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Wang L, Western D, Timsina J, Repaci C, Song WM, Norton J, Kohlfeld P, Budde J, Climer S, Butt OH, Jacobson D, Garvin M, Templeton AR, Campagna S, O’Halloran J, Presti R, Goss CW, Mudd PA, Ances BM, Zhang B, Sung YJ, Cruchaga C. Plasma proteomics of SARS-CoV-2 infection and severity reveals impact on Alzheimer's and coronary disease pathways. iScience 2023; 26:106408. [PMID: 36974157 PMCID: PMC10010831 DOI: 10.1016/j.isci.2023.106408] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 01/21/2023] [Accepted: 03/10/2023] [Indexed: 03/14/2023] Open
Abstract
Identification of proteins dysregulated by COVID-19 infection is critically important for better understanding of its pathophysiology, building prognostic models, and identifying new targets. Plasma proteomic profiling of 4,301 proteins was performed in two independent datasets and tested for the association for three COVID-19 outcomes (infection, ventilation, and death). We identified 1,449 proteins consistently associated in both datasets with any of these three outcomes. We subsequently created highly accurate models that distinctively predict infection, ventilation, and death. These proteins were enriched in specific biological processes including cytokine signaling, Alzheimer's disease, and coronary artery disease. Mendelian randomization and gene network analyses identified eight causal proteins and 141 highly connected hub proteins including 35 with known drug targets. Our findings provide distinctive prognostic biomarkers for two severe COVID-19 outcomes, reveal their relationship to Alzheimer's disease and coronary artery disease, and identify potential therapeutic targets for COVID-19 outcomes.
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Affiliation(s)
- Lihua Wang
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St Louis, MO, USA
| | - Daniel Western
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St Louis, MO, USA
| | - Jigyasha Timsina
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St Louis, MO, USA
| | - Charlie Repaci
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St Louis, MO, USA
| | - Won-Min Song
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joanne Norton
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St Louis, MO, USA
| | - Pat Kohlfeld
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St Louis, MO, USA
| | - John Budde
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St Louis, MO, USA
| | - Sharlee Climer
- Department of Computer Science, University of Missouri-St. Louis, St. Louis, MO, USA
| | - Omar H. Butt
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Daniel Jacobson
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Michael Garvin
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Alan R. Templeton
- Department of Biology, Washington University School of Medicine, St Louis, MO, USA
| | - Shawn Campagna
- Department of Chemistry, University of Tennessee, Knoxville, TN, USA
| | - Jane O’Halloran
- Division of Infectious Diseases, Washington University School of Medicine, St Louis, MO, USA
| | - Rachel Presti
- Division of Infectious Diseases, Washington University School of Medicine, St Louis, MO, USA
| | - Charles W. Goss
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Philip A. Mudd
- Department of Emergency Medicine, Washington University School of Medicine, St Louis, MO, USA
| | - Beau M. Ances
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yun Ju Sung
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St Louis, MO, USA
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St Louis, MO, USA
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO, USA
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Beyond GWAS-Could Genetic Differentiation within the Allograft Rejection Pathway Shape Natural Immunity to COVID-19? Int J Mol Sci 2022; 23:ijms23116272. [PMID: 35682950 PMCID: PMC9181155 DOI: 10.3390/ijms23116272] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 05/26/2022] [Accepted: 05/30/2022] [Indexed: 01/25/2023] Open
Abstract
COVID-19 infections pose a serious global health concern so it is crucial to identify the biomarkers for the susceptibility to and resistance against this disease that could help in a rapid risk assessment and reliable decisions being made on patients' treatment and their potential hospitalisation. Several studies investigated the factors associated with severe COVID-19 outcomes that can be either environmental, population based, or genetic. It was demonstrated that the genetics of the host plays an important role in the various immune responses and, therefore, there are different clinical presentations of COVID-19 infection. In this study, we aimed to use variant descriptive statistics from GWAS (Genome-Wide Association Study) and variant genomic annotations to identify metabolic pathways that are associated with a severe COVID-19 infection as well as pathways related to resistance to COVID-19. For this purpose, we applied a custom-designed mixed linear model implemented into custom-written software. Our analysis of more than 12.5 million SNPs did not indicate any pathway that was significant for a severe COVID-19 infection. However, the Allograft rejection pathway (hsa05330) was significant (p = 0.01087) for resistance to the infection. The majority of the 27 SNP marking genes constituting the Allograft rejection pathway were located on chromosome 6 (19 SNPs) and the remainder were mapped to chromosomes 2, 3, 10, 12, 20, and X. This pathway comprises several immune system components crucial for the self versus non-self recognition, but also the components of antiviral immunity. Our study demonstrated that not only single variants are important for resistance to COVID-19, but also the cumulative impact of several SNPs within the same pathway matters.
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