Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID-19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID-19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID-19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype.- Pfizer Pharmaceuticals(undefined

Targeted Genotyping of MIS-C Patients Reveals a Potential Alternative Pathway Mediated Complement Dysregulation during COVID-19 Infection

Complement dysregulation has been documented in adults with COVID-19 and implicated in relevant pediatric inflammatory responses against SARS-CoV-2. We propose that signatures of complement missense coding SNPs associated with dysregulation could also be identified in children with multisystem inflammatory syndrome (MIS-C). We investigated 71 pediatric patients with RT-PCR validated SARS-CoV-2 hospitalized in pediatric COVID-19 care units (November 2020–March 2021) in three major groups. Seven (7) patients suffered from MIS-C (MIS-C group), 32 suffered from COVID-19 and were hospitalized (admitted group), whereas 32 suffered from COVID-19, but were sent home. All patients survived and were genotyped for variations in the C3, C5, CFB, CFD, CFH, CFHR1, CFI, CD46, CD55, MASP1, MASP2, MBL2, COLEC11, FCN1, and FCN3 genes. Upon evaluation of the missense coding SNP distribution patterns along the three study groups, we noticed similarities, but also considerably increased frequencies of the alternative pathway (AP) associated with SNPs rs12614 CFB, rs1061170, and rs1065489 CFH in the MIS-C patients. Our analysis suggests that the corresponding substitutions potentially reduce the C3b-inactivation efficiency and promote slower and weaker AP C3bBb pre-convertase assembly on virions. Under these circumstances, the complement AP opsonization capacity may be impaired, leading to compromised immune clearance and systemic inflammation in the MIS-C syndrome

Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID-19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID-19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID-19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype