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Large-Scale Multi-omic Analysis of COVID-19 Severity
Open accessPublished version of a preprint
Abstract
We performed RNA-seq and high-resolution mass spectrometry on 128 blood samples from COVID-19-positive and COVID-19-negative patients with diverse disease severities and outcomes. Quantified transcripts, proteins, metabolites, and lipids were associated with clinical outcomes in a curated relational database, uniquely enabling systems analysis and cross-ome correlations to molecules and patient prognoses. We mapped 219 molecular features with high significance to COVID-19 status and severity, many of which were involved in complement activation, dysregulated lipid transport, and neutrophil activation. We identified sets of covarying molecules, e.g., protein gelsolin and metabolite citrate or plasmalogens and apolipoproteins, offering pathophysiological insights and therapeutic suggestions. The observed dysregulation of platelet function, blood coagulation, acute phase response, and endotheliopathy further illuminated the unique COVID-19 phenotype. We present a web-based tool (covid-omics.app) enabling interactive exploration of our compendium and illustrate its utility through a machine learning approach for prediction of COVID-19 severity.
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400
Charlson, M. E., Pompei, P., Ales, K. L., & MacKenzie, C. R. (1987) · Journal of Chronic Diseases · doi:10.1016/0021-9681(87)90171-8 · Cited by 49,742
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077
Clinical characteristics of coronavirus disease 2019 in China
Guan, W.-J., Ni, Z.-Y., Hu, Y., Liang, W.-H., Ou, C.-Q., He, J.-X., Liu, L., Shan, H., Lei, C.-L., Hui, D. S. C., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2002032 · Cited by 30,246
Modern Applied Statistics with S
Venables, W. N., & Ripley, B. D. (2002) · Statistics and Computing · doi:10.1007/978-0-387-21706-2 · Cited by 24,305
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179
Research works printed but not linked: 3
- Duke-Cohan, 1998, Attractin (DPPT-L), a member of the CUB family of cell adhesion and guidance proteins, is secreted by activated human T lymphocytes and modulates immune cell interactions, Proc. Natl. Acad. Sci. USA, 95, 11336, 10.1073/pnas.95.19.11336 · doi:10.1073/pnas.95.19.11336
- Omarjee, 2020, Can ticagrelor be used to prevent sepsis-induced coagulopathy in COVID-19?, Clin. Immunol., 216, 108468, 10.1016/j.clim.2020.108468 · doi:10.1016/j.clim.2020.108468
- Stukalov, 2020, Multi-level proteomics reveals host-perturbation strategies of SARS-CoV-2 and SARS-CoV, bioRxiv
Cited by
Using machine learning approaches for multi-omics data analysis: A review
Reel, P. S., Reel, S., Pearson, E., Trucco, E., & Jefferson, E. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107739 · Cited by 903
Ultra-fast proteomics with Scanning SWATH
Messner, C. B., Demichev, V., Bloomfield, N., Yu, J. S. L., White, M., Kreidl, M., Egger, A.-S., Freiwald, A., Ivosev, G., Wasim, F., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00860-4 · Cited by 308
A time-resolved proteomic and prognostic map of COVID-19
Demichev, V., Tober-Lau, P., Lemke, O., Nazarenko, T., Thibeault, C., Whitwell, H., Röhl, A., Freiwald, A., Szyrwiel, L., Ludwig, D., et al. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.005 · Cited by 200
From Omics to Multi-Omics: A Review of Advantages and Tradeoffs
Hayes, C. N., Nakahara, H., Ono, A., Tsuge, M., & Oka, S. (2024) · Genes · doi:10.3390/genes15121551 · Cited by 119
Li, J., Li, L., You, P., Wei, Y., & Xu, B. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.02.009 · Cited by 55
Metadata
- Authors
- Overmyer, K. A., Shishkova, E., Miller, I. J., Balnis, J., Bernstein, M. N., Peters-Clarke, T. M., Meyer, J. G., Quan, Q., Muehlbauer, L. K., Trujillo, E. A., He, Y., Chopra, A., Chieng, H. C., Tiwari, A., Judson, M. A., Paulson, B., Brademan, D. R., Zhu, Y., Serrano, L. R., Linke, V., Drake, L. A., Adam, A. P., Schwartz, B. S., Singer, H. A., Swanson, S., Mosher, D. F., Stewart, R., Coon, J. J., & Jaitovich, A.
- Venue
- Cell Systems
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 12
- Issue
- 1
- Pages
- 23-40.e7
- ISSN
- 2405-4712 (ISSN-L)
- OpenAlex
- W3091839106
- Cited by
- 606
- References
- 114
- FWCI
- 12.74
- Type
- journal article
- Language
- en
- Open access
- hybrid
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
- Funders
- National Human Genome Research Institute (5T32HG002760)
- National Institute of General Medical Sciences (1R01GM124133)
- National Heart, Lung, and Blood Institute (5R01HL-049426, K01-HL-130704)
- National Institutes of Health
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- Overmyer, K. A., Shishkova, E., Miller, I. J., Balnis, J., Bernstein, M. N., Peters-Clarke, T. M., Meyer, J. G., Quan, Q., Muehlbauer, L. K., Trujillo, E. A., He, Y., Chopra, A., Chieng, H. C., Tiwari, A., Judson, M. A., Paulson, B., Brademan, D. R., Zhu, Y., Serrano, L. R., . . . Jaitovich, A. (2021). Large-Scale Multi-omic Analysis of COVID-19 Severity. Cell Systems. https://doi.org/10.1016/j.cels.2020.10.003
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