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Artículo de investigación

Farmacología de redes, transcriptómica de GEO y acoplamiento molecular de la decocción Zhenwu con el polvo Wuling para la insuficiencia cardíaca crónica

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11 de septiembre de 2026

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

En este artículo

Resumen

Este estudio integra el mapeo de dianas basado en redes, datos de expresión de GEO y acoplamiento molecular para clasificar candidatos relacionados con la combinación Zhenwu-Wuling en la insuficiencia cardíaca crónica.

Resumen

Se utiliza la Decocción Zhenwu combinada con el Polvo Wuling en la insuficiencia cardíaca crónica (CHF), pero su base molecular no está completamente definida. Integraremos la identificación de objetivos basada en redes, datos transcriptómicos del Gene Expression Omnibus (GEO) y el acoplamiento molecular para priorizar objetivos y vías candidatas. Los registros de compuestos para las ocho hierbas se obtuvieron a partir de bases de datos farmacológicas y químicas y se sometieron a cribado de compuestos y objetivos. Los genes asociados a CHF se recopilaron a partir de seis recursos de enfermedades, y se examinó su superposición con los objetivos relacionados con la fórmula mediante análisis de redes de interacción de proteínas (PPI) y enriquecimiento funcional. GSE5406 proporcionó datos de expresión miocárdica para muestras con insuficiencia y sin insuficiencia. El flujo de trabajo produjo 89 compuestos seleccionados, 314 objetivos no redundantes de compuestos y 2.515 objetivos relacionados con CHF, con 143 genes compartidos. AKT1, IL6, TNF, SRC y ESR1 ocuparon los primeros lugares en la red de enfermedad-objetivo. El análisis de expresión arrojó 1.348 genes expresados diferencialmente (DEG) y seleccionó HSP90AA1 y STAT1 como candidatos principales transcriptómicos. Los términos enriquecidos se agruparon en torno a hipoxia, circulación, señalización relacionada con TNF, vías PI3K-Akt y MAPK. El acoplamiento produjo puntuaciones predichas negativas para pares representativos de constituyentes-objetivos; la puntuación más negativa se observó para STAT1 y alisol C (-9,2 kcal/mol). Estos hallazgos generan una hipótesis computacional comprobable y priorizan candidatos para validación experimental.

Introducción

La IC abarca un síndrome heterogéneo que surge a partir de anomalías miocárdicas estructurales o funcionales. Dichos cambios pueden comprometer el llenado ventricular o la eyección sistólica y a menudo se acompañan de congestión pulmonar o sistémica. Los síntomas típicos incluyen disnea, fatiga y retención de líquidos1. Un análisis retrospectivo de los datos chinos del Estudio de la Carga Global de Enfermedades desde 1990 hasta 2023 mostró que la prevalencia nacional de casos recién diagnosticados de IC aumentó en un 208,4 % durante tres décadas2.

En la medicina tradicional china (MTCh), la IC corresponde al edema cardíaco, el síndrome de disnea y el edema generalizado. Su patogénesis se describe comúnmente como deficiencia en la raíz y exceso en la rama. La deficiencia de yin, yang y qi constituye la base patológica subyacente, mientras que la acumulación interna de líquidos y la estasis sanguínea representan el exceso manifiesto. Por consiguiente, el tratamiento en la MTCh hace hincapié en calentar el yang, eliminar los líquidos retenidos, promover la circulación y resolver la estasis.

El uso combinado de la Decocción Zhenwu y el Polvo Wuling es una estrategia herbaria clásica para el manejo clínico de la IC. La Decocción Zhenwu calienta el yang y promueve la diuresis, mientras que el Polvo Wuling apoya el qi yang y regula el metabolismo de líquidos. Juntas, las dos fórmulas pueden reforzar el yang y eliminar la humedad interna. Evidencia clínica sugiere que esta combinación puede aliviar los síntomas, mejorar los indicadores de función cardíaca y ralentizar el deterioro de la IC3. Primero mapeamos los constituyentes de las fórmulas y los objetivos candidatos para la IC mediante farmacología de redes, luego utilizamos acoplamiento molecular para clasificar los pares seleccionados constituyente-objetivo. Posteriormente, se utilizaron perfiles de expresión derivados de GEO para situar los candidatos de la red en un contexto de enfermedad miocárdica y examinar las vías descendentes. El estudio proporciona un marco computacional generador de hipótesis para priorizar candidatos destinados a validación experimental posterior; sin embargo, no establece eficacia clínica ni un mecanismo validado.

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Protocolo

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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Resultados

Ingredientes activos y dianas de la Decocción Zhenwu combinada con el Polvo Wuling
Se obtuvieron registros de componentes-dianas para cada hierba. Las cantidades correspondientes de dianas por hierba fueron: Aconiti Lateralis Radix Praeparata, 178; Poria, 21; Atractylodis Macrocephalae Rhizoma, 18; Paeoniae Radix Alba, 86; Zingiberis Rhizoma Recens, 127; Alismatis Rhizoma, 96; Polyporus, 4; y Cinnamomi Ramulus, 45. Tras la normalización de símbolos y la eliminación de duplicados, el conjunto integr...

