1. Introduction

2. Materials and Methods

3. Results

3.1. Hub data of human liver during COVID-19

As mentioned in the Introduction, we carefully selected 11 projects [13,14,15,16,17,18,19,20,21,22,23] out of the 18 projects identified in the scientific literature between 2021 and 2023. These papers deal with the characterization of hepatic metabolic processes that are viral targets in patients affected by COVID-19. The distinguishing feature of these projects is the utilization of different techniques to conduct bioinformatic analyses on profiled patient genes. In particular, the authors studied the hub genes that coordinated the metabolic activities of the human liver during COVID-19 infection. They have considered them as potential drug targets for this liver pathology. Owing to their high significant rank, HUB nodes can also serve as functional seeds to extract related functions from the human proteome. By appropriately enriching the nodes that express these functions, it is possible to broaden the functional spectrum of action of the virus, accessing the mechanisms used by SARS-CoV-2 to manipulate human proteins and metabolic processes, as well as information on the molecular strategy adopted. The surprising discovery is that the hub nodes highlighted by these projects are too numerous and different from each other (Table 1). Since they concern the same disease and the same virus, we should have a set of similar hub genes that control the viral strategy by inducing dysregulations in metabolic processes, but we could also come across hub nodes that coordinate normal metabolic activities (housekeeping activities).

Table 1. HUB genes found in the liver by different scientific projects during COVID-19 (2021-2023).

Table 1. HUB genes found in the liver by different scientific projects during COVID-19 (2021-2023).

Article title	HUB GENES
Demonstration of the impact of COVID-19 on metabolic associated fatty liver disease by bioinformatics and system biology approach [2023].	SERPINE1, IL1RN, THBS1, TNFAIP6, GADD45B, TNFRSF12A, PLA2G7, PTGES, PTX3, and GADD45G.
Comprehensive DNA methylation profiling of COVID-19 and hepatocellular carcinoma to identify common pathogenesis and potential therapeutic targets [2023].	MYLK2, FAM83D, STC2, CCDC112, EPHX4, and MMP1.
Exploration and verification of COVID-19-related hub genes in liver physiological and pathological regeneration. [2023].	ASPM, BUB1B, CDC20, CENPF, CEP55, KIF11, KIF4, NCAPG, NUF2, NUSAP1, PBK, PTTG1, RRM2, TPX2, UBE2C.
Systems biology approach reveals a common molecular basis for COVID-19 and non-alcoholic fatty liver disease [NAFLD] [2022].	IL6, IL1B, PTGS2, JUN, FOS, ATF3, SOCS3, CSF3, NFKB2, and HBEGF.
To investigate the internal association between SARS-CoV-2 infections and cancer through bioinformatics [2022].	MMP9, FOS, COL1A2, COL2A1, DKK3, IHH, CYP3A4, PPARGC1A, MMP11, and APOD.
Target and drug predictions for SARS-CoV-2 infection in hepatocellular carcinoma patients [2022].	Up regulated, PDGFRB, MMP14, VWF, CD34, NES, MCAM, CSPG4, MMP1, SPARCL1, and MMP10. Down-regulated, IL1B, S100A12, FCGR3B, CCR1, S100A8, CCL3, CCL2, CCL4, CLEC4D, and LILRA1.
Bioinformatics analysis reveals molecular connections between non-alcoholic fatty liver disease [NAFLD] and COVID-19 [2022].	ACE, ADAM17, DPP4, TMPRSS2 and NAFLD-related genes such as TNF, AKT1, MAPK14, HIF1A, SP1, IL10.
Organ-specific or personalized treatment for COVID-19: rationale, evidence, and potential candidates [2022].	CCL2, CCL5, CXCL10, HAO2, BAAT, and SLC27A2.
Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic [2021].	IL6, IL18, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A.
A systems biology approach for investigating significantly expressed genes among COVID-19, hepatocellular carcinoma, and chronic hepatitis B [2022].	ACTB, ATM, CDC42, DHX15, EPRS, GAPDH, HIF1A, HNRNPA1, HRAS, HSP90AB1, HSPA8, IL1B, JUN, POLR2B, PTPRC, RPS27A, SFRS1, SMARCA4, SRC, TNF, UBE2I, and VEGFA.
Identification of Key Pathways and Genes in SARS-CoV-2 Infecting Human Intestines by Bioinformatics Analysis [2022]	AKT1, TIMP1, NOTCH, CCNA2, RRM2, TTK, BUB1B, KIF20A, and PLK1.

Note: In bold red, hub genes found in common between different projects.

From these papers, we have collected 142 hub nodes of the liver cells landscape found connected to COVID-19, of which 21.12% comprises a group of 30 genes in common between different projects, while all the others are different. 126 hub genes remain after removing those in common. Barabasi’s research consistently showed that biological networks exhibit scale-free properties, with a few genes controlling multiple connections within different functional modules, while most genes have only a few connections [49,50]. It is rather suspicious that the same tissue has a metabolic network operated by such a disproportionate number of hub genes during viral aggression. This suggests heterogeneity of networks. The differences in databases used to extract relationships are a common cause of conflicting results [39,40]. The relationships between the virus and the host occur at the molecular level, mainly through protein interactions. These interactions occur between viral proteins and human proteins and are determined by both human defensive strategies and viral attack strategies. Therefore, it is likely that hub nodes unrelated to the pathology have also been identified. To understand how and why, we applied a biological protocol that involves the identification of the real physical relationships established between the nodes that implement the liver network, with no a priori knowledge of the computational protocols. The fundamental biological events between virus and host drive these interactions, thus necessitating a biological evaluation of each individual interaction (see Methods for details).

Considering the ongoing SARS-CoV-2 pandemic, BioGRID implemented a project called the BioGRID COVID-19 Coronavirus Curation Project. BioGRID is a biomedical interaction repository with experimental data compiled through curation [43]. In these years, BioGRID has accumulated fundamental experimental data supporting the role of SARS-CoV-2 in human infection. This Project collected the comprehensive datasets of all the Known physical interactions between the proteins of the human proteome and those of SARS-CoV-2. In the purification processes of these proteins, researchers overwhelmingly used physical methods such as Affinity Capture-MS and Proximity Label-MS and curators of BioGRID have specifically selected and classified both interactors and physical interactions into various levels of statistical significance. The reason lies because some interactions may be random because the laboratory method does not reproduce the cellular environment. Indeed, the breaking of cells to favor bait-prey interaction also allows for random encounters that do not happen.

Today we have a vast number of over 30 thousand interactions (as of July 2023) from the human proteome when its proteins interact singularly with the 31 viral proteins of SARS-CoV-2. These interactions are unique in being non-redundant and having high confidence interactions at high throughput, associated with score values of statistical filtering, as determined by using SAINT (Significance Analysis of INTeractome) express version 3.6.0. [51].

We have successfully acquired the entire dataset comprising the entire viral genome (31 proteins) and its interactions with human proteome. With it, we have created a unique database of the human-virus relationships to search for physical/functional interaction between a viral protein and a human protein. Using our proposed conceptual application framework, we can gain a large understanding of the molecular mechanism of a viral infection. A similar approach has already helped researchers recognize targeted viral complexes of five common human viruses [52]. This recognition is based on biological information.

Because of its small genome, a virus must get maximum performance in interfering with the functional processes determined by human cellular proteins aimed at ensuring normal organic homeostasis. The virus learns over time to implement its attack strategy on specific animal targets by evolutionary studying the structure of the target proteins. Many viruses use proteins containing large segments of intrinsic disorder [53] to facilitate “encounter”, but every single interaction must have specific and well-defined structural bases to be successful, even if transient. To get this knowledge, the virus employs lengthy periods of co-evolution, parasitizing humans, or similar species [54]. Therefore, if an interaction is present in this peculiar archive, it means that it has a strategic value of attack or defense, for the virus and for humans, respectively. The database also searches for multiple interactions of a human protein with different viral proteins.

Therefore, prioritizing the characterization of the 126 hub genes is an important issue. They should represent the highest-ranking genes, most affected by the virus, and therefore optimal to use as functional seeds. This should significantly facilitate the identification of genes truly associated with the pathology and genes involved in normal metabolic regulation, but also uncertified genes included in networks with no experimental certainty. STRING uses many standardized databases [39] as a source of data and information for calculating network models. It produces a detailed analysis of all the scientific articles underlying each single interaction, and corroborating the models calculated also with biological analyses, such as GO or KEGG, and with structural analyzes using systems such as UniProt. Using STRING, we can manage 6 data channels that parametrize the network calculation differently and influenced by various confidence levels. In this way, we can modulate results with very different parameters of reliability, origin, and statistical significance.

On STRING, we inputted the 126 hub genes as functional seeds to extract their relationships from the entire human proteome. We show this gene list in the Supplements as Table S1. These genes, decoded by STRING, should interact to form a protein-protein network model showing also compact sub-graphs. Therefore, we left the six channels open to make the most of all the information from each source, but we set the interaction score to 0.900. As STRING networks usually have a lot of low-scoring interactions, if we want to limit their number per protein, we should use a filter. We used the highest confidence score cut-off to limit the number of interactions to those that have the highest confidence and then are more likely to be true positives. By implementing this strategy, we can narrow down the information only to our input proteins and their network pattern.

3.2. Comprehensive Liver interactome during COVID-19.

The graph in Figure S1 of the Supplements shows numerous nodes not connected (31%). A significant number of the remaining elements do not form a compact and connected graph, with only a portion exhibiting connectivity. This is an indicator of poor functional connectivity, but it also says that many of these hubs may not possess the basis of significant experimental certainty. The manipulation of genomic data in the pipeline, from input to the extraction of functional properties of the network, suffers from a lack of accurate data and an indifference for control over know-how. This makes it impossible to carry out any robust analysis, because the disconnected nodes make any topological analysis or functional consideration unreliable [55,56,57,58]. To overcome these shortcomings, we can extend the interactions by setting an enrichment of our network with new interaction partners (seeds), always depending on confidence value. This allows us to know whether the input shows evidence of statistical enrichment for any known biological function or pathway. The various external databases, including Gene Ontology, KEGG pathways, UniProt Keywords, PubMed publications, and others, which annotate the STRING maps, can provide considerable help. The STRING enrichment method retrieves functional enrichment for the set of input proteins. This will show which input protein has enriched terms and the description of each term with all its annotations, providing only answers with FDR =<0.05. About publications, STRING extracts automatically all available scientific texts from PubMed to cover the maximum knowledge about each interaction information, also including full-text articles. Figure S2 shows the network of Figure S1 implemented with 500 first-order (direct) nodes and 500 second-order (indirect) nodes. Despite its compactness and size, the resulting graph still shows some unconnected nodes. We removed the 15 unconnected nodes (APOD, BAAT, CCDC112, CSPG4, CYP3A4, DKK3, EPHX4, HAO2, MMP11, NES, PLA2G7, SLC27A2, SPARCL1, STC2, and UGT2B7) using an appropriate tool present in STRING to ensure a fully connected network. Pruning has also the aim of minimizing non-informative enrichment. As a result, we still have 111 residual original hub proteins within the final network, which clearly suggests that we are in the presence of enrichments consistent with the functional seeds used. In TABLE S2, we report the list of the 111 remaining hub nodes. It is also important to note that STRING in all the calculated networks has always used data and information extracted from no less than 10,000 scientific articles from PubMed (fully downloadable), which have generated a specific knowledge base for interactions used in the calculation. By employing a sequential cleaning approach, we can get a collection of highly precise information and data, which is ensured by the exceptional dependability of each individual interaction among nodes, unveiling their authentic biological credibility.

