Interactome of the hepatitis C virus: Literature mining with ANDSystem

Prioritization of potential pharmacological targets for the development of anti-hepatocarcinoma drugs modulating the extrinsic apoptosis pathway: the reconstruction and analysis of associative gene networks help

Hepatocellular carcinoma (HCC) is a common severe type of liver cancer characterized by an extremely aggressive course and low survival rates. It is known that disruptions in the regulation of apoptosis activation are some of the key features inherent in most cancer cells, which determines the pharmacological induction of apoptosis as an important strategy for cancer therapy. The computer design of chemical compounds capable of specifically regulating the external signaling pathway of apoptosis induction represents a promising approach for creating new effective ways of therapy for liver cancer and other oncological diseases. However, at present, most of the studies are devoted to pharmacological effects on the internal (mitochondrial) apoptosis pathway. In contrast, the external pathway induced via cell death receptors remains out of focus. Aberrant gene methylation, along with hepatitis C virus (HCV) infection, are important risk factors for the development of hepatocellular carcinoma. The reconstruction of gene networks describing the molecular mechanisms of interaction of aberrantly methylated genes with key participants of the extrinsic apoptosis pathway and their regulation by HCV proteins can provide important information when searching for pharmacological targets. In the present study, 13 criteria were proposed for prioritizing potential pharmacological targets for developing anti-hepatocarcinoma drugs modulating the extrinsic apoptosis pathway. The criteria are based on indicators of the structural and functional organization of reconstructed gene networks of hepatocarcinoma, the extrinsic apoptosis pathway, and regulatory pathways of virus-extrinsic apoptosis pathway interaction and aberrant gene methylation-extrinsic apoptosis pathway interaction using ANDSystem. The list of the top 100 gene targets ranked according to the prioritization rating was statistically significantly (p-value = 0.0002) enriched for known pharmacological targets approved by the FDA, indicating the correctness of the prioritization method. Among the promising potential pharmacological targets, six highly ranked genes (JUN, IL10, STAT3, MYC, TLR4, and KHDRBS1) are likely to deserve close attention.

Molecular-genetic pathways of hepatitis C virus regulation of the expression of cellular factors PREB and PLA2G4C, which play an important role in virus replication

The participants of Hepatitis C virus (HCV) replication are both viral and host proteins. Therapeutic approaches based on activity inhibition of viral non-structural proteins NS3, NS5A, and NS5B are undergoing clinical trials. However, rapid mutation processes in the viral genome and acquisition of drug resistance to the existing drugs remain the main obstacles to fighting HCV. Identifying the host factors, exploring their role in HCV RNA replication, and studying viral effects on their expression is essential for understanding the mechanisms of viral replication and developing novel, effective curative approaches. It is known that the host factors PREB (prolactin regulatory element binding) and PLA2G4C (cytosolic phospholipase A2 gamma) are important for the functioning of the viral replicase complex and the formation of the platforms of HCV genome replication. The expression of PREB and PLA2G4C was significantly elevated in the presence of the HCV genome. However, the mechanisms of its regulation by HCV remain unknown. In this paper, using a text-mining technology provided by ANDSystem, we reconstructed and analyzed gene networks describing regulatory effects on the expression of PREB and PLA2G4C by HCV proteins. On the basis of the gene network analysis performed, we put forward hypotheses about the modulation of the host factors functions resulting from protein-protein interaction with HCV proteins. Among the viral proteins, NS3 showed the greatest number of regulatory linkages. We assumed that NS3 could inhibit the function of host transcription factor (TF) NOTCH1 by protein-protein interaction, leading to upregulation of PREB and PLA2G4C. Analysis of the gene networks and data on differential gene expression in HCV-infected cells allowed us to hypothesize further how HCV could regulate the expression of TFs, the binding sites of which are localized within PREB and PLA2G4C gene regions. The results obtained can be used for planning studies of the molecular-genetic mechanisms of viral-host interaction and searching for potential targets for anti-HCV therapy.

