Therapeutic potential of Nelumbo nucifera Linn. in systemic lupus erythematosus: Network pharmacology and molecular modeling insights

Abstract

Background

Systemic lupus erythematosus is a chronic autoimmune inflammatory disease characterized by multiple symptoms. The phenolic acids and other flavonoids in Nelumbo nucifera have anti-oxidants, anti-inflammatory, and immunomodulatory activities that are essential for managing SLE through natural sources. This study employs network pharmacology to unveil the multi-target and multi-pathway mechanisms of Nelumbo nucifera as a complementary therapy. The findings are validated through molecular modeling, which includes molecular docking followed by a molecular dynamics study.

Methods

Active compounds and targets of SLE were obtained from IMPPAT, KNApAcKFamily and SwissTargetPrediction databases. SLE-related targets were retrieved from GeneCards and OMIM databases. A protein–protein interaction (PPI) network was built to screen out the core targets using Cytoscape software. ShinyGO was used for GO and KEGG pathway enrichment analyses. Interactions between potential targets and active compounds were assessed by molecular docking and molecular dynamics simulation study.

Results

In total, 12 active compounds and 1190 targets of N. nucifera’s were identified. A network analysis of the PPI network revealed 10 core targets. GO and KEGG pathway enrichment analyses indicated that the effects of N. nucifera are mediated mainly by AGE-RAGE and other associated signalling pathways. Molecular docking indicated favourable binding affinities, particularly leucocianidol exhibiting less than −4.5 kcal/mol for all 10 targets. Subsequent molecular dynamics simulations of the leucocianidol-ESR1 complex aimed to elucidate the optimal binding complex’s stability and flexibility.

Conclusions

Our study unveiled the potential therapeutic mechanism of N. nucifera in managing SLE. These findings provide insights for subsequent experimental validation and open up new avenues for further research in this field.

Keywords

systemic lupus erythematosus network pharmacology molecular docking AGE-RAGE signaling pathway

Introduction

Systemic lupus erythematosus (SLE) is a chronic autoimmune condition characterised by immunological abnormalities, notably the production of antinuclear antibodies along with several other phenotypes, from minor skin rashes to serious consequences such as hematologic abnormalities, renal impairment, and neuropsychiatric problems.^1,2 This condition has an overwhelming preference for females, with a ninefold higher frequency than males, highlighting the significant role of female hormones.³ Characterized by the potential for relapses, the onset of SLE is intricate and involves a multifaceted interplay of genetic predisposition, environmental factors, immunological responses, and hormonal influences, with a distinct inclination observed in women of childbearing age.^4–6 Globally, the estimated incidence of SLE is 5.14 per 100,000 person-years, resulting in an annual diagnosis of 0.40 million people. Among women, the rates are higher, with an incidence of 8.82 per 100,000 person-years and an annual diagnosis of 0.34 million people, while in men, the estimates are lower, with an incidence of 1.53 per 100,000 person-years and an annual diagnosis of 0.06 million people.⁷

Diagnosing SLE can be challenging due to its pattern of flare-ups and remissions compounded by the lack of a standardized treatment approach.⁸ The prevailing approach to SLE treatment encompasses a blend of glucocorticoids, immunosuppressive medications, and antimalarials, with hydroxychloroquine playing a pivotal role.⁹ Limited data suggests that hydroxychloroquine, a common antimalarial, may impact the cardiovascular disease risk in lupus patients.¹⁰ The side effects associated with glucocorticoids encompass a spectrum of issues affecting the musculoskeletal, endocrine, gastrointestinal, neuropsychiatric, cardiovascular, dermatologic, ocular, and immunologic systems. Prolonged use raises the risk of severe complications, particularly cardiovascular issues and new malignancies.¹¹ Although certain immunosuppressive medicines provide symptomatic relief, their narrow therapeutic range makes SLE patients more susceptible to opportunistic infections.^12–14

