A combination of mechanisms—including persistent inflammation, metabolic dysfunction, and insulin resistance— are responsible for non-alcoholic steatohepatitis (NASH). Thus, the multifactorial nature of NASH requires a systems biology approach to the study of the disease to decipher the complexity associated with its initiation and progression. The present study aimed to gain a holistic PPI-level insight into biological processes driving the NASH progression. To this end, we first compared the gene expression profiles of NASH patients’ samples with the healthy subjects to identify the significantly overexpressed genes in the patients. Next, we reconstructed and analyzed the PPI network of DEGs to identify key NAFLD associated genes at the network level.
Although the number of our samples was limited, we used previous gene expression studies to gather gene signatures within the different stages of the disease, from obesity to progressive NASH.
Several genes related to immunity, oxidative stress responses, and metabolic functions were determined to play a role in NAFLD (12, 18-20). We found a satisfactory agreement between our results and previous findings on the role of genes in the disease's modulation. Also consistent with our results, biological processes such as focal adhesion, inflammatory response, fibrosis, and cellular response to chemical stimuli are highlighted in the literature (19-21).
The centrality analysis of DEGs' PPI networks identified the network's hub genes, including PPARA, CREBBP, CCL2, SERPINE1, ABCB11, SOD2, Foxo1, IL-1B, INSR, NF-ϰB. These are both theoretical and experimental shreds of evidence to demonstrate that the PPI network hubs often play important roles in regulating the biological processes involving disease (4, 22) (23). Previous studies consistently show the significance of our identified hub genes as critical modulators of NAFLD initiation and progression (12, 18, 22, 24). Interestingly, several genes absent from the list of top DEGs, including PRS6KA5, CFLAR, TNFSF11, ADRB2, RAF1, RAGE, MEF2A, NFE2L2, ID2, GART, ZFP36 were found among the high-score hub genes. Among them, four proteins stand out as relatively unknown factors in NASH development.
Most notable among hubs is the Zfp36, which encodes tristetraprolin protein (a zinc finger transcription factor), which plays a significant role in negatively regulating TNFα production by destabilizing its mRNA (25). A recent study has demonstrated that tristetraprolin post-transcriptionally regulates systemic insulin sensitivity and hepatic metabolism through the modulation of liver-derived FGF21 (26). Another study found that insulin resistance in obese mice was associated with enhanced Zfp36 expression in the hepatic macrophage (m1). The study of Caracciolo, V. et al., revealed that the myeloid-specific deletion of Zfp36 protects against insulin resistance and fatty liver in mice whose obesity is diet-induced (27). Moreover, a significant correlation has been reported between TTP and hepatocarcinogenesis (28).
Another less explored gene is NFE2L2, which encodes nuclear factor erythroid 2-related factor 2 (NRF2), a transcription factor that mediates protection against oxidative damage triggered by injury or inflammation. Lu, C. et al. demonstrate the role of curcumin in increasing NRF2 expression (29). Due to its antioxidant and anti-inflammatory properties, curcumin's natural polyphenol has long been proposed as a potentially viable treatment for NAFLD. A recent systematic review and meta-analysis of a randomized controlled trial demonstrated that curcumin supplementation has favorable effects on metabolic markers and anthropometric parameters in patients with NAFLD (30). Chambel, S.S. has reported that NRF2 also mediates the impact of lipid metabolism on antioxidant defense, as observed in NAFLD experimental models (31). In another research, the pharmacological activation of NRF2 in obese and insulin-resistant mice was found to reverse insulin resistance, suppress hepatic steatosis, and mitigate NASH and liver fibrosis (19). Hence, the pharmacological induction of NRF2 appears to be a promising strategy for NAFLD prevention and treatment.
