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ABSTRACT:
PROVIDER: PRJEB74559 | ENA |
REPOSITORIES: ENA
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Project description:BACKGROUND. Lower respiratory tract infection (LRTI) is a leading cause of death in children worldwide. LRTI diagnosis is challenging since non-infectious respiratory illnesses appear clinically similar and existing microbiologic tests are often falsely negative or detect incidentally-carried microbes common in children. These challenges result in antimicrobial overuse and adverse patient outcomes. Lower airway metagenomics has the potential to detect host and microbial signatures of LRTI. Whether it can be applied at scale and in a pediatric population to enable improved diagnosis and precision treatment remains unclear. METHODS. We used tracheal aspirate RNA-sequencing to profile host gene expression and respiratory microbiota in 261 children with acute respiratory failure. We developed a random forest gene expression classifier for LRTI by training on patients with an established diagnosis of LRTI (n=117) or of non-infectious respiratory failure (n=50). We then developed a classifier that integrates the: i) host LRTI probability, ii) abundance of respiratory viruses, and iii) dominance in the lung microbiome of bacteria/fungi considered pathogenic by a rules-based algorithm. RESULTS. The host classifier achieved a median AUC of 0.967 by 5-fold cross-validation, driven by activation markers of T cells, alveolar macrophages and the interferon response. The integrated classifier achieved a median AUC of 0.986 and significantly increased the confidence of patient classifications. When applied to patients with an uncertain diagnosis (n=94), the integrated classifier indicated LRTI in 52% of cases and nominated likely causal pathogens in 98% of those. CONCLUSIONS. Lower airway metagenomics enables accurate LRTI diagnosis and pathogen identification in a heterogeneous cohort of critically ill children through integration of host, pathogen, and microbiome features.
2022-12-06 | GSE212532 | GEO
Project description:The Virochip microarray (version 4.0) was used to detect viruses in patients from North America with unexplained influenza-like illness at the onset of the 2009 H1N1 pandemic. We used metagenomics-based technologies (the Virochip microarray) and deep sequencing to analyze nasal swab samples from individuals with 2009 H1N1 infection. This Series includes the Virochip microarray data only.
2010-11-01 | E-GEOD-24034 | biostudies-arrayexpress
Project description:To identify diagnostic and prognostic biomarkers, we compared methylation profiles of LUNC tissues and normal blood at 485,000 CpG markers and identified a marker panel differently methylated in LUNC. We developed diagnostic and prognostic prediction models with the selected panel and compared their efficacy in ctDNA to current available approaches. Our data indicate that cfDNA methylation patterns provide reliable biomarkers in the diagnosis, surveillance, and prognosis of LUNC.
2020-01-01 | GSE97932 | GEO
Project description:To identify diagnostic and prognostic biomarkers, we compared methylation profiles of COAD tissues and normal blood at 485,000 CpG markers and identified a marker panel differently methylated in COAD. We developed diagnostic and prognostic prediction models with the selected panel and compared their efficacy in ctDNA to current available approaches. Our data indicate that cfDNA methylation patterns provide reliable biomarkers in the diagnosis, surveillance, and prognosis of COAD.
2020-01-01 | GSE97923 | GEO
Project description:Comparative Study of Platform-Specific and Hybrid Assembly Approaches in SARS-CoV-2 Genome Sequencing
| PRJEB73346 | ENA