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Onderzoeksartikel

Netwerkfarmacologie, GEO-transcriptomics en moleculaire docking van Zhenwu Decoction met Wuling Powder voor chronisch hartfalen

116 weergaven

⸱

11 september 2026

 ,  ,  ,  , 
1National Clinical Research Center for Traditional Chinese Medicine in Cardiovascular Diseases, Xiyuan Hospital

In dit artikel

Samenvatting

Deze studie integreert netwerkgebaseerde target-mapping, GEO-expressiedata en docking om kandidaten te rangschikken die gerelateerd zijn aan de Zhenwu-Wuling-combinatie bij chronisch hartfalen.

Samenvatting

Zhenwu Decoction in combinatie met Wuling Powder wordt gebruikt bij chronisch hartfalen (CHF), maar de moleculaire basis hiervan is nog niet volledig gedefinieerd. We hebben netwerkgebaseerde target-mapping, transcriptomische gegevens van de Gene Expression Omnibus (GEO) en moleculaire docking geïntegreerd om kandidaat-targets en signaalpaden te prioriteren. De gegevens over verbindingen voor de acht kruiden werden verkregen uit farmacologische en chemische databases en onderworpen aan screening van verbindingen en targets. CHF-geassocieerde genen werden verzameld uit zes ziektebronnen, en hun overlap met formuleringsgerelateerde targets werd onderzocht met behulp van PPI-netwerk- en functionele verrijkingsanalyses. GSE5406 leverde myocardiale expressiegegevens van falende en niet-falende monsters. De workflow resulteerde in 89 gescreende verbindingen, 314 niet-redundante compound-targets en 2.515 CHF-gerelateerde targets, met 143 gedeelde genen. AKT1, IL6, TNF, SRC en ESR1 scoorden het hoogst in het ziekte-targetnetwerk. De expressieanalyse leverde 1.348 differentieel tot expressie gebrachte genen (DEGs) op, waarbij HSP90AA1 en STAT1 werden geselecteerd als transcriptomische hub-kandidaten. Verrijkte termen clusterden rond hypoxie, circulatie, TNF-gerelateerde signalering, PI3K-Akt- en MAPK-paden. Docking resulteerde in negatieve voorspelde scores voor representatieve constituent-targetparen; de meest negatieve score werd waargenomen voor STAT1 en alisol C (-9,2 kcal/mol). Deze bevindingen vormen een testbare computationele hypothese en prioriteren kandidaten voor experimentele validatie.

Inleiding

CHF omvat een heterogeen syndroom dat voortkomt uit structurele of functionele myocardiale abnormaliteiten. Dergelijke veranderingen kunnen de ventriculaire vulling of systolische ejectie belemmeren en gaan vaak gepaard met pulmonale of systemische congestie. Typische symptomen zijn dyspneu, vermoeidheid en vochtretentie1. Een retrospectieve analyse van de Global Burden of Disease-gegevens van China van 1990 tot 2023 toonde aan dat de nationale prevalentie van nieuw gediagnosticeerde CHF in drie decennia met 208,4% is toegenomen2.

In de traditionele Chinese geneeskunde (TCG) komt CHF overeen met cardiaal oedeem, dyspneu-syndroom en gegeneraliseerd oedeem. De pathogenese wordt gewoonlijk beschreven als een deficiëntie in de wortel en een overmaat in de tak. Deficiënties van yin, yang en qi vormen de onderliggende pathologische basis, terwijl interne vochtophoping en bloedstase de manifeste overmaat vertegenwoordigen. Bijgevolg legt de TCG-behandeling de nadruk op het verwarmen van yang, het verwijderen van vastgehouden vocht, het bevorderen van de circulatie en het oplossen van stase.

