Abstract

Background

   Sepsis is a critical condition characterized by an atypical immune
   response to infection, resulting in systemic inflammatory response
   syndrome. Honeysuckle, a traditional Chinese medicinal plant with
   anti-inflammatory, anti-tumor, and antioxidant characteristics, shows
   promise in mitigating sepsis-related inflammatory responses. The
   precise mechanism by which honeysuckle confers protection against
   sepsis remains uncertain. This research utilized network pharmacology
   to explore the mechanisms through which honeysuckle mitigates sepsis.

Methods

   Bioactive compounds of honeysuckle were sourced from the Traditional
   Chinese Medicine System Pharmacology database, and their corresponding
   targets were identified. Sepsis-associated genes were gathered from the
   GeneCards and Online Mendelian Inheritance in Man databases. The
   regulatory network depicting “active ingredients-target” relationships
   was constructed utilizing Cytoscape 3.9.1 software. Target genes
   affected by honeysuckle in sepsis were analyzed utilizing the String
   database to create a protein-protein interaction network. Kyoto
   Encyclopedia of Genes and Genomes and Gene Ontology analyses were
   performed through the Database for Annotation, Visualization, and
   Integrated Discovery platform, aiming to identify pathways associated
   with these pivotal targets. Subsequently, molecular docking confirmed
   the binding capacity of these bioactive ingredients to pivotal targets.
   Subsequent in vitro experiments were conducted for additional
   validation. Network pharmacology analysis employed R software for
   extensive data processing. A Student’s T-test compared the mean values
   of the two groups in the in vitro experimental data. Statistical
   analysis for multiple group comparisons was carried out utilizing
   one-way ANOVA, with Tukey’s post hoc test applied for further analysis.

Results

   The screening process identified 23 active ingredients in honeysuckle,
   which are linked to 291 targets. Molecular docking demonstrated strong
   binding affinities of kaempferol, luteolin, and quercetin to the target
   proteins TP53, TLR4, and MYD88. In vitro experiments demonstrated that
   honeysuckle and its active components mitigate sepsis by inhibiting the
   TLR4/MYD88 signaling pathways, thereby reducing proinflammatory
   cytokine production.

Conclusion

   These findings indicate that honeysuckle may be an effective treatment
   for sepsis by modulating key proteins such as TP53, TLR4, and MYD88.

   Keywords: network pharmacology, sepsis, molecular docking, traditional
   Chinese medicine, inflammatory response syndrome

Introduction

   Sepsis, a critical systemic inflammatory reaction to microbial
   invasion, is notably triggered by lipopolysaccharide (LPS).[32]^1 It is
   a systemic inflammatory response syndrome that may progress to septic
   shock, marked by significant mortality, morbidity, and treatment
   expenses.[33]^2 When the infection triggers widespread systemic
   symptoms like fever, increased heart rate, or elevated respiratory
   rate, it is identified as sepsis. If the systemic response escalates
   and leads to organ dysfunction remote from the infection site, the
   condition is classified as severe sepsis. Septic shock, on the other
   hand, is characterized by severe organ dysfunction accompanied by
   persistent hypotension or hypoperfusion that does not improve with
   initial fluid resuscitation.[34]^3^,[35]^4 Sepsis is a prevalent,
   life-threatening condition in clinical practice and a leading cause of
   death in ICU patients. Annually, sepsis impacts approximately 31.5
   million individuals worldwide, resulting in 5.3 million deaths.[36]^5
   In China, sepsis patients have a mortality rate of 35.5%, with severe
   cases experiencing rates over 50%.[37]^6 The management of sepsis
   primarily involves fluid resuscitation, antibiotics administration,
   anticoagulation therapy, glucocorticoid usage, and renal replacement
   therapy;[38]^3^,[39]^7–9 however, recurrent infections and multi-drug
   resistance pose significant challenges in clinical treatment.[40]^10–12
   Recently, the utilization of Chinese herbal medicine in the management
   of sepsis has progressively emerged as a pivotal and efficacious
   therapeutic approach, significantly reducing sepsis-related mortality
   rates.[41]^13–15 Sepsis falls within the domain of heat disorders in
   traditional Chinese medicine, where the Inner Canon advocates for
   “treating heat with coldness”, thus predominantly emphasizing the
   selection of drugs that possess heat-clearing and detoxifying
   properties.[42]^16^,[43]^17

   Honeysuckle (Lonicera japonica Thunb)., a widely recognized traditional
   Chinese medicinal herb, has a rich history of therapeutic use. The
   first recorded mention appears in the Miscellaneous Records of Famous
   Physicians (Ming Yi Bie Lu). Ethnobotanically, honeysuckle is valued
   for its ability to clear heat, detoxify, expel wind, and eliminate
   dampness.[44]^18^,[45]^19 According to the Chinese Pharmacopoeia (2020
   edition), honeysuckle is described as sweet in taste and cooling
   properties, primarily acting on the stomach and lung
   meridians.[46]^20^,[47]^21 Its clinical applications include the
   treatment of febrile diseases, sore throats, dysentery, heat toxicity,
   and wind-heat colds. Morphologically, honeysuckle is a perennial,
   twining vine with opposite leaves and fragrant, tubular flowers that
   transition from white to yellow as they age. It is distributed across
   East Asia, including China, Japan, and Korea, thriving in temperate and
   subtropical climates. Recent pharmacological research indicates that
   honeysuckle exhibits anti-inflammatory, antiviral, antioxidative,
   antibacterial, and hepatoprotective properties.[48]^22–24 Recent
   research indicates that honeysuckle improves the prognosis and
   complications of sepsis by exerting anti-inflammatory, improving
   endothelial function, improving renal injury, improving myocardial
   injury, and regulating gastrointestinal function.[49]^25–27 However,
   the key components and specific mechanisms of honeysuckle in sepsis
   therapy are still unclear.