Reconstruction of the regulatory hypermethylation network controlling hepatocellular carcinoma development during hepatitis C viral infection

Hepatocellular carcinoma (HCC) has been associated with hepatitis C viral (HCV) infection as a potential risk factor. Nonetheless, the precise genetic regulatory mechanisms triggered by the virus, leading to virus-induced hepatocarcinogenesis, remain unclear. We hypothesized that HCV proteins might modulate the activity of aberrantly methylated HCC genes through regulatory pathways. Virus-host regulatory pathways, interactions between proteins, gene expression, transport, and stability regulation, were reconstructed using the ANDSystem. Gene expression regulation was statistically significant. Gene network analysis identified four out of 70 HCC marker genes whose expression regulation by viral proteins may be associated with HCC: DNA-binding protein inhibitor ID - 1 (ID1), flap endonuclease 1 (FEN1), cyclin-dependent kinase inhibitor 2A (CDKN2A), and telomerase reverse transcriptase (TERT). It suggested the following viral protein effects in HCV/human protein heterocomplexes: HCV NS3(p70) protein activates human STAT3 and NOTC1; NS2-3(p23), NS5B(p68), NS1(E2), and core(p21) activate SETD2; NS5A inhibits SMYD3; and NS3 inhibits CCN2. Interestingly, NS3 and E1(gp32) activate c-Jun when it positively regulates CDKN2A and inhibit it when it represses TERT. The discovered regulatory mechanisms might be key areas of focus for creating medications and preventative therapies to decrease the likelihood of HCC development during HCV infection.

Primary and Secondary micro-RNA Modulation the Extrinsic Pathway of Apoptosis in Hepatocellular Carcinoma

Abstract-One of the most common malignant liver diseases is hepatocellular carcinoma, which has a high recurrence rate and a low five-year survival rate. It is very heterogeneous both in structure and between patients, which complicates the diagnosis, prognosis and response to treatment. In this regard, an individualized, patient-centered approach becomes important, in which the use of mimetics and hsa-miRNA inhibitors involved in the pathogenesis of the disease may be determinative. From this point of view hsa-miRNAs are of interest, their aberrant expression is associated with poor prognosis for patients and is associated with tumor progression due to dysregulation of programmed cell death (apoptosis). However, the effect of hsa-miRNA on tumor development depends not only on its direct effect on expression of genes, the primary targets, but also on secondary targets mediated by regulatory pathways. While the former are actively studied, the role of secondary targets of these hsa-miRNAs in modulating apoptosis is still unclear. The present work summarizes data on hsa-miRNAs whose primary targets are key genes of the extrinsic pathway of apoptosis. Their aberrant expression is associated with early disease relapse and poor patient outcome. For these hsa-miRNAs, using the software package ANDSystem, we reconstructed the regulation of the expression of secondary targets and analyzed their impact on the activity of the extrinsic pathway of apoptosis. The potential effect of hsa-miRNAs mediated by action on secondary targets is shown to negatively correlate with the number of primary targets. It is also shown that hsa-miR-373, hsa-miR-106b and hsa-miR-96 have the highest priority as markers of hepatocellular carcinoma, whose action on secondary targets enhances their anti-apoptotic effect.

Computer analysis of regulation of hepatocarcinoma marker genes hypermethylated by HCV proteins

Hepatitis C virus (HCV) is a risk factor that leads to hepatocellular carcinoma (HCC) development. Epigenetic changes are known to play an important role in the molecular genetic mechanisms of virus-induced oncogenesis. Aberrant DNA methylation is a mediator of epigenetic changes that are closely associated with the HCC pathogenesis and considered a biomarker for its early diagnosis. The ANDSystem software package was used to reconstruct and evaluate the statistical significance of the pathways HCV could potentially use to regulate 32 hypermethylated genes in HCC, including both oncosuppressor and protumorigenic ones identified by genome-wide analysis of DNA methylation. The reconstructed pathways included those affecting protein-protein interactions (PPI), gene expression, protein activity, stability, and transport regulations, the expression regulation pathways being statistically significant. It has been shown that 8 out of 10 HCV proteins were involved in these pathways, the HCV NS3 protein being implicated in the largest number of regulatory pathways. NS3 was associated with the regulation of 5 tumor-suppressor genes, which may be the evidence of its central role in HCC pathogenesis. Analysis of the reconstructed pathways has demonstrated that following the transcription factor inhibition caused by binding to viral proteins, the expression of a number of oncosuppressors (WT1, MGMT, SOCS1, P53) was suppressed, while the expression of others (RASF1, RUNX3, WIF1, DAPK1) was activated. Thus, the performed gene-network reconstruction has shown that HCV proteins can influence not only the methylation status of oncosuppressor genes, but also their transcriptional regulation. The results obtained can be used in the search for pharmacological targets to develop new drugs against HCV-induced HCC.

Plasma metabolomics and gene regulatory networks analysis reveal the role of nonstructural SARS-CoV-2 viral proteins in metabolic dysregulation in COVID-19 patients

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.

