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10.1038/s41591-020-1021-2

http://scihub22266oqcxt.onion/10.1038/s41591-020-1021-2
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32747830!7809634!32747830
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suck abstract from ncbi


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pmid32747830      Nat+Med 2020 ; 26 (10): 1609-1615
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  • Immune complement and coagulation dysfunction in adverse outcomes of SARS-CoV-2 infection #MMPMID32747830
  • Ramlall V; Thangaraj PM; Meydan C; Foox J; Butler D; Kim J; May B; De Freitas JK; Glicksberg BS; Mason CE; Tatonetti NP; Shapira SD
  • Nat Med 2020[Oct]; 26 (10): 1609-1615 PMID32747830show ga
  • Understanding the pathophysiology of SARS-CoV-2 infection is critical for therapeutic and public health strategies. Viral-host interactions can guide discovery of disease regulators, and protein structure function analysis points to several immune pathways, including complement and coagulation, as targets of coronaviruses. To determine whether conditions associated with dysregulated complement or coagulation systems impact disease, we performed a retrospective observational study and found that history of macular degeneration (a proxy for complement-activation disorders) and history of coagulation disorders (thrombocytopenia, thrombosis and hemorrhage) are risk factors for SARS-CoV-2-associated morbidity and mortality-effects that are independent of age, sex or history of smoking. Transcriptional profiling of nasopharyngeal swabs demonstrated that in addition to type-I interferon and interleukin-6-dependent inflammatory responses, infection results in robust engagement of the complement and coagulation pathways. Finally, in a candidate-driven genetic association study of severe SARS-CoV-2 disease, we identified putative complement and coagulation-associated loci including missense, eQTL and sQTL variants of critical complement and coagulation regulators. In addition to providing evidence that complement function modulates SARS-CoV-2 infection outcome, the data point to putative transcriptional genetic markers of susceptibility. The results highlight the value of using a multimodal analytical approach to reveal determinants and predictors of immunity, susceptibility and clinical outcome associated with infection.
  • |Adult[MESH]
  • |Age Factors[MESH]
  • |Aged[MESH]
  • |Aged, 80 and over[MESH]
  • |Betacoronavirus[MESH]
  • |Blood Coagulation Disorders/epidemiology[MESH]
  • |Blood Coagulation/genetics[MESH]
  • |COVID-19[MESH]
  • |Complement Activation/genetics/*immunology[MESH]
  • |Coronavirus Infections/blood/genetics/immunology/*mortality[MESH]
  • |Diabetes Mellitus, Type 2/epidemiology[MESH]
  • |Female[MESH]
  • |Gene Expression[MESH]
  • |Hemorrhage/blood/*epidemiology/immunology[MESH]
  • |Hereditary Complement Deficiency Diseases/epidemiology/immunology[MESH]
  • |Humans[MESH]
  • |Hypertension/epidemiology[MESH]
  • |Intubation, Intratracheal[MESH]
  • |Macular Degeneration/*epidemiology[MESH]
  • |Male[MESH]
  • |Middle Aged[MESH]
  • |New York City/epidemiology[MESH]
  • |Obesity/epidemiology[MESH]
  • |Pandemics[MESH]
  • |Pneumonia, Viral/blood/genetics/immunology/*mortality[MESH]
  • |Proportional Hazards Models[MESH]
  • |Respiration, Artificial[MESH]
  • |Retrospective Studies[MESH]
  • |Risk Factors[MESH]
  • |SARS-CoV-2[MESH]
  • |Severity of Illness Index[MESH]
  • |Sex Factors[MESH]
  • |Thrombocytopenia/blood/*epidemiology[MESH]


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