BackgroundLong-living adults often maintain cognitive function despite neuropathological changes, which is often attributed to cognitive resilience (CR)—a combined effect of cognitive and cerebral reserves. CR is influenced by genetic, clinical, sociodemographic, and environmental factors.Materials and methodsWe investigated genetic, clinical, and environmental predictors of CR in 198 dementia-free long-living adults via two neuropsychological examinations over a 2-year period, a geriatric assessment, and a genome-wide association study (GWAS).ResultsLimited mobility, reduced walking, hearing impairment, depression, anemia, lower quality of life, and decreased BMI were key accelerators of CI. Depression, hypercholesterolemia, and lack of hobbies increased the risk of mild cognitive impairment (MCI)-to-dementia progression. GWAS identified CR-associated genetic variants, including a missense mutation in SYNGAP1 (Ile1115Thr) not previously linked to cognitive disorders.ConclusionOur findings corroborated established risk factors for cardiovascular diseases and identified population-specific patterns, with APOE ε4 showing no significant association. Both protein-coding regions and non-coding elements were implicated in CI, suggesting that it is underlain by complex regulatory mechanisms.

ObjectiveElevated cholesterol levels are associated with the risk of the most socially significant cardiovascular diseases, such as atherosclerosis, ischemic heart disease, and myocardial infarction.MethodsIn this study, we sought to study the genetic architecture of lipid metabolism by conducting genome-wide association studies of total cholesterol, LDL-C, and HDL-C levels in a sample of the Russian population (n = 8,732) who were not carriers of variants linked to familial hypercholesterolemia and did not take lipid-regulating agents. Based on the detected associations and machine learning methods, several polygenic score models were constructed for each lipid type, and the link between polygenic scores and atherosclerosis, ischemic heart disease, and myocardial infarction was examined in an additional sample of patients diagnosed with either of these diseases (n = 3,954).ResultsA meta-analysis of the results of the conducted genome-wide association studies showed that total cholesterol and LDL-C were largely associated with the same variants located in the HMGCR, CERT1, POLK, ANKDD1B, APOC3, BCL3, CBLC, BCAM, NECTIN2, TOMM40, APOE, APOC1, APOC4, and SMARCA4 genes, while HDL-C was associated with variants located in the LPL, ALDH1A2, LIPC, and CETP genes. Men and women differed in genetic predictors of lipid levels, with variants in the SMARCA4 and LDLR genes associated with cholesterol levels only in women.ConclusionThe models developed in this study consider age and a modifiable factor, BMI. Therefore, the personalized polygenic profiling approach presented in this study enables life-long CVD risk assessment.

Most genomic studies compare the genomes of long-living adults to those of the general population to identify potential genetic markers of longevity. We propose a refined approach: focusing on the genetic makeup of healthy, long-living adults to detect mechanisms promoting both longer lifespan and improved quality of life. To this end, we analyzed medical and genomic data from 3,703 long-living adults aged ≥90 years and 22,354 individuals aged 18-75 years (total N = 26,057). Using whole-genome sequencing (WGS) and a genome-wide association study (GWAS), we found that variants with significant and negative associations with longevity in the GWAS were located in genes such as APOE, APOC1, and CFAP46, which are implicated in an increased risk of age-related diseases. However, the presence or absence of these variants should not be considered a definitive determinant of longevity or sustained health after the age of 90. We found that healthy longevity was positively associated with variants within the MYO18B, TBC1D28, and LOC105376454 genes. To demonstrate the multifactorial nature of the examined phenotypes, we constructed polygenic score models that accounted for nonlinear interactions among the predictors. Trial registration: Clinical Trials NCT06268132 (for long-living adults). Registered 22 February 2024 (retrospectively registered).

