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

2.1 Analysis of genes exhibiting hypermethylation in HCC

Data on genes exhibiting aberrant methylation patterns in HCC were obtained from the scientific literature (Supplementary Table S1). Only those genes identified by the authors as the most significant were considered, creating a list of 70 genes. The largest gene groups were taken from a meta-analysis of differentially methylated genes [31] and an integrated analysis of microarray-based mRNA expression and methylation profiles [32]. Authors [31] used data on differential gene methylation in HCC tumor and adjacent tissue pairs, HCC tumor and normal tissue pairs, and HCC and normal serum pairs. They reported 22 marker genes, of which we excluded seven because their names did not allow us to accurately determine their identifiers in the Gene database: p16, p14, CDH1, p15, hMLH1, and p73. Ma et al. [32] identified novel aberrantly methylated DEGs using data from the GEO database: GSE19665 and GSE62232 for mRNA expression and GSE60753 for methylation. They identified 185 genes, including those downregulated and hypermethylated and those upregulated and hypomethylated. We took 35 genes for further analysis, which the authors listed among the central genes in their PPI network.

2.2 DEGs in HCV-infected cells

Two distinct sets of DEGs between mock-infected control and HCV-infected cells were created [1]: a compilation of 1844 significantly differentially expressed annotated genes identified in Huh 7.5 cells undergoing acute infection and proliferation at a 72-h mark [2, 30] a collection of 1886 genes derived from the analysis of the microarray-based dataset GSE66842 [29]. The GSE66842 dataset encompasses gene expression profiles of differentiated Huh7.5.1 cells subjected to infection with the HCV Jc1 clone. We used GEO2R [33] to screen for DEGs in 10-day post-infection samples in the dataset. We utilized an established threshold defined by a false discovery rate (FDR) below 0.05 to recognize differentially expressed genes.

2.3 Reconstructing regulatory pathways using the ANDSystem

Reconstruction of viral-protein-associated regulatory pathways containing HCC-associated genes (Table S1) was performed using the ANDSystem tool [21]. The ANDSystem knowledge base comprises a comprehensive gene network describing interactions between the objects of the ANDSystem’s ontology. This network was obtained via automated mining of scientific literature and factual databases. The ontology of the ANDSystem incorporates 13 distinct object types, including but not limited to proteins, genes, and metabolites, alongside 24 unique interaction types, such as expression, activity and stability regulation, physical interaction. Specific templates were used to search for pathways within the ANDSystem base of knowledge that dictate the structure of subgraphs embedded within the overarching gene network graph. Templates are defined as a sequential series of vertices that correspond to objects, where the edges that connect these vertices encapsulate the interactions between objects. Objects may be delineated either by a compilation of names or identifiers or, alternatively, by object type. In the first scenario, pathways containing objects from among those listed in the template will be searched. In the second case, any object of a given type can be included in the searched pathways.

We used seven different pathway templates (Table 1). The first object in all templates comprised HCV proteins, provided as a list of UniProt identifiers. The last object comprised a list of protein identifiers encoded by the genes in Table S1. The length of the templates varied from two to five objects. All intermediate objects were specified by their object type. Only human proteins and genes were considered intermediate objects. Interactions between objects in templates included PPIs, several regulatory interactions, and an expression-type relationship between a gene and its protein product. HCV proteins were only involved in PPIs with human proteins.

3.1 Identification of marker genes that can be regulated by viral proteins

We compiled a list of genes whose promoter hypermethylation or hypomethylation was accompanied by aberrant expression and associated with HCC in published data (Table S1). This list included 70 methylation-related DEGs. Most of these genes had been reported as HCC markers whose aberrant expression correlated with unfavorable disease prognoses. Regulatory pathways were reconstructed with the application of ANDSystem [21], intended to identify marker genes potentially regulated by viral proteins at the transcriptional regulation level, as illustrated in Table 1. We hypothesized that viral proteins could regulate such genes in normal and cancer cells, influencing the disease course and underlying risk factors. By deploying the ANDSystem software, the regulatory pathways that could potentially be modulated by HCV proteins influencing HCC marker genes were reassembled. This process was facilitated by templates that delineate various types of interactions between objects, as detailed in Table 2.

