Analysis of inflammatory protein profiles in the circulation of COVID-19 patients identifies patients with severe disease phenotypes

Abstract

Background: The coronavirus disease (COVID-19) caused by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) can present with a broad range of clinical manifestations, ranging from asymptomatic to severe multiple organ failure. The severity of the disease can vary depending on factors such as age, sex, ethnicity, and pre-existing medical conditions. Despite multiple efforts to identify reliable prognostic factors and biomarkers, the predictive capacity of these markers for clinical outcomes remains poor. Circulating proteins, which reflect the active mechanisms in an individual, can be easily measured in clinical practice and therefore may be useful as biomarkers for COVID-19 disease severity. In this study, we sought to identify protein biomarkers and endotypes for COVID-19 severity and evaluate their reproducibility in an independent cohort.

Methods: We investigated a cohort of 153 Greek patients with confirmed SARS-CoV-2 infection in which plasma protein levels were measured using the Olink Explore 1536 panel, which consists of 1472 proteins. We compared the protein profiles from severe and moderate COVID-19 patients to identify proteins associated with disease severity. To evaluate the reproducibility of our findings, we compared the protein profiles of 174 patients with comparable COVID-19 severities in a US COVID-19 cohort to identify proteins consistently correlated with COVID-19 severity in both groups.

Results: We identified 218 differentially regulated proteins associated with severity, 20 proteins were also replicated in an external cohort which we used for validation. Moreover, we performed unsupervised clustering of patients based on 97 proteins with the highest log2 fold changes in order to identify COVID-19 endotypes. Clustering of patients based on differentially regulated proteins revealed the presence of three clinical endotypes. While endotypes 2 and 3 were enriched for severe COVID-19 patients, endotypes 3 represented the most severe form of the disease.

Conclusions: These results suggest that identified circulating proteins may be useful for identifying COVID-19 patients with worse outcomes, and this potential utility may extend to other populations.

Trial registration: NCT04357366.

Keywords: Biomarker; COVID-19; Clustering; Cytokine; Endotypes; Proteomics.

Fig. 1

Identification of proteins associated with severity in the SAVE cohort. (a) The volcano plot shows the proteins associated with severity in the SAVE cohort. Linear models are fitted for each protein using the WHO severity score (WHO score 4 = 48, WHO score 5 = 105). Proteins with a positive log2 fold change indicate that an increase in protein level is associated with increased disease severity and proteins with a negative log2 fold change indicate decreasing protein levels with severity. P-values are adjusted using the Bonferroni adjustment. Proteins colored in red have both, a log2 fold change > ± 0.4 and adjusted p-value <0.05, whereas proteins colored in blue have a log2 fold change < ± 0.4 and are below the < adjusted p-value 0.05, finally proteins in grey are non-significant. (b) Boxplot shows the four most significant proteins in both directions. (blue = Up-regulated, red = Down-regulated) The x-axis shows the WHO score for each individual. (c) Heatmap visualizing enriched terms and the associated proteins. We selected proteins that both have a log2 fold change > ± 0.4 and adjusted p-value <0.05. Rows represent enriched terms, whereas the columns represent proteins.

Fig. 2

Inter-cohort identification of proteins associated with severity.(a) Volcano plot visualizing proteins associated with acuity score in MGH cohort. For each protein, a linear model is fitted using the Acuity score from day 0. Positive values for log2 fold changes indicate increasing protein levels with increased severity, while negative values mean the opposite. (b) Boxplot shows the four most significant proteins associated with Acuity levels in both directions. (c) Venn-diagram visualizing overlapping significant associations proteins from each differential abundance analysis between our own cohort of Covid-19 patients and the MGH Covid-19 cohort including age, chronic liver disease, lung disease, heart disease, and kidney disease as covariables. For both analyses, we used the Bonferroni correction to correct for multiple testing and used 0.05 as the significance threshold. (d) Heatmap visualization which shows the effect size and p-values for the overlapping proteins.

Fig. 3

Heatmap with DE proteins and pathways associated with severity (A) Heatmap visualizing proteins associated with severity. We selected all proteins that were found to be significantly associated with severity after multiple testing corrections. (log2FC > ± 0.4 and adjusted p-value <0.05). Rows represent proteins, whereas the columns represent individuals. Proteins and individuals are both ordered by hierarchical clustering. Top-annotation shows the WHO score for each individual. (B) Heatmap shows the comparison of selected terms in individual samples between Cluster 1 and Cluster 3. The y-axis represents the enriched pathway term, whereas the x-axis represents individual samples. The color indicates the aggregated z-score of each enriched term per sample, red indicates an overall increased expression (activated) of the corresponding enriched term, whereas the blue color indicates decreased expression (repressed).