BackgroundThe purpose of this study was to investigate genetic risk factors associated with ischemic heart disease (IHD) in Russian adults, to develop prognostic PRS models for IHD risk stratification, and to gain a better understanding of molecular mechanisms underlying IHD through a transcriptome analysis.Materials and methodsThe study analyzed data from three independent cohorts: a population sample of 69,500 individuals, a cardiac sample of 5,875 IHD patients, and a sample of 2,870 long-living adults. To identify genomic loci associated with IHD, a GWAS was performed, and its findings were used to develop a PRS model for the IHD phenotype. Additionally, a transcriptome analysis was conducted.ResultsThe GWAS for IHD adjusted for sex, age, and the first ten principal component accounting for population structure identified 75 variants, with 67 located at 9p21.3. Based on the GWAS findings, PRS models were developed and validated for IHD. The risk of IHD adjusted for sex, age, and BMI was the lowest in the sample of long-living adults. The TWAS revealed a significant association between IHD and increased CDKN2B expression in the aorta. According to the DEG results, the CDKN1A gene, a member of the same gene family as CDKN2B, was significantly hypoexpressed in the lower limb veins of patients with IHD.ConclusionPRS models offer great benefits for IHD risk prediction, particularly in individuals at the e tremes of the heritable risk distribution. lncRNAs, such as CDKN2BAS1 and lncRNA-MAP3K4, as well as P2X2 receptors, could be potential therapeutic targets for IHD.

Aging is associated with a high prevalence of insomnia, which is linked to somatic and neuropsychiatric diseases, as well as metabolic and immunological dysfunction. This study aims to identify alterations in the transcriptome profiles and functional metabolic pathways in older adults with different types of sleep disorders. This cross-sectional study included 1002 participants (60-90 years) who were screened for sleep disorders using the Pittsburgh Sleep Quality Index (PSQI) questionnaire. Two types of sleep disorders were identified in the study cohort, i.e., sleep onset insomnia and sleep maintenance insomnia. Both types of insomnia were further analyzed for associations with clinical characteristics, laboratory testing results, and socioeconomic backgrounds. The transcriptomic profiles of peripheral blood samples were examined in 236 individuals, supplemented with differential gene and dsRNA expression analyses (DESeq2). Both sleep onset insomnia and middle insomnia were associated with depression, chronic pain syndrome, and osteoarthritis, while only middle insomnia was associated with cardiometabolic diseases. No associations were observed between sleep onset insomnia or reduced sleep duration and transcriptomic profiles. In contrast, 244 genes were differentially expressed in patients with middle insomnia, indicating the activation of pathways related to viral infection response and inhibition of protein synthesis. Additionally, differential expression analysis of double-stranded RNA (dsRNA) identified 2139 significant changes. Middle insomnia in older adults is associated with transcriptomic changes indicative of an activated antiviral immune response, likely resulting from changes in dsRNA expression levels. The chronic inflammation arising from these transcriptomic alterations may underlie the observed association between middle insomnia and cardiometabolic conditions.

Background: Cardiovascular diseases remain a leading cause of death worldwide, yet the prevalence of pathogenic and likely pathogenic genetic variants associated with them is still underassessed in some populations. This study aimed to assess the frequency and geographic distribution of such variants within a representative sample of the Russian population. Additionally, it explored potential links between genotype and phenotype in a cohort of long-lived adults. Methods: We analyzed whole-genome sequencing data from 75,144 adults and 2,872 individuals aged 90 and older. Variants within 37 ACMG v3.1 genes were examined using InterVar, focusing on nonsynonymous variants and indels across exons and splicing sites. Variants were grouped based on ClinVar (as of 24 April 2023) annotations, with most subjected to manual review to confirm their significance. Results: Among the adult participants, 3,817 (5.1%) carried at least one of the variants under consideration. Of these, 141 (0.19%) carried pathogenic, 580 (0.77%) likely pathogenic, and 3,127 (4.16%) variants of uncertain significance. Variants not registered in ClinVar were found in 1,782 individuals (2.37%). Notably, one participant with cardiomyopathy carried a heterozygous TTN variant. In the long-lived cohort, 15 variants were classified as pathogenic or likely pathogenic, alongside 72 uncertain variants; overall, 19 individuals (0.66%) carried pathogenic or likely pathogenic variants. No significant difference was observed in variant frequency between the adult and long-lived groups. Conclusions: This study provided essential insights into the prevalence and geographic distribution of cardiovascular disease-related variants in Russia, laying the foundation for targeted genetic screening disease prevention strategies within this population.

