The course of COVID-19 is characterized by wide variability, with genetics playing a contributing role. Through large-scale genetic association studies, a significant link between genetic variants and disease severity was established. However, individual genetic variants identified thus far have shown modest effects, indicating a polygenic nature of this trait. To address this, a polygenic risk score (PRS) can be employed to aggregate the effects of multiple single nucleotide polymorphisms (SNPs) into a single number, allowing practical application to individuals within a population. In this work, we investigated the performance of a PRS model in the context of COVID-19 severity in 1,085 Russian participants using low-coverage NGS sequencing. By developing a genome-wide PRS model based on summary statistics from the COVID-19 Host Genetics Initiative consortium, we demonstrated that the PRS, which incorporates information from over a million common genetic variants, can effectively identify individuals at significantly higher risk for severe COVID-19. The findings revealed that individuals in the top 10% of the PRS distribution had a markedly elevated risk of severe COVID-19, with an odds ratio (OR) of 2.1 (95% confidence interval (CI): 1.4–3.2, p-value = 0.00046). Furthermore, incorporating the PRS into the prediction model significantly improved its accuracy compared to a model that solely relied on demographic information (p-value < 0.0001). This study highlights the potential of PRS as a valuable tool for identifying individuals at increased risk of severe COVID-19 based on their genetic profile.

BACKGROUND: Human genotype is a factor that determines the severity of COVID-19. Previously, a large-scale whole-genome association study of the COVID-19 Host Genetics Initiative (2021) investigated the association of genetic variants at multiple loci with COVID-19 severity. The genetic variants that have the greatest effect on COVID-19 severity are expected to have a low frequency in the population. Therefore, the study of rare variants may provide additional insights into the disease pathogenesis and thus help in the development of prevention and treatment options. AIM: To search for genes enriched for rare genetic variants associated with COVID-19 severity in the Russian population by replication analysis. METHODS: The clinical exome of a Russian cohort of patients was sequenced based on the St. Petersburg State Budgetary Institution “City Hospital No. 40” and St Petersburg University. The study used biomaterial from patients hospitalized at City Hospital No. 40 diagnosed with COVID-19 and healthy individuals (population control group). The severity of the course of COVID-19 was determined according to the results of lung computed tomography. The list of genes for subsequent replication was generated by a literature review. Burden test methods were used for the replication analysis of genes associated with COVID-19 severity. RESULTS: In total, 701 clinical exomes were sequenced from 263 individuals with severe COVID-19 and 438 healthy individuals. In the literature review, 18 genes associated with severe COVID-19 were included in the replication analysis. The replication analysis did not identify any genes whose association with severe COVID-19 was confirmed in the study cohort. CONCLUSION: The replication analysis did not identify any genes that showed a significant association between the functional variant enrichment and COVID-19 severity. However, the direction of the correlation was consistent with the findings of previous studies. Expanding the study cohort would increase the power of the tests and allow us to detect additional rare variants that influence the severity of COVID-19 progression.

Background/Objectives: The COVID-19 pandemic has posed a significant challenge to global healthcare systems and has prompted a need for a better understanding of the molecular mechanisms underlying SARS-CoV-2 infection. This study aims to analyze differential gene expression in COVID-19 patients to identify regulatory genes influencing key pathways involved in disease progression. Methods: We conducted a transcriptomic analysis of patients admitted to the Infectious Disease Department of City Hospital No. 40, confirmed with SARS-CoV-2 via PCR. The study received ethical approval (protocol No. 171, 18 May 2020), and all participants provided informed consent. Total RNA was extracted from blood samples, followed by RNA sequencing using the DNBSEQ-G400 platform. Differential gene expression was analyzed using the Mann–Whitney test, and Gene Ontology enrichment analysis was performed to identify relevant biological processes. Results: Our analysis revealed significant number of differentially expressed genes within studied groups (severity, outcome, cytokine storm and paired samples). These genes are involved in key regulatory and signal transduction pathways governing immune responses, intercellular communication, and the metabolism of various compounds. Furthermore, we identified genes ALOX15, PRL, FLT3, S100A8, S100A12, IL4, IL13, and a few others as master regulators within the studied pathways, which represent promising candidates for further investigation as potential therapeutic targets. Conclusions: This study highlights critical gene expression changes associated with COVID-19 severity and outcomes, identifying potential biomarkers. Our findings contribute to the understanding of the molecular drivers of COVID-19 and suggest new avenues for therapeutic interventions aimed at modulating immune responses.

