BackgroundConsideration of tissue-specific gene expression in reconstruction and analysis of molecular genetic networks is necessary for a proper description of the processes occurring in a specified tissue. Currently, there are a number of computer systems that allow the user to reconstruct molecular-genetic networks using the data automatically extracted from the texts of scientific publications. Examples of such systems are STRING, Pathway Commons, MetaCore and Ingenuity. The MetaCore and Ingenuity systems permit taking into account tissue-specific gene expression during the reconstruction of gene networks. Previously, we developed the ANDSystem tool, which also provides an automated extraction of knowledge from scientific texts and allows the reconstruction of gene networks. The main difference between our system and other tools is in the different types of interactions between objects, which makes the ANDSystem complementary to existing well-known systems. However, previous versions of the ANDSystem did not contain any information on tissue-specific expression.ResultsA new version of the ANDSystem has been developed. It offers the reconstruction of associative gene networks while taking into account the tissue-specific gene expression. The ANDSystem knowledge base features information on tissue-specific expression for 272 tissues. The system allows the reconstruction of combined gene networks, as well as performing the filtering of genes from such networks using the information on their tissue-specific expression. As an example of the application of such filtering, the gene network of the extrinsic apoptotic signaling pathway was analyzed. It was shown that considering different tissues can lead to changes in gene network structure, including changes in such indicators as betweenness centrality of vertices, clustering coefficient, network centralization, network density, etc.ConclusionsThe consideration of tissue specificity can play an important role in the analysis of gene networks, in particular solving the problem of finding the most significant central genes. Thus, the new version of ANDSystem can be employed for a wide range of tasks related to biomedical studies of individual tissues. It is available at http://www-bionet.sscc.ru/and/cell/.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2567-6) contains supplementary material, which is available to authorized users.

A mathematical model for suppression of the hepatitis C virus RNA replicon replication in Huh-7 cell culture in the presence of potential drugs was built. There was a good agreement between the experimental and theoretical kinetic data for the decrease in the level of viral RNA in the cell in the presence of the competitive HCV NS3 protease inhibitor. Using the model, we verified the estimates for the efficiency of the effect of potential drugs on replication of viral RNA and viral protein processing. It was demonstrated that the tested drugs are most efficient at the replication step of viral RNA. The efficiency of the combined action of real and putative inhibitors target on the host and viral proteins was also studied. It was found that the action of the inhibitor at low concentrations on the host factors considerably enhances the suppressive effect on viral RNA replication in the presence of even the low affine NS3 protease inhibitors. The developed mathematical model may serve as a tool for the evaluation of the efficiency of potential drugs on the HCV genome.

Modelling of gene networks is widely used in systems biology to study the functioning of complex biological systems. Most of the existing mathematical modelling techniques are useful for analysis of well-studied biological processes, for which information on rates of reactions is available. However, complex biological processes such as those determining the phenotypic traits of organisms or pathological disease processes, including pathogen-host interactions, involve complicated cross-talk between interacting networks. Furthermore, the intrinsic details of the interactions between these networks are often missing. In this study, we developed an approach, which we call mosaic network modelling, that allows the combination of independent mathematical models of gene regulatory networks and, thereby, description of complex biological systems. The advantage of this approach is that it allows us to generate the integrated model despite the fact that information on molecular interactions between parts of the model (so-called mosaic fragments) might be missing. To generate a mosaic mathematical model, we used control theory and mathematical models, written in the form of a system of ordinary differential equations (ODEs). In the present study, we investigated the efficiency of this method in modelling the dynamics of more than 10,000 simulated mosaic regulatory networks consisting of two pieces. Analysis revealed that this approach was highly efficient, as the mean deviation of the dynamics of mosaic network elements from the behaviour of the initial parts of the model was less than 10%. It turned out that for construction of the control functional, data on perturbation of one or two vertices of the mosaic piece are sufficient. Further, we used the developed method to construct a mosaic gene regulatory network including hepatitis C virus (HCV) as the first piece and the tumour necrosis factor (TNF)-induced apoptosis and NF-κB induction pathways as the second piece. Thus, the mosaic model integrates the model of HCV subgenomic replicon replication with the model of TNF-induced apoptosis and NF-κB induction. Analysis of the mosaic model revealed that the regulation of TNF-induced signaling by the HCV network is crucially dependent on the RIP1, TRADD, TRAF2, FADD, IKK, IκBα, c-FLIP, and BAR genes. Overall, the developed mosaic gene network modelling approach demonstrated good predictive power and allowed the prediction of new regulatory nodes in HCV action on apoptosis and the NF-κB pathway. Those theoretical predictions could be a basis for further experimental verification.