The regular use of psychoactive substances, resulting in substance use disorders, represents a significant public health concern globally. Due to genetic variability and the substantial impact of environmental factors, identifying specific genes associated with predisposition to addictive behaviors presents a significant challenge. Regular exposure to psychoactive substances is known to change gene expression levels in the brain regions for reward and motivation. These changes affect the dopaminergic, glutamatergic, and cannabinoid systems of the brain. The existing data on cellular gene expression in the central nervous system has been predominantly derived from postmortem brain samples or animal models, which limits their clinical applicability. Therefore, analyzing gene expression levels in peripheral blood may provide considerable advantages for clinical practice. In this study, we conducted a differential gene expression analysis followed by a pathway analysis in individuals with long-term substance use disorders, such as opiates, psychostimulants, and cannabinoids. Our results revealed a significant number of genes exhibiting differential expression in peripheral blood. The pathway analysis suggested that the metabolic changes associated with regular substance use primarily impact energy catabolism, toxin metabolism, anabolic processes, and cell signaling pathways. The examination of peripheral blood transcriptome profiles provided valuable insights into the overall health status of individuals with substance use disorders related to various classes of psychoactive substances. Transcriptome analysis has the potential to significantly enhance the diagnosis of substance use disorders.

Orthostatic hypotension is a sharp decrease in blood pressure when an individual transitions from a supine to an upright position. OH affects at least 30% of older adults. It is attributed to the dysfunction of the autonomic innervation and decreased vascular bed capacity. Genomic (n = 2526), methylomic (n = 910), and transcriptomic (n = 391) data from centenarians aged 90 years and older were used to examine molecular and genetic factors for OH. No statistically significant genetic predictors of OH were identified. However, the study revealed numerous epigenetic markers of OH indicative of general aging, such as DNA hypomethylation. The predictive DNA methylation-based model for orthostatic hypotension demonstrated an average accuracy of 79%. The transcriptome analyses highlighted associations between OH and inflammation pathways, as well as other age-related biological processes. Integrated omics and clinical data have identified six key mechanisms associated with orthostatic hypotension: metabolic dysregulation, impaired muscle tone, altered cell proliferation, inflammation, humoral regulation, and neural regulation.

BackgroundCervical screening, aimed at detecting precancerous lesions and preventing cancer, is based on cytology and HPV testing. Both methods have limitations, the main ones being the variable diagnostic sensitivity of cytology and the moderate specificity of HPV testing. Various molecular biomarkers are proposed in recent years to improve cervical cancer management, including a number of mRNAs encoded by human genes involved in carcinogenesis. Many scientific papers have shown that the expression patterns of cellular mRNAs reflect the severity of the lesion, and their analysis in cervical smears may outperform HPV testing in terms of diagnostic specificity. However, such analysis has not yet been implemented in broad clinical practice. Our aim was to devise an assay detecting severe cervical lesions (≥HSIL) via analysis of cellular mRNA expression in cytological smears.MethodsThrough logistic regression analysis of a reverse-transcription quantitative PCR (RT-qPCR) dataset generated from analysis of six mRNAs in 167 cervical smears with various cytological diagnoses, we generated a family of linear classifiers based on paired mRNA concentration ratios. Each classifier outputs a dimensionless decision function (DF) value that increases with lesion severity. Additionally, in the same specimens, the HPV genotyping, viral load assessment, diagnosis of cervicovaginal microbiome imbalance and profiling of some relevant mRNAs and miRNAs were performed by qPCR-based methods.ResultsThe best classifiers were obtained with pairs of mRNAs whose expression changes in opposite directions during lesion progression. With this approach based on a five-mRNA combination (CDKN2A, MAL, TMPRSS4, CRNN, and ECM1), we generated a classifier having ROC AUC 0.935, diagnostic sensitivity 89.7%, and specificity 87.6% for ≥HSIL detection. Based on this classifier, a two-tube RT-qPCR based assay was developed and it confirmed the preliminary characteristics on 120 cervical smears from the test sample. DF values weakly correlated with HPV loads and cervicovaginal microbiome imbalance, thus being independent markers of ≥HSIL risk.ConclusionThus, we propose a high-throughput method for detecting ≥HSIL cervical lesions by RT-qPCR analysis of several cellular mRNAs. The method is suitable for the analysis of cervical cytological smears prepared by a routine method. Further clinical validation is necessary to clarify its clinical potential.

