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Research Article
Network pharmacology to uncover potential anti-inflammatory and immunomodulatory constituents in Curcuma longa rhizome as complementary treatment in COVID-19
expand article infoRaden Bayu Indradi‡, Dian Ayu Eka Pitaloka‡, Suryani Suryani§
‡ Universitas Padjadjaran, Sumedang, Indonesia
§ Universitas Jenderal Achmad Yani, Cimahi, Indonesia

Corresponding author: Raden Bayu Indradi ( bayu.indradi@unpad.ac.id )

Academic editor: Georgi Momekov
© 2022 Raden Bayu Indradi, Dian Ayu Eka Pitaloka, Suryani Suryani.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation: Indradi RB, Pitaloka DAE, Suryani (2022) Network pharmacology to uncover potential anti-inflammatory and immunomodulatory constituents in Curcuma longa rhizome as complementary treatment in COVID-19. Pharmacia 69(4): 995-1003. https://doi.org/10.3897/pharmacia.69.e89799
Open Access

Abstract

The immune status of patients plays an essential role in COVID-19. Herbal medicine with immunomodulatory and anti-inflammatory effect could have potential as a complementary therapeutic along with modern medicine. This study aims to investigate the anti-inflammatory and immunomodulatory constituents of Curcuma longa (C. longa) and its possible mechanisms in COVID-19. We systematically sorted the biochemical of C. longa rhizome from literature and repository. Next, we investigated targets related to COVID-19 in the selected active phytochemical constituents and analyzed the possible mechanisms against COVID-19 and performed molecular docking with four essential target proteins in COVID-19 for further verification. Ten active phytochemical constituents of C. longa were predicted to interact with four protein targets. The epidermal growth factor was the most interacted protein targeted by Calebin A, curcumin, cyclocurcumin, demethoxycurcumin, turmeronol a, turmeronol b, caffeic acid, and quercetin. Interferon-gamma was performed as the most critical protein targeted by 4-hydroxycinnamic acid. Curcumin was also predicted to interact with toll-like receptor 4 and Ar-turmerone with angiotensin II receptor type 2. We also reported four signaling pathways associated with target proteins-active phytochemical constituents against COVID-19: cytokine-cytokine receptor interaction, toll-like receptor signaling pathway, Jak-STAT signaling pathway, and PI3K-Akt signaling pathway. In conclusion, multi compounds in C. longa might act synergistically against COVID-19 by affecting the inflammatory and immune responses, and other pathological processes through multiple targets and pathways.

Keywords

anti-inflammatory, Curcuma longa, immunomodulatory, COVID-19

Introduction

It has been highlighted that the emergence of coronavirus disease 2019 (COVID-19) pandemic in the world, including in Indonesia, posed a new challenge on the efforts to control this infectious disease. According to the latest data, the number of COVID-19 infection cases in Indonesia was estimated to be 6,088,460, total deaths of 156,737 per July 1, 2022 (Worldometer 2022), which significantly impacted the national healthcare system (Parikh et al. 2020). COVID-19 is caused by the SARS-CoV-2 virus that is highly contagious (Gorbalenya et al. 2020). Transmission of this virus is through droplets containing viruses, either directly or indirectly (Chan et al. 2020). Up to now, approximately 552 million positive cases of COVID-19 in all age groups have been recorded worldwide (Worldometer 2022).

The integrative treatment between modern and traditional medicines is useful for COVID-19 that currently has no specific treatment. Since the immune status of patients plays an essential role in COVID-19 infection, a herbal medicine, which has an immunomodulatory effect, could have the potential as a preventive measure and complementary therapeutic agent for patients with COVID-19 (Sharma et al. 2009; Zhang and Liu 2020). Curcuma longa (C. longa), widely known as turmeric, has been used traditionally by many countries and gained much interest in the scientific field (Kocaadam and Sanlier 2017). Curcumin as the medicinal part isolated from C. longa reported protecting from acute respiratory distress syndrome via targeting NF-κB, inflammasome, IL-6 trans signal, and HMGB1 pathways in COVID-19 patients (Thimmulappa et al. 2021). However, the synergistic mechanism as an anti-inflammatory and immunomodulator from multi-compound found in C. longa against COVID-19 is remain unclear.