With the highlighted concerns on the safety of allopathic drugs among SLE patients, it is recommended that herbal medicines be considered owing to their potential use across efficacy in the management of SLE. Recent studies and reports reveal how certain herbal remedies with immunomodulatory, anti-inflammatory and anti-oxidant properties will be beneficial to people with this disorder.¹⁵ Nelumbo nucifera Linn. (N. nucifera), commonly known as the lotus plant or sacred lotus, stands as a renowned plant in India, harbouring a diverse array of beneficial compounds. Employed for centuries in traditional medicine, this is a aquatic plant, indigenous to tropical and subtropical regions of Asia, belongs to Nelumbonaceae family. Every part of the lotus plant, including the base, stem, leaf, flower and seed, holds not only edible properties but has widespread use in traditional medicine and Ayurveda.^16,17 Numerous bioactive compounds reported to be present in lotus extracts, such as phenolic acids, alkaloids, flavonoids, and steroids provide anti-oxidants, anti-inflammatory and immunomodulatory properties.^18,19

Advancements in computer-aided drug design have opened avenues for identifying plant-derived compounds that may enhance existing medicational therapy.²⁰ Network pharmacology is one such approach used in this study to determine the molecular pathways and potential proteins that can be targeted by N. nucifera in managing SLE. This approach combines integrated protein-protein network analysis with the identification of potential targets, key compounds, and relevant pathways. The aim is to unravel the crucial targets, potential compounds and pathways for managing systemic lupus erythematosus (SLE) using Nelumbo nucifera. Lastly, in order to validate the interactions, molecular modeling is done to establish the mode of action on selected disease targets and potential compounds of N. nucifera against SLE.

Materials and method

Extraction of active compounds in N. nucifera

The phytocompounds of the N. nucifera was extracted from IMPPAT (Indian Medicinal Plants, Phytochemistry and Therapeutics) (https://cb.imsc.res.in/imppat/) and KNApAcKFamily (https://www.knapsackfamily.com/). PubChem (https://pubchem.ncbi.nlm.nih.go/) was used to confirm compounds and download the canonical smiles.²¹

Screening of active compounds

The evaluation and screening of active compounds were based on ADMET criteria (absorption, distribution, metabolism, excretion, and toxicity). Initially, the extracted compounds were assessed for Oral Bioavailability using SwissADME (https://www.swissadme.ch/). Next, the compounds were evaluated for Drug-Likeness and Blood–Brain Barrier permeability using MOLSOFT (https://molsoft.com). Finally, toxicity prediction (hepatotoxicity, carcinogenicity, immunotoxicity, mutagenicity and cytotoxicity) was conducted using ProTox II (https://tox-new.charite.de/). The target prediction for all screened compounds was performed using Swiss Target Prediction (https://www.swisstargetprediction.ch/).²²

Disease gene identification

The genes related to SLE was extracted from the GeneCard human database (https://www.genecards.org/) and OMIM (Online Mendelian Inheritance in Man) (https://www.omim.org/). The duplicate genes were then filtered and the data was used for further processing.²³

PPI network analysis

The common target was obtained through Venn Diagram (Bioinformatics and Evolutionary Genomics) (https://bioinformatics.psb.ugent.be/). The obtained common targets were uploaded in String (https://string-db.org/) and Protein-protein interactions (PPI) were created. The PPI network obtained from String was then visualized under Cytoscape 3.8.2 (https://www.cytoscape.org/).²⁴ and the network parameters were calculated using Cytoscape’s network analyzer. The top potential targets were acquired from the Cytoscape plug-in, Cytohubba.

Construction of the compound-target-disease (C-T-D) network

Cytoscape version 3.8.2 was employed for constructing the C-T-D network model. This involved the integration of active compounds of N. nucifera, common targets, and disease to elucidate the intricate relationships among these elements. The regulatory network was meticulously established, leveraging the degree value for internal ranking to ensure a comprehensive and unbiased representation.