In pathway templates, besides “expression regulation” interactions, we considered various interaction types, including PPIs; regulation of protein activity, stability, and transport (release); and post-translational modifications. Interestingly, among pathways involving all the regulation types mentioned above, only gene expression regulation represented by templates 4 and 5 was significant (Table 2). These pathways describe potential regulatory associations between HCV proteins and HCC marker genes with the participation of host mediator proteins and genes (Figures S1 and S2).

Nine HCV proteins, 19 host mediator proteins, and 20 HCC marker genes were involved in pathways associated with template 4 (Figures S1). Pathways reconstructed using template 5 contained 10 viral proteins, 51 HCC marker genes, 254 host mediator genes, and 344 host mediator proteins (Figures S2). According to the reconstructed regulatory pathways, HCV proteins possess the potential to modulate the expression of these hepatocellular carcinoma marker genes.

3.2 The employment of differential gene expression data derived from HCV-infected cell cultures

It should be noted that all reconstructed regulatory pathways are predictions and require further confirmation. In order to gain further insights, we analyzed previously published data on differential gene expression in HCV-infected cell cultures. Data concerning differential gene expression in Huh 7.5 cells acutely infected with HCV (72 h post-infection) were derived from the study by Papic et al. [30]. Additionally, another dataset, sourced from the work of Crouchet et al. [29] available in the GEO database (accession number GSE66842), included gene expression profiles of differentiated Huh 7.5.1 cells following infection with the HCV Jc1 clone. We used data representing 10 days post-infection in our analysis. In both studies, controls were mock-infected cells.

3.3 Reconstruction of regulatory pathways to differentially expressed genes in cell lines and patients

Of the 70 marker genes (Table S1), 17 were differentially expressed in HCV-infected cells (Table 3), of which seven were present in regulatory pathways (Figures S1 and S2). The data presented in Table 3 indicate a consistent pattern of alterations in gene expression among HCV-infected patients with HCC and corresponding cell lines. However, there are differences, which might reflect different aberrant methylation mechanisms in HCV presence and absence. The observed downregulation of tumor suppressor genes in patients, contrasted with their upregulation in HCV-infected cell lines, inversely mimicking the behavior of tumorigenic genes, suggests that not all regulatory pathways implicated in the transcriptional regulation of viral proteins necessarily contribute to the risk factors associated with HCC development. For further work, we considered only the gene group whose expression changed unidirectionally in patients and cell lines. Such genes may be of particular interest for studying the mechanisms through which HCV proteins influence HCC.

3.4 Pathways with differentially expressed target marker and mediator genes

The next step in our analysis was reconstructing regulatory pathways in which both target marker and mediator genes are differentially expressed in HCV-infected cell lines (Figure 1). Our assumption was predicated on the notion that alterations in gene expression can correlate with the functional activities of viral proteins implicated in these regulatory pathways. The requirement for differential expression did not apply to proteins involved in PPIs with HCV proteins since their expression might not depend on viral proteins.

Figure 1:

Regulatory pathways associated with viral protein modulation of the expression of HCC marker genes differentially expressed in infected cells, reconstructed using the ANDSystem according to combined templates 4 and 5. Balls with green frames denote viral proteins, other balls denote host proteins, spirals in the middle of the picture denote intermediate participant genes, and spirals on the right side denote marker genes. Black links between objects mean physical interaction, pink arrows – regulation of expression, blue arrows – production of a protein product from a gene.

3.5 ID1, FEN1, TERT, and CDKN2A regulatory pathways

Seven HCV proteins, 39 host mediator proteins, and four HCC marker genes are involved in pathways according to templates 4 and 5 (Figure 1). These marker genes were ID1, FEN1, TERT, and CDKN2A.

We reviewed individual regulatory pathways for each gene (Figure 2a–d). Three viral proteins, 10 human proteins, and six human genes were involved in regulating ID1 expression (Figure 2a). The viral proteins included gp32, core protein (p21), and NS3 (p70). NS3 had the most PPIs with human proteins.

