Molecular battle against COVID-19
Characteristic Blood Biomarkers for COVID-19 identified in Westlake University
Shortly after our last release ofcoronavirus research findings , Westlake University released anotherbreakthrough in COVID-19 research. Tiannan Guo and co-workers identifiedcharacteristic molecular changes in the sera from severe #COVID-19 cases,allowing prediction of severe cases using a machine learning model based onserum protein and metabolite biomarkers.
The Guomics Laboratory of Big ProteomicData, led by Assistant Professor Tiannan Guo, performed the first proteomic andmetabolomic characterization of #COVID-19 sera, and managed to identified aseries of characteristic biomarkers indicating the severity of COVID-19patients.
The manuscript was made available onmedRxiv around 0:15 am on April 8th (Beijing time).
COVID-19 is an ongoing unprecedented globalthreat. More than 1,5 million individuals are confirmed cases worldwide andthis number is rapidly increasing. Studies investigating the clinical symptomsand epidemiology have been reported, however, little is known about themolecular pathogenesis of SARS-CoV-2, the pathogen of COVID-19. Little clue areavailable for clinicians to determine why certain patients develop into severecases, and how to treat them effectively.
In collaboration with clinicians andmetabolomics scientists, the team performed a rigorous proteomic andmetabolomic analysis of 99 sera samples from four groups including healthydonors, non COVID-19 patients with similar clinical characteristics as COVID-19patients, non-severe COVID-19 patients, and severe COVID-19 patients. Together,they quantified 894 proteins and 941 metabolites. This study identifiedcharacteristic molecules expressed in the blood of severe patients.
In the blood from severe COVID-19 patients,93 proteins and 204 metabolites were found to be significantly dysregulatedcompared to the non-severe COVID-19 cases. Specifically, 50 proteins areinvolved in pathways including macrophage functions, the complement system andthe platelet degranulation. They also found a significant drop of more than 100amino acids and more than 100 lipids, which may indicate huge consumption ofthe relevant metabolites during the replication of virus. These findings mayprovide clues to medical care.
Furthermore, Guo’s team established aclassifier composed of 22 proteins and 7 metabolites using machine learning toidentify severe COVID-19 cases. COVID-19 patients who carry such features intheir sera exhibited a high risk of getting worse. A model based on the abovefindings might be used to predict severe cases, facilitating efficientlyallocation of medical resources, although further clinical studies may berequired to verify it.
Mass spectrometer-based proteomics is acrucial tool for clinical diagnosis and therapeutics. By integrating datafrom clinic, proteomics, and metabolomics, the team revealed a holisticlandscape of the characteristic molecular changes in the blood of severeCOVID-19 cases.