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The proteomic characteristics of airway mucus from critical ill COVID-19 patients
Open accessPublished version of a preprint
Abstract
Background The pandemic of the coronavirus disease 2019 (COVID-19) has brought a global public health crisis. However, the pathogenesis underlying COVID-19 are barely understood. Methods In this study, we performed proteomic analyses of airway mucus obtained by bronchoscopy from severe COVID-19 patients. In total, 2351 and 2073 proteins were identified and quantified in COVID-19 patients and healthy controls, respectively. Results Among them, 92 differentiated expressed proteins (DEPs) (46 up-regulated and 46 down-regulated) were found with a fold change >1.5 or <0.67 and a p-value <0.05, and 375 proteins were uniquely present in airway mucus from COVID-19 patients. Pathway and network enrichment analyses revealed that the 92 DEPs were mostly associated with metabolic, complement and coagulation cascades, lysosome, and cholesterol metabolism pathways, and the 375 COVID-19 only proteins were mainly enriched in amino acid degradation (Valine, Leucine and Isoleucine degradation), amino acid metabolism (beta-Alanine, Tryptophan, Cysteine and Methionine metabolism), oxidative phosphorylation, phagosome, and cholesterol metabolism pathways. Conclusions This study aims to provide fundamental data for elucidating proteomic changes of COVID-19, which may implicate further investigation of molecular targets directing at specific therapy.
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Cites
Global quantification of mammalian gene expression control
Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2011) · Nature · doi:10.1038/nature10098 · Cited by 6,566 · cited 3×
“For label-free quanti cation, protein expression levels were estimated using the iBAQ (Intensity Based Absolute Quanti cation) algorithm embedded in MaxQuant (Schwanhausser et al. 2011).”
InfluentialHenry, B. M., de Oliveira, M. H. S., Benoit, S., Plebani, M., & Lippi, G. (2020) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2020-0369 · Cited by 1,964
McGonagle, D., Sharif, K., O'Regan, A., & Bridgewood, C. (2020) · Autoimmunity Reviews · doi:10.1016/j.autrev.2020.102537 · Cited by 1,654
Metformin reduces liver glucose production by inhibition of fructose-1-6-bisphosphatase
Hunter, R. W., Hughey, C. C., Lantier, L., Sundelin, E. I., Peggie, M., Zeqiraj, E., Sicheri, F., Jessen, N., Wasserman, D. H., & Sakamoto, K. (2018) · Nature Medicine · doi:10.1038/s41591-018-0159-7 · Cited by 313
Flagged first, then by citations
Research works printed but not linked: 20
- 2020, Zhonghua Liu Xing Bing Xue Za Zhi, 41, 145
- Yang, 2020, Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study, Lancet Respir. Med., 8, 475, 10.1016/S2213-2600(20)30079-5 · DOI · Paper page
- Lu, 2020, Elevated MUC1 and MUC5AC mucin protein levels in airway mucus of critical ill COVID-19 patients, J. Med. Virol., 93, 582, 10.1002/jmv.26406 · DOI
- Wang, 2019, Reproducibility of fluid-phase measurements in PBS-treated sputum supernatant of healthy and stable COPD subjects, Int J Chron Obstruct Pulmon Dis, 14, 835, 10.2147/COPD.S187661 · DOI
- Thaker, 2019, Viral hijacking of cellular metabolism, BMC Biol., 17, 59, 10.1186/s12915-019-0678-9 · DOI
- Snaebjornsson, 2018, Non-canonical functions of enzymes facilitate cross-talk between cell metabolic and regulatory pathways, Exp. Mol. Med., 50, 10.1038/s12276-018-0065-6 · DOI