GO and KEGG enrichment analysis

To acquire more information about gene ontology and discovery of the biological processes (BP), cellular components (CC), and molecular functions (MF) ShinyGO 0.77 (https://bioinformatics.sdstate.edu/go/) was used, that were closely associated to the targeted genes. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway (https://www.genome.jp/kegg/pathway.html) was used to download the specific pathway.²⁵

Molecular docking

The crystal structures of all 10 targets were obtained from the Protein Data Bank. We selected protein with highest resolution available, for each protein’s crystal structure. To prepare the structures of the proteins we used Schrodingers Maestro Molecular modeling suit (2021-4) and its protein preparation module. The various 3D structures and energy minimization of the identified compounds were obtained using the LigPrep module from Maestro modeling package. For all docking simulations and calculations, we utilized XP Glide. We visualized the interactions, between ligands and targets including hydrogen bonds, ion pair interactions, hydrophobic interactions and binding modes of the identified compounds using Maestro interface.²⁶

Molecular dynamics simulation

Molecular dynamics (MD) simulation is important for understanding the characteristics of the protein-ligand interaction in the aqueous medium of living systems. It was performed after gaining information on the highest affinity docked conformation for the protein. The software used for the MD simulation was Desmond software for the time period of 50 nanoseconds.²⁷ Maestro’s Protein Preparation Wizard was used for the preliminary processing of the protein-ligand complex, which includes optimization, minimization and filling of any residual gap as needed. The System Builder tool is used to construct the system. The TIP3P (Intermolecular Interaction Potential 3 Points Transferable) as a solvent model was used in MD simulation, with an orthorhombic box set to 300K in temperature. 1 atm in pressure and the OPLS_2005 force field. Counter ions were added, and the system was neutralised using 0.15 M NaCl to preserve the physiological conditions. Before running the simulation, the models were equilibrated, and the trajectories were captured for further examination and analysis at intervals of 100 picoseconds. Root mean square deviation (RMSD) and root mean square fluctuations (RMSF) analysis was done to check the stability dynamics of the protein-ligand forming complex throughout the simulation run.

Results

Compound extraction and screening of Nelumbo nucifera

Total of 297 compounds were extracted from IMPPAT and KNApSAcK Family by using a keyword “Nelumbo nucifera” and 169 compounds were verified among them from PubChem for further processing. For further screening filters like OB, DL, BBB, Lipinski and toxicity were applied and 12 compounds were obtained shown in Table 1. Then, the screened compounds were sent to Swiss target prediction that gave 1190 targets.

Table 1.

ADMET profiling of 12 compounds N. nucifera using Swiss ADME, Protox-II and Molsoft publicly available databases.

Compounds	PubChem ID	DL (≥0.18)	OBS (≥0.55)	Lipinski rule^#	BBB (0-6)	Hepatotoxicity	Carcinogenicity	Immunotoxicity	Mutagenicity	Cytotoxicity
Kaempferol	5280863	0.5	0.55	pass	2.78	inactive	inactive	Inactive	inactive	inactive
Quercetin	5280343	0.52	0.55	pass	2.55	inactive	inactive	Inactive	inactive	inactive
Luteolin	5280445	0.29	0.55	pass	4.25	inactive	inactive	Inactive	inactive	inactive
Leucocianidol	440833	0.53	0.55	pass	2.51	inactive	inactive	Inactive	inactive	inactive
Maslinic acid	73659	0.55	0.56	pass	2.97	inactive	inactive	Inactive	inactive	inactive
Eburicol	9803310	0.32	0.55	pass	3.82	inactive	inactive	Inactive	inactive	inactive
Lotusine A	101229399	0.57	0.55	pass	3.13	inactive	inactive	Inactive	inactive	inactive
Ascorbic acid	54670067	0.74	0.56	pass	1.93	inactive	inactive	Inactive	inactive	inactive
beta-ionone	638014	0.33	0.55	pass	4.41	inactive	inactive	Inactive	inactive	inactive
Isorhamnetin	5281654	0.39	0.55	pass	2.66	inactive	inactive	Inactive	inactive	inactive
Armepavine	442169	1.29	0.55	pass	5.3	inactive	inactive	Inactive	inactive	inactive
Leucocyanidin	71629	0.53	0.55	pass	2.51	inactive	inactive	Inactive	inactive	inactive

Acquisition of SLE related target genes

Using the words “Systemic Lupus Erythematosus” on the GeneCards and OMIM database obtained targets were 5902 and 355, respectively. After removing the duplicates, the total target genes identified was 5930.

