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Forschungsartikel

Netzwerkpharmakologie, GEO-Transkriptomik und molekulares Docking von Zhenwu Decoction mit Wuling Powder bei chronischer Herzinsuffizienz

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11. September 2026

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

In diesem Artikel

Zusammenfassung

Diese Studie integriert netzwerkbasierte Target-Zuordnung, GEO-Expressionsdaten und Docking, um Kandidaten im Zusammenhang mit der Zhenwu-Wuling-Kombination bei chronischer Herzinsuffizienz zu bewerten.

Zusammenfassung

Zhenwu-Decoktion in Kombination mit Wuling-Pulver wird bei chronischer Herzinsuffizienz (CHF) eingesetzt, doch ihre molekulare Grundlage ist bisher nicht vollständig geklärt. Wir kombinierten netzwerkbasierte Target-Zuordnung, Transkriptomdaten aus der Gene Expression Omnibus (GEO)-Datenbank und molekulares Docking, um Kandidaten-Ziele und -wege zu priorisieren. Verbindungsdatensätze für die acht Kräuter wurden aus pharmakologischen und chemischen Datenbanken gewonnen und einer Screening-Analyse für Verbindungen und Zielstrukturen unterzogen. CHF-assoziierte Gene wurden aus sechs Krankheitsressourcen zusammengestellt, und ihre Überschneidung mit formelbezogenen Targets wurde mittels PPI-Netzwerk- und Funktionsanreicherungsanalysen untersucht. GSE5406 lieferte myokardiale Expressionsdaten für insuffiziente und nicht-insuffiziente Proben. Der Arbeitsablauf ergab 89 ausgewählte Verbindungen, 314 nicht-redundante Verbindungsziele und 2.515 CHF-bezogene Ziele mit 143 gemeinsamen Genen. AKT1, IL6, TNF, SRC und ESR1 wiesen im Krankheits-Target-Netzwerk die höchsten Rangwerte auf. Die Expressionsanalyse identifizierte 1.348 differentiell exprimierte Gene (DEGs) und wählte HSP90AA1 und STAT1 als transkriptomische Hub-Kandidaten aus. Angereicherte Begriffe gruppierten sich um Hypoxie, Durchblutung, TNF-vermittelte Signalwege, PI3K-Akt- und MAPK-Signalwege. Das Docking ergab negative vorhergesagte Scores für repräsentative Verbindung-Target-Paare; der niedrigste Score wurde für STAT1 und Alisol C beobachtet (−9,2 kcal/mol). Diese Ergebnisse liefern eine überprüfbare rechnergestützte Hypothese und priorisieren Kandidaten für die experimentelle Validierung.

Einleitung

Die chronische Herzinsuffizienz (CHF) umfasst ein heterogenes Krankheitsbild, das auf strukturellen oder funktionellen myokardialen Anomalien beruht. Solche Veränderungen können die ventrikuläre Füllung oder die systolische Ejektion beeinträchtigen und gehen häufig mit pulmonaler oder systemischer Stauung einher. Typische Symptome sind Dyspnoe, Müdigkeit und Flüssigkeitsretention1. Eine retrospektive Analyse der Daten zur globalen Krankheitslast Chinas von 1990 bis 2023 zeigte, dass die nationale Prävalenz neu diagnostizierter CHF über drei Jahrzehnte um 208,4 % angestiegen ist2.

In der traditionellen chinesischen Medizin (TCM) entspricht CHF Herzödem, Dyspnoe-Syndrom und generalisiertem Ödem. Die Pathogenese wird üblicherweise als Mangel im Wurzelbereich und Überfluss im Zweigbereich beschrieben. Der Mangel an Yin, Yang und Qi bildet die zugrunde liegende pathologische Basis, während innere Flüssigkeitsansammlung und Blutstagnation den manifesten Überfluss darstellen. Demgemäß betont die TCM-Behandlung das Wärmen des Yang, das Entfernen von zurückgehaltener Flüssigkeit, die Förderung der Durchblutung und die Auflösung von Stagnation.