The enrichment produced a network that includes all principal human proteins in liver tissues during COVID-19. According to STRING, the net shows 7313 functional associations with biological processes spanning 14 categories. A set of 2344 Biological Processes (GO), 195 KEGG Pathways, and 960 Reactome Pathways characterizes the breadth of functional activities. This network appears very well organized and contains all those functional relationships that also involve the original hub proteins. The compact groupings of certain nodes suggest molecular complexes, even very large ones. We can see these molecular complexes in the peripheral areas of the network. They operate as metabolic nano-machines that carry out specific molecular processes [59,60]. For example, the subgraph at the bottom left is rich in proteins of the Splicing Factor 3B complex that, together with other 17S U2 small nuclear ribonucleoprotein particle (snRNP) components, may play a role in Spliceosome during the selective processing of microRNAs (miRNAs) [61]. This sub-graph also collects many of the proteins involved in transforming the molecules of pre-mRNA (precursor messenger RNA) into mature mRNA. The involvement of this complex is not random because RNA splicing is among the major down-regulated proteomic signatures in COVID-19 patients [62]. Certainly, the virus needs to manipulate the host splicing machinery to its advantage to control the production of its proteome [63]. In fact, going back along the periphery of the network, we encounter compact sets of genes involved in all phases of cellular translational processes and the entire ribosomal complex, just to mention the most important. At least in the liver, these appear to be the most obvious targets of SARS2. The Excel File 1 reports all the nodes of the interactome in Figure 1 with their degrees. These nodes also include all the remaining original hubs (111 nodes). In the Excel file 1, we can also note a few dozen high-ranking genes, all specific for the various phases of the cytoplasmic translation processes. However, before proceeding with other observations, we have reported in the Excel file 2 all 26,990 interactions relating to the interactome in Figure 1. The file also reports the sources of each single binary interaction and the combined score. The interest in this file released by STRING lies because it shows (in red) the quantitative impact of the component deriving from the experimental data alone on the combined value of the score. Thus, this file is useful as a reference in evaluating each individual interaction for the score of 0.900 (highest confidence) we have always used. As these results show, even for a binary relationship with a score of 0.900, the experimental certification that makes it certain can many times be missing, thus introducing serious and not easily visible anomalies into the graph. We then processed in our SARS2-Human Proteome Interaction Database (SHPID) each single protein of the entire interactome (1111 nodes) to find out which viral proteins had interacted with the network proteins, as well as with the remaining original hub-proteins. Some of these proteins no longer exhibit the high connectivity characteristics that were crucial when they were designated as HUBs in the original papers. For example, hub nodes like MCAM, LILRA1, GDI2, COL2A1, TNFAIP6 or PTX3 now have low ranks. What happened reveals that their COVID-19-associated high functional rank disappear because they are likely proteins very inflated by high studying frequency because of their relevance in diseases or for their functional importance in the cell or because they are poorly characterized. A quick check using the Excel file 2 highlighted the widespread lack of valid biochemical and biophysical experimental data for these proteins, meaning that they did not provide adequate evidence for the functional hypotheses in which they had been implicated. Although determining the cellular localization of events through PPI networks is experimentally challenging, in this interactome, we find proteins localize to a precise range of modules, represented by specific molecular complexes.

Figure 1. Comprehensive interactome of liver tissue proteins during COVID-19. STRING calculated the graph through enrichment, using as seeds the set of 111 hub proteins got after pruning. We enriched this network with 500 first-order (direct) nodes and 500 second-order (indirect) nodes. Settings: interaction score of 0.900 (highest confidence); all six channels open. Network parameters: number of nodes, 1111; number of edges, 13,494, while its expected statistical number is 8,838; average node degree, 24.3; avg. local clustering coefficient, 0.623; PPI p-value, <1.0e-16; network diameter, 7; network density, 0.022; network heterogeneity, 1.030; network centralizations, 0.128; connected components, 1. (Topological parameters calculated by Cytoscape).

Figure 1. Comprehensive interactome of liver tissue proteins during COVID-19. STRING calculated the graph through enrichment, using as seeds the set of 111 hub proteins got after pruning. We enriched this network with 500 first-order (direct) nodes and 500 second-order (indirect) nodes. Settings: interaction score of 0.900 (highest confidence); all six channels open. Network parameters: number of nodes, 1111; number of edges, 13,494, while its expected statistical number is 8,838; average node degree, 24.3; avg. local clustering coefficient, 0.623; PPI p-value, <1.0e-16; network diameter, 7; network density, 0.022; network heterogeneity, 1.030; network centralizations, 0.128; connected components, 1. (Topological parameters calculated by Cytoscape).

3.3. Metabolic Stress Related to COVID-19 in the Liver.

The Excel file 1 shows the protein RPS27A, with a degree of 161, serves as the primary hub. The original hub nodes list (refer to TABLE 1) also contained RPS27A. One alias of RPS27A, Ubiquitin-40S Ribosomal Protein S27a, explicitly suggests its function as a remarkably conserved protein responsible for directing cellular proteins toward degradation by the 26S proteasome [64]. Thus, its role in the liver holds significance. It assembles into ribosomes, but also functions independently of them. We also know RPS27A plays a significant role in the progression of various human cancers, including HCC [65]. Its landscape of action during viral infection of the liver is interesting. Investigations of SARS-CoV-2 infection have shown large-scale chromatin structural changes because of metabolic stress [66,67]. In situations of oxidative stress [68], induced by phases of the viral cycle [69,70], both oxidizing agents and the need to signal this stress, as well as variations in sensitivity to oxygen, have highlighted the importance of HIF in signaling [71]. These effects are a common feature of both tumors and COVID-19 [72,73]. In both cases, cells must switch from the TCA cycle to the energetically less efficient glycolysis pathway, and so many glycolytic enzymes are up regulated. One of the transcriptional regulators involved in the response to oxidative stress is HIF1A [74], which remains inactive in normoxic conditions because of its interaction with HIF1AN, an oxygen sensor that hinders interactions with other transcriptional co-activators. SIRT1 serves as an energetic sensor [75], connecting transcriptional regulation to intracellular energetic demands, while TP53BP1 acts as a p53 binding protein, participating in the response to DNA damage.