   Sepsis pathogenesis involves complex genetic and molecular changes,
   making it challenging for single-target therapies to achieve optimal
   outcomes.[50]^28–30 Network pharmacology is extensively applied in
   traditional Chinese medicine research to clarify drug molecular
   mechanisms and aid in predicting individual drugs or
   compounds.[51]^31^,[52]^32 It can examine the overarching relationship
   between drugs and diseases. This study employs molecular docking and
   network pharmacology to identify the bioactive ingredients, relevant
   targets, and signaling pathways of honeysuckle in sepsis therapy,
   aiming to elucidate its mechanism of action and offer insights for
   clinical application and research.

Methods

Identification of Bioactive Compounds in Honeysuckle

   The active ingredients of honeysuckle were screened through the use of
   the Traditional Chinese Medicine System Pharmacology (TCMSP) database
   and analysis platform ([53]https://old.tcmsp-e.com/tcmsp.php). The
   TCMSP platform is an integrated database designed to analyze the
   pharmacokinetic properties of compounds in Chinese herbal medicine. It
   facilitates the identification of bioactive components and their
   potential targets through criteria such as drug-likeness (DL) and oral
   bioavailability (OB). To identify the active ingredients, the term
   “honeysuckle” was searched within the TCMSP database. OB and DL were
   chosen as evaluation indicators due to their roles in the human body’s
   metabolism, distribution, absorption, and excretion processes. OB
   denotes the proportion of a drug’s effective concentration that reaches
   systemic circulation following oral administration. A higher OB
   indicates that the compound can be efficiently absorbed through the
   gastrointestinal tract and retain significant concentrations even after
   first-pass metabolism in the liver. Compounds with OB ≥ 30% generally
   ensure sufficient biological efficacy without requiring high doses,
   which is crucial for reducing toxicity and improving patient
   compliance. If OB is insufficient, alternative administration methods
   like injection or structural modifications might be required, leading
   to increased development costs and time. This assessment is grounded in
   the chemical properties of approved medications to determine if a
   compound adheres to druggability criteria, such as Lipinski’s Rule of
   Five and Veber’s Rule. Statistical analyses suggest that compounds with
   a DL value ≥ 0.18 are more structurally and chemically similar to known
   drugs. Conversely, compounds with a DL below 0.18 may fall outside the
   chemical space of drugs and require extensive optimization to meet drug
   development requirements. Selecting compounds with DL ≥ 0.18 enhances
   the accuracy of druggability predictions, avoiding excessive resource
   allocation to potential failures during early screening. Compounds with
   OB ≥ 30% and DL ≥ 0.18 are likely to possess favorable ADME properties
   and align with the chemical space criteria for drug development.

Screening Targets of Honeysuckle

   The TCMSP was utilized to predict the targets of honeysuckle, which
   were then uploaded to the UniProt database for search, selecting Homo
   sapiens as the target species. The 2D structures of these active
   components saved in SDF format were obtained from a PubChem search for
   honeysuckle. The PharmMapper platform was employed to predict
   honeysuckle-related targets through reverse pharmacophore matching,
   which was also uploaded to the UniProt database for screening against
   human targets. Finally, the BATMAN-TCM database was used to predict
   high-relevance targets of honeysuckle by uploading its PubChem ID. The
   integration of these three methods facilitated the elimination of
   redundant, non-human, and unqualified targets, thereby identifying
   honeysuckle-related targets.

Identification of Sepsis-Related Targets

   The databases GeneCards ([54]https://www.genecards.org) and OMIM
   ([55]https://www.omim.org) were searched using the keyword “Sepsis” to
   find target genes related to sepsis. Then, Excel software was used to
   summarize and analyze the relevant target genes corresponding to the
   disease, and duplicate genes were deleted to build the target database
   of sepsis. To determine the crucial intersection between honeysuckle
   and sepsis, the Venn website
   ([56]http://bioinformatics.psb.ugent.be/webtools/Venn/) was employed.

Construction of Active Ingredient-Target Network

   To identify common targets between honeysuckle and sepsis, we employed
   the online Venn diagram tool for visualization. The study identified
   common targets between sepsis and honeysuckle’s active ingredient by
   intersecting their respective targets and visualizing the overlap using
   Venn diagrams. These overlapping genes indicate potential targets of
   honeysuckle in the therapy of sepsis. Additionally, we predicted the
   target genes related to active components and sepsis and standardized
   their nomenclature into gene names. To build a network diagram of
   active components and targets, sepsis-related targets were brought into
   Cytoscape 3.9.1.