PPI network analysis

1190 targets of 12 compounds of N. nucifera and 5930 of disease genes were introduced to Bioinformatics & Evolutionary Genomics online tool for obtaining the common targets between the two shown in Table S1. The above step resulted in identifying total 234 targets as depicted in Figure 1(a). The common targets were then initiated in STRING database that generated 234 nodes, 3087 edges, average node degree 26.4, average local clustering coefficient 0.502 and PPI enrichment p-value <1.0e-16 as shown in Figure 1(b). The STRING file was then transferred to Cytoscape, using the Cytohubba plugin top 10 potential targets were identified including ESR1 (Estrogen Receptor-1), AKT1 (AKT Serine/Threonine Kinase 1), STAT3 (signal transducer and activator of transcription 3), SRC (proto-oncogene tyrosine-protein kinase), EGFR (epidermal growth factor receptor), BCL2 (B-cell lymphoma-2), HIF1A (Hypoxia-inducible factor-1α), JUN (Jun Proto-oncogene), PTGS2 (prostaglandin-endoperoxide synthetase (2) and PPARG (Peroxisome Proliferator Activated Receptor Gamma) as depicted in Figure 1(c). Cytohubba plugin shows AKT1 has the highest score value of 126 and PPARG has the lowest score value of 88, and the ranking of all 10 top targets was identified according to the degree method depicted in Table S2.

Compound-target-disease (C-T-D) network

The C-T-D network representing the therapeutic effects of N. nucifera on SLE is depicted in Figure 2, illustrating the intricate relationship involving a total of 248 nodes. These nodes comprise 12 compound nodes, 234 target nodes, one N. nucifera node, and one SLE node, interconnected by 808 edges.

Within the active compound section, the degrees of luteolin, beta-ionone, kaempferol, leucocyanidin, leucocianidol, armepavine, isorhamnetin, eburicol, maslinic acid, lotusine a, ascorbic acid and quercetin were found to be 54, 54, 54, 52, 52, 52, 52, 52, 51, 47, 29 and 26, respectively shown in Table S3. This indicates that luteolin, beta-ionone, kaempferol, leucocyanidin, leucocianidol, armepavine, isorhamnetin, eburicol might play a pivotal role as the most significant active compounds in the management of SLE.

Pathway analysis by gene ontology (GO) and KEGG enrichment analysis

ShinyGO v.0.741 was used for the enrichment analysis of 234 common targets that were introduced for GO and KEGG analysis. The following three categories were considered: Biological processes (BP), Cellular components (CC) and Molecular functions (MF). Among the top 20 constituents of bar graph enrichment of BP, CC and MF. The top five biological processes include cellular response to organonitrogen compound, cellular response to nitrogen compound, cellular response to organic cyclic compound, response to organonitrogen compound and cellular response to oxygen-containing compound as shown in Figure 3(a). The top five cellular components involve caveola, integral component of presynaptic membrane, plasma membrane raft, integral component of synaptic membrane and intrinsic component of synaptic membrane as shown in Figure 3(b). The top five molecular function are G protein-coupled amine receptor activity, nuclear receptor activity, ligand-activated transcription factor activity, neurotransmitter receptor activity and protein tyrosine kinase activity as shown in Figure 3(c). With the same settings, KEGG enrichment analysis has also been evaluated and the top five pathways, including the AGE-RAGE signalling pathway in diabetics complications, EGFR tyrosine kinase inhibitor resistance, endocrine resistance, PD-L1 expression and PD-1 checkpoint pathway in cancer and HIF-1 signalling pathway as shown in Figure 3(d).