In tumor progression, the stressful events described affect the p53 protein. The p53 (gene TP53) role is to inhibit the proliferation of cancer cells through cell cycle arrest [76]. Therefore, it normally performs a protective cellular action. The main cellular antagonist of p53 is MDM2, as it triggers the degradation of p53 [77] and effectively supports cancerous growth. MDM2 and p53 establish a feedback loop to preserve balance, complemented by the involvement of RPL11, a ribosomal protein that inhibits MDM2 and enhances p53 stabilization and activation in normal conditions [78]. Therefore, RPL11-MDM2-p53 form an axis regulated precisely by RPS27A [78]. When activated by cellular stress phenomena, RPS27A hinders the interaction between RPL11 and MDM2, promoting the degradation activity of p53 through the catalytic activity of a free MDM2, thus starting the oncogenic process. Hence, this system of proteins works as a sensor and regulator of cellular stress, acting on p53 and RPS27A to regulate their specific activity.

Figure 2 demonstrates the influence of DNA damage and oxidative stress on these same metabolic players during COVID-19. By highlighting the proteins involved in these processes through a tool that colors the nodes specifically involved (refer to Materials and Methods for further information) we can identify them within the liver protein interactome, also visualizing their role and functional relationships. TABLE 2 shows the activated biological processes, their statistical value, and the colors of the nodes in the network.

The analysis of Figure 2 reveals that RPL11 and RPS27A are not implicated in the pathways through which cellular stress is detected and transmitted to TP53 and MDM2. These two proteins are not colored; thus, they do not display any stimulation from their interconnected nodes. The non-involvement of RPS27A also suggests that RPL11 continues its activity of blocking the biological function of MDM2 towards TP53. This analysis hypothesizes an activity of TP53 in protecting liver cells by interfering in viral action. Clearly, only data got from laboratory experiments can offer certainties, even though clinical observations of mild liver damage appear to corroborate this hypothesis. However, the Excel file 2 shows that the experimental component of all the interactions highlighted in Figure 2 and used to evaluate the hypothesis on the functional activity of TP53 during infection is very high for each protein, so the interactions all rely on solid experimental basis, which strongly supports this conclusion.

Figure 2. Role of TP53 (p53) and RPS27A in liver infection by SARS-CoV-2. The network is that of Figure 1 and the nodes at the top left have been carefully extrapolated to highlight both the mutual relationships and the abundance of functional connections with the central core of the network. The degree for each single node is RPL11, 104; MDM2, 45; TP53, 133; RPS27A, 161; TP53BP1, 23; SIRT1, 26; HIF1A, 35; HIF1AN, 5. The colours of the individual nodes show the type of metabolic stress (DNA damage and/or hypoxia) induced by COVID-19 in the liver. The biological stress processes (GO) activated are those shown in TABLE 2.

Figure 2. Role of TP53 (p53) and RPS27A in liver infection by SARS-CoV-2. The network is that of Figure 1 and the nodes at the top left have been carefully extrapolated to highlight both the mutual relationships and the abundance of functional connections with the central core of the network. The degree for each single node is RPL11, 104; MDM2, 45; TP53, 133; RPS27A, 161; TP53BP1, 23; SIRT1, 26; HIF1A, 35; HIF1AN, 5. The colours of the individual nodes show the type of metabolic stress (DNA damage and/or hypoxia) induced by COVID-19 in the liver. The biological stress processes (GO) activated are those shown in TABLE 2.

TABLE 2. Biological processes related to COVID metabolic stress in the liver.

TABLE 2. Biological processes related to COVID metabolic stress in the liver.

3.4. Individual Human proteins interacting with many viral proteins and their distribution graph.

In the Excel 3 file, we can see that some human liver proteins interact with many viral proteins. It is a known fact that multiple viral proteins have the ability to target specific human proteins (90). These interactions described in Excel file 3 could be a resource for researchers aiming to identify important specific host-virus interactions in the dynamics of disease transmission [89]. In particular, to describe the viral diversity associated with different hosts and different tissues, as well as detect shared associations useful for identifying who, where and how they are shared [88,89]. However, some authors report that, in viral infections, the most common ratio of protein-protein interactions between virus and host is 1:1 [90]. Viral proteins, as well as human proteins, are tightly integrated and interact in a specific functional context. This explains much of the binding specificity between proteins. However, even in the best-case scenario, only a handful of viral proteins could interact with a single human protein. This limitation arises from the physical impossibility of locating suitable binding surfaces on a single molecule and the potential electrostatic repulsions and structural constraints caused by proximity on a crowded structure. In the absence of temporal data on the frequency and specificity of these attacks, we can reasonably think that this massive attack is likely directed towards the entire ribosome and its ancillary complexes, of which the targeted protein is a component, given that the most targeted proteins are the ribosomal ones. But this hypothesis also has another side. It shows the total lack, even in the best databases, of the spatio-temporal characteristics relating to individual human proteins. Given the unlikelihood of crowding on a single protein, the attack is more likely to be sequential, i.e., at different times. A comprehensive understanding of human biology, and that of other living beings, requires acknowledging the dynamic nature of metabolism.

TABLE 3 shows the human proteins most attacked by viral proteins in the range 12 - 20. Its main purpose is to showcase the different levels of affected human proteins, both high and low. The degree of each protein (see Excel file 1) is in the bracket. The high degree is justified because the majority are proteins organized into complexes.

Table 3.  .

Table 3.  .

