Ontology highlight
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PROVIDER: PRJNA1017160 | ENA |
REPOSITORIES: ENA
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Project description:Introduction: Diagnosis of severe influenza pneumonia remains challenging because of the lack of correlation between presence of influenza virus and patient’s clinical status. We conducted gene expression profiling in the whole blood of critically ill patients to identify a gene signature that would allow clinicians to distinguish influenza infection from other causes of severe respiratory failure (e.g. bacterial pneumonia, non-infective systemic inflammatory response syndrome). Methods: Whole blood samples were collected from critically ill individuals and assayed on Illumina HT-12 gene expression beadarrays. Differentially expressed genes were determined by linear mixed model analysis and over-represented biological pathways determined using GeneGo MetaCore. Results: The gene expression profile of H1N1 influenza A pneumonia was distinctly different from bacterial pneumonia and systemic inflammatory response syndrome. The influenza gene expression profile is characterized by up-regulation of genes from cell cycle regulation, apoptosis and DNA-damage response pathways. In contrast, no distinctive gene-expression signature was found in patients with bacterial pneumonia or systemic inflammatory response syndrome. The gene expression profile of influenza infection persisted through five days of follow-up. Furthermore, in patients with primary H1N1 influenza A infection who subsequently developed bacterial co-infection, the influenza gene-expression signature remained unaltered, despite the presence of a super-imposed bacterial infection. Conclusions: The whole blood expression profiling data indicates that the host response to influenza pneumonia is distinctly different from that caused by bacterial pathogens. This information may speed up identification of the cause of infection in patients presenting with severe respiratory failure, allowing appropriate patient care to be undertaken more rapidly. Daily PAXgene samples for up to 5 days for; influenza A pneumonia patients (n=8), bacterial pneumonia patients (n=16), mixed bacterial and influenza A pneumonia patients (n=3), systemic inflammatory response patients (SIRS, n=13). Days 1 and 5 PAXgene samples for healthy control individuals
2012-08-14 | E-GEOD-40012 | biostudies-arrayexpress
Project description:To test whether airway epithelial and immune cells in patients with severe SARS-CoV-2 pneumonia exhibit specific gene expression signatures (namely interferon-response signature) we have performed bronchoscopy on intubated patients with severe SARS-CoV-2 pneumonia and obtained bronchial brushings.
2021-03-05 | GSE168215 | GEO
Project description:Recent progress in unbiased metagenomic next-generation sequencing (mNGS) allows simultaneous examination of microbial and host genetic material in a single test. Leveraging affordable bronchoalveolar lavage fluid (BALF) mNGS data, we employed machine learning to create a diagnostic approach distinguishing lung cancer from pulmonary infections, conditions prone to misdiagnosis in clinical settings. This prospective study analyzed BALF-mNGS data from lung cancer and pulmonary infection patients, delineating differences in DNA/RNA microbial composition, bacteriophage abundances, and host responses, including gene expression, transposable element levels, immune cell composition, and tumor fraction derived from copy number variation (CNV). Integrating these metrics into a host/microbe metagenomics-driven machine learning model (Model VI) demonstrated robustness, achieving an AUC of 0.87 (95% CI = 0.857-0.883), sensitivity = 73.8%, and specificity = 84.5% in the training cohort, and an AUC of 0.831 (95% CI = 0.819-0.843), sensitivity = 67.1%, and specificity = 94.4% in the validation cohort for distinguishing lung cancer from pulmonary infections. The application of a rule-in and rule-out strategy-based composite predictive model significantly enhances accuracy (ACC) in distinguishing between lung cancer and tuberculosis (ACC=0.913), fungal infection (ACC=0.955), and bacterial infection (ACC=0.836). These findings highlight the potential of cost-effective mNGS-based analysis as a valuable tool for early differentiation between lung cancer and pulmonary infections, offering significant benefits through a single comprehensive testing.
