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Article de recherche

Pharmacologie de réseau, transcriptomique GEO et amarrage moléculaire de la décoction Zhenwu associée à la poudre Wuling pour l'insuffisance cardiaque chronique

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11 septembre 2026

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

Dans cet article

Résumé

Cette étude intègre la cartographie des cibles basée sur les réseaux, les données d'expression GEO et le docking pour classer les candidats associés à la combinaison Zhenwu-Wuling dans l'insuffisance cardiaque chronique.

Résumé

La décoction Zhenwu combinée au Wuling San est utilisée dans l'insuffisance cardiaque chronique (ICC), mais sa base moléculaire n'est pas entièrement définie. Nous avons combiné une cartographie des cibles basée sur les réseaux, des données transcriptomiques du Gene Expression Omnibus (GEO) et un ancrage moléculaire afin de hiérarchiser les cibles et voies candidates. Les données sur les composés des huit plantes ont été extraites de bases de données pharmacologiques et chimiques, puis soumises à un criblage des composés et de leurs cibles. Les gènes associés à l'ICC ont été regroupés à partir de six ressources sur la maladie, et leur chevauchement avec les cibles liées à la formule a été analysé par des réseaux d'interactions protéine-protéine (PPI) et des analyses d'enrichissement fonctionnel. La série GSE5406 a fourni les données d'expression myocardique des échantillons défaillants et non défaillants. Ce flux de travail a permis d'obtenir 89 composés sélectionnés, 314 cibles non redondantes des composés et 2 515 cibles liées à l'ICC, avec 143 gènes communs. AKT1, IL6, TNF, SRC et ESR1 se sont classés au plus haut rang dans le réseau maladie-cible. L'analyse d'expression a révélé 1 348 gènes différentiellement exprimés (DEGs) et a sélectionné HSP90AA1 et STAT1 comme candidats clés au niveau transcriptomique. Les termes enrichis se regroupaient autour de l'hypoxie, de la circulation, des voies de signalisation liées au TNF, de la voie PI3K-Akt et de la voie MAPK. L'ancrage moléculaire a produit des scores prédits négatifs pour les paires représentatives composé-cible ; le score le plus négatif a été observé pour STAT1 et l'alisol C (-9,2 kcal/mol). Ces résultats génèrent une hypothèse computationnelle testable et permettent de hiérarchiser des candidats pour une validation expérimentale.

Introduction

L'insuffisance cardiaque (CHF) comprend un syndrome hétérogène résultant d'anomalies myocardiques structurelles ou fonctionnelles. De tels changements peuvent compromettre le remplissage ventriculaire ou l'éjection systolique et s'accompagnent souvent de congestion pulmonaire ou systémique. Les symptômes typiques incluent la dyspnée, la fatigue et la rétention de liquide1. Une analyse rétrospective des données chinoises de l'étude mondiale sur la charge mondiale des maladies entre 1990 et 2023 a montré que la prévalence nationale des nouveaux cas d'insuffisance cardiaque a augmenté de 208,4 % sur trois décennies2.

En médecine traditionnelle chinoise (MTC), l'IC correspond à l'œdème cardiaque, au syndrome de dyspnée et à l'œdème généralisé. Sa pathogenèse est couramment décrite comme une déficience à la racine et un excès en périphérie. La déficience du yin, du yang et du qi constitue la base pathologique sous-jacente, tandis que l'accumulation interne de liquide et la stase sanguine représentent l'excès manifeste. En conséquence, la prise en charge en MTC met l'accent sur le réchauffement du yang, l'élimination des liquides rétentionnés, la promotion de la circulation et la résolution de la stase.

L'utilisation combinée de la décoction de Zhenwu et de la poudre de Wuling constitue une stratégie classique à base de plantes médicinales pour la prise en charge clinique de l'IC. La décoction de Zhenwu réchauffe le yang et favorise la diurèse, tandis que la poudre de Wuling soutient le qi yang et régule le métabolisme des liquides. Ensemble, ces deux formules peuvent renforcer le yang et éliminer l'humidité interne. Des preuves cliniques suggèrent que cette association peut atténuer les symptômes, améliorer les indicateurs de fonction cardiaque et ralentir la détérioration de l'IC3. Nous avons tout d'abord cartographié les constituants des formules et les cibles candidates de l'IC à l'aide de la pharmacologie en réseau, puis utilisé le docking moléculaire pour classer les paires constituants-cibles sélectionnées. Des profils d'expression dérivés de GEO ont ensuite été utilisés pour situer les candidats du réseau dans un contexte de maladie myocardique et examiner les voies en aval. Cette étude propose un cadre computationnel générant des hypothèses pour hiérarchiser les candidats en vue d'une validation expérimentale ultérieure ; elle n'établit toutefois ni l'efficacité clinique ni un mécanisme validé.

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Protocole

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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Résultats

Ingrédients actifs et cibles de la décoction de Zhenwu combinée au Poudre de Wuling
Des enregistrements constituants-cibles ont été obtenus pour chaque plante. Les nombres respectifs de cibles par plante étaient les suivants : Aconiti Lateralis Radix Praeparata, 178 ; Poria, 21 ; Atractylodis Macrocephalae Rhizoma, 18 ; Paeoniae Radix Alba, 86 ; Zingiberis Rhizoma Recens, 127 ; Alismatis Rhizoma, 96 ; Polyporus, 4 ; et Cinnamomi Ramulus, 45. Après standardisation des symboles et élimination des dou...