BackgroundAs the field of probiotic research continues to expand, new beneficial strains are being discovered. The Christensenellaceae family and its newly described member, Christensenella minuta, have been shown to offer great health benefits. We aimed to extensively review the existing literature on these microorganisms to highlight the advantages of their use as probiotics and address some of the most challenging aspects of their commercial production and potential solutions.MethodsWe applied a simple search algorithm using the key words “Christensenellaceae” and “Christensenella minuta” to find all articles reporting the biotherapeutic effects of these microorganisms. Only articles reporting evidence-based results were reviewed.ResultsThe review showed that Christensenella minuta has demonstrated numerous beneficial properties and a wider range of uses than previously thought. Moreover, it has been shown to be oxygen-tolerant, which is an immense advantage in the manufacturing and production of Christensenella minuta-based biotherapeutics. The results suggest that Christensenellaceae and Christensenella munita specifically can play a crucial role in maintaining a healthy gut microbiome. Furthermore, Christensenellaceae have been associated with weight management. Preliminary studies suggest that this probiotic strain could have a positive impact on metabolic disorders like diabetes and obesity, as well as inflammatory bowel disease.ConclusionChristensenellaceae and Christensenella munita specifically offer immense health benefits and could be used in the management and therapy of a wide range of health conditions. In addition to the impressive biotherapeutic effect, Christensenella munita is oxygen-tolerant, which facilitates commercial production and storage.

BackgroundPopulation studies are essential for gathering critical disease prevalence data. Automated pathogenicity assessment tools enhance the capacity to interpret and annotate large amounts of genetic data. In this study, we assessed the prevalence of cancer-associated germline variants in Russia using a semiautomated variant interpretation algorithm.MethodsWe examined 74,996 Russian adults (Group 1) and 2,872 long-living individuals aged ≥ 90 years (Group 2) for variants in 28 ACMG-recommended cancer-associated genes in three steps: InterVar annotation; ClinVar interpretation; and a manual review of the prioritized variants based on the available data. Using the data on the place of birth and the region of residence, we determined the geographical distribution of the detected variants and tracked the migration dynamics of their carriers.ResultsWe report 175 novel del-VUSs. We detected 232 pathogenic variants, 46 likely pathogenic variants, and 216 del-VUSs in Group 1 and 19 pathogenic variants, 2 likely pathogenic variants, and 16 del-VUSs in Group 2. For each detected variant, we provide a description of its functional significance and geographical distribution.ConclusionThe present study offers extensive genetic data on the Russian population, critical for future genetic research and improved primary cancer prevention and genetic screening strategies. The proposed hybrid assessment algorithm streamlines variant prioritization and pathogenicity assessment and offers a reliable and verifiable way of identifying variants of uncertain significance that need to be manually reviewed.

Introduction Modification of natural enzymes to introduce new properties and enhance existing ones is a central challenge in bioengineering. This study is focused on the development of Taq polymerase mutants that show enhanced reverse transcriptase (RTase) activity while retaining other desirable properties such as fidelity, 5 '- 3 ' exonuclease activity, effective deoxyuracyl incorporation, and tolerance to locked nucleic acid (LNA)-containing substrates. Our objective was to use AI-driven rational design combined with multiparametric wet-lab analysis to identify and validate Taq polymerase mutants with an optimal combination of these properties.Methods The experimental procedure was conducted in several stages: 1) On the basis of a foundational paper, we selected 18 candidate mutations known to affect RTase activity across six sites. These candidates, along with the wild type, were assessed in the wet lab for multiple properties to establish an initial training dataset. 2) Using embeddings of Taq polymerase variants generated by a protein language model, we trained a Ridge regression model to predict multiple enzyme properties. This model guided the selection of 14 new candidates for experimental validation, expanding the dataset for further refinement. 3) To better manage risk by assessing confidence intervals on predictions, we transitioned to Gaussian process regression and trained this model on an expanded dataset comprising 33 data points. 4) With this enhanced model, we conducted an in silico screen of over 18 million potential mutations, narrowing the field to 16 top candidates for comprehensive wet-lab evaluation.Results and Discussion This iterative, data-driven strategy ultimately led to the identification of 18 enzyme variants that exhibited markedly improved RTase activity while maintaining a favorable balance of other key properties. These enhancements were generally accompanied by lower Kd, moderately reduced fidelity, and greater tolerance to noncanonical substrates, thereby illustrating a strong interdependence among these traits. Several enzymes validated via this procedure were effective in single-enzyme real-time reverse-transcription PCR setups, implying their utility for the development of new tools for real-time reverse-transcription PCR technologies, such as pathogen RNA detection and gene expression analysis. This study illustrates how AI can be effectively integrated with experimental bioengineering to enhance enzyme functionality systematically. Our approach offers a robust framework for designing enzyme mutants tailored to specific biotechnological applications. The results of our biological activity predictions for mutated Taq polymerases can be accessed at https://huggingface.co/datasets/nerusskikh/taqpol_insilico_dms

