Bogomoletz Institute of Physiology

Arterial hypertension (AH) and heart failure (HF) are age-associated conditions usually accompanied by chronic inflammation. Neutrophil extracellular traps (NET) are important during inflammation and left ventricular remodeling but their functions in HF are poorly studied. Myeloid-derived suppressor cells (MDSC) that fulfill anti-inflammatory functions are also not studied during HF. The goal of this work is to evaluate the balance between proinflammatory NET-producing neutrophils and anti-inflammatory MDSC in pathogenesis of AH and HF in elder rats. Wistar and spontaneously hypertensive rats (SHR) aged 5 and 16months were subjected to cardiodynamic parameters monitoring. NET formation was examined using fluorescence microscopy. MDSC were determined as CD11b/c+-His28+-RP1+/low (granulocytic) and CD11b/c+-His28+-RP1- (monocytic) cells by flow cytometry. Signs of HF were observed at AH: aged Wistar rats had decreased by 20.4 ± 15.5% ejection fraction (p = 0.006) compared to young ones, and old SHR by 29.4 ± 21% (p = 0.0007). Aged Wistar rats had the level of NET 3.0 times higher (p = 0.03) than young ones; old SHR-2.9 times higher compared to young SHR (p < 0.001). In old SHR the percentage of granulocytic MDSC from the total number of leukocytes in peripheral blood was 3.98 times higher than in young SHR (p = 0.006), and 3.3 times higher than in old Wistar rats (p = 0.01); and monocytic MDSC-4.75 times higher than in young SHR (p = 0.003), and 2.3 times higher than in old Wistar rats (p = 0.04). Here, we show that HF in old rats leads to significant increase of the NET and MDSC amount versus young animals.

Pharmacological studies in vitro demonstrate the preventive and therapeutic potential of green tea and its constituent epigallocatechin-3-gallate (EGCG) in the fight against coronavirus disease 2019 (COVID-19). Previously reported correlations between per capita green tea consumption and COVID-19 morbidity/mortality suggest similar effects in vivo. Considering that some recent SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) sub-variants are less influenced by EGCG, this study aimed to determine whether this affects the aforementioned correlations, focusing on comparisons between the periods before (2021) and after (2022-2024) the emergence of the Omicron variant. Correlations between per capita green tea consumption and COVID-19 morbidity/mortality were calculated using multiple regression models accounting for several confounding factors in a subset (n=84) of countries/territories worldwide with Human Development Index (HDI) above 0.55. Higher per capita green tea consumption was associated with lower COVID-19 morbidity and mortality. Statistically significant correlations were observed in 2021-2024. Compared with 2021, the strength of both correlations decreased; the relative decrease in the strength of the correlation between per capita green tea consumption and COVID-19 mortality was notably less pronounced. This differential decrease at the epidemiological level supports the idea that green tea consumption may have not only preventive but also therapeutic value regarding COVID-19. This aligns with in vitro pharmacological evidence indicating that green tea constituents target distinct molecular pathways responsible for the entry of the virus and its replication. While promising, these findings require further assessment in observational and interventional studies focused on potential therapeutic benefits.

