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Associations of miRNAs with blood phenotypes and ICU admission in COVID-19 patients

ABSTRACT: This SuperSeries is composed of the SubSeries listed below.

ORGANISM(S): Homo sapiens

PROVIDER: GSE220078 | GEO | 2023/06/14

REPOSITORIES: GEO

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Associations of miRNAs with blood phenotypes and ICU admission in COVID-19 patients [miRNA-seq]

Project description:Individuals infected with SARS-CoV-2 vary greatly in their symptomatology and disease progression, likely as a result of numerous genetic, biological and environmental factors and their complex interactions. Meanwhile, the potential roles of microRNAs (miRNAs) in SARS-CoV-2 infection have not been fully described. MiRNAs have emerged as key post-transcriptional regulators of gene expression, and their dysregulation can be indicative of aberrant immune function. In this study, we characterize the potential roles of mIRNAs in early COVID-19 disease progression. We studied a diverse cohort of 259 patients admitted to hospitals in Abu Dhabi, United Arab Emirates to understand the clinical and biological factors associated with ICU admission during COVID-19 treatment, integrating electronic health records (EHR), global miRNA and RNA expression, and genotyping data. Using EHR, we identified 26 factors correlated with ICU admission, including 8 blood phenotypes such as neutrophil-to-lymphocyte ratio, Interleukin-6, and C-reactive protein levels. Using genome-wide miRNA expression data for a subset of 96 individuals from Southeast Asia and the Middle East and North Africa, we identified 27 miRNAs significantly associated with ICU admission (p < 0.01), and 97 miRNAs associated with at least one of the 8 blood phenotypes. [cross-cor] Integrating expression data for 632 miRNAs and genotyping data for ~260,000 SNPs, we identified 168 significant cis-expression quantitative trait loci (cis-eQTLs), of which 59 were associated with either ICU admission or one of the 8 blood phentoypes. Overall, our findings characterize the miRNA architecture of blood phenotypes during the early stages of COVID-19 infection, identify miRNAs associated with ICU admission and therefore COVID-19 disease severity, and suggest a potential genetic control of miRNA expression during early COVID-19 disease progression.

2023-06-14 | GSE220077 | GEO

Associations of miRNAs with blood phenotypes and ICU admission in COVID-19 patients [mRNA-seq]

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Associations of miRNAs with blood phenotypes and ICU admission in COVID-19 patients

Project description:Associations of miRNAs with blood phenotypes and ICU admission in COVID-19 patients

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Associations of miRNAs with blood phenotypes and ICU admission in COVID-19 patients [mRNA-seq]

Project description:Associations of miRNAs with blood phenotypes and ICU admission in COVID-19 patients [mRNA-seq]

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Associations of miRNAs with blood phenotypes and ICU admission in COVID-19 patients [miRNA-seq]

Project description:Associations of miRNAs with blood phenotypes and ICU admission in COVID-19 patients [miRNA-seq]

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Dynamic Activity in cis-Regulatory Elements of Leukocytes Identifies Transcription Factor Activation and Stratifies COVID-19 Severity in ICU Patients

Project description:This SuperSeries is composed of the SubSeries listed below.

2023-03-02 | GSE221067 | GEO

Transcriptomic clustering of critically-ill COVID-19 patients

Project description:This SuperSeries is composed of the SubSeries listed below.

2022-02-28 | GSE197259 | GEO

Unsupervised clustering reveals phenotypes of AKI in ICU COVID-19 patients.

Project description:BackgroundAcute Kidney Injury (AKI) is a very frequent condition, occurring in about one in three patients admitted to an intensive care unit (ICU). AKI is a syndrome defined as a sudden decrease in glomerular filtration rate. However, this unified definition does not reflect the various mechanisms involved in AKI pathophysiology, each with its own characteristics and sensitivity to therapy. In this study, we aimed at developing an innovative machine learning based method able to subphenotype AKI according to its pattern of risk factors.MethodsWe adopted a three-step pipeline of analyses. First, we looked for factors associated with AKI using a generalized additive model. Second, we calculated the importance of each identified AKI related factor in the estimated AKI risk to find the main risk factor for AKI, at the single patient level. Lastly, we clusterized AKI patients according to their profile of risk factors and compared the clinical characteristics and outcome of every cluster. We applied this method to a cohort of severe COVID-19 patients hospitalized in the ICU of the Geneva University Hospitals.ResultsAmong the 248 patients analyzed, we found 7 factors associated with AKI development. Using the individual expression of these factors, we identified three groups of AKI patients, based on the use of Lopinavir/Ritonavir, baseline eGFR, use of dexamethasone and AKI severity. The three clusters expressed distinct characteristics in terms of AKI severity and recovery, metabolic patterns and hospital mortality.ConclusionWe propose here a new method to phenotype AKI patients according to their most important individual risk factors for AKI development. When applied to an ICU cohort of COVID-19 patients, we were able to differentiate three groups of patients. Each expressed specific AKI characteristics and outcomes, which probably reflect a distinct pathophysiology.

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Proteomic blood profiling in mild, severe and critical COVID-19 patients

Project description: Not available

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