Results of expression studies can be useful to clarify the genotype-phenotype relationship. However, according to data from recent literature, there is a large group of genes that are revealed as differentially expressed (DE) in many studies, regardless of the biological context. Additional analyses could shed more light on the relationships between genes, their differential expression, and diseases. We generated a set of 9972 disease genes from five gene-phenotype databases (OMIM, ORPHANET, DDG2P, DisGeNet and MalaCards) and a report of the International Union of Immunological Societies. To study transcriptomics of disease and non-disease genes in healthy tissues, we obtained data from the Human Protein Atlas (HPA) website. We analyzed the dependency between expression in healthy tissues and gene occurrence in Gene Expression Omnibus series using tools within the Enrichr libraries. The results of expression studies were annotated with Gene Ontology (GO) and Human Phenotype Ontology (HPO) terms. Using transcriptomics analysis of healthy tissues, we validated the previous findings of higher expression levels of disease genes in pathologically linked tissues compared to other tissues. Preferentially DE genes were generally highly expressed in one or multiple tissues and were enriched for disease genes. According to the results of GO enrichment analyses, both down- and up-regulated DE genes most often took part in immune response, translation and tissue-specific processes. A connection between DE-related pathology and the diversity of HPO terms was found. Investigating a link between expression and phenotype contributes to understanding the mode of development and progression of human diseases.

COVID-19 patients with acute respiratory distress syndrome (ARDS) have an immune imbalance when systemic inflammation and dysfunction of circulating T and B cells lead to a more severe disease. Using TREC/KREC analysis, we studied the level of mature naive T and B cells in peripheral blood of COVID-19 patients and its relationship with clinical and laboratory data. TREC/KREC analysis was performed by multiplex real-time quantitative PCR on a sample of 36 patients aged 45 years or younger. The reduced TREC/KREC level was observed in ARDS patients compared with non-ARDS patients, and similar results were found for the deceased patients. During days 6 to 20 of hospitalization, a higher neutrophil-to-lymphocyte ratio (NLR) was detected in ARDS patients compared with non-ARDS patients. TREC/KREC negatively correlated with NLR; the highest correlation was recorded for TREC per 100,000 cells with the coefficient of determination R² = 0.527. Thus, TREC/KREC analysis is a potential prognostic marker for assessing the severity and outcome in COVID-19.

Pneumonia is an acute infectious disease with high morbidity and mortality rates. Pneumonia’s development, severity and outcome depend on age, comorbidities and the host immune response. In this study, we combined theoretical and experimental investigations to characterize pneumonia and its comorbidities as well as to assess the host immune response measured by TREC/KREC levels in patients with pneumonia. The theoretical study was carried out using the Columbia Open Health Data (COHD) resource, which provides access to clinical concept prevalence and co-occurrence from electronic health records. The experimental study included TREC/KREC assays in young adults (18–40 years) with community-acquired (CAP) (n = 164) or nosocomial (NP) (n = 99) pneumonia and healthy controls (n = 170). Co-occurring rates between pneumonia, sepsis, acute respiratory distress syndrome (ARDS) and some other related conditions common in intensive care units were the top among 4170, 3382 and 963 comorbidities in pneumonia, sepsis and ARDS, respectively. CAP patients had higher TREC levels, while NP patients had lower TREC/KREC levels compared to controls. Low TREC and KREC levels were predictive for the development of NP, ARDS, sepsis and lethal outcome (AUC_TREC in the range 0.71–0.82, AUC_KREC in the range 0.67–0.74). TREC/KREC analysis can be considered as a potential prognostic test in patients with pneumonia.