Metabolomic analysis of blood plasma samples from COVID-19 patients is a promising approach allowing for the evaluation of disease progression. We performed the metabolomic analysis of plasma samples of 30 COVID-19 patients and the 19 controls using the high-performance liquid chromatography (HPLC) coupled with tandem mass spectrometric detection (LC–MS/MS). In our analysis, we identified 103 metabolites enriched in KEGG metabolic pathways such as amino acid metabolism and the biosynthesis of aminoacyl-tRNAs, which differed significantly between the COVID-19 patients and the controls. Using ANDSystem software, we performed the reconstruction of gene networks describing the potential genetic regulation of metabolic pathways perturbed in COVID-19 patients by SARS-CoV-2 proteins. The nonstructural proteins of SARS-CoV-2 (orf8 and nsp5) and structural protein E were involved in the greater number of regulatory pathways. The reconstructed gene networks suggest the hypotheses on the molecular mechanisms of virus-host interactions in COVID-19 pathology and provide a basis for the further experimental and computer studies of the regulation of metabolic pathways by SARS-CoV-2 proteins. Our metabolomic analysis suggests the need for nonstructural protein-based vaccines and the control strategy to reduce the disease progression of COVID-19.

As an RNA virus, hepatitis C virus (HCV) is able to rapidly acquire drug resistance, and for this reason the design of effective anti-HCV drugs is a real challenge. The HCV subgenomic replicon-containing cells are widely used for experimental studies of the HCV genome replication mechanisms, for drug testing in vitro and in studies of HCV drug resistance. The NS3/4A protease is essential for virus replication and, therefore, it is one of the most attractive targets for developing specific antiviral agents against HCV. We have developed a stochastic model of subgenomic HCV replicon replication, in which the emergence and selection of drug resistant mutant viral RNAs in replicon cells is taken into account. Incorporation into the model of key NS3 protease mutations leading to resistance to BILN-2061 (A156T, D168V, R155Q), VX-950 (A156S, A156T, T54A) and SCH 503034 (A156T, A156S, T54A) inhibitors allows us to describe the long term dynamics of the viral RNA suppression for various inhibitor concentrations. We theoretically showed that the observable difference between the viral RNA kinetics for different inhibitor concentrations can be explained by differences in the replication rate and inhibitor sensitivity of the mutant RNAs. The pre-existing mutants of the NS3 protease contribute more significantly to appearance of new resistant mutants during treatment with inhibitors than wild-type replicon. The model can be used to interpret the results of anti-HCV drug testing on replicon systems, as well as to estimate the efficacy of potential drugs and predict optimal schemes of their usage.

Coronaviruses (CoVs) belong to the subfamily Orthocoronavirinae of the family Coronaviridae. CoVs are enveloped (+) RNA viruses with unusually long genomes. Severe acute respiratory syndrome CoV (SARS-CoV), Middle East respiratory syndrome CoV (MERS-CoV), and the novel coronavirus (2019-nCoV, SARS-CoV-2) have been identif ied as causing global pandemics. Clinically tested vaccines are widely used to control rapidly spreading, acute, and often severe infections; however, effective drugs are still not available. The genomes of SARS-CoV-2 and SARS-CoV are approximately 80 % identical, while the genomes of SARS-CoV-2 and MERS-CoV are approximately 50 % identical. This indicates that there may be common mechanisms of coronavirus pathogenesis and, therefore, potential therapeutic targets for each virus may be the same. The enzymes and effector proteins that make up the replicationtranscription complex (RTC) of coronaviruses are encoded by a large replicase gene. These enzymes and effector proteins represent promising targets for potential therapeutic drugs. The enzyme targets include papain- and 3C-like cysteine proteinases that process two large viral polyproteins, RNA-dependent RNA polymerase, RNA helicase, viral genome-modifying enzymes, and enzymes with 3’–5’ exoribonuclease or uridylate-specif ic endonuclease activity. Currently, there are many studies investigating the complex molecular mechanisms involved in the assembly and function of the RTC. This review will encompass current, modern studies on the properties and complexes of individual non-structural subunits of the RTC, the structures of individual coronavirus RTC subunits, domain organization and functions of subunits, protein-protein interactions, properties and architectures of subunit complexes, the effect of mutations, and the identif ication of mutations affecting the viability of the virus in cell culture.

A terrible disease of the cardiovascular system, atherosclerosis, develops in the areas of bends and branches of arteries, where the direction and modulus of the blood flow velocity vector change, and consequently so does the mechanical effect on endothelial cells in contact with the blood flow. The review focuses on topical research studies on the development of atherosclerosis – mechanobiochemical events that transform the proatherogenic mechanical stimulus of blood flow – low and low/oscillatory arterial wall shear stress in the chains of biochemical reactions in endothelial cells, leading to the expression of specific proteins that cause the progression of the pathological process. The stages of atherogenesis, systemic risk factors for atherogenesis and its important hemodynamic factor, low and low/oscillatory wall shear stress exerted by blood flow on the endothelial cells lining the arterial walls, have been described. The interactions of cell adhesion molecules responsible for the development of atherosclerosis under low and low/oscillating shear stress conditions have been demonstrated. The activation of the regulator of the expression of cell adhesion molecules, the transcription factor NF­κB, and the factors regulating its activation under these conditions have been described. Mechanosensitive signaling pathways leading to the expression of NF­κB in endothelial cells have been described. Studies of the mechanobiochemical signaling pathways and interactions involved in the progression of atherosclerosis provide valuable information for the development of approaches that delay or block the development of this disease.

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