Therapeutic effect and potential mechanism of curcumin, an active ingredient in Tongnao Decoction, on COVID-19 combined with stroke: a network pharmacology study and GEO database mining

doi:10.21203/rs.3.rs-4329762/v1

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Therapeutic effect and potential mechanism of curcumin, an active ingredient in Tongnao Decoction, on COVID-19 combined with stroke: a network pharmacology study and GEO database mining

https://doi.org/10.21203/rs.3.rs-4329762/v1

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Background

The onset of severe acute respiratory syndrome coronavirus type 2 at the end of 2019 led to a global pandemic of acute respiratory diseases, commonly known as COVID-19, caused by this highly infectious and pathogenic coronavirus. As members of the Fifth-batch National TCM Medical Team sent by the Affiliated Hospital of Nanjing University of Chinese Medicine to assist Wuhan Jiangxia Mobile Cabin Hospital, it was observed that patients with COVID-19 had an elevated incidence of stroke and are prone to progression. The possible mechanisms included neuroinvasion by the virus, an imbalance between ACE1 and ACE2, hypercoagulability, and a pre-thrombotic state. However, effective treatment options are not yet available. Developed by the Department of Neurology, Affiliated Hospital of Nanjing University of Chinese Medicine, Tongnao Decoction (TND) is a prescribed medication. The chemical components within the TND extract were previously examined using Waters ACQUITY UPLC ultra-performance liquid chromatography. Notably, identified chemical constituents, including curcumin, exhibited protective effects on brain cells. In the current investigation, bioinformatics techniques were employed to mine the GEO database, aiming to assess the functional role and potential action mechanism of curcumin as a therapeutic agent for COVID-19 combined with stroke.Tongnao Decoction

Methods

The datasets of COVID-19 and stroke were retrieved from the GEO database to identify the differentially intersecting genes (DIGs) between the two diseases. These DIGs were then exposed to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses employing the clusterProfiler to extract TF genes in the Trrust database (TF in TRRUST). Core TF genes were found to be target genes in DEGs-related TF in Stroke and DEGs-related TF in COVID. The overlapping set of these target genes yielded intersected target genes, which were then imported into the DGIdb website for drug prediction. Subsequently, the selected core drugs were subjected to drug validation on the cMAP website. Target prediction was performed on four online platforms: SEA, SwissTargetPrediction, TargetNet, and TCMSP. Subsequently, the obtained results were imported into the String database to create an interaction network comprising core target genes and enriched pathways. Additionally, molecular docking was performed using AutoDock software. Finally, molecular docking scoring was applied to the core targets.

Results

The intersection of DEGs from stroke dataset GSE16561 with DEGs from COVID dataset GSE211979 yielded 205 intersected genes. These intersected genes, including E2F3, BCL6, ETS2, CEBPD, STAT1, MXD1, NFIL3, HDAC4, MMP9, CD163, FCGR1A, IFIT3, LY96, PLSCR1, TNFSF10:E2F3, BCL6, ETS2, CEBPD, STAT1, MXD1, NFIL3, HDAC4, MMP9, CD163, FCGR1A, IFIT3, LY96, PLSCR1, and TNFSF10, were subjected to GO and KEGG pathway enrichment analyses utilizing the clusterProfiler. Subsequently, the core drug curcumin was selected from the 17 intersected drugs. Curcumin inhibits NF-κb and MAPKs and possesses various pharmacological effects encompassing anti-proliferative, anti-oxidant, anti-inflammatory, and anti-angiogenic effects. The six core targets CCNA2, JAK2, MMP9, PPARG, PTGS2, and STAT3 had molecular docking scores of -2.86, -2.98, -5.01, -2.42, -3.83, and − 2.28, respectively.

Conclusion

Curcumin, the active ingredient in TND, can effectively intervene in COVID-19 combined with stroke through CCNA2, JAK2, MMP9, PPARG, PTGS2, and STAT3 target pathways, of which the most important target is MMP9.

SARS-CoV-2 emerged at the end of 2019, and by 2023, the WHO declared the conclusion of the emergency state associated with the COVID-19 pandemic. Over the span of three years, COVID-19 posed a significant global threat to public health. Presently, in China, COVID-19 retains its classification as a Category B infectious disease. The ongoing evolution of the COVID-19 strain has resulted in varying degrees of changes in latency, pathogenicity, and infectiousness.