Construction of the Protein-Protein Interaction (PPI) Network

   To establish the PPI network, we first identified the intersection of
   targets related to honeysuckle and sepsis. These targets were submitted
   to the STRING database (version 12.0), with the species restricted to
   “Homo sapiens” to ensure the results are specific to human biology. The
   STRING database calculates interactions based on experimental data,
   curated databases, and computational predictions, assigning confidence
   scores to indicate interaction reliability. To improve data
   reliability, we employed “Homo sapiens” PPI data with a minimum
   confidence score of 0.7, prioritizing the largest connected component
   in the PPI network. The network created by STRING was brought into
   Cytoscape 3.9.1 for additional analysis. Using the CytoHubba plugin in
   Cytoscape, we applied topological algorithms (Betweenness, Degree, and
   Closeness) to analyze the network’s structure and identify key targets.
   These algorithms evaluate node significance in a network by: 1) Degree,
   which counts a node’s direct connections to indicate centrality; 2)
   Betweenness, which gauges a node’s presence on shortest paths between
   others to reflect its connectivity role; and 3) Closeness, which
   measures a node’s communication efficiency with all other nodes. The
   value of these parameters indicates the importance of a node among all
   targets, with higher values indicating greater significance. Targets
   surpassing average parameters were chosen, and the CytoHubba plugin was
   employed to determine the leading 10 nodes according to their ranking,
   as previously outlined. This systematic approach ensures the
   identification of significant targets that are most likely to influence
   the pathophysiology of sepsis and the therapeutic effects of
   honeysuckle.

Functional Enrichment Analysis

   The intersection of honeysuckle- and sepsis-related key targets were
   input into the DAVID database ([57]http://david.ncifcrf.gov/), with the
   parameters set to human (Homo sapiens). An analysis was conducted on
   key targets to determine their enrichment in biological processes from
   Gene Ontology (GO) and pathways from KEGG. The GO biological enrichment
   analysis included molecular function (MF), cellular component (CC), and
   biological process (BP). Genes that had a P-value under 0.05 were
   identified, with the leading 10 from GO analysis and the leading 20
   from KEGG pathway analysis chosen for more detailed exploration.

Molecular Docking of Active Ingredients with Primary Targets

   A 3D structure of the active ingredient was created from its 2D
   structure, which was sourced from PubChem, utilizing Chem3D software
   version 14.0. The target protein’s crystal structure was retrieved from
   the PDB database ([58]https://www.rcsb.org/) and processed in PyMOL
   software (version 2.5) to delete water molecules. Subsequently,
   AutoDock Tools software was utilized to process the target protein by
   adding hydrogens, charges, etc. The 2D structure of honeysuckle’s
   active compounds was imported into ChemBio3D software and saved in mol2
   format to obtain its 3D structure. Both the target protein and original
   ligand were set in pdbqt format recognized by AutoDock Tools software
   with the original ligand’s position serving as an active pocket on the
   target protein. Molecular docking analysis between honeysuckle’s active
   ingredients and the target proteins was conducted to evaluate their
   affinity. The binding energy serves as a crucial indicator for
   assessing effective binding between ligand and receptor molecules;
   lower values indicate better binding performance.

Cell Culture and Reagents

   The key active compounds of honeysuckle (Kaempferol, Quercetin, and
   Luteolin) were investigated in this research. Kaempferol (CAS No.
   520–18-3), Luteolin (CAS No. 491–70-3), and Quercetin (CAS No.117–39-5)
   were acquired from Shanghai Haoyuan Medchemexpress Co., Ltd, China.
   Sigma-Aldrich in St. Louis, MO, USA, supplied LPS (L2880). The RAW264.7
   murine macrophages were sourced from the Stem Cell Bank of the Chinese
   Academy of Sciences in Shanghai, China, and obtained from ATCC. The
   culture medium for the cells was DMEM with 100 μg/mL streptomycin, 2 mM
   L-glutamine, 50 U/mL penicillin, and 10% FBS (GIBCO), and incubated at
   37°C with 5% CO2. The EZ-PCR Mycoplasma Test Kit (Beijing Novo
   Biotechnology Co., LTD) confirmed the absence of mycoplasma
   contamination. Cells were pretreated with PBS or a 160 μM solution of
   kaempferol, luteolin, and quercetin for 2 hours, then incubated with
   100 ng/mL LPS for 6 hours. Cells and supernatants were subsequently
   collected for enzyme-linked immunosorbent assay (ELISA) and
   quantitative real-time PCR (qRT-PCR).

ELISA

   An ELISA Kit from ExCell Bio in Shanghai, China, was used to measure
   IL-1β and IL-6 concentrations in the supernatant, following the
   instructions provided by the manufacturer.

qRT-PCR

   The isolation of total RNA from RAW264.7 cells was carried out using
   TRIzol reagent (Solarbio, China) following the manufacturer’s
   instructions. RNA purity and concentration were assessed using
   ultraviolet spectrophotometry. Following extraction, the RNA was
   reverse-transcribed to generate complementary DNA. An RT-qPCR detection
   system and SYBR Premix Ex Taq II were employed for RT-qPCR assays.
   Normalization of mRNA expression levels was performed with β-actin as
   the internal control, and the 2−^ΔΔCt method was used to determine
   relative expression levels. [59]Table 1 presents the RT-qPCR primer
   sequences for IL-6, IL-1β, TP53, TLR4, and MYD88.

