3.1 The landscape of predicted human-HSV-1 PPIs
In this work, we applied an integrative computational framework to predict the interactions between 74 different proteins of HSV-1 strain KOS and 20,412 reviewed human proteins. Our computational framework used four methods (IM, DDI, DMI and ML) to predict whether two proteins interact. Then, the four interaction probability scores (PrIM, PrDDI, PrDMI, and PrML) were combined into an integration score (Pr) representing the interaction probability of the human-HSV-1 protein pair.
We separately calculated the number of PPIs predicted by each individual method. As shown in Fig. 2A, the number of PPIs predicted by DMI was the largest (41,828), followed by DDI (13,579), IM (7,805), and ML (6,341). In general, the percentages of overlapping PPIs among different methods are low, implying that different methods are distinctive and complementary. Due to the methodology similarity, DDI achieved a relatively more consistent PPI prediction results with IM and DMI (the overlap rate in both cases accounts for about 10% of its total). After integrating the results of the four methods, the number of predicted PPIs with Pr > 0 was 65,673. Although higher Pr should correspond to higher reliability, it is still necessary to set a reasonable and convincing threshold for high-confidence predictions. We sought the solution from high-throughput human-virus PPI identification studies. Taking the number of experimentally validated PPIs between HIV-1 and human as a reference, each HIV-1 protein was identified to have 100 ~ 200 interactions with human proteins in some high-throughput experimental studies . Thus, we set Pr > 0.5 as the threshold of high-confidence PPIs (Additional file 4 Fig. S2), and 10,432 PPIs were singled out as the most likely interacting protein pairs. We found that 690 of 728 experimentally verified PPIs (collected from HPIDB database and used in the ML method) overlap with our 10,432 predicted results (Additional file 3 Data set S3) and 601 of these 690 PPIs can be predicted by more than one method. Figure 2B showed that the IM method accounts for the largest proportion among these 10,432 high-confidence PPIs.
3.2 Functional and network analysis showing the reliability of predicted human-HSV-1 PPIs
We further analyzed these 10,432 high-confidence PPIs. First, we counted the number of human proteins targeted by each HSV-1 protein (Fig. 3). On average, one HSV-1 protein interacts with 145 human proteins and the top ten HSV-1 proteins contribute 5,963 interactions (approximately 57%) in the predicted human-HSV-1 interactome. We found that the HSV-1 protein UL22 was predicted to have the most interactions with human proteins, and the predicted interaction partners are significantly enriched in the category of membrane-bounded organelle components (hypergeometric test, corrected P value = 3.37 × 10− 51). Previous studies have suggested that UL22, also called envelope glycoprotein H (gH), complexed with glycoprotein L (gL, UL1) and interacted with glycoproteins B (gB, UL27) and D (gD, US6) to form a viral membrane fusion machine, thereby driving the fusion of the virus with the host membranes to allow the virus to enter or spread between host cells . It is therefore reasonable to predict that this viral protein to interact with multiple human proteins especially membrane proteins. RL2/RL2_1, E3 ubiquitin ligase (ICP0), was predicted to interact with many human proteins belonging to the category of host cellular interferon-related proteins (hypergeometric test, corrected P value = 1.32 × 10− 9), which may indicate that RL2/RL2_1 is an HSV-1’s weapon to counteract the intrinsic- and interferon-based antiviral responses. Thus, the predicted viral targets play an important role in viral infection process, which indicates the reliability of our human-virus PPI prediction.
To hijack and utilize host cells to complete viral life cycles, viral proteins tend to target some important host (human) proteins, such as the “hub” (high-degree centrality) and “bottleneck” (high-betweenness centrality) nodes of the human PPI network. Therefore, we also calculated the degree centrality and betweenness centrality of target proteins (proteins in the human PPI network that are targeted by HSV-1) and non-target proteins (proteins in the human PPI network that are not targeted by HSV-1) from the perspective of network biology. It can be clearly seen from Fig. 4 that, whether in degree or betweenness centrality, the values of target proteins were significantly higher than that of non-target proteins (Wilcoxon rank sum test, P value < 2.2 × 10− 16), which is in accordance with previous observations inferred from human-pathogen PPI network analyses.
3.3 Functional analysis of brain-specific human-HSV-1 PPIs
Among several diseases caused by the infection of HSV-1, sporadic but often fatal HSE is of great concern, which is caused by HSV-1 infection in the brain. Therefore, we further paid our attention to the PPIs in which the human proteins are specifically expressed in the brain tissue. We selected 569 PPIs containing 283 brain-specific human proteins from the 10,432 high-confidence PPIs. According to the GO enrichment analysis (Fig. 5), we found that the cell adhesion-related BP terms such as “cell adhesion”, “biological adhesion” and “cell-cell adhesion” were significantly enriched (Fig. 5A, corrected P value = 2.33 × 10− 7, 2.33 × 10− 7 and 2.2 × 10− 14, respectively), which indicated that HSV-1 relies on the intricate events of attachment and fusion to enter cells, specifically by utilizing its own envelope proteins (envelope glycoproteins) to interact with cell adhesion molecules to mediate this process . In our results, 55 cellular adhesion molecules were predicted to interact with HSV-1 proteins. In the category of CC, we found that human proteins were significantly enriched in microtubule or microtubule cytoskeleton (Fig. 5B, corrected P value = 1.14 × 10− 4 and 4.82 × 10− 4, respectively). As we know, microtubules are major components of the cytoskeleton and are involved in transport in all eukaryotic cells. Therefore, the above enriched GO terms are in accordance with previous knowledge that after entering the host cell, the viral capsids need to be transported to and from the nucleus to complete the replication cycle. This is particularly relevant to the processes of establishment of latent infection and reactivation in neurons during which transport of capsids along microtubules in long axons is required.