Another underscored genes less focused in NAFLD in the connective tissue growth factor (CTGF), a multicellular component of the extracellular matrix-associated heparin-binding proteins involved in many biological processes, including cell adhesion, migration, proliferation, angiogenesis, and wound healing. The overexpression of CTGF has been considered a hallmark of fibrosis (32, 33). Besides, aberrant CTGF expression is associated with many types of malignancies, diabetic nephropathy and retinopathy, arthritis, and cardiovascular diseases. Yoshino, J. showed that CTGF overexpression was associated with adipose tissue expansion and multi-organ insulin resistance in obese subjects (34). Colak, Y. et al. demonstrated that CTGF serum levels might be a clinical marker for distinguishing those NAFLD patients with advanced fibrosis from those who do not (35).
Lastly, CFLAR (a regulator of apoptosis that is structurally similar to caspase-8) functions as a suppressor of steatohepatitis and its metabolic disorders. According to Wang P.X. et al., CFLAR attenuates steatohepatitis progression in both mice and monkeys (36). Liu Y. et al. demonstrate that Silibinin, a flavonolignan from milk thistle, performs its function by activating the CFLAR-JNK pathway (37). In so doing, it regulates downstream target genes involved in lipid metabolism (PPARα, SREBP-1C, and PNPLA3), glucose uptake (PI3K-Akt), oxidative stress (NRF2, CYP2E1, and CYP4A), an inflammatory response. Analyses of treated HepG2 cells have confirmed its potential use in improving various symptoms of NASH (38). Silymarin is the extract of Silybum marianum, or milk thistle, which has been used to treat various liver disorders—particularly chronic liver diseases, cirrhosis, and hepatocellular carcinoma—because of its antioxidant and anti-inflammatory properties. (39)
The translational relevance of this research is accelerated by our authentication of the PPI networks of central genes, our interpretation of rationales, and the broad mechanisms we have drawn from the progression of the disease to an irreversible stage of non-alcoholic steatohepatitis in a previously healthy patient. Our biological function enrichment analysis enabled us to narrow the functional spectrum of NASH-related genes. Summarizing the results from our pathway and network analyses, we were able to pinpoint the vital pathways to metabolic perturbation, stress-related responses, and fibrosis initiation. The Foxo signaling pathway, the PI3k-Akt signaling pathway, and select pathways in cancer produced the highest combined scores. This result was consistent with previous research, thus offering valuable evidence to study the complicated connection underlying the NASH disease. Accordingly, greater emphasis is placed on the AMPK signaling pathway, the insulin resistance pathway, and the Foxo signaling pathway in several research lines on NAFLD progression (21, 40). Also included are several pathways involved in the cellular physiological processes linked to tumor cell proliferation, such as apoptosis, necroptosis, adherence junction, and cell cycle. Following previous research, some pathways are related to the HCC signaling pathway, such as the transforming growth factor-beta signaling pathway, the NF-κB signaling pathway, and the Hippo and the JAK-STAT signaling pathway.
The clustering of the interactions between hub genes resulted in the PPI network's dissection into three distinct modules. According to previous studies, dense interactions between a particular set of proteins may underlie the biological functions coordinated by those proteins (41). Therefore, the clustering of the PPI network provides insights into the modules' biological functions and processes that might otherwise be challenging to uncover. Pathway enrichment analysis is broadly used to interpret PPIs in terms of their biological functions and processes. Discovering the pathways and processes most likely to be coordinated by each PPI module in a particular disease can reveal the molecular mechanisms that drive specific diseases. Owing to the complex and multifaceted nature of NASH, we could not attribute the individual clusters to specific aspects of the disease. Nevertheless, the groups of hub genes in each module constitute a mixture of specific characteristics. We can find genes that are metabolically related to oncogenic/apoptosis or inflammation/fibro-genesis (Figure 7,8).