Molecular docking result

We used Schrödinger Maestro software to visualize the complexes that formed between compounds and proteins after conducting docking experiments. Each compound was selected based on its binding energy score, for the specific target protein as depicted in Table S4. By examining the resulting binding, we were able to understand how these compounds interacted with protein pockets. The top three docking result were found to be leucocianidol binding to ESR1, SRC and PPARG respectively with binding affinity of −11.153 kcal/mol, −9.618 kcal/mol and −9.51 kcal/mol respectively as depicted in Figure 5, in which leucocianidol with ESR1 shown best binding among the top three, with hydroxyl groups formed hydrogen bond interactions with important amino acid residues like GLU 353, ARG 394 and HIS 524. Interestingly, these hydrogen bonds were quite close with a distance of 3.0 Å or less, significantly shorter than the typical 3.5 Å length for hydrogen bonds. This close interaction played a role in stabilizing the molecule structure. In summary compounds such as leucocianidol, quercetin, kaempferol, luteolin, leucocyanidin and isorhamnetin showed better affinity towards their respective target proteins as can be clearly seen in the heat map as depicted in Figure 4. Established robust connections with the proteins indicated their potential regulatory effects on most identified targets, including ESR1, SRC, PPARG, PTSG2, JUN, AKT1, HIF1A and EGFR. Most of these compounds show binding energy below −4.5 kcal/mol, which signifies a strong bond between the compound and core proteins.

Molecular dynamics simulation result analysis

RMSD is calculated as the most often applied quantitative metric for comparing the similarity of two overlaid atomic positions. Here, RMSD were employed for the stability analysis of the ESR1-leucocianidol complex throughout the simulation of 50 ns, which is about 2.1 Å as shown in Figure 6(a). The root mean square fluctuation (RMSF) plot depicts the fluctuations at the residual level in different secondary structures as the alpha helices and beta strands are more rigid; therefore, no such fluctuations can be seen at these parts as compared to the unstructured or other parts of the protein. The fluctuation ranges between 1.5 Å to 3.4 Å, indicating more flexibility in the loop region, which permits the protein ESR1 to obtain an accurate secondary structure to hold the ligand, mentioned in Figure 6(b).

MD simulation also gives an idea for the protein-ligand contacts, including H-bonds, hydrophobic, ionic and water bridges. The LEU387 amino acid residue of leucocianidol interacted with ESR1 via H-bonding with the H₂O molecule, and 37% interacting persistence was seen. MET421, HIS524, GLY521 and GLU353 formed direct H-bond interaction with OH-group for 79%, 98%, 55% and 100% interacting persistence, respectively, shown in Figure 6(c) with the normalized value of approximately 0.8, 1.0, 0.6 and 2.0, respectively. Furthermore, GLU353 showed multiple interactions with the ligand, as presented in the dark orange colour as mentioned in Figure 6(d). Thus, the study of molecular dynamics shows the important interactions for the stability of the protein-ligand complex.

Discussion

Systemic Lupus Erythematosus (SLE) is a condition characterized by a wide range of clinical symptoms that affect multiple organs.²⁸ Managing SLE using medicine poses challenges due to its various side effects such as weakened immune system, limited effectiveness and complicated treatment plans. The medication may not fully relieve symptoms and prolonged usage can result in consequences.²⁹ Furthermore, SLE flare ups and the financial burden further contribute to the intricacy of managing this condition. Consequently, there is an increasing interest in treatments that combines both integrative therapy and conventional treatment approach. Several herbal plants have demonstrated promising results in aiding individuals with systemic lupus erythematosus. Herbal medications in SLE offers complementary relief, potentially easing symptoms like inflammation and stress.³⁰ They provide a holistic approach, focusing on immune modulation and antioxidants activity. One such plant is lotus, scientific name Nelumbo nucifera, having incredible health benefits and ability to manage various disorders and are believed to be linked to the existence of compounds, such, as polyphenols, flavonoids, phenolic acids, alkaloids, terpenoids, steroids, fatty acids and glycosides.^31,19

