卷 59, 编号 5 (2025)

Antimicrobial peptides, particularly proline-arginine-rich cationic antimicrobial peptides (PRAMPs), have recently attracted the attention of researchers as potential candidates for the development of new generation antibacterial drugs. This interest stems from PRAMPs’ ability to target bacteria resistant to currently used antibiotics, through a unique mechanism involving interaction with bacterial ribosomes and inhibition of protein synthesis. Additionally, PRAMPs exhibit a broad spectrum of activity against Gram-negative bacteria, low rates of bacterial resistance, and relative ease of structural modification. The use of antimicrobial peptides in practice is limited due to several factors, including their susceptibility to proteolytic degradation in biological media and their insufficiently broad spectrum of antibacterial activity against Gram-positive bacteria. Additionally, there is a possibility of resistance developing in bacteria against AMPs, as well as toxic effects resulting from the interaction of PRAMPs with certain components of eukaryotic cells. To overcome these challenges, researchers have explored various strategies, such as modifying the structure of PRAMPs and conjugating them with other molecules. This review examines recent literature on analogues and conjugates of PRAMPs, along with information on methods of modifying PRAMPs. The analysis of new properties of these compounds highlights the potential for creating effective antibacterial agents based on PRAMPs.

Traditional methods of treatment and prevention of cardiovascular disease (CVD) are not always effective, especially in severe myocardial injury. One of the promising areas for the treatment of cardiac pathologies is cell transplantation using tissue-engineered constructs from allogeneic stem cells, such as cell sheets. The success of cell therapy depends on the severity of local inflammatory reactions, angiogenesis activity, resistance of transplant cells to hypoxia and apoptosis, as well as on their production of extracellular matrix. Single nucleotide polymorphisms (SNPs) in genes involved in the processes associated with CDV can serve as markers of genetic dysfunction of these genes in the cardiovascular system and be used to predict the efficacy of the heart disease therapy based on tissue-engineered constructs. The review systematizes the information, allowing us to form a panel of such SNPs and analyze it. We identified seven genes at the intersection of pathways that are key to the survival of cellular constructs, VEGFA, TGFB1, FN1, IL6R, ITIH4, NRP1 and CDH13, and selected SNPs rs998584, rs8108632, rs1250259, rs6689306, rs77347777, rs75082222 and rs6565060, which are located in the regions of these genes and associated with CVD according to the Genome-Wide Association Study (GWAS). These polymorphisms may constitute a minimal panel to search for an association with the efficacy of cell therapy in heart disease.

Efficient analysis of large amounts of transcriptome data requires fast and easy access to raw gene expression data. In our work, we localized the available expression data of more than 50000 genes in 54 tissues from approximately 1000 individuals from the GTEx system and created an easy-to-use interface for accessing and selecting these data. Using the capabilities of the localized system, we selected seven genes with highly stable expression from the housekeeping genes, investigated the changes in the number and activity of mast cells in the tibial artery with aging, studied changes in the components of the intestinal barrier and the state of mucosal immunity in old age in connection with the increased incidence of ulcerative colitis after 60 years. These examples demonstrate the applicability of the localized GTEx database in various biomedical projects and applications.

Pancreatic Ductal AdenoCarcinoma (PDAC) is one of the most therapy-resistant tumors. Cultured cells originating from different stages of PDAC development are characterized by different levels of expression of a number of transcription factors. In particular, poorly differentiated high-grade PDAC cells are characterized by increased expression of ZEB1 gene encoding multifunctional transcription factor ZEB1, one of the main regulators of epithelial-mesenchymal transition. By the method of Circular Chromosome Conformation Capture (4C-seq) we studied the spatial organization of chromatin of regulatory region of ZEB1 gene in cultures of highly differentiated PDAC cells (Capan2) with low level of ZEB1 expression and poorly differentiated PDAC MIA PaCa2 cells with a high level of expression of this gene, and compared it with the chromatin organization of KLF5 gene. The number and distribution of contacts of the ZEB1 regulatory region with other chromatin regions are similar in these cell types and differ significantly from the pattern of distribution of contacts characteristic for KLF5 gene studied earlier. In Capan2 cells, the contacts of the regulatory region of the ZEB1 gene are tend to locate in regions with an increased level of H3K27ac modification, whereas in MIA PaCa2 cells these contacts are predominantly located in regions with a decreased level of H3K27ac. Consequently, the probability of contact of distant chromatin regions is primarily determined not by the degree of chromatin openness/activity of this region. To explain the data obtained, we assumed that the main regulator of the ZEB1 gene transcription level in the studied cells is a transcriptional repressor, whereas for the KLF5 gene main regulator is a transcriptional activator. According to a number of properties, one of the possible candidates for the role of this repressor may be the product of the ZNF438 gene. In addition, we have characterized a number of regions in contact with the ZEB1 promoter that are specific for MIA PaCa2 cells and contain potential regulators of this gene activity.