We found the first cluster with members of the topologically remarkable gene (of the entire network of hub genes)(Table 4) receiving the highest enrichment scores through the apoptosis signaling pathway (P-value: 3.000E-10), the PI3K-Akt signaling pathway (P-value: 9.799E-10), the Foxo signaling pathway (P-value: 2.46E-07), and other cancerrelated pathways (P-value: 6.935E-08) (Figure 8). This cluster study confirms that the majority of the functional genes presented in this module are involved in the interaction between the tumor-suppressing and oncogenic signals through the PI3K-Akt signaling pathway. This cluster also explains the prognosis of severe liver fibrosis or hepatocellular carcinoma (42). Many studies have demonstrated that the PI3K-Akt signaling pathway balances oncogenesis and cell survival signals by regulating pro-and anti-apoptotic genes (40, 43). Conversely, the activation of the pathways such as apoptosis and Foxo signaling pathways in this module may indicate the activation of the cellular defensive mechanisms that counter the disease's progressive traits.
Moreover, enrichment of these pathways with a significant p-value can imply dysfunction in the Akt to regulate its downstream pathways in favor of maintaining balance. From this specific module, we found tumor suppressors (e.g., PTEN, PHLPP-1ATM, BCL6, KLF2, ERN1) and protein oncogenes (e.g., MDM2, XIAP, ACTB, RAF1, CFLAR, and MCL1) among our top hub gene which are overexpressed. Furthermore, they may play a prominent role in exacerbating disease and insulin resistance as a hallmark of NASH progression.
It is worth noting that Akt is also considered a master regulator in insulin-mediated glucose homeostasis. Evidence indicates that Akt is negatively regulated by tumor-suppressing proteins whose activity interferes with insulinmediated glucose homeostasis. Hub genes PHLPP1 and PTEN in this cluster are both tumor suppressors and negative regulators of Akt. Recently studies suggest that the overexpression of PHLPP1 may contribute to type 2 diabetes by interfering with Akt-mediated insulin signaling (44-46). Similarly, the PTEN overexpression is induced and mediated by high levels of free fatty acids and inflammatory cytokines (47-49)and can be involved in controlling cell division, .whereas it said that it could exacerbate insulin unresponsiveness. Consequently, despite the several findings that demonstrate inhibition of Akt’s downstream signaling by these suppressors, it might decrease oxidative stress and DNA damage response, and this appears to contradict with glucose homeostasis function of Akt (43, 50). The clustering of all of these genes together in a single module may reflect the difficulties associated with treating insulin resistance with an antagonizing approach—a well-known challenge in managing cancers and diabetes.
A study of pathways enriched in the second highly populated cluster highlights insulin unresponsiveness and the impaired metabolic balance that justify another molecular aspect of NASH. This fact is reflected in the high enrichment scores of the AMPK signaling pathway (P-value=8.187E-14), the Foxo signaling pathway (Pvalue=8.187E-14), the insulin resistance pathway (P-value=6.149E-09), and the adipocytokine signaling pathway (Pvalue= 2.272E-6) (Fig. 8). Remarkably, we found that almost all of these enriched pathways might shed light on feedback against cells' disorganized bioenergetics. This disorganization can happen through nutrient-sensing signals and glucose hemostasis. The activation of these pathways reinforces the fact that several mechanisms, including elevated gluconeogenesis, lipolysis, and increased fatty acid metabolism oxidation, at the onset of NASH development. These pathways represent maintaining cell metabolism; nevertheless, the combination of prolonged insulin unresponsiveness, accumulated stress, and metabolism perturbation can lead to pathologic conditions, making them "double-edged swords in the pathology of the disease.
