Proteomic profiling identifies biomarkers of COVID-19 severity

Distinct proteomic signatures in Ethiopians predict acute and long-term sequelae of COVID-19

Introduction

Little is known about the acute and long-term sequelae of COVID-19 and its pathophysiology in African patients, who are known to have a distinct immunological profile compared to Caucasian populations. Here, we established protein signatures to define severe outcomes of acute COVID-19 and determined whether unique protein signatures during the first week of acute illness predict the risk of post-acute sequelae of COVID-19 (Long COVID) in a low-income country (LIC) setting.

Method

Using the Olink inflammatory panel, we measured the abundance of 92 proteins in the plasma of COVID-19 patients (n=55) and non-COVID-19 individuals (n=23). We investigated distinct inflammatory protein signatures in acute severe COVID-19 individuals (n=22) compared to asymptomatic or mild/moderate COVID-19 cases (n=33), and non-COVID-19 controls.

Results

Levels of SLAMF1, CCL25, IL2RB, IL10RA, IL15RA, IL18 and CST5 were significantly upregulated in patients with critical COVID-19 illness compared to individuals negative for COVID-19. The cohort was followed for an average of 20 months, and 23 individuals developed Long COVID, based on the WHO's case definition, while 32 COVID-19 patients recovered fully. Whereas upregulated levels of SLAMF1, TNF, TSLP, IL15RA, IL18, ADA, CXCL9, CXCL10, IL17C, and NT3 at the acute phase of the illness were associated with increased Long COVID risk, upregulated TRANCE was associated with a reduced risk of developing Long COVID. Protein levels of SLAMF1, IL15RA, and IL18 associated with critical illness during the acute phase of COVID-19 also predicted Long COVID risk.

Discussion

Patients with severe COVID-19 and Long COVID outcomes exhibited distinct proteomic signatures. Unravelling the pathophysiology of severe acute COVID-19 and Long COVID before its advent may contribute to designing novel interventions for diagnosing, treating, and monitoring of SARS-CoV-2 infection and its associated acute and long-term consequences.

MixOmics Integration of Biological Datasets Identifies Highly Correlated Variables of COVID-19 Severity

Despite several years passing since the COVID-19 pandemic was declared, challenges remain in understanding the factors that can predict the severity of COVID-19 disease and complications of SARS-CoV-2 infection. While many large-scale multi-omic datasets have been published, integration of these datasets has the potential to substantially increase the biological insight gained, allowing a more complex comprehension of the disease pathogenesis. Such insight may improve our ability to predict disease progression, detect severe cases more rapidly and develop effective therapeutics. In this study, we have applied an innovative machine learning algorithm to delineate COVID severity based on the integration of paired samples of proteomic and transcriptomic data from a small cohort of patients testing positive for SARS-CoV-2 infection with differential disease severity. Targeted plasma proteomics and an onco-immune targeted transcriptomic panel were performed on sequential samples from a cohort of 23 severe, 21 moderate and 10 mild COVID-19 patients. We applied DIABLO, a new integrative method, to identify multi-omics biomarker panels that can discriminate between multiple phenotypic groups, such as the varied severity of disease in COVID-19 patients. As COVID-19 severity is known among our sample group, we can train models using this as the outcome variable and calculate features that are important predictors of severe disease. In this study, we detect highly correlated key variables of severe COVID-19 using transcriptomic discriminant analysis and multi-omics integration methods. This approach highlights the power of data integration from a small cohort of patients, offering a better biological understanding of the molecular mechanisms driving COVID-19 severity and an opportunity to improve the prediction of disease trajectories and targeted therapeutics.

Utility of Protein Markers in COVID-19 Patients

COVID-19 has been a challenge at the healthcare level not only in the early stages of the pandemic, but also in the subsequent appearance of long-term COVID-19. Several investigations have attempted to identify proteomic biomarkers in an attempt to improve clinical care, guide treatment and predict possible patient outcomes. Proteins such as C-reactive protein (CRP) or interleukin 6 (IL-6) are clear markers of severe disease, but many others have been proposed that could help in risk stratification and in the prediction of specific complications. This review aims to bring together the most relevant studies in this regard, providing information to identify the most notable biomarkers in relation to COVID-19 found to date.

Plasma Proteins Associated with COVID-19 Severity in Puerto Rico

Viral strains, age, and host factors are associated with variable immune responses against SARS-CoV-2 and disease severity. Puerto Ricans have a genetic mixture of races: European, African, and Native American. We hypothesized that unique host proteins/pathways are associated with COVID-19 disease severity in Puerto Rico. Following IRB approval, a total of 95 unvaccinated men and women aged 21-71 years old were recruited in Puerto Rico from 2020-2021. Plasma samples were collected from COVID-19-positive subjects (n = 39) and COVID-19-negative individuals (n = 56) during acute disease. COVID-19-positive individuals were stratified based on symptomatology as follows: mild (n = 18), moderate (n = 13), and severe (n = 8). Quantitative proteomics was performed in plasma samples using tandem mass tag (TMT) labeling. Labeled peptides were subjected to LC/MS/MS and analyzed by Proteome Discoverer (version 2.5), Limma software (version 3.41.15), and Ingenuity Pathways Analysis (IPA, version 22.0.2). Cytokines were quantified using a human cytokine array. Proteomics analyses of severely affected COVID-19-positive individuals revealed 58 differentially expressed proteins. Cadherin-13, which participates in synaptogenesis, was downregulated in severe patients and validated by ELISA. Cytokine immunoassay showed that TNF-α levels decreased with disease severity. This study uncovers potential host predictors of COVID-19 severity and new avenues for treatment in Puerto Ricans.