Figure 2:

Regulatory networks of marker genes (a) ID1, (b) CDKN2A, (c) TERT, and (d) FEN1 modulated by HCV proteins. Black links between objects mean physical interaction, pink arrows – regulation of expression, blue arrows – production of a protein product from a gene.

Seven viral proteins, 19 human proteins, and eight human genes were involved in regulating CDKN2A (Figure 2b). The viral proteins included gp32, core protein (p21), NS5B (p68), protease NS2-3 (p23), NS5B (p56), NS1, and NS3 (p70).

The FEN1 regulatory network (Figure 2c) contained only one viral NS3 protein (p70) and two intermediate participants (the human YYAP1 protein and TYY1 gene and protein).

The TERT regulatory network involved seven viral proteins (gp32, p21, p23, p56, p68, p70, and NS1), 21 human proteins, and 10 human genes (Figure 2d). Among viral proteins, the NS3 protein had the most interactions with human proteins, consistent with the previous cases.

4.1 ID1’s regulatory network

ID1’s regulatory network is shown in Figure 2a. ID1 expression was upregulated in HCC patients and HCV-infected cells [41] (Table 3). There are three short pathways in the regulatory network that bind viral proteins to ID1; all involve NS3 (NS3/STAT3/ID1, NS3/NOTC1/ID1, and NS3/TAF1/ID1). While physical interaction between NS3 viral protein and STAT3 has been previously shown [49], their effects remain unclear. STAT3 is directly associated with ID1 upregulation [50]. In addition, STAT3 can regulate ID1 by regulating MYC and FGF2, whose expression is upregulated by STAT3 [51, 52]. It should be noted that MYC and FGF2 expression was elevated in HCV-infected Huh 7.5 cells [30]. Likewise, while NS3’s physical interaction with NOTC1 has been previously shown [49], its effects on NOTC1 function remain unclear. NS3 can also positively regulate NOTCH1 expression via STAT3. Data on physical interactions between STAT3 and NOTCH1 have been reported [53]. NOTCH1 then positively regulates ID1 expression [54].

Therefore, an analysis of the regulatory network indicates that PPIs between viral NS3 with human STAT3 accompany ID1 activation. In addition, NS3’s interaction with NOTC1 also positively affects its activity.

4.2 CDKN2A’s regulatory network

CDKN2A expression was upregulated in HCC patients and HCV-infected cells (Table 3). Similar to ID1, NS3 (p70) occupies a central place in the network with respect to interaction numbers with human proteins (Figure 2b).

SETD2 was the human protein targeted by the most viral proteins, interacting with five: gp32, core protein (p21), NS5B (p68), NS2-3 (p23), and NS1. The regulatory network contains five short pathways linking viral proteins to CDKN2A: NS3/CCN2/CDKN2A, NS3/JUN/CDKN2A, NS3/STAT3/CDKN2A, NS3/SMUF2/CDKN2A, and NS5A(p56)/SMYD3/CDKN2A. Four of these pathways involve the viral protein NS3.

Published data has documented PPIs between NS5A and SMYD3 [49, 55, 56]. These studies demonstrated that NS5A co-localizes with SMYD3 solely in the cytoplasm, inhibiting SMYD3’s nuclear localization [55]. The capacity of SMYD3 to suppress CDKN2A expression has also been explored in the literature [57]. The regulatory network analysis indicated that NS5A’s interaction with SMYD3 inhibits SMYD3, suppressing its ability to inhibit CDKN2A expression, consistent with increased CDKN2A expression with HCV infection. Another CDKN2A activation pathway involves viral protein-mediated suppression of its inhibitors and may involve NS3/CCN2/CDKN2A interactions. For example, data on CCN2’s ability to negatively affect CDKN2A expression has been reported [58]. NS3 can physically interact with CCN2 [49]. Therefore, it can be hypothesized that NS3’s interaction with CCN2 might prevent CCN2 from inhibiting CDKN2A expression.