Another sub-network constructed in this module elaborates on the complicated link between metabolic perturbation and inflammatory responses. This linkage is highlighted with the enrichment of pathways such as the MAPK signaling pathway (P-value=3.603E-08) and the AGE/RAGE signaling pathway (P-value=9.832E-06) in this specific module may account for the stress that may originate from insulin-mediated lipogenesis that subsequently gives rise to the progression of NASH. Meanwhile, several lines of evidence demonstrate a disruption in the balanced input and output of hepatic FFA manifested in active ROS generation (51). We can understand how hyperlipidemia due to obesity and hyperglycemia in the NASH cases and inflammation and oxidative stress may cause AGE products' formation through the glycosylation process (52). Generally, we can interpret this cluster's biological functions as a reflection of the mechanisms of transforming NAFLD characterized in the two-hit hypothesis. This NASH incidence theory implicates excessive fatty acid and insulin resistance as a "first hit," which are supposed to be connected to mitochondrial dysfunction and oxidative stress (36).A modular study of the third module explains the disease, which moves toward the steato-apoptotic stage. Our analysis indicates two pathways—the TNF signaling pathway (P value=4.908E-07) and the NF-kappa B signaling pathway (P value= 4.908E-07)—were also enriched with genes involved in NASH development. (Figure 8). These two pathways have been studied extensively for their role in inflammation and immunity responses . The AGE/RAGE signaling pathway (P value=2.120E-07) and its downstream gene are prevalent in our hub gene enrichment; in fact, they appear to explain contribution to the progression of the disease. The enrichment of these pathways highlights the over-activity of pro-inflammatory components in this pathway, which triggers positive feedback mechanisms via AGE/RAGE. These inter-related activities—which are known to insulin resistance and systemic complications and the progression of NASH to fibrosis—are presented in our clustering analysis of the disease PPI network. RAGE ligands (AGEs), which are generated by the non-enzymatic glycation of accumulated lipids and glucose, trigger oxidative stress pathways and cause the over-activation of receptors through a positive feedback loop. This phenomenon has detrimental effects on hepatic insulin resistance, steatosis, fibrosis, ischemic and non-ischemic liver disease, and the growth and metastasis of HCC. Consequently, receptor blockages or restrictions in dietary AGEs appear to be an influential therapeutic target for these progressive hepatic disorders, as supported by numerous studies(53) (54) (55)(56).
The network-based analysis offers important insights about functional genes (e.g., RAF1, PTGS2 ATM, TNFSF11, CXCR4, ICAM-1, PTEN, SMARCA2, H6PD, SERPINE1, GLS, NAMPT), but it can also help to identify novel candidate targets and markers. On the one hand, some genes exhibit the pathological characteristics found in other disease contexts, including inflammatory diseases (e.g., rheumatoid arthritis, diabetes, metabolic syndrome, and various cancers) that can be repurposed as a pharmacological target in new disease. This complexity can cause challenges in determining the roles these genes playing in the context of fatty liver disease. On the other hand, some hub genes targeted by FDA-approved drugs—such as ADRB2, PTGS2, and AGTR1—are related to different diseases (e.g., cardiovascular disease) creates opportunities for repositioning and poly-pharmacological strategies about NASH disease. As illustrated in the pathway enrichment, two interconnected mechanisms—including RAS activation and lipolysis regulation—appear to be stimulated by the sympathetic system. The beta-receptor (ADRB2) is among the top-ranked hub genes whose activity in the adrenergic system may lead to RAS activation through multiple signaling steps. RAS is a hallmark of several manifestations of NASH (including ROS formation and fibrosis initiation) (57).
Similarly, the receptor of angiotensin, AGTR1, is among the network hubs that cause aldosterone's biosynthesis through its function. This result can be reflected by a high score of the aldosterone synthesis pathway in the functional enrichments. Also, pathways pertaining to lipolysis regulation in adipocytes, which correlate with beta-receptor overactivity, are highly enriched. Thus, beta-receptor and angiotensin II receptor inhibitors, which are widely available in the market, might be considered for therapeutic use in the future to mitigate the devastating effects of fatty liver on the body.
