Metabolic modeling of single bronchoalveolar macrophages reveals regulators of hyperinflammation in COVID-19.

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Author
Zhao, Qiuchen
Yu, Zhenyang
Zhang, Shengyuan
Shen, Xu-Rui
Yang, Hao
Xu, Yangyang
Liu, Yang
Yang, Lin
Zhang, Qing
Chen, Jiaqi
Date
2022-10-10
Journal
iScience
Type
Article

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https://doi.org/10.1016/j.isci.2022.105319
Abstract
SARS-CoV-2 infection induces imbalanced immune response such as hyperinflammation in patients with severe COVID-19. Here we studied the immunometabolic regulatory mechanisms for the pathogenesis of COVID-19. We depicted the metabolic landscape of immune cells, especially macrophages, from bronchoalveolar lavage fluid of COVID-19 patients at single-cell level. We found that most metabolic processes were upregulated in macrophages from lungs of mild COVID-19 patients compared to cells from heathy controls, whereas macrophages from severe COVID-19 showed downregulation of most of the core metabolic pathways including glutamate metabolism, fatty acid oxidation, citrate cycle and oxidative phosphorylation, and upregulation of a few pathways such as glycolysis. Rewiring cellular metabolism by amino acid supplementation, glycolysis inhibition or PPARγ stimulation reduces inflammation in macrophages stimulated with SARS-CoV-2. Altogether, this study demonstrates that metabolic imbalance of bronchoalveolar macrophages may contribute to hyperinflammation in patients with severe COVID-19, and provides insights into treating COVID-19 by immunometabolic modulation.
Data Availibility
ScRNA-seq data from BALF of COVID-19 patients used in all studies except in Figure S3 can be accessed in Gene Expression Omnibus under the accession number GSE145926 and GSM3660650. Data from paired PBMCs can be acquired in the Genome Sequence Archive (GSA) under accession number HRA000297. For data from BALF, we removed cells with gene number less than 200 or large than 6,000. Cells with unique molecular identifier (UMI) count less than 1,000 or mitochondrial gene percentage more than 10% (15% for PBMC) were also removed in the study. All samples were integrated with Seurat (version 3.2.2) to avoid the influence of batch effects. We utilized first 50 dimensions of canonical correlation analysis (CCA) and principal component analysis (PCA) for further analysis. ScRNA-seq data from BALF of another cohort of COVID-19 patients analyzed in Figure S3 can be accessed in the EGA European Genome-Phenome Archive database under the accession number EGAS00001004717. We removed cells with gene number less than 151 and large than 6,000, UMI count less than 301, and mitochondrial gene percentage more than 20% as reported In Figure 4, integrated analysis of CD14 598 + monocytes and macrophages from paired BALFand PBMCs samples was performed by extracting CD14 + monocytes and macrophages and using CCA to integrated. In Figure S7, integrated analysis of total monocytes or macrophages from paired PBMCs and BALF samples was performed by extracting all monocytes and macrophages and using CCA to integrate.
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© 2022 The Authors.
Identifier to cite or link to this item
http://hdl.handle.net/10713/20024
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