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.
Jun DingDavid Earl HostalleroMohamed Reda El KhiliGregory Joseph FonsecaSimon MiletteNuzha NoorahMyriam Guay-BelzileJonathan D SpicerNoriko DaneshtalabMartin SiroisKarine TremblayAmin EmadSimon RousseauPublished in: PLoS computational biology (2021)
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.
Keyphrases
- sars cov
- respiratory syndrome coronavirus
- coronavirus disease
- pulmonary embolism
- end stage renal disease
- infectious diseases
- oxidative stress
- chronic kidney disease
- risk factors
- machine learning
- multiple sclerosis
- early onset
- ejection fraction
- nitric oxide
- peritoneal dialysis
- deep learning
- bioinformatics analysis
- risk assessment
- prognostic factors
- transcription factor