Human Protein	Number of Interacting Viral Proteins**
RPL18A*(84)	20
RPL13(84)	19
ALDOA(4), CDC42(52), EIF2S1(45)	18
RRM2B(3)	17
RPL13A(98), RPL21*(87), RPL30*(85)	16
PSMC1(30), RPL26*(96), RPL7A(85), RPL(9)	15
BUB3(19), RPL7(95), RPL8(95), RPS24(90), RPS6(93), RPS9*(102), SNRPD1(38), SRC(97), STIP1(12)	14
BAG2(7), RAC1(11), RPL12(93), RPL27A(85), RPS27L(82).	13
EIF6(46), MCM7(20), HYOU1, PTGES3(23), RPL27(84), RPL13(84), RPL35A(84), RPS10(87), RPS11*(108), RPSA(99).	12

Note: * Proteins marked with an asterisk also interact with ORF1ab. ** For more extensive details about interactions, see the EXCEL file 3.

That some human proteins interact with many viral proteins presupposes many shared structural motifs. But this also suggests that viral motifs in their evolution must gain host-like mechanisms to be successful in invasion. Consequentially, this supports the observations that conformational flexibility, spatial diversity, abundance, and slow evolution are the characteristic features of the human proteins targeted by viral proteins [91]. Viral proteins mimic host binding surfaces of domains to interact with human proteins, which occur through domain-motif interactions. In the Excel file 3, we can also observe the interacting viral proteins are not only NSPs (non-structural proteins), but we have also a significant presence of accessory proteins. However, viral proteins intervene in large numbers, targeting mostly the proteins of the ribosomal system. This allows the virus to take control of protein biosynthesis and redirecting it towards the synthesis of the viral genome and its own proteins. That many viral proteins attack one host protein also means that many of them have mimicked the same human motif. In addition, we must consider an average of around 47% of disordered segments in coronavirus proteins [92,93]. This favours attacks on specific cellular targets of the host. An interesting discovery is that among the viral proteins that interact with ribosomal proteins (RPL18A, RPL21, RPL30, RPL26, RPS9, and RPS11) there is also the long viral polypeptide ORF1ab. Since ORF1ab is certainly not a target to be blocked but is the viral polypeptide that must be translated, the asterisked proteins mentioned above could represent points of structural contact of the viral protein ORF1ab with the human ribosome. In fact, some of them (RPL18A, RPL21, RPL30, and RPL26) are specific components of the large ribosomal subunit, the complex responsible for peptide chain elongation and the synthesis of proteins in the cell, while RPS9 and RPS11, are components of the small ribosomal subunit as part of ribosomal process, which couples processing steps of RNA folding, and RNA cleavage [94,95]. Most ribosomes end translation at a stop codon present in the first stem of the pseudo-knot. While, the coronavirus protein-synthesis employs regulatory mechanisms, such as ribosomal frameshifting, promoted by a conserved stem-loop of RNA that forms a promoting pseudo-knot structure [96]. Ribosomes stall at the pseudo-knot and undergo a -1 frameshift at the slippery sequence, leading to the translation of ORF1ab fusion polypeptide [97,98]. In coronavirus, this phenomenon allows the virus to encode multiple types of proteins from a single mRNA, compacting the information. In this way, virus translation dominates host translation because of high levels of virus transcripts.

In Table 3, we also find the involvement of lower-degree human proteins that are not ribosomal proteins. Some of them are key because involved in crucial metabolic functions of the liver. We report as examples, ALDOA, RRM2B, BAG2, and HGS. ALDOA is the tetramer of hepatic-type aldolase B that specifically binds to the hepatic cytoskeleton, particularly to actin-containing stress fibers. The presence of disordered segments in the C-terminals favours the possibility of scaffolding and suggests that aldolase can regulate cell contraction [99,100]. RRM2B forms a complex with RRM1 where it plays a key catalytic role in repairing damaged DNA together with p53 and provides deoxyribonucleotides in G1/G2-locked cells [101,102]. While BAG2 is a co-chaperone regulator of the HSP70 and HSC70 chaperones. It acts as a nucleotide exchange factor by promoting the release of ADP from HSP70 and HSC70 proteins, triggering the release of the client/substrate protein [103,104]. In the end, HGS, Hepatocyte Growth Factor, is involved in intracellular signal transduction mediated by cytokines and growth factors. It regulates endosomal sorting and plays a critical role in the recycling and degradation of membrane receptors [105,106,107]. The liver serves as the site of localization for many of these proteins, emphasizing their tissue specificity.

3.5. Distribution of viral proteins interacting with single human proteins.

Instead, Figure 3 shows that the distribution graph of the entire set of human liver proteins (626 proteins) interacting with viral proteins (see also Excel file 3). Each point on the curve reports the set of human proteins that have the same number of interacting viral proteins. The fit shows that the distribution conforms to a power law, albeit with an R² value of 0.5278, suggesting an acceptable fit. This value is at the low limits of reliability and may imply the existence of heterogeneities in the distribution, which makes the results difficult to explain. This should not be surprising because the distribution accurately reflects the overall structural and functional behaviour of the entire set of human proteins with different roles from each other and subjected to sequential functional stress by viral proteins in complex and metabolically differentiated cellular environments. Hundreds of interactions are mainly one-to-one (those on the left side of the curve), while others involve multiple interactions (multi-to-one), to up to 20 viral proteins per single human protein (in the tail). The connectivity distribution in Figure 3 is quite consistent with the power law’s prediction of preferential attachment [108]. Thus, our model should show the emergence of a scale-free topology [109] from interaction results. So, if the connectivity distribution follows a power law, then new nodes will have a better chance of connecting to those with already many neighbours because of the preferential attachment rule.

Figure 3. Distribution of viral proteins interacting with single human proteins. The curve is the exponential fit (displayed at the top right). Data calculated from the Excel file 3. The figure also shows, for the experimental points involving the most targeted human proteins (from 10 onwards), the list of which they are. The asterisked proteins are those that also interact with ORF1ab.