2024-01-08 | GSE252118 | GEO
Project description:To screen and validate abnormally expressed circRNAs in exosomes from BALF of patients with ARDS caused by severe pneumonia, and then evaluate the diagnostic values of these circRNA for ARDS
2022-11-11 | GSE217763 | GEO
Project description:Introduction: Diagnosis of severe influenza pneumonia remains challenging because of the lack of correlation between presence of influenza virus and patient’s clinical status. We conducted gene expression profiling in the whole blood of critically ill patients to identify a gene signature that would allow clinicians to distinguish influenza infection from other causes of severe respiratory failure (e.g. bacterial pneumonia, non-infective systemic inflammatory response syndrome). Methods: Whole blood samples were collected from critically ill individuals and assayed on Illumina HT-12 gene expression beadarrays. Differentially expressed genes were determined by linear mixed model analysis and over-represented biological pathways determined using GeneGo MetaCore. Results: The gene expression profile of H1N1 influenza A pneumonia was distinctly different from bacterial pneumonia and systemic inflammatory response syndrome. The influenza gene expression profile is characterized by up-regulation of genes from cell cycle regulation, apoptosis and DNA-damage response pathways. In contrast, no distinctive gene-expression signature was found in patients with bacterial pneumonia or systemic inflammatory response syndrome. The gene expression profile of influenza infection persisted through five days of follow-up. Furthermore, in patients with primary H1N1 influenza A infection who subsequently developed bacterial co-infection, the influenza gene-expression signature remained unaltered, despite the presence of a super-imposed bacterial infection. Conclusions: The whole blood expression profiling data indicates that the host response to influenza pneumonia is distinctly different from that caused by bacterial pathogens. This information may speed up identification of the cause of infection in patients presenting with severe respiratory failure, allowing appropriate patient care to be undertaken more rapidly.
2012-08-15 | GSE40012 | GEO
Project description:We assayed leukocyte global gene expression for a prospective discovery cohort of 265 adult patients admitted to UK intensive care units with severe sepsis due to community acquired pneumonia.
2016-02-25 | E-MTAB-4421 | biostudies-arrayexpress
Project description:We assayed leukocyte global gene expression for a prospective validation cohort of 106 adult patients admitted to UK intensive care units with severe sepsis due to community acquired pneumonia.
2016-02-25 | E-MTAB-4451 | biostudies-arrayexpress
Project description:Longitudinal Gene expression profiling of whole blood from critically ill influenza and bacterial pneumonia patients. In addition before vs 7 days post influenza vaccination volunteer samples are assayed. 3 groups of samples. First is bacterial pneumonia patients with 6 subjects sampled for up to 5 days. Second group is severe influenza infection with 4 subjects sampled for up to 5 days. Third group is influenza vaccination with 18 subjects sampled before and 7 days post vaccination.
2011-06-30 | E-GEOD-20346 | biostudies-arrayexpress
Project description:Some patients infected with Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) develop severe pneumonia and the acute respiratory distress syndrome (ARDS). Distinct clinical features in these patients have led to speculation that the immune response to virus in the SARS-CoV-2-infected alveolus differs from other types of pneumonia. We collected bronchoalveolar lavage fluid samples from 88 patients with SARS-CoV-2-induced respiratory failure and 211 patients with known or suspected pneumonia from other pathogens and subjected them to flow cytometry and bulk transcriptomic profiling. We performed single-cell RNA-seq on 10 bronchoalveolar lavage fluid samples collected from patients with severe COVID-19 within 48 hours of intubation. In the majority of patients with SARS-CoV-2 infection, the alveolar space was persistently enriched in T cells and monocytes. Bulk and single-cell transcriptomic profiling suggested that SARS-CoV-2 infects alveolar macrophages, which in turn respond by producing T cell chemoattractants. These T cells produce interferon-gamma to induce inflammatory cytokine release from alveolar macrophages and further promote T cell activation. Collectively, our results suggest that SARS-CoV-2 causes a slowly-unfolding, spatially-limited alveolitis in which alveolar macrophages harboring SARS-CoV-2 and T cells form a positive feedback loop that drives persistent alveolar inflammation.
2020-08-05 | GSE155249 | GEO