Virus-Like Particles as Preventive and Therapeutic Cancer Vaccines

Virus-like particles (VLPs) are self-assembled viral protein complexes that mimic the native virus structure without being infectious. VLPs, similarly to wild type viruses, are able to efficiently target and activate dendritic cells (DCs) triggering the B and T cell immunities. Therefore, VLPs hold great promise for the development of effective and affordable vaccines in infectious diseases and cancers. Vaccine formulations based on VLPs, compared to other nanoparticles, have the advantage of incorporating multiple antigens derived from different proteins. Moreover, such antigens can be functionalized by chemical modifications without affecting the structural conformation or the antigenicity. This review summarizes the current status of preventive and therapeutic VLP-based vaccines developed against human oncoviruses as well as cancers.

Profiling the HCV immune response in patients with chronic liver diseases and hepatocellular carcinoma by peptide microarray analysis

BACKGROUND

Chronic infection with hepatitis C virus (HCV) is among the major causes of hepatic fibrosis, cirrhosis, as well as hepatocellular carcinoma (HCC), and it is associated with a significant risk of developing lymphoproliferative disorders. The rate of clinical disease progression is variable depending on multiple host and viral factors, including immune response.

METHODS

To perform a comprehensive epitope mapping of anti-HCV antibodies in patients suffering from HCV-related liver or lymphoproliferative diseases, we analyzed clinical samples on a peptide microarray platform made of 5952 overlapping 15-mer synthetic peptides derived from the whole HCV proteome. We evaluated the antibody profile of 71 HCV-positive patients diagnosed with HCC, mixed cryoglobulinemia (MC), and HCV chronic infection. Antibody reactivity against virus peptides was detected in all HCV-positive patients. Importantly, the signal amplitude varied significantly within and between diverse patient groups.

RESULTS

Antibody reactivity against C peptides were found generally low in HCV chronically infected asymptomatic subjects and increasingly high in HCC and MC patients. Moreover, we found a statistically significant higher IgG response in HCC and MC patients against specific domains of HCV C, E2, NS3, NS4A, NS4B, NS5A, and p7 compared to HCV-positive subjects.

CONCLUSION

In conclusion, our data suggest that immune response against specific HCV protein domains may represent useful biomarkers of disease progression among HCV-positive patients and suggest that peptide microarrays are good tools for the screening of immunotherapy targets in preclinical HCV research.

Reconstruction of the Cytokine Signaling in Lysosomal Storage Diseases by Literature Mining and Network Analysis

Lysosomal storage diseases (LSDs) are characterized by the abnormal accumulation of substrates in tissues due to the deficiency of lysosomal proteins. Among the numerous clinical manifestations, chronic inflammation has been consistently reported for several LSDs. However, the molecular mechanisms involved in the inflammatory response are still not completely understood. In this study, we performed text-mining and systems biology analyses to investigate the inflammatory signals in three LSDs characterized by sphingolipid accumulation: Gaucher disease, Acid Sphingomyelinase Deficiency (ASMD), and Fabry Disease. We first identified the cytokines linked to the LSDs, and then built on the extracted knowledge to investigate the inflammatory signals. We found numerous transcription factors that are putative regulators of cytokine expression in a cell-specific context, such as the signaling axes controlled by STAT2, JUN, and NR4A2 as candidate regulators of the monocyte Gaucher disease cytokine network. Overall, our results suggest the presence of a complex inflammatory signaling in LSDs involving many cellular and molecular players that could be further investigated as putative targets of anti-inflammatory therapies.

Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions

The microbiome, by virtue of its interactions with the host, is implicated in various host functions including its influence on nutrition and homeostasis. Many chronic diseases such as diabetes, cancer, inflammatory bowel diseases are characterized by a disruption of microbial communities in at least one biological niche/organ system. Various molecular mechanisms between microbial and host components such as proteins, RNAs, metabolites have recently been identified, thus filling many gaps in our understanding of how the microbiome modulates host processes. Concurrently, high-throughput technologies have enabled the profiling of heterogeneous datasets capturing community level changes in the microbiome as well as the host responses. However, due to limitations in parallel sampling and analytical procedures, big gaps still exist in terms of how the microbiome mechanistically influences host functions at a system and community level. In the past decade, computational biology and machine learning methodologies have been developed with the aim of filling the existing gaps. Due to the agnostic nature of the tools, they have been applied in diverse disease contexts to analyze and infer the interactions between the microbiome and host molecular components. Some of these approaches allow the identification and analysis of affected downstream host processes. Most of the tools statistically or mechanistically integrate different types of -omic and meta -omic datasets followed by functional/biological interpretation. In this review, we provide an overview of the landscape of computational approaches for investigating mechanistic interactions between individual microbes/microbiome and the host and the opportunities for basic and clinical research. These could include but are not limited to the development of activity- and mechanism-based biomarkers, uncovering mechanisms for therapeutic interventions and generating integrated signatures to stratify patients.