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Discusión

La IC requiere un manejo clínico a largo plazo y a menudo implica regímenes medicamentosos complejos. Estudios realizados por Wang JX y Zhang S11 y otros estudios indican que la farmacoterapia convencional combinada a largo plazo puede reducir la adherencia al tratamiento. Una mala adherencia puede aumentar el riesgo de reingresos y de resultados emocionales adversos. Los medicamentos disponibles se centran principalmente en el control de los síntomas y en la desaceleración del remodelado ventricu...

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Divulgaciones

Los autores no declaran intereses competitivos relacionados con este estudio.

Agradecimientos

No aplicable.

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Materiales

Lista de materiales utilizados en este artículo
NombreEmpresaNúmero de catálogoComentarios
AutoDock VinaCCSB-Scrippsv1.2.6Versión de línea de comandos utilizada; versión oficial actual v1.2.7; las configuraciones usaron los valores predeterminados de exhaustividad 8 y num_modes 9. https://github.com/ccsb-scripps/AutoDock-Vina/releases
AutoDockToolsThe Scripps Research Institutev1.5.7Preparación de receptor/ligando; versión utilizada en el análisis. https://autodock.scripps.edu/
BioconductorBioconductor Projectversión compatible 3.16Ambiente compatible con R 4.2; versión actual 3.23. https://bioconductor.org/about/release-announcements/
Cytoscape DesktopCytoscape Consortiumv3.7.1Versión utilizada para visualización y análisis de redes; versión actual v3.10.4. https://cytoscape.org/download.html
DisGeNETMedBioinformatics Solutionsv25.4Recuperación de genes asociados a ICC; búsqueda realizada el 2026-02-10. https://disgenet.com/
DrugBankOMx Personal Health Analyticsv6.0Recuperación de genes asociados a ICC; búsqueda realizada el 2026-02-12. https://go.drugbank.com/
GeneCardsWeizmann Institute of ScienceBase de datos webRecuperación de genes asociados a ICC; búsqueda realizada el 2026-02-10. https://www.genecards.org/
GEO / GSE5406NCBIGSE5406; GPL96Serie de microarreglos normalizada por RMA del ventrículo izquierdo humano. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5406
hgu133a.dbBioconductorAmbiente Bioconductor 3.16Anotación de sondas del genoma humano Affymetrix U133A; la versión exacta del parche no se conservó. https://bioconductor.org/packages/hgu133a.db/
limmaBioconductorAmbiente Bioconductor 3.16Análisis de expresión diferencial; la versión exacta del parche no se conservó. https://bioconductor.org/packages/limma/
MacBookAppleProcesador M4Estación de trabajo computacional local; la especificación de memoria no se conservó. https://www.apple.com/mac/
MetascapeMetascapev3.5Enriquecimiento GO/KEGG; la marca de tiempo exacta de acceso no se conservó. https://metascape.org/
Microsoft ExcelMicrosoftAplicación de escritorioCuración/exportación de datos; la versión de la aplicación no se conservó. https://www.microsoft.com/microsoft-365/excel
OMIMJohns Hopkins UniversityBase de datos webRecuperación de genes asociados a ICC; búsqueda realizada el 2026-02-10. https://www.omim.org/
Open BabelOpen Babel ProjectVersión no conservadaConversión de estructuras en el flujo de trabajo de acoplamiento molecular. https://openbabel.org/
PharmGKBPharmGKBBase de datos webRecuperación de genes asociados a ICC; búsqueda realizada el 2026-02-12. https://www.pharmgkb.org/
PubChemNCBIBase de datos webSMILES y completación de estructuras. https://pubchem.ncbi.nlm.nih.gov/
PyMOLSchrödingerVersión no conservadaPreprocesamiento de proteínas y visualización de conformaciones. https://www.pymol.org/
RR Foundation4.2.0+Familia de versiones registrada para el análisis; versión actual 4.6. https://www.r-project.org/
RCSB Protein Data BankRCSB PDBArchivo webEstructuras cristalinas de proteínas. https://www.rcsb.org/
RStudioPosit2026.04.0+526IDE de análisis registrado en la nota de revisión. https://posit.co/download/rstudio-desktop/
STRINGSTRING ConsortiumServicio webHomo sapiens; confianza >0.9; metadatos de versión/sesión no conservados. https://string-db.org/
SwissADMESIB Swiss Institute of BioinformaticsServicio webExamen de ADME y reglas de Lipinski; versión local no aplicable. https://www.swissadme.ch/
SwissTargetPredictionSIB Swiss Institute of BioinformaticsActualización principal de 2019Homo sapiens; probabilidad >0.02; búsqueda realizada el 2026-02-10. https://www.swisstargetprediction.ch/
TCMSPNorthwest A&F Universityv2.3Recuperación de compuestos; búsqueda realizada el 2026-02-07. https://tcmsp-e.com/
TTDTherapeutic Target Databaseversión 2026Recuperación de genes asociados a ICC; búsqueda realizada el 2026-02-12. https://ttd.idrblab.cn/
UniProtKBUniProt Consortiumversión 2026_01Normalización revisada de símbolos génicos de Homo sapiens. https://www.uniprot.org/
VennyCNB-CSICv2.1.0Visualización de intersección de conjuntos de dianas. https://bioinfogp.cnb.csic.es/tools/venny/

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