DataSheet_1_Alterations of Urinary Microbial Metabolites and Immune Indexes Linked With COVID-19 Infection and Prognosis.xlsx
Coronavirus disease 2019 (COVID-19) has evolved into an established global pandemic. Metabolomic studies in COVID-19 patients is worth exploring for further available screening methods. In our study, we recruited a study cohort of 350 subjects comprising 248 COVID-19 patients (161 non-severe cases, 60 asymptomatic cases, and 27 severe cases) and 102 healthy controls (HCs), and herein present data with respect to their demographic features, urinary metabolome, immunological indices, and follow-up health status. We found that COVID-19 resulted in alterations of 39 urinary, mainly microbial, metabolites. Using random forest analysis, a simplified marker panel including three microbial metabolites (oxoglutaric acid, indoxyl, and phenylacetamide) was constructed (AUC=0.963, 95% CI, 0.930-0.983), which exhibited higher diagnostic performance than immune feature-based panels between COVID-19 and HC groups (P<0.0001). Meanwhile, we observed that urine metabolic markers enabled discriminating asymptomatic patients (ASY) from HCs (AUC = 0.981, 95% CI, 0.946-0.996), and predicting the incidence of high-risk sequalae in COVID-19 individuals (AUC=0.931, 95% CI, 0.877-0.966). Co-expression network analysis showed that 13 urinary microbial metabolites (e.g., oxoglutaric acid) were significantly correlated with alterations of CD4+, CD3+, and CD8+ T-cells, as well as IFN-γ, IL-2 and IL-4 levels, suggesting close interactions between microbial metabolites and host immune dysregulation in COVID-19. Taken together, our findings indicate that urinary metabolites may have promising potential for screening of COVID-19 in different application scenarios, and provide a new entry point to understand the microbial metabolites and related immune dysfunction in COVID-19.
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References
- https://doi.org//10.3390/jcm9020330
- https://doi.org//10.1038/d41586-020-01221-y
- https://doi.org//10.1126/sciimmunol.abh2259
- https://doi.org//10.1016/j.pathol.2021.08.001
- https://doi.org//10.1053/j.gastro.2020.06.048
- https://doi.org//10.1053/j.gastro.2020.05.048
- https://doi.org//10.1136/gutjnl-2020-323826
- https://doi.org//10.3389/fimmu.2020.00827
- https://doi.org//10.1136/gutjnl-2020-323020
- https://doi.org//10.1016/S2215-0366(21)00084-5
- https://doi.org//10.1007/s11606-021-06731-7
- https://doi.org//10.1097/SLA.0000000000005111
- https://doi.org//10.1111/ene.14357
- https://doi.org//10.1155/2019/4851323
- https://doi.org//10.1002/14651858.CD014641
- https://doi.org//10.2147/IJGM.S321292
- https://doi.org//10.1093/sleep/34.5.601
- https://doi.org//10.1016/j.bbih.2021.100315
- https://doi.org//10.1016/j.paid.2020.110131
- https://doi.org//10.1002/advs.201902862
- https://doi.org//10.1126/sciadv.aba8555
- https://doi.org//10.3389/fendo.2021.712855
- https://doi.org//10.1038/s41380-020-0744-2
- https://doi.org//10.3389/fcell.2021.620730
- https://doi.org//10.1016/j.kint.2021.05.032
- https://doi.org//10.1038/nature06245
- https://doi.org//10.1080/19490976.2020.1840765
- https://doi.org//10.1186/s13054-021-03544-2
- https://doi.org//10.1038/s41591-020-0965-6
- https://doi.org//10.3748/wjg.v27.i29.4763
- https://doi.org//10.1016/j.cell.2020.05.032
- https://doi.org//10.1038/s41392-021-00614-3
- https://doi.org//10.1021/acs.analchem.0c04497
- https://doi.org//10.1016/S0140-6736(20)30628-0
- https://doi.org//10.18632/aging.101805
- https://doi.org//10.1016/j.molmet.2018.03.015
- https://doi.org//10.1016/j.celrep.2021.109760
- https://doi.org//10.2337/db20-0765
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Categories
- Transplantation Immunology
- Tumour Immunology
- Immunology not elsewhere classified
- Immunology
- Veterinary Immunology
- Animal Immunology
- Genetic Immunology
- Applied Immunology (incl. Antibody Engineering, Xenotransplantation and T-cell Therapies)
- Autoimmunity
- Cellular Immunology
- Humoural Immunology and Immunochemistry
- Immunogenetics (incl. Genetic Immunology)
- Innate Immunity