Figure 3. Distribution of viral proteins interacting with single human proteins. The curve is the exponential fit (displayed at the top right). Data calculated from the Excel file 3. The figure also shows, for the experimental points involving the most targeted human proteins (from 10 onwards), the list of which they are. The asterisked proteins are those that also interact with ORF1ab.

Comparative and evolutionary genomic analyses support the birth in the cell of complex structures that make up organized and complicated cellular nano-machines [110]. Genomics has also shown that parts associate with each other to form integrated systems with modular and hierarchical structures [111]. This organizational process should also be intrinsic in the modelling of liver metabolic reactions that arise from protein-protein interactions. In accordance, complex networks exhibit higher-order organization in connectivity, showing links that can be modulated and modelled using subgraphs of the network [112]. Some authors have also shown that networks contain within themselves information about the organization of these compact modules (subgraphs) such as emergence of the protein complexes [113,114]. The peculiarity of these models is the emergence of an important intrinsic structural characteristic of biological networks, namely hierarchical modularity, i.e., a higher level of organization, the growing mechanisms of which, unfortunately, remain unknown. Researchers have never quantitatively tested these qualitative and observational relationships in real biological interaction networks. Our network model, related to liver tissue, shows human protein complexes strongly involved in viral infection. We believe that the preferences of viral proteins toward the interior of these complexes should reflect the mechanisms used by viruses to manipulate host protein complexes.

Based on our collective data, it is evident that the evaluation of virus action should be conducted within the framework of viral preferential attack strategies on intricate protein organizations. However, how viruses manipulate sub-graphs of local host networks, such as human protein complexes, have never been addressed from a topological-computational perspective, preferring to focus on the preferential targeting of viral proteins with hub or bottleneck nodes, despite that no formal definition exists to separate hub proteins from non-hub proteins [12,115].

A systematic analysis of the protein complexes, identified as direct protein-protein targets, has been done to discover new drugs (127) or even through bioinformatic approaches (52), almost never considering a topological point of view. In such type of analysis, both local topological aspects of the network and evolutionary ones should contribute, but, to date, discrimination of the topological and functional properties of complex viral targets during an infection is lacking. Our analysis identified compact sub-networks of human proteins targeted by multiple viral pathogen proteins. But what is perplexing is that during the infection, the targeting process of a complex protein system, such as the ribosome, seems to depend on the connectivity of neighbouring proteins in the network (due to the preferential attachment, which is a topological parameter). Conversely, the interaction of a viral protein ought to be primarily determined by the likelihood of a physical encounter associated with the decrease in free energy because of binding, so exploiting chemical-physical parameters from evolutionary laws.

We can hypothesize, from the analysis presented in Figure 3, that multiple types of interaction activities could compete concurrently. If this is the case, upon closer analysis, we should be able to discern more exponential decays that would better characterize the distribution. In Figure 4 (upper side), we observe that the degree distribution seems to follow a single power law. But the fit in the log-log scale shows that the single power law distribution is at the lower limit to significantly meet or explain the data characteristics. One-to-one and one-to-many interactions behave differently and make the analytical representation heterogeneous when considered together. The bottom side shows that the distribution, always in the log–log scale, displays two different slopes, unlike what happens when fitting with a single power law. In both fits, the values of R2 are very good, suggesting a combination of two solutions (or two decays) linearly independent. The biphasic distribution opens the hypothesis that there may be at least two dominant classes of coexisting proteins with differentiated functional responses. One class (in black) should contain human proteins essential for metabolic adaptations following viral infection. These proteins can be under-expressed or lost when pathophysiological conditions induce profound metabolic changes. Proteins belonging to the other class (depicted in red) are essential for critical physiological processes of viruses and hosts but are also essential for the virus to gain energy. Thus, these human proteins, highly expressed, exhibit enhanced resistance to pathological processes that induce functional variability. Depending on the characteristics of the local context, it is possible for all proteins to transmigrate between both classes. In the lower Figure 4, there is on the x axis the transition degree, TD. Its value breaks into two parts the distribution and identifies the boundary between nodes with interaction degree less than 12 (in black, made up of proteins that are on average poorly connected), and nodes having degree greater than TD (in red, composed of evolutionarily older proteins that are on average much more connected). In our analysis, each of these sub-networks follow well a single power-law degree distribution, while differing in the value of power-law exponents.

Figure 4. – Linear distributions of interacting viral proteins with a single human protein (log-log scales). Upper figure – Distribution graph considered as a single power law. Fitting: f(x) = 431.26 x^-1.66 and R² is 0.3675. Lower figure - Biphasic representation of the power law. The graph displays the fitting equations. TD is the transition degree, the estimated point (marked by blue star) at which the slope of the distribution sharply changes. Its value is around 12.

Figure 4. – Linear distributions of interacting viral proteins with a single human protein (log-log scales). Upper figure – Distribution graph considered as a single power law. Fitting: f(x) = 431.26 x^-1.66 and R² is 0.3675. Lower figure - Biphasic representation of the power law. The graph displays the fitting equations. TD is the transition degree, the estimated point (marked by blue star) at which the slope of the distribution sharply changes. Its value is around 12.

This biphasic model suggests all proteins can gain new interactions with rate (greater slope) and number of interactions (the rich get richer) always increasing, as happens for older proteins (red ones). Proteins can also lose their interactions, both with and without the loss of their connecting partners. It is a kinetic model which through the different slopes reflects the evolutionary behavior of proteins, considering two classes of proteins, one with a rapid action but also with a fast residence time, the second, with opposite properties of greater resilience. Both classes adequately describe, both in topological and evolutionary terms, the nature of the bi-exponential model. The model, in fact, shows a situation in which the oldest proteins, the most conserved by evolution, increase their interactions because of the establishment of new and specific kinetic conditions. Although our results are built on solid foundations of statistics and experimentation, it is important to interpret them with caution due to all the limitations previously described.