Table 1.

   qRT-PCR Primer Sequences
    Gene              Forward                         Reverse
   TP53    5′-CCAGTGGTAATCTACTGGGACG-3′   5′-CTGACGCACACCTATTGCAAGC-3′
   TLR4    5′-GACTCTGATCCCAGCCATGG-3′     5′-AGGAACCACCTCCACGCA-3′
   MYD88   5′-ATGTCTGCGGGAGACCCCCGCGTG-3′ 5′-TCAGGGCAGGGACAAAGCCTTGG-3′
   IL-6    5′-ATGAACTCCTTCTCCACAAGCGC-3′  5′-GAAGAGCCCTCAGGCTGGACTG-3′
   IL-1β   5′-ATGGCAGAAGTACCTAAGCTCGC-3′  5′-ACACAAATTGCATGGTGAAGTCAGTT-3′
   β-actin 5′-CATGGATGATGATATCGCCGCG-3′   5-CCTAAGGTGTGCACTTTTATTCAAC-3′
   [60]Open in a new tab

MTT Assay

   MTT (CAS 298–93-1) from Sigma-Aldrich was dissolved in PBS at pH 7.2 to
   create a stock solution with a concentration of 5 mg/mL. A final
   concentration of 0.5 mg/mL was obtained by diluting the stock solution
   with RPMI 1640 medium, creating the MTT working solution. Samples were
   applied at different concentrations to RAW264.7 cells (5 × 10^4) seeded
   in 96-well plates for a duration of 6.5 hours. Following treatment,
   wells were rinsed with PBS and then 100 μL of MTT solution was added.
   To dissolve the formazan crystals, 100 µL of dimethyl sulfoxide was
   added to each well after a 4-hour incubation at 37 °C. Next, the plates
   underwent incubation for a further 10 minutes at 37 °C. To assess cell
   viability, absorbance was measured at 580 nm utilizing a Tecan
   Infinite^® M1000 Microplate Reader (Tecan, Männedorf, Switzerland). To
   calculate cell viability percentage, the formula was applied: (%) =
   [100 × (sample absorbance) / (control absorbance)].

Statistical Analyses

   Network pharmacology analysis involved comprehensive data processing
   using R software. In vitro experimental data were statistically
   analyzed with the help of GraphPad Prism 8.0.1 (GraphPad Software Inc.,
   San Diego, CA, USA). A Student’s T-test compared mean values between
   two groups, and One-way ANOVA with Tukey’s post hoc test enabled
   multiple comparisons. Statistical significance is represented by a
   p-value of less than 0.05.

Results

Identification of Active Compounds in Honeysuckle and their Primary Target
Genes

   Active compounds in honeysuckle were identified using the TCMSP
   database. Applying the criteria of DL ≥ 0.18 and OB ≥ 30%, 23 bioactive
   compounds were identified ([61]Table 2). The TCMSP database was then
   utilized to identify the targets linked to these active compounds,
   resulting in 291 unique targets after deduplication and summarization.

Table 2.

   Active Compounds in Honeysuckle
   Serial Number CAS Active Ingredients Chemical Structure
   MOL000098 73,123–10-1 Quercetin graphic file with name
   IDR-18-3257-i0001.jpg
   MOL000006 491–70-3 Luteolin graphic file with name
   IDR-18-3257-i0002.jpg
   MOL000422 520–18-3 Kaempferol graphic file with name
   IDR-18-3257-i0003.jpg
   MOL000449 83–48-7 Stigmasterol graphic file with name
   IDR-18-3257-i0004.jpg
   MOL000358 83–46-5 Beta-sitosterol graphic file with name
   IDR-18-3257-i0005.jpg
   MOL003117 Na Ioniceracetalides B_qt graphic file with name
   IDR-18-3257-i0006.jpg
   MOL003124 74,518–57-3 XYLOSTOSIDINE graphic file with name
   IDR-18-3257-i0007.jpg
   MOL003128 25,488–59-9 Dinethylsecologanoside graphic file with name
   IDR-18-3257-i0008.jpg
   MOL003101 Na 7-epi-Vogeloside graphic file with name
   IDR-18-3257-i0009.jpg
   MOL003108 Na Caeruloside C graphic file with name IDR-18-3257-i0010.jpg
   MOL003111 Na Centauroside_qt graphic file with name
   IDR-18-3257-i0011.jpg
   MOL001494 544–35-4 Mandenol graphic file with name
   IDR-18-3257-i0012.jpg
   MOL001495 1191–41-9, 34,079–45-3 Ethyl linolenate graphic file with
   name IDR-18-3257-i0013.jpg
   MOL002707 540–05-6 Phytofluene graphic file with name
   IDR-18-3257-i0014.jpg
   MOL002914 4049–38-1 Eriodyctiol (flavanone) graphic file with name
   IDR-18-3257-i0015.jpg
   MOL003006 Na
   (-)-(3R,8S,9R,9aS,10aS)-9-ethenyl-8-(beta-D-glucopyranosyloxy)-2,3,9,9a
   ,10,10a-hexahydro-5-oxo-5H,8H-pyrano[4,3-d]oxazolo[3,2-a]pyridine-3-car
   boxylic acid_qt graphic file with name IDR-18-3257-i0016.jpg
   MOL003014 Na Secologanic dibutylacetal_qt graphic file with name
   IDR-18-3257-i0017.jpg
   MOL002773 7235–40-7 Beta-carotene graphic file with name
   IDR-18-3257-i0018.jpg
   MOL003036 19,716–26-8 ZINC03978781 graphic file with name
   IDR-18-3257-i0019.jpg
   MOL003044 491–71-4 Chryseriol graphic file with name
   IDR-18-3257-i0020.jpg
   MOL003059 472–70-8 Kryptoxanthin graphic file with name
   IDR-18-3257-i0021.jpg
   MOL003062 116–30-3 4,5′-Retro-.beta.,.beta.-Carotene-3,3′-dione,
   4′,5′-didehydro- graphic file with name IDR-18-3257-i0022.jpg
   MOL003095 18,103–41-8
   5-hydroxy-7-methoxy-2-(3,4,5-trimethoxyphenyl)chromone graphic file
   with name IDR-18-3257-i0023.jpg
   [62]Open in a new tab

