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Articolo di ricerca

Farmacologia di rete, trascrittomica GEO e docking molecolare della Decozione Zhenwu con Polvere Wuling per lo scompenso cardiaco cronico

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11 settembre 2026

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

In questo articolo

Sommario

Questo studio integra il mappaggio dei bersagli basato su reti, i dati di espressione GEO e il docking per classificare i candidati associati alla combinazione Zhenwu-Wuling nell'insufficienza cardiaca cronica.

Abstract

La Decozione Zhenwu combinata con la Polvere Wuling è utilizzata nell'insufficienza cardiaca cronica (CHF), ma la sua base molecolare non è completamente definita. Abbiamo integrato il mappaggio delle target basato su reti, dati trascrittomici del Gene Expression Omnibus (GEO) e docking molecolare per individuare le target e le vie candidate più promettenti. I dati sui composti delle otto erbe sono stati ottenuti da database farmacologici e chimici e sottoposti a screening di composti e target. I geni associati alla CHF sono stati raccolti da sei risorse relative alla malattia e il loro sovrapposizione con le target della formula è stata analizzata mediante reti di interazione proteina-proteina (PPI) e analisi di arricchimento funzionale. Il dataset GSE5406 ha fornito dati di espressione miocardica da campioni con insufficienza e senza insufficienza. Il flusso di lavoro ha prodotto 89 composti selezionati, 314 target non ridondanti dei composti e 2.515 target associati alla CHF, con 143 geni condivisi. AKT1, IL6, TNF, SRC ed ESR1 hanno ottenuto i punteggi più elevati nella rete malattia-target. L'analisi dell'espressione ha identificato 1.348 geni differenzialmente espressi (DEG) e ha selezionato HSP90AA1 e STAT1 come candidati principali a livello trascrittomico. I termini arricchiti si sono raggruppati attorno a ipossia, circolazione, segnalazione legata al TNF, vie PI3K-Akt e MAPK. Il docking ha prodotto punteggi predittivi negativi per coppie rappresentative costituenti-target; il punteggio più negativo è stato osservato per STAT1 e alisol C (-9,2 kcal/mol). Questi risultati generano un'ipotesi computazionale verificabile e individuano candidati prioritari per la validazione sperimentale.

Introduzione

L'insufficienza cardiaca congestizia comprende un quadro eterogeneo derivante da anomalie miocardiche strutturali o funzionali. Tali alterazioni possono compromettere il riempimento ventricolare o l'eiezione sistolica e sono spesso accompagnate da congestione polmonare o sistemica. I sintomi tipici includono dispnea, affaticamento e ritenzione idrica1. Un'analisi retrospettiva dei dati cinesi del Global Burden of Disease dal 1990 al 2023 ha mostrato che la prevalenza nazionale dei nuovi casi di insufficienza cardiaca congestizia è aumentata del 208,4% nell'arco di tre decenni2.

Nella medicina tradizionale cinese (MTC), l'insufficienza cardiaca congestizia (CHF) corrisponde all'edema cardiaco, al sindrome da dispnea e all'edema generalizzato. La sua patogenesi è comunemente descritta come un deficit alla radice e un eccesso nella ramificazione. La carenza di yin, yang e qi costituisce la base patologica sottostante, mentre l'accumulo interno di liquidi e la stasi del sangue rappresentano l'eccesso manifesto. Di conseguenza, il trattamento nella MTC pone l'accento sul riscaldamento del yang, sull'eliminazione dei liquidi ristagnanti, sul miglioramento della circolazione e sulla risoluzione della stasi.

L'uso combinato della Decozione Zhenwu e della Polvere Wuling rappresenta una strategia erboristica classica per la gestione clinica dell'insufficienza cardiaca cronica (CHF). La Decozione Zhenwu riscalda lo yang e promuove la diuresi, mentre la Polvere Wuling sostiene lo qi yang e regola il metabolismo dei liquidi. Insieme, le due formulazioni possono rafforzare lo yang ed eliminare l'umidità interna. Evidenze cliniche suggeriscono che questa combinazione possa alleviare i sintomi, migliorare gli indicatori della funzione cardiaca e rallentare il deterioramento dell'insufficienza cardiaca cronica3. Abbiamo innanzitutto mappato i costituenti delle formulazioni e i possibili bersagli per la CHF mediante farmacologia di rete, quindi abbiamo utilizzato il docking per ordinare le coppie costituente-bersaglio selezionate. Successivamente, profili di espressione derivati da GEO sono stati impiegati per collocare i candidati della rete in un contesto di malattia miocardica ed esaminare i percorsi a valle. Lo studio fornisce un quadro computazionale generatore di ipotesi per la selezione di candidati da sottoporre a successiva validazione sperimentale; tuttavia, non stabilisce l'efficacia clinica né un meccanismo validato.

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Protocollo

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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Risultati

Principi attivi e bersagli della Decozione Zhenwu combinata con la Polvere Wuling
Sono stati ottenuti i record costituenti-bersaglio per ciascuna erba. I corrispondenti numeri di bersagli per erba erano: Aconiti Lateralis Radix Praeparata, 178; Poria, 21; Atractylodis Macrocephalae Rhizoma, 18; Paeoniae Radix Alba, 86; Zingiberis Rhizoma Recens, 127; Alismatis Rhizoma, 96; Polyporus, 4; e Cinnamomi Ramulus, 45. Dopo standardizzazione dei simboli ed eliminazione dei duplicati, l'insieme integrato re...