3.6. Comprehensive analysis of liver metabolic activities during COVID-19

To support the structural and functional organizational events previously found for these proteins and the complexes involved, we analyze the data using the many specific databases that STRING maps onto the protein data of calculated networks. Table 4 reports some analysis of biological processes made by STRING on the interactome data in Figure 1. The table shows the most statistically reliable results. Although all data used in this study have a high intrinsic significance, analyzes on extensive sets, where gene expression variability could also play a fundamental role, must be carefully evaluated. Therefore, in their evaluation, the value of the intensity of the expression of the genes that code for the proteins of a process, contained in the Strength parameter (see Methods), was also considered. The results show that the p-value (fdr) is important, but the level of gene expression influences its significance. Then, the intensity of the biological action also depends on the intensity of gene expression.

The gene expression depends on cellular signals, but the biological results depend on the phenotype "interpretation" of that information, which is displayed by the synthesis of proteins (and non-coding RNA). Thus, this parameter allows for the definition of a similarity metric between gene expressions, which we can use to reposition and compare biological processes [128,129].

Table 4.  .

Table 4.  .

1 - NORMAL BIOLOGICAL PROCESSES RELATED TO NODES CERTIFIED BY REVERSE ENGINEERING IN THE LIVER INFECTED BY COVID
GO-term Biological Process	Description		P*		p-value	Strength
GO:0019221	Cytokine-mediated signaling pathway		47.50		8.51e-57	0.82
GO:0002181	Cytoplasmic translation		46.53		2.05e-44	1.05
GO:0071345	Cellular response to cytokine stimulus		42.97		1.59e-63	0.68
GO:0033044	Regulation of chromosome separation		37.73		9.62e-36	1.02
GO:0010965	Regulation of mitotic sister chromatid separation		36.30		8.03e-34	1.04
GO:0033045	Regulation of sister chromatid segregation		36.20		6.46e-34	1.02
GO:0051983	Regulation of chromosome segregation		34.60		4.60e-35	0.97
GO:0030071	Regulation of mitotic metaphase/anaphase transition		33.87		3.68e-32	1.04
GO:0033044	Regulation of chromosome organization		32.37		3.03e-39	0.82
GO:0007346	Regulation of mitotic cell cycle		32.25		1.18e-46	0.70
GO:1901987	Regulation of cell cycle phase transition		30.16		2.98e-42	0.71
GO:0006412	Translation		29.30		4.59e-40	0.72
GO:1901990	Regulation of mitotic cell cycle phase transition		27.66		2.42e-37	0.74
GO:1990869	Cellular response to chemokine		23.92		8.36e-24	0.96
GO:0034243	Regulation of transcript. elongat. from RNA polym. II		17.94		5.25e-19	0.91
GO:0007088	Regulation of mitotic nuclear division		17.50		3.89e-20	0.85
2 - NEGATIVE REGULATION OF BIOLOGICAL PROCESSES RELATED TO NODES CERTIFIED BY REVERSE ENGINEERING IN THE LIVER INFECTED BY COVID
GO-term Biological Process	Description		P		p-value	Strength
GO:0043069	Negative regulation of programmed cell death		18.94		2.65e-36	0.52
GO:0043066	Negative regulation of apoptotic process		18.31		7.95e-35	0.51
GO:1901988	Negative regulation of cell cycle phase transition		15.97		3.11e-22	0.71
GO:0045786	Negative regulation of cell cycle		15.25		1.63e-24	0.63
GO:0010948	Negative regulation of cell cycle process		14.98		1.08e-22	0.68
GO:0009892	Negative regulation of metabolic process		14.36		3.19e-43	0.33
GO:0010605	Neg. regulation of macromolecule metabolic process		14.22		6.61e-41	0.34
GO:1901991	Neg. regulation of mitotic cell cycle phase transition		13.83		8.82e-18	0.73
GO:0045930	Negative regulation of mitotic cell cycle		13.33		2.12e-19	0.69
GO:0031324	Negative regulation of cellular metabolic process		12.03		2.37e-34	0.35
GO:0060548	Negative regulation of cell death		11.95		1.43e-34	0.35
GO:2000816	Neg. regulation of mitotic sister chromatid separation		11.88		7.56e-11	1.0
GO:0045841	Neg. regulation mitotic metaphase/anaphase transition		10.46		2.29e-10	1.01
GO:2001237	Neg. regulation of extrinsic apoptotic signaling pathway		9.67		5.60e-12	0.76
GO:0051348	Negative regulation of transferase activity		8.90		1.17e-15	0.59
3 - DYSREGULATED BIOLOGICAL PROCESSES RELATED TO NODES CERTIFIED BY REVERSE ENGINEERING IN THE LIVER INFECTED BY COVID
3A -Local network clustering (STRING)**	Description			P	p-value	Strength
CL.152	Viral mRNA Translation			89.03	7.21e-46	1.19
CL:159	Viral mRNA Translation			55.38	1.06e-45	1.23
CL:162	Cytoplasmic ribosomal proteins			54.16	1.41e-43	1.23
CL.143	Viral mRNA Transl. and Sec61 translocon complex			53.10	6.93e-47	1.11
3B - REACTOME PATHWAYS	Description			P	p-value	Strength
HSA-192823	Viral mRNA Translation			64.09	2.56e-53	1.2
HSA-72764	Eukaryotic Translational Termination			61.79	2.32e-52	1.18
HSA-72689	Formation of a pool of free 40S subunits			58.97	1.91e-51	1.15
HSA-72737	CAP-dependent Translation Initiation			53.73	1.98e-49	1.09
HSA-1799339	SRP-dependent cotranslational prot. targeting to membr.			53.17	2.20e-48	1.1
HSA-9679506	SARS-CoV infections			38.58	5.77e-50	0.76
HSA-9754678	SARS-CoV-2 modulation of host translational machinery			26.18	2.39e-23	1.12
HSA-9692914	SARS-CoV-1 host interactions			32.98	1.06e-32	1.03
HSA-9705683	SARS-CoV-2 host interactions			31.14	1.61e-36	0.86
HSA-9678108	SARS-CoV-1 infection			30.73	1.12e-33	0.93
HSA-9735869	SARS-CoV-1 modulates host translational machinery			28.19	1.28e-23	1.22
HAS-9754678	SARS-CoV-2 modulation of host translational machinery			26.18	2.39e-23	1.12
HSA-9694516	SARS-CoV-2 infections			25.52	1.07e-34	0.75
HSA-9705671	SARS-CoV-2 activates/modulates innate/adaptative immune responses			11.06	5.57e-14	0.75
HSA-597592	Post-translational protein modification			7.95	1.28e-22	0.36
HSA-9772572	Early SARS-CoV-2 Infection Events			3.68	1.3e-05	0.72
4 - PROTEIN DOMAIN CHARACTERISTICS IN THE LIVER INFECTED BY COVID
4A – Prot. Domains (InterPro)	Description	Count in network		P	p-value	Strength
IPR036048	Chemokine interleukin-8-like superfamily	29 of 44		15.03	1.11e-14	1.07
IPR039809	Chemokine beta/gamma/delta	15 of 26		8.03	8.90e-07	1.01
IPR033899	CXC Chemokine domain	12 of 14		7.30	1.54e-06	1.18
IPR011332	Zinc-binding ribosomal protein	9 of 10		7.01	6.92e-05	1.2
IPR011029	Death-like domain superfamily	29 of 97		6.01	2.23e-08	0.72
IPR008271	Serine/threonine-protein kinase, active site	52 of 310		4.21	9.00e-08	0.47
IPR001875	Death effector domain	5 of 7		3.84	3.10e-03	1.1
IPR0000488	Death domain	11 of 35		2.81	6.3e-03	0.74
4B - Prot. Domains (SMART)	Description	Count in network		P	p-value	Strength
SM00199	Intercrine alpha family (small cyt/chem CXC)	28 of 42		16.80	5.07e-15	1.07
SM00252	Src homology 2 domains	22 of 104		3.24	2.5e-05	0.6
SM00219	Tyrosine kinase, catalytic domain	20 of 88		2.64	2.5e-04	0.6
4C - Annotated Keywords (UniProt)	Description	Count in network		P	p-value	Strength
KW-0689	Ribosomal protein	90 of 175		44.83	5.05e-46	0.96
KW-0687	Ribonucleoprotein	112 / 278		42.17	4.14e-49	0.85
KW-0945	Host-virus interaction	148 / 540		33.03	3.81e-48	0.68
KW-0747	Spliceosome	50 of 138		16.77	5.14e-20	0.81
KW-0395	Inflammatory response	56 of 163		16.56	1.73e-21	0.78
KW-0132	Cell division	88 of 384		14.25	2.31e-23	0.61
KW-0498	Mitosis	69 of 75		13.43	4.53e-20	0.65
KW-0131	Cell cycle	137 / 651		13.13	1.09e-23	0.57
KW-0647	Proteasome	25 of 52		11.57	2.74e-12	0.93