Background Schizophrenia varies greatly from person to person, mainly because of its polygenic nature. Consequently, schizophrenia patients form distinct subphenotypes of schizophrenia, with specific symptom patterns and outcomes. Methods This study included 4257 adults, with long-term schizophrenia (control - 8955 individuals) who were assessed for schizophrenia with potentially severe outcomes based on following criteria: disability in functional and/or physical domains before the age of 40; severe negative symptoms (present in infancy or shortly after onset); a continuous course of the disease. Additionally, the time of the onset and aggressive/antisocial tendencies were assessed as one the predictors of potentially severe outcomes. A total of 817 participants met at least three of these criteria, i.e., had disruptive schizophrenia. A genome-wide and transcriptome-wide association study was conducted using linear regression and the PrediXcan algorithm. The obtained data were used to develop a polygenic risk model for early risk prediction of schizophrenia with potentially severe outcomes. Results Significant associations were found between schizophrenia and variants in CAMTA1, TRHDE, NELFE, and others. The PRS model demonstrated high performance in training, internal and external validation (ROC AUC of 0.9, 0.89, and 0.68, respectively). The functional pathway analysis highlighted pathways involved in ATP metabolism, myeloid cell differentiation, and apoptotic processes. Conclusion Subphenotyping schizophrenia may enhance the discovery of genetic factors affecting its development and progression. The GWAS and TWAS findings revealed general mechanisms involved in the development of schizophrenia with potentially severe outcomes, such as synapse regulation, inflammation, and apoptosis.

Previous studies examining the molecular and genetic basis of cognitive impairment, particularly in cohorts of long-living adults, have mainly focused on associations at the genome or transcriptome level. Dozens of significant dementia-associated genes have been identified, including APOE, APOC1, and TOMM40. However, most of these studies did not consider the intergenic interactions and functional gene modules involved in cognitive function, nor did they assess the metabolic changes in individual brain regions. By combining functional analysis with a transcriptome-wide association study, we aimed to address this gap and examine metabolic pathways in different areas of the brain of older adults. The findings from our previous genome-wide association study in 1155 older adults, 179 of whom had cognitive impairment, were used as input for the PrediXcan gene prediction algorithm. Based on the predicted changes in gene expression levels, we conducted a transcriptome-wide association study and functional analysis using the KEGG and HALLMARK databases. For a subsample of long-living adults, we used logistic regression to examine the associations between blood biochemical markers and cognitive impairment. The functional analysis revealed a significant association between cognitive impairment and the expression of NADH oxidoreductase in the cerebral cortex. Significant associations were also detected between cognitive impairment and signaling pathways involved in peroxisome function, apoptosis, and the degradation of lysine and glycan in other brain regions. Our approach combined the strengths of a transcriptome-wide association study with the advantages of functional analysis. It demonstrated that apoptosis and oxidative stress play important roles in cognitive impairment.

Multiple myeloma (MM) is characterized by the uncontrolled proliferation of monoclonal plasma cells and accounts for approximately 10% of all hematologic malignancies. The clinical outcomes of MM can exhibit considerable variability. Variability in both the genetic and epigenetic characteristics of MM undeniably contributes to tumor dynamics. The aim of the present study was to identify biomarkers with the potential to improve the accuracy of prognosis assessment in MM. Initially, miRNA sequencing was conducted on bone marrow (BM) samples from patients with MM. Subsequently, the expression levels of 27 microRNAs (miRNA) and the gene expression levels of ASF1B, CD82B, CRISP3, FN1, MEF2B, PD-L1, PPARγ, TERT, TIMP1, TOP2A, and TP53 were evaluated via real-time reverse transcription polymerase chain reaction in BM samples from patients with MM exhibiting favorable and unfavorable prognoses. Additionally, the analysis involved the bone marrow samples from patients undergoing examinations for non-cancerous blood diseases (NCBD). The findings indicate a statistically significant increase in the expression levels of miRNA-124, -138, -10a, -126, -143, -146b, -20a, -21, -29b, and let-7a and a decrease in the expression level of miRNA-96 in the MM group compared with NCBD (p < 0.05). No statistically significant differences were detected in the expression levels of the selected miRNAs between the unfavorable and favorable prognoses in MM groups. The expression levels of ASF1B, CD82B, and CRISP3 were significantly decreased, while those of FN1, MEF2B, PDL1, PPARγ, and TERT were significantly increased in the MM group compared to the NCBD group (p < 0.05). The MM group with a favorable prognosis demonstrated a statistically significant decline in TIMP1 expression and a significant increase in CD82B and CRISP3 expression compared to the MM group with an unfavorable prognosis (p < 0.05). From an empirical point of view, we have established that the complex biomarker encompassing the CRISP3/TIMP1 expression ratio holds promise as a prognostic marker in MM. From a fundamental point of view, we have demonstrated that the development of MM is rooted in a cascade of complex molecular pathways, demonstrating the interplay of genetic and epigenetic factors.

