Репозиторий Университета

Background: Malaria is a disease annually causing over 400,000 deaths. Deep understanding of molecular and genetic processes underlying its life cycle and pathogenicity is required to efficiently resist it. RNA interference is a mechanism of the gene expression regulation typical for a wide variety of species. Even though the existence of this phenomenon in Plasmodium falciparum has long been rejected, several recent works pose hypotheses and provide direct and indirect evidence of the existence of mechanisms similar to RNA interference in this organism. In particular, the possibility of regulation of P. falciparum gene expression through human microRNAs is of great importance both for fundamental biology and for medicine. In the present work we address the problem of possibility of the existence in the P. falciparum genome of the nucleotide sequences such that mRNAs transcribed from genes containing these sequences could form duplexes with human microRNAs. Using bioinformatics methods we have analysed genomes of 15 P. falciparum isolates for sequences homological to these microRNAs. Results: The analysis has demonstrated the existence of a vast number of genes that could potentially be regulated by the human microRNAs in the plasmodial genome. Conclusions: Despite the fact that the numbers of homological intervals vary significantly between isolates, the hsa-miR-451a and hsa-miR-223-3p microRNAs are expected to make the most notable contribution to the pathogenesis of P. falciparum malaria. The majority of homological intervals occur in genes encoding cell adhesion proteins.

© 2019 Elsevier B.V. A massive-scale production of engineered nanoparticles (ENPs) becomes one of the most important environmental issues. The mechanisms of ENPs' (eco)toxic action are not fully understood, and the estimation of those mechanisms is a complicated task because even slight changes in particle characteristics could dramatically change their toxicity. As a result of continuous manufacturing of ENPs with specific functionality and different physicochemical properties, conventional methods of in vivo and in vitro testing would not be able to fill the existing knowledge gap in nanotoxicology. The objectives of this review are to overlook the current achievements based on the new approaches of ENPs' risk assessment, such as bioinformatics approaches and machine learning tools. These methods confirmed their ability to reliable prediction and evaluation of ENPs' behavior and their toxic endpoints. Databases and projects based on these methods and approaches would be highly useful in addressing the problem of ENPs’ regulation.

© Springer Science+Business Media, LLC, part of Springer Nature 2020. We describe here the Oncobox method for scoring efficiencies of anticancer target drugs (ATDs) using high throughput gene expression data. The method rationale, design, and validation are given along with the examples of its practical applications in biomedicine. The method is based on the analysis of intracellular molecular pathways activation and measuring expressions of molecular target genes for every ATD under consideration. Using Oncobox method requires collection of normal (control) expression profiles and annotated databases of molecular pathways and drug target genes. Both microarray and RNA sequencing profiles are acceptable, although the latter type of data prevails in the most recent applications of this technique.

© Springer Science+Business Media, LLC, part of Springer Nature 2020. Intracellular molecular pathways (IMPs) control all major events in the living cell. IMPs are considered hotspots in biomedical sciences and thousands of IMPs have been discovered for humans and model organisms. Knowledge of IMPs activation is essential for understanding biological functions and differences between the biological objects at the molecular level. Here we describe the Oncobox system for accurate quantitative scoring activities of up to several thousand molecular pathways based on high throughput molecular data. Although initially designed for gene expression and mainly RNA sequencing data, Oncobox is now also applicable for quantitative proteomics, microRNA and transcription factor binding sites mapping data. The Oncobox system includes modules of gene expression data harmonization, aggregation and comparison and a recursive algorithm for automatic annotation of molecular pathways. The universal rationale of Oncobox enables scoring of signaling, metabolic, cytoskeleton, immunity, DNA repair, and other pathways in a multitude of biological objects. The Oncobox system can be helpful to all those working in the fields of genetics, biochemistry, interactomics, and big data analytics in molecular biomedicine.

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Sequential courses of anticancer target therapy lead to selection of drug-resistant cells, which results in continuous decrease of clinical response. Here we present a new approach for predicting effective combinations of target drugs, which act in a synergistic manner. Synergistic combinations of drugs may prevent or postpone acquired resistance, thus increasing treatment efficiency. We cultured human ovarian carcinoma SKOV-3 and neuroblastoma NGP-127 cancer cell lines in the presence of Tyrosine Kinase Inhibitors (Pazopanib, Sorafenib, and Sunitinib) and Rapalogues (Temsirolimus and Everolimus) for four months and obtained cell lines demonstrating increased drug resistance. We investigated gene expression profiles of intact and resistant cells by microarrays and analyzed alterations in 378 cancer-related signaling pathways using the bioinformatical platform Oncobox. This revealed numerous pathways linked with development of drug resistant phenotypes. Our approach is based on targeting proteins involved in as many as possible signaling pathways upregulated in resistant cells. We tested 13 combinations of drugs and/or selective inhibitors predicted by Oncobox and 10 random combinations. Synergy scores for Oncobox predictions were significantly higher than for randomly selected drug combinations. Thus, the proposed approach significantly outperforms random selection of drugs and can be adopted to enhance discovery of new synergistic combinations of anticancer target drugs.

Sequential courses of anticancer target therapy lead to selection of drug-resistant cells, which results in continuous decrease of clinical response. Here we present a new approach for predicting effective combinations of target drugs, which act in a synergistic manner. Synergistic combinations of drugs may prevent or postpone acquired resistance, thus increasing treatment efficiency. We cultured human ovarian carcinoma SKOV-3 and neuroblastoma NGP-127 cancer cell lines in the presence of Tyrosine Kinase Inhibitors (Pazopanib, Sorafenib, and Sunitinib) and Rapalogues (Temsirolimus and Everolimus) for four months and obtained cell lines demonstrating increased drug resistance. We investigated gene expression profiles of intact and resistant cells by microarrays and analyzed alterations in 378 cancer-related signaling pathways using the bioinformatical platform Oncobox. This revealed numerous pathways linked with development of drug resistant phenotypes. Our approach is based on targeting proteins involved in as many as possible signaling pathways upregulated in resistant cells. We tested 13 combinations of drugs and/or selective inhibitors predicted by Oncobox and 10 random combinations. Synergy scores for Oncobox predictions were significantly higher than for randomly selected drug combinations. Thus, the proposed approach significantly outperforms random selection of drugs and can be adopted to enhance discovery of new synergistic combinations of anticancer target drugs. View Full-Text

Sequential courses of anticancer target therapy lead to selection of drug-resistant cells, which results in continuous decrease of clinical response. Here we present a new approach for predicting effective combinations of target drugs, which act in a synergistic manner. Synergistic combinations of drugs may prevent or postpone acquired resistance, thus increasing treatment efficiency. We cultured human ovarian carcinoma SKOV-3 and neuroblastoma NGP-127 cancer cell lines in the presence of Tyrosine Kinase Inhibitors (Pazopanib, Sorafenib, and Sunitinib) and Rapalogues (Temsirolimus and Everolimus) for four months and obtained cell lines demonstrating increased drug resistance. We investigated gene expression profiles of intact and resistant cells by microarrays and analyzed alterations in 378 cancer-related signaling pathways using the bioinformatical platform Oncobox. This revealed numerous pathways linked with development of drug resistant phenotypes. Our approach is based on targeting proteins involved in as many as possible signaling pathways upregulated in resistant cells. We tested 13 combinations of drugs and/or selective inhibitors predicted by Oncobox and 10 random combinations. Synergy scores for Oncobox predictions were significantly higher than for randomly selected drug combinations. Thus, the proposed approach significantly outperforms random selection of drugs and can be adopted to enhance discovery of new synergistic combinations of anticancer target drugs. View Full-Text