In addition, one of the strategies usurped by HSV-1 is to guide the entry pathway by manipulating the cell signaling cascades . We found that in the GO enrichment analysis results of MF entries (Fig. 5C), the GO term of “calcium ion binding” was significantly enriched. Ca2+ is one of the most prominent and common signal carriers and is known to modulate several steps during virus replication. The entry of HSV-1 is triggered by the interaction of gH with cellular integrin, which eventually triggers Ca2+-mediated signaling pathways within the cell to ensure effective nucleocapsid translocation into the cytoplasm . Although the relationship between chloride channels and viral infections has received less attention, previous studies have shown that chloride channels play an important role in HSV-1 entry . Here we also found the significant CC enrichment of the chloride channel complex and the MF enrichment of the chloride channel activity, which further supports the association between chloride channel and HSV-1 entry.
Collectively, the GO enrichment results of HSV-1-interacting human brain-specific proteins are consistent with known functions associated with the HSV-1 replication cycle, suggesting that the PPIs between HSV-1 and human disrupt proteins normal functions in brain cells, which may cause inflammation and damage leading to HSE. These data also support the overall reliability of the predicted PPIs. We expect that the 569 PPIs can form a vital subnetwork (Additional file 4 Fig. S3) of the human-HSV-1 interactome, which may enhance mechanistic understanding of diseases related to HSV-1 infection (e.g. HSE) as well as providing new hints to therapeutic target discovery.
3.4 The association of HSV-1 with AD in the context of human-HSV-1 PPIs
Increasing evidence points to the association of HSV-1 brain infection with AD. HSV-1 is present in the latent state in a high proportion of elderly brains. Intermittent reactivation from the latent state may cause local damage and inflammation, accumulation of which might eventually lead to AD . In order to investigate whether the emergence of AD is related to the HSV-1 infection, we compared 1,947 AD-related human genes with our 4,546 predicted HSV-1 target proteins (human proteins present in the 10,432 high-confidence PPIs), and 635 were found to be overlapping (Fig. 6A, hypergeometric test, P value = 1.10 × 10− 28). Meanwhile, we calculated the overlap between AD-related genes and target proteins specifically expressed in brain tissue and found that the overlap was still significant (hypergeometric test, P value = 9.18 × 10− 10). We also calculated the average network distance of AD-related genes to target proteins and non-target proteins in the human PPI network, and the results showed that AD-related genes were closer to target proteins (Fig. 6B). The above network analyses suggest that, to a large extent, HSV-1 target proteins are heavily associated with AD, and it can be hypothesized that the virus may also indirectly affect these AD-related genes by interacting with other proteins to enhance their ability to influence AD risk and predisposition.
Among our predicted PPIs, three HSV-1 proteins (UL2, UL21, and UL45) interact with amyloid precursor protein (APP). APP is a single-pass transmembrane protein that is widely expressed in tissues, especially at high levels in brain neurons and then rapidly metabolized . There are two pathways for the proteolysis of APP, one of which is cleaved by α-secretase generating the sAPPα fragment and the other is cleaved by β-secretase (BACE1) producing neurotoxic amyloid β (Aβ) . One of the commonly recognized hallmarks of AD is the accumulation of Aβ. Our results may raise the possibility that HSV-1 infection appears to contribute to the Aβ deposition process through PPIs (Fig. 6C). First of all, two of our three predicted interactions are consistent with the experimental observations that HSV-1 uses its capsid proteins (UL21 and UL45) to physically interact with APP, thereby hijacking APP to transport newly generated virions in infected cells through a rapid anterograde transport mechanism . Although such behavior changes the intracellular distribution of APP and seems to prevent APP from converting to Aβ to some extent, HSV-1 infection triggers an intra-CNS anti-microbial innate immune response to induce APP phosphorylation and activates BACE1 activity, which jointly promote the production of Aβ . Aβ would encapsulate the HSV-1 virions to facilitate their clearance by autophagy [51, 52]. HSV-1 also employs its virulence factors (RL1 and UL45 were predicted to play a role) to counterattack, inhibiting the autophagy-lysosome pathway of Aβ by interacting with Beclin-1 . The imbalance between the production and elimination of Aβ caused by HSV-1 infection accounted for excessive intracellular neurotoxic Aβ deposition within autophagosomes and endosomes, therefore inducing neuronal apoptosis, which in turn can drive degeneration of CNS tissue and development of AD. In summary, the recapitulated interactions among HSV-1, APP and Aβ argue for a mechanistic basis for the association between HSV1 infection and the risk of AD.
3.5 Interactive web interface
We stored our predicted 10,432 high confidence PPIs in a database and provided an interactive web interface (http://www.zzdlab.com/HintHSV/index.php) to facilitate user access. We have provided a search box for 72 HSV-1 proteins participating in these 10,432 PPIs, so users can select any protein to view the corresponding interactions. For each HSV-1 protein, we provide a table to display all the prediction scores for each human target protein (including four individual prediction scores and one integrative score) and a subnetwork to show the PPIs, which are available for download. The 569 brain-specific PPIs, 690 known PPIs and other datasets we used in this work are also downloadable in the web interface.