Following infection with SARS-CoV-2, patients typically undergo an incubation period lasting 1–14 days. Common symptoms during this period include fever, fatigue, dry cough, and diarrhea. Furthermore, manifestations involving multiple organs, such as the heart, brain, and kidneys, become apparent, reflecting the diverse symptoms and signs associated with COVID-19 (Hu et al., 2021). As members of the Fifth-batch National TCM Medical Team sent by the Affiliated Hospital of Nanjing University of Chinese Medicine to assist Wuhan Jiangxia Mobile Cabin Hospital, a common occurrence was observed among patients with COVID-19. Neurological lesions ༈Li et al., 2020༉, including acute cerebrovascular disease, insomnia, dizziness, headache, anxiety, and depression, were observed༈Kremer et al., 2020; Mao et al., 2020༉. The incidence of COVID-19 combined with acute large vessel occlusion in New York City has increased twofold compared with the pre-COVID-19 outbreak ༈Majidi et al., 2020༉, suggesting a certain pathophysiologic association between COVID-19 and stroke. A retrospective cohort study compared the incidence of ischemic stroke between individuals with COVID-19 and influenza. The statistics indicated an elevated risk of acute ischemic stroke in patients with COVID-19 relative to those with influenza. This elevated risk was more prevalent among the elderly and patients with traditional stroke risk factors ༈Merkler et al., 2020༉. Mechanistically, the virus binds to cells expressing angiotensin-converting enzyme 2 and transmembrane serine protease 2 receptor, which releases cytokines and chemokines, produces pro-inflammatory cytokines ༈Broman et al., 2021༉, and triggers a cytokine storm ༈Napoli et al., 2021༉, thereby inducing exacerbation of vascular lesions and thrombotic events in hypoperfusion. Further, extensive microvascular and macrovascular thrombosis was observed, leading to massive cerebral embolism.

Tongnao Decoction (TND), a prescription formulated by the Department of Neurology, Affiliated Hospital of Nanjing University of Chinese Medicine (Wang et al., 2022; Zhang et al., 2022; Jiang et al., 2022; Zhang et al., 2022), consists of processed Rhizoma Arisaematis, Irkutsk Anemone Rhizome, Uncaria rhynchophylla, Ligusticum wallichii, Rhodiola rosea, Rhodiola rosea, hirudo, and earthworm. Previously, the chemical constituents in the TND extract were analyzed by Waters ACQUITY UPLC ultra-performance liquid chromatography. The identified chemical constituents, such as curcumin ༈Wang et al., 2020; Wang et al., 2021༉, can exert antioxidant and anti-inflammatory effects and protect brain cells by binding to molecular and cellular targets ༈Yang et al., 2023༉. Therefore, curcumin can ameliorate neurological damage in patients with COVID-19 combined with stroke. In addition, this scientific hypothesis was validated, and potential action mechanisms were explored through web pharmacology and GEO database mining.

2.1 Data Acquisition and Processing

Applying "COVID-19" and "Stroke" as search terms, relevant microarray data of humans was retrieved from the Gene Expression Omnibus (GEO) platform (https://www.ncbi.nlm.nih.gov/gds). Two datasets were identified: GSE211979, comprising 16 COVID-19 tissues and five control tissues, and GSE16561, comprising 39 stroke tissues and 24 control tissues. The detection platforms for GSE211979 and GSE16561 were GPL16791 and GPL6883, respectively. Probe expression value matrices for GSE211979 and GSE16561, along with their respective annotation files (GPL16791 and GPL6883), were acquired.

For data quality assurance, probes lacking alignment with gene symbols were excluded. In cases where distinct probes mapped to the same gene, the mean value of the probes was calculated for subsequent analysis(Yu et al., 2021). The normalization of gene expression profiles in GSE211979 and GSE16561 was performed using the normalizeBetweenArrays function within the limma package, ensuring standardized and reliable data for subsequent analyses༈Zou et al., 2019༉.