Automated Extraction of Information From Texts of Scientific Publications: Insights Into HIV Treatment Strategies

Text analysis can help to identify named entities (NEs) of small molecules, proteins, and genes. Such data are very important for the analysis of molecular mechanisms of disease progression and development of new strategies for the treatment of various diseases and pathological conditions. The texts of publications represent a primary source of information, which is especially important to collect the data of the highest quality due to the immediate obtaining information, in comparison with databases. In our study, we aimed at the development and testing of an approach to the named entity recognition in the abstracts of publications. More specifically, we have developed and tested an algorithm based on the conditional random fields, which provides recognition of NEs of (i) genes and proteins and (ii) chemicals. Careful selection of abstracts strictly related to the subject of interest leads to the possibility of extracting the NEs strongly associated with the subject. To test the applicability of our approach, we have applied it for the extraction of (i) potential HIV inhibitors and (ii) a set of proteins and genes potentially responsible for viremic control in HIV-positive patients. The computational experiments performed provide the estimations of evaluating the accuracy of recognition of chemical NEs and proteins (genes). The precision of the chemical NEs recognition is over 0.91; recall is 0.86, and the F1-score (harmonic mean of precision and recall) is 0.89; the precision of recognition of proteins and genes names is over 0.86; recall is 0.83; while F1-score is above 0.85. Evaluation of the algorithm on two case studies related to HIV treatment confirms our suggestion about the possibility of extracting the NEs strongly relevant to (i) HIV inhibitors and (ii) a group of patients i.e., the group of HIV-positive individuals with an ability to maintain an undetectable HIV-1 viral load overtime in the absence of antiretroviral therapy. Analysis of the results obtained provides insights into the function of proteins that can be responsible for viremic control. Our study demonstrated the applicability of the developed approach for the extraction of useful data on HIV treatment.

ANDDigest: a new web-based module of ANDSystem for the search of knowledge in the scientific literature

BACKGROUND

The rapid growth of scientific literature has rendered the task of finding relevant information one of the critical problems in almost any research. Search engines, like Google Scholar, Web of Knowledge, PubMed, Scopus, and others, are highly effective in document search; however, they do not allow knowledge extraction. In contrast to the search engines, text-mining systems provide extraction of knowledge with representations in the form of semantic networks. Of particular interest are tools performing a full cycle of knowledge management and engineering, including automated retrieval, integration, and representation of knowledge in the form of semantic networks, their visualization, and analysis. STRING, Pathway Studio, MetaCore, and others are well-known examples of such products. Previously, we developed the Associative Network Discovery System (ANDSystem), which also implements such a cycle. However, the drawback of these systems is dependence on the employed ontologies describing the subject area, which limits their functionality in searching information based on user-specified queries.

RESULTS

The ANDDigest system is a new web-based module of the ANDSystem tool, permitting searching within PubMed by using dictionaries from the ANDSystem tool and sets of user-defined keywords. ANDDigest allows performing the search based on complex queries simultaneously, taking into account many types of objects from the ANDSystem's ontology. The system has a user-friendly interface, providing sorting, visualization, and filtering of the found information, including mapping of mentioned objects in text, linking to external databases, sorting of data by publication date, citations number, journal H-indices, etc. The system provides data on trends for identified entities based on dynamics of interest according to the frequency of their mentions in PubMed by years.

CONCLUSIONS

The main feature of ANDDigest is its functionality, serving as a specialized search for information about multiple associative relationships of objects from the ANDSystem's ontology vocabularies, taking into account user-specified keywords. The tool can be applied to the interpretation of experimental genetics data, the search for associations between molecular genetics objects, and the preparation of scientific and analytical reviews. It is presently available at https://anddigest.sysbio.ru/ .

