High-resolution longitudinal serum proteome trajectories in COVID-19 reveal patients-specific seroconversion

Abstract

Biomarkers for COVID-19 are urgently needed. Here we bring the powerful technology of mass spectrometry (MS)-based proteomics to bear on this challenge. We measured serum proteomes of COVID-19 patients and symptomatic, but PCR-negative controls, in a time-resolved manner. In 262 controls and 458 longitudinal samples (average of 31 days) of 31 patients, hospitalized for COVID-19, a remarkable 26% of proteins changed significantly. Bioinformatics analyses revealed co-regulated groups and shared biological functions. Proteins of the innate immune system such as CRP, SAA1, CD14, LBP and LGALS3BP decreased early in the time course. In contrast, regulators of coagulation (APOH, FN1, HRG, KNG1, PLG) and lipid homeostasis (APOA1, APOC1, APOC2, APOC3, PON1) increased over the course of the disease. A global correlation map provides a systems-wide functional association between proteins, biological processes and clinical chemistry parameters. Importantly, five SARS-CoV-2 immunoassays against antibodies revealed excellent correlations with an extensive range of immunoglobulin regions, which were quantified by MS-based proteomics. The high-resolution profile of all immunoglobulin regions showed individual-specific differences and commonalities of potential pathophysiological relevance.

A serum proteome signature to predict mortality in severe COVID-19 patients

“…Among others, these included several complement related proteins (which are not statistically differential in our data). Other proteins observed to be significantly differential in the plasma proteomics and SOMAscan analyses of Galbraith et al ( 6 ) include SERPINA3 , SERPINC1 , PLG , and KNG1 , which follow the same trend as in the data of Geyer et al ( 31 Preprint ), Demichev et al ( 8 Preprint ) as well as our work. Overall, our data is in better agreement with the Geyer et al and Demichev et al data sets, but that may also be due to the fact that the classification made by Galbraith et al was based on seroconversion, and thus different from the classifications made by the other groups.…”

“…In the last months, the research efforts on COVID-19 have expanded enormously. In this period, quite a few multi-omics and plasma proteomics studies have appeared studying COVID-19 patients, generating data comparable to ours, but with different research questions and thus also different study designs ( 2 , 5 , 6 , 7 , 8 Preprint , 31 Preprint ). Still, the outcome of these studies and their conclusions can be compared with the data obtained in our cohort (further termed “Ferrara cohort”).…”

“…Another recent plasma proteome profiling study related to ours has been reported by Geyer et al ( 31 Preprint ). They primarily studied differences between the plasma proteomes of 31 COVID-19 patients versus 263 PCR-negative controls, from the University Hospital of LMU Munich (here termed “Munich cohort”).…”

Protease inhibitor plasma concentrations associate with COVID-19 infection

“…Our data are consistent with data from mass spectrometry-based proteomic analyses of plasma and sera samples from COVID-19 patients. C1-INH was the protein with the most significantly reduced levels in samples from 31 COVID-19 patients compared with 262 controls [ 23 ]. From the same study, levels of ITIH4 were increased at first sampling in COVID-19 patients and inpatients who died from COVID-19 [24].…”

Individual COVID‐19 disease trajectories revealed by plasma proteomics

“…However, these studies were based on a single time point measurement. In this issue of EMBO Molecular Medicine , Geyer and colleagues report on the study of plasma proteome changes for a longitudinal cohort of 31 COVID‐19 patients with an average of 14 samples per patient, during an average period of 31 days (Fig 1A) (2021). Around 300 proteins per sample were quantified in a total of 720 samples, including controls.…”