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Discussione

L'insufficienza cardiaca cronica richiede un trattamento clinico a lungo termine e spesso comporta regimi terapeutici complessi. Studi condotti da Wang JX e Zhang S11 e altre ricerche indicano che una terapia farmacologica convenzionale combinata a lungo termine può ridurre l'aderenza alla terapia. Una scarsa aderenza può aumentare il rischio di ricovero e di esiti emotivi avversi. I farmaci disponibili si concentrano principalmente sul controllo dei sintomi e sul rallentamento del rimodellamento ...

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Dichiarazioni

Gli autori dichiarano di non avere interessi concorrenti relativi a questo studio.

Ringraziamenti

Non applicabile.

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Materiali

Elenco dei materiali utilizzati in questo articolo
NomeAziendaNumero di catalogoCommenti
AutoDock VinaCCSB-Scrippsv1.2.6Versione da riga di comando utilizzata; versione ufficiale attuale v1.2.7; le configurazioni hanno utilizzato i valori predefiniti CLI exhaustiveness 8 e num_modes 9. https://github.com/ccsb-scripps/AutoDock-Vina/releases
AutoDockToolsThe Scripps Research Institutev1.5.7Preparazione di recettore/ligando; versione utilizzata nell'analisi. https://autodock.scripps.edu/
BioconductorBioconductor Projectversione compatibile 3.16Ambiente compatibile con R 4.2; versione attuale 3.23. https://bioconductor.org/about/release-announcements/
Cytoscape DesktopCytoscape Consortiumv3.7.1Versione utilizzata per la visualizzazione e l'analisi delle reti; versione attuale v3.10.4. https://cytoscape.org/download.html
DisGeNETMedBioinformatics Solutionsv25.4Recupero di geni associati all'insufficienza cardiaca congestizia (CHF); ricerca effettuata il 2026-02-10. https://disgenet.com/
DrugBankOMx Personal Health Analyticsv6.0Recupero di geni associati all'insufficienza cardiaca congestizia (CHF); ricerca effettuata il 2026-02-12. https://go.drugbank.com/
GeneCardsWeizmann Institute of ScienceDatabase webRecupero di geni associati all'insufficienza cardiaca congestizia (CHF); ricerca effettuata il 2026-02-10. https://www.genecards.org/
GEO / GSE5406NCBIGSE5406; GPL96Serie di microarray normalizzata RMA per il ventricolo sinistro umano. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5406
hgu133a.dbBioconductorAmbiente Bioconductor 3.16Annotazione delle sonde Affymetrix Human Genome U133A; la versione esatta della patch non è stata conservata. https://bioconductor.org/packages/hgu133a.db/
limmaBioconductorAmbiente Bioconductor 3.16Analisi dell'espressione differenziale; la versione esatta della patch non è stata conservata. https://bioconductor.org/packages/limma/
MacBookAppleProcessore M4Workstation computazionale locale; la specifica della memoria non è stata conservata. https://www.apple.com/mac/
MetascapeMetascapev3.5Arricchimento GO/KEGG; il timestamp esatto di accesso non è stato conservato. https://metascape.org/
Microsoft ExcelMicrosoftApplicazione desktopCurazione/esportazione dati; la build dell'applicazione non è stata conservata. https://www.microsoft.com/microsoft-365/excel
OMIMJohns Hopkins UniversityDatabase webRecupero di geni associati all'insufficienza cardiaca congestizia (CHF); ricerca effettuata il 2026-02-10. https://www.omim.org/
Open BabelOpen Babel ProjectVersione non conservataConversione di strutture nel flusso di lavoro del docking. https://openbabel.org/
PharmGKBPharmGKBDatabase webRecupero di geni associati all'insufficienza cardiaca congestizia (CHF); ricerca effettuata il 2026-02-12. https://www.pharmgkb.org/
PubChemNCBIDatabase webSMILES e completamento della struttura. https://pubchem.ncbi.nlm.nih.gov/
PyMOLSchrödingerVersione non conservataPre-elaborazione delle proteine e visualizzazione dei pose. https://www.pymol.org/
RR Foundation4.2.0+Famiglia di versioni registrata per l'analisi; versione attuale 4.6. https://www.r-project.org/
RCSB Protein Data BankRCSB PDBArchivio webStrutture cristalline delle proteine. https://www.rcsb.org/
RStudioPosit2026.04.0+526IDE per l'analisi registrato nella nota di revisione. https://posit.co/download/rstudio-desktop/
STRINGSTRING ConsortiumServizio webHomo sapiens; confidenza >0.9; i metadati della versione/della sessione non sono stati conservati. https://string-db.org/
SwissADMESIB Swiss Institute of BioinformaticsServizio webScreening ADME e delle regole di Lipinski; versione locale non applicabile. https://www.swissadme.ch/
SwissTargetPredictionSIB Swiss Institute of BioinformaticsAggiornamento principale del 2019Homo sapiens; probabilità >0.02; ricerca effettuata il 2026-02-10. https://www.swisstargetprediction.ch/
TCMSPNorthwest A&F Universityv2.3Recupero di composti; ricerca effettuata il 2026-02-07. https://tcmsp-e.com/
TTDTherapeutic Target DatabaseVersione 2026Recupero di geni associati all'insufficienza cardiaca congestizia (CHF); ricerca effettuata il 2026-02-12. https://ttd.idrblab.cn/
UniProtKBUniProt ConsortiumRelease 2026_01Standardizzazione dei simboli genici Homo sapiens verificata. https://www.uniprot.org/
VennyCNB-CSICv2.1.0Visualizzazione dell'intersezione tra insiemi di target. https://bioinfogp.cnb.csic.es/tools/venny/

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