2.2 Differential Expression Analysis

The Limma package (v4.1.1, http://www.bioconductor.org/packages/release/bioc/html/limma.html) served as the tool for analyzing differentially expressed genes (DEGs) between diseased and control tissues in GSE16561 and GSE211979. Following the t-test, the obtained P-values underwent correction by multiple tests utilizing the Benjamini & Hochberg (BH) method. Subsequently, the outcomes were scrutinized based on both difference multiples and statistical significance to ensure a comprehensive evaluation. DEGs were identified based on criteria set at an adjusted P-value < 0.05 and |log fold change (FC)| > 0.5, establishing stringent standards for significance and fold change(Yu et al., 2021).

Moreover, the R "ggplot2" was employed to generate volcano plots as a present of differential expression of DEGs.

2.3 Enrichment Analysis

To delve into the roles and pathways associated with DEGs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. The "clusterProfiler" (http://www.bioconductor.org/packages/release/bioc/html/clusterProfiler.html) was utilized for these analyses. GO functions comprised biological process (BP), cellular component (CC), and molecular function (MF). Statistical significance was established at BH adjusted P < 0.05(Yu et al., 2021).

Additionally, Metascape (https://metascape.org) was utilized to validate the functional enrichment of hub genes. A cutoff value of P < 0.05 was established(Huang et al., 2020).

2.4 Transcription Factor–Target Networks

For predicting human and mouse transcriptional regulatory networks, the TRRUST database (https://www.grnpedia.org/trrust/) emerged as a valuable tool. This database encompasses 8,444 transcription factor (TF)-target regulatory relationships involving 800 human TFs(Huang et al., 2020)

The differently expressed TFs in COVID-19 and Stroke, which could regulate DEGs in COVID-19/Stroke, were identified as key TFs. The differently expressed TFs and target genes were utilized to develop the TF-target networks by means of Cytoscape.(Zhang et al., 2021)

2.5 Protein-Protein Interaction Networks

A PPI network comprising significant DEGs was developed using the online tool STRING (https://string-db.org/). An interaction score threshold of > 0.4 (default parameters) from the Search Tool for the Retrieval of Interacting Genes (STRING) was employed to predict PPIs. Subsequently, the PPI network was generated and visualized utilizing Cytoscape (V3.8.2). Within this PPI network, each node represents a target gene, and the lines between nodes depict related interactions, providing a comprehensive view of molecular connections(Shen et al., 2022).

2.6 Drug Prediction of COVID-19/Stroke

The initial step involved downloading the catalog of small molecules interacting with COVID-19/Stroke from the CTD Database(Pang et al., 2022). To prioritize drugs for COVID-19 and stroke, the DGIbd database was employed, utilizing key TFs and target genes to identify potential candidate drugs. Subsequently, the selected candidate drugs were further validated for their relevance to COVID-19 and stroke using the Connectivity Map (CMap) database༈Zhang et al., 2021༉.

2.7 Acquisition of curcumin-pharmacological target in COVID-19/Stroke

Pharmacological targets of curcumin were systematically screened and collected from various online resources, namely the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, http://tcmspw.com/tcmsp.php), SwissTargetPrediction (http://swisstargetprediction.ch/), TargetNet (http://targetnet.scbdd.com/), and SEA(https://sea.bkslab.org/).

The UniProt database was then employed to correct and identify overlapping candidate genes, ensuring accuracy and consistency in the collected data.

2.8 Molecular Docking

Molecular docking serves as a crucial technique for structurally docking small molecules with target proteins and assessing their binding affinities to specific binding sites. A negative docking binding energy indicates the effective autonomous binding of the small molecule to the target protein. It is generally believed that the lower the energy the conformation in which a ligand binds to a receptor is stabilized, the more likely the effect will be.

This study conducted molecular docking of the main active compounds and hub targets of curcumin. The structure diagrams of the primary active compounds were acquired from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and saved in SDF format. Concurrently, the 3D structure diagrams of hub targets were retrieved from the PDB database (https://www.rcsb.org/), saved in PDB format, and subsequently imported into AutoDock software for the molecular docking process. The outcomes of the docking were visualized utilizing PyMol.

3.1 Identification of COVID-19/Stroke–associated genes

Figure 1 illustrates the workflow of this research.

