Next Article in Journal
Better Dietary Knowledge and Socioeconomic Status (SES), Better Body Mass Index? Evidence from China—An Unconditional Quantile Regression Approach
Previous Article in Journal
Social-Psychological Factors in Food Consumption of Rural Residents: The Role of Perceived Need and Habit within the Theory of Planned Behavior
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Perspective

A Novel Combination of Vitamin C, Curcumin and Glycyrrhizic Acid Potentially Regulates Immune and Inflammatory Response Associated with Coronavirus Infections: A Perspective from System Biology Analysis

1
Nutrilite Health Institute, 720 Cailun Road, Shanghai 201203, China
2
Nutrilite Health Institute, 5600 Beach Boulevard, Buena Park, CA 90621, USA
3
Nutrilite Health Institute, 7575 East Fulton Avenue, Ada, MI 49355, USA
*
Author to whom correspondence should be addressed.
Nutrients 2020, 12(4), 1193; https://doi.org/10.3390/nu12041193
Submission received: 4 March 2020 / Revised: 21 April 2020 / Accepted: 22 April 2020 / Published: 24 April 2020
(This article belongs to the Section Phytochemicals and Human Health)

Abstract

:
Novel coronaviruses (CoV) have emerged periodically around the world in recent years. The recurrent spreading of CoVs imposes an ongoing threat to global health and the economy. Since no specific therapy for these CoVs is available, any beneficial approach (including nutritional and dietary approach) is worth investigation. Based on recent advances in nutrients and phytonutrients research, a novel combination of vitamin C, curcumin and glycyrrhizic acid (VCG Plus) was developed that has potential against CoV infection. System biology tools were applied to explore the potential of VCG Plus in modulating targets and pathways relevant to immune and inflammation responses. Gene target acquisition, gene ontology and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment were conducted consecutively along with network analysis. The results show that VCG Plus can act on 88 hub targets which are closely connected and associated with immune and inflammatory responses. Specifically, VCG Plus has the potential to regulate innate immune response by acting on NOD-like and Toll-like signaling pathways to promote interferons production, activate and balance T-cells, and regulate the inflammatory response by inhibiting PI3K/AKT, NF-κB and MAPK signaling pathways. All these biological processes and pathways have been well documented in CoV infections studies. Therefore, our findings suggest that VCG Plus may be helpful in regulating immune response to combat CoV infections and inhibit excessive inflammatory responses to prevent the onset of cytokine storm. However, further in vitro and in vivo experiments are warranted to validate the current findings with system biology tools. Our current approach provides a new strategy in predicting formulation rationale when developing new dietary supplements.

1. Introduction

Coronaviruses (CoVs) belong to the Coronaviridae virus family and are enveloped, positive-sense RNA viruses [1]. CoVs infect various host species, including humans and other vertebrates. In recent years, novel CoVs emerged periodically in different regions around the globe, such as severe acute respiratory syndrome CoV (SARS-CoV) in 2002, Middle East respiratory syndrome CoV (MERS-CoV) in 2012 and SARS-CoV-2 in late 2019 [2]. These viruses predominantly cause respiratory and intestinal tract infections and induce various clinical manifestations [3]. Although the pathologies of these virus are not yet completely understood, viral proteins and host factors play key roles in the infection process [4]. A well-coordinated immune response is essential against virus infection. In contrast, an out of control immune response is associated with immunopathogenesis and excessive inflammatory response, which may result in poor outcomes such as severe pulmonary damage and multi-organ failure [5,6]. Due to the challenges of developing antiviral drugs and vaccines, the outbreaks of CoV infections often cause major public health issues [7]. CoV-infected people must rely on their own immune defense to control the progress of infection. These diseases are classified as self-limiting diseases, meaning that an individual’s immune function will determine whether early symptoms will advance into severe acute respiratory tract symptoms (i.e., pneumonia) or recovery from infection.
Phytonutrients are a variety of bioactive non-nutrient plant compounds that exhibit the capacity to alter biochemical reactions and consequently influence human health after ingestion [8,9]. Commonly known phytonutrients in dietary supplements include flavonoids, anthocyanin, carotenoids, polyphenols, triterpenoids and phytosterols, many of which have been reported to play important roles in human health with potential as therapeutic agents [10,11]. It is well-known that adequate intake of nutrients and phytonutrients may help regulate immune function, including enhancing defense and resistance to infection, while maintaining tolerance [12]. Several plant food sources, such as acerola berry (Malpighia glabra L., M. emarginata D.C.), roxburgh rose fruit (Rosa roxburghii Tratt.), camu camu (Myrciaria dubia (Kunth) McVaugh), amla (Phyllanthus emblica L.) and sea buckthorn berry (Hippophae rhamnoides L.) are known as rich sources of vitamin C (VC). VC regulates immunity by enhancing differentiation and proliferation of B- and T-cells, and it is beneficial in preventing and treating respiratory and systemic infections [13,14,15]. VC potentially protects against infection caused by CoVs due to its benefits on immune function [16]. High doses of VC were recommended for prevention of SARS-CoV-2 infections by the Chinese Center for Disease Control and Prevention and Chinese Nutrition Society. Currently, VC is under investigation in a clinical trial for its benefit in patients with severe SARS-CoV-2 infection (https://clinicaltrials.gov/).
Glycyrrhizic acid (GA) is a major phytonutrient found in licorice root (Glycyrrhiza uralensis Fisch. ex DC., G. inflata Bat., G. glabra L.), which is considered an ingredient for both food and medicinal use in China [17]. GA exhibits anti-viral [18], anti-inflammatory [19] and hepatoprotective activities [20]. Traditional Chinese medicine (TCM) treatments for SARS-CoV-2 infection pneumonia were recommended by National Health Commission of China, and licorice root was one of the commonly used TCM herbs [21]. GA has been reported recently for its binding capability with angiotensin-converting enzyme 2 (ACE2) to prevent SARS-CoV-2 infection [22]. Intriguingly, the effect of diammonium glycyrrhizinate combined with vitamin C tablets on common pneumonia infected with SARS-CoV-2 is being tested in clinical trials (http://www.chictr.org.cn/).
Curcumin (CC) and its analogues are the main phytonutrients of turmeric (Curcuma longa L.) and other Curcuma spp., which are widely used around the world as culinary spices, traditional medicine as well as a popular dietary supplement ingredient due to its wide range of health benefits including anti-inflammation [23], anti-cancer [24], cardiovascular regulation [25], respiratory [26] and immune system benefits [27]. In addition, the suppression of multiple cytokines by curcumin suggested that it may be a useful approach in treating Ebola patients against cytokine storm [28]. CC also inhibited aminopeptidase N (APN) which was identified as a cellular receptor for alpha CoV [29].
Since VC, CC and GA are popular in nutrition, and more importantly, they have been used to regulate immune responses and recommended to intervene in CoV infections, a combination of VC, CC and GA (VCG Plus) was proposed for its potential to prevent CoVs infection. In the present study, our objective is to apply system biology techniques to investigate biological processes and pathways that are regulated by VCG Plus, and to illustrate how these biological processes and pathways could be associated with protection against CoV infections.

2. Method

2.1. Gene Target Acquisition and Screening

Comprehensive determination of potential compound–target interaction profiles is a critical step for the system biology analysis [30]. Currently, multiple databases/platforms, such as DrugBank Database, Comparative Toxicogenomics Database (CTD), Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and Integrative Pharmacology-based Research Platform of Traditional Chinese Medicine (TCMIP), were commonly applied to acquire potential targets of small molecular compounds [31,32,33]. DrugBank contains detailed drug, drug-target, drug action and drug interaction information about FDA-approved drugs as well as experimental drugs [34]. CTD provides core information on chemical-gene interactions that are manually curated from scientific literature [35,36]. While TCMIP predicts the potential targets for herbal chemical compounds using MedChem Studio (version 3.0), an efficient drug similarity search tool to identify herbal chemical compounds with high structural similarity (Tanimoto score > 0.8) to known drugs [37]. Basically, the target information in these three databases is complementary, a combination of which could provide relatively comprehensive compound-target interactions. In this work, the target acquisition of VC, CC and GA was conducted separately, using direct text mining of DrugBank, CTD and TCMIP with their chemical names as keywords. The targets of VC and CC from CTD with interaction counts less than 5 were excluded. All acquired targets of VC, CC and GA were limited to Homo sapiens and mapped to UniProt [38] for correction to remove redundant and erroneous ones.

