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Artigo de investigação

Farmacologia de Redes, Transcriptômica de GEO e Docagem Molecular da Decocção Zhenwu com Pó Wuling para Insuficiência Cardíaca Crônica

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⸱

11 de setembro de 2026

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

Neste artigo

Resumo

Este estudo integra mapeamento de alvos baseado em redes, dados de expressão do GEO e docking para classificar candidatos relacionados à combinação Zhenwu-Wuling na insuficiência cardíaca crônica.

Resumo

A Decocção Zhenwu combinada com o Pó Wuling é usada na insuficiência cardíaca crônica (ICC), mas sua base molecular não está totalmente definida. Integramos mapeamento de alvos baseado em rede, dados transcriptômicos do Gene Expression Omnibus (GEO) e acoplamento molecular para priorizar alvos e vias candidatas. Registros de compostos para as oito ervas foram obtidos de bancos de dados farmacológicos e químicos e submetidos à triagem de compostos e alvos. Genes associados à ICC foram reunidos a partir de seis recursos de doenças, e sua sobreposição com alvos relacionados à fórmula foi examinada por meio de análise de rede de interação proteína-proteína (PPI) e enriquecimento funcional. O conjunto de dados GSE5406 forneceu dados de expressão miocárdica de amostras com e sem insuficiência. O fluxo de trabalho gerou 89 compostos triados, 314 alvos não redundantes dos compostos e 2.515 alvos relacionados à ICC, com 143 genes compartilhados. AKT1, IL6, TNF, SRC e ESR1 apresentaram as classificações mais altas na rede de alvos da doença. A análise de expressão identificou 1.348 genes diferencialmente expressos (DEGs) e selecionou HSP90AA1 e STAT1 como candidatos a húbulos transcriptômicos. Os termos enriquecidos agruparam-se em torno de hipóxia, circulação, sinalização relacionada ao TNF, vias PI3K-Akt e MAPK. O acoplamento molecular gerou escores preditivos negativos para pares representativos constituinte-alvo; o escore mais negativo foi observado para STAT1 e alisol C (-9,2 kcal/mol). Esses achados geram uma hipótese computacional testável e priorizam candidatos para validação experimental.

Introdução

A IC agrega uma síndrome heterogênea decorrente de anormalidades miocárdicas estruturais ou funcionais. Tais alterações podem comprometer o enchimento ventricular ou a ejeção sistólica e frequentemente são acompanhadas por congestão pulmonar ou sistêmica. Os sintomas típicos incluem dispneia, fadiga e retenção de líquidos1. Uma análise retrospectiva dos dados chineses do Estudo Global de Doenças de 1990 a 2023 mostrou que a prevalência nacional de IC recém-diagnosticada aumentou em 208,4% ao longo de três décadas2.

Na medicina tradicional chinesa (MTC), a IC corresponde ao edema cardíaco, síndrome de dispneia e edema generalizado. Sua patogênese é comumente descrita como deficiência na raiz e excesso no ramo. A deficiência de yin, yang e qi constitui a base patológica subjacente, enquanto o acúmulo interno de fluidos e a estase sanguínea representam o excesso manifesto. Assim, o tratamento na MTC enfatiza o aquecimento do yang, a eliminação de fluidos retidos, a promoção da circulação e a resolução da estase.

O uso combinado de Zhenwu Decoction e Wuling Powder é uma estratégia clássica à base de ervas para o manejo clínico da IC. Zhenwu Decoction aquece o yang e promove a diurese, enquanto Wuling Powder sustenta o qi yang e regula o metabolismo dos fluidos. Juntas, as duas fórmulas podem reforçar o yang e eliminar a umidade interna. Evidências clínicas sugerem que essa combinação pode aliviar sintomas, melhorar indicadores da função cardíaca e retardar a deterioração da IC3. Primeiramente, mapeamos os constituintes das fórmulas e os alvos candidatos para IC utilizando farmacologia de rede, seguido pelo uso de docking para classificar os pares selecionados entre constituintes e alvos. Em seguida, perfis de expressão derivados do GEO foram utilizados para situar os candidatos da rede em um contexto de doença miocárdica e examinar as vias downstream. O estudo fornece um arcabouço computacional gerador de hipóteses para priorizar candidatos a uma posterior validação experimental; no entanto, não estabelece eficácia clínica nem um 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 ativos e alvos da Decocção Zhenwu combinada com o Pó Wuling
Foram obtidos registros de constituintes-alvo para cada erva. As contagens correspondentes de alvos por erva foram: 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. Após padronização dos símbolos e remoção de duplicatas, o conjunto integrado relacionado à fórmula cont...

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Discussão

A ICC exige manejo clínico de longo prazo e frequentemente envolve regimes medicamentosos complexos. Estudos de Wang JX e Zhang S11 e outros estudos indicam que a farmacoterapia convencional combinada de longo prazo pode reduzir a adesão medicamentosa. A baixa adesão pode aumentar o risco de reinternação e de desfechos emocionais adversos. Os medicamentos disponíveis focam principalmente no controle dos sintomas e na desaceleração do remodelamento ventricular. As Diretrizes Nacionais de 2023 para ...