Acquisition of Target Genes Related to Sepsis

   A total of 947 potential target genes associated with sepsis were
   identified by screening and removing redundant entries using the
   GeneCards, OMIM, DrugBank, and TTD databases. The intersection between
   these 947 sepsis-related targets and the 291 active ingredients-related
   targets yielded 78 key targets as depicted in [63]Figure 1.

Figure 1.

   [64]Figure 1
   [65]Open in a new tab

   Venn diagram of honeysuckle’s active ingredients and sepsis-related
   target genes.

Construction of the PPI Networks

   The STRINC11.0 database received 78 potential target genes, with Homo
   sapiens set as the species, for the purpose of constructing a PPI
   network. The network was subsequently analyzed using Cytoscape 3.9.1
   software. The network analysis employed degree, betweenness, and
   closeness as primary criteria for node selection. Nodes were selected
   if their degree exceeded twice the network nodes’ median degree, their
   betweenness surpassed the median degree, and their closeness was
   greater than the median degree. The results were visualized using
   Cytoscape 3.9.1, as depicted in [66]Figure 2A. There are 867 nodes and
   893 edges in the network, which has an average node degree of 2.06 and
   a local clustering coefficient of 0.980. Using Cytoscape 3.9.1 and the
   NetworkAnalyzer plug-in, a PPI network diagram ([67]Figure 2B) was
   created from the imported network data, focusing on node density
   values. A node’s size and color intensity increase with its degree. The
   higher the interaction score between proteins, the thicker the line
   connecting the nodes, and the deeper the color. The proteins TP53,
   TLR4, EP300, MYD88, IL1-β, FN1, CTNN1, JUN, AKT1, and CXCR4 ranked
   highest in degree centrality measures, including Degree, Betweenness,
   and Closeness ([68]Figure 2C). TP53, TLR4, and MYD88 were pinpointed at
   the intersection of targets identified by the three methods used
   ([69]Figure 3 and [70]Table 3). These target proteins are likely to be
   an important part of the pivotal targets for honeysuckle in sepsis
   therapy.

Figure 2.

   [71]Figure 2
   [72]Open in a new tab

   The protein-protein interaction (PPI) network of honeysuckle and sepsis
   targets. (A) PPI network created using STRING. (B) PPI network
   visualized with Cytoscape 3.9.1. Nodes signify proteins, with colors
   ranging from pink to red indicating the degree of binding between
   proteins. Edges represent protein-protein associations. (C) Histograms
   displaying the top 10 target genes based on their degree values within
   the PPI network.

Figure 3.

   [73]Figure 3
   [74]Open in a new tab

   Selection of key target genes. Key targets identified by intersecting
   centrality measures (Degree, Closeness, and Betweenness).

Table 3.

   Information on the Top 10 Key Targets for Honeysuckle in the Treatment
   of Sepsis
   Top 10 in Network PPI Ranked by Degree Top 10 in Network PPI Ranked by
   Betweenness Centrality Top 10 in network PPI ranked by Closeness
   Centrality
   Rank Name Score Rank Name Score Rank Name Score
   1 MYD88 26 1 TLR4 0.30490603 1 TLR4 0.3010279
   2 TP53 24 2 FN1 0.23825175 2 MYD88 0.27424749
   3 TLR4 24 3 TP53 0.13204362 3 HSP90AA1 0.27333333
   3 EP300 23 4 MYD88 0.12701142 3 HSPA4 0.27296937
   3 IL1B 17 5 HSP90AA1 0.12585132 5 HMGB1 0.26797386
   6 FN1 17 6 CD44 0.10872326 6 TRAF6 0.26797386
   7 CTNNB1 16 7 CD40 0.08693913 7 IL1B 0.266061
   7 JUN 15 8 VWF 0.08432187 8 TLR1 0.266061
   9 AKT1 15 9 ALB 0.07720472 9 TP53 0.26131294
   10 CXCR4 14 10 HMGB1 0.07711109 10 BECN1 0.26015228
   [75]Open in a new tab

Results of GO Biological Enrichment Analysis

   Using the DAVID database for GO enrichment analysis, the key targets of
   honeysuckle were primarily associated with 1600 biological processes
   (BP), 183 cellular components (CC), and 280 molecular functions (MF).
   [76]Figure 4 illustrates the leading 10 biological processes (BP),
   cellular components (CC), and molecular functions (MF) identified by a
   screening criterion of P<0.05, ranked by target numbers. The BP
   included inflammatory and innate immune responses, cellular response to
   lipopolysaccharide, positive modulation of gene expression and
   angiogenesis, response to hypoxia, and production of tumor necrosis
   factor and interleukin-8. The CC included the extracellular space and
   region, extracellular exosome, plasma membrane’s external side, cell
   surface, blood microparticle, endoplasmic reticulum lumen,
   macromolecular complex, and platelet alpha granule lumen. The MF
   included receptor binding, cytokine and hormone activity, identical
   protein, integrin, enzyme, and protease binding, transmembrane
   signaling receptor activity, and protein kinase binding. The results of
   the GO enrichment analysis indicate that these crucial targets are
   mainly associated with the progression of sepsis, particularly in
   regulating the inflammatory response, though further experimental
   validation is required.

