Identification of Serum Prognostic Biomarkers of Severe COVID-19 by Quantitative Proteomic Approach

Abstract

The COVID-19 pandemic is an unprecedented threat to humanity provoking global health concerns. Since the etio-pathogenesis of this illness is not fully characterized, the prognostic factors enabling treatment decisions have not been well documented. An accurate prediction of the disease progression can aid in appropriate patient categorization to determine the best treatment option. Here, we have introduced a proteomic approach utilizing data-independent acquisition mass spectrometry (DIA-MS) to identify the serum proteins closely associated with the prognosis of COVID-19. We observed 27 proteins to be differentially expressed between the cohorts of severely ill COVID-19 patients with adverse and favorable prognosis. Ingenuity pathway analysis revealed that 15 out of the 27 proteins might be regulated by cytokine signalling relevant to interleukin (IL)-1b, IL-6 and tumor necrosis factor (TNF), and their differential expression was possibly implicated in the systemic inflammatory response and cardiovascular disorders. We further evaluated the practical prognosticators for the clinical prognosis of severe COVID-19 patients. Subsequent ELISA analyses further uncovered that CHI3L1 and IGFALS could be potent prognostic markers with a high sensitivity. Our findings can help in formulating a diagnostic approach for accurately discriminating severe COVID-19 patients and provide appropriate treatment based on their predicted prognosis.

Combinatorial peptides: A library that continuously probes low‐abundance proteins

“…Among other unexpected applications, a recent article reported the use of CPLL along with immunodepletion to evidence protein expression differences and to identify prognostic biomarkers of severe COVID-19 giving an accurate prediction of the disease progression [100]. Fifteen proteins involved in systemic inflammation response and related disorders have been found; however, the attention was focused on two of them, chitinase-3-like protein 1 and the acid-labile Direct bead trypsination to produce peptides followed by MudPit analysis of peptides for protein identification.…”