2.2. Hub Target Identification and Protein–Protein Interaction (PPI) Analysis

Hub targets were identified by taking following steps:
(1) Combine the targets of VC, CC and GA and remove the duplicates;
(2) Map them into the CTD website, choose “virus diseases” and “immune system diseases” gene database for comparison, select the overlapping targets for the next analysis;
(3) Map selected targets into STRING (Version 11.0) to perform PPI analysis [39], set the cut-off degree of PPI as high confidence (0.700), and download the information of PPI as TSV file format;
(4) Import the file to Cytoscape software (Version 3.6.1) [40] to analyze the topological parameters of the interactions, select the hub targets whose node degree is greater than the median value. After these steps, STRING and Cytoscape are used subsequently to construct and analyze the PPI network of hub targets. In constructed networks, the targets are represented by nodes while the interactions among them are represented by edges.

2.3. Distribution Analysis of Targets in Tissues/System and Gene Ontology (GO) Enrichment and Analysis

Gene ORGANizer [41] was employed to perform the target-system location analysis. DAVID Bioinformatics Resources 6.8 [42] was applied to perform GO analysis for the hub targets. The biological process, cell component and molecular function were three basic outputs of GO. The cut-off value of the p-value was set to 0.05, and the p-value was adjusted using the Benjamini–Hochberg method. In addition, the analysis of specific GO annotation involved in immune system processes was carried out with ClueGo (Version 2.5.6) [43], a Cytoscape plug-in integrating EBI-Uniport GO annotation database (updated in Mar 2019). Generally, the targets from VC, CC and GA were imported to ClueGo separately and represented by different colors. The visual style of ClueGo analysis was set as “cluster”. The GO term/pathway was added to a specific cluster term if at least 80% of genes in this term is contributed by an individual (phyto-) nutrient. Only terms with a p-value less than 0.05 were presented after two-side hypergenometric test and bonferroni step down adjustment were conducted.

2.4. Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Analysis

KEGG pathway enrichment and analysis were performed on ClueGo integrating with KEGG database (updated in 17 February 2020). The procedures were similar to the immune system process GO term analysis, briefly described below:
(1) import the targets of VC, CC and GA to ClueGo separately, represent by different colors;
(2) set visual style as “cluster”, and set statistical method as two-side hypergenometric test and bonferroni step down adjustment, only pathways with p-value less than 0.05 are shown;
(3) start analysis, download the protein-pathway interactions information in Excel format for analysis. According to KEGG database, pathways are clustered into the following categories: (A) metabolism, (B) genetic information processing, (C) environmental information processing, (D) cellular processes, (E) organismal systems, and (F) human diseases. Finally, the top 15 protein–pathway interactions related to immune and inflammatory responses were extracted and shown.

3. Results

3.1. Hub Target Identification and Analysis

Three public databases were used to mine the potential targets for the three (phyto-) nutrients in VCG Plus. The number of qualified targets identified for VC, CC and GA were 109, 146, and 65, respectively (Supplementary Table S1), and a total of 248 unique targets were identified for the combination of VCG Plus (phyto-) nutrients. Comparing the results with “virus diseases” and “immune system disease” gene data in CTD, it was found that 179 targets existed in both the “virus diseases” and “immune system disease” gene database. These 179 targets were then selected to perform PPI analysis and network topological analysis. As a result, 88 tightly connected targets (hub targets, node degree ≥ 12) were identified for further analysis. Detailed information of the 88 hub targets is shown in Table 1. A Venn diagram (Figure 1A) shows that 13 targets overlap for the combination of VCG Plus (phyto-) nutrients, which include ALB, CASP3, CXCL8, HMOX1, NFKB1, NFKBIA, PTGS2, RELA, TGFB1 NOS2, SOD2, IFNG and TNF. In addition, there are nine overlapping targets for CC and GA, and 22 overlapping targets for VC and CC. Furthermore, the PPI of hub targets was constructed by STRING and they are shown in Figure 1B. The PPI network was assembled by 88 nodes (targets) and 1153 edges (interactions), with clustering coefficients of 0.59 and an average number of neighbors of 26.21. The targets are closely connected, suggesting that they may position in similar biological pathways with similar health benefits.

3.2. Enrichment and Analysis of Target Distribution in Tissues and Systems

We analyzed the system distribution of 88 targets to better explore the potential function on a system level. The top 10 systems are shown in Figure 2A. The respiratory system was found as the most significant location which contained 78 targets, followed by the urinary (74 targets), cardiovascular (84 targets), digestive (83 targets) and immune systems (64 targets). In addition, the tissue distribution of the targets for each (phyto-) nutrient was analyzed. The top three significant tissues of each individual compound were shown in Figure 2B. It is interesting that targets of these (phyto-) nutrients are all enriched in the heart. However, targets of CC are also enriched in the lung and liver, while targets of GA are enriched in the intestine and large intestine, and targets of VC are enriched in the peripheral nerves and coagulation system.
CoV infections may lead to inflammation and alter immune responses, which are generally associated with the respiratory and immune systems [4,44]. Some digestive and cardiovascular events, such as diarrhea [45], heart palpitations [46] and abnormal coagulation parameters [47] were observed in clinical studies, suggesting that coronavirus infection may result in systemic damage. In this sense, the VCG Plus targets could cover most systems and tissues, indicating the potential to systematically intervene in the process of virus infection. The results also indicate that VCG Plus may have the potential to improve systematic immune and inflammatory responses caused by virus infections.

3.3. Enrichment and Analysis of GO Term

All enriched GO terms are available in Supplementary Table S2. The top 10 significant terms in biological process, molecular function and cellular component categories, respectively, are shown in Figure 3. VCG Plus is active in regulating transcription from RNA polymerase II promoter and transcription of DNA-templated via binding of transcription factor and chromatin. VCG Plus regulates the apoptotic process, nitric oxide biosynthetic process and lipopolysaccharide-mediated signaling pathway through cytokine activity, enzyme binding and/or protein binding. The biological process result for responding to hypoxia is worth mentioning, since a decline in oxygen saturation is commonly observed in SARS-CoV-2 infected patients [45]. The hypoxic response is a systemic process that regulates multiple cellular activities to maintain homeostasis under hypoxic condition [48]. In the present work, we note that both VC and CC could act on hypoxia inducible factor 1 alpha subunit (HIF-1A), suggesting their potential benefits on maintaining homeostasis under hypoxic conditions.
In addition, GO analysis of biological processes related to the immune system was performed using ClueGo. ClueGo was used to generate the targets-processes network of VC, CC and GA and shown as clusters, so that the role of each nutrient contributing to pathway regulation could be visualized (Figure 4). As a result, nine significant immune system processes were obtained, including differentiations of macrophage, leukocyte, myeloid cell and myeloid leukocyte, activation of macrophage and T-cell, T cell lineage commitment and hemopoiesis. These results suggest that VCG Plus may enhance immunity by modulating the regulation of immune cell differentiation and activation.

3.4. KEGG Pathway Enrichment and Analysis

All 88 identified targets were imported to ClueGo for KEGG pathway enrichment, resulting in 110 statistically significant pathways (Supplementary Table S3). According to the KEGG database, the obtained pathways are mainly concentrated on categories of signal transduction involved in environmental information processes, immune systems involved in organismal systems, infectious diseases involved in human diseases and other pathways. The top 15 pathways which are closely related to immunity, inflammation and RNA virus infections, along with effective target interactions were demonstrated in Figure 5. PI3K-AKT signaling pathway is associated with the most targets (30 targets), followed by TNF signaling pathway (25 targets), HIF-1 signaling pathway (23 targets), IL-17 signaling pathway (22 targets), NOD-like receptor signaling pathway (22 targets), Influenza A (21 targets), FoxO signaling pathway (20 targets), Toll-like receptor signaling pathway (19 targets), NF-κB signaling pathway (17 targets) and T helper (Th)17 cell differentiation (16 targets). Other pathways which belong to the immune system include T-cell receptor, Th17 cell differentiation and C-type lectin receptor signaling, and inflammation-related pathways including JAK-STAT signaling and apoptosis are also shown.

