ABSTRACT In silico prediction of cell line cytotoxicity considerably decreases time and financial costs during drug development of new antineoplastic agents. (Q)SAR models for the prediction of drug-like compound cytotoxicity in relation to nine breast cancer cell lines (T47D, ZR-75-1, MX1, Hs-578T, MCF7-DOX, MCF7, Bcap37, MCF7R, BT-20) were created by GUSAR software based on the data from ChEMBL database (v. 30). The separate datasets related with IC50 and IG50 values were used for the creation of (Q)SAR models for each cell line. Based on leave-one-out and 5F CV procedures, 24 reasonable (Q)SAR models were selected for the creation of a freely available web-application (BC CLC-Pred: https://www.way2drug.com/bc/) to predict substance cytotoxicity in relation to human breast cancer cell lines. The mean accuracies of prediction r 2, RMSE, Balance Accuracy for the selected (Q)SAR models calculated by 5F CV were 0.599, 0.679 and 0.875, respectively. As a result, BC CLC-Pred provides simultaneous quantitative and qualitative predictions of IC50 and IG50 values for most of the nine breast cancer cell lines, which may be helpful in selecting promising compounds and optimizing lead compounds during the development of new antineoplastic agents against breast cancer.

BACKGROUND: Prostate cancer is the most frequently diagnosed type of cancer in men in developed countries. It is dependent upon androgens and could be effectively combated by androgen deprivation therapy. Reduction of androgen synthesis can be accomplished through the inhibition of the enzyme 17α-hydroxylase/17.20-lyase (CYP17A1), which catalyzes two sequential reactions in the production of androgens. Steroid derivatives modified with nitrogen-containing heterocycles attract attention as antineoplastic agents for prostate cancer treatment due to their inhibitory potential against CYP17A1.
 AIM: Evaluate cytotoxic activity and antitumor effects of the synthesized alsevirone in comparison with abiraterone.
 METHODS: Cytotoxicity was evaluated using MTT test. Anticancer effect was researched in vivo in prostate cancer xenograft models 22Rv1 and DU145 in Balb/c nude mice. Testosterone concentration was determined using an enzyme-linked immunosorbent assay in blood serum of BDF1 mice.
 RESULTS: Alsevirone demonstrated cytotoxic activity in prostate cancer cells: DU145 (23.8±1.2 µM vs 151.4±23.7 µM for abiraterone), 22Rv1 (35.9±5.6 µM vs 109.9±35.2 µM for abiraterone) and LNCaP (22.9±0.5 µM vs 28.8±1.6 µM for abiraterone). Testosterone concentration in blood serum of BDF1 mice reduced by 80% after 10-day treatment. Inhibition of the tumors’ growth in 22Rv1 xenograft model was statistically significant when using alsevirone in comparison with the control group: average tumor volume was 171.6±50.1 mm3 (р=0.022) vs 424.2±70.3 mm3 in control, with tumor growth inhibition index of 59%.
 CONCLUSIONS: Alsevirone has a higher cytotoxic potential against prostate cancer cells (DU145, LNCaP and 22Rv1) compared to abiraterone. Alsevirone demonstrated the ability to reduce the concentration of testosterone in the blood serum of BDF1 mice, and statistically significant antitumor activity in 22Rv1 xenograft models.

Metabolic pathway prediction and reconstruction play crucial roles in solving fundamental and applied biomedical problems. In the case of fundamental research, annotation of metabolic pathways allows one to study human health in normal, stressed, and diseased conditions. In applied research, it allows one to identify novel drugs and drug targets and to design mimetics (biomolecules with tailored properties), as well as contributes to the development of such disciplines as toxicology and nutrigenomics. It is important to understand the role of a metabolite as a substrate (the product or intermediate participant of an enzymatic reaction) in cellular signaling and phenotype implementation according to the pivotal paradigm of biology: “one gene–one protein–one function (one trait)”. Due to the development of omics technologies, a vast body of data on the metabolome composition of living organisms has been accumulated over the past two decades. Systematization of the information on the roles played by metabolites in implementation of cellular signaling, as well as metabolic pathway reconstruction and refinement, have necessitated the development of bioinformatic tools for performing large-scale omics data mining. This paper reviews web-accessible databases relevant to metabolic pathways and considers the applications of the three types of bioinformatics methods for constructing metabolic networks (graphs for substrate–enzyme–product transformation; stoichiometric analysis of substrate–product transformation; and product retrosynthesis). It describes, step by step, a generalized algorithm for constructing biological pathway maps which explains to the researcher the workflow implemented in available bioinformatics tools and can be used to create new tools in projects requiring pathway reconstruction.

