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Suvarna K, Biswas D, Pai MGJ, Acharjee A, Bankar R, Palanivel V, Salkar A, Verma A, Mukherjee A, Choudhury M, Ghantasala S, Ghosh S, Singh A, Banerjee A, Badaya A, Bihani S, Loya G, Mantri K, Burli A, Roy J, Srivastava A, Agrawal S, Shrivastav O, Shastri J, Srivastava S. Proteomics and Machine Learning Approaches Reveal a Set of Prognostic Markers for COVID-19 Severity With Drug Repurposing Potential. Front Physiol 2021; 12:652799. [PMID: 33995121 PMCID: PMC8120435 DOI: 10.3389/fphys.2021.652799] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 03/12/2021] [Indexed: 12/13/2022] Open
Abstract
The pestilential pathogen SARS-CoV-2 has led to a seemingly ceaseless pandemic of COVID-19. The healthcare sector is under a tremendous burden, thus necessitating the prognosis of COVID-19 severity. This in-depth study of plasma proteome alteration provides insights into the host physiological response towards the infection and also reveals the potential prognostic markers of the disease. Using label-free quantitative proteomics, we performed deep plasma proteome analysis in a cohort of 71 patients (20 COVID-19 negative, 18 COVID-19 non-severe, and 33 severe) to understand the disease dynamics. Of the 1200 proteins detected in the patient plasma, 38 proteins were identified to be differentially expressed between non-severe and severe groups. The altered plasma proteome revealed significant dysregulation in the pathways related to peptidase activity, regulated exocytosis, blood coagulation, complement activation, leukocyte activation involved in immune response, and response to glucocorticoid biological processes in severe cases of SARS-CoV-2 infection. Furthermore, we employed supervised machine learning (ML) approaches using a linear support vector machine model to identify the classifiers of patients with non-severe and severe COVID-19. The model used a selected panel of 20 proteins and classified the samples based on the severity with a classification accuracy of 0.84. Putative biomarkers such as angiotensinogen and SERPING1 and ML-derived classifiers including the apolipoprotein B, SERPINA3, and fibrinogen gamma chain were validated by targeted mass spectrometry-based multiple reaction monitoring (MRM) assays. We also employed an in silico screening approach against the identified target proteins for the therapeutic management of COVID-19. We shortlisted two FDA-approved drugs, namely, selinexor and ponatinib, which showed the potential of being repurposed for COVID-19 therapeutics. Overall, this is the first most comprehensive plasma proteome investigation of COVID-19 patients from the Indian population, and provides a set of potential biomarkers for the disease severity progression and targets for therapeutic interventions.
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Affiliation(s)
- Kruthi Suvarna
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Deeptarup Biswas
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Medha Gayathri J. Pai
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Arup Acharjee
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Renuka Bankar
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Viswanthram Palanivel
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Akanksha Salkar
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Ayushi Verma
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Amrita Mukherjee
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Manisha Choudhury
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Saicharan Ghantasala
- Centre for Research in Nanotechnology and Sciences, Indian Institute of Technology Bombay, Mumbai, India
| | - Susmita Ghosh
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Avinash Singh
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Arghya Banerjee
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Apoorva Badaya
- Department of Chemistry, Indian Institute of Technology Bombay, Mumbai, India
| | - Surbhi Bihani
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Gaurish Loya
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Krishi Mantri
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Ananya Burli
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Jyotirmoy Roy
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Alisha Srivastava
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
- Department of Genetics, University of Delhi, New Delhi, India
| | - Sachee Agrawal
- Kasturba Hospital for Infectious Diseases, Mumbai, India
| | - Om Shrivastav
- Kasturba Hospital for Infectious Diseases, Mumbai, India
| | | | - Sanjeeva Srivastava
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
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