4. Discussion

The interaction between CoV spike (S) protein and its receptor is the primary determinant for such virions attachment to human cells [49]. Multiple peptidases have been well described as CoV cellular receptors, including APN as the receptor for alpha CoV, angiotensin-converting enzyme 2 (ACE2) as the receptor for SARS-CoV and dipeptidyl-peptidase 4 (DPP4) as the receptor for MERS-CoV [1]. Inhibitors of S protein binding to receptor is a strategy for preventing and treating infection [7,50]. Although our data did not show that VCG Plus (phyto-) nutrients act on CoV cellular receptor, the potential capability of GA binding to ACE2 was reported recently [22]. Moreover, CC has been reported as the inhibitor of APN with potential to be a cancer chemoprevention agent [29]. The interactions between CC and APN, and GA and ACE2 were not included in our current analysis, mainly due to our strict rules for target screening. Through Venn analysis of targets from VCG Plus, silent mating type information regulation 2 homolog 1 (SIRT1) was found to only interact with GA. SIRT 1 belongs to the sirtuin family which contains seven proteins (SIRT1-7) that are class III NAD+-dependent histone deacetylases (HDACs) [51]. It is interesting that SIRT1 has been shown to play both pro-viral and anti-viral roles, depending on the type of virus. The SIRT1 inhibitor showed a suppressive effect on hepatitis B virus (HBV) replication [51,52], while the SIRT1 activator showed a suppressive effect on human T-cell leukemia virus type 1 (HTLV-1) [53] and MERS-CoV [54]. Han [55] found that SIRT1 inhibited viral RNA transcription and translation in enterovirus 71 (EV 71, a RNA virus)-infected human rhabdomyosarcoma (RD) cells. Based on these results, it is possible that SIRT 1 could be an antiviral for RNA virus infections like MERS-CoV and EV 71. Containing the key phytochemical GA, licorice is generally associated with detoxication in TCM [56], and exhibits antiviral effect [57,58,59]. Others have found that GA activates SIRT1 in diabetic db/db mice [60] and increases the expression of SIRT1 in renal tubular epithelial cell line [61]. Hence, it is speculated that GA may exert anti-CoV effects via regulating SIRT 1 protein. However, further experimental research is needed to clarify the antivirus mechanism of GA as well as the role of SIRT1 in various CoV infections.
The innate immune system is the first line of defense against virus infection. A rapid and well-coordinated innate immune response to sense invading viruses, and subsequent signal transduction pathways targeted to inhibit infection [62]. During a viral infection, host pathogen-recognition receptors (PRRs) initially sensitized by viral pathogen-associated molecular patterns and cascades of signaling pathways are activated to produce type 1 interferons (IFNs). IFNs are the prominent cytokines in innate immune response, and are thought to enhance the release of antiviral proteins for the protection of uninfected cells [5,63]. CoV can be sensed by three types of PRR, including Toll-like receptors, retinoic acid-inducible gene I (RIG-I)-like receptors, and nucleotide-binding and oligomerization domain (NOD)-like receptors [4]. Sometimes, accessory proteins of SARS-CoV and MERS-CoV can interfere with PRRs, antagonize IFNs’ response and evade the immune response. The delayed IFNs’ response may result in uncontrolled inflammatory response [64,65]. In our present study, the results demonstrate the involvement of PRR signaling-related pathways including NOD-like receptors, Toll-like receptors (Figure 5) and RIG-I like receptors signaling (Supplementary Table S3) pathways in the biological functions of VCG Plus, as well as the IFNs (IFNG, IFNB1 in Table 1). Previous studies have revealed that CC significantly stimulated the production of IFN-β (IFNB1) in mice infected with influenza A virus (IAV), resulting in the increased survival rate and improvement of pulmonary histopathological changes [66]. Similarly, VC improved the production of IFN α/β (IFNA1/B1), activated anti-viral immune responses and remarkably increased the survival rate of VC-depleted mice infected with IAV [67,68]. In addition, multiple groups have demonstrated that GA improves IFN-γ (IFNG) production and ameliorates immune function [69,70,71]. These results indicate that VCG Plus may be beneficial in regulating innate immune response against invading viruses, through regulating NOD-like, Toll-like receptor signaling pathways, and promoting the production of IFNs.
T-cells, including CD4+ cells, and CD8+ cells play an antiviral role not only by combating against virions but also restricting the development of autoimmunity or overwhelming inflammation [4]. CD4+ cells promote the production of virus-specific antibodies via activating T-dependent B-cells, whereas CD8+ cells kill viral infected cells [72]. However, some CoVs are thought to induce T-cell apoptosis by the activation of apoptosis pathways [73], while depletion of CD4+ cells in later stages is associated with immune-mediated interstitial pneumonitis and delayed clearance of pathogen [74]. In SARS-CoV-2 infected patients, both the counts of CD4 + cells and CD8+ cells in severe pneumonia patients were lower than non-severe patients [75]. Similar results were observed in SARS-CoV infected patients [76,77]. In our current study, the significant interactions of VCG Plus related to immune cell differentiation and activation pathways were observed (Figure 4). The VCG Plus (phyto-) nutrients in this combination can co-regulate T-cell activation and other related biological processes by acting on different targets, suggesting the existence of a potential synergy. The literature has shown that VCG Plus (phyto-) nutrients positively regulate T-cells. For instance, VC positively influences lymphocyte development and function, and enhances T-cell proliferation and T-cell function [14,78]. CC could target regulatory T-cells and convert them into CD4+ Th1 cells to process anti-tumor effects [79,80], and improve the imbalance of Th1/Th2 subsets to process anti-inflammatory and anti-autoimmune effects [27,81]. GA showed anti-allergic effect by restoring the imbalance of Th1/Th2 subsets [82,83]. These results suggest that VCG Plus could promote the proliferation of Th1 cells and the production of virus-specific antibodies to compete CoV infections, and simultaneously regulate the Th1/Th2 subsets to prevent autoimmune and excessive inflammatory response in the later stage of infection.
A cytokine storm, the massive overproduction of cytokines by the immune system, often appears in the terminal stage of some viral diseases (SARS, MERS, SARS-CoV-2). It is partially responsible for high fatality rates in patients infected with viruses [3]. In a cytokine storm, numerous pro-inflammatory cytokines such as IL-1, IL-6 and TNF-α, and inflammatory chemokines CCL3, CCL5, CCL2, and CXCL10 are released, leading to hypotension, hemorrhage, and eventually multiorgan failure [84]. MAPKs signaling [85], NF-κB signaling [86,87], TNF signaling [88] and PI3K/AKT signaling pathways [85,89], play important roles in mediating CoV infection-induced inflammatory responses. As a matter of fact, the anti-inflammatory effects of VC, CC and GA have been well documented. VC decreases IL-4, IL-6 and IL-8 level via inhibition of NF-κB signaling pathway in concanavalin A- induced liver injury mice [90]. Many studies have shown that CC presents anti-inflammatory function via NF-κB signaling [91,92], PI3K/AKT signaling [93], MAPK signaling [66] and TLRs signaling pathways [94]. In addition, GA alleviated inflammation via NF-kB and p38/ERK pathways in the reduction in multiple cytokines, including IL-6, TNF-α, IL-8, IL-1β and HMGB1 [95]. Consistently, the pathways mentioned above were successfully enriched and demonstrated in our result (Figure 5). Together with the evidence from the literature, our findings suggest that this combination may prevent the onset of cytokine storm.
VC is an essential nutrient derived from plant sources, GA is derived from licorice, which is the most popular herb in TCM and other traditional medicine, and CC is derived from turmeric which is the most popular botanical source for Ayurveda medicine and culinary herbs. The combination of these three (phyto-) nutrients has not been reported previously, despite the single use of each ingredient has been widely studied. In this study, we first collected gene targets of VC, CC and GA, followed by target enrichment and analysis including distribution in tissues and systems, GO function and KEGG pathways. As target acquisition is the critical step for the whole analysis, an optimized strategy was used in our study. Briefly, we compared the targets from multiple databases, set high, reliable cut-off values and reviewed the text description of interactions, to ensure the high credibility of targets. In addition, we narrowed down the range by mapping to “immune system disease” and “virus diseases” related gene databases in CTD, to ensure a more focused analysis. After step by step system biology analysis, combined with up to date molecular mechanism investigations of CoV infections, our results suggest VCG Plus may regulate immune and inflammatory responses to prevent CoV infections by acting on multiple targets and pathways. Regulating NOD-like and Toll-like receptor signaling, promoting IFNs production, inhibition of PI3K/AKT, NF-κB and MAPK signaling, and activating and balancing T cells are the main functional mechanisms identified. In addition to the function of the individual (phyto-) nutrients in the VCG plus, they appear to be complementary and synergistic by modulating a variety of targets through similar or different signal pathways.
There are limitations of the current investigation. For example, the pathogenic mechanism of CoV infection is not clearly understood yet, and the study of specific protections against CoV infections of VC, CC and GA was very limited. We only conducted the analysis on our best knowledge at the time. We started the analysis from known potential targets of VCG Plus, followed by enrichment analysis of biological processes and pathways which were generally associated with the immune system and viral infection. Based on the recent advances in the knowledge of CoV infection pathogenic mechanism and the findings from our analysis, VCG Plus regulates CoV infection pathways and were highlighted in our discussion. The results may not comprehensively illustrate how this combination would help immune system defense to CoV infections, but it demonstrates the potential of VCG Plus. In addition, the dose and route of administration of VCG or ADME were not taken into consideration in the current work. However, technologies to enhance bioavailability have been widely studied and indicated that advanced formulation processes could minimize these issues. Further in vitro mechanistic and preclinical studies are warranted in order to verify the directional prediction obtained from our current analysis.