Prioritization of potato genes involved in the formation of agronomically valuable traits using the SOLANUM TUBEROSUM knowledge base

The development of highly efficient technologies in genomics, transcriptomics, proteomics and metabolomics, as well as new technologies in agriculture has led to an “information explosion” in plant biology and crop production, including potato production. Only a small part of the information reaches formalized databases (for example, Uniprot, NCBI Gene, BioGRID, IntAct, etc.). One of the main sources of reliable biological data is the scientific literature. The well-known PubMed database contains more than 18 thousand abstracts of articles on potato. The effective use of knowledge presented in such a number of non-formalized documents in natural language requires the use of modern intellectual methods of analysis. However, in the literature, there is no evidence of a widespread use of intelligent methods for automatically extracting knowledge from scientific publications on cultures such as potatoes. Earlier we developed the SOLANUM TUBEROSUM knowledge base (http://www-bionet.sysbio.cytogen. ru/and/plant/). Integrated into the knowledge base information about the molecular genetic mechanisms underlying the selection of significant traits helps to accelerate the identification of candidate genes for the breeding characteristics of potatoes and the development of diagnostic markers for breeding. The article searches for new potential participants of the molecular genetic mechanisms of resistance to adverse factors in plants. Prioritizing candidate genes has shown that the PHYA, GF14, CNIH1, RCI1A, ABI5, CPK1, RGS1, NHL3, GRF8, and CYP21-4 genes are the most promising for further testing of their relationships with resistance to adverse factors. As a result of the analysis, it was shown that the molecular genetic relationships responsible for the formation of significant agricultural traits are complex and include many direct and indirect interactions. The construction of associative gene networks and their analysis using the SOLANUM TUBEROSUM knowledge base is the basis for searching for target genes for targeted mutagenesis and marker-oriented selection of potato varieties with valuable agricultural characteristics.

Construction and analysis of a comprehensive protein interaction network of HCV with its host Homo sapiens

Background

Hepatitis C Virus is becoming a major health problem in Asia and across the globe since it is causing serious liver diseases including liver cirrhosis, chronic hepatitis and hepatocarcinoma (HCC). Protein interaction networks presents us innumerable novel insights into functional constitution of proteome and helps us finding potential candidates for targeting the drugs.

Methods

Here we present a comprehensive protein interaction network of Hepatitis C Virus with its host, constructed by literature curated interactions. The network was constructed and explored using Cytoscape and the results were further analyzed using KEGG pathway, Gene Ontology enrichment analysis and MCODE.

Results

We found 1325 interactions between 12 HCV proteins and 940 human genes, among which 21 were intraviral and 1304 were HCV-Human. By analyzing the network, we found potential human gene list with their number of interactions with HCV proteins. ANXA2 and NR4A1 were interacting with 6 HCV proteins while we found 11 human genes which were interacting with 5 HCV proteins. Furthermore, the enrichment analysis and Gene Ontology of the top genes to find the pathways and the biological processes enriched with those genes. Among the viral proteins, NS3 was interacting with most number of interactors followed by NS5A and so on. KEGG pathway analysis of three set of most HCV- associated human genes was performed to find out which gene products are involved in certain disease pathways. Top 5, 10 and 20 human genes with most interactions were analyzed which revealed some striking results among which the top 10 host genes came up to be significant because they were more related to Influenza A viral infection previously. This insight provides us with a clue that the set of genes are highly enriched in HCV but are not well studied in its infection pathway.

Conclusions

We found out a group of proteins which were rich in HCV viral pathway but there were no drugs targeting them according to the drug repurposing hub. It can be concluded that the cluster we obtained from MCODE contains potential targets for HCV treatment and could be implemented for molecular docking and drug designing further by the scientists.

A new version of the ANDSystem tool for automatic extraction of knowledge from scientific publications with expanded functionality for reconstruction of associative gene networks by considering tissue-specific gene expression

BACKGROUND

Consideration 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.

RESULTS

A 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. CONCLUSIONS: The 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 /.

Evolution of efficacious pangenotypic hepatitis C virus therapies

Hepatitis C compromises the quality of life of more than 350 million individuals worldwide. Over the last decade, therapeutic regimens for treating hepatitis C virus (HCV) infections have undergone rapid advancements. Initially, structure-based drug design was used to develop molecules that inhibit viral enzymes. Subsequently, establishment of cell-based replicon systems enabled investigations into various stages of HCV life cycle including its entry, replication, translation, and assembly, as well as role of host proteins. Collectively, these approaches have facilitated identification of important molecules that are deemed essential for HCV life cycle. The expanded set of putative virus and host-encoded targets has brought us one step closer to developing robust strategies for efficacious, pangenotypic, and well-tolerated medicines against HCV. Herein, we provide an overview of the development of various classes of virus and host-directed therapies that are currently in use along with others that are undergoing clinical evaluation.