Currently, there is great interest in the development of highly sensitive bioanalytical systems for diagnosing diseases at an early stage, when pathological biomarkers are present in biological fluids at low concentrations and there are no clinical manifestations. A promising direction is the use of molecular detectors-highly sensitive devices that detect signals from single biomacromolecules. A typical detector in this class is the atomic force microscope (AFM). The high sensitivity of an AFM-based bioanalysis system is determined by the size of the sensing element of an atomic force microscope-the cantilever-the radius of the curvature of which is comparable to that of a biomolecule. Biospecific molecular probe-target interactions are used to ensure detection system specificity. Antibodies, aptamers, synthetic antibodies, and peptides can be used as molecular probes. This study has demonstrated the possibility of using aptamers as molecular probes for AFM-based detection of the ovarian cancer biomarker CA125. Antigen detection in a nanomolar solution was carried out using AFM chips with immobilized aptamers, commercially available or synthesized based on sequences from open sources. Both aptamer types can be used for antigen detection, but the availability of sequence information enables additional modeling of the aptamer structure with allowance for modifications necessary for immobilization of the aptamer on an AFM chip surface. Information on the structure and oligomeric composition of aptamers in the solution was acquired by combining small-angle X-ray scattering and molecular modeling. Modeling enabled pre-selection, before the experimental stage, of aptamers for use as surface-immobilized molecular probes.

The CD133 cell membrane glycoprotein, also termed prominin-1, is expressed on some of the tumor cells of both solid and blood malignancies. The CD133-positive tumor cells were shown to exhibit higher proliferative activity, greater chemo- and radioresistance, and enhanced tumorigenicity compared to their CD133-negative counterparts. For this reason, CD133 is regarded as a potential prognostic biomarker in oncology. The CD133-positive cells are related to the cancer stem cell subpopulation in many types of cancer. Recent studies demonstrated the involvement of CD133 in the regulation of proliferation, autophagy, and apoptosis in cancer cells. There is also evidence of its participation in the epithelial-mesenchymal transition associated with tumor progression. For a number of malignant tumor types, high CD133 expression is associated with poor prognosis, and the prognostic significance of CD133 has been confirmed in a number of meta-analyses. However, some published papers suggest that CD133 has no prognostic significance or even demonstrate a certain correlation between high CD133 levels and a positive prognosis. This review summarizes and discusses the existing evidence for and against the prognostic significance of CD133 in cancer. We also consider possible reasons for conflicting findings from the studies of the clinical significance of CD133.

Aim. To develop the BioVitrina web repository for visualizing data on biological samples available in the organization (https://bb.ibmc.msk.ru).Material and methods. The following set of tools was used: PostgreSQL 12, Python 3.8 scripting language, Django 3.2.7, Angular 12.2.13, Nginx, Docker 3, Redis 6.Results. Biological samples are systematized according to the International Classification of Diseases, 10th revision (ICD-10). After selecting the most suitable biosamples, the User has the opportunity to submit a request for their receipt. A necessary condition for obtaining biological samples is the subsequent publication of the results of experimental studies in the BioVitrina web repository. Such an exchange of analytical research results will avoid repetition of similar studies and will contribute to the conservation of material resources.Conclusion. "BioVitrina" was developed to optimize the infrastructure of biobanks of organizations and ensure the availability of information about their existing bioresource collections. This will solve many issues related to the use of different types of biomaterials, both for research and clinical purposes in personalized biomedicine.

p53 is arguably one of the most important tumor suppressor genes in humans. Due to the paramount importance of p53 in the onset of cell cycle arrest and apoptosis, the p53 gene is found either silenced or mutated in the vast majority of cancers. Furthermore, activated wild-type p53 exhibits a strong bystander effect, thereby activating apoptosis in surrounding cells without being physically present there. For these reasons, p53-targeted therapy that is designed to restore the function of wild-type p53 in cancer cells seems to be a very appealing therapeutic approach. Systemic delivery of p53-coding DNA or RNA using nanoparticles proved to be feasible both in vitro and in vivo. In fact, one p53-based therapeutic (gendicine) is currently approved for commercial use in China. However, the broad use of p53-based therapy in p53-inactivated cancers is severely restricted by its inadequate efficacy. This review highlights the current state-of-the-art in this area of biomedical research and also discusses novel approaches that may help overcome the shortcomings of p53-targeting nanomedicine.