5. Conclusions

In summary, since no specific therapy for CoV infections is available, any potential way of protecting against CoV infections is worth studying and discussing. This paper investigated the potential protective effect of VCG Plus against CoV infections using systems biology. Our results suggest that VCG Plus is predicted to be helpful in regulating immune response against CoV infections and inhibiting excessive inflammatory response to prevent the onset of cytokine storm. However, further in vitro/in vivo experiments are warranted for validation. The analytical approach in this study provides a new thinking process to support the formulation strategy for the development of new dietary supplements with potential immune benefits.

Supplementary Materials

The following are available online at https://www.mdpi.com/2072-6643/12/4/1193/s1, Table S1: Acquired targets of VC (vitamin C), CC (curcumin) and GA (glycyrrhizic acid)., Table S2: GO (Gene ontology) enrichment results from DAVID Bioinformatics Resources 6.8 (https://david.ncifcrf.gov/tools.jsp), including biological process (BP), cell component (CC) and molecular function (MF). Table S3: KEGG pathways enrichment results from ClueGo (integrates the latest KEGG database).

Author Contributions

Conceptualization, L.C. and J.D.; methodology, L.C.; validation, C.H., M.H. and J.D.; formal analysis, L.C.; investigation, L.C., X.Z., L.Z. and J.K.; resources, X.Z., L.Z. and J.K.; data curation, L.C.; writing—original draft preparation, L.C.; writing—review and editing, L.C., C.H., M.H. and J.D.; supervision, J.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Acknowledgments

This study did not receive any specific grant from funding agencies in public, commercial, or not-for-profit sectors.

Conflicts of Interest

The authors declare that there are no conflicts of interest.