Het gecombineerde gebruik van Zhenwu Decoction en Wuling Powder is een klassieke kruidenstrategie voor het klinische beheer van CHF. Zhenwu Decoction verwarmt yang en bevordert diurese, terwijl Wuling Powder yang qi ondersteunt en het vloeistofmetabolisme reguleert. Samen kunnen de twee formules yang versterken en interne vochtigheid elimineren. Klinisch bewijs suggereert dat deze combinatie symptomen kan verlichten, indicatoren van de hartfunctie kan verbeteren en de verslechtering van CHF kan vertragen3. We hebben eerst de bestanddelen van de formules en kandidaat-CHF-targets in kaart gebracht met behulp van netwerkfarmacologie, waarna we docking hebben gebruikt om geselecteerde paar-combinaties van bestanddeel-target te rangschikken. Vervolgens zijn GEO-afgeleide expressieprofielen gebruikt om de netwerkkandidaten in een context van myocardziekten te plaatsen en om downstream-paden te onderzoeken. De studie biedt een hypothese-genererend computationeel kader voor het prioriteren van kandidaten voor daaropvolgende experimentele validatie; het stelt geen klinische werkzaamheid of een gevalideerd mechanisme vast.

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Protocol

This study analyzed public database resources and did not involve new human participants, human specimens, or animal experiments. All databases, web-based resources, software, analysis packages, visualization tools, molecular docking tools, and other computational resources used in this study, together with their details, are listed in the Table of Materials.

Formula composition and botanical provenance
The eight distinct crude drugs were aligned to current botanical and pharmacopoeial naming as follows: Aconiti Lateralis Radix Praeparata, the processed lateral root of Aconitum carmichaelii Debeaux; Poria, the sclerotium of Wolfiporia cocos (F.A.Wolf) Ryvarden & Gilb.; Atractylodis Macrocephalae Rhizoma, the rhizome of Atractylodes macrocephala Koidz.; Paeoniae Radix Alba, the root of Paeonia lactiflora Pall.; Zingiberis Rhizoma Recens, the fresh rhizome of Zingiber officinale Roscoe; Alismatis Rhizoma, the rhizome of Alisma orientale (Sam.) Juzep.; Polyporus, the sclerotium of Polyporus umbellatus (Pers.) Fr.; and Cinnamomi Ramulus, the young twig of Cinnamomum cassia (L.) J.Presl. The prescription comprised 9 g Aconiti Lateralis Radix Praeparata, 12 g Poria, 9 g Paeoniae Radix Alba, 12 g Atractylodis Macrocephalae Rhizoma (stir-fried Atractylodes), 9 g Zingiberis Rhizoma Recens, 9 g Polyporus, 15 g Alismatis Rhizoma, and 6 g Cinnamomi Ramulus; the corresponding ratio was 3:4:3:4:3:3:5:2. Aconiti Lateralis Radix Praeparata was treated as processed material. This was an entirely in silico study: no decoction batch was prepared, purchased, chemically assayed, or administered. Manufacturer, lot number, batch-specific processing records, and voucher specimens are therefore not applicable; the gram amounts define the formula composition and relative ratio, not experimental exposure. Poria and Atractylodis Macrocephalae Rhizoma are shared by the two formulae. Botanical names and medicinal-part terminology were checked against the 2025 Pharmacopoeia of the People’s Republic of China4.

Screening and target prediction of active ingredients in Zhenwu Decoction and Wuling Powder
TCMSP was queried separately for compounds recorded for Zhenwu Decoction and Wuling Powder, including Aconiti Lateralis Radix Praeparata, Poria, Atractylodis Macrocephalae Rhizoma, Paeoniae Radix Alba, Zingiberis Rhizoma Recens, Alismatis Rhizoma, Polyporus, and Cinnamomi Ramulus. Initial filtering required oral bioavailability (OB) of at least 30% and drug-likeness (DL) of at least 0.18. When a simplified molecular-input line-entry system (SMILES) record was unavailable, the corresponding structure was retrieved from a public chemical database. SMILES records were evaluated for physicochemical and drug-likeness properties. A compound passed the secondary filter when its molecular weight was ≤500, lipophilicity ≤5, hydrogen-bond donors ≤5, hydrogen-bond acceptors ≤10, at least three of the Lipinski, Ghose, Veber, Egan, and Muegge rule sets were positive, and predicted gastrointestinal absorption was high5. Eligible structures were subjected to target prediction, and only predictions with probability >0.02 were included in the target list. TCMSP and target-prediction records were combined at the herb-compound-target level, duplicate identifiers were collapsed, and gene names were standardized to official gene symbols.

