rdo5e9ho

Property	Value
?:abstract	COVID-19 patients show heterogeneity in clinical presentation and outcomes that makes pandemic control and strategy difficult; optimizing management requires a systems biology approach of understanding the disease. Here we sought to potentially understand and infer complex disease progression, immune regulation, and symptoms in patients infected with coronaviruses (35 SARS-CoV and 3 SARS-CoV-2 patients and 57 samples) at two different disease progression stages. Further, we compared coronavirus data with healthy individuals (n = 16) and patients with other infections (n = 144; all publicly available data). We applied inferential statistics (the COVID-engine platform) to RNA profiles (from limited number of samples) derived from peripheral blood mononuclear cells (PBMCs). Compared to healthy individuals, a subset of integrated blood-based gene profiles (signatures) distinguished acute-like (mimicking coronavirus-infected patients with prolonged hospitalization) from recovering-like patients. These signatures also hierarchically represented multiple (at the system level) parameters associated with PBMC including dysregulated cytokines, genes, pathways, networks of pathways/concepts, immune status, and cell types. Proof-of-principle observations included PBMC-based increases in cytokine storm-associated IL6, enhanced innate immunity (macrophages and neutrophils), and lower adaptive T and B cell immunity in patients with acute-like disease compared to those with recovery-like disease. Patients in the recovery-like stage showed significantly enhanced TNF, IFN-γ, anti-viral, HLA-DQA1, and HLA-F gene expression and cytolytic activity, and reduced pro-viral gene expression compared to those in the acute-like stage in PBMC. Besides, our analysis revealed overlapping genes associated with potential comorbidities (associated diabetes) and disease-like conditions (associated with thromboembolism, pneumonia, lung disease, and septicemia). Overall, our COVID-engine inferential statistics platform and study involving PBMC-based RNA profiling may help understand complex and variable system-wide responses displayed by coronavirus-infected patients with further validation.
is ?:annotates of	<https://research.tib.eu/covid-19/entity/rdo5e9ho_hasAnnotation_C0242656> <https://research.tib.eu/covid-19/entity/rdo5e9ho_hasAnnotation_C1457887> <https://research.tib.eu/covid-19/entity/rdo5e9ho_hasAnnotation_C3178810>
?:creator	<https://research.tib.eu/covid-19/entity/Sadanandam%2C_Anguraj%3B_Bopp%2C_Tobias%3B_Dixit%2C_Santosh%3B_Knapp%2C_David_J._H._F.%3B_Emperumal%2C_Chitra_Priya%3B_Vergidis%2C_Paschalis%3B_Rajalingam%2C_Krishnaraj%3B_Melcher%2C_Alan%3B_Kannan%2C_Nagarajan>
?:doi	10.1038/s41420-020-00376-x
?:doi	<https://research.tib.eu/covid-19/entity/10.1038/s41420-020-00376-x>
?:journal	Cell_Death_Discov
?:license	cc-by
?:pdf_json_files	document_parses/pdf_json/bdd3e172362aaad4befc684edc1fa4ebd64f93cd.json
?:pmc_json_files	document_parses/pmc_json/PMC7721861.xml.json
?:pmcid	<https://research.tib.eu/covid-19/entity/PMC7721861>
?:pmid	<https://research.tib.eu/covid-19/entity/33293514.0>
?:pmid	33293514.0
?:publication_isRelatedTo_Disease	<https://research.tib.eu/covid-19/entity/COVID-19>
?:sha_id	<https://research.tib.eu/covid-19/entity/bdd3e172362aaad4befc684edc1fa4ebd64f93cd>
?:source	Medline; PMC
?:title	A blood transcriptome-based analysis of disease progression, immune regulation, and symptoms in coronavirus-infected patients
?:type	<https://research.tib.eu/covid-19/vocab/Publication>
?:year	2020-12-08

Property

Value

?:abstract

COVID-19 patients show heterogeneity in clinical presentation and outcomes that makes pandemic control and strategy difficult; optimizing management requires a systems biology approach of understanding the disease. Here we sought to potentially understand and infer complex disease progression, immune regulation, and symptoms in patients infected with coronaviruses (35 SARS-CoV and 3 SARS-CoV-2 patients and 57 samples) at two different disease progression stages. Further, we compared coronavirus data with healthy individuals (n = 16) and patients with other infections (n = 144; all publicly available data). We applied inferential statistics (the COVID-engine platform) to RNA profiles (from limited number of samples) derived from peripheral blood mononuclear cells (PBMCs). Compared to healthy individuals, a subset of integrated blood-based gene profiles (signatures) distinguished acute-like (mimicking coronavirus-infected patients with prolonged hospitalization) from recovering-like patients. These signatures also hierarchically represented multiple (at the system level) parameters associated with PBMC including dysregulated cytokines, genes, pathways, networks of pathways/concepts, immune status, and cell types. Proof-of-principle observations included PBMC-based increases in cytokine storm-associated IL6, enhanced innate immunity (macrophages and neutrophils), and lower adaptive T and B cell immunity in patients with acute-like disease compared to those with recovery-like disease. Patients in the recovery-like stage showed significantly enhanced TNF, IFN-γ, anti-viral, HLA-DQA1, and HLA-F gene expression and cytolytic activity, and reduced pro-viral gene expression compared to those in the acute-like stage in PBMC. Besides, our analysis revealed overlapping genes associated with potential comorbidities (associated diabetes) and disease-like conditions (associated with thromboembolism, pneumonia, lung disease, and septicemia). Overall, our COVID-engine inferential statistics platform and study involving PBMC-based RNA profiling may help understand complex and variable system-wide responses displayed by coronavirus-infected patients with further validation.

is ?:annotates of