples of diffuse large B-cell lymphoma tissue. Furthermore, loss of heterozygosity was revealed only in the subgroup of patients with altered status of the gene (mutations were detected in five patients and promoter methylation in one case). Thus, the results suggest that there are two sequential events in the formation of diffuse large B-cell lymphoma in at least some cases. The first event is mutation or methylation of the TP53 promoter, leading to appearance of a cell with increased risk of malignant transformation. The second event is the loss of an intact allele of the gene; this change is necessary for tumorigenesis. We identified TP53 mutation patterns in a Russian cohort of patients with de novo DLBCL who were treated with R-CHOP and R-CHOP-like regimens and confirmed that TP53 mutation status is a valuable prognostic biomarker.

Background: To study the changes in protein composition of atherosclerotic plaques at different stages of their development in coronary atherosclerosis using proteomics. Methods: The object of research consisted of homogenates of atherosclerotic plaques from coronary arteries at different stages of development, obtained from 15 patients. Plaque proteins were separated by two-dimensional electrophoresis. The resultant protein spots were identified by the matrix-assisted laser desorption ionization method with peptide mass mapping. Results: Groups of differentially expressed proteins, in which the amounts of proteins differed more than twofold (p < 0.05), were identified in pools of homogenates of atherosclerotic plaques at three stages of development. The amounts of the following proteins were increased in stable atherosclerotic plaques at the stage of lipidosis and fibrosis: vimentin, tropomyosin β-chain, actin, keratin, tubulin β-chain, microfibril-associated glycoprotein 4, serum amyloid P-component, and annexin 5. In plaques at the stage of fibrosis and calcification, the amounts of mimecan and fibrinogen were increased. In unstable atherosclerotic plaque of the necrotic–dystrophic type, the amounts of human serum albumin, mimecan, fibrinogen, serum amyloid P-component and annexin were increased. Conclusion: This proteomic study identifies the proteins present in atherosclerotic plaques of coronary arteries by comparing their proteomes at three different stages of plaque development during coronary atherosclerosis.

Maturity onset diabetes of the young (MODY) is a congenital form of diabetes characterized by onset at a young age and a primary defect in pancreatic-β-cell function. Currently, 14 subtypes of MODY are known, and each is associated with mutations in a specific gene: HNF4A, GCK, HNF1A, PDX1, HNF1B, NEUROD1, KLF11, CEL, PAX4, INS, BLK, KCNJ11, ABCC8, and APPL1. The most common subtypes of MODY are associated with mutations in the genes GCK, HNF1A, HNF4A, and HNF1B. Among them, up to 70% of cases are caused by mutations in GCK and HNF1A. Here, an analysis of 14 MODY genes was performed in 178 patients with a MODY phenotype in Western Siberia. Multiplex ligation-dependent probe amplification analysis of DNA samples from 50 randomly selected patients without detectable mutations did not reveal large rearrangements in the MODY genes. In 38 patients (37% males) among the 178 subjects, mutations were identified in HNF4A, GCK, HNF1A, and ABCC8. We identified novel potentially causative mutations p.Lys142*, Leu146Val, Ala173Glnfs*30, Val181Asp, Gly261Ala, IVS7 c.864 −1G>T, Cys371*, and Glu443Lys in GCK and Ser6Arg, IVS 2 c.526 +1 G>T, IVS3 c.713 +2 T>A, and Arg238Lys in HNF1A.

Background: A non-invasive, easy-to-access marker of accelerated cardiac ageing would provide novel insights into the mechanisms and aetiology of cardiovascular disease (CVD) as well as contribute to risk stratification of those who have not had a heart or circulatory event. Our hypothesis is that differences between an ECG-predicted and chronologic age of participants (δage) would reflect accelerated or decelerated cardiovascular ageing Methods: A convolutional neural network model trained on over 700,000 ECGs from the Mayo Clinic in the U.S.A was used to predict the age of 4,542 participants in the Know Your Heart study conducted in two cities in Russia (2015-2018). Thereafter, δage was used in linear regression models to assess associations with known CVD risk factors and markers of cardiac abnormalities. Results: The biomarker δage (mean: +5.32 years) was strongly and positively associated with established risk factors for CVD: blood pressure, body mass index (BMI), total cholesterol and smoking. Additionally, δage had strong independent positive associations with markers of structural cardiac abnormalities: N-terminal pro b-type natriuretic peptide (NT-proBNP), high sensitivity cardiac troponin T (hs-cTnT) and pulse wave velocity, a valid marker of vascular ageing. Conclusion: The difference between the ECG-age obtained from a convolutional neural network and chronologic age (δage) contains information about the level of exposure of an individual to established CVD risk factors and to markers of cardiac damage in a way that is consistent with it being a biomarker of accelerated cardiovascular (vascular) ageing. Further research is needed to explore whether these associations are seen in populations with different risks of CVD events, and to better understand the underlying mechanisms involved.

The aim of this study is to investigate the 14-year risk of type 2 diabetes mellitus (T2DM) and develop a risk score for T2DM in the Siberian cohort. A random population sample (males/females, 45–69 years old) was examined at baseline in 2003–2005 (Health, Alcohol, and Psychosocial Factors in Eastern Europe (HAPIEE) project, n = 9360, Novosibirsk) and re-examined in 2006–2008 and 2015–2017. After excluding those with baseline T2DM, the final analysis included 7739 participants. The risk of incident T2DM during a 14-year follow-up was analysed using Cox regression. In age-adjusted models, male and female hazard ratios (HR) of incident T2DM were 5.02 (95% CI 3.62; 6.96) and 5.13 (95% CI 3.56; 7.37) for BMI ≥ 25 kg/m²; 4.38 (3.37; 5.69) and 4.70 (0.27; 6.75) for abdominal obesity (AO); 3.31 (2.65; 4.14) and 3.61 (3.06; 4.27) for fasting hyperglycaemia (FHG); 2.34 (1.58; 3.49) and 3.27 (2.50; 4.26) for high triglyceride (TG); 2.25 (1.74; 2.91) and 2.82 (2.27; 3.49) for hypertension (HT); and 1.57 (1.14; 2.16) and 1.69 (1.38; 2.07) for family history of diabetes mellitus (DM). In addition, secondary education, low physical activity (PA), and history of cardiovascular disease (CVD) were also significantly associated with T2DM in females. A simple T2DM risk calculator was generated based on non-laboratory parameters. A scale with the best quality included waist circumference >95 cm, HT history, and family history of T2DM (area under the curve (AUC) = 0.71). The proposed 10-year risk score of T2DM represents a simple, non-invasive, and reliable tool for identifying individuals at a high risk of future T2DM.