Network pharmacology is based on high-throughput omics data analysis and network database retrieval, which combines systems biology with multidirectional pharmacology. It focuses on pattern changing from a single protein target and a single drug to multiple protein target and multiple drugs (Hopkins 2008). Currently, network pharmacology has been extensively utilized to explore multiple targets and unknown additional mechanisms against diverse diseases (Liang et al. 2016).

In this study, we applied a network pharmacology to investigate the active anti-inflammatory and immunomodulatory constituents of C. longa rhizome and their possible molecular mechanism & synergism effect of various compounds as complementary treatment against COVID-19. Firstly, we selected chemical compounds of C. longa rhizome via public websites and the COVID-19 related target proteins. Next, the extracted overlapping target proteins were discovered as target proteins for analyzing anti-COVID-19 properties. Finally, pathway enrichment analysis was performed to reveal the mechanisms of the most potent constituents against COVID-19. We also performed the molecular docking analysis to validate the interaction between the respective constituents and potential targets.

Materials and methods

Our protocol involved five main steps: (1) finding active phytochemical ingredients of C. longa rhizome from the literature database and public repository; (2) finding known targets and candidate genes related to COVID-19; (3) developing gene ontology and pathway analysis; (4) constructing different types of molecule-target networks and analyzing these networks; (5) performing molecular docking.

Active phytochemical components of C. longa

Phytochemical components of C. longa were collected from Dr. Duke’s Phytochemical and Ethnobotanical Databases (U.S. Department of Agriculture 2021) (https://phytochem.nal.usda.gov/phytochem/search), and KNApSAcK Family (Afendi et al. 2012) (http://www.knapsackfamily.com/KNApSAcK_Family/). We focus on the rhizome part of C. longa which previously known to have biological activities and collected a total of 39 phytochemical components. We analyzed the Absorption, Distribution, Metabolism, and Extraction (ADME) of phytochemical components using SwissADME (Daina et al. 2017) (http://www.swissadme.ch/index.php) as SMILES chemical notation obtained from PubChem (Kim et al. 2021) (https://pubchem.ncbi.nlm.nih.gov/). These components were screened based on three following parameters: high gastrointestinal absorption, no Lipinski violation, and bioavailability ≥ 0.55 (Benet et al. 2016). We downloaded the 3D structure of selected phytochemical components in C. longa from PubChem (Kim et al. 2021) (one phytochemical component was excluded because of no 3D molecular structure provided at the website).

Potential targets of the herbal pair for COVID‑19

We identified the potential target of selected phytochemical components using PharmMapper (Wang et al. 2017) (http://www.lilab-ecust.cn/pharmmapper/). We selected only human protein as target sets and chose the targets with normalized fit scores ≥ 0.8 to be considered potential targets for C. longa (Gordon et al. 2020). We also performed another analysis to uncover more potential targets using SwissTargetPrediction (Daina et al. 2019) (http://www.swisstargetprediction.ch/) (the criteria of probability ≥ 0.5) and BATMAN (Liu et al. 2016) (http://bionet.ncpsb.org.cn/batman-tcm/) (score cut off 80 and Adjusted P-value cut off 0.05).

COVID-19 related human gene annotations were downloaded from National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/gene/), and a total of 165 targets were obtained. We merged the selected phytochemical component targets of C. longa and COVID-19 related targets using Venny 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/) to identify the targets of C. longa related to COVID-19. Overlapped targets were considered as C. longa targets related to COVID-19.

To identify the significant cluster of COVID-19 related targets, protein-protein interaction (PPI) data from COVID-19 related targets were gained from STRING (Szklarczyk et al. 2021) (https://string-db.org/). The interaction was set with parameter limited to “Homo sapiens” and confidence score set to highest (>0.900) (Patel et al. 2014). The PPI data was generated and clustered using CytoCluster tools in Cytoscape v.3.8.2 (Shannon et al. 2003) (https://cytoscape.org/) with ClusterONE algorithm, the minimum size of 10, the minimum density of 0.5, and seeding method from every node. We chose the highest cluster (lowest p-values) as significant cluster of COVID-19 related targets (Nepusz et al. 2012).