References

  1. Fehr, A.R.; Perlman, S. Coronaviruses: An Overview of Their Replication and Pathogenesis. In Coronaviruses; Maier, H.J., Bickerton, E., Britton, P., Eds.; Springer New York: New York, NY, USA, 2015; Volume 1282, pp. 1–23. ISBN 978-1-4939-2437-0. [Google Scholar]
  2. Xu, B.; Kraemer, M.U.G.; Xu, B.; Gutierrez, B.; Mekaru, S.; Sewalk, K.; Loskill, A.; Wang, L.; Cohn, E.; Hill, S.; et al. Open access epidemiological data from the COVID-19 outbreak. Lancet Infect. Dis. 2020. [Google Scholar] [CrossRef] [Green Version]
  3. Channappanavar, R.; Perlman, S. Pathogenic human coronavirus infections: Causes and consequences of cytokine storm and immunopathology. Semin. Immunopathol. 2017, 39, 529–539. [Google Scholar] [CrossRef]
  4. Li, G.; Fan, Y.; Lai, Y.; Han, T.; Li, Z.; Zhou, P.; Pan, P.; Wang, W.; Hu, D.; Liu, X.; et al. Coronavirus infections and immune responses. J. Med. Virol. 2020, 92, 424–432. [Google Scholar] [CrossRef]
  5. Zheng, J.; Perlman, S. Immune responses in influenza A virus and human coronavirus infections: An ongoing battle between the virus and host. Curr. Opin. Virol. 2018, 28, 43–52. [Google Scholar] [CrossRef]
  6. Lai, C.-C.; Shih, T.-P.; Ko, W.-C.; Tang, H.-J.; Hsueh, P.-R. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and corona virus disease-2019 (COVID-19): The epidemic and the challenges. Int. J. Antimicrob. Agents 2020, 105924. [Google Scholar] [CrossRef]
  7. Zumla, A.; Chan, J.F.W.; Azhar, E.I.; Hui, D.S.C.; Yuen, K.-Y. Coronaviruses—Drug discovery and therapeutic options. Nat. Rev. Drug Discov. 2016, 15, 327–347. [Google Scholar] [CrossRef] [Green Version]
  8. Liu, R.H. Health benefits of fruit and vegetables are from additive and synergistic combinations of phytochemicals. Am. J. Clin. Nutr. 2003, 78, 517S–520S. [Google Scholar] [CrossRef]
  9. Chang, S.K.; Alasalvar, C.; Shahidi, F. Review of dried fruits: Phytochemicals, antioxidant efficacies, and health benefits. J. Funct. Foods 2016, 21, 113–132. [Google Scholar] [CrossRef]
  10. Oh, J.; Wall, E.H.; Bravo, D.M.; Hristov, A.N. Host-mediated effects of phytonutrients in ruminants: A review. J. Dairy Sci. 2017, 100, 5974–5983. [Google Scholar] [CrossRef] [Green Version]
  11. Gupta, C.; Prakash, D. Phytonutrients as therapeutic agents. J. Complement. Integr. Med. 2014, 11, 151–169. [Google Scholar] [CrossRef]
  12. Wu, D.; Lewis, E.D.; Pae, M.; Meydani, S.N. Nutritional Modulation of Immune Function: Analysis of Evidence, Mechanisms, and Clinical Relevance. Front. Immunol. 2019, 9. [Google Scholar] [CrossRef]
  13. Wintergerst, E.S.; Maggini, S.; Hornig, D.H. Immune-enhancing role of vitamin C and zinc and effect on clinical conditions. Ann. Nutr. Metab. 2006, 50, 85–94. [Google Scholar] [CrossRef] [Green Version]
  14. Ang, A.; Pullar, J.M.; Currie, M.J.; Vissers, M.C.M. Vitamin C and immune cell function in inflammation and cancer. Biochem. Soc. Trans. 2018, 46, 1147–1159. [Google Scholar] [CrossRef] [Green Version]
  15. Carr, A.C.; Maggini, S. Vitamin C and Immune Function. Nutrients 2017, 9, 1211. [Google Scholar] [CrossRef] [Green Version]
  16. Zhang, L.; Liu, Y. Potential interventions for novel coronavirus in China: A systematic review. J. Med. Virol. 2020, 92, 479–490. [Google Scholar] [CrossRef] [Green Version]
  17. Hu, C. Chapter 21—Historical Change of Raw Materials and Claims of Health Food Regulations in China. In Nutraceutical and Functional Food Regulations in the United States and Around the World, 2nd ed.; Bagchi, D., Ed.; Academic Press: San Diego, CA, USA, 2014; pp. 363–388. ISBN 978-0-12-405870-5. [Google Scholar]
  18. Pompei, R.; Laconi, S.; Ingianni, A. Antiviral properties of glycyrrhizic acid and its semisynthetic derivatives. Mini. Rev. Med. Chem. 2009, 9, 996–1001. [Google Scholar] [CrossRef]
  19. Ming, L.J.; Yin, A.C.Y. Therapeutic effects of glycyrrhizic acid. Nat. Prod. Commun. 2013, 8, 415–418. [Google Scholar] [CrossRef] [Green Version]
  20. Li, J.; Cao, H.; Liu, P.; Cheng, G.; Sun, M. Glycyrrhizic acid in the treatment of liver diseases: Literature review. Biomed. Res. Int. 2014, 2014, 872139. [Google Scholar] [CrossRef]
  21. Luo, H.; Tang, Q.; Shang, Y.; Liang, S.; Yang, M.; Robinson, N.; Liu, J. Can Chinese Medicine Be Used for Prevention of Corona Virus Disease 2019 (COVID-19)? A Review of Historical Classics, Research Evidence and Current Prevention Programs. Chin. J. Integr. Med. 2020, 26, 243–250. [Google Scholar] [CrossRef] [Green Version]
  22. Chen, H.; Du, Q. Potential natural compounds for preventing 2019-nCoV infection. Preprints 2020, 2020010358. [Google Scholar] [CrossRef]
  23. Deguchi, A. Curcumin targets in inflammation and cancer. Endocr. Metab. Immune Disord. Drug Targets 2015, 15, 88–96. [Google Scholar] [CrossRef] [PubMed]
  24. Pulido-Moran, M.; Moreno-Fernandez, J.; Ramirez-Tortosa, C.; Ramirez-Tortosa, M. Curcumin and Health. Molecules 2016, 21, 264. [Google Scholar] [CrossRef] [PubMed]
  25. Kim, Y.; Clifton, P. Curcumin, Cardiometabolic Health and Dementia. Int. J. Environ. Res. Public Health 2018, 15, 2093. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Lelli, D.; Sahebkar, A.; Johnston, T.P.; Pedone, C. Curcumin use in pulmonary diseases: State of the art and future perspectives. Pharmacol. Res. 2017, 115, 133–148. [Google Scholar] [CrossRef] [PubMed]
  27. Bright, J.J. Curcumin and autoimmune disease. Adv. Exp. Med. Biol. 2007, 595, 425–451. [Google Scholar] [CrossRef] [PubMed]
  28. Sordillo, P.P.; Helson, L. Curcumin Suppression of Cytokine Release and Cytokine Storm. A Potential Therapy for Patients with Ebola and Other Severe Viral Infections. In Vivo 2015, 29, 1–4. [Google Scholar]
  29. Bauvois, B.; Dauzonne, D. Aminopeptidase-N/CD13 (EC 3.4.11.2) inhibitors: Chemistry, biological evaluations, and therapeutic prospects. Med. Res. Rev. 2006, 26, 88–130. [Google Scholar] [CrossRef]
  30. Yadav, B.S.; Tripathi, V. Recent Advances in the System Biology-based Target Identification and Drug Discovery. CTMC 2018, 18, 1737–1744. [Google Scholar] [CrossRef]
  31. Zhang, W.; Huai, Y.; Miao, Z.; Qian, A.; Wang, Y. Systems Pharmacology for Investigation of the Mechanisms of Action of Traditional Chinese Medicine in Drug Discovery. Front. Pharmacol. 2019, 10, 743. [Google Scholar] [CrossRef]
  32. Chen, L.; Hu, C.; Hood, M.; Kan, J.; Gan, X.; Zhang, X.; Zhang, Y.; Du, J. An Integrated Approach Exploring the Synergistic Mechanism of Herbal Pairs in a Botanical Dietary Supplement: A Case Study of a Liver Protection Health Food. Int. J. Genom. 2020, 2020, 1–14. [Google Scholar] [CrossRef]
  33. Yue, S.-J.; Liu, J.; Feng, W.-W.; Zhang, F.-L.; Chen, J.-X.; Xin, L.-T.; Peng, C.; Guan, H.-S.; Wang, C.-Y.; Yan, D. System Pharmacology-Based Dissection of the Synergistic Mechanism of Huangqi and Huanglian for Diabetes Mellitus. Front. Pharmacol. 2017, 8. [Google Scholar] [CrossRef] [Green Version]
  34. Wishart, D.S.; Feunang, Y.D.; Guo, A.C.; Lo, E.J.; Marcu, A.; Grant, J.R.; Sajed, T.; Johnson, D.; Li, C.; Sayeeda, Z.; et al. DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Res. 2018, 46, D1074–D1082. [Google Scholar] [CrossRef] [PubMed]
  35. Davis, A.P.; Wiegers, T.C.; Johnson, R.J.; Lay, J.M.; Lennon-Hopkins, K.; Saraceni-Richards, C.; Sciaky, D.; Murphy, C.G.; Mattingly, C.J. Text Mining Effectively Scores and Ranks the Literature for Improving Chemical-Gene-Disease Curation at the Comparative Toxicogenomics Database. PLoS ONE 2013, 8, e58201. [Google Scholar] [CrossRef] [Green Version]
  36. Davis, A.P.; Grondin, C.J.; Johnson, R.J.; Sciaky, D.; McMorran, R.; Wiegers, J.; Wiegers, T.C.; Mattingly, C.J. The Comparative Toxicogenomics Database: Update 2019. Nucleic Acids Res. 2019, 47, D948–D954. [Google Scholar] [CrossRef] [PubMed]
  37. Xu, H.-Y.; Zhang, Y.-Q.; Liu, Z.-M.; Chen, T.; Lv, C.-Y.; Tang, S.-H.; Zhang, X.-B.; Zhang, W.; Li, Z.-Y.; Zhou, R.-R.; et al. ETCM: An encyclopaedia of traditional Chinese medicine. Nucleic Acids Res. 2019, 47, D976–D982. [Google Scholar] [CrossRef] [PubMed]
  38. Wu, C.H. The Universal Protein Resource (UniProt): An expanding universe of protein information. Nucleic Acids Res. 2006, 34, D187–D191. [Google Scholar] [CrossRef] [PubMed]
  39. Szklarczyk, D.; Gable, A.L.; Lyon, D.; Junge, A.; Wyder, S.; Huerta-Cepas, J.; Simonovic, M.; Doncheva, N.T.; Morris, J.H.; Bork, P.; et al. STRING v11: Protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019, 47, D607–D613. [Google Scholar] [CrossRef] [Green Version]
  40. Su, G.; Morris, J.H.; Demchak, B.; Bader, G.D. Biological Network Exploration with Cytoscape 3. Curr. Protoc. Bioinform. 2014, 47, 8.13.1–8.13.24. [Google Scholar] [CrossRef] [Green Version]
  41. Gokhman, D.; Kelman, G.; Amartely, A.; Gershon, G.; Tsur, S.; Carmel, L. Gene ORGANizer: Linking genes to the organs they affect. Nucleic Acids Res. 2017, 45, W138–W145. [Google Scholar] [CrossRef] [Green Version]
  42. Huang, D.W.; Sherman, B.T.; Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 2009, 4, 44–57. [Google Scholar] [CrossRef]
  43. Bindea, G.; Mlecnik, B.; Hackl, H.; Charoentong, P.; Tosolini, M.; Kirilovsky, A.; Fridman, W.-H.; Pagès, F.; Trajanoski, Z.; Galon, J. ClueGO: A Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 2009, 25, 1091–1093. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Yin, Y.; Wunderink, R.G. MERS, SARS and other coronaviruses as causes of pneumonia: MERS, SARS and coronaviruses. Respirology 2018, 23, 130–137. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Huang, C.; Wang, Y.; Li, X.; Ren, L.; Zhao, J.; Hu, Y.; Zhang, L.; Fan, G.; Xu, J.; Gu, X.; et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020, 395, 497–506. [Google Scholar] [CrossRef] [Green Version]
  46. Kui, L.; Fang, Y.-Y.; Deng, Y.; Liu, W.; Wang, M.-F.; Ma, J.-P.; Xiao, W.; Wang, Y.-N.; Zhong, M.-H.; Li, C.-H.; et al. Clinical characteristics of novel coronavirus cases in tertiary hospitals in Hubei Province. Chin. Med. J. (Engl.) 2020. [Google Scholar] [CrossRef]
  47. Tang, N.; Li, D.; Wang, X.; Sun, Z. Abnormal Coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J. Thromb. Haemost. 2020. [Google Scholar] [CrossRef] [Green Version]
  48. Nakayama, K.; Kataoka, N. Regulation of Gene Expression under Hypoxic Conditions. Int. J. Mol. Sci. 2019, 20, 3278. [Google Scholar] [CrossRef] [Green Version]
  49. Cong, Y.; Ren, X. Coronavirus entry and release in polarized epithelial cells: A review: Polarized infection of coronaviruses. Rev. Med. Virol. 2014, 24, 308–315. [Google Scholar] [CrossRef] [PubMed]
  50. Yeung, K.-S.; Yamanaka, G.A.; Meanwell, N.A. Severe acute respiratory syndrome coronavirus entry into host cells: Opportunities for therapeutic intervention. Med. Res. Rev. 2006, 26, 414–433. [Google Scholar] [CrossRef]