Die kombinierte Anwendung von Zhenwu Decoction und Wuling Powder ist eine klassische pflanzliche Strategie zur klinischen Behandlung von CHF. Zhenwu Decoction wärmt das Yang und fördert die Diurese, während Wuling Powder das Yang-Qi stärkt und den Flüssigkeitsstoffwechsel reguliert. Zusammen können beide Formulierungen das Yang verstärken und innere Feuchtigkeit beseitigen. Klinische Belege deuten darauf hin, dass diese Kombination Symptome lindern, kardiale Funktionsparameter verbessern und das Fortschreiten der CHF verlangsamen kann3. Zunächst kartierten wir die Bestandteile der Formulierungen und mögliche CHF-Zielstrukturen mittels Netzwerkpharmakologie und nutzten anschließend Docking-Verfahren, um ausgewählte Wirkstoff-Zielstruktur-Paare zu bewerten. Anschließend wurden aus GEO abgeleitete Expressionsprofile verwendet, um die netzwerkbasierenden Kandidaten in einen myokardialen Krankheitskontext einzuordnen und nachgeschaltete Signalwege zu untersuchen. Die Studie liefert einen hypothesengenerierenden rechnergestützten Rahmen zur Priorisierung von Kandidaten für spätere experimentelle Validierungen; sie belegt jedoch nicht die klinische Wirksamkeit oder einen validierten Wirkmechanismus.

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Protokoll

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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Ergebnisse

Wirkstoffe und Zielstrukturen von Zhenwu Decoction in Kombination mit Wuling Powder
Für jedes Kraut wurden Bestandteil-Zielstruktur-Datensätze erhalten. Die entsprechenden Anzahlen zugeordneter Zielstrukturen pro Kraut betrugen Aconiti Lateralis Radix Praeparata, 178; Poria, 21; Atractylodis Macrocephalae Rhizoma, 18; Paeoniae Radix Alba, 86; Zingiberis Rhizoma Recens, 127; Alismatis Rhizoma, 96; Polyporus, 4; und Cinnamomi Ramulus, 45. Nach Standardisierung der Bezeichnungen und Entfernung von Dub...

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Diskussion

Die chronische Herzinsuffizienz erfordert eine langfristige klinische Betreuung und ist häufig mit komplexen Medikamentenschemata verbunden. Studien von Wang JX und Zhang S11 sowie weitere Untersuchungen zeigen, dass eine langfristige kombinierte konventionelle Pharmakotherapie die Medikamentenadhärenz beeinträchtigen kann. Eine unzureichende Adhärenz kann das Risiko von Wiedereinweisungen und negativen emotionalen Folgen erhöhen. Die verfügbaren Arzneimittel konzentrieren sich überwiegend auf die...

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Offenlegungen

Die Autoren erklären keine konkurrierenden Interessen in Bezug auf diese Studie.

Danksagungen

Nicht zutreffend.

