Workshop metagenomics
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB12659 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Metagenomic analysis of ruminal microbes by shotgun pyrosequencing
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Project description:Two molecular phenotypes of sepsis and acute respiratory distress syndrome, termed hyperinflammatory and hypoinflammatory, have been consistently identified by latent class analysis in numerous cohorts, with widely divergent clinical outcomes and differential responses to some treatments; however, the key biological differences between these phenotypes remain poorly understood. We used host and microbe metagenomic sequencing data from blood to deepen our understanding of biological differences between latent class analysis-derived phenotypes and to assess concordance between the latent class analysis-derived phenotypes and phenotypes reported by other investigative groups (e.g., SRS1-2, MARS1-4, reactive/uninflamed). We analyzed data from 113 hypoinflammatory and 76 hyperinflammatory sepsis patients enrolled in a two-hospital prospective cohort study. Molecular phenotypes had been previously assigned using latent class analysis. The hyperinflammatory and hypoinflammatory phenotypes of sepsis had distinct gene expression signatures, with 5,755 genes (31%) differentially expressed. The hyperinflammatory phenotype was associated with elevated expression of innate immune response genes, while the hypoinflammatory phenotype was associated with elevated expression of adaptive immune response genes, and notably, T-cell response genes. Plasma metagenomic analysis identified differences in prevalence of bacteremia, bacterial DNA abundance and composition between the phenotypes, with an increased presence and abundance of Enterobacteriaceae in the hyperinflammatory phenotype. Significant overlap was observed between these phenotypes and previously identified transcriptional subtypes of acute respiratory distress syndrome (reactive/uninflamed) and sepsis (SRS1-2). Analysis of data from the VANISH trial indicated that corticosteroids might have a detrimental effect in hypoinflammatory patients. The hyperinflammatory and hypoinflammatory phenotypes have distinct transcriptional and metagenomic features that could be leveraged for precision treatment strategies.
2024-01-08 | GSE236892 | GEO
Project description:Here we developed a new approach to sepsis diagnosis that integrates host transcriptional profiling with metagenomic broad-range pathogen detection from cell-free plasma RNA and DNA.
2022-08-03 | GSE189400 | GEO
Project description:Here we developed a new approach to sepsis diagnosis that integrates host transcriptional profiling with metagenomic broad-range pathogen detection from cell-free plasma RNA and DNA.
2022-08-03 | GSE189401 | GEO
Project description:Metagenomic Study of North East Lakes and Ponds
| PRJEB6293 | ENA
Project description:Untangling Syntrophic associations between bacteria and methanogenic archaea in ruminal anaerobic microbial ecosystem
| PRJNA630069 | ENA
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