FunGeneNet: a web tool to estimate enrichment of functional interactions in experimental gene sets

Background

Estimation of functional connectivity in gene sets derived from genome-wide or other biological experiments is one of the essential tasks of bioinformatics. A promising approach for solving this problem is to compare gene networks built using experimental gene sets with random networks. One of the resources that make such an analysis possible is CrossTalkZ, which uses the FunCoup database. However, existing methods, including CrossTalkZ, do not take into account individual types of interactions, such as protein/protein interactions, expression regulation, transport regulation, catalytic reactions, etc., but rather work with generalized types characterizing the existence of any connection between network members.

Results

We developed the online tool FunGeneNet, which utilizes the ANDSystem and STRING to reconstruct gene networks using experimental gene sets and to estimate their difference from random networks. To compare the reconstructed networks with random ones, the node permutation algorithm implemented in CrossTalkZ was taken as a basis. To study the FunGeneNet applicability, the functional connectivity analysis of networks constructed for gene sets involved in the Gene Ontology biological processes was conducted. We showed that the method sensitivity exceeds 0.8 at a specificity of 0.95. We found that the significance level of the difference between gene networks of biological processes and random networks is determined by the type of connections considered between objects. At the same time, the highest reliability is achieved for the generalized form of connections that takes into account all the individual types of connections. By taking examples of the thyroid cancer networks and the apoptosis network, it is demonstrated that key participants in these processes are involved in the interactions of those types by which these networks differ from random ones.

Conclusions

FunGeneNet is a web tool aimed at proving the functionality of networks in a wide range of sizes of experimental gene sets, both for different global networks and for different types of interactions. Using examples of thyroid cancer and apoptosis networks, we have shown that the links over-represented in the analyzed network in comparison with the random ones make possible a biological interpretation of the original gene/protein sets. The FunGeneNet web tool for assessment of the functional enrichment of networks is available at http://www-bionet.sscc.ru/fungenenet/.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4474-7) contains supplementary material, which is available to authorized users.

Computer Analysis of Glioma Transcriptome Profiling: Alternative Splicing Events

Here we present the analysis of alternative splicing events on an example of glioblastoma cell culture samples using a set of computer tools in combination with database integration. The gene expression profiles of glioblastoma were obtained from cell culture samples of primary glioblastoma which were isolated and processed for RNA extraction. Transcriptome profiling of normal brain samples and glioblastoma were done by Illumina sequencing. The significant differentially expressed exon-level probes and their corresponding genes were identified using a combination of the splicing index method. Previous studies indicated that tumor-specific alternative splicing is important in the regulation of gene expression and corresponding protein functions during cancer development. Multiple alternative splicing transcripts have been identified as progression markers, including generalized splicing abnormalities and tumor- and stage-specific events. We used a set of computer tools which were recently applied to analysis of gene expression in laboratory animals to study differential splicing events. We found 69 transcripts that are differentially alternatively spliced. Three cancer-associated genes were considered in detail, in particular: APP (amyloid beta precursor protein), CASC4 (cancer susceptibility candidate 4) and TP53. Such alternative splicing opens new perspectives for cancer research.

Copy number variation of genes involved in the hepatitis C virus-human interactome

Copy number variation (CNV) is a newly discovered form of intra-species genetic polymorphism that is defined as deletions or duplications of genome segments ranging from 1 kbp to several Mbp. CNV accounts for the majority of the genetic variation observed in humans (CNV regions cover more than 10% of the human genome); therefore, it may significantly influence both the phenotype and susceptibility to various diseases. Unfortunately, the impact of CNV on a number of diseases, including hepatitis C virus (HCV) infection, remains largely unexplored. Here, we analyzed 421 human genes encoding proteins that have been shown to interact with HCV proteins or genomic RNA (proteins from the HCV-human interactome). We found that 19 of the 421 candidate genes are located in putative CNV regions. For all of these genes, copy numbers were determined for European, Asiatic and African populations using the multiplex ligation-dependent amplification (MLPA) method. As a result, we identified 4 genes, IGLL1, MLLT4, PDPK1, PPP1R13L, for which the CN-genotype ranged from 1 to 6. All of these genes are involved in host-virus interaction; thus, their polymorphism has a potential impact on the development of HCV infection and/or therapy outcome.