Validating a Proteomic Signature of Severe COVID-19

Longitudinal plasma proteomic analysis of 1117 hospitalized patients with COVID-19 identifies features associated with severity and outcomes

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is characterized by highly heterogeneous manifestations ranging from asymptomatic cases to death for still incompletely understood reasons. As part of the IMmunoPhenotyping Assessment in a COVID-19 Cohort study, we mapped the plasma proteomes of 1117 hospitalized patients with COVID-19 from 15 hospitals across the United States. Up to six samples were collected within ~28 days of hospitalization resulting in one of the largest COVID-19 plasma proteomics cohorts with 2934 samples. Using perchloric acid to deplete the most abundant plasma proteins allowed for detecting 2910 proteins. Our findings show that increased levels of neutrophil extracellular trap and heart damage markers are associated with fatal outcomes. Our analysis also identified prognostic biomarkers for worsening severity and death. Our comprehensive longitudinal plasma proteomics study, involving 1117 participants and 2934 samples, allowed for testing the generalizability of the findings of many previous COVID-19 plasma proteomics studies using much smaller cohorts.

Vascular endothelial-derived SPARCL1 exacerbates viral pneumonia through pro-inflammatory macrophage activation

Inflammation induced by lung infection is a double-edged sword, moderating both anti-viral and immune pathogenesis effects; the mechanism of the latter is not fully understood. Previous studies suggest the vasculature is involved in tissue injury. Here, we report that expression of Sparcl1, a secreted matricellular protein, is upregulated in pulmonary capillary endothelial cells (EC) during influenza-induced lung injury. Endothelial overexpression of SPARCL1 promotes detrimental lung inflammation, with SPARCL1 inducing 'M1-like' macrophages and related pro-inflammatory cytokines, while SPARCL1 deletion alleviates these effects. Mechanistically, SPARCL1 functions through TLR4 on macrophages in vitro, while TLR4 inhibition in vivo ameliorates excessive inflammation caused by endothelial Sparcl1 overexpression. Finally, SPARCL1 expression is increased in lung ECs from COVID-19 patients when compared with healthy donors, while fatal COVID-19 correlates with higher circulating SPARCL1 protein levels in the plasma. Our results thus implicate SPARCL1 as a potential prognosis biomarker for deadly COVID-19 pneumonia and as a therapeutic target for taming hyperinflammation in pneumonia.

Proteomics of serum-derived extracellular vesicles are associated with the severity and different clinical profiles of patients with COVID-19: An exploratory secondary analysis

BACKGROUND AIMS

Coronavirus disease 2019 (COVID-19) is characterized by a broad spectrum of clinical manifestations with the potential to progress to multiple organ dysfunction in severe cases. Extracellular vesicles (EVs) carry a range of biological cargoes, which may be used as biomarkers of disease state.

METHODS

An exploratory secondary analysis of the SARITA-2 and SARITA-1 datasets (randomized clinical trials on patients with mild and moderate/severe COVID-19) was performed. Serum-derived EVs were used for proteomic analysis to identify enriched biological processes and key proteins, thus providing insights into differences in disease severity. Serum-derived EVs were separated from patients with COVID-19 by size exclusion chromatography and nanoparticle tracking analysis was used to determine particle concentration and diameter. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) was performed to identify and quantify protein signatures. Bioinformatics and multivariate statistical analysis were applied to distinguish candidate proteins associated with disease severity (mild versus moderate/severe COVID-19).

RESULTS

No differences were observed in terms of the concentration and diameter of enriched EVs between mild (n = 14) and moderate/severe (n = 30) COVID-19. A total of 414 proteins were found to be present in EVs, of which 360 were shared while 48 were uniquely present in severe/moderate compared to mild COVID-19. The main biological signatures in moderate/severe COVID-19 were associated with platelet degranulation, exocytosis, complement activation, immune effector activation, and humoral immune response. Von Willebrand factor, serum amyloid A-2 protein, histone H4 and H2A type 2-C, and fibrinogen β-chain were the most differentially expressed proteins between severity groups.