Construction of “component-target-disease” network

Network constructions were visualized using Cytoscape v.3.8.2 for the following networks: 1) network between C. longa phytochemical components – common target and 2) network among C. longa common targets, COVID-19 related targets, and pathways. The nodes represent targets, compounds, and pathways, while edges represent interactions.

Enrichment analysis

We performed GO- and KEGG- enrichment analysis using The Database for Annotation, Visualization, and Integrated Discovery, DAVID v.6.8 (Jiao et al. 2012) (https://david.ncifcrf.gov/) to further investigate functional annotation and pathway involved in C. longa targets related to COVID-19. We used only significant cluster of COVID-19 targets listed to help pinpoint the significant pathways. The threshold was set on 10 (except 5 for molecular function) and p < 0.01 to be considered as significant (Reimand et al. 2019).

Molecular docking method

We performed molecular docking for each selected phytochemical constituent which was found potential against COVID-19 using AutoDock Vina (Trott and Olson 2010). All phytochemical structures were optimized by the molecular mechanic’s optimization method based on the MMFF94 force field using AutoDockTools-1.5.6 (Morris et al. 2009) The X-ray crystal structures of 4 known targets were downloaded from Protein Data Bank (PDB) (https://www.rcsb.org/). These protein targets were EGFR (PDB ID: 5FED) (Lelais et al. 2016), TLR4 (PDB ID: 4G8A) (Ohto and Shimizu 2012), IFNG (PDB ID: 6E3K) (Jude et al. 2019), and AGTR2 (PDB ID: 5XJM) (Asada et al. 2018). Protein rigid used in the docking and water molecules were removed in preparation step before running, and exhaustiveness used were 16. This protocol used the docking score between native ligand and molecular protein complex and known targets proteins as the cutoff value. For all target proteins, the docking score of a phytochemical constituent and a target were compared to the native ligand (energy & amino acid residues), and when it gave a binding similarity, they were considered effective docking and could be regarded as nodes (Li et al. 2012). Their interaction could be further regarded as edges.

Results

The active phytochemical components of C. longa

We firstly acquired the phytochemical constituents and corresponding targets of the COVID-19-related C. longa. We collected a total of 39 phytochemical components of C. longa rhizome from two databases (Suppl. material 1: S1). We only focused on the rhizome part of C. longa, the most utilized in these herbs. ADME evaluation found that 25 phytochemical components of C. longa rhizome were met all the criteria (Suppl. material 1: S2). Using three different websites, identification of protein target for COVID-19 resulted in 253 different targets from 25 phytochemical components (280 nodes & 649 edges). These components predicted to interacts with several targets, except for 1,7-bis(4-hydroxyphenyl)-1-heptene-3,5-dione and 1,7-Bis(4-hydroxyphenyl)hepta-1,6-diene-3,5-dione compound which only predicted interact with one target (Suppl. material 1: S3).

“C. longa components common target” network

A total of 165 COVID-19 targets were obtained from NCBI (Suppl. material 1: S4) and six targets intersection of C. longa and COVID-19 were obtained by merging the phytochemical component targets with COVID-19 targets. The intersection of targets was regarded as the potential targets of C. longa against COVID-19 (Fig. 1). Then, the “C. longa-component-target-disease” network was constructed by Cytoscape, as depicted. The six overlapped targets were EGFR, F2, F3, TLR4, AGTR2, and IFNG from a total of 10 active ingredients. According to the degree value, the most critical ingredient of C. longa is curcumin which predicted to interact with EGFR, F3, and TLR4 targets of COVID-19, respectively (Fig. 2).

Figure 1. 

Venn diagram of C. longa phytochemical compound targets and COVID-19 related targets.

Figure 2. 

Compound – common target of C. longa and COVID-19 related network. Yellow ellipse represents C. longa compounds, blue diamond represents COVID-19 related protein targeted by C. longa.