The table is split into four sections that show the primary aspects of the metabolic context encountered by the liver during COVID-19. The data are shown in decreasing order determined by the P value. As we note, some p-values, despite being remarkably low, are repositioned due to variability in the intensity of gene expression. In the first part of the table (Section 1) we can see that cellular activity is mainly involved in promoting cytokine signaling processes, cellular translation, and the cell cycle. In the second part (Section 2), we have the negative regulations resulting from the viral attack. Surprisingly, one of the main viral activities is to alter the programmed processes of cell death, followed by strong interference to alter the processes of the cell cycle in its various phases. These data suggest a viral activity that aims to implement a systemic spread of intact but infected cells, very similar in result to the spread of cancerous metastases. If we observe the interaction data in Excel file 3, we can see that the virus attacks proteins of the cellular matrix and cytoskeleton, such as ACTB, ACTR3, FN1, CDC42, COL2A1, COL18A1, ITGA3, ITGA5, ITGAV, FLNA, ACTL6A, ACTR2/3, and others, similar to what the cancer cell does to spread metastasis. Other researchers have noticed similar strategies [130], such as extending particular stages of the cell cycle and managing programmed cell death. Section 3A shows some of clusters calculated by STRING which show the involvement of the virus in mRNA translation and in ribosomal cytoplasmic proteins. Local STRING network clusters are pre-computed protein clusters derived by hierarchically clustering the full STRING network.

The Supplements (under Clustering) provide a comprehensive overview of all four clusters of the Section 3A, featuring their topological parameters and a GO analysis for each, to facilitate the identification of the metabolic framework of action. Extremely low FDR values characterize all these contexts, demonstrating that the cytoplasmic translational system, including ribosomes, is the most statistically significant virus target.

The Section 3B (Reactome) shows the most reliable metabolic pathways that involve extensive virus-host interactions and identifies sets of proteins that also perform the same action as SARS-CoV-1. Section 4 highlights the specific human protein domains targeted by viruses. One interesting aspect is that they have quantized the presence and incidence (count in the net) of these proteins. Many of these domains (Sections 4A and 4B) are involved in the molecular mechanisms of chemokine/cytokine signaling, and in the reprogramming processes of programmed cell death. The last section, 4C, shows in which downregulated biological processes we find these domains and in what abundance, including spliceosome-mediated RNA processing. The set of this information is in excellent agreement with that discussed earlier and also opens up to other observations. Although our results are built on solid foundations of statistics and experimentation, it is important to interpret them with caution due to all the limitations previously described. In this study, we did not discuss one-to-one interactions of the proteins of this viral pathogen with other human proteins. The most surprising of these observations (see the Excel file 3), is the large number of one-to-one interactions, that, for instance, characterize the S1 viral protein (Spike), which interacts with many individual human proteins involved in different metabolic processes (manuscript in preparation).

4. Discussion

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