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.

The altered molecular proteins and pathways in response to COVID-19 infection are still unclear. Here, we performed a comprehensive proteomics-based investigation of nasopharyngeal swab samples from COVID-19 patients to study the host response by employing simple extraction strategies. Few of the host proteins such as Interleukin-6, L-lactate dehydrogenase, C-reactive protein, Ferritin and Aspartate aminotransferase were found to be up-regulated only in COVID-19 positive patients using targeted Multiple Reaction Monitoring studies. The most important pathways identified by enrichment analysis were neutrophil degranulation, interleukin-12 signaling pathways and mRNA translation of proteins thus providing the detailed investigation of host response in COVID-19 infection. Thus, we conclude that mass spectrometry-detected host proteins have a potential for disease severity progression; however, suitable validation strategies should be deployed for the clinical translation. Furthermore, the in-silico docking of host proteins involved in the interleukin-12 signaling pathway might aid in COVID-19 therapeutic interventions.

The coronavirus disease 2019 (COVID-19) pandemic continues to ravage the world, with many hospitals overwhelmed by the large number of patients presenting during major outbreaks. A rapid triage for COVID-19 patient requiring hospitalization and intensive care is urgently needed. Age and comorbidities have been associated with a higher risk of severe COVID-19 but are not sufficient to triage patients. Here, we investigated the potential of attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy as a rapid blood test for classification of COVID-19 disease severity using a cohort of 160 COVID-19 patients. A simple plasma processing and ATR-FTIR data acquisition procedure was established using 75% ethanol for viral inactivation. Next, partial least-squares-discriminant analysis (PLS-DA) models were developed and tested using data from 130 and 30 patients, respectively. Addition of the ATR-FTIR spectra to the clinical parameters (age, sex, diabetes mellitus, and hypertension) increased the area under the ROC curve (C-statistics) for both the training and test data sets, from 69.3% (95% CI 59.8–78.9%) to 85.7% (78.6–92.8%) and 77.8% (61.3–94.4%) to 85.1% (71.3–98.8%), respectively. The independent test set achieved 69.2% specificity (42.4–87.3%) and 94.1% sensitivity (73.0–99.0%). Diabetes mellitus was the strongest predictor in the model, followed by FTIR regions 1020–1090 and 1588–1592 cm –1 . In summary, this study demonstrates the potential of ATR-FTIR spectroscopy as a rapid, low-cost COVID-19 severity triage tool to facilitate COVID-19 patient management during an outbreak.

With emerging SARS-CoV-2 new strains and their increased pathogenicity, diagnosis has become more challenging. Molecular diagnosis often involves the use of nasopharyngeal swab and subsequent real-time PCR-based test. While this test is the gold standard, it has several limitations and more complementary assays are required. This protocol describes how to identify SARS-CoV-2 protein from patients' nasopharyngeal swab samples. We first introduce the approach of label-free quantitative proteomics. We then detail target verification by triple quadrupole mass spectrometry (M.S.)-based targeted proteomics.

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