Effects and Mechanism of Morinda Officinalis How on Metabolism-Associated Fatty Liver Disease.

IF 1.6 4区 医学 Q4 BIOCHEMICAL RESEARCH METHODS
Long Chen, Xiaohua Jiang, Hui Liu, Jin Zhang
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引用次数: 0

Abstract

Aims: This study aims to investigate the effects and mechanism of Morinda Officinalis How (MOH), a lianoid shrub with potential therapeutic properties, on Metabolism- Associated Fatty Liver Disease (MAFLD). bjective: The objective of this study was to construct a MOH-MAFLD network prediction model and explore the effect of MOH on MAFLD and its underlying mechanism in vivo.

Methods: Screening of MAFLD targets was performed using the DisGeNET database. Venny database was used to establish the MOH-MAFLD interaction network map, while the STRING database was applied to assess the Protein-Protein Interaction (PPI) network. The central target gene was screened using Gene Ontology (GO) function analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway.

Results: GO function enrichment analysis revealed that MOH affected MAFLD through apoptosis and estrogen-related pathways. KEGG pathway enrichment and PPI network analysis indicated that MOH might mitigate MAFLD by reducing apoptosis and improving lipid metabolism. Additionally, 6 weeks of MOH treatment in rats decreased caspase-3 levels and increased Bcl-2, Estrogen receptor α(Esr1), and JUN proteins, thus ameliorating MAFLD progression.

Conclusion: MOH could delay the progression of MAFLD by affecting estrogen-related pathways, reducing cell stress, and inhibiting apoptosis.

巴戟天对代谢相关性脂肪肝的作用和机制
目的:本研究旨在探讨具有潜在治疗作用的巴戟天灌木(MOH)对代谢相关性脂肪肝(MAFLD)的影响及其机制:本研究的目的是构建一个 MOH-MAFLD 网络预测模型,并探索 MOH 对 MAFLD 的影响及其体内潜在机制:方法:使用DisGeNET数据库筛选MAFLD靶标。方法:利用DisGeNET数据库筛选MAFLD靶标,利用Venny数据库建立MOH-MAFLD相互作用网络图,利用STRING数据库评估蛋白质-蛋白质相互作用(PPI)网络。利用基因本体(GO)功能分析和京都基因组百科全书(KEGG)通路筛选中心靶基因:结果:GO功能富集分析表明,MOH通过凋亡和雌激素相关通路影响MAFLD。KEGG通路富集和PPI网络分析表明,MOH可通过减少细胞凋亡和改善脂质代谢来缓解MAFLD。此外,对大鼠进行6周的MOH治疗可降低Caspase-3水平,增加Bcl-2、雌激素受体α(Esr1)和JUN蛋白,从而改善MAFLD的进展:结论:MOH可通过影响雌激素相关通路、降低细胞应激和抑制细胞凋亡来延缓MAFLD的进展。
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来源期刊
CiteScore
3.10
自引率
5.60%
发文量
327
审稿时长
7.5 months
期刊介绍: Combinatorial Chemistry & High Throughput Screening (CCHTS) publishes full length original research articles and reviews/mini-reviews dealing with various topics related to chemical biology (High Throughput Screening, Combinatorial Chemistry, Chemoinformatics, Laboratory Automation and Compound management) in advancing drug discovery research. Original research articles and reviews in the following areas are of special interest to the readers of this journal: Target identification and validation Assay design, development, miniaturization and comparison High throughput/high content/in silico screening and associated technologies Label-free detection technologies and applications Stem cell technologies Biomarkers ADMET/PK/PD methodologies and screening Probe discovery and development, hit to lead optimization Combinatorial chemistry (e.g. small molecules, peptide, nucleic acid or phage display libraries) Chemical library design and chemical diversity Chemo/bio-informatics, data mining Compound management Pharmacognosy Natural Products Research (Chemistry, Biology and Pharmacology of Natural Products) Natural Product Analytical Studies Bipharmaceutical studies of Natural products Drug repurposing Data management and statistical analysis Laboratory automation, robotics, microfluidics, signal detection technologies Current & Future Institutional Research Profile Technology transfer, legal and licensing issues Patents.
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