Traumatic brain injury (TBI) is the leading cause of global mortality and morbidity. Because TBI is accident-related, the role of genetics in predisposing to TBI has been largely unexplored. However, the likelihood of injury may not be entirely random and may be associated with certain physical and mental characteristics. In this study, we analyzed the exomes of 50 patients undergoing rehabilitation after TBI. Patients were divided into three groups according to rehabilitation outcome: improvement, no change, and deterioration/death. We focused on rare, potentially functional missense and high-impact variants in genes intolerant to these variants. The concordant results from the three independent groups of patients allowed for the suggestion of the existence of a genetic predisposition to TBI, associated with rare functional variations in intolerant genes, with a prevalent dominant mode of inheritance and neurological manifestations in the genetic phenotypes according to the OMIM database. Forty-four of the 50 patients had one or more rare, potentially deleterious variants in one or more neurological genes. Comparison of these results with those of a 50-sampled matched non-TBI cohort revealed significant differences: P = 2.6 × 10⁻³, OR = 4.89 (1.77–13.47). There were no differences in the distribution of the genes of interest between the TBI patient groups. Our exploratory study provides new insights into the impact of genetics on TBI risk and is the first to address potential genetic susceptibility to TBI.

Changes in red blood cell (RBC)-related parameters and anemia are common in both severe chronic and acute diseases. RBC-related phenotypes have a heritable component. However, it is unclear whether the contribution of genetic variability is pronounced when hematological parameters are affected by physiological stress. In this study, we analyzed RBC-related phenotypes and phenotype–genotype correlations in two exome-sequenced patient cohorts with or at a high risk for a critical illness: chronic TBI patients admitted for rehabilitation and patients with acute COVID-19. In the analysis of exome data, we focused on the cumulative effects of rare high-impact variants (qualifying variants, QVs) in specific gene sets, represented by Notch signaling pathway genes, based on the results of enrichment analysis in anemic TBI patients and three predefined gene sets for phenotypes of interest derived from GO, GWAS, and HPO resources. In both patient cohorts, anemia was associated with the cumulative effects of QVs in the GO (TBI: p = 0.0003, OR = 2.47 (1.54–4.88); COVID-19: p = 0.0004, OR = 2.12 (1.39–3.25)) and Notch pathway-derived (TBI: p = 0.0017, OR = 2.33 (1.35–4.02); COVID-19: p = 0.0012, OR =8.00 (1.79–35.74)) gene sets. In the multiple linear regression analysis, genetic variables contributed to RBC indices in patients with TBI. In COVID-19 patients, QVs in Notch pathway genes influenced RBC, HGB, and HCT levels, whereas genes from other sets influenced MCHC levels. Thus, in this exploratory study, exome data analysis yielded similar and different results in the two patient cohorts, supporting the view that genetic factors may contribute to RBC-related phenotypic performance in both severe chronic and acute health conditions.

The aim of this study was to assess the applicability of the bacterial lux biosensors for genotoxicological studies. Biosensors are the strains of E. coli MG1655 carrying a recombinant plasmid with the lux operon of the luminescent bacterium P. luminescens fused with the promoters of inducible genes: recA, colD, alkA, soxS, and katG. The genotoxicity of forty-seven chemical compounds was tested on a set of three biosensors pSoxS-lux, pKatG-lux and pColD-lux, which allowed us to estimate the oxidative and DNA-damaging activity of the analyzed drugs. The comparison of the results with the data on the mutagenic activity of these drugs from the Ames test showed a complete coincidence of the results for the 42 substances. First, using lux biosensors, we have described the enhancing effect of the heavy non-radioactive isotope of hydrogen deuterium (D₂O) on the genotoxicity of chemical compounds as possible mechanisms of this effect. The study of the modifying effect of 29 antioxidants and radioprotectors on the genotoxic effects of chemical agents showed the applicability of a pair of biosensors pSoxS-lux and pKatG-lux for the primary assessment of the potential antioxidant and radioprotective activity of chemical compounds. Thus, the results obtained showed that lux biosensors can be successfully used to identify potential genotoxicants, radioprotectors, antioxidants, and comutagens among chemical compounds, as well as to study the probable mechanism of genotoxic action of test substance.