In the current investigation, we synthesized data from previously published research and openly accessible databases to identify the interactions between active compounds found in N. nucifera and their potential protein targets associated with SLE, as well as explored various signaling pathways and networks in which the potential targets of N. nucifera are involved. Additionally, by conducting docking studies, we gained insights into how specifically therapeutic compounds interact with targets associated with SLE. This comprehensive approach enhances our understanding of the basis clinical presentations and underlying molecular mechanisms of SLE. It also holds promise for developing personalized and alternative treatment strategies that open up avenues for further research.

We initially found 12 potential compounds, from N. nucifera using the IMPPAT and KNApAcKFamily database thereafter applying the ADMET criteria using various publicly available databases. The majority of compounds classified under group polyphenol have been previously demonstrated to exhibit a protective effect against SLE.³² When we analyzed these 12 compounds using the SwissTargetPrediction server, we discovered that they have the potential to interact with a total of 1190 targets. This extensive profiling of targets indicates that these compounds may have various effects on biological pathways and systems. Further, we used a Venn diagram to find common targets by looking at the overlap (Figure 1(a)) between 12 compounds’ unique targets and the 5930 genes associated with the disease. This overlap showed that there are 234 common targets that give valuable insight into the potential of these compounds as PPI network found to be highly significant p-value (<1.0e 16). Using Cytoscape, we further visualized this network that gave the top 10 potential targets using cytohubba. 10 pivotal genes exhibited high betweenness centrality and degree values within the protein-protein interaction network. Previous literature has already established the involvement of some of these pivotal genes in both the pathophysiology and treatment of autoimmune disorders. PTGS2, commonly referred to as cyclooxygenase 2 (COX-2), serves as a pivotal enzyme in the synthesis of prostaglandin D2 (PGD2), playing a crucial role in inflammation, contributes to the progression of lupus disease through basophil accumulation in lymphoid organs.³³ Elevated activity of AKT kinases has been observed in B cells derived from SLE patients.³⁴ Genetic polymorphisms in the ESR1 gene can potentially influence the clinical manifestations observed in individuals with systemic lupus erythematosus.³⁵ EGFR, with inherent tyrosine kinase activity, is known to promote cell division and proliferation and induces increased ROS production and endoplasmic reticulum stress, which is crucial in the pathogenesis of lupus nephritis.³⁶ In Foxp3Cre × Stat3fl/fl mice, reduced CCR6 expression on Tregs hampers renal infiltration, highlighting Stat3-induced Treg17 cells as novel anti-inflammatory mediators.³⁷ PPARG agonism was found to have a protective effect by improving vascular and metabolic dysfunction in SLE patients.³⁸ Overexpression of HIF-1α in lymphocytes and associated with enhanced Th17 cell activation thereby increase in inflammatory cytokines production.³⁹

In an in-vitro study, (S)-armepavine from N. nucifera was shown to alleviate disease progression in MRL/MpJ-lpr/lpr mice by inhibiting T cell function and the formation of anti-dsDNA autoantibodies.⁴⁰ The cumulative evidence strongly indicates that N. nucifera has the potential to combat SLE by influencing the regulation of key genes. Through the C-T-D network, it was determined that luteolin, beta-ionone, kaempferol, leucocyanidin, leucocianidol, armepavine, isorhamnetin, eburicol were the most important potential compounds of N. nucifera. These evidences deepen our understanding of the pathophysiology and the distinct roles these genes and compounds play in SLE.