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Divulgações

Os autores declaram não haver interesses concorrentes relacionados a este estudo.

Agradecimentos

Não aplicável.

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Materiais

Lista de materiais utilizados neste artigo
NomeEmpresaNúmero de catálogoComentários
AutoDock VinaCCSB-Scrippsv1.2.6Versão em linha de comando utilizada; versão oficial atual v1.2.7; configurações usaram os valores padrão da CLI: exhaustividade 8 e num_modes 9. https://github.com/ccsb-scripps/AutoDock-Vina/releases
AutoDockToolsThe Scripps Research Institutev1.5.7Preparação de receptor/ligante; versão utilizada na análise. https://autodock.scripps.edu/
BioconductorProjeto Bioconductorlançamento compatível 3.16Ambiente compatível com R 4.2; versão atual 3.23. https://bioconductor.org/about/release-announcements/
Cytoscape DesktopConsortium Cytoscapev3.7.1Versão utilizada para visualização/análise de redes; versão atual v3.10.4. https://cytoscape.org/download.html
DisGeNETSoluções MedBioinformaticsv25.4Recuperação de genes associados à ICC; pesquisa realizada em 2026-02-10. https://disgenet.com/
DrugBankOMx Personal Health Analyticsv6.0Recuperação de genes associados à ICC; pesquisa realizada em 2026-02-12. https://go.drugbank.com/
GeneCardsInstituto Weizmann de CiênciaBanco de dados webRecuperação de genes associados à ICC; pesquisa realizada em 2026-02-10. https://www.genecards.org/
GEO / GSE5406NCBIGSE5406; GPL96Série de microarranjos normalizados por RMA do ventrículo esquerdo humano. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5406
hgu133a.dbBioconductorAmbiente Bioconductor 3.16Anotação de sonda do Genoma Humano Affymetrix U133A; versão exata do patch não registrada. https://bioconductor.org/packages/hgu133a.db/
limmaBioconductorAmbiente Bioconductor 3.16Análise de expressão diferencial; versão exata do patch não registrada. https://bioconductor.org/packages/limma/
MacBookAppleprocessador M4Estação de trabalho computacional local; especificação de memória não registrada. https://www.apple.com/mac/
MetascapeMetascapev3.5Enriquecimento GO/KEGG; carimbo de data/hora exato de acesso não registrado. https://metascape.org/
Microsoft ExcelMicrosoftAplicativo desktopCuradoria/exportação de dados; versão do aplicativo não registrada. https://www.microsoft.com/microsoft-365/excel
OMIMUniversidade Johns HopkinsBanco de dados webRecuperação de genes associados à ICC; pesquisa realizada em 2026-02-10. https://www.omim.org/
Open BabelProjeto Open BabelVersão não registradaConversão de estrutura no fluxo de trabalho de docking. https://openbabel.org/
PharmGKBPharmGKBBanco de dados webRecuperação de genes associados à ICC; pesquisa realizada em 2026-02-12. https://www.pharmgkb.org/
PubChemNCBIBanco de dados webSMILES e complementação de estrutura. https://pubchem.ncbi.nlm.nih.gov/
PyMOLSchrödingerVersão não registradaPré-processamento de proteínas e visualização de poses. https://www.pymol.org/
RFundação R4.2.0+Família de versões registrada para análise; versão atual 4.6. https://www.r-project.org/
RCSB Protein Data BankRCSB PDBArquivo webEstruturas cristalográficas de proteínas. https://www.rcsb.org/
RStudioPosit2026.04.0+526IDE de análise registrada na nota de revisão. https://posit.co/download/rstudio-desktop/
STRINGConsortium STRINGServiço webHomo sapiens; confiança >0,9; metadados de versão/sessão não registrados. https://string-db.org/
SwissADMESIB Swiss Institute of BioinformaticsServiço webTriagem de ADME e regras de Lipinski; versão local não aplicável. https://www.swissadme.ch/
SwissTargetPredictionSIB Swiss Institute of Bioinformaticsatualização principal de 2019Homo sapiens; probabilidade >0,02; pesquisa realizada em 2026-02-10. https://www.swisstargetprediction.ch/
TCMSPUniversidade Northwest A&Fv2.3Recuperação de compostos; pesquisa realizada em 2026-02-07. https://tcmsp-e.com/
TTDTherapeutic Target Databaselançamento 2026Recuperação de genes associados à ICC; pesquisa realizada em 2026-02-12. https://ttd.idrblab.cn/
UniProtKBConsortium UniProtLançamento 2026_01Padronização de símbolos gênicos Homo sapiens revisada. https://www.uniprot.org/
VennyCNB-CSICv2.1.0Visualização de interseção de conjuntos de alvos. https://bioinfogp.cnb.csic.es/tools/venny/

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