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Multiomics Personalized Network Analyses Highlight Progressive Immune Disruption of Central Metabolism Associated with COVID-19 Severity
54 Pages Posted: 17 Dec 2021 Publication Status: Published
More...Abstract
The system-wide metabolomics profile in COVID-19 patients identified several biomarkers that shed mechanistic insights into how SARS-CoV-2 infection is associated with gender, age, ethnicity, and co-morbidities. However, the clinical outcome and disease severity are heterogenous and cannot explain by a single factor. In this study, we used system-wide network-based system biology analysis using whole blood RNA-seq, immune-phenotyping by flow cytometry, plasma metabolomics, and single cell-type metabolomics to identify the metabo-transcriptomics mechanism of COVID-19 severity at the personalized and group level. Digital cell quantification and immune-phenotyping of the mononuclear phagocytes (MNPs) indicated a substantial role in coordinating the immune cells that mediate the COVID-19 disease severity. Stratum-specific and personalized genome-scale metabolic modeling indicated that transporter genes such as SLC16A6 and SLC29A1, and metabolites such as α-ketoglutarate, succinate, malate, and butyrate, could play a crucial role in COVID-19 severity can be the potential targetable elements for COVID-19 treatment depending on disease severity.
Funding: The study is funded by the Swedish Research Council grants 2021-00993 (UN), 2017-01330 (UN), 2018-06156 (UN), and 2021-03035 (SG) and support received from Karolinska Institutet Stiftelser och Fonder grant 2020-01554 (UN) and 2020-02153 (SG), The Center for Medical Innovation grant CIMED-FoUI-093304 (SG) and Åke Wiberg Stiftelse grant M20-0220 (SG).
Declaration of Interests: Authors declare that they have no competing interests.
Ethics Approval Statement: The study was approved by the regional Ethics Committee in Stockholm, Sweden, and performed in accordance with the Declaration of Helsinki.
Keywords: COVID-19, Similarity Network Fusion, personalised genome scale metabolic model
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