Figure 4.

   [77]Figure 4
   [78]Open in a new tab

   Analysis of GO enrichment.

Results of KEGG Pathway Enrichment Analysis

   Through KEGG pathway enrichment analysis utilizing the DAVID database,
   172 signaling pathways were found. We chose and displayed the top 20
   signaling pathways by using a screening criterion of P < 0.05, focusing
   on target numbers ([79]Figure 5). In the visualization, each bubble’s
   size and darkness indicate the significance and abundance of enriched
   key targets within the pathway. The top 20 signaling pathways encompass
   those associated with cancer, atherosclerosis, lipid metabolism,
   PI3K-Akt, cytokine-cytokine receptor interaction, NF-kappa B,
   neutrophil extracellular trap formation, TNF, tuberculosis, human
   papillomavirus, NOD-like receptor, Kaposi sarcoma-associated
   herpesvirus, JAK-STAT, neurodegeneration, human cytomegalovirus,
   Epstein-Barr virus, neuroactive ligand-receptor interaction, MAPK,
   influenza A, Herpes simplex virus 1, and Toll-like receptor signaling.
   Using Cytoscape software, a “target-pathway” network diagram was
   generated to visually represent the primary pathways and their
   associated targets ([80]Figure 6). The development of sepsis heavily
   relies on the TLR signaling pathway, which is also significantly
   related to inflammatory infiltration. [81]Figure 7 illustrates that TLR
   signaling pathways, influenced by honeysuckle’s active ingredient
   targets, play a role in sepsis development, as evidenced by data
   integration from KEGG and the Genome Encyclopedia. The Pi3k-Akt and
   NF-kappa B pathways play a role in creating an inflammatory
   microenvironment and are implicated in sepsis development. Overall, the
   findings indicate that honeysuckle primarily influences mechanisms
   related to the inflammatory response, offering potential treatment for
   sepsis.

Figure 5.

   [82]Figure 5
   [83]Open in a new tab

   KEGG pathway enrichment analysis.

Figure 6.

   [84]Figure 6
   [85]Open in a new tab

   [86]Figure 8 Diagram depicting the network connections between targets
   and signaling pathways.

Figure 7.

   [87]Figure 7
   [88]Open in a new tab

   TLR signaling pathways by which honeysuckle protects against sepsis.

Results of Molecular Docking

   The protein targets of honeysuckle’s active compounds were connected to
   their respective genes using the UniProt protein database. Cytoscape
   3.9.1 was used to create a network diagram depicting the interactions
   between honeysuckle, its active components, and target genes. As
   illustrated in [89]Figure 8, this network comprises a total of 315
   nodes (including 1 Traditional Chinese Medicine node, 23 nodes
   representing active ingredients, and 291 nodes representing
   corresponding target proteins). Notably, kaempferol, luteolin, and
   quercetin showed the highest degree of centrality and stronger binding
   to TP53, TLR4, and MYD88 ([90]Figure 9). The main active compounds in
   honeysuckle demonstrated significant binding energies with the targets
   TLR4 (−4.39, −4.59, and −4.81 kcal/mol), TP53 (−6.31, −4.59, and −5.05
   kcal/mol), and MYD88 (−5.6, −4.84, and −4.69 kcal/mol). These results
   indicate robust binding interactions between honeysuckle’s main active
   ingredients and their target proteins. In molecular docking analyses
   ([91]Figures 10–12), TP53 established hydrogen bonds with kaempferol
   through GLU-1575, GLU-1573, and PHE-1553 residues, whereas TLR4
   interacted via the ALA-287 residue. MYD88 engaged with kaempferol
   through GLN-181, TRP-286, THR-287, and LYS-282 residues, forming
   multiple hydrogen bonding sites. Similarly, luteolin utilized GLN-181
   and TYR-276 residues for binding to MYD88, while it binds to TP53 via
   MET1584, LEU-1547, and PHE-1553 residues, and binds to TLR4 by GLY-291,
   LYS-288, SER-286 residues. Quercetin interacted with TLR4 via ASN-260,
   LYS-288, and THR-259 residues, TP53 by GLU-1575 and SER-1554, and MYD88
   through ASP-162, ARG-160, LEU-191, VAL-1, and CYS-203. These findings
   underscore the significant binding affinity between honeysuckle’s
   active components and their respective protein targets.

Figure 8.

   [92]Figure 8
   [93]Open in a new tab

   The active ingredient-target network diagram illustrating active
   ingredients and targets for honeysuckle in sepsis treatment. The green
   circular node signifies the active components of honeysuckle, whereas
   the other nodes denote their respective targets.

Figure 9.

   [94]Figure 9
   [95]Open in a new tab

   Heat map displaying binding affinities between active ingredients and
   selected key targets based on molecular docking results.

Figure 10.

   [96]Figure 10
   [97]Open in a new tab

   Molecular docking analysis of active compounds with TP53. (A)
   kaempferol-TP53; (B) luteolin-TP53; (C) quercetin-TP53.