To scrutinize the relationships and implications between COVID-19 and stroke, human RNA-seq and microarray datasets from the GEO were analyzed to identify genes that are disrupted in both conditions. A total of 3566 DEGs were obtained from the COVID-19 dataset, comprising 483 upregulated DEGs and 3,083 downregulated genes. Similarly, in the stroke blood dataset, a total of 511 DEGs were identified through differential expression analysis, with 267 genes exhibiting upregulation and 244 genes exhibiting downregulation.

The two volcano plots presented in Fig. 2 visually represent the comprehensive transcriptional gene expression patterns for COVID-19 and stroke. In these plots, red dots signify genes exhibiting upregulation, while blue dots indicate genes exhibiting downregulation with significant differences (Figs. 2A, 2B).(Lu et al., 2022)

Additionally, Fig. 2C illustrates 205 overlapping DEGs that were identified in both COVID-19 and stroke.

3.2 Functions and Pathways Enrichment Analyses

Utilizing the 205 overlapped DEGs, enrichment analyses were conducted to delve into their functional roles and associated pathways. The outcome revealed enrichment in 150 GO-BP, 26 GO-CC, 2 GO-MF, and 7 KEGG pathways. The top 10 results of biological processes as per BH adjusted P-value < 0.05 and 7 KEGG pathways were chosen for visualization in a scatter plot (Figs. 3A and 3B). Notably, the DEGs exhibited prominent enrichment in GO-BP terms related to the immune response to activating signal transduction and the immune response to activating cell surface receptor signaling pathways, among others. Additionally, KEGG pathways, encompassing Th17 cell differentiation and Th1 and Th2 cell differentiation, among others, were highlighted.

Consistent with these findings, enrichment analysis conducted through the Metascape database revealed significant enrichment in pathways related to inflammation, signaling, immune responses, and regulatory processes (Fig. 3C). Furthermore, the enrichment analysis demonstrated that overlapped DEGs were related to diseases associated with inflammation, vascular issues, and infections in DisGeNET (Fig. 3D). Transcription factor–target analysis demonstrated that numerous overlapped DEGs were regulated by TFs in the TRRUST database (Fig. 3E), highlighting that the TF regulatory network might participate in the progression of COVID-19 and stroke.

3.3 The TF Regulatory Network and Potential Drugs for COVID-19 and Stroke

To further unveil the TFs-target network in COVID-19 and stroke, 795 individual TFs and their corresponding target genes retrieved from the TRRUST database served as the foundational data. The criteria for selection involved identifying TFs present in COVID-19 and DEGs in stroke that coexist in COVID-19. Specifically, TFs in stroke DEGs and its downstream target gene in at least COVID-19 and one disease DEGs in stroke are key genes in COVID-19 and stroke.

Six hub TFs are determined in Figure 4A. The TF regulatory network in COVID-19 and stroke is depicted in Figure 4B and Table S1.

Subsequently, an analysis of potential drugs for treating both COVID-19 and stroke was undertaken. Utilizing the DGIbd database (https: //dgidb.org/), 64 drugs that could be used to treat both COVID-19 and stroke were identified. A comparison was made between 4130 COVID-19 drugs and 5892 stroke drugs obtained from the CTD database, and a Venn diagram analysis was performed. The results revealed 17 drugs predicted by the DGIbd database capable of treating both COVID-19 and stroke (Figure 5A). Finally, the relationship between these potential drugs and their respective targets was visualized using the R-language Sankeywheel package to create a Sankey diagram (Figure 5B)（Zhang et al., 2021）.

Among these potential drugs, curcumin emerged as a promising candidate due to its ability to inhibit NF-κB and MAPKs and its recognized pharmacological impacts, comprising anti-inflammatory, anti-oxidant, anti-proliferation, and anti-angiogenesis properties. Curcumin was predicted as a candidate drug utilizing the CMap database (Table S2). Consequently, curcumin was selected for subsequent functional assay.