Deduplication produced 646 candidate associations; 71 rows without a mapped official gene symbol were excluded, leaving 575 standardized associations. The independent herb-level integration yielded 314 nonredundant target genes. The compound and target screening criteria are supported by the TCMSP and SwissTargetPrediction source papers6,7.

Screening of disease targets for CHF
CHF-related gene entries were downloaded from GeneCards, OMIM, TTD, DisGeNET, DrugBank, and PharmGKB. The disease search used four phrases: heart failure, congestive heart failure, diastolic heart failure, and systolic heart failure. For GeneCards, entries with relevance scores below 0.20 were excluded. The six database-derived target sets were integrated after duplicate identifiers were removed, while preserving the source database for each record. All screened gene names were standardized to official gene symbols. The overlap between the formula and CHF target lists was visualized using a Venn diagram. The shared identifiers were treated as candidate formula-related targets for CHF.

TCMSP was searched on 7 February 2026; SwissTargetPrediction, GeneCards, OMIM, and DisGeNET on 10 February 2026; and PharmGKB, TTD, and DrugBank on 12 February 2026. Disease queries were entered as heart failure, congestive heart failure, heart failure, diastolic, and heart failure, systolic; the search was restricted to Homo sapiens where supported. Retrieved entries were standardized to official gene symbols before deduplication.

Construction of the protein-protein interaction (PPI) network and screening of core targets
The shared target identifiers were queried in STRING for PPI analysis. The query used Homo sapiens interactions and a confidence cutoff above 0.9. Isolated nodes were omitted, and the resulting STRING interaction data were used for network visualization and topological analysis. Candidate hubs were ranked by network topology. Betweenness, closeness, degree, and eigenvector centralities were calculated as network descriptors. Degree was the primary ranking variable, and the five highest-degree targets were retained as candidate hubs.

Official gene symbols were used for gene-level analyses, including AKT1, IL6, TNF, SRC, ESR1, BCL2, CASP3, PPARG, PTGS2, and MMP9; protein names are written in full only when needed for clarity. The initial PPI network contained 143 nodes and 584 interaction records before visualization-specific filtering; after network processing, the graph contained 143 nodes and 292 edges.

Construction of the drug-disease-component-target network
Once names were standardized, the network data were assembled and used to construct the drug-disease-component-target graph.

GO and KEGG enrichment analyses
The candidate CHF target list was submitted to Metascape for functional annotation and pathway enrichment analysis. The analysis was run for Homo sapiens. The raw p-value cutoff was set at 0.01, a minimum overlap of 3, and a minimum enrichment score of 1.5. GO bar charts and KEGG bubble plots were generated for visualization. The 143-gene input set was used for Gene Ontology biological-process (GO-BP), molecular-function (GO-MF), and KEGG enrichment analyses. The analysis thresholds were raw p < 0.01, minimum overlap 3, and minimum enrichment 1.58. The GO-CC analysis was based on a 60-gene hit list against a 30,230-gene library. Because this input differed from the 143-gene input used for the BP, MF, and KEGG analyses, the GO-CC results were reported separately and were not quantitatively pooled with the other categories.

Screening of heart failure-related targets based on the GEO database
The GSE5406 dataset9 was used for differential-expression analysis. The series included 210 myocardial samples: 108 with ischemic cardiomyopathy, 86 with idiopathic dilated cardiomyopathy, and 16 nonfailing donor controls. The GEO record identifies the platform as GPL96, the Affymetrix Human Genome U133A Array. The series matrix was already robust multi-array average (RMA)-normalized. Probes without matched SYMBOL annotations were removed after matching to the platform annotation, and rowSums(expression > 0.5) ≥ 3 was applied before limma fitting. Group levels were defined as Control, DCM, and ICM, with a no-intercept design matrix (~0 + group) and the contrasts (DCM+ICM)/2-Control, DCM-Control, and ICM-Control. Differential expression was defined as adjusted p value < 0.05 and |log2(Fold Change)| > 0.3. Because all samples were processed within one GEO series and no separate batch variable was available, no batch-correction term was introduced. Differential-expression results were visualized using heatmaps and volcano plots. DEGs were subjected to PPI analysis followed by network-based hub ranking. Docking and pose visualization were conducted for the selected targets and prescription compounds.