Figure 11.

   [98]Figure 11
   [99]Open in a new tab

   Molecular docking analysis of active compounds with TLR4. (A)
   kaempferol-TLR4; (B) luteolin-TLR4; (C) quercetin-TLR4.

Figure 12.

   [100]Figure 12
   [101]Open in a new tab

   Molecular docking analysis of active compounds with MYD88. (A)
   kaempferol-MYD88; (B) luteolin-MYD88; (C) quercetin-MYD88.

Honeysuckle Mitigates LPS-Induced Sepsis in Macrophages by Decreasing
Proinflammatory Cytokine Production

   Excessive proinflammatory cytokine production during infections can
   trigger a cytokine storm, which significantly increases mortality in
   sepsis syndrome.[102]^33^,[103]^34 Macrophages are essential to innate
   immunity, initiating inflammatory responses against diverse pathogens.
   To investigate the impact of honeysuckle and its active constituents on
   sepsis, RAW 264.7 cells were subjected to different concentrations of
   honeysuckle and its active components, kaempferol, luteolin, and
   quercetin, to assess their effects on sepsis. The MTT assay indicated
   that 160 μM concentrations of honeysuckle, kaempferol, luteolin, and
   quercetin were non-cytotoxic to RAW 264.7 cells ([104]Figure 13A).
   Therefore, subsequent experiments were conducted using a concentration
   of 160 μM. We investigated the impact of honeysuckle and its active
   components on LPS-induced inflammation in RAW264.7 macrophages by
   assessing mRNA levels of proinflammatory cytokines. Our results
   demonstrated that honeysuckle and its active components significantly
   lowered the mRNA expression of IL-1β and IL-6 induced by LPS in
   RAW264.7 cells ([105]Figure 13B–E). ELISA assays demonstrated that
   honeysuckle and its active components reduced LPS-induced IL-1β and
   IL-6 protein production in macrophages ([106]Figure 13F–I). The results
   indicate that honeysuckle reduces LPS-induced inflammation by
   suppressing inflammatory cytokine production in both macrophages and
   mice.

Figure 13.

   [107]Figure 13
   [108]Open in a new tab

   Effect of honeysuckle on the production of proinflammatory cytokines in
   macrophages. (A) The MTT assay indicated that treatment with 160 μM of
   honeysuckle, kaempferol, luteolin, and quercetin did not cause
   cytotoxic effects in RAW 264.7 cells (n = 3). (B–E) IL-1β and IL-6 mRNA
   levels in macrophages were measured by qRT-PCR (n = 5). The data are
   presented as mean ± standard deviation (SDW). ***P < 0.001, ****p <
   0.0001. (F–I) The secretion of IL-1β and IL-6 in macrophages was
   detected by ELISA (n = 5). The data are presented as mean ± standard
   deviation (SD), determined using a two-tailed Student’s t-test or
   one-way analysis of variance (ANOVA). ***P < 0.001, ****p < 0.0001 vs
   control, ^####p < 0.0001, ^###p < 0.001, ^##p < 0.01 vs LPS. ns: no
   significance vs control.

Active Components of Honeysuckle Suppress LPS-Induced Activation of the
TLR4/MYD88 Signaling Pathways

   Molecular docking and network pharmacology experiments revealed a
   strong binding affinity between honeysuckle’s active ingredient and the
   key target genes TLR4, TP53, and MYD88. The planar structures of
   kaempferol, luteolin, and quercetin in silico active compounds are
   shown in [109]Figure 14A. Prior research has emphasized the crucial
   involvement of TLR4, TP53, and MYD88 in sepsis development.[110]^35–37
   LPS, acting as a TLR4 agonist, primarily induces proinflammatory
   responses via the TLR4/MYD88 signaling pathway. To validate the impact
   of honeysuckle’s active components on these core targets, qRT-PCR
   assays were conducted. The results indicated significant reductions in
   TLR4, TP53, and MYD88 expression levels following treatment with
   honeysuckle, kaempferol, luteolin, and quercetin ([111]Figure 14B–D).
   The study indicates that honeysuckle’s active components alleviate
   LPS-induced sepsis by inhibiting the TLR4/MYD88 signaling pathways,
   supporting conclusions from molecular docking and network pharmacology
   analyses.

Figure 14.

   [112]Figure 14
   [113]Open in a new tab

   Effect of active constituents of honeysuckle on expression of key
   target genes and TLR4/MYD88 signaling pathway. LPS-induced RAW 264.7
   cells were exposed to 160 μM concentrations of honeysuckle, kaempferol,
   luteolin, and quercetin. (A) The planar structures of the in silico
   kaempferol, luteolin, and quercetin. (B–D) Real-time qRT-PCR was
   utilized to analyze the mRNA levels of TP53, TLR4, and MYD88 in
   LPS-induced RAW 264.7 cells treated with these active compounds (n =
   5). The data are presented as mean ±SD, determined using a two-tailed
   Student’s t-test or ANOVA. **P < 0.01, ***P < 0.001 vs model.

Discussion

   Honeysuckle, a traditional tonic medicine, demonstrates
   anti-inflammatory, antiviral, antioxidative, and antibacterial
   properties.[114]^38^,[115]^39 This research employed molecular docking
   and network pharmacology to explore the mechanisms by which honeysuckle
   combats sepsis. Our findings suggest that kaempferol, luteolin, and
   quercetin are the principal bioactive compounds responsible for
   honeysuckle’s therapeutic effects, particularly through their
   interaction with key proteins TP53, TLR4, and MYD88. These compounds
   play crucial roles in modulating inflammation-related pathways, as
   confirmed by both computational docking and experimental validation.