3.4 Identification of Curcumin Targets in COVID-19 and Stroke

Pharmacological targets of curcumin were downloaded from four public online databases: SEA, Swiss Target Prediction, TargetNet, and TCMSP. After identification, correction, and elimination of duplicate values through the UniProt database, 1890 pharmacological targets associated with curcumin were acquired. In the previous analysis, 29 common TF and its downstream target genes for COVID-19 and stroke were retrieved from the TRRUST database, and the above two groups of genes were assessed utilizing the Venn diagram. Ten overlapping genes were identified among the pharmacological targets of curcumin and the TFs-target genes shared by COVID-19 and stroke. These shared genes encompass ANPEP, MMP9, PTGS2, STAT3, XBP1, AURKA, CCNA2, JAK2, PPARG, PLK1, which are pharmacological targets of curcumin against both COVID-19 and stroke (Figure 6A).

Figure 6B shows the PPI network of the ten intersection targets assessed utilizing STRING. Subsequently, in Figures 6C and 6D, the six core gene targets identified by cytoHubba—namely, PTGS2, STAT3, CCNA2, JAK2, PPARG, and MMP9—are presented. Utilizing the MCC algorithm in cytoHubba, the top 6 nodes in terms of importance were summarized in Table 1.

Rank	Name	Score
1	STAT3	28
2	MMP9	26
2	PPARG	26
4	PTGS2	24
4	JAK2	24
6	CCNA2	4

Table 1 | Top 6 core target genes in the PPI network.

3.5 Development of network diagram

The GO enrichment analysis was executed utilizing the six curcumin anti-stroke and COVID-19 pharmacological targets. The top 20 pathways for BP, CC, and MF are illustrated in Figures 7A, 7B, and 7C, respectively. The outcomes highlight significant enrichment in pharmacological target genes of curcumin, particularly in essential biological processes such as smooth muscle cell proliferation, regulation of smooth muscle cell proliferation, and regulation of cysteine-type endopeptidase activity involved in the apoptotic signaling pathway, among others.

Moreover, KEGG enrichment analysis was conducted on these curcumin anti-stroke and COVID-19 pharmacological targets, followed by visualization analysis of the top 20 enrichment pathways (Figure 7D). The results indicated that the major enrichment pathways of curcumin anti-stroke and COVID-19 pharmacological target genes included Transcriptional misregulation in cancer, TNF signaling pathway, Toxoplasmosis, Th17 cell differentiation, etc.

Utilizing Cytoscape, a network visualization of curcumin against COVID-19/stroke targets and an interaction diagram for core target-related pathways (top 20 pathways) were generated (Figure 8).

3.6 Binding of curcumin to intersecting potential target genes in COVID-19 and Stroke

To gain deeper insights into the interaction of curcumin with pharmacological targets against COVID-19 and stroke, AutoDock Vina software was employed to analyze potential binding between the protein macromolecules of curcumin and the six core targets (CCNA2, JAK2, MMP9, PPARG, PTGS2, STAT3). The molecular docking scores were as follows: -2.86, -2.98, -5.01, -2.42, -3.83, -2.28. The evaluation of molecular docking was based on the principle of energy minimization, where a higher absolute value of the score indicates a better binding degree（Lu et al., 2022）. Figure 9(A, B, C, D, E) illustrates the docking interactions of CCNA2, JAK2, MMP9, PPARG, PTGS2, and STAT3 with curcumin.

This study delved into the possible mechanisms of curcumin in treating COVID-19 combined with stroke. In addition, 17 chemical constituents were extracted through the CTP database and DGIdb website. The major chemical constituents included abexinostat, apicidin, belinostat, carboplatin, celecoxib, cisplatin, curcumin, daunorubicin, entinostat, fenretinide, garcinol, marimastat, mocetinostat, panobinostat, prinomastat, romidepsin, temozolomide, ursolic acid, valproic acid, and vorinostat. These chemical components work together with the other active ingredients on the multiple targets in the network. Among the predicted active constituents, curcumin has a favorable therapeutic effect on COVID-19 combined with stroke. Curcumin, derived from turmeric in 1970, falls into the category of hydrophobic polyphenolic compounds, exhibiting anti-inflammatory, antioxidant, antitumor, lipid-lowering, and neuroprotective effects （Ghoneim et al., 2002）. Curcumin attenuates ischemia-reperfusion injury occurring in multiple organs such as the heart, liver, kidney, lungs, testes, spinal cord, and retina （Yavarpour et al., 2019; Hongtao et al., 2018）

