Proteomic and Metabolomic Characterization of COVID-19 Patient Sera
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Abstract
Early detection and effective treatment of severe COVID-19 patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model was validated using 10 independent patients, 7 of which were correctly classified. Targeted proteomics and metabolomics assays were employed to further validate this molecular classifier in a second test cohort of 19 COVID-19 patients, leading to 16 correct assignments. We identified molecular changes in the sera of COVID-19 patients compared to other groups implicating dysregulation of macrophage, platelet degranulation, complement system pathways, and massive metabolic suppression. This study revealed characteristic protein and metabolite changes in the sera of severe COVID-19 patients, which might be used in selection of potential blood biomarkers for severity evaluation.
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MS and array-based proteomics serology has screened for potential biomarkers and antibodies against infection.
“MS and array-based proteomics serology has screened for potential biomarkers and Abs against infection156,157.”
From A high-stringency blueprint of the human proteome (2020) · cites this paper as [157]
Severe COVID-19 is associated with elevated acute phase proteins including SAA1, SAA2, SAA4, SAP/APCS, and SERPINA3.
“the serum amyloids A-1 (SAA1), A-2 (SAA2), and A-4 (SAA4), the serum amyloid P-component (SAP/APCS), and alpha-1-antichymotrypsin (SERPINA3) were found to be elevated in severe COVID-19”
From COVID-19 and the human innate immune system (2021) · cites this paper as Shen et al., 2020
Complement components and regulators are elevated in severe COVID-19, pointing to complement activation as an early innate immune mechanism.
“point toward complement activation as an early innate immune activation mechanism”
From COVID-19 and the human innate immune system (2021) · cites this paper as Shen et al., 2020
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Self-citation
- Authors
- Shen, B., Yi, X., Sun, Y., Bi, X., Du, J., Zhang, C., Quan, S., Zhang, F., Sun, R., Qian, L., Ge, W., Liu, W., Liang, S., Chen, H., Zhang, Y., Li, J., Xu, J., He, Z., Chen, B., Wang, J., Yan, H., Zheng, Y., Wang, D., Zhu, J., Kong, Z., Kang, Z., Liang, X., Ding, X., Ruan, G., Xiang, N., Cai, X., Gao, H., Li, L., Li, S., Xiao, Q., Lu, T., Zhu, Y., Liu, H., Chen, H., & Guo, T.
- Affiliations
- Wenzhou Medical University (CN)
- Westlake University (CN)
- Dian Diagnostics (China) (CN)
- Corresponding author
- Zhu, Yi
- Liu, Huafen
- Chen, Haixiao
- Guo, Tiannan
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2020-07
- Volume
- 182
- Issue
- 1
- Pages
- 59-72.e15
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC7254001
- OpenAlex
- W3028711203
- Cited by
- 1,514
- References
- 62
- Citations by year
- 2026: 79 · 2025: 108 · 2024: 221 · 2023: 249 · 2022: 325 · 2021: 380 · 2020: 151
- FWCI
- 36.45
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- SSRN Electronic Journal (publishedVersion)
- medRxiv (acceptedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (81972492, 21904107, 81672086)
- Natural Science Foundation of Zhejiang Province (LR19C050001)
- SCIEX
- Topics
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Keywords
- COVID-19 pandemic, severe COVID-19, serum proteomics, serum metabolomics, blood biomarkers, disease severity classification, machine learning classifiers, macrophage activation, platelet secretion, complement system, metabolic suppression
- MeSH terms
- Machine Learning, COVID-19, Adult, Amino Acids, Female, Humans, Macrophages, Male, Middle Aged, Pneumonia, Viral, Severity of Illness Index, Biomarkers, Cluster Analysis, Coronavirus Infections, Proteomics, Lipid Metabolism, Metabolomics, Pandemics
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