   In this study, we collected 23 chemical components from the TCMSP
   database, with kaempferol, luteolin, and quercetin identified as the
   principal ones. These compounds target multiple nodes within the
   network alongside other active components. Kaempferol, luteolin, and
   quercetin, among the identified active ingredients, have demonstrated
   significant therapeutic efficacy against sepsis. A functional
   enrichment analysis involving 78 core targets was executed with the
   help of the GO and KEGG databases. Protein binding was highlighted as
   the most enriched GO term by the results, suggesting that honeysuckle
   could modulate sepsis progression by influencing protein-protein
   interactions. Furthermore, analysis of 172 KEGG pathways revealed
   enrichment of sepsis-related genes primarily within inflammatory
   pathways, indicating that honeysuckle’s intervention in sepsis could
   involve modulation of inflammation-associated signaling pathways.

   We explored the molecular mechanisms by which honeysuckle may treat
   sepsis using network pharmacology, focusing on active ingredient and
   target screening within the PPI network. We identified three primary
   bioactive ingredients and their corresponding representative targets.
   Specifically, kaempferol, luteolin, and quercetin were molecularly
   docked with TP53, TLR4, and MYD88 proteins to validate our network
   pharmacology predictions. The docking results confirmed that these
   ingredients bind effectively to the proteins. Additionally, our
   findings demonstrated that honeysuckle mitigates LPS-induced sepsis in
   macrophages and mice by reducing proinflammatory cytokine production.
   Kaempferol, luteolin, and quercetin, the active components of
   honeysuckle, inhibit LPS-induced activation of the TLR4/MYD88 signaling
   pathways. TP53, a well-documented tumor suppressor protein, has been
   increasingly recognized for its role in regulating immune responses.
   Recent studies have highlighted TP53’s involvement in modulating immune
   response and ferroptosis, which are critical during sepsis.[116]^40 For
   example, p53 is closely associated with poor outcomes in sepsis and
   exerts an immunosuppressive effect by promoting the expansion of
   functional Tregs during the development of sepsis.[117]^41 Similarly,
   TLR4 and MYD88, as pivotal components of innate immunity, initiate
   macrophage proinflammatory cascades and regulate macrophage
   polarization during sepsis.[118]^42^,[119]^43 Suppression of these
   targets has been shown to alleviate sepsis-related organ dysfunction,
   consistent with the findings of our study. Kaempferol, luteolin, and
   quercetin exhibited high docking affinities with TP53, TLR4, and MYD88,
   suggesting their potential to disrupt the progression of sepsis by
   modulating these proteins. Comparatively, prior studies have shown that
   traditional Chinese medicines mitigate sepsis-related multiple organ
   dysfunction in rats by modulating the inflammatory response and
   macrophage activation via the TLR4/MYD88 signaling pathways. For
   instance, Xijiao Dihuang decoction was reported to reduce
   sepsis-induced cardiac inflammation by inhibiting TLR4/NF-κB signaling
   and modulating cytokine expression.[120]^44 Similarly, Wang et al
   demonstrated that puerarin attenuates sepsis-induced multiple-organ
   injury and Inflammatory response via regulation of TLR and MAPK
   signaling.[121]^45 In line with these studies, our findings revealed
   that honeysuckle’s active components inhibit the TLR4/MYD88 pathway, a
   key mediator of proinflammatory cytokine release in sepsis. This
   consistency reinforces the role of honeysuckle as an anti-sepsis agent
   targeting inflammatory signaling.

   This study has certain limitations. Our research on honeysuckle’s
   effects on sepsis employed modern bioinformatics methods, such as
   network pharmacology and molecular docking. The existing network
   information technology necessitates further enhancement, and the
   precision and promptness of database information require scientific
   verification. Our analysis may exclude compounds or targets that are
   unconfirmed or unrecorded. While luteolin, kaempferol, and quercetin
   were identified as the main bioactive ingredients of honeysuckle for
   sepsis treatment, they do not fully encapsulate the plant’s therapeutic
   potential. Therefore, additional pharmacodynamic and molecular biology
   experiments are necessary to expand our current findings. Furthermore,
   some of our results have limited precedent in previous studies. The
   absence of prior explanations and validations regarding the effects and
   mechanisms of these potential active ingredients on sepsis presents
   substantial opportunities for future research.

Conclusion

   The research utilized network pharmacology to determine the active
   compounds and crucial targets of honeysuckle in addressing sepsis.
   Molecular docking analysis demonstrated a high binding affinity between
   honeysuckle’s active compounds and key targets, including TP53, TLR4,
   and MYD88. In vitro experiment results indicated that honeysuckle has a
   protective effect against sepsis by reducing proinflammatory cytokine
   production. Additionally, the active compounds in honeysuckle
   (kaempferol, luteolin, and quercetin) exhibit therapeutic potential by
   inhibiting the TLR4/MYD88 signaling pathways. These findings highlight
   the significance of understanding honeysuckle’s mechanisms, providing
   valuable insights for developing new therapeutic strategies for sepsis
   management.

Disclosure

   The authors report no conflicts of interest in this work.

References