Renal cell carcinoma (RCC) is a prevalent malignancy with highly variable outcomes. MicroRNA-15a (miR-15a) has emerged as a promising prognostic biomarker in RCC, linked to angiogenesis, apoptosis, and proliferation. Radiogenomics integrates radiological features with molecular data to non-invasively predict biomarkers, offering valuable insights for precision medicine. This study aimed to develop a machine learning-assisted radiogenomic model to predict miR-15a expression in RCC. A retrospective analysis was conducted on 64 RCC patients who underwent preoperative multiphase contrast-enhanced CT or MRI. Radiological features, including tumor size, necrosis, and nodular enhancement, were evaluated. MiR-15a expression was quantified using real-time qPCR from archived tissue samples. Polynomial regression and Random Forest models were employed for prediction, and hierarchical clustering with K-means analysis was used for phenotypic stratification. Statistical significance was assessed using non-parametric tests and machine learning performance metrics. Tumor size was the strongest radiological predictor of miR-15a expression (adjusted R2 = 0.8281, p < 0.001). High miR-15a levels correlated with aggressive features, including necrosis and nodular enhancement (p < 0.05), while lower levels were associated with cystic components and macroscopic fat. The Random Forest regression model explained 65.8% of the variance in miR-15a expression (R2 = 0.658). For classification, the Random Forest classifier demonstrated exceptional performance, achieving an AUC of 1.0, a precision of 1.0, a recall of 0.9, and an F1-score of 0.95. Hierarchical clustering effectively segregated tumors into aggressive and indolent phenotypes, consistent with clinical expectations. Radiogenomic analysis using machine learning provides a robust, non-invasive approach to predicting miR-15a expression, enabling enhanced tumor stratification and personalized RCC management. These findings underscore the clinical utility of integrating radiological and molecular data, paving the way for broader adoption of precision medicine in oncology.

Abstract The Novel Foods Risk Assessment Data Modelling and Extraction (NORA) project, commissioned by the European Food Safety Authority (EFSA) and implemented by EcoMole s.r.o., aimed to develop a structured database to support the risk assessment process for novel foods (NF). The project encompassed three key objectives: (1) designing a comprehensive data model for NF opinions, leveraging and extending existing frameworks like OpenFoodTox 2.0; (2) extracting and integrating data from published NF opinions into the database; and (3) creating a guideline for systematic data entry to support future use. The resulting data model successfully captured administrative, compositional, toxicological, and allergenic properties of NFs with approximately 35% of the data model adopted from OpenFoodTox 2.0 and 65% representing new or refined elements specific to NF assessment. Relevant data from 196 NF opinions published by June 2024 were extracted and integrated into the database. The database supports advanced querying, filtering, and exporting functionalities, while a user interface facilitates efficient data entry and visualisation.

One of the serious cardiovascular complications during the COVID-19 pandemic was myocardial inflammation, which often affected young patients but was particularly dangerous for the elderly. This condition, triggered by systemic inflammation in acute respiratory distress syndrome, contributed to the progression of heart failure. Due to the lack of effective treatment methods, this led to high mortality rates among affected patients. The lipopolysaccharide-induced model of myocardial injury can replicate the pathogenic myocardial changes characteristic of inflammation seen in COVID-19, providing a valuable tool for experimental studies of potential anti-inflammatory therapies, including stem cell treatments. Objective. This study aimed to develop and validate a lipopolysaccharide-induced model of inflammatory myocardial injury to investigate the regenerative potential of stem cells in myocarditis of various origins. Materials and methods. The study was conducted on female FVB «wild-type» mice aged 4–5 months. A single intraperitoneal injection of E. coli lipopolysaccharide was administered at doses of 1 mg/kg or 5 mg/kg to induce a systemic inflammatory response. Electrocardiographic assessments were performed before the injection and on days 7 and 14 post-injection. On these days, histological sections of the heart were prepared to analyze morphological markers of inflammatory myocardial injury, and immunohistochemical staining for the apoptosis marker CD95 was conducted. Results. One week after the administration of lipopolysaccharide, electrocardiographic studies in mice revealed an increase in heart rate compared to baseline, with increases of 19.6 % and 13.2 % following the administration of lipopolysaccharide (LPS) at doses of 1 mg/kg and 5 mg/kg, respectively. This response indicates a compensatory reaction to the impairment of the heart’s contractile function due to myocardial injury. Additionally, signs of intraventricular conduction disturbances were observed, including R wave deformation and ventricular extrasystoles, which persisted in the group receiving LPS at a dose of 5 mg/kg after two weeks. Histological sections in both animal subgroups, one week after LPS administration, revealed cytoplasmic swelling and eosinophilia resulting from myofibril contraction, along with significant vascular congestion characterized by stasis and aggregation of erythrocytes. These changes were more pronounced in the group receiving the 5 mg/kg LPS dose. Two weeks after administration of LPS at a dose of 1 mg/kg, significantly less severe manifestations of myocardial injury were observed, indicating partial recovery due to endogenous repair mechanisms. In contrast, the group receiving the 5 mg/kg dose exhibited morphological signs of persistent inflammation in the heart tissue. Immunohistochemical analysis revealed the expression of the apoptosis marker CD95 in cells throughout the entire section of myocardium in mice with LPS-induced inflammation. Conclusions. A model of lipopolysaccharide-induced inflammatory myocardial injury in mice has been developed and validated for investigating the regenerative potential of stem cells in inflammatory heart diseases. Electrophysiological and morphological studies indicate that a dose of 5 mg/kg of E. coli lipopolysaccharide is optimal for inducing more pronounced inflammatory changes in the myocardium of laboratory mice compared to a dose of 1 mg/kg.

