The coronavirus disease 2019 (COVID-19) is accompanied by a cytokine storm with the release of many proinflammatory factors and development of respiratory syndrome. Several SARS-CoV-2 lineages have been identified, and the Delta variant (B.1.617), linked with high mortality risk, has become dominant in many countries. Understanding the immune responses associated with COVID-19 lineages may therefore aid the development of therapeutic and diagnostic strategies. Multiple single-cell gene expression studies revealed innate and adaptive immunological factors and pathways correlated with COVID-19 severity. Additional investigations covering host–pathogen response characteristics for infection caused by different lineages are required. Here, we performed single-cell transcriptome profiling of blood mononuclear cells from the individuals with different severity of the COVID-19 and virus lineages to uncover variant specific molecular factors associated with immunity. We identified significant changes in lymphoid and myeloid cells. Our study highlights that an abundant population of monocytes with specific gene expression signatures accompanies Delta lineage of SARS-CoV-2 and contributes to COVID-19 pathogenesis inferring immune components for targeted therapy.

Introduction: Morphofunctional changes of the circulatory system organs detected in athletes may remain without due attention, as clinical (phenotypic) signs of pathological abnormalities are very similar to manifestations of cardiovascular system adaptation to intensive physical loads. The aim of the study is to propose a personalized algorithm for biomedical support of professional athletes with abnormalities and diseases of the circulatory organs based on clinical and genomic data.Materials and methods: The results of in-depth medical examination (2021-2023) of 15,464 athletes who are members of Russian sports teams were analyzed. The structure of circulatory system diseases according to the codes of the International Classification of Diseases, 10th revision (ICD-10), which were included in the summary report of the last examination, was analyzed. Fifty athletes with abnormalities and diseases of the circulatory system organs, experiencing different degrees of intensity of dynamic and static loads in accordance with the Mitchell classification, were selected from the study sample for full genome sequencing and subsequent clinical interpretation of the obtained data.Results: In the study sample the number of people with pathologic conditions of the circulatory system organs amounted to 6 946 people (45 %). Mitchell classification groups had statistically significant differences with respect to the prevalence of 10 diseases of the circulatory system organs. In 50 DNA samples of professional athletes, 5 probably pathogenic variants (10%), 19 variants with uncertain clinical significance (38%), relevant to the phenotype of a monogenic disease with circulatory system organ damage, were detected.Conclusion: Molecular genetic testing is an effective tool for differential diagnostics of pathologic and adaptive changes in the organs of the circulatory system. Carrying causative genes in combination with clinical signs allows to change the tactics of medical and biological support of an athlete according to the proposed algorithm.

This case report describes a novel intronic mutation, CYP21A2:c.738+75C>T (rs1463196531), identified in a 4-year-old male with congenital adrenal insufficiency, and expands the known mutation spectrum associated with this condition. The patient, born full-term to unrelated parents, presented with adrenal failure within the first month of life, characterized by acute adrenal crisis symptoms such as vomiting, dehydration, weight loss, hypotension, and electrolyte imbalances. Hormonal evaluations confirmed primary adrenocortical insufficiency, necessitating ongoing hydrocortisone and fludrocortisone therapy. Using family trio-based amplicon sequencing of the CYP21A2 gene, we identified compound heterozygosity consisting of a full gene deletion and a novel pathogenic intronic mutation. Additionally, analysis of WGS data was performed to rule out pathogenic variants in genes that might lead to a similar phenotype, thereby eliminating the possibility of other genes contributing to the proband’s disease. This case demonstrates the potential of using amplicon sequencing in molecular genetic diagnostic testing to detect rare intronic variants in the CYP21A2 gene in cases of early-onset adrenal failure. It also contributes to a better understanding of the genetic basis of congenital adrenal hyperplasia (CAH), which remains a significant autosomal recessive disorder affecting cortisol and aldosterone production, with an incidence of 1 in 10,000 to 1 in 15,000 globally.

Type 1 Diabetes Mellitus (T1D) is an autoimmune disease characterized by the destruction of pancreatic β-cells, predominantly manifesting in childhood or adolescence. The lack of clearly interpretable biological markers in the early stages, combined with the insidious onset of the disease, poses significant challenges to early diagnosis and the implementation of preventive strategies. The applicability of classic T1D biomarkers for understanding the mechanisms of the autoimmune process, preclinical diagnostics and treatment efficiency is limited. Despite advances in next-generation sequencing (NGS) technologies, which have enabled large-scale genome-wide association studies (GWASs) and the identification of polygenic risk scores (PRSs) associated with T1D predisposition, as well as progress in bioinformatics approaches for assessing dysregulated gene expression, no universally accepted risk assessment model or definitive predictive biomarker has been established. Until now, the use of new promising biomarkers for T1D diagnostics is limited by insufficient evidence base. However, they have great potential for the development of diagnostic methods on their basis, which has been shown in single or serial large-scale studies. This critical review covers both well-known biomarkers widely used in clinical practice, such as HLA-haplotype, non-HLA SNPs, islet antigen autoantibodies, C-peptide, and the promising ones, such as cytokines, cfDNA, microRNA, T1D-specific immune cells, islet-TCR, and T1D-specific vibrational bands. Additionally, we highlight new approaches that have been gaining popularity and have already demonstrated their potential: GWAS, single-cell transcriptomics, identification of antigen-specific T cells using scRNA-seq, and FTIR spectroscopy. Although some of the biomarkers, in our opinion, are still limited to a research context or are far from being implemented in clinical diagnostics of T1D, they have the greatest potential of being applied in clinical practice. When integrated with the monitoring of the classical autoimmune diabetes markers, they would increase the sensitivity and specificity during diagnostics of early and preclinical stages of the disease. This critical review aims to evaluate the current landscape of classical and emerging biomarkers in autoimmune diabetes, with a focus on those enabling early detection—prior to extensive destruction of pancreatic islets. Another goal of the review is to focus the attention of the scientific community on the gaps in early T1D diagnostics, and to help in the selection of markers, targets, and methods for scientific studies on creating novel diagnostic panels.

Objective: The goal of this study was to compare the results of CNV detection by three different methods using 13 paired carcinoma samples, as well as to perform a statistical analysis of the agreement. Methods: CNV was studied using NanoString nCounter v2 Cancer CN Assay (Nanostring), Illumina Infinium CoreExome microarrays (CoreExome microarrays) and digital droplet PCR (ddPCR). Results: There was a good level of agreement (PABAK score > 0.6) between the CoreExome microarrays and the ddPCR results for finding CNVs. There was a moderate level of agreement (PABAK values ≈ 0.3–0.6) between the NanoString Assay results and microarrays or ddPCR. For 83 out of 87 target genes studied (95%), the agreement between the CoreExome microarrays and NanoString nCounter was characterized by PABAK values < 0.75, except for MAGI3, PDGFRA, NKX2-1 and KDR genes (>0.75). The MET, HMGA2, KDR, C8orf4, PAX9, CDK6, and CCND2 genes had the highest agreement among all three approaches. Conclusions: Therefore, to get a better idea of how to genotype an unknown CNV spectrum in tumor or normal tissue samples that are very different molecularly, it makes sense to use at least two CNV detection methods. One of them, like ddPCR, should be able to quantitatively confirm the results of the other.