PPI network of C. longa against COVID-19

PPI network has been widely used to identify many different interactions of the protein targets in the context of a complex disease. There was a total of 116 nodes and 429 interactions lines in the STRING PPI network. Due to the complexity of the original network obtained from the STRING database, we imported the PPI data into Cytoscape to explore the importance of potential targets in the protein networks and the main cluster in this network. We used ClusterONE to obtain the significant cluster. The highest cluster with lowest p-value contained 27 nodes (27 targets) and 181 edges (interactions) with a network density of 0,516 (Fig. 3), which were JAK1, TYJ2, IFNG, CCL2, CSF3, IL17A, CCL3, IL18, IL1A, IL2RA, CXCL2, IL4, CSF2, IL10, CXCL1, CXCL10, STAT 3, NFKB1, IL1B, IL6, JAK2, CXCL8, STAT1, TNF, CD4, CD8A, and IL2. We found that IFNG was among the proteins in significant cluster and also targeted directly by C. longa compound as in Fig. 2.

Figure 3. 

Significant cluster of COVID-19 related targets.

Enrichment of potential targets of C. longa

To further explore the underlying mechanisms of C. longa as a therapy against COVID-19, we performed GO-enrichment analysis with the 27 COVID-19 related targets from significant cluster identified by DAVID (Fig. 4). GO enrichment consists of three parts, biological process (BP), cellular component (CC), and molecular function (MF). There were 31 GO enrichment terms for BP. The top 5 enriched terms included immune response, defense response to the virus, type I interferon signaling pathway, inflammatory response, and innate immune response. Besides, a total of 11 CC items were obtained, and the most enriched terms included extracellular space, extracellular region, external side of the plasma membrane, and cell surface. There were 10 GO terms for MF enrichment, and the most enriched terms included cytokine activity, chemokine activity, double-stranded RNA binding, receptor binding, and growth factor activity.

Figure 4. 

Enrichment analysis of 27 COVID-19 related targets from significant cluster. A biological process; B chemical component; C molecular function; D KEGG.

From KEGG analysis, we obtained a total of 32 pathways which four of them were related to COVID-19: cytokine-cytokine receptor interaction, toll-like receptor signaling pathway, Jak-STAT signaling pathway, and PI3K-Akt signaling pathway. All this pathway showed a low p-value (<0.0001) and considered as an important pathway of C. longa anti-inflammatory and immunomodulatory activity against COVID-19.

We create network between C. longa target of COVID-19, other COVID-19 related target, and pathway to understand the interaction (Fig. 5). COVID-19 related targets involved in those 4 KEGG pathway were considered as interaction between them, resulting in 47 nodes and 100 edges in the network with 5 direct interactions between C. longa compounds common target with a pathway. The rest were considered as indirect interaction.

Figure 5. 

Network between C. longa common targets of COVID-19 (blue diamonds, upper left side) – other COVID-19 related targets (green diamonds, upper right side) – pathways (red arrows, down side). Yellow diamond depicts targets included in significant cluster for COVID-19. Red edges represent direct interaction while blue edges represent indirect interaction of C. longa COVID-19 related targets from significant cluster with pathways.

Molecular docking

The main active phytochemical constituents in C. longa, namely Quercetin, Ar-turmerone, 4-hydroxycinnamic acid, calebin A, curcumin, cyclocurcumin, demethoxycurcumin, turmeronol A, turmeronol B, and caffeic acid were used to dock with IFNG, AGTR2, EGFR, and TLR4 respectively. Protein rigid used in the docking and water molecules were removed in preparation step before running. It is generally believed that the lower the binding energy of ligand and receptor, the more stable the conformation and the greater the possibility of action. The molecular docking results showed that the binding energies of the main phytochemical constituents in C. longa were predicted interact with COVID-19 related targets based on comparison to the native ligand (energy & amino acid residues) (Table 1). The interaction were visualized using Discovery Studio (Biovia 2021) could be seen in (Suppl. material 1: S9).

Table 1.
Download as
CSV
XLSX

Molecular docking result of found compound that predicted interact with COVID-19 targets.

