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Multi-omics analysis identifies potential mechanisms by which high glucose accelerates macrophage foaming

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Abstract

Atherosclerotic morbidity is significantly higher in the diabetic population. Hyperglycemia, a typical feature of diabetes, has been proven to accelerate foam cell formation. However, the molecular mechanisms behind this process remain unclear. In this study, LPS and IFN-γ were used to convert THP-1-derived macrophages into M1 macrophages, which were then activated with ox-LDL in either high glucose or normal condition. We identified lipids within macrophages by Oil red O staining and total cholesterol detection. The genes involved in lipid absorption, efflux, inflammation, and metabolism were analyzed using qRT-PCR. The mechanisms of high glucose-induced foam cell formation were further investigated through metabolomics and transcriptomics analysis. We discovered that high glucose speed up lipid accumulation in macrophages (both lipid droplets and total cholesterol increased), diminished lipid efflux (ABCG1 down-regulation), and aggravated inflammation (IL1B and TNF up-regulation). Following multi-omics analysis, it was determined that glucose altered the metabolic and transcriptional profiles of macrophages, identifying 392 differently expressed metabolites and 293 differentially expressed genes, respectively. Joint pathway analysis suggested that glucose predominantly disrupted the glycerolipid, glycerophospholipid, and arachidonic acid metabolic pathways in macrophages. High glucose in the glyceride metabolic pathway, for instance, suppressed the transcription of triglyceride hydrolase (LIPG and LPL), causing cells to deposit excess triglycerides into lipid droplets and encouraging foam cell formation. More importantly, high glucose triggered the accumulation of pro-atherosclerotic lipids (7-ketocholesterol, lysophosphatidylcholine, and glycerophosphatidylcholine). In conclusion, this work elucidated mechanisms of glucose-induced foam cell formation via a multi-omics approach.

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Data availability

All data generated or analyzed during this study are included in this published article and its supplementary information files. Further any enquiries can be directed to the corresponding author.

Abbreviations

7-KC:

7-Ketocholesterol

AS:

Atherosclerosis

CE:

Cholesteryl esters

Control:

Blank control group

DEGs:

Differentially expressed genes

DEMs:

Differentially expressed metabolites

FC:

Fold change

GPC:

Glycerophosphocholine

IFN-γ:

Interferon γ

KEGG:

Kyoto Encyclopedia of Genes and Genomes

KOBAS:

KEGG Orthology-based Annotation System

LPS:

Lipopolysaccharide

LysoPC:

Lysophosphatidylcholine.

OPLS-DA:

Orthogonal projections to latent structures-discriminant analysis

ORO:

Oil red O

Ox-LDL:

Oxidized low-density lipoprotein

oxLDL:

oxLDL-induced group

oxLDL + HG:

oxLDL plus high glucose-induced group

PC:

Phosphatidylcholine

PCA:

Principal component analysis

PGH2:

Prostaglandin H2

PMA:

Phorbol myristic acid

qRT-PCR:

Quantitative real-time PCR

RNA-seq:

RNA sequencing

TG:

Triglycerides

VIP:

Variable importance for the projection

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Acknowledgements

Thanks to the members of our laboratory for their contributions.

Funding

This work was supported by the Natural Science Basic Research Program of Shaanxi province (Grant No. 2019JM-440), and The Science and Technology Project of Shaanxi Province (Grant No. 2014K11-03-01-07, 2022SF-152).

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Authors and Affiliations

  1. Cardiovascular Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, No. 227 Yanta West Road, Xi’an, 710061, Shaanxi, China

    Jie Qi & Chao-Feng Sun

  2. Second Department of Cardiovascular Medicine, Shaanxi Provincial People’s Hospital, No. 256 Youyi West Road, Xi’an, 710068, Shaanxi, China

    Jie Qi, Ni-Er Zhong, Wen-Qi Han & Qi-Ling Gou

  3. First Department of Cardiovascular Medicine, Shaanxi Provincial People’s Hospital, No. 256 Youyi West Road, Xi’an, 710068, Shaanxi, China

    Ying Lv

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Contributions

JQ, YL and CS contributed to the conception and design of the study. JQ, YL, NZ and WH performed the experiments and performed statistical analysis. JQ and QG analyzed transcriptome and metabolome data. JQ wrote the manuscript. All authors have reviewed and edited the article. JQ and CS provided funding acquisition and supervision. All authors contributed to the article and approved the submitted version.

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Correspondence to Chao-Feng Sun.

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Qi, J., Lv, Y., Zhong, NE. et al. Multi-omics analysis identifies potential mechanisms by which high glucose accelerates macrophage foaming. Mol Cell Biochem 478, 665–678 (2023). https://doi.org/10.1007/s11010-022-04542-w

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