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Multi-organ proteomic landscape of COVID-19 autopsies
Open accessAbstract
The molecular pathology of multi-organ injuries in COVID-19 patients remains unclear, preventing effective therapeutics development. Here, we report an in-depth multi-organ proteomic landscape of COVID-19 patient autopsy samples. By integrative analysis of proteomes of seven organs, namely lung, spleen, liver, heart, kidney, thyroid and testis, we characterized 11,394 proteins, in which 5336 were perturbed in COVID-19 patients compared to controls. Our data showed that CTSL, rather than ACE2, was significantly upregulated in the lung from COVID-19 patients. Dysregulation of protein translation, glucose metabolism, fatty acid metabolism was detected in multiple organs. Our data suggested upon SARS-CoV-2 infection, hyperinflammation might be triggered which in turn induces damage of gas exchange barrier in the lung, leading to hypoxia, angiogenesis, coagulation and fibrosis in the lung, kidney, spleen, liver, heart and thyroid. Evidence for testicular injuries included reduced Leydig cells, suppressed cholesterol biosynthesis and sperm mobility. In summary, this study depicts the multi-organ proteomic landscape of COVID-19 autopsies, and uncovered dysregulated proteins and biological processes, offering novel therapeutic clues.
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Cites
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
The third international consensus definitions for sepsis and septic shock (Sepsis-3).
Singer, M., Deutschman, C. S., Seymour, C. W., Shankar-Hari, M., Annane, D., Bauer, M., Bellomo, R., Bernard, G. R., Chiche, J.-D., Coopersmith, C. M., et al. (2016) · JAMA · doi:10.1001/jama.2016.0287 · Cited by 28,250
Hoffmann, M., Kleine-Weber, H., Schroeder, S., Krüger, N., Herrler, T., Erichsen, S., Schiergens, T. S., Herrler, G., Wu, N.-H., Nitsche, A., et al. (2020) · Cell · doi:10.1016/j.cell.2020.02.052 · Cited by 20,286
Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N. T., Morris, J. H., Bork, P., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1131 · Cited by 17,749
Tissue-based map of the human proteome
Uhlén, M., Fagerberg, L., Hallström, B. M., Lindskog, C., Oksvold, P., Mardinoglu, A., Sivertsson, Å., Kampf, C., Sjöstedt, E., Asplund, A., et al. (2015) · Science · doi:10.1126/science.1260419 · Cited by 15,286
Metascape provides a biologist-oriented resource for the analysis of systems-level datasets
Zhou, Y., Zhou, B., Pache, L., Chang, M., Khodabakhshi, A. H., Tanaseichuk, O., Benner, C., & Chanda, S. K. (2019) · Nature Communications · doi:10.1038/s41467-019-09234-6 · Cited by 14,611
Chalasani, N., Younossi, Z., Lavine, J. E., Charlton, M., Cusi, K., Rinella, M., Harrison, S. A., Brunt, E. M., & Sanyal, A. J. (2018) · Hepatology · doi:10.1002/hep.29367 · Cited by 7,239
Bindea, G., Mlecnik, B., Hackl, H., Charoentong, P., Tosolini, M., Kirilovsky, A., Fridman, W.-H., Pagès, F., Trajanoski, Z., & Galon, J. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp101 · Cited by 6,280
Risau, W. (1997) · Nature · doi:10.1038/386671a0 · Cited by 5,727
Causal analysis approaches in Ingenuity Pathway Analysis
Krämer, A., Green, J., Pollard, J., & Tugendreich, S. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt703 · Cited by 5,724
Cited by
Urine proteome of COVID-19 patients
(2020) · doi:10.1016/j.urine.2021.02.001 · Cited by 68
The neurobiology of SARS-CoV-2 infection
Meinhardt, J., Streit, S., Dittmayer, C., Manitius, R. V., Radbruch, H., & Heppner, F. L. (2024) · Nature Reviews Neuroscience · doi:10.1038/s41583-023-00769-8 · Cited by 64
Brain-wide alterations revealed by spatial transcriptomics and proteomics in COVID-19 infection
Zhang, T., Li, Y., Pan, L., Sha, J., Bailey, M., Faure-Kumar, E., Williams, C. K., Wohlschlegel, J., Magaki, S., Niu, C., et al. (2024) · Nature Aging · doi:10.1038/s43587-024-00730-z · Cited by 20
Self-citationZhang, K., Eldin, P., Ciesla, J. H., Briant, L., Lentini, J. M., Ramos, J., Cobb, J., Munger, J., & Fu, D. (2024) · eLife · doi:10.7554/elife.90316.3 · Cited by 9
Zhang, K., Eldin, P., Ciesla, J. H., Briant, L., Lentini, J. M., Ramos, J., Cobb, J., Munger, J., & Fu, D. (2023) · doi:10.1101/2023.02.10.527147 · Cited by 3
Metadata
- Authors
- Nie, X., Qian, L., Sun, R., Huang, B., Dong, X., Xiao, Q., Zhang, Q., Lu, T., Yue, L., Chen, S., Li, X., Sun, Y., Li, L., Xu, L., Li, Y., Yang, M., Xue, Z., Liang, S., Ding, X., Yuan, C., Peng, L., Liu, W., Yi, X., Lyu, M., Xiao, G., Xu, X., Ge, W., He, J., Fan, J., Wu, J., Luo, M., Chang, X., Pan, H., Cai, X., Zhou, J., Yu, J., Gao, H., Xie, M., Wang, S., Ruan, G., Chen, H., Su, H., Mei, H., Luo, D., Zhao, D., Xu, F., Li, Y., Zhu, Y., Xia, J., Hu, Y., & Guo, T.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2021-02
- Volume
- 184
- Issue
- 3
- Pages
- 775-791.e14
- ISSN
- 0092-8674 (ISSN-L)
- OpenAlex
- W3120750707
- Cited by
- 408
- References
- 352
- FWCI
- 26.65
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Nie, X., Qian, L., Sun, R., Huang, B., Dong, X., Xiao, Q., Zhang, Q., Lu, T., Yue, L., Chen, S., Li, X., Sun, Y., Li, L., Xu, L., Li, Y., Yang, M., Xue, Z., Liang, S., Ding, X., . . . Guo, T. (2021). Multi-organ proteomic landscape of COVID-19 autopsies. Cell. https://doi.org/10.1016/j.cell.2021.01.004
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