Curcumin can block SARS-CoV-2 S1 subunit binding to ACE2 by inhibiting SARS-CoV-2 entry into cells （Maurya et al., 2020）. Additionally, it can reduce the TMPRSS-2 expression （Saidi et al., 2019） and exert anti-COVID-19 effects. Furthermore, curcumin regulates ACE-related pathways, promotes ACE2 expression, inactivates Ang II, binds to SARS-CoV-2 RdRp and Mpro, and prevents viral replication （Ting et al., 2018）. Liu （Liu et al., 2020） confirmed that curcumin inhibits cytokine storm in COVID-19. In an inflammatory lung injury model, curcumin exerts protection on alveolar ATII cells （Shi et al., 2019）, repairs the cells, and prevents lung cell carcinogenesis （Huang et al., 2018）. In comorbid ischemic stroke, curcumin can easily cross the blood-brain barrier due to its lipophilic advantage and act directly on the brain. Inflammatory response is one of the key mechanisms in the development of ischemic stroke （Ao et al., 2018）. Zheng et al. （Zheng et al., 2021） simulated an in vitro model of cerebral ischemia by oxygen-glucose deprivation treatment of BV2 microglia. It was found that curcumin promoted miR-182 expression, inhibited the expression of Toll-like receptor-4, and decreased the levels of tumor necrosis factor α, interleukin-6, and IL-1β. However, miR-182 inhibitors counteracted this effect. Similarly, oxidative stress is an important part of the pathophysiological process of ischemic stroke. In the SH-SY5Y cell OGD/R model, curcumin upregulated miR-1287-5p expression, decreased peroxisome synthesis, reduced ROS generation, attenuated oxygen radical damage, and exerted anti-oxidant effects （Zheng et al., 2021）.

The major hub genes involved in curcumin treatment of COVID-19 combined with stroke included E2F3, BCL6, ETS2, CEBPD, STAT1, MXD1, NFIL3, HDAC4, MMP9, CD163, FCGR1A, IFIT3, LY96, PLSCR1, and TNFSF10. In addition. curcumin target prediction yielded six hub genes, including STAT3, MMP9, PPARG, PTGS2, JAK2, and CCNA2. Potential pathways involved primarily included adipocytokine signaling pathway, AGE-RAGE signaling pathway in diabetic complications, AMPK signaling pathway, EGFR tyrosine kinase inhibitor resistance, IL-17 signaling pathway, lipid and atherosclerosis, stem cell pluripotency regulation signaling pathway, Th17 cell differentiation, and TNF signaling pathway. Among them, the most important target gene was MMP9. The matrix metalloproteinase family (MPPs) has the potential to disrupt the equilibrium between the synthesis and degradation of the extracellular matrix in the bronchial wall. This imbalance can lead to airway remodeling and hyperresponsiveness （He et al., 2019）. MMP-2, MMP-9, and MMP-12 are all members of this family. Among them, MMP-9 is the main degradation mediator of extracellular matrix components, including collagen, fibronectin, aggregated proteoglycan, and basement membranes. Overexpression of MMP-9 can cause increased degradation of the extracellular matrix in the wall of the fine bronchiole and can prompt inflammatory cytokines to disrupt the endothelial and epithelial structures, thereby participating in respiratory inflammatory responses and airway remodeling （Wang et al., 2022）. Curcumin attenuates the signs and symptoms of airway mucus hypersecretion by interfering with MMP-9 targets, reducing airway mucus secretion, and attenuating airway obstruction （Cai et al., 2017）. GRZELA K et al. （Grzela et al., 2016） found that the MMP-9 protein level can be an important biological marker for the early diagnosis of lung inflammation. According to Zhang et al. （Zhang et al., 2022）, the expression levels of caspase-3 and MMP-9 in the curcumin group were reduced relative to the model group, and the expression levels in the high-dose group were reduced relative to the medium- and low-dose groups. Their findings suggest that curcumin inhibits caspase-3 and MMP-9 expression and attenuates oxidative stress and inflammatory responses in rats. Moreover, MMP9 is significantly associated with ischemic stroke, and its impact on ischemic stroke is mediated primarily through the disturbance of the blood-brain barrier. The MMP9 level is significantly elevated in the early stage of ischemic stroke （Yang et al., 2007）. Zhong et al. found that elevated serum MMP9 levels in patients with ischemic stroke increased the risk of poor prognosis in these patients （Planas et al., 2001）. Moreover, MMP-9 gene-deficient mice have a stronger blood-brain barrier protection effect than normal mice （Chaturvedi et al., 2014）. According to clinical studies, MMP-9 expression was significantly upregulated in the infarct core and ischemic penumbra regions of patients with cerebral infarction. In the infarct core region, MMP-9 was expressed in infiltrating neutrophils and activated microglia, whereas it was primarily expressed in macrophages in the ischemic penumbra region, suggesting that MMP-9 is closely related to ischemic brain injury and secondary brain edema （Guo et al., 2019）. Therefore, curcumin inhibits the expression of MMP-9, thereby attenuating blood-brain barrier dysfunction and ischemic brain injury.