The GEO record identifies the platform as GPL96, the Affymetrix Human Genome U133A Array. Group levels were defined as Control, DCM, and ICM, with a no-intercept design matrix (~0 + group) and the contrasts (DCM+ICM)/2-Control, DCM-Control, and ICM-Control. The series matrix was already robust multi-array average (RMA)-normalized; probes with missing SYMBOL annotations were removed after matching to the platform annotation, and the code applies rowSums(expression > 0.5) ≥ 3 before limma fitting. Because all samples were processed within one GEO series and no separate batch variable was available, no batch-correction term was introduced.

Molecular docking verification
A three-dimensional PDB structure for each receptor was obtained from the RCSB Protein Data Bank. Protein structures were prepared by removing crystallographic waters and non-receptor ligands. Structure-data file (SDF) structures for the five highest-degree compounds were obtained from a public chemical database.

The receptor structures used for docking were AKT1/1H10 (chain A, 1.40 Å, 4IP), IL6/1ALU (chain A, 1.90 Å, TLA), TNF/1A8M (chain A, 2.30 Å, no co-crystallized ligand), SRC/1A09 (chain A, 2.00 Å, no ligand), ESR1/1A52 (chain A, 2.80 Å, EST), STAT1/1BF5 (chain A, 2.90 Å, no ligand), and HSP90AA1/3OWB (chain A, 2.05 Å, BSM). The center, box dimensions, and random seed were specified for each tested pair. The search boxes were defined to encompass the receptor for exploratory global docking.

Receptor and ligand files were prepared with the docking-preparation tools. Waters were deleted and polar hydrogens were added to receptor structures. SDF ligands were converted to PDB, then dehydrated and hydrogenated before PDBQT export. The prepared receptor-ligand files were assigned to the docking models. PDBQT files were loaded into AutoDock Vina. Vina runs were executed and scores were recorded. Scores below -5 kcal/mol were treated as comparatively more favorable and scores below -7 kcal/mol as more strongly predicted interactions10; these are not experimental thresholds. The three complexes with the best predicted scores were selected for visualization.

No search-depth or output-mode override was specified; therefore, the documented command-line defaults were used: exhaustiveness 8 and at most 9 modes. Pair-specific random seeds were used, and mode 1 (the best-scoring returned pose) was selected for representative visualization. The HSP90AA1 output-file assignment was confirmed because each mode-1 score matched the corresponding HSP90AA1 value in the docking-score matrix. Molecular dynamics simulations were not performed, so the scores are exploratory computational predictions only.

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Resultaten

Actieve bestanddelen en targets van Zhenwu Decoction gecombineerd met Wuling Powder
Er zijn records van bestanddelen en targets verkregen voor elk kruid. De overeenkomstige target-aantallen per kruid waren Aconiti Lateralis Radix Praeparata, 178; Poria, 21; Atractylodis Macrocephalae Rhizoma, 18; Paeoniae Radix Alba, 86; Zingiberis Rhizoma Recens, 127; Alismatis Rhizoma, 96; Polyporus, 4; en Cinnamomi Ramulus, 45. Na symbolstandaardisatie en deduplicatie bevatte de geïntegreerde formule-gerelateerd...

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Discussie

CHF vereist langdurig klinisch beheer en omvat vaak complexe medicatieschema's. Studies door Wang JX en Zhang S11 en andere studies wijzen uit dat langdurige gecombineerde conventionele farmacotherapie de medicatietrouw kan verminderen. Slechte therapietrouw kan het risico op heropname en negatieve emotionele uitkomsten verhogen. De beschikbare geneesmiddelen richten zich grotendeels op symptoombeheersing en het vertragen van ventriculaire remodellering. De National Guidelines for the Management o...

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Openbaarmakingen

De auteurs melden geen concurrerende belangen met betrekking tot deze studie.

Dankbetuigingen

Niet van toepassing.