The NS3/STAT3/MYC/CDKN2A pathway might be the CDKN2A activation pathway resulting from the positive effects of viral proteins on human proteins. The NS3 protein’s effects on human gene expression have been previously discussed [59]. It should be noted that positive CDKN2A regulators, such as MYC, JUN, and GLI1, are upregulated in HCV-infected cells [29, 30].

Therefore, this regulatory network analysis supports the following assumptions about viral protein effects on human protein functions due to their physical PPIs: NS5A inhibits SMYD3, NS3 inhibits CCN2, and NS3, gp32 activates c-Jun.

4.3 FEN1’s regulatory network

FEN1 is controlled by the following participants: viral NS3 (p70), human YYAP1 and YY1 (p70; Figure 2c). While a physical interaction between NS3 and YYAP1 has been previously reported [49], the effects of PPIs between NS3 and YYAP1 on YYAP1 function have not been previously described. YYAP1 can negatively regulate FEN1 expression by activating YY1 expression, a FEN1 transcriptional repressor [40, 60]. Interestingly, our reconstructed pathway indicates that increased FEN1 expression with HCV infection can be explained by NS3 interacting with YYAP1, suppressing its ability to activate YY1.

4.4 TERT’s regulatory network

Like ID1, FEN1, and CDKN2A, TERT expression was upregulated in HCC patients and HCV-infected cells (Table 3). Four short pathways from viral proteins to TERT can be seen in the regulatory net-work: NS3(p70)/STAT3/TERT, NS3(p70)/gp32/JUN/TERT, p23/p68/NS1/p21/SETD2/TERT, and NS3/NOTC1/TERT (Figure 2d). Three participants in these pathways (STAT3, SETD2, and NOTC1) are positively associated with TERT [61–65], while JUN inhibits TERT expression [66, 67]. Since TERT was activated with HCV infection, it can be hypothesized that the physical interactions of viral proteins with positive TERT regulators STAT3, SETD2, and NOTC1 may positively affect their activity.

Physical interactions between c-JUN and viral proteins NS3 and E1 appeared to inhibit c-JUN. Interestingly, c-JUN was involved in CDKN2A upregulation in the CDKN2A regulatory network, and it was assumed that viral proteins activated c-JUN. However, c-JUN is inhibited in the TERT network, and it can be hypothesized that these same viral proteins interfere with its activity.

STAT3 was a member of the NS3/STAT3/TERT pathway and was associated with nine other nodes in the network. Therefore, it can be called the hub of this regulatory network. STAT3 activation by NS3 was discussed above for the ID1 regulatory network. The effects of NOTCH signaling pathway activation by the NS3 protein have been previously discussed [68]. However, it should again be noted that our data did not support the effect of NS3’s physical interaction with NOTC1. There is also no published information on the functional effects of SETD2’s physical interactions with viral proteins. Based on our analysis, SET2D’s interactions with viral proteins NS2-3 (p23), NS5B (p68), NS1, and NS3 (p70) might be expected to positively affect its ability to activate TERT.

Therefore, this regulatory network analysis is consistent with our assumption that the NS3 protein activates STAT3, and also activates NOTC1. Interestingly, the expected effects of NS3 and gp32 interaction with c-Jun are likely opposing when c-Jun acts as a CDKN2A activator and a TERT repressor.

We discussed the expected effects of physical interactions between viral NS3 protein and STAT3 in the ID1, CDKN2A, and TERT regulatory networks. We also hypothesized that viral NS3 interaction with STAT3 positively affects STAT3 activity for all three networks. Consequently, the reconstituted regulatory networks serve as a foundation for generating hypotheses about the molecular mechanisms underpinning HCV proteins’ impact on pivotal genes associated with HCC. Our qualitative analyses of these networks allowed us to propose several hypotheses about the mechanisms of host-virus interactions and the role of specific viral proteins in them. However, it should be noted that a rigorous quantitative analysis of the reconstructed networks requires mathematical modeling of their dynamics. Many interactions in gene networks can contribute non-linearly to the regulation of gene expression, the signal interference from opposing pathways can also have non-linear effects.