Project description:Circulating miRNAs in patients who underwent ARDS and needed mechanical ventilation were analyzed by next generation sequencing (NGS) in comparison with patients who had COVID-19 poor symptoms but without intensive care unit requirement.
Project description:Although some studies reported the comprehensive mRNA expression analysis of coronavirus disease 2019 (COVID-19) using blood samples to understand its pathogenesis, the characteristics of RNA expression in COVID-19 and sepsis have not been compared. We compared the transcriptome expression of whole blood samples from patients with COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and patients with sepsis caused by other bacteria who entered the intensive care unit to clarify the COVID-19-specific RNA expression and understand its pathogenesis. Transcriptomes related to mitochondria were upregulated in COVID-19, whereas those related to neutrophils were upregulated in sepsis. However, the transcriptomes related to neutrophils were upregulated in both COVID-19 and sepsis compared to in healthy controls, whereas the mitochondrial transcriptomes were upregulated in COVID-19 and downregulated in sepsis compared to in healthy controls. Moreover, sepsis showed sub-optimal intrinsic apoptotic features compared with COVID-19. The transcriptome expression of COVID-19 has been examined in vitro but has not been widely validated using human specimens. This study improves the understanding of the pathogenesis of COVID-19 and can contribute to the development of treatments.
Project description:To go further insight into the involvement of neutrophils in COVID-19 clinical expression, we performed a proteomic analysis of this blood cell type in COVID-19 patients and two non-infected SARS-CoV-2 control groups composed of healthy subjects and ARDS patients hospitalized in intensive care unit (ICU) respectively. All patients were from Guadeloupe and represent a homogeneous population. We have performed a quantitative proteomic study of neutrophiles from French hot spot COVID region, Guadeloupe, confirming the activation of type I IFN pathway and in some target of IFN as TAP proteins, specifically in COVID patients, but not in hospitalized ARDS non-COVID patients and described modification of the NET proteome potentially associated with ARDS.
Project description:We performed a whole-genome DNA methylation analysis using EPIC Illumina BeadArrays on a cohort of 187 “Italian” COVID-19 patients (123 mild and 64 severe) to identify epigenetic markers underlying the worst evolution of the disease (death or Intensive Care Unit admission for mechanical ventilation support).
Project description:As coronavirus disease 2019 (COVID-19) and aging are both accompanied by cognitive decline, we hypothesized that COVID-19 might lead to molecular signatures similar to aging. We performed whole-transcriptome analysis of the frontal cortex, a critical area for cognitive function, in individuals with COVID-19, age-matched and sex-matched uninfected controls, and uninfected individuals with intensive care unit/ventilator treatment. Our findings indicate that COVID-19 is associated with molecular signatures of brain aging and emphasize the value of neurological follow-up in recovered individuals.
Project description:Infections caused by SARS-CoV-2 may cause a severe disease, termed COVID-19, with significant mortality. Host responses to this infection, mainly in terms of systemic inflammation, have emerged as key pathogenetic mechanisms, and their modulation is the only therapeutic strategy that has shown a mortality benefit. Herein, we used peripheral blood transcriptomes of critically-ill COVID-19 patients obtained at admission in an Intensive Care Unit, to identify two clusters that, in spite of no major clinical differences, have different gene expression profiles that reveal different underlying pathogenetic mechanisms and ultimately have different ICU outcome. A transcriptomic signature was used to identify these clusters in an external validation cohort, yielding a similar result. These results illustrate the potential of transcriptomic profiles to identify patient endotypes and point to relevant pathogenetic mechanisms in COVID-19.
Project description:Infections caused by SARS-CoV-2 may cause a severe disease, termed COVID-19, with significant mortality. Host responses to this infection, mainly in terms of systemic inflammation, have emerged as key pathogenetic mechanisms, and their modulation is the only therapeutic strategy that has shown a mortality benefit. Herein, we used peripheral blood transcriptomes of critically-ill COVID-19 patients obtained at admission in an Intensive Care Unit, to identify two clusters that, in spite of no major clinical differences, have different gene expression profiles that reveal different underlying pathogenetic mechanisms and ultimately have different ICU outcome. A transcriptomic signature was used to identify these clusters in an external validation cohort, yielding a similar result. These results illustrate the potential of transcriptomic profiles to identify patient endotypes and point to relevant pathogenetic mechanisms in COVID-19.
Project description:Infections caused by SARS-CoV-2 may cause a severe disease, termed COVID-19, with significant mortality. Host responses to this infection, mainly in terms of systemic inflammation, have emerged as key pathogenetic mechanisms, and their modulation is the only therapeutic strategy that has shown a mortality benefit. Herein, we used peripheral blood transcriptomes of critically-ill COVID-19 patients obtained at admission in an Intensive Care Unit, to identify two clusters that, in spite of no major clinical differences, have different gene expression profiles that reveal different underlying pathogenetic mechanisms and ultimately have different ICU outcome. A transcriptomic signature was used to identify these clusters in an external validation cohort, yielding a similar result. These results illustrate the potential of transcriptomic profiles to identify patient endotypes and point to relevant pathogenetic mechanisms in COVID-19.
Project description:Prognostic characteristics inform risk stratification in intensive care unit (ICU) patients with coronavirus disease 2019 (COVID-19). We obtained blood samples (n = 474) from hospitalized COVID-19 patients (n = 123), non-COVID-19 ICU sepsis patients (n = 25) and healthy controls (n = 30). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA was detected in plasma or serum (RNAemia) of COVID-19 ICU patients when neutralizing antibody response was low. RNAemia was associated with higher 28-day ICU mortality (hazard ratio [HR], 1.84 [95% CI, 1.22–2.77] adjusted for age and sex). In longitudinal comparisons, COVID-19 ICU patients had a distinct proteomic trajectory associated with RNAemia and mortality. Among COVID-19-enriched proteins, galectin-3 binding protein (LGALS3BP) and proteins of the complement system were identified as interaction partners of SARS-CoV-2 spike glycoprotein. Finally, machine learning identified ‘Age, RNAemia’ and ‘Age, pentraxin-3 (PTX3)’ as the best binary signatures associated with 28-day ICU mortality.
Project description:Prognostic characteristics inform risk stratification in intensive care unit (ICU) patients with coronavirus disease 2019 (COVID-19). We obtained blood samples (n = 474) from hospitalized COVID-19 patients (n = 123), non-COVID-19 ICU sepsis patients (n = 25) and healthy controls (n = 30). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA was detected in plasma or serum (RNAemia) of COVID-19 ICU patients when neutralizing antibody response was low. RNAemia was associated with higher 28-day ICU mortality (hazard ratio [HR], 1.84 [95% CI, 1.22–2.77] adjusted for age and sex). In longitudinal comparisons, COVID-19 ICU patients had a distinct proteomic trajectory associated with RNAemia and mortality. Among COVID-19-enriched proteins, galectin-3 binding protein (LGALS3BP) and proteins of the complement system were identified as interaction partners of SARS-CoV-2 spike glycoprotein. Finally, machine learning identified ‘Age, RNAemia’ and ‘Age, pentraxin-3 (PTX3)’ as the best binary signatures associated with 28-day ICU mortality.