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Materialen

Lijst van materialen gebruikt in dit artikel
NaamBedrijfCatalogusnummerOpmerkingen
AutoDock VinaCCSB-Scrippsv1.2.6Gebruikte command-line versie; huidige officiële release v1.2.7; gebruikte configuraties waren CLI-standaardwaarden exhaustiveness 8 en num_modes 9. https://github.com/ccsb-scripps/AutoDock-Vina/releases
AutoDockToolsThe Scripps Research Institutev1.5.7Receptor/ligand-preparatie; versie gebruikt bij analyse. https://autodock.scripps.edu/
BioconductorBioconductor Project3.16 compatibility releaseR 4.2-compatibele omgeving; huidige release 3.23. https://bioconductor.org/about/release-announcements/
Cytoscape DesktopCytoscape Consortiumv3.7.1Gebruikte versie voor netwerkvisualisatie/analyse; huidige release v3.10.4. https://cytoscape.org/download.html
DisGeNETMedBioinformatics Solutionsv25.4Ophalen van CHF-geassocieerde genen; doorzocht op 2026-02-10. https://disgenet.com/
DrugBankOMx Personal Health Analyticsv6.0Ophalen van CHF-geassocieerde genen; doorzocht op 2026-02-12. https://go.drugbank.com/
GeneCardsWeizmann Institute of ScienceWebdatabaseOphalen van CHF-geassocieerde genen; doorzocht op 2026-02-10. https://www.genecards.org/
GEO / GSE5406NCBIGSE5406; GPL96RMA-genormaliseerde microarray-serie van de menselijke linker ventrikel. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5406
hgu133a.dbBioconductorBioconductor 3.16 environmentAffymetrix Human Genome U133A probe-annotatie; exacte patchversie niet bewaard. https://bioconductor.org/packages/hgu133a.db/
limmaBioconductorBioconductor 3.16 environmentAnalyse van differentiële expressie; exacte patchversie niet bewaard. https://bioconductor.org/packages/limma/
MacBookAppleM4 processorLokale computationele werkstation; geheugenspecificaties niet bewaard. https://www.apple.com/mac/
MetascapeMetascapev3.5GO/KEGG-verrijking; exacte tijdstempel van toegang niet bewaard. https://metascape.org/
Microsoft ExcelMicrosoftDesktopapplicatieDatacuratie/export; applicatiebuild niet bewaard. https://www.microsoft.com/microsoft-365/excel
OMIMJohns Hopkins UniversityWebdatabaseOphalen van CHF-geassocieerde genen; doorzocht op 2026-02-10. https://www.omim.org/
Open BabelOpen Babel ProjectVersie niet bewaardStructuurconversie in docking-workflow. https://openbabel.org/
PharmGKBPharmGKBWebdatabaseOphalen van CHF-geassocieerde genen; doorzocht op 2026-02-12. https://www.pharmgkb.org/
PubChemNCBIWebdatabaseSMILES- en structuuraanvulling. https://pubchem.ncbi.nlm.nih.gov/
PyMOLSchrödingerVersie niet bewaardProtein-preprocessing en pose-visualisatie. https://www.pymol.org/
RR Foundation4.2.0+Versiefamilie geregistreerd voor analyse; huidige release 4.6. https://www.r-project.org/
RCSB Protein Data BankRCSB PDBWebarchiefProteïnekristalstructuren. https://www.rcsb.org/
RStudioPosit2026.04.0+526Analyse-IDE geregistreerd in revisienota. https://posit.co/download/rstudio-desktop/
STRINGSTRING ConsortiumWebserviceHomo sapiens; betrouwbaarheid >0.9; release/sessie-metadata niet bewaard. https://string-db.org/
SwissADMESIB Swiss Institute of BioinformaticsWebserviceADME- en Lipinski-regel-screening; lokale versie niet van toepassing. https://www.swissadme.ch/
SwissTargetPredictionSIB Swiss Institute of Bioinformatics2019 major updateHomo sapiens; waarschijnlijkheid >0.02; doorzocht op 2026-02-10. https://www.swisstargetprediction.ch/
TCMSPNorthwest A&F Universityv2.3Ophalen van verbindingen; doorzocht op 2026-02-07. https://tcmsp-e.com/
TTDTherapeutic Target Database2026 releaseOphalen van CHF-geassocieerde genen; doorzocht op 2026-02-12. https://ttd.idrblab.cn/
UniProtKBUniProt ConsortiumRelease 2026_01Gereviewde standaardisatie van Homo sapiens gen-symbolen. https://www.uniprot.org/
VennyCNB-CSICv2.1.0Visualisatie van intersectie van target-sets. https://bioinfogp.cnb.csic.es/tools/venny/

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