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Araştırma makalesi

Kronik Kalp Yetersizliğinde Zhenwu Decoction ile Wuling Powder Kombinasyonunun Ağ Farmakolojisi, GEO Transkriptomiği ve Moleküler Kenetlenme Analizi

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11 Eylül 2026

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

Bu makalede

Özet

Bu çalışma, kronik kalp yetmezliğinde Zhenwu-Wuling kombinasyonu ile ilişkili adayları sıralamak için ağ tabanlı hedef eşleştirmeyi, GEO ekspresyon verilerini ve kenetlemeyi entegre etmektedir.

Özet

Zhenwu Decoction'ın Wuling Powder ile kombinasyonu kronik kalp yetmezliğinde (CHF) kullanılmaktadır, ancak moleküler temeli tam olarak tanımlanmamıştır. Aday hedefleri ve yolakları önceliklendirmek için ağ tabanlı hedef eşleme, Gene Expression Omnibus (GEO) transkriptomik verileri ve moleküler kenetlemeyi entegre ettik. Sekiz bitki için bileşik kayıtları farmakolojik ve kimyasal veri tabanlarından elde edilmiş ve bileşik ile hedef taramasına tabi tutulmuştur. CHF ile ilişkili genler altı hastalık kaynağından derlenmiş ve formülle ilişkili hedeflerle olan örtüşmeleri PPI ağı ve fonksiyonel zenginleştirme analizleri kullanılarak incelenmiştir. GSE5406, yetmezlik gösteren ve göstermeyen örnekler için miyokardiyal ekspresyon verilerini sağlamıştır. İş akışı sonucunda 89 taranmış bileşik, 314 tekrarsız bileşik hedefi ve 2.515 CHF ile ilgili hedef belirlenmiş; 143 ortak gen saptanmıştır. Hastalık-hedef ağında AKT1, IL6, TNF, SRC ve ESR1 en üst sıralarda yer almıştır. Ekspresyon analizi 1.348 farklı eksprese edilen gen (DEG) ortaya çıkarmış ve HSP90AA1 ile STAT1'i transkriptomik hub adayları olarak seçmiştir. Zenginleştirilmiş terimler hipoksi, dolaşım, TNF ile ilişkili sinyalizasyon, PI3K-Akt ve MAPK yolakları etrafında kümelenmiştir. Kenetleme, temsili bileşen-hedef çiftleri için negatif öngörülen skorlar üretmiştir; en negatif skor STAT1 ve alisol C (-9.2 kcal/mol) için gözlemlenmiştir. Bu bulgular, test edilebilir hesaplamalı bir hipotez oluşturmakta ve deneysel doğrulama için adayları önceliklendirmektedir.

Giriş

KKY, yapısal veya fonksiyonel miyokard anomalilerinden kaynaklanan heterojen bir sendromdur. Bu tür değişiklikler ventrikül dolumunu veya sistolik ejeksiyonu bozabilir ve sıklıkla pulmoner veya sistemik konjesyona eşlik eder. Tipik semptomlar arasında dispne, yorgunluk ve sıvı tutulumu yer alır1. Çin'in 190'dan 2023'e kadar olan Küresel Hastalık Yükü verilerinin retrospektif bir analizi, yeni teşhis edilen KKY'nin ulusal prevalansının üç on yılda %208,4 oranında arttığını göstermiştir2.

Geleneksel Çin tıbbında (TCM), CHF; kardiyak ödem, dispne sendromu ve generalize ödem ile karşılık bulur. Patogenezi yaygın olarak kökte eksiklik ve dalda fazlalık şeklinde tanımlanır. Yin, yang ve qi eksikliği temel patolojik bazı oluştururken; dahili sıvı birikimi ve kan stazı belirgin fazlalığı temsil eder. Buna göre TCM yönetimi; yang'ı ısıtmaya, tutulan sıvıyı uzaklaştırmaya, dolaşımı desteklemeye ve stazı çözmeye odaklanır.