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Materialien

Liste der in diesem Artikel verwendeten Materialien
NameUnternehmenKatalognummerKommentare
AutoDock VinaCCSB-Scrippsv1.2.6Befehlszeilenversion verwendet; aktuelle offizielle Version v1.2.7; verwendete Konfigurationen: Standardwerte für Exhaustivität 8 und Anzahl der Modi 9. https://github.com/ccsb-scripps/AutoDock-Vina/releases
AutoDockToolsThe Scripps Research Institutev1.5.7Vorbereitung von Rezeptor/Ligand; verwendete Version für die Analyse. https://autodock.scripps.edu/
BioconductorBioconductor ProjectKompatibilitätsversion 3.16R 4.2-kompatible Umgebung; aktuelle Version 3.23. https://bioconductor.org/about/release-announcements/
Cytoscape DesktopCytoscape Consortiumv3.7.1Version zur Netzwerkvisualisierung/-analyse; aktuelle Version v3.10.4. https://cytoscape.org/download.html
DisGeNETMedBioinformatics Solutionsv25.4Abfrage von Genen im Zusammenhang mit CHF; durchgeführt am 2026-02-10. https://disgenet.com/
DrugBankOMx Personal Health Analyticsv6.0Abfrage von Genen im Zusammenhang mit CHF; durchgeführt am 2026-02-12. https://go.drugbank.com/
GeneCardsWeizmann Institute of ScienceWebdatenbankAbfrage von Genen im Zusammenhang mit CHF; durchgeführt am 2026-02-10. https://www.genecards.org/
GEO / GSE5406NCBIGSE5406; GPL96RMA-normalisierte Mikroarray-Serie des menschlichen linken Ventrikels. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5406
hgu133a.dbBioconductorBioconductor 3.16 UmgebungProbe-Anmerkung für Affymetrix Human Genome U133A; exakte Patch-Version nicht dokumentiert. https://bioconductor.org/packages/hgu133a.db/
limmaBioconductorBioconductor 3.16 UmgebungAnalysen zur differentiellen Expression; exakte Patch-Version nicht dokumentiert. https://bioconductor.org/packages/limma/
MacBookAppleM4-ProzessorLokale Rechenworkstation; Spezifikation des Arbeitsspeichers nicht dokumentiert. https://www.apple.com/mac/
MetascapeMetascapev3.5GO/KEGG-Anreicherung; exakter Zugriffszeitpunkt nicht dokumentiert. https://metascape.org/
Microsoft ExcelMicrosoftDesktop-AnwendungDatenbereinigung/-export; Anwendungsversion nicht dokumentiert. https://www.microsoft.com/microsoft-365/excel
OMIMJohns Hopkins UniversityWebdatenbankAbfrage von Genen im Zusammenhang mit CHF; durchgeführt am 2026-02-10. https://www.omim.org/
Open BabelOpen Babel ProjectVersion nicht dokumentiertStrukturumwandlung im Docking-Workflow. https://openbabel.org/
PharmGKBPharmGKBWebdatenbankAbfrage von Genen im Zusammenhang mit CHF; durchgeführt am 2026-02-12. https://www.pharmgkb.org/
PubChemNCBIWebdatenbankSMILES und Strukturergänzung. https://pubchem.ncbi.nlm.nih.gov/
PyMOLSchrödingerVersion nicht dokumentiertProtein-Vorverarbeitung und Visualisierung der Bindungsmodi. https://www.pymol.org/
RR Foundation4.2.0+Aufgezeichnete Versionsserie für die Analyse; aktuelle Version 4.6. https://www.r-project.org/
RCSB Protein Data BankRCSB PDBWebarchivProteinkristallstrukturen. https://www.rcsb.org/
RStudioPosit2026.04.0+526IDE für die Analyse, dokumentiert in der Versionsnotiz. https://posit.co/download/rstudio-desktop/
STRINGSTRING ConsortiumWebserviceHomo sapiens; Konfidenz >0,9; Release-/Sitzungsmetadaten nicht dokumentiert. https://string-db.org/
SwissADMESIB Swiss Institute of BioinformaticsWebserviceADME- und Lipinski-Regel-Prüfung; lokale Version nicht anwendbar. https://www.swissadme.ch/
SwissTargetPredictionSIB Swiss Institute of Bioinformatics2019 HauptaktualisierungHomo sapiens; Wahrscheinlichkeit >0,02; durchgeführt am 2026-02-10. https://www.swisstargetprediction.ch/
TCMSPNorthwest A&F Universityv2.3Abfrage von Verbindungen; durchgeführt am 2026-02-07. https://tcmsp-e.com/
TTDTherapeutic Target Database2026-VersionAbfrage von Genen im Zusammenhang mit CHF; durchgeführt am 2026-02-12. https://ttd.idrblab.cn/
UniProtKBUniProt ConsortiumVersion 2026_01Überprüfte Standardisierung menschlicher Gen-Symbole (Homo sapiens). https://www.uniprot.org/
VennyCNB-CSICv2.1.0Visualisierung der Schnittmenge von Zielgruppen. https://bioinfogp.cnb.csic.es/tools/venny/

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