The course of COVID-19 is determined by various factors. Studies worldwide have shown a correlation between changes in the expression profile and the severity of the disease. Thus, an in-depth study of differentially expressed genes will allow a more detailed investigation of the metabolic changes occurring in the background of coronavirus infection. The technique of RNA sequencing and subsequent bioinformatics analysis is suitable for such research tasks. In our study, we compared groups of samples from patients with mild and severe disease course and identified a number of differentially expressed genes. These genes are involved in metabolic pathways responsible for immune response, signaling, intercellular communication, metabolism of various compounds etc. We then identified master regulators whose function and role in pathways enriched by differentially expressed genes makes them potential targets for biochemical and meta-studies.

The current paper is dedicated to the research of the differential expression of blood leukocyte fraction (LF) genes in patients with colorectal cancer (CRC) and polyposis of the lower part of the gastrointestinal tract (GIT). Aim. Determination of gene expression profiles in peripheral blood LF cells in patients with CRC and polyps of the lower GIT, and creation of logistic regression models, which would make it possible to differentiate patients with CRC, polyposis. Material and methods. Patients diagnosed with CRC (n=33), polyposis (n=22), as well as control group (n=30) were included in the study. Gene expression was determined by means of the NGS method. Libraries were prepared with the use of the RNAHyperPrep&HyperCap kit. NGS was performed on the MiSeq sequencer. Results. Groups of differentially expressed genes were identified, and logistic regression models were built to differentiate patients diagnosed with CRC (accuracy 93.7%) and polyposis (accuracy 84.6%), in comparison with the control group, as well as patients diagnosed with CRC from patients with polyposis with the accuracy up to 78.3%. Conclusion. It is necessary to study the expression of genes with the highest diagnostic potential in patients from an independent sampling, using digital RT-PCR, to validate and advance the models we have created. В работе представлены результаты разработки малоинвазивного метода дифференциальной диагностики колоректального рака (КРР) и полипоза на основе анализа транкриптома клеток периферической крови. Цель исследования. Определение профилей экспрессии отобранных по данным литературы генов, в клетках лейкоцитарной фракции (ЛФ) периферической крови у пациентов с КРР и полипами нижних отделов желудочно-кишечного тракта (ЖКТ), и создание на основе этого прогностической модели, позволяющей дифференцированно диагностировать КРР и полипоз. Материал и методы. В исследование были включены образцы периферической крови пациентов с диагнозами КРР (n=33) и полипоз (n=22), а также контрольной группы пациентов (n=30). Выделенную из ЛФ РНК использовали для изготовления библиотек с помощью набора RNAHyperPrep&HyperCap (Roche NimbleGen, США) с двойным обогащением таргетными последовательностями. NGS осуществляли на аппарате MiSeq (Illumina, США). Количество прочтений, выровнявшихся с исследуемыми генами, принимали за оценку уровня экспрессии. Результаты. Были выделены группы генов, дифференциально экспрессирующихся у пациентов с диагнозами КРР и полипоз и в контроле и построены модели логистической регрессии, позволяющие осуществить диагностику КРР, на основании анализа экспрессии генов FRMD3, ANXA3, DPEP1, CPEB4, MMD и TLR1 (точность 93,7%), и полипоза, на основании анализа экспрессии SERPINB5, CEACAM5, DPEP1 и STC1 (точность 84,6%) при сравнении с контрольной группой и отличить пациентов с диагнозом КРР от пациентов с диагнозом полипоз на основании анализа экспрессии генов MMD и MDM2 с точностью 78,3%. Заключение. В дальнейшем необходимо исследование экспрессии генов с наибольшим диагностическим потенциалом у пациентов независимой выборки методом цифровой ОТ-ПЦР и уточнение созданных нами моделей.

Aim. To study the applicability of RNA (ribonucleic acid) sequencing with master regulator identification for predicting the effectiveness of targeted therapy in patients with colorectal cancer.Materials and methods. Tissue samples from three patients with colorectal cancer obtained from postoperative material were used. All patients received palliative antitumor therapy in standard regimens in accordance with the tumor location and the status of RAS and BRAF gene mutations. The transcriptome of tumor and healthy tissue of each patient was sequenced, and master regulators in the tumor tissue were analyzed.Results. A list of master regulators was found for each patient and possible therapeutic agents most suitable for suppressing the tumor process were predicted.Conclusion. The potential of computer analysis of the molecular profile of colorectal adenocarcinoma in predicting the effectiveness of therapy is shown. However, to determine the clinical potential of this technique, a study on a wider sample is required.