Following the construction of the C-T-D network, we conducted Gene Ontology (GO) and KEGG pathway analyses, which revealed 232 pathways associated with the common targets uploaded. Notably, the AGE-RAGE signalling pathway in diabetic complications encompasses key pathways that are associated with other popular signalling pathways such as PI3K, MAPK, calcium signalling, and the JAK-STAT pathway.^41–43 A study reported that the PI3K/Akt pathway activates mTOR and contributes to various autoimmune disorders, with increased regulation in murine lupus nephritis.⁴⁴ These pathways have been previously reported to be associated with SLE in various studies and scientific literature. Understanding their involvement in SLE may provide insights into the disease’s pathogenesis and potential therapeutic targets.

In order to enhance the validation of our network pharmacology study, we executed molecular docking analyses using the Schrödinger Maestro software, enabling the visualization of potent interactions between ligands and proteins. Specifically, leucocianidol exhibited noteworthy binding affinity with all 10 potential targets, demonstrating a binding energy below −4.5 kcal/mol. Compounds, primarily belonging to polyphenol groups such as luteolin, beta-ionone, kaempferol, leucocyanidin, leucocianidol, armepavine, isorhamnetin, and eburicol, displayed favorable binding affinities for a range of targets, including ESR1, SRC, PPARG, PTSG2, JUN, AKT1, HIF1A, and EGFR. These outcomes not only validate the results obtained from our network pharmacology screening but also provide preliminary confirmation of the reliability of the employed network pharmacology methods.

To further scrutinize the stability and adaptability of the ESR1 and leucocianidol complex, we conducted comprehensive molecular dynamics simulations. Examination of the root mean square deviation (RMSD) plots exhibited consistent and stable patterns over the entire 50ns simulation run, as depicted. Additionally, analysis of the root mean square fluctuation (RMSF) graphs highlighted notable flexibility in ESR1, enabling it to conform to the ligand within its binding pocket and form enduring hydrogen bonds with multiple residues throughout the simulation period. In summary, these in-depth molecular dynamics simulations furnish compelling evidence supporting the stability and dependable behaviour of the ESR1 and leucocianidol complex, imparting valuable insights into the intricate dynamics of protein-ligand interactions.

Conclusion

In conclusion, the integration of network pharmacology and molecular modeling in this study sheds light on the molecular and pharmacological mechanisms underlying N. nucifera ‘s efficacy against SLE. Key players such as ESR1, SRC, PPARG, PTSG2, JUN, AKT1, HIF1A and EGFR along with AGE-RAGE signalling pathway in diabetic complications are implicated in N. nucifera ‘s mechanism. Additionally, leucocianidol, luteolin, beta-ionone, kaempferol, leucocyanidin, armepavine, isorhamnetin, eburicol emerge as potential key compounds. Our study’s results indicate that N. nucifera can be employed as a multi-targeted therapy with the potential to modulate multiple pathways and promote disease improvement by interacting with key biomarkers. Our study demonstrates the multi-target and multi-pathway mechanisms of N. nucifera in combating SLE, serving as a foundation for further research to explore N. nucifera ‘s potential towards the management of SLE.

Supplemental Material

Supplemental Material - Therapeutic potential of Nelumbo nucifera Linn. In systemic lupus erythematosus: Network pharmacology and molecular modeling insights

Supplemental Material for Therapeutic potential of Nelumbo nucifera Linn. In systemic lupus erythematosus: Network pharmacology and molecular modeling insights by Sugandha Jaiswal, Satish Kumar, Biswatrish Sarkar and Rakesh Kumar Sinha in Lupus

Footnotes

Author contributions

B. Sarkar and R. K Sinha shaped the study’s conception and design, while S. Jaiswal and S. Kumar executed the experiments. S. Jaiswal conducted data acquisition, analysis, and interpretation. S. Kumar authored the primary manuscript, created figures, and performed statistical analysis. B. Sarkar and R. K Sinha undertook manuscript revisions. All authors have reviewed and endorsed the final manuscript.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Sugandha Jaiswal

Satish Kumar

Biswatrish Sarkar

Rakesh Kumar Sinha

Supplemental Material

Supplemental material for this article is available online.

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