Protein Ligand Binding Energy Hydrogen Bond Residue
EGFR Native ligand -8.8 4 LYS645, LYS745, CYS797, MET793
Calebin A -6.9 5 LYS745, THR790, THR854, ASP855, MET793
Curcumin -7.2 4 LYS745, MET793, ARG841, THR854
Cyclocurcumin -7.9 3 LYS745, MET793, THR854
Demethoxycurcumin -7.5 5 LYS745, MET793, THR854, ARG841, ASN842
Turmeronol a -6.7 4 LYS745, MET793, THR854, GLN791
Turmeronol b -6.7 4 LYS745, LYS745, THR854, GLN791
Caffeic acid -6.1 5 LYS745, MET793, THR854, MET793, MET793
Quercetin -7.9 6 LYS745, LYS745, LYS745, MET793, THR854, THR854
TLR4 Native ligand -4.4 4 ASN173, GLU142, GLY147, HIS148
Curcumin -5.7 3 ASN173, GLU169, GLU142
AGTR2 Native ligand -6.0 3 ARG2, ARG182, LYS215
Ar-Turmerone -6.4 1 ARG182
IFNG Native ligand -2.3 3 ASN69, SER71, ASP72
4-Hydroxycinnamic acid -1.5 1 ASN26

Discussion

Until now, people around the world are still actively fighting against COVID-19, and herbal medicine has played an important role in the complementary therapy of this disease. The result of our network analysis provides insights into the identification of active constituents of C. longa for COVID-19, identification of protein target related to COVID-19, active constituents-target interaction, and signaling pathway associated with target proteins of active constituents in C. longa.

Ten active constituents of C. longa rhizome were predicted to interact with six common protein targets of COVID-19. We found that among these active constituents, curcumin appears in two pairs of active constituents-target interaction as seen in the Fig. 5: EGFR and TLR4 protein. IFNG protein was also found as the most critical target found in the PPI network. We reported four signaling pathways associated with target proteins of active constituents in C. longa rhizome against COVID-19: cytokine-cytokine receptor interaction, toll-like receptor signaling pathway, Jak-STAT signaling, and PI3K-Akt signaling pathway. The proposed mechanism of action are visualized in Fig. 6.

Figure 6. 

The proposed mechanism of action of multi compounds in C. longa rhizome on trans-signaling pathways (JAK/STAT, PI3K/Akt, and TLR) and their interaction with target proteins against COVID-19. JAK/STAT, The Janus kinase (JAK)/signal transducer and activator of transcription (STAT); PI3K, Phosphatidylinositol-3-kinase (PI3K); MAPK, Mitogenactivated protein kinase; NF-KB, Nuclear Factor kappa-light-chain-enhancer of activated B cells; TLR, Toll-Like receptor; 4HA, 4-Hydroxycinnamic acid; CC, Cyclocurcumin; CA, Caffeic acid; DC, demetoxycurcumin. The figure was created using Biorender (https://biorender.com/).

Numerous studies have been reported in curcumin as a potential treatment for COVID-19. Nanocurcumin was reported to modulate pro-inflammatory cytokines in patients with COVID-19. Patients showed high mRNA expression and secretion of IL-1β, IL-6, TNF-α, and IL-18 but showed a significant reduction in IL-6 and IL-1β after treatment with nano curcumin (Valizadeh et al. 2020). The previous study also demonstrated that the number of Th17 cells, gene expression, and serum Th17-mediated factors levels (IL-17, IL-21, IL-23, and GM-CSF) were significantly reduced in the group of patients with COVID-19 treated with nano curcumin (Tahmasebi et al. 2021).

IFNG played a vital role in COVID-19, although based on our result, it is only targeted by the 4-hydroxycinnamic acid of C. longa. A recent study reported that IFNG had driven differentiation of immature secretory cells into the largely ACE2+ ciliated cells in patients with COVID-19. ACE2 has upregulated in epithelial cells at least partially through IFNG signaling by immune cells in the patients with moderate COVID-19. Cytotoxic T lymphocytes displayed the characteristic transcriptional profile of high expression of IFNG and TNF together with genes encoding for cytotoxic receptors (KLRB1, KLRC1, and KLRD1) (Chua, et al., 2020). Upregulation of ACE2 in COVID-19 patients, which correlated to activation of IFNG signaling by 4-hydroxycinnamic acid of C. longa, might counteract viral infection, which is in line with the protective function of immune cells (Imai et al. 2005; Verdecchia et al. 2020).