- Zhang, 2013, Far upstream element binding protein 1: a commander of transcription, translation and beyond, Oncogene, 32, 2907, 10.1038/onc.2012.350 · DOI
- Chang, 2018, Roles of aldolase family genes in human cancers and diseases, Trends Endocrinol. Metab., 29, 549, 10.1016/j.tem.2018.05.003 · DOI
- Wang, 2007, Differential gene expression of aldolase C (ALDOC) and hypoxic adaptation in chickens, Anim. Genet., 38, 203, 10.1111/j.1365-2052.2007.01605.x · DOI
- Ali, 1998, Hereditary fructose intolerance, J. Med. Genet., 35, 353, 10.1136/jmg.35.5.353 · DOI
- Schatz-Jakobsen, 2016, Structural insight into proteolytic activation and regulation of the complement system, Immunol. Rev., 274, 59, 10.1111/imr.12465 · DOI
- Pandya, 2014, Complement system in lung disease, Am. J. Respir. Cell Mol. Biol., 51, 467, 10.1165/rcmb.2013-0485TR · DOI
- Sarma, 2006, Complement in lung disease, Autoimmunity, 39, 387, 10.1080/08916930600739456 · DOI
- Gralinski, 2018, Complement activation contributes to severe acute respiratory syndrome coronavirus pathogenesis, mBio, 9, 10.1128/mBio.01753-18 · DOI
- Kuang, 2019, FGB and FGG derived from plasma exosomes as potential biomarkers to distinguish benign from malignant pulmonary nodules, Clin. Exp. Med., 19, 557, 10.1007/s10238-019-00581-8 · DOI
- Li, 2020, Risk factors for severity and mortality in adult COVID-19 inpatients in Wuhan, J. Allergy Clin. Immunol., 146, 110, 10.1016/j.jaci.2020.04.006 · DOI
- Drent, 1996, Usefulness of lactate dehydrogenase and its isoenzymes as indicators of lung damage or inflammation, Eur. Respir. J., 9, 1736, 10.1183/09031936.96.09081736 · DOI
- Lopez-Collazo, 2020, Immune response and COVID-19: a mirror image of sepsis, Int. J. Biol. Sci., 16, 2479, 10.7150/ijbs.48400 · DOI
- Henry, 2020, Lactate dehydrogenase levels predict coronavirus disease 2019 (COVID-19) severity and mortality: a pooled analysis, Am. J. Emerg. Med., 38, 1722, 10.1016/j.ajem.2020.05.073 · DOI
- Favaloro, 2020, Reporting of D-dimer data in COVID-19: some confusion and potential for misinformation, Clin. Chem. Lab. Med., 58, 1191, 10.1515/cclm-2020-0573 · DOI
Metadata
- Authors
- Zhang, Z., Wang, T., Liu, F., Zhu, A., Gu, G., Luo, J., Xu, J., Zhao, J., Li, Y., Li, Y., Liu, X., Zhong, N., & Lu, W.
- Affiliations
- First Affiliated Hospital of Guangzhou Medical University (CN)
- Corresponding author
- Liu, Xiaoqing
- Zhong, Nanshan
- Lu, Wenju
- ORCID
- Venue
- Life Sciences
- Publisher
- Elsevier BV
- Published
- 2021-03
- Volume
- 269
- Pages
- 119046
- ISSN
- 0024-3205 (ISSN-L)
- PMC
- PMC7806453
- OpenAlex
- W3108974328
- Cited by
- 12
- References
- 4
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 3 · 2023: 2 · 2022: 2 · 2021: 2
- FWCI
- 0.71
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Life Sciences (publishedVersion)
- Research Square (acceptedVersion, cc-by)
- SSRN Electronic Journal (acceptedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Key Research and Development Program of China (2016YFC0903700)
- National Natural Science Foundation of China (81520108001, 81770043)
- 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, airway mucus, proteomics, critically ill patients, differentially expressed proteins, complement and coagulation pathways, lysosomes, cholesterol metabolism, amino acid metabolism, oxidative phosphorylation, phagosome, bronchoscopy
- MeSH terms
- COVID-19, Aged, Amino Acids, Bronchoscopy, Cholesterol, Female, Humans, Male, Middle Aged, Mucus, Proteins, Severity of Illness Index, Case-Control Studies, Critical Illness, Proteomics
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