Zhenwu Dekoksiyonu ve Wuling Tozu'nun birlikte kullanımı, klinik KKY yönetimi için klasik bir bitkisel stratejidir. Zhenwu Dekoksiyonu yang'ı ısıtır ve diyürezi teşvik ederken, Wuling Tozu yang qi'yi destekler ve sıvı metabolizmasını düzenler. Bu iki formül birlikte yang'ı güçlendirebilir ve iç nemi ortadan kaldırabilir. Klinik kanıtlar, bu kombinasyonun semptomları hafifletebileceğini, kardiyak fonksiyon göstergelerini iyileştirebileceğini ve KKY kötüleşmesini yavaşlatabileceğini göstermektedir3. İlk olarak, ağ nefarmakolojisi kullanarak formül bileşenlerini ve aday KKY hedeflerini eşleştirdik, ardından seçilen bileşen-hedef çiftlerini sıralamak için kenetleme (docking) kullandık. Daha sonra, ağ adaylarını miyokardiyal hastalık bağlamına yerleştirmek ve alt yolları incelemek için GEO kaynaklı ekspresyon profilleri kullanıldı. Bu çalışma, sonraki deneysel doğrulama için adayları önceliklendirmeye yönelik hipotez üreten hesaplamalı bir çerçeve sunmaktadır; klinik etkinliği veya doğrulanmış bir mekanizmayı kanıtlamaz.

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Protokol

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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Sonuçlar

Wuling Tozu ile kombine edilen Zhenwu Dekoksiyonunun aktif bileşenleri ve hedefleri
Her bir bitki için bileşen-hedef kayıtları elde edilmiştir. Bitki başına denk gelen hedef sayıları şu şekildedir: Aconiti Lateralis Radix Praeparata, 178; Poria, 21; Atractylodis Macrocephalae Rhizoma, 18; Paeoniae Radix Alba, 86; Zingiberis Rhizoma Recens, 127; Alismatis Rhizoma, 96; Polyporus, 4 ve Cinnamomi Ramulus, 45. Sembol standardizasyonu ve yinelenenlerin ayıklanmasının ardından, entegre formül ilişkili set...

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Tartışma

KKY, uzun süreli klinik yönetim gerektirir ve genellikle karmaşık ilaç rejimlerini içerir. Wang JX ve Zhang S1 tarafından yapılan çalışmalar ve diğer çalışmalar, uzun süreli kombine konvansiyonel farmakoterapinin ilaç uyumunu azaltabileceğini göstermektedir. Düşük uyum, yeniden yatış riskini ve olumsuz duygusal sonuçları artırabilir. Mevcut ilaçlar büyük ölçüde semptom kontrolüne ve ventriküler yeniden şekillenmeyi yavaşlatmaya odaklanmaktadır. 2023 yılı Çin Kalp Yetersizliği Yönetimi Ulusal Kılav...

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Açıklamalar

Yazarlar, bu çalışma ile ilgili herhangi bir çıkar çatışması olmadığını beyan etmektedir.

Teşekkürler

Uygulanamaz.

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Malzemeler

Bu makalede kullanılan malzemelerin listesi
AdŞirketKatalog numarasıYorumlar
AutoDock VinaCCSB-Scrippsv1.2.6Kullanılan komut satırı sürümü; güncel resmi sürüm v1.2.7; kullanılan yapılandırmalar CLI varsayılanları exhaustiveness 8 ve num_modes 9. https://github.com/ccsb-scripps/AutoDock-Vina/releases
AutoDockToolsThe Scripps Research Institutev1.5.7Reseptör/ligand hazırlığı; analizde kullanılan sürüm. https://autodock.scripps.edu/
BioconductorBioconductor Project3.16 uyumluluk sürümüR 4.2 ile uyumlu ortam; güncel sürüm 3.23. https://bioconductor.org/about/release-announcements/
Cytoscape DesktopCytoscape Consortiumv3.7.1Kullanılan ağ görselleştirme/analiz sürümü; güncel sürüm v3.10.4. https://cytoscape.org/download.html
DisGeNETMedBioinformatics Solutionsv25.4CHF ilişkili genlerin elde edilmesi; 2026-02-10 tarihinde sorgulandı. https://disgenet.com/
DrugBankOMx Personal Health Analyticsv6.0CHF ilişkili genlerin elde edilmesi; 2026-02-12 tarihinde sorgulandı. https://go.drugbank.com/
GeneCardsWeizmann Institute of ScienceWeb veri tabanıCHF ilişkili genlerin elde edilmesi; 2026-02-10 tarihinde sorgulandı. https://www.genecards.org/
GEO / GSE5406NCBIGSE5406; GPL96RMA-normalizasyonlu insan sol ventrikül mikroarray serisi. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5406
hgu13a.dbBioconductorBioconductor 3.16 ortamıAffymetrix Human Genome U13A prob anotasyonu; kesin yama sürümü kaydedilmedi. https://bioconductor.org/packages/hgu13a.db/
limmaBioconductorBioconductor 3.16 ortamıDiferansiyel ekspresyon analizi; kesin yama sürümü kaydedilmedi. https://bioconductor.org/packages/limma/
MacBookAppleM4 işlemciYerel hesaplama iş istasyonu; bellek özellikleri kaydedilmedi. https://www.apple.com/mac/
MetascapeMetascapev3.5GO/KEGG zenginleştirme; kesin erişim zaman damgası kaydedilmedi. https://metascape.org/
Microsoft ExcelMicrosoftMasaüstü uygulamasıVeri düzenleme/aktarma; uygulama derlemesi kaydedilmedi. https://www.microsoft.com/microsoft-365/excel
OMIMJohns Hopkins UniversityWeb veri tabanıCHF ilişkili genlerin elde edilmesi; 2026-02-10 tarihinde sorgulandı. https://www.omim.org/
Open BabelOpen Babel ProjectSürüm kaydedilmediDocking iş akışında yapı dönüştürme. https://openbabel.org/
PharmGKBPharmGKBWeb veri tabanıCHF ilişkili genlerin elde edilmesi; 2026-02-12 tarihinde sorgulandı. https://www.pharmgkb.org/
PubChemNCBIWeb veri tabanıSMILES ve yapı tamamlama. https://pubchem.ncbi.nlm.nih.gov/
PyMOLSchrödingerSürüm kaydedilmediProtein ön işleme ve poz görselleştirme. https://www.pymol.org/
RR Foundation4.2.0+Analiz için kaydedilen sürüm ailesi; güncel sürüm 4.6. https://www.r-project.org/
RCSB Protein Data BankRCSB PDBWeb arşiviProtein kristal yapıları. https://www.rcsb.org/
RStudioPosit2026.04.0+526Revizyon notunda kaydedilen analiz IDE'si. https://posit.co/download/rstudio-desktop/
STRINGSTRING ConsortiumWeb servisiHomo sapiens; güven düzeyi >0.9; sürüm/oturum meta verileri kaydedilmedi. https://string-db.org/
SwissADMESIB Swiss Institute of BioinformaticsWeb servisiADME ve Lipinski kuralı taraması; yerel sürüm uygulanamaz. https://www.swissadme.ch/
SwissTargetPredictionSIB Swiss Institute of Bioinformatics2019 ana güncellemesiHomo sapiens; olasılık >0.02; 2026-02-10 tarihinde sorgulandı. https://www.swisstargetprediction.ch/
TCMSPNorthwest A&F Universityv2.3Bileşik elde etme; 2026-02-07 tarihinde sorgulandı. https://tcmsp-e.com/
TTDTherapeutic Target Database2026 sürümüCHF ilişkili genlerin elde edilmesi; 2026-02-12 tarihinde sorgulandı. https://ttd.idrblab.cn/
UniProtKBUniProt ConsortiumSürüm 2026_01Gözden geçirilmiş Homo sapiens gen sembolü standardizasyonu. https://www.uniprot.org/
VennyCNB-CSICv2.1.0Hedef seti kesişim görselleştirmesi. https://bioinfogp.cnb.csic.es/tools/venny/

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