Proteomic characteristics of bronchoalveolar lavage fluid in critical COVID‐19 patients

Abstract

Up to 10–20% of patients with coronavirus disease 2019 (COVID‐19) develop a severe pulmonary disease due to immune dysfunction and cytokine dysregulation. However, the extracellular proteomic characteristics in respiratory tract of these critical COVID‐19 patients still remain to be investigated. In the present study, we performed a quantitative proteomic analysis of the bronchoalveolar lavage fluid (BALF) from patients with critical COVID‐19 and from non‐COVID‐19 controls. Our study identified 358 differentially expressed BALF proteins (P < 0.05), among which 41 were significantly changed after using the Benjamini–Hochberg correction (q < 0.05). The up‐regulated signaling was found to be mainly involved in inflammatory signaling and response to oxidative stress. A series of increased extracellular factors including Tenascin‐C (TNC), Mucin‐1 (KL‐6 or MUC1), Lipocalin‐2 (LCN2), periostin (POSTN), Chitinase 3‐like 1 (CHI3L1 or YKL40), and S100A12, and the antigens including lymphocyte antigen 6D/E48 antigen (LY6D), CD9 antigen, CD177 antigen, and prostate stem cell antigen (PSCA) were identified, among which the proinflammatory factors TNC and KL‐6 were further validated in serum of another thirty‐nine COVID‐19 patients and healthy controls, showing high potentials of being biomarkers or therapeutic candidates for COVID‐19. This BALF proteome associated with COVID‐19 would also be a valuable resource for researches on anti‐inflammatory medication and understanding the molecular mechanisms of host response. Database Proteomic raw data are available in ProteomeXchange (http://proteomecentral.proteomexchange.org) under the accession number PXD022085, and in iProX (http://www.iprox.org) under the accession number IPX0002429000.

Identification of COVID-19 prognostic markers and therapeutic targets through meta-analysis and validation of Omics data from nasopharyngeal samples

“…A subset of upregulated proteins is secretory including S100A12, S100A8, S100A9, SERPINB3, DEFA3, LCN2 and TXN. LCN2, which came up in our study was previously shown to be an important biomarker for viral infection (72,73), and was also reported to be upregulated in transcriptomic and proteomic studies in COVID-19 patients (74,75). It has been shown that virus replication and inflammation goes down when thioredoxin reductase, a reducing agent of TXN is inhibited using auranofin, pointing towards the importance of redox environment during SARS-CoV2 pathogenesis (76), akin to many inflammatory responses which are governed by the redox status of the cell (77).…”

Novel Human Tenascin-C Function-Blocking Camel Single Domain Nanobodies

“…As a major binding site for the envelope protein in HIV was found in TN5 (10), Nb3 and Nb4 may be useful to modulate this interaction. Finally, Nb3 and Nb4 may also be useful to target TNC actions in COVID19 as high TNC levels correlated with severity of the disease symptoms (13). Our results provide a rationale for a future clinical evaluation of the hTNCspecific Nbs.…”

“…In addition to tumors, TNC is also highly up-regulated in wound healing, fibrosis and chronic inflammation (11,12). Recently, high TNC levels were also associated with more severe COVID19 symptoms (13). Using stochastic tumorigenesis models with engineered high and low levels of TNC it was formally proven that TNC indeed is a promoter of tumor progression (14).…”

A rigorous evaluation of optimal peptide targets for MS-based clinical diagnostics of Coronavirus Disease 2019 (COVID-19)

“…For establishing workflows to evaluate virus-specific peptides, three published cell culture datasets [22][23][24] which used SARS-COV2 infected Vero cell lines were chosen, along with five clinical datasets [25][26][27][28][29] .…”

“…In order to evaluate the most robustly detectable SARS-CoV-2 peptides, and make the detection of these viral peptides in human samples in a clinical setting all the more feasible, we set out to examine proteomic datasets from three cell culture-based studies [22][23][24] and five clinical studies [25][26][27][28][29] . We utilized automated workflows implemented in the Galaxy platform and made accessible via the European Galaxy public instance to first identify as many SARS-CoV-2 peptides possible in all samples, creating a master list of SARS-CoV-2 peptides identified across the samples.…”