In summary, network pharmacology and GEO database mining showed that anti-inflammatory, anti-oxidative stress, and immunomodulation are the key pathways for curcumin, the active ingredient in TND, to intervene in COVID-19 combined with Stroke. Curcumin can be utilized in the clinical treatment of COVID-19 combined with stroke. In addition, the effective targets of curcumin were identified, which paved the way for further clinical trials. However, this research has also some limitations, including a small sample size and the absence of validation analysis. Consequently, further validation studies with large sample sizes are needed.

6 Data availability

The data used to support the results of this study are available from the first and corresponding authors.

7 Additional Points

COVID-19 and Stroke were analyzed in combination for the first time. CCNA2, JAK2, MMP9, PPARG, PTGS2, and STAT3 may be potential therapeutic targets for curcumin treatment of COVID-19 combined with stroke. Therapeutic pathways may be related to adipocytokine signaling pathway, AGE-RAGE signaling pathway in diabetic complications, AMPK signaling pathway, EGFR tyrosine kinase inhibitor resistance, IL-17 signaling pathway, lipid and atherosclerosis, stem cell pluripotency regulation signaling pathway, Th17 cell differentiation, and TNF signaling pathway.

8 Declaration of interest:

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

9 Author contributions

Li Yunze and Yao Yangjing: Conceptualization; Data curation; Formal analysis; Funding acquisition; Maorui, Wu Jiacheng, and Chen Weiwei: Investigation; Methodology; Li Jianjun, Wu Minghua: Project administration; Resources; Supervision; Validation; Liu Yan, Feng Qinghua, and Hu Manyan: Software; Visualization; Roles/Writing - original draft; Li Yunze, Yao Yangjing: Writing - review & editing.

10 Acknowledgments

The authors thank all patients who participated in this study for their cooperation.

11 Funding

This work was supported by Jiangsu Province Administration of Chinese Medicine （grant numbers ZT202102, MS2022016）; National Administration of Traditional Chinese Medicine: Evidence-Based Capacity Building Project （2019XZZX-NB007）; 333 High-Level Talents Training Project in Jiangsu （grant no. BRA 2016507）; Jiangsu Province Cadres Health Research Project （BJ21024）. Project of Jiangsu Province Hospital of Chinese medicine(Y19044; Y20025; Y2019CX10);

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Therapeutic effect and potential mechanism of curcumin, an active ingredient in Tongnao Decoction, on COVID-19 combined with stroke: a network pharmacology study and GEO database mining

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

1 Introduction

2 Materials and Methods

2.1 Data Acquisition and Processing

2.2 Differential Expression Analysis

2.3 Enrichment Analysis

2.4 Transcription Factor–Target Networks

2.5 Protein-Protein Interaction Networks

2.6 Drug Prediction of COVID-19/Stroke

2.7 Acquisition of curcumin-pharmacological target in COVID-19/Stroke

2.8 Molecular Docking

3. Result

3.1 Identification of COVID-19/Stroke–associated genes

3.2 Functions and Pathways Enrichment Analyses

Discussion

Conclusion

Declarations

References

Tables S1 and S2

Additional Declarations

Status:

Version 1