Aging is a natural process with varying effects. As we grow older, our bodies become more susceptible to aging-associated diseases. These diseases, individually or collectively, lead to the formation of distinct aging phenotypes. Identifying these aging phenotypes and understanding the complex interplay between coexistent diseases would facilitate more personalized patient management, a better prognosis, and a prolonged lifespan. Many studies distinguish between successful aging and frailty. However, this simple distinction fails to reflect the diversity of underlying causes. In this study, we sought to establish the underlying causes of frailty and determine the patterns in which these causes converge to form aging phenotypes. We conducted a comprehensive geriatric examination, cognitive assessment, and survival analysis of 2,688 long-living adults (median age = 92 years). The obtained data were clustered and used as input data for the Aging Phenotype Calculator, a multiclass classification model validated on an independent dataset of 96 older adults. The accuracy of the model was assessed using the receiver operating characteristic curve and the area under the curve. Additionally, we analyzed socioeconomic factors that could contribute to specific aging patterns. We identified five aging phenotypes: non-frailty, multimorbid frailty, metabolic frailty, cognitive frailty, and functional frailty. For each phenotype, we determined the underlying diseases and conditions and assessed the survival rate. Additionally, we provided management recommendations for each of the five phenotypes based on their distinct features and associated challenges. The identified aging phenotypes may facilitate better-informed decision-making. The Aging Phenotype Calculator (ROC AUC = 92%) may greatly assist geriatricians in patient management.

Disturbed cervicovaginal-microbiome (CVM) structure promotes human papillomavirus (HPV) persistence and reflects risks of cervical lesions and cancer onset and recurrence. Therefore, microbiomic biomarkers may be useful for cervical disease screening and patient management. Here, by 16S rRNA gene sequencing and commercial PCR-based diagnostic kits, we profiled CVM in cytological preparations from 140 HPV-tested women (from Novosibirsk, Russia) with normal cytological findings, cervical lesions, or cancer and from 101 women who had recently received different cancer therapies. An increase in lesion severity was accompanied by higher HPV prevalence and elevated CVM biodiversity. Post-treatment CVM was found to be enriched with well-known microbial biomarkers of dysbiosis, just as in cervical disease. Nonetheless, concentrations of some skin-borne and environmental species (which gradually increased with increasing lesion severity)-especially Cutibacterium spp., Achromobacter spp., and Ralstonia pickettii-was low in post-treatment patients and depended on treatment types. Frequency of Lactobacillus iners dominance was high in all groups and depended on treatment types in post-treatment patients. Microbiome analysis via PCR-based kits revealed statistically significant differences among all groups of patients. Thus, microbiome profiling may help to find diagnostic and prognostic markers for management of cervical lesions; quantitative PCR-based kits may be suitable for these purposes.

We previously developed a technique that uses the anti-HBs monoclonal antibody panel (MAbs) to determine the major variants of hepatitis B virus (HBV) genotypes and surface antigen (HBsAg) subtypes circulating in the Russian Federation: D/ayw2, D/ayw3, A/adw2, C/adrq+. To identify HBV genotypes and HBsAg subtypes in HBsAg-positive blood-serum samples of patients with chronic HBV infection in three Russian regions using the designed reagents; to compare the obtained data with the results obtained by the molecular biological methods of HBV genotype and HBsAg subtype identification and to combine them with the results of the previous tests. The comparative study included 127 HBsAg-positive serum samples of patients with chronic HBV infection from Barnaul, Krasnodar, and Blagoveshchensk collected in 2017–2019. HBV genotypes and HBsAg subtypes were determined in ELISA (enzyme immunoassay) using the designed reagents and by molecular-biology methods (DNA sequence analysis of detected HBV isolates). Among the 127 HBsAg-positive samples in which HBV genotypes/HBsAg subtypes were identified using the designed reagents, the results were distributed as follows: 65 (51.6