In recent decades, growing evidence has highlighted the AGE/RAGE axis's pathological role in various diseases, such as diabetes and fatty liver disease. The mechanisms through which the AGE-RAGE pathway influences inflammatory reactions include rising oxidative stress generation and inflammatory responses in its downstream. Four proteins involved in this pathway (SMAD4, ICAM-1, EDN1, SERPINE) can be considered the key players in further translational applications in the context of NASH. The first key hub gene, SMAD4, is part of the protein complexes in this study. This hub gene encodes a protein that acts as a signal transducer in the activation of the fibrogenic pathway and apoptosis through HSC activation and can lead to organ damage such as diabetic nephropathy (58-60). One research has highlighted the role of SMAD4 as a risk factor for fibrosis in conjunction with BMI, TG, LDL-C, ALT, and AST (58). Studies in favor of the deletion of SMAD4 overexpression have shown that improvements to lipid metabolism, liver function, inflammation, or fibrosis could confirm its targeted role in improving fibrosis in the NASH context.
Similarly, the second Hub gene, ICAM-1, is associated with the increased adhesion of leukocytes via increased intercellular adhesion molecule-1 in the membrane of leukocyte and endothelial cells. As a result, more immune responses are activated, and more reactive oxygen is generated. Our data agree with previous findings explaining that hepato-steatosis is associated with increased hepatic ICAM-1 expression. Therefore, setting this glycoprotein as a target in graft protection in fatty liver subjects (and as a biomarker for susceptibility to organ injury, as discussed in previous studies) would be more promising.
The overexpression of the endothelin-1 gene (EDN1) due to the transcriptional regulator's hypoxia-induced activation could explain oxygen hemostasis irregularity in the NASH state (61). Previous studies have found higher levels of endothelin-1 in NASH patients correlated with the grade of their hepatic fibrosis. Such studies have also demonstrated the role of EDN1 as an angiogenic factor in tumor metastasis, and studies on zebrafishes have mentioned the liverspecific expression of EDN1-induced HCC. Hence, commercially available endothelin-1 receptor antagonists might be an excellent therapeutic target both for hypoxia-induced fibrosis and tumor growth that may occur in NASH disease. The activation of the AGE/RAGE axis could also produce an extrahepatic reaction. Studies indicate that the overexpression of SERPINE1 downstream of the AGE/RAGE axis predisposes patients to severe fibrosis and systemic vascular complications in the fatty liver (including atherosclerosis), which further complicates the condition (62).
Additionally, microvascular dysfunction occurs due to the overexpression of ICAM-1 in the endothelial cells may facilitate atherosclerosis formation. As a result, these two genes might be considered CVD risk factors, which could direct drug development efforts toward treating the extrahepatic complications of NAFLD. In agreement with other studies, we confirmed the involvement of these four genes in NASH pathogenesis.
Studying the PPI network could also highlight genes with promising pharmacological targets for further investigation. For instance, recent literature has suggested that BCL6 (a master immune system regulator) could play a role in metabolic regulation, which is not related directly to its function through immunity response (63, 64). A study of knockout mice (2014) illustrated that BCL6 deletion decreased lipogenesis through alteration in SREBP1c Fasn and Scd1. It also reduced adipogenesis and fatty acid oxidation (via PPAR) (64). The BCL6 hub gene—which is coexpressed with hub genes like Foxo1, KLF2, and ATM in our study—supports new findings that BCL6 acts as an intermediary, relating stress response the metabolic irregularity. As the co-expression illustrates, it may form a bridge with another hub genes such as JUN, IL-1b, REL, CCl2, and SERPINE1. Likewise, considering the significance of BCL6 in cross-talk between apoptosis and metabolic regulation, the inhibition of BCL6 could be a plausible target for future treatment of fatty liver disease and insulin resistance (65, 66).
Our pathway analysis also revealed that the disease's progression through oxidative response ultimately leads to defects in cell bioenergetics. We can understand some changes as subordinate to metabolic reactions related to energy hemostasis (e.g., purine and glutamine metabolism), which are said to compensate for energy imbalance failures. In terms of dominant hub genes related to metabolic signaling pathways, those most strongly aligned with the metabolism were GART, GLS, and H6PD.