Breast cancer remains one of the leading causes of cancer mortality among women, and the study of epigenetic mechanisms is an important task of molecular oncology in breast cancer. In this study, we analyzed the expression levels of 8 microRNAs (miR-125b-5p, -127-5p, -129-5p, -132-3p, -148a-3p, -193a-5p, -24-2-5p, -34b-3p) and methylation of promoter regions of 7 microRNA genes in a representative sample of 40 and 70 paired samples of tumor and normal breast tissue, respectively, and showed hypermethylation of promoter regions of 7 genes and statistically significant decrease in expression levels of 8 microRNAs in tumor. For three genes (MIR125B-1, MIR129-2, MIR148A), inverse relationships between methylation and expression (r_s <–0.5) were revealed, indicating their possible epigenetic regulation. Statistically significant positive correlations of expression levels were revealed for 7 pairwise combinations of miRNAs, suggesting their coordinated functioning. Indeed, for the pairs miR-127-5p/miR-125b-5p, miR-148a-3p/miR-125b-5p, miR-148a-3p/miR-132-3p, miR-34b-3p/miR-193a-5p, common mRNA targets and involvement in biological processes, including pathways associated with epigenetic regulation, proliferation and metastasis, were revealed. The miRNA–mRNA regulatory network constructed involving DNMTs and EZH2 highlights their potential role in breast cancer progression and demonstrates diagnostic and prognostic significance.

Bacterial purine nucleoside phosphorylase (PNP) E. coli can be considered as an attractive model enzyme for investigation of metabolism and stability of biologically active nucleosides in a cell. This work is devoted to the investigation of the substrate specificity of E. coli PNP in the reactions of phosphorolytic cleavage of the glycoside bond in adenosine derivatives containing a cyclic terpene fragment as precursors for the production of biologically active purine derivatives. A number of N⁶-terpene substituted adenosine derivatives with different structures of the hydrocarbon substituent were obtained. By measuring the kinetic parameters of the phosphorolysis reaction, it was shown that adenosine derivatives containing a bicyclic hydrocarbon fragment bind to the enzyme more efficiently than a monocyclic derivative. The obtained results make it possible to produce new purine derivatives containing a lipophilic terpene fragment under mild reaction conditions.

We present GeneLens, a Python package for comprehensive analysis of differentially expressed genes and biomarker discovery. The package consists of two core modules: FSelector for biomarker identification by utilizing Monte Carlo simulations of L1-regularized models, and NetAnalyzer for functional prediction of selected gene sets based on the topology of their protein-protein interaction networks.The FSelector includes: (1) automated gene selection through iterative bootstrap sampling; (2) calculation of gene significance weights taking into account ROC-AUC model performance and their number in simulations; (3) adaptive thresholding for feature space reduction. NetAnalyzer performs pathway enrichment analysis while integrating significance weights from FSelector. Implemented as a PIP module, GeneLens provides standardized algorithms for applying machine learning and network analysis methods in differential gene expression studies, along with automated model hyperparameter tuning and visualization tools.

The N_ε-acetylation of lysine residues is one of the most common processes of post-translational protein modification. As a result of the reaction between the ε-amino group of the side chain of Lys and the activated acetyl, an amide bond is formed, which leads to a change in the charge of the protein in the region of the modification. The growing interest in such sites is due to the influence of N_ε-acetylation of Lys residues on the regulation of cellular activity, the disruption of which can lead to pathological conditions. Furthermore, the prediction of the N_ε-acetylation sites of Lys residues serves as a tool for planning an experiment design in modern proteomics, since the presence of a forecast simplifies the choice of proteolysis strategy, the interpretation of controversial mass spectra and the selection of proteotypic peptides. Here, were propose a new approach for predicting the N_ε-acetylation sites of Lys residues in human proteins using machine learning techniques. A feature of the approach is the use of structural formulas of peptides containing a potential N_ε-acetylation site and their description in the form of Multilevel Neighborhoods of Atoms (MNA) descriptors. Such descriptors are recursively generated for each atom of the molecule. A level zero descriptor represents the atom itself, the first level descriptor includes the atom and all atoms one bond away from it, and so on. Classification models for predicting N_ε-acetylation sites of Lys residues were built using the previously developed MultiPASS program based on the analysis of more than 23 000 sites from the PhosphoSitePlus database. The best model was obtained when the peptide length of 35 amino acid residues and using level 9 MNA descriptors. In fivefold cross-validation, the sensitivity, specificity, and ROC-AUC of the developed model were 0.71, 0.74, and 0.82, respectively. The model identified 1,136 previously unknown potential sites in 418 proteins of the human reference proteome at a classification threshold defined as the difference in the probabilities of site assignment to positive (Pa) and negative (Pi) classes, (Pa – Pi) ≥ 0.7. The obtained data can serve as a basis for further proteomic studies aimed at identifying and functionally annotating new N_ε-acetylation sites of Lys in human proteins.