  51. Li, W.-Y.; Ren, J.-H.; Tao, N.-N.; Ran, L.-K.; Chen, X.; Zhou, H.-Z.; Liu, B.; Li, X.-S.; Huang, A.-L.; Chen, J. The SIRT1 inhibitor, nicotinamide, inhibits hepatitis B virus replication in vitro and in vivo. Arch. Virol. 2016, 161, 621–630. [Google Scholar] [CrossRef]
  52. Deng, J.-J.; Kong, K.-Y.E.; Gao, W.-W.; Tang, H.-M.V.; Chaudhary, V.; Cheng, Y.; Zhou, J.; Chan, C.-P.; Wong, D.K.-H.; Yuen, M.-F.; et al. Interplay between SIRT1 and hepatitis B virus X protein in the activation of viral transcription. Biochim. Biophys. Acta (BBA)—Gene Regul. Mech. 2017, 1860, 491–501. [Google Scholar] [CrossRef] [Green Version]
  53. Tang, H.-M.V.; Gao, W.-W.; Chan, C.-P.; Cheng, Y.; Deng, J.-J.; Yuen, K.-S.; Iha, H.; Jin, D.-Y. SIRT1 Suppresses Human T-Cell Leukemia Virus Type 1 Transcription. J. Virol. 2015, 89, 8623–8631. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Lin, S.-C.; Ho, C.-T.; Chuo, W.-H.; Li, S.; Wang, T.T.; Lin, C.-C. Effective inhibition of MERS-CoV infection by resveratrol. BMC Infect. Dis. 2017, 17. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Han, Y.; Wang, L.; Cui, J.; Song, Y.; Luo, Z.; Chen, J.; Xiong, Y.; Zhang, Q.; Liu, F.; Ho, W.; et al. SIRT1 inhibits EV71 genome replication and RNA translation by interfering with the viral polymerase and 5′UTR RNA. J. Cell Sci. 2016, 129, 4534–4547. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Saito, K. Studies on glycyrrhizin, an active principle of radix liquiritine. (3.) on the mechanism of detoxicating action. Gunma J. Med. Sci. 1964, 13, 275–282. [Google Scholar]
  57. Pu, J.-Y.; He, L.; Wu, S.-Y.; Zhang, P.; Huang, X. Anti-virus research of triterpenoids in licorice. Bing Du Xue Bao 2013, 29, 673–679. [Google Scholar]
  58. Wang, J.; Chen, X.; Wang, W.; Zhang, Y.; Yang, Z.; Jin, Y.; Ge, H.M.; Li, E.; Yang, G. Glycyrrhizic acid as the antiviral component of Glycyrrhiza uralensis Fisch. against coxsackievirus A16 and enterovirus 71 of hand foot and mouth disease. J. Ethnopharmacol. 2013, 147, 114–121. [Google Scholar] [CrossRef]
  59. Feng Yeh, C.; Wang, K.C.; Chiang, L.C.; Shieh, D.E.; Yen, M.H.; San Chang, J. Water extract of licorice had anti-viral activity against human respiratory syncytial virus in human respiratory tract cell lines. J. Ethnopharmacol. 2013, 148, 466–473. [Google Scholar] [CrossRef]
  60. Hou, S.; Zhang, T.; Li, Y.; Guo, F.; Jin, X. Glycyrrhizic Acid Prevents Diabetic Nephropathy by Activating AMPK/SIRT1/PGC-1 α Signaling in db/db Mice. J. Diabetes Res. 2017, 2017, 1–10. [Google Scholar] [CrossRef] [Green Version]
  61. Hou, S.; Zheng, F.; Li, Y.; Gao, L.; Zhang, J. The Protective Effect of Glycyrrhizic Acid on Renal Tubular Epithelial Cell Injury Induced by High Glucose. Int. J. Mol. Sci. 2014, 15, 15026–15043. [Google Scholar] [CrossRef] [Green Version]
  62. Nelemans, T.; Kikkert, M. Viral Innate Immune Evasion and the Pathogenesis of Emerging RNA Virus Infections. Viruses 2019, 11, 961. [Google Scholar] [CrossRef] [Green Version]
  63. Channappanavar, R.; Fehr, A.R.; Vijay, R.; Mack, M.; Zhao, J.; Meyerholz, D.K.; Perlman, S. Dysregulated Type I Interferon and Inflammatory Monocyte-Macrophage Responses Cause Lethal Pneumonia in SARS-CoV-Infected Mice. Cell Host Microbe 2016, 19, 181–193. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Shokri, S.; Mahmoudvand, S.; Taherkhani, R.; Farshadpour, F. Modulation of the immune response by Middle East respiratory syndrome coronavirus. J. Cell. Physiol. 2019, 234, 2143–2151. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Peiris, M. Pathogenesis of avian flu H5N1 and SARS. Novartis Found. Symp. 2006, 279, 56–219. [Google Scholar]
  66. Dai, J.; Gu, L.; Su, Y.; Wang, Q.; Zhao, Y.; Chen, X.; Deng, H.; Li, W.; Wang, G.; Li, K. Inhibition of curcumin on influenza A virus infection and influenzal pneumonia via oxidative stress, TLR2/4, p38/JNK MAPK and NF-κB pathways. Int. Immunopharmacol. 2018, 54, 177–187. [Google Scholar] [CrossRef] [PubMed]
  67. Kim, Y.; Kim, H.; Bae, S.; Choi, J.; Lim, S.Y.; Lee, N.; Kong, J.M.; Hwang, Y.-I.; Kang, J.S.; Lee, W.J. Vitamin C Is an Essential Factor on the Anti-viral Immune Responses through the Production of Interferon-α/β at the Initial Stage of Influenza A Virus (H3N2) Infection. Immune Netw. 2013, 13, 70–74. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  68. Kim, H.; Jang, M.; Kim, Y.; Choi, J.; Jeon, J.; Kim, J.; Hwang, Y.-I.; Kang, J.S.; Lee, W.J. Red ginseng and vitamin C increase immune cell activity and decrease lung inflammation induced by influenza A virus/H1N1 infection. J. Pharm. Pharmacol. 2016, 68, 406–420. [Google Scholar] [CrossRef]
  69. Ram, A.; Mabalirajan, U.; Das, M.; Bhattacharya, I.; Dinda, A.K.; Gangal, S.V.; Ghosh, B. Glycyrrhizin alleviates experimental allergic asthma in mice. Int. Immunopharmacol. 2006, 6, 1468–1477. [Google Scholar] [CrossRef]
  70. Wang, Y.; Chai, J.; Sun, M.; He, W.; Hu, X.; Zou, W.; Li, H.; Lu, Y.; Xie, C. Glycyrrhizinic acid modulates the immunity of MRL/lpr mice and related mechanism. Xi Bao Yu Fen Zi Mian Yi Xue Za Zhi 2017, 33, 305–309. [Google Scholar]
  71. Ma, C.; Ma, Z.; Liao, X.; Liu, J.; Fu, Q.; Ma, S. Immunoregulatory effects of glycyrrhizic acid exerts anti-asthmatic effects via modulation of Th1/Th2 cytokines and enhancement of CD4(+)CD25(+)Foxp3+ regulatory T cells in ovalbumin-sensitized mice. J. Ethnopharmacol. 2013, 148, 755–762. [Google Scholar] [CrossRef]
  72. Cecere, T.E.; Todd, S.M.; Leroith, T. Regulatory T cells in arterivirus and coronavirus infections: Do they protect against disease or enhance it? Viruses 2012, 4, 833–846. [Google Scholar] [CrossRef] [Green Version]
  73. Chu, H.; Zhou, J.; Wong, B.H.-Y.; Li, C.; Chan, J.F.-W.; Cheng, Z.-S.; Yang, D.; Wang, D.; Lee, A.C.-Y.; Li, C.; et al. Middle East Respiratory Syndrome Coronavirus Efficiently Infects Human Primary T Lymphocytes and Activates the Extrinsic and Intrinsic Apoptosis Pathways. J. Infect. Dis. 2016, 213, 904–914. [Google Scholar] [CrossRef] [Green Version]
  74. Chen, J.; Lau, Y.F.; Lamirande, E.W.; Paddock, C.D.; Bartlett, J.H.; Zaki, S.R.; Subbarao, K. Cellular immune responses to severe acute respiratory syndrome coronavirus (SARS-CoV) infection in senescent BALB/c mice: CD4+ T cells are important in control of SARS-CoV infection. J. Virol. 2010, 84, 1289–1301. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  75. Zheng, Y.; Huang, Z.; Ying, G.; Zhang, X.; Ye, W.; Hu, Z.; Hu, C.; Wei, H.; Zeng, Y.; Chi, Y.; et al. Study of the lymphocyte change between COVID-19 and non-COVID-19 pneumonia cases suggesting other factors besides uncontrolled inflammation contributed to multi-organ injury. Preprints 2020. [Google Scholar] [CrossRef]
  76. National Research Project For SARS Beijing Group Beijing 100020 China. Dynamic changes of T-lymphocytes and immunoglobulins in patients with severe acute respiratory syndrome. Zhonghua Yi Xue Za Zhi 2003, 83, 1014–1017. [Google Scholar]
  77. Cui, W.; Fan, Y.; Wu, W.; Zhang, F.; Wang, J.; Ni, A. Expression of lymphocytes and lymphocyte subsets in patients with severe acute respiratory syndrome. Clin. Infect. Dis. 2003, 37, 857–859. [Google Scholar] [CrossRef] [PubMed]
  78. Van Gorkom, G.N.Y.; Klein Wolterink, R.G.J.; Van Elssen, C.H.M.J.; Wieten, L.; Germeraad, W.T.V.; Bos, G.M.J. Influence of Vitamin C on Lymphocytes: An Overview. Antioxidants (Basel) 2018, 7, 41. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  79. Shafabakhsh, R.; Pourhanifeh, M.H.; Mirzaei, H.R.; Sahebkar, A.; Asemi, Z.; Mirzaei, H. Targeting regulatory T cells by curcumin: A potential for cancer immunotherapy. Pharmacol. Res. 2019, 147, 104353. [Google Scholar] [CrossRef]
  80. Zou, J.Y.; Su, C.H.; Luo, H.H.; Lei, Y.Y.; Zeng, B.; Zhu, H.S.; Chen, Z.G. Curcumin converts Foxp3+ regulatory T cells to T helper 1 cells in patients with lung cancer. J. Cell. Biochem. 2018, 119, 1420–1428. [Google Scholar] [CrossRef]
  81. Rahimi, K.; Ahmadi, A.; Hassanzadeh, K.; Soleimani, Z.; Sathyapalan, T.; Mohammadi, A.; Sahebkar, A. Targeting the balance of T helper cell responses by curcumin in inflammatory and autoimmune states. Autoimmun. Rev. 2019, 18, 738–748. [Google Scholar] [CrossRef]
  82. Han, S.; Sun, L.; He, F.; Che, H. Anti-allergic activity of glycyrrhizic acid on IgE-mediated allergic reaction by regulation of allergy-related immune cells. Sci. Rep. 2017, 7, 7222. [Google Scholar] [CrossRef] [Green Version]
  83. Wu, Q.; Tang, Y.; Hu, X.; Wang, Q.; Lei, W.; Zhou, L.; Huang, J. Regulation of Th1/Th2 balance through OX40/OX40L signalling by glycyrrhizic acid in a murine model of asthma. Respirology 2016, 21, 102–111. [Google Scholar] [CrossRef]
  84. Lau, S.K.P.; Lau, C.C.Y.; Chan, K.-H.; Li, C.P.Y.; Chen, H.; Jin, D.-Y.; Chan, J.F.W.; Woo, P.C.Y.; Yuen, K.-Y. Delayed induction of proinflammatory cytokines and suppression of innate antiviral response by the novel Middle East respiratory syndrome coronavirus: Implications for pathogenesis and treatment. J. Gen. Virol. 2013, 94, 2679–2690. [Google Scholar] [CrossRef] [PubMed]
  85. Kindrachuk, J.; Ork, B.; Hart, B.J.; Mazur, S.; Holbrook, M.R.; Frieman, M.B.; Traynor, D.; Johnson, R.F.; Dyall, J.; Kuhn, J.H.; et al. Antiviral Potential of ERK/MAPK and PI3K/AKT/mTOR Signaling Modulation for Middle East Respiratory Syndrome Coronavirus Infection as Identified by Temporal Kinome Analysis. Antimicrob. Agents Chemother. 2015, 59, 1088–1099. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  86. DeDiego, M.L.; Nieto-Torres, J.L.; Regla-Nava, J.A.; Jimenez-Guardeño, J.M.; Fernandez-Delgado, R.; Fett, C.; Castaño-Rodriguez, C.; Perlman, S.; Enjuanes, L. Inhibition of NF-κB-mediated inflammation in severe acute respiratory syndrome coronavirus-infected mice increases survival. J. Virol. 2014, 88, 913–924. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  87. Dosch, S.F.; Mahajan, S.D.; Collins, A.R. SARS coronavirus spike protein-induced innate immune response occurs via activation of the NF-κB pathway in human monocyte macrophages in vitro. Virus Res. 2009, 142, 19–27. [Google Scholar] [CrossRef] [PubMed]
  88. Peteranderl, C.; Herold, S. The Impact of the Interferon/TNF-Related Apoptosis-Inducing Ligand Signaling Axis on Disease Progression in Respiratory Viral Infection and Beyond. Front. Immunol. 2017, 8. [Google Scholar] [CrossRef] [Green Version]
  89. Mizutani, T. Signal Transduction in SARS-CoV-Infected Cells. Ann. N. Y. Acad. Sci. 2007, 1102, 86–95. [Google Scholar] [CrossRef]
  90. Liang, T.; Chen, X.; Su, M.; Chen, H.; Lu, G.; Liang, K. Vitamin C exerts beneficial hepatoprotection against Concanavalin A-induced immunological hepatic injury in mice through inhibition of NF-κB signal pathway. Food Funct. 2014, 5, 2175–2182. [Google Scholar] [CrossRef]
  91. Zhu, H.; Bian, C.; Yuan, J.; Chu, W.; Xiang, X.; Chen, F.; Wang, C.; Feng, H.; Lin, J.-K. Curcumin attenuates acute inflammatory injury by inhibiting the TLR4/MyD88/NF-κB signaling pathway in experimental traumatic brain injury. J. Neuroinflammation 2014, 11, 59. [Google Scholar] [CrossRef] [Green Version]