GART, which is involved in the de-novo biosynthesis of purine, causes noticeable changes in purine metabolism. Many studies have identified an elevation in uric acid in NAFLD patients (67, 68); this appears to occur after the upstream elevation of purine biosynthesis and after the activation of additional metabolic signals through purine metabolism. It produces uric acid as a byproduct. However, some studies reflected that uric acid's elevated formation could be a compensative mechanism against disease progression. Other studies established a high uric acid level in increasing insulin resistance and lipogenesis conditions (69). In addition, many studies point out that purine metabolism plays a significant role in cancer prognosis (70, 71). Based on this evidence, it is likely that alterations to this metabolic pathway in favor of cell proliferation contribute to the disease's cancer-like progression. Ultimately, we can conclude that GART-expressed enzymes, not uric acid, can probably act as promising biomarkers for earlier detection of NAFLD progression.
The GLS gene overexpression represents a metabolic alteration in glutamine catabolism. A recent study by J. Simón et al. revealed that glutaminase (GLS), regulated by c-myc proto-oncogene, is overexpressed in the late stages of NASH and the early stages of HCC (72). They concluded that the enzyme's inhibition could deactivate the Krebs cycle and the electron transport chain (ETC) and decrease β-oxidation, which contributes to reduced ROS formation (73). Another study by Miller RA et al. shows that increased activity of haptic glutaminase (GLS2) is closely related to carbon generation. This carbon is needed for gluconeogenesis through mitochondrial stimulation anaplerotic reactions in response to glucagon (74). This study indicates the role of glutamine degradation in the gluconeogenic pathway under insulin resistance conditions. Afterward, Hyper-ammonia can also occur during increased glutaminase activity. Javier Ampuero et al. demonstrated the role of metformin as a hepatic glutaminase inhibitor. So this medicine can primarily be proposed to treat encephalopathic cirrhosis caused by excess ammonia in the brain (75). A more consistent occurrence is the dysregulated expression of liver aminotransferase, such as ornithine transaminase produced through OAT expression. So, we may find overexpression of OAT in our hub sets of genes as a scavenger that responds to the hyper-ammonia state (76). Our study hypothesizes that GLS2 can present promise not only for the treatment of hyperglycemic diabetes but also for the termination of hyper-gluconeogenic activity due to insulin resistance in fatty liver disease (74).
Hub gene H6PD—the proteins of which are known to activate 11- β HSD1 by generating NADPH (77)—influences the rise in corticosteroid-associated metabolic activity (e.g., gluconeogenesis, biosynthesis, insulin resistance, accumulation of visceral fat, vascular reactivity, vascular remodeling, and sodium reabsorption). Since excess glucocorticoids promote obesity, hyperlipidemia, insulin resistance, and antagonizing approach in the therapeutic strategies for all metabolic syndromes could be more promising in the treatment of fatty liver disease.
Despite decades of research, NAFLD remains one of the most complex diseases with no efficient cure. While previous studies have demonstrated this disease's multifactorial nature, our study shed more light on the complexity associated with it at the molecular level. Our study confirms many genes well associated with NASH from previous reports. It was able to identify several genes less studied in the NAFLD context, an inspection of which in future research may help better characterization and develop a more effective treatment. Our gene expression, PPI network, and enrichment analysis also provide useful insights into NASH's development at the pathway level. Indeed an extensive body of the current knowledge of pathways contributing to NASH was reflected in our network and enrichment analysis. Clustering analysis of DEGs' PPI network also revealed more in-depth associations among genes that support disease progression, being otherwise challenging to identify. Our research demonstrates the usefulness of network and systems biology in facilitating the integration of separate NAFLD molecular data in a coherent framework, which enables consistent interpretation of the data in the context of molecular disease mechanisms and therapeutic target identification.