Auditory neuropathy spectrum disorder (ANSD) associated with mutations of the OTOF gene is one of the common types of sensorineural hearing loss of a hereditary nature. Due to its high genetic heterogeneity, ANSD is considered one of the most difficult hearing disorders to diagnose. The dataset from 270 known annotated single amino acid substitutions (SAV) related to ANSD was created. It was used to estimate the accuracy of pathogenicity prediction using the known (from dbNSFP4.4) method and a new one. The new method (ConStruct) for the creation of the protein-centric classification model is based on the use of Random Forest for the analysis of missense variants in exons of the OTOF gene. A system of predictor variables was developed based on the modern understanding of the structure and function of the otoferlin protein and reflecting the location of changes in the tertiary structure of the protein due to mutations in the OTOF gene. The conservation values of nucleotide substitutions in genomes of 100 vertebrates and 30 primates were also used as variables. The average prediction of balanced accuracy and the AUC value calculated by the 5-fold cross-validation procedure were 0.866 and 0.903, respectively. The model shows good results for interpreting data from the targeted sequencing of the OTOF gene and can be implemented as an auxiliary tool for the diagnosis of ANSD in the early stages of ontogenesis. The created model, together with the results of the pathogenicity prediction of SAVs via other known accurate methods, were used for the evaluation of a manually created set of 1302 VUS related to ANSD. Based on the analysis of predicted results, 16 SAVs were selected as the new most probable pathogenic variants.

The long-read RNA sequencing developed by Oxford Nanopore Technologies provides a direct quantification of transcript isoforms, thereby making it possible to present alternative splicing (AS) profiles as arrays of single splice variants with different abundances. Additionally, AS profiles can be presented as arrays of genes characterized by the degree of alternative splicing (the DAS-the number of detected splice variants per gene). Here, we successfully utilized the DAS to reveal biological pathways influenced by the alterations in AS in human liver tissue and the hepatocyte-derived malignant cell lines HepG2 and Huh7, thus employing the mathematical algorithm of gene set enrichment analysis. Furthermore, analysis of the AS profiles as abundances of single splice variants by using the graded tissue specificity index τ provided the selection of the groups of genes expressing particular splice variants specifically in liver tissue, HepG2 cells, and Huh7 cells. The majority of these splice variants were translated into proteins products and appeal to be in focus regarding further insights into the mechanisms underlying cell malignization. The used metrics are intrinsically suitable for transcriptome-wide AS profiling using long-read sequencing.

AbstractEvaluation of the influence of primary and secondary aging on the manifestation of molecular and cellular hallmarks of aging is a challenging and currently unresolved issue. Our study represents the first demonstration of the distinct role of primary aging and chronic inflammation/physical inactivity – the most important drivers of secondary aging, in the regulation of transcriptomic and proteomic profiles in human skeletal muscle. To achieve this purpose, young healthy people (n=15), young (n=8) and older (n=37) patients with knee/hip osteoarthritis, a model to study the effect of long-term inactivity and chronic inflammation on the vastus lateralis muscle, were included in the study. It was revealed that widespread and substantial age-related changes in gene expression in older patients relative to young healthy people (∼4,000 genes regulating mitochondrial function, proteostasis, cell membrane, secretory and immune response) were related to the long-term physical inactivity and chronic inflammation rather than primary aging. Primary aging contributed mainly to the regulation of genes (∼200) encoding nuclear proteins (regulators of DNA repair, RNA processing, and transcription), mitochondrial proteins (genes encoding respiratory enzymes, mitochondrial complex assembly factors, regulators of cristae formation and mitochondrial reactive oxygen species production), as well as regulators of proteostasis. It was found that proteins associated with aging were regulated mainly at the post-transcriptional level. The set of putative primary aging genes and their potential transcriptional regulators can be used as a resource for further targeted studies investigating the role of individual genes and related transcription factors in the emergence of a senescent cell phenotype.