Voprosy virusologii · 2025
[Identification of human genes potentially involved in the pathogenesis of viral hepatitis C based on multi-network bioinformatics analysis].
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Anufrieva E V, Ostankova Y V, Davydenko V S, Schemelev A N, Totolian A A
Аннотация
AIM: The aim of this study was to search for human genes potentially involved in the pathogenesis of hepatitis C by multi-network bioinformatics linkage analysis of proteins involved in the stages of hepatitis C virus (HСV) attachment and entry.
MATERIALS AND METHODS: A number of web applications with complementary algorithms and databases were used to analyze genetic and protein-protein networks. The following genes were used as basic genes: CD81, CLDN1, LDLR, OCLN, SCARB1, the products of which are involved in interaction with viral glycoproteins E1 and E2 at the stage of HCV attachment and penetration into the cell. Data analysis was performed, including a two-stage scoring ranking of the identified candidate genes based on their interaction with basic genes and their presence in the results of network analysis of different web resources.
RESULTS: Candidate genes were initially identified using three web resources: HumanNet - 100 candidate genes, GeneMania - 20, STRING - 98. Based on the intersection of the three web resources, the total number of candidate genes associated with basic genes was 170. The total number of genes with a rank higher than 4 points was 35. Candidate genes were grouped into functional sets: cellular barriers and intercellular contacts (17 genes, 48.6%); lipid metabolism and lipoproteins (9 genes, 25.7%); immune response and interaction with the virus (5 genes, 14.3%); signaling pathways, proteolysis and cytoskeleton (4 genes, 11.4%). The following candidate genes potentially involved in the pathogenesis of HCV have been identified: APOA1, CLDN3, APOE, LIPC, LRPAP1, CSNK1E, APOB, CD19, CLDN6, CLDN9, ESAM, F11R, IFITM1, LDLRAP1, PCSK9, TJP1, CD9, CLDN11, CLDN17, CLDN2, CLDN5, IGSF8, MMP2, PDZK1, ADAM10, APOA2, C3, CLDN12, DAB1, GJB1, ITGB1, MYLIP, NEDD4L, PTGFRN. Conclusion. In the future, a detailed study of the functional features and polymorphic variants of the identified genes using bioinformatics and laboratory methods can significantly expand current understanding of the involvement of human genes in the development of HCV infection and discover new targets for the development of drugs and therapeutic strategies.
MATERIALS AND METHODS: A number of web applications with complementary algorithms and databases were used to analyze genetic and protein-protein networks. The following genes were used as basic genes: CD81, CLDN1, LDLR, OCLN, SCARB1, the products of which are involved in interaction with viral glycoproteins E1 and E2 at the stage of HCV attachment and penetration into the cell. Data analysis was performed, including a two-stage scoring ranking of the identified candidate genes based on their interaction with basic genes and their presence in the results of network analysis of different web resources.
RESULTS: Candidate genes were initially identified using three web resources: HumanNet - 100 candidate genes, GeneMania - 20, STRING - 98. Based on the intersection of the three web resources, the total number of candidate genes associated with basic genes was 170. The total number of genes with a rank higher than 4 points was 35. Candidate genes were grouped into functional sets: cellular barriers and intercellular contacts (17 genes, 48.6%); lipid metabolism and lipoproteins (9 genes, 25.7%); immune response and interaction with the virus (5 genes, 14.3%); signaling pathways, proteolysis and cytoskeleton (4 genes, 11.4%). The following candidate genes potentially involved in the pathogenesis of HCV have been identified: APOA1, CLDN3, APOE, LIPC, LRPAP1, CSNK1E, APOB, CD19, CLDN6, CLDN9, ESAM, F11R, IFITM1, LDLRAP1, PCSK9, TJP1, CD9, CLDN11, CLDN17, CLDN2, CLDN5, IGSF8, MMP2, PDZK1, ADAM10, APOA2, C3, CLDN12, DAB1, GJB1, ITGB1, MYLIP, NEDD4L, PTGFRN. Conclusion. In the future, a detailed study of the functional features and polymorphic variants of the identified genes using bioinformatics and laboratory methods can significantly expand current understanding of the involvement of human genes in the development of HCV infection and discover new targets for the development of drugs and therapeutic strategies.