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Discussion

L'insuffisance cardiaque chronique (ICC) nécessite une prise en charge clinique à long terme et implique souvent des régimes médicamenteux complexes. Des études menées par Wang JX et Zhang S11 ainsi que d'autres travaux indiquent qu'un traitement pharmacologique conventionnel combiné à long terme peut réduire l'observance thérapeutique. Une mauvaise observance peut augmenter le risque de réadmission et d'effets émotionnels indésirables. Les médicaments disponibles visent principalement le contrôle...

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Déclarations de divulgation

Les auteurs déclarent ne pas avoir d'intérêts concurrents liés à cette étude.

Remerciements

Non applicable.

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Matériaux

Liste des matériaux utilisés dans cet article
NomEntrepriseNuméro de catalogueCommentaires
AutoDock VinaCCSB-Scrippsv1.2.6Version en ligne de commande utilisée ; version officielle actuelle v1.2.7 ; les configurations ont utilisé les valeurs par défaut CLI exhaustiveness 8 et num_modes 9. https://github.com/ccsb-scripps/AutoDock-Vina/releases
AutoDockToolsThe Scripps Research Institutev1.5.7Préparation du récepteur/ligand ; version utilisée dans l'analyse. https://autodock.scripps.edu/
BioconductorBioconductor Projectversion compatible 3.16Environnement compatible R 4.2 ; version actuelle 3.23. https://bioconductor.org/about/release-announcements/
Cytoscape DesktopCytoscape Consortiumv3.7.1Version utilisée pour la visualisation et l'analyse de réseaux ; version actuelle v3.10.4. https://cytoscape.org/download.html
DisGeNETMedBioinformatics Solutionsv25.4Récupération des gènes associés à l'IC ; recherche effectuée le 2026-02-10. https://disgenet.com/
DrugBankOMx Personal Health Analyticsv6.0Récupération des gènes associés à l'IC ; recherche effectuée le 2026-02-12. https://go.drugbank.com/
GeneCardsWeizmann Institute of ScienceBase de données webRécupération des gènes associés à l'IC ; recherche effectuée le 2026-02-10. https://www.genecards.org/
GEO / GSE5406NCBIGSE5406 ; GPL96Série de puces à ADN normalisées RMA provenant du ventricule gauche humain. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5406
hgu133a.dbBioconductorEnvironnement Bioconductor 3.16Annotation des sondes Affymetrix Human Genome U133A ; la version exacte du correctif n’a pas été conservée. https://bioconductor.org/packages/hgu133a.db/
limmaBioconductorEnvironnement Bioconductor 3.16Analyse d'expression différentielle ; la version exacte du correctif n’a pas été conservée. https://bioconductor.org/packages/limma/
MacBookAppleProcesseur M4Station de travail informatique locale ; la spécification mémoire n’a pas été conservée. https://www.apple.com/mac/
MetascapeMetascapev3.5Enrichissement GO/KEGG ; l'horodatage exact d'accès n’a pas été conservé. https://metascape.org/
Microsoft ExcelMicrosoftApplication de bureauCuration/exportation des données ; la version de l'application n’a pas été conservée. https://www.microsoft.com/microsoft-365/excel
OMIMJohns Hopkins UniversityBase de données webRécupération des gènes associés à l'IC ; recherche effectuée le 2026-02-10. https://www.omim.org/
Open BabelOpen Babel ProjectVersion non conservéeConversion de structures dans le flux de travail de docking. https://openbabel.org/
PharmGKBPharmGKBBase de données webRécupération des gènes associés à l'IC ; recherche effectuée le 2026-02-12. https://www.pharmgkb.org/
PubChemNCBIBase de données webSMILES et complétion de structure. https://pubchem.ncbi.nlm.nih.gov/
PyMOLSchrödingerVersion non conservéePrétraitement des protéines et visualisation des poses. https://www.pymol.org/
RR Foundation4.2.0+Famille de versions enregistrée pour l'analyse ; version actuelle 4.6. https://www.r-project.org/
RCSB Protein Data BankRCSB PDBArchive webStructures cristallines des protéines. https://www.rcsb.org/
RStudioPosit2026.04.0+526IDE d'analyse enregistré dans la note de révision. https://posit.co/download/rstudio-desktop/
STRINGSTRING ConsortiumService webHomo sapiens ; confiance >0,9 ; les métadonnées de version ou de session n’ont pas été conservées. https://string-db.org/
SwissADMESIB Swiss Institute of BioinformaticsService webAnalyse ADME et criblage selon les règles de Lipinski ; version locale non applicable. https://www.swissadme.ch/
SwissTargetPredictionSIB Swiss Institute of BioinformaticsMise à jour majeure de 2019Homo sapiens ; probabilité >0,02 ; recherche effectuée le 2026-02-10. https://www.swisstargetprediction.ch/
TCMSPNorthwest A&F Universityv2.3Récupération de composés ; recherche effectuée le 2026-02-07. https://tcmsp-e.com/
TTDTherapeutic Target DatabaseVersion 2026Récupération des gènes associés à l'IC ; recherche effectuée le 2026-02-12. https://ttd.idrblab.cn/
UniProtKBUniProt ConsortiumVersion 2026_01Normalisation des symboles de gènes Homo sapiens validés. https://www.uniprot.org/
VennyCNB-CSICv2.1.0Visualisation des intersections d'ensembles cibles. https://bioinfogp.cnb.csic.es/tools/venny/

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