Multi-omics analysis of the lipid-regulating effects of metformin in a glucose concentration-dependent manner in macrophage-derived foam cells.

IF 1.8 4区 生物学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY
Jie Qi, Mengya Dong, Qiling Gou, Huolan Zhu
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Abstract

Metformin has a long history of clinical application and has been shown to have outstanding ability in lowering glucose. Recent advances have further revealed its broad modulatory ability beyond glucose-lowering, expanding the scope of metformin applications. Metformin has now been applied as a viable lipid-lowering strategy in non-hyperglycemic obese patients. However, the benefits and underlying pharmacological mechanisms of metformin administration in non-hyperglycemic populations remain to be explained. Our study aimed to systematically investigate the differences in the lipid-lowering function and pharmacological mechanisms of metformin in high- and low-sugar conditions to facilitate the development of individualized metformin use regimens for different clinical patients. We constructed macrophage-derived foam cell models in vitro for subsequent analysis. ORO results showed that metformin significantly reduced lipid accumulation in macrophages in both high and low glucose environments, but the lipid decline was higher in the high glucose environment. By mutual validation and joint analysis of transcriptomics and metabolomics, significant differences in metformin transcriptional and metabolic patterns existed among high and normal glucose environments. The significant alterations of genes such as DGKA, LPL, DGAT2 and lipid metabolites such as LysPA and LysPC partially explained the glucose-dependent pharmacological function of metformin. In conclusion, our study confirmed that the lipid-lowering effect of metformin depends on the extracellular glucose concentration, and systematically studied the molecular mechanism of metformin in different glycemic environments, which provides a certain reference value for the subsequent in-depth study and clinical application.

Abstract Image

多组学分析二甲双胍以葡萄糖浓度依赖性方式对巨噬细胞衍生泡沫细胞的脂质调节作用
二甲双胍的临床应用历史悠久,降糖能力突出。最近的研究进展进一步揭示了二甲双胍在降糖之外的广泛调节能力,扩大了二甲双胍的应用范围。目前,二甲双胍已作为一种可行的降脂策略应用于非高血糖肥胖患者。然而,二甲双胍在非高血糖人群中的益处和潜在药理机制仍有待解释。我们的研究旨在系统研究二甲双胍在高糖和低糖条件下降脂功能和药理机制的差异,以促进针对不同临床患者制定个性化的二甲双胍使用方案。我们在体外构建了巨噬细胞衍生的泡沫细胞模型,以便进行后续分析。ORO结果显示,二甲双胍可显著减少高糖和低糖环境下巨噬细胞中的脂质积累,但高糖环境下脂质下降幅度更大。通过转录组学和代谢组学的相互验证和联合分析,二甲双胍的转录和代谢模式在高血糖和正常血糖环境中存在显著差异。DGKA、LPL、DGAT2 等基因以及 LysPA 和 LysPC 等脂质代谢物的明显改变部分解释了二甲双胍的葡萄糖依赖性药理作用。总之,我们的研究证实了二甲双胍的降脂作用依赖于细胞外葡萄糖浓度,并系统研究了二甲双胍在不同血糖环境下的分子机制,为后续的深入研究和临床应用提供了一定的参考价值。
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来源期刊
Cell Biochemistry and Biophysics
Cell Biochemistry and Biophysics 生物-生化与分子生物学
CiteScore
4.40
自引率
0.00%
发文量
72
审稿时长
7.5 months
期刊介绍: Cell Biochemistry and Biophysics (CBB) aims to publish papers on the nature of the biochemical and biophysical mechanisms underlying the structure, control and function of cellular systems The reports should be within the framework of modern biochemistry and chemistry, biophysics and cell physiology, physics and engineering, molecular and structural biology. The relationship between molecular structure and function under investigation is emphasized. Examples of subject areas that CBB publishes are: · biochemical and biophysical aspects of cell structure and function; · interactions of cells and their molecular/macromolecular constituents; · innovative developments in genetic and biomolecular engineering; · computer-based analysis of tissues, cells, cell networks, organelles, and molecular/macromolecular assemblies; · photometric, spectroscopic, microscopic, mechanical, and electrical methodologies/techniques in analytical cytology, cytometry and innovative instrument design For articles that focus on computational aspects, authors should be clear about which docking and molecular dynamics algorithms or software packages are being used as well as details on the system parameterization, simulations conditions etc. In addition, docking calculations (virtual screening, QSAR, etc.) should be validated either by experimental studies or one or more reliable theoretical cross-validation methods.
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