Ar-turmerone, as one of the bioactive compounds in C. longa rhizome, was predicted to interact with AGTR2, and this result is in line with our previous idea. AGTR2 belongs to the G-protein coupled receptor one family and functions as a receptor for angiotensin II (Pubchem 2016). It is reported that infection of SARS-CoV-2 results in the down regulation of ACE2 and triggers severe inflammatory lesions in the lungs (Silhol et al. 2020). This inflammatory reaction also appears to be mediated by AGTR2 via activation of the ERK/MAPK signaling pathway, which was found upregulated in COVID-19 patients (Guo et al. 2020). AGTR2 is also found to promote activation of the PI3K-Akt signaling pathway via ADAM17 resulting in NF-κB activation (Sun et al. 2010). Inhibition of AGTR2 by Ar-turmerone might prevent these signaling activation, and inflammation can be minimized.

EGFR is the most interacted protein targeted by active constituents of C. longa. A total of 8/10 was predicted to interact with this target protein. EGFR is known to have a role in processing the membrane form of IL-6Rα into its soluble form sIL-6Rα. Complex sIL-6Rα-IL-6 leads to activation of the Jak-STAT signaling pathway via gp130, with it being important for complete activation of the NF-κB signaling pathway (Eguchi et al. 2018; Murakami et al. 2019). AGTR, as one of the protein targets from active constituents of C. longa, is also found to promote activation of the PI3K-Akt signaling pathway via ADAM17 resulting in NF-κB activation (Sun et al. 2010), preventing the NF-κB activation via EGFR and AGTR by active constituents of C. longa may prevent the release of pro-inflammatory cytokines leading to cytokine storm in COVID-19 patients.

Toll-like receptor (TLR) is also one of the signaling pathways regulated by active constituents of C. longa. As a component of innate immunity, it plays an important role in activating innate immunity, regulating cytokine expression, indirectly activating the adaptive immune system, and recognizing pathogen‐associated molecular patterns (PAMPs) (Hedayat et al. 2011; Birra et al. 2020; Debnath et al. 2020). Khanmohammadi et al. (2021) reported that activation of TLR pathways leads to the secretion of pro‐inflammatory cytokines, such as interleukin‐1 (IL‐1), IL‐6, and tumor necrosis factor‐α, as well as type 1 interferon, which are potentially important in COVID-19 infection and its cytokine storms. Regulation of TLR signaling pathway by curcumin of C. longa could be a potential target in controlling infection against SARS-CoV-2.

Limitations

The network pharmacology analysis has identified ten candidates of bioactive compounds in C. longa against COVID-19 infection, this was obtained based on its binding to the six target proteins. The possible mechanism of bioactive compounds in C. longa against COVID-19 has been known in this study, that four pathways are targeted to control infection COVID-19. However, this study still has limitations, the effectiveness, and the mechanism of bioactive compounds in C. longa against COVID-19 needs to be re-validated through in vitro analysis, that is directly effect of bioactive compound to the virus which will support the result of the pathway analysis. The pharmacological effect in inhibiting the pathological process from COVID-19 by C. longa compound also could be validated through in vivo analysis. The in vitro and in vivo analysis could be the further research from this study.

Conclusions

The mechanism of multi compounds in C. longa rhizome were firstly investigated through network pharmacology. The finding of this research suggested 10 compounds (curcumin, turmeronol A, turmeronol B, cyclocurcumin, calebin A, 4-Hydroxycinnamic acid, ar-turmerone, caffeic acid, demetoxycurcumin, quercetin) were connected to 4 considered significant target proteins (EGFR, TLR4, IFNG, and AGTR2). The promising mechanism of C. longa rhizome against COVID-19 were connected to 4 pathways (cytokine-cytokine receptor interactions, P13K/Akt, JAK/STAT, and TLR4 signaling pathways) and blocking the activation of these pathway. Overall, multi compounds in C. longa rhizome might act synergistically against COVID-19 by affecting the inflammatory and immune responses, cell apoptosis, and other pathological processes through multiple targets and pathways.

Supplementary data

The data underpinning the analysis reported in this paper are deposited at Figshare data repository at https://doi.org/10.6084/m9.figshare.21184429.v1.

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Supplementary material

Supplementary material 1 

Datasets of Network Pharmacology Process

Author: Raden Bayu Indradi

Data type: datasets

Explanation note: This supplementary file contain raw datasets and results used for Network Pharmacology Process.

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
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