  92. Kong, F.; Ye, B.; Cao, J.; Cai, X.; Lin, L.; Huang, S.; Huang, W.; Huang, Z. Curcumin Represses NLRP3 Inflammasome Activation via TLR4/MyD88/NF-κB and P2X7R Signaling in PMA-Induced Macrophages. Front. Pharmacol. 2016, 7, 369. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  93. Vucic, M.; Cojbasic, I.; Vucic, J.; Pavlovic, V. The effect of curcumin and PI3K/Akt inhibitor on monosodium glutamate-induced rat thymocytes toxicity. Gen. Physiol. Biophys. 2018, 37, 329–336. [Google Scholar] [CrossRef] [PubMed]
  94. Gong, P.; Liu, M.; Hong, G.; Li, Y.; Xue, P.; Zheng, M.; Wu, M.; Shen, L.; Yang, M.; Diao, Z.; et al. Curcumin improves LPS-induced preeclampsia-like phenotype in rat by inhibiting the TLR4 signaling pathway. Placenta 2016, 41, 45–52. [Google Scholar] [CrossRef] [PubMed]
  95. Yao, L.; Sun, T. Glycyrrhizin administration ameliorates Streptococcus aureus-induced acute lung injury. Int. Immunopharmacol. 2019, 70, 504–511. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Hub target analysis of VCG Plus. A Venn diagram of hub target distribution in VC, CC and GA, respectively (A). PPI network of 88 hub targets of VCG Plus (B). OmicsBean (http://www.omicsbean.cn/) was employed to draw Figure 1A. Cytoscape software (Version 3.6.1) was employed to draw Figure 1B. In Figure 1B, all the targets are represented by nodes, whereas the interaction between the targets are represented by edges. The node size is proportional to the node degree. The intersect targets of VC, CC and GA are represented by green. VCG Plus, the combination of vitamin C, curcumin and glycyrrhizic acid. VC, vitamin C (group1); CC, curcumin (group 2); GA, glycyrrhizic acid (group 3). PPI, protein-protein interaction.
Figure 1. Hub target analysis of VCG Plus. A Venn diagram of hub target distribution in VC, CC and GA, respectively (A). PPI network of 88 hub targets of VCG Plus (B). OmicsBean (http://www.omicsbean.cn/) was employed to draw Figure 1A. Cytoscape software (Version 3.6.1) was employed to draw Figure 1B. In Figure 1B, all the targets are represented by nodes, whereas the interaction between the targets are represented by edges. The node size is proportional to the node degree. The intersect targets of VC, CC and GA are represented by green. VCG Plus, the combination of vitamin C, curcumin and glycyrrhizic acid. VC, vitamin C (group1); CC, curcumin (group 2); GA, glycyrrhizic acid (group 3). PPI, protein-protein interaction.
Nutrients 12 01193 g001
Figure 2. Distribution analysis of targets in tissues and systems. The bubble plots were made using JMP software 14.2.0 (SAS institute Inc. USA). Distribution of targets of VCG Plus in system (A), distribution of targets of VC, CC and GA in tissues (B). In Figure 2A, the bubble size is proportional to the targets number, and the shade of bubble is inversely proportional to the p-value. In Figure 2B, the bubble size is proportional to the targets number. The targets distribution of VC is represented by blue bubble, CC is represented by red bubble, and GA are represented by green bubble. VC, vitamin C; CC, curcumin; GA, glycyrrhizic acid.
Figure 2. Distribution analysis of targets in tissues and systems. The bubble plots were made using JMP software 14.2.0 (SAS institute Inc. USA). Distribution of targets of VCG Plus in system (A), distribution of targets of VC, CC and GA in tissues (B). In Figure 2A, the bubble size is proportional to the targets number, and the shade of bubble is inversely proportional to the p-value. In Figure 2B, the bubble size is proportional to the targets number. The targets distribution of VC is represented by blue bubble, CC is represented by red bubble, and GA are represented by green bubble. VC, vitamin C; CC, curcumin; GA, glycyrrhizic acid.
Nutrients 12 01193 g002
Figure 3. Top 10 gene ontology (GO) terms of biologic process, molecular function and cellular component, respectively. The bubble plot was made using JMP software 14.2.0 (SAS institute Inc. USA). The bubble size is proportional to the targets number, and the shade of bubble is inversely proportional to the p-value.
Figure 3. Top 10 gene ontology (GO) terms of biologic process, molecular function and cellular component, respectively. The bubble plot was made using JMP software 14.2.0 (SAS institute Inc. USA). The bubble size is proportional to the targets number, and the shade of bubble is inversely proportional to the p-value.
Nutrients 12 01193 g003
Figure 4. Target immune-related biologic process network. The network was constructed by ClueGo (Latest Version 2.5.6), integrating immune process EBI-Uniport GO annotation database. Only pathways with p < 0.05 are shown. The targets and biologic processes are represented by nodes while the interactions among them are represented by edges. Contribution of VC (vitamin c) in targets and pathways is represented by red, while CC (curcumin) is represented by blue, and GA (glycyrrhizic acid) is represented by green.
Figure 4. Target immune-related biologic process network. The network was constructed by ClueGo (Latest Version 2.5.6), integrating immune process EBI-Uniport GO annotation database. Only pathways with p < 0.05 are shown. The targets and biologic processes are represented by nodes while the interactions among them are represented by edges. Contribution of VC (vitamin c) in targets and pathways is represented by red, while CC (curcumin) is represented by blue, and GA (glycyrrhizic acid) is represented by green.
Nutrients 12 01193 g004
Figure 5. Target KEGG pathways network of VCG Plus. The network was constructed by ClueGo (Latest Version 2.5.6), integrating the latest KEGG pathway database. The targets and pathways are represented by nodes while the interactions among them are represented by edges. Contribution of VC (vitamin c) in targets and pathways is represented by red, while CC (curcumin) is represented by blue, and GA (glycyrrhizic acid) is represented by green.
Figure 5. Target KEGG pathways network of VCG Plus. The network was constructed by ClueGo (Latest Version 2.5.6), integrating the latest KEGG pathway database. The targets and pathways are represented by nodes while the interactions among them are represented by edges. Contribution of VC (vitamin c) in targets and pathways is represented by red, while CC (curcumin) is represented by blue, and GA (glycyrrhizic acid) is represented by green.
Nutrients 12 01193 g005
Table 1. Hub targets identified for VCG Plus. VCG Plus, the combination of vitamin C, curcumin and glycyrrhizic acid. VC, vitamin C; CC, curcumin; GA, glycyrrhizic acid.
Table 1. Hub targets identified for VCG Plus. VCG Plus, the combination of vitamin C, curcumin and glycyrrhizic acid. VC, vitamin C; CC, curcumin; GA, glycyrrhizic acid.
GENE_SYMBOLNameDistribution
EP300E1A binding protein p300CC only
VCAM1vascular cell adhesion molecule 1CC only
CCN2cellular communication network factor 2CC only
MYCMYC proto-oncogene, bHLH transcription factorCC only
VEGFAvascular endothelial growth factor ACC only
ADIPOQadiponectin, C1Q and collagen domain containingCC only
IKBKBinhibitor of nuclear factor kappa B kinase subunit betaCC only
FN1fibronectin 1CC only
ESR1estrogen receptor 1CC only
MAPK8mitogen-activated protein kinase 8CC only
GSTP1glutathione S-transferase pi 1CC only
FOSFos proto-oncogene, AP-1 transcription factor subunitCC only
AKT1AKT serine/threonine kinase 1CC only
IFNB1interferon beta 1CC only
MDM2MDM2 proto-oncogeneCC only
CXCL1C-X-C motif chemokine ligand 1CC only
CXCL2C-X-C motif chemokine ligand 2CC only
PDGFBplatelet derived growth factor subunit BCC only
AHRaryl hydrocarbon receptorCC only
CYP2E1cytochrome P450 family 2 subfamily E member 1CC only
EGFRepidermal growth factor receptorCC only
EGR1early growth response 1CC only
IGF1Rinsulin like growth factor 1 receptorCC only
BIRC3baculoviral IAP repeat containing 3CC only
IGFBP3insulin like growth factor binding protein 3CC only
STAT3signal transducer and activator of transcription 3CC only
EGFepidermal growth factorCC only
IL18interleukin 18CC only
CCND1cyclin D1CC only
MMP9matrix metallopeptidase 9CC only
BCL2L1BCL2 like 1CC only
JUNJun proto-oncogene, AP-1 transcription factor subunitCC only
IL10interleukin 10CC only
HMGB1high mobility group box 1CC_GA_intersect
IL6interleukin 6CC_GA_intersect
CREB1cAMP responsive element binding protein 1CC_GA_intersect
IFNGinterferon gammaCC_GA_intersect
BDNFbrain derived neurotrophic factorCC_GA_intersect
MMP2matrix metallopeptidase 2CC_GA_intersect
CCL2C-C motif chemokine ligand 2CC_GA_intersect
CASP9caspase 9CC_GA_intersect
ARandrogen receptorCC_GA_intersect
CASP8caspase 8CC_GA_intersect
SIRT1silent mating type information regulation 2 homolog 1GA only
BMP2bone morphogenetic protein 2VC only
TIMP1TIMP metallopeptidase inhibitor 1VC only
TLR2toll like receptor 2VC only
SPP1secreted phosphoprotein 1VC only
MMP13matrix metallopeptidase 13VC only
NOS3nitric oxide synthase 3VC only
TFtransferrinVC only
RUNX2RUNX family transcription factor 2VC only
EZH2enhancer of zeste 2 polycomb repressive complex 2 subunitVC only
CD44CD44 molecule VC only
HMOX1heme oxygenase 1VC_CC_GA_intersect
RELARELA proto-oncogene, NF-κB subunitVC_CC_GA_intersect
TGFB1transforming growth factor beta 1VC_CC_GA_intersect
PTGS2prostaglandin-endoperoxide synthase 2VC_CC_GA_intersect
NFKBIANF-κB inhibitor alphaVC_CC_GA_intersect
NFKB1nuclear factor kappa B subunit 1VC_CC_GA_intersect
CXCL8C-X-C motif chemokine ligand 8VC_CC_GA_intersect
SOD2superoxide dismutase 2, mitochondrialVC_CC_GA_intersect
ALBalbuminVC_CC_GA_intersect
TNFtumor necrosis factorVC_CC_GA_intersect
NOS2nitric oxide synthase 2VC_CC_GA_intersect
CASP3caspase 3VC_CC_GA_intersect
PARP1poly (ADP-ribose) polymerase 1VC_CC_intersect
CTNNB1catenin beta 1VC_CC_intersect
NQO1NAD(P)H quinone dehydrogenase 1VC_CC_intersect
NFE2L2nuclear factor, erythroid 2 like 2VC_CC_intersect
PPARGperoxisome proliferator activated receptor gammaVC_CC_intersect
IL1Binterleukin 1 betaVC_CC_intersect
MAPK3mitogen-activated protein kinase 3VC_CC_intersect
MAPK1mitogen-activated protein kinase 1VC_CC_intersect
MPOmyeloperoxidaseVC_CC_intersect
TLR4toll like receptor 4VC_CC_intersect
COL1A1collagen type I alpha 1 chainVC_CC_intersect
AGTangiotensinogenVC_CC_intersect
APPamyloid beta precursor proteinVC_CC_intersect
HIF1Ahypoxia inducible factor 1 alpha subunitVC_CC_intersect
CDKN1Acyclin dependent kinase inhibitor 1AVC_CC_intersect
IGF1insulin like growth factor 1VC_CC_intersect
SOD1superoxide dismutase 1VC_CC_intersect
CYP1A1cytochrome P450 family 1 subfamily A member 1VC_CC_intersect
BCL2BCL2, apoptosis regulatorVC_CC_intersect
TP53tumor protein p53VC_CC_intersect
CATcatalaseVC_CC_intersect
ICAM1intercellular adhesion molecule 1VC_CC_intersect