Background.Monoclonal antibodies (mABs) and their fragments are widely used for therapeutic, diagnostic, research and other purposes.At the same time, the de ve lop ment of this group of pharmacotherapeutic agents still encounters a number of challenges.One of the areas of improvement in translational (T) studies is the use of pharmacometrics in silico, which can be explained by significant progress in the computing facilities, increased data volume and information availability.Aim.The aim of the study was to identify trends and directions of using physiologically based pharmacokinetic modelling (PBPK) in silico in translational studies of mABs.Methods.The analysis of scientific publications of the PubMed database for the period up to May 2024 inclusive was used.The keywords used in the advanced search were combinations of the words "PBPK" and "mAB", "antibody", "antibody drug conjugate", and combinations of "PBPK" with drugs (mABs) that fit our scope.Results.In total, 129 scientific articles were identified according to the selected criteria, including 110 (85%) original and 19 (15%) review articles.Although the first paper was published in 1994, 90% (116 publications) were published in the last 10 years (2013-May 2024), including 61% (79) in the last 5 years (2019-May 2024).Over the past 5 years, more than two thirds of original publications were related to the basic (T0) and preclinical (T1) stages of translational studies.43% of the papers were devoted to the study of parameters that may affect the absorption, distribution, metabolism and excre tion (ADME) of mABs, including physicochemical char

Acid-sensing ion channels (ASICs) play a key role in the perception and response to extracellular acidification changes. These proton-gated cation channels are critical for neuronal functions, like learning and memory, fear, mechanosensation and internal adjustments like synaptic plasticity. Moreover, they play a key role in neuronal degeneration, ischemic neuronal injury, seizure termination, pain-sensing, etc. Functional ASICs are homo or heterotrimers formed with (ASIC1-ASIC3) homologous subunits. ASIC1a, a major ASIC isoform in the central nervous system (CNS), possesses an acidic pocket in the extracellular region, which is a key regulator of channel gating. Growing data suggest that ASIC1a channels are a potential therapeutic target for treating a variety of neurological disorders, including stroke, epilepsy and pain. Many studies were aimed at identifying allosteric modulators of ASIC channels. However, the regulation of ASICs remains poorly understood. Using all available crystal structures, which correspond to different functional states of ASIC1, and a molecular dynamics simulation (MD) protocol, we analyzed the process of channel inactivation. Then we applied a molecular docking procedure to predict the protein conformation suitable for the amiloride binding. To confirm the effect of its sole active blocker against the ASIC1 state transition route we studied the complex with another MD simulation run. Further experiments evaluated various compounds in the Enamine library that emerge with a detectable ASIC inhibitory activity. We performed a detailed analysis of the structural basis of ASIC1a inhibition by amiloride, using a combination of in silico approaches to visualize its interaction with the ion pore in the open state. An artificial activation (otherwise, expansion of the central pore) causes a complex modification of the channel structure, namely its transmembrane domain. The output protein conformations were used as a set of docking models, suitable for a high-throughput virtual screening of the Enamine chemical library. The outcome of the virtual screening was confirmed by electrophysiological assays with the best results shown for three hit compounds.