CONCLUSION

Exploratory proteomic analysis of serum-derived EVs from patients with COVID-19 detected key proteins related to immune response and activation of coagulation and complement pathways, which are associated with disease severity. Our data suggest that EV proteins may be relevant biomarkers of disease state and prognosis.

Simple and Scalable Algorithms for Cluster-Aware Precision Medicine

AI-enabled precision medicine promises a transformational improvement in healthcare outcomes. However, training on biomedical data presents significant challenges as they are often high dimensional, clustered, and of limited sample size. To overcome these challenges, we propose a simple and scalable approach for cluster-aware embedding that combines latent factor methods with a convex clustering penalty in a modular way. Our novel approach overcomes the complexity and limitations of current joint embedding and clustering methods and enables hierarchically clustered principal component analysis (PCA), locally linear embedding (LLE), and canonical correlation analysis (CCA). Through numerical experiments and real-world examples, we demonstrate that our approach outperforms fourteen clustering methods on highly underdetermined problems (e.g., with limited sample size) as well as on large sample datasets. Importantly, our approach does not require the user to choose the desired number of clusters, yields improved model selection if they do, and yields interpretable hierarchically clustered embedding dendrograms. Thus, our approach improves significantly on existing methods for identifying patient subgroups in multiomics and neuroimaging data and enables scalable and interpretable biomarkers for precision medicine.

Deep phenotyping of the lipidomic response in COVID-19 and non-COVID-19 sepsis

BACKGROUND

Lipids may influence cellular penetrance by viral pathogens and the immune response that they evoke. We deeply phenotyped the lipidomic response to SARs-CoV-2 and compared that with infection with other pathogens in patients admitted with acute respiratory distress syndrome to an intensive care unit (ICU).

METHODS

Mass spectrometry was used to characterise lipids and relate them to proteins, peripheral cell immunotypes and disease severity.

RESULTS

Circulating phospholipases (sPLA2, cPLA2 (PLA2G4A) and PLA2G2D) were elevated on admission in all ICU groups. Cyclooxygenase, lipoxygenase and epoxygenase products of arachidonic acid (AA) were elevated in all ICU groups compared with controls. sPLA2 predicted severity in COVID-19 and correlated with TxA2, LTE4 and the isoprostane, iPF2α-III, while PLA2G2D correlated with LTE4. The elevation in PGD2, like PGI2 and 12-HETE, exhibited relative specificity for COVID-19 and correlated with sPLA2 and the interleukin-13 receptor to drive lymphopenia, a marker of disease severity. Pro-inflammatory eicosanoids remained correlated with severity in COVID-19 28 days after admission. Amongst non-COVID ICU patients, elevations in 5- and 15-HETE and 9- and 13-HODE reflected viral rather than bacterial disease. Linoleic acid (LA) binds directly to SARS-CoV-2 and both LA and its di-HOME products reflected disease severity in COVID-19. In healthy marines, these lipids rose with seroconversion. Eicosanoids linked variably to the peripheral cellular immune response. PGE2, TxA2 and LTE4 correlated with T cell activation, as did PGD2 with non-B non-T cell activation. In COVID-19, LPS stimulated peripheral blood mononuclear cell PGF2α correlated with memory T cells, dendritic and NK cells while LA and DiHOMEs correlated with exhausted T cells. Three high abundance lipids - ChoE 18:3, LPC-O-16:0 and PC-O-30:0 - were altered specifically in COVID. LPC-O-16:0 was strongly correlated with T helper follicular cell activation and all three negatively correlated with multi-omic inflammatory pathways and disease severity.

CONCLUSIONS

A broad based lipidomic storm is a predictor of poor prognosis in ARDS. Alterations in sPLA2, PGD2 and 12-HETE and the high abundance lipids, ChoE 18:3, LPC-O-16:0 and PC-O-30:0 exhibit relative specificity for COVID-19 amongst such patients and correlate with the inflammatory response to link to disease severity.