Molecular pathology of acute respiratory distress syndrome, mechanical ventilation and abnormal coagulation in severe COVID-19 (preprint)

Systemic inflammation in critically ill patients can lead to serious consequences such as acute respiratory distress syndrome (ARDS), a condition characterized by the presence of lung inflammation, edema, and impaired gas exchange, associated with poor survival. Understanding molecular pathobiology is essential to improve critical care of these patients. To this end, we use multimodal profiles of SARS-CoV-2 infected hospitalized participants to the Biobanque Quebecoise de la COVID-19 (BQC-19) to characterize endophenotypes associated with different degrees of disease severity. Proteomic, metabolomic, and genomic characterization supported a role for neutrophil-associated procoagulant activity in severe COVID-19 ARDS that is inversely correlated with sphinghosine-1 phosphate plasma levels. Fibroblast Growth Factor Receptor (FGFR) and SH2-containing transforming protein 4 (SHC4) signaling were identified as molecular features associated with endophenotype 6 (EP6). Mechanical ventilation in EP6 was associated with alterations in lipoprotein metabolism. These findings help define the molecular mechanisms related to specific severe outcomes, that can be used to identify early unfavorable clinical trajectories and treatable traits to improve the survival of critically ill patients.

RAMEN identifies effective indicators for severe COVID and Long COVID patients (preprint)

The outbreak of the COVID-19 pandemic caused catastrophic socioeconomic consequences and fundamentally reshaped the lives of billions across the globe. Our current understanding of the relationships between clinical variables (demographics, symptoms, follow-up symptoms, comorbidities, treatments, lab results, complications, and other clinical measurements) and COVID-19 outcomes remains obscure. Various computational approaches have been employed to elucidate the relationships between different COVID-19 clinical variables and their contributions to the disease outcomes. However, it is often challenging to capture the indirect relationships, as well as the direction of those relationships, with the conventional pairwise correlation methods. Graphical models (e.g., Bayesian networks) can address these limitations but are computationally expensive, which substantially limits their applications in reconstructing relationship networks ofumpteen clinical variables. In this study, we have developed a method named RAMEN, which employs Genetic Algorithm and random walks to infer the Bayesian relationship network between clinical variables. We applied RAMEN to a comprehensive COVID-19 dataset, Biobanque Quebecoise de la COVID-19 (BQC19). Most of the clinical variables in our reconstructed Bayesian network associated with COVID-19 severity, or long COVID, are supported by existing literature. We further computationally verified the effectiveness of the RAMEN method with statistical examinations of the multi-omics measurements (Clinical variables, RNA-seq, and Somascan) of the BQC19 data and simulations. The accurate inference of the relationships between clinical variables and disease outcomes powered by RAMEN will significantly advance the development of effective and early diagnostics of severe COVID-19 and long COVID, which can help save millions of lives.

Unsupervised clustering of SARS-CoV-2 positive hospitalized patients identifies six endophenotypes of COVID-19 and points to FGFR and SHC4-signaling in acute respiratory distress syndrome (preprint)

Defining the molecular mechanisms of novel emerging diseases like COVID-19 is crucial to identify treatable traits to improve patient care. To circumvent a priori bias and the lack of in-depth knowledge of a new disease, we opted for an unsupervised approach, using the detailed circulating proteome, as measured by 4985 aptamers (SOMAmers), of 731 SARS-CoV-2 PCR-positive hospitalized participants to Biobanque quebecoise de la COVID-19 (BQC19). The consensus clustering identified six endophenotypes (EPs) present in this cohort, with varying degrees of disease severity. One endophenotype, EP6, was associated with a greater proportion of ICU admission, mechanical ventilation, acute respiratory distress syndrome (ARDS) and death. Clinical features of this endophenotype, showed increased levels of C-reactive protein, D-dimers, elevated neutrophils, and depleted lymphocytes. Moreover, metabolomic analysis supported a role for immunothrombosis in severe COVID-19 ARDS. Furthermore, the approach enabled the identification of Fibroblast Growth Factor Receptor (FGFR) and SH2-containing transforming protein 4 (SHC4) signaling as features of the molecular pathways associated with severe COVID-19. Finally, this information was sufficient to train an accurate predictive model solely based on clinical laboratory measurements, suggesting the use of blood markers as surrogates for generalizing these EPs to new patients and automating identification of high-risk groups in the clinic.

Identification of COVID-19-relevant transcriptional regulatory networks and associated kinases as potential therapeutic targets (preprint)

Identification of transcriptional regulatory mechanisms and signaling networks involved in the response of host to infection by SARS-CoV-2 is a powerful approach that provides a systems biology view of gene expression programs involved in COVID-19 and may enable identification of novel therapeutic targets and strategies to mitigate the impact of this disease. In this study, we combined a series of recently developed computational tools to identify transcriptional regulatory networks involved in the response of epithelial cells to infection by SARS-CoV-2, and particularly regulatory mechanisms that are specific to this virus. In addition, using network-guided analyses, we identified signaling pathways that are associated with these networks and kinases that may regulate them. The results identified classical antiviral response pathways including Interferon response factors (IRFs), interferons (IFNs), and JAK-STAT signaling as key elements upregulated by SARS-CoV-2 in comparison to mock-treated cells. In addition, comparing SARS-Cov-2 infection of airway epithelial cells to other respiratory viruses identified pathways associated with regulation of inflammation (MAPK14) and immunity (BTK, MBX) that may contribute to exacerbate organ damage linked with complications of COVID-19. The regulatory networks identified herein reflect a combination of experimentally validated hits and novel pathways supporting the computational pipeline to quickly narrow down promising avenue of investigations when facing an emerging and novel disease such as COVID-19.

A network-informed analysis of SARS-CoV-2 and hemophagocytic lymphohistiocytosis genes' interactions points to Neutrophil Extracellular Traps as mediators of thrombosis in COVID-19 (preprint)

Abnormal coagulation and an increased risk of thrombosis are features of severe COVID-19, with parallels proposed with hemophagocytic lymphohistiocytosis (HLH), a life-threating condition associated with hyperinflammation. The presence of HLH was described in severely ill patients during the H1N1 influenza epidemic, presenting with pulmonary vascular thrombosis. We tested the hypothesis that genes causing primary HLH regulate pathways linking pulmonary thromboembolism to the presence of SARS-CoV-2 using novel network-informed computational algorithms. This approach led to the identification of Neutrophils Extracellular Traps (NETs) as plausible mediators of vascular thrombosis in severe COVID-19 in children and adults. Taken together, the network-informed analysis led us to propose the following model: the release of NETs in response to inflammatory signals acting in concert with SARS-CoV-2 damage the endothelium and direct platelet-activation promoting abnormal coagulation leading to serious complications of COVID-19. The underlying hypothesis is that genetic and/or environmental conditions that favor the release of NETs may predispose individuals to thrombotic complications of COVID-19 due to an increase risk of abnormal coagulation. This would be a common pathogenic mechanism in conditions including autoimmune/infectious diseases, hematologic and metabolic disorders.