Share and Cite

MDPI and ACS Style

Chen, L.; Hu, C.; Hood, M.; Zhang, X.; Zhang, L.; Kan, J.; Du, J. A Novel Combination of Vitamin C, Curcumin and Glycyrrhizic Acid Potentially Regulates Immune and Inflammatory Response Associated with Coronavirus Infections: A Perspective from System Biology Analysis. Nutrients 2020, 12, 1193. https://doi.org/10.3390/nu12041193

AMA Style

Chen L, Hu C, Hood M, Zhang X, Zhang L, Kan J, Du J. A Novel Combination of Vitamin C, Curcumin and Glycyrrhizic Acid Potentially Regulates Immune and Inflammatory Response Associated with Coronavirus Infections: A Perspective from System Biology Analysis. Nutrients. 2020; 12(4):1193. https://doi.org/10.3390/nu12041193

Chicago/Turabian Style

Chen, Liang, Chun Hu, Molly Hood, Xue Zhang, Lu Zhang, Juntao Kan, and Jun Du. 2020. "A Novel Combination of Vitamin C, Curcumin and Glycyrrhizic Acid Potentially Regulates Immune and Inflammatory Response Associated with Coronavirus Infections: A Perspective from System Biology Analysis" Nutrients 12, no. 4: 1193. https://doi.org/10.3390/nu12041193

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop