Protective effects of Fufang Ejiao Jiang against aplastic anemia assessed by network pharmacology and metabolomics strategy

• Главные авторы: He Dan (Автор), Zhang Haichao (Автор), Yi Ziyang (Автор), Zhao Di (Автор), Zhang Shuihan (Автор)
• Формат: ‌
• Опубликовано: KeAi Communications Co., Ltd., 2021-12-01T00:00:00Z.
• Предметы: article
• Online-ссылка: Connect to this object online.
• Notes: 2589-3777; 10.1016/j.dcmed.2021.12.007
• Keywords: Fufang Ejiao Jiang (复方阿胶浆; FFEJJ); aplastic anemia; network pharmacology; metabolomics; lipid metabolomics; Medicine; R; Other systems of medicine; RZ201-999
• Source: Digital Chinese Medicine, Vol 4, Iss 4, Pp 328-342 (2021)

Objective: To elucidate the mechanisms underlying the therapeutic effects of Fufang Ejiao Jiang (复方阿胶浆, FFEJJ) on aplastic anemia (AA) using integrated network pharmacology and serum metabolomics. Methods: Traditional Chinese Medicine Systems Pharmacology (TCMSP), Pubmed, integrative pharmacology-based research platform of traditional Chinese medicine (TCMIP), and Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine (BATMAN-TCM) were used to identify the constituents and putative targets of FFEJJ. GeneCards and DisGeNET databases were used to identify AA-associated targets. We constructed a herb-component-target network and analyzed the protein-protein interaction (PPI) network. Potential mechanisms were determined using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. In addition, an AA model was established using acetylphenylhydrazine (APH) and cetylphenylhydrazine (CTX). Ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS)-based serum metabolomics was applied to screen potential metabolites and the related pathways associated with AA and the potential anti-anemic effects of FFEJJ. Results: A total of 30 active components of FFEJJ and 24 targets were related to AA. PPI network analysis showed that VEGFA, AKT1, IL-6, CASP3, and ICAM1 were key nodes overlapping with proteins known to be related to AA. KEGG pathway enrichment analysis revealed that the presumed targets of FFEJJ were mainly associated with pathways linked to the promotion of hematopoiesis and improvement of the hematopoietic microenvironment. A total of 423 metabolite biomarkers were identified between the control and AA models, which are involved in the development of AA. In contrast, FFEJJ reversed the 79 differential metabolites altered by AA. Pathway analysis suggested that the synergistic effects of FFEJJ were mainly enriched in 24 metabolic pathways. Among them, sphingolipid metabolism, glycerophospholipid metabolism, and arachidonic acid metabolism were related to promoting hematopoiesis and improving the hematopoietic microenvironment, which partially conforms with network pharmacology. The interaction network formed by three key differential metabolites, including hydroxy-eicosatetraenoic acid (HETE), sphingosine 1-phosphate (S1P), and lysophosphati-dylcholine (lysoPC), and three predicted network targets (VEGFA, CASP3, and ICAM1) may be the potential mechanism underlying the anti-AA action of the multi-component of FFEJJ. Conclusion: FFEJJ could be an alternative treatment option for AA. It acts by promoting hematopoiesis and improving the hematopoietic microenvironment. Network pharmacology-integrated metabolomics makes it possible to analyze TCMs from a systems perspective and at the molecular level.