Host gene expression profiling of fungal (candidemia), bacterial, and viral infection, and non-infectious illness [validation cohort]
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ABSTRACT:
PROVIDER: PRJNA735656 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Improved diagnostics are necessary to differentiate between multipe potential etiologies of acute illness in a hospitalized patient. The peripheral blood host gene expression response can act as a supplementary diagnostic tool and better inform the full host immune response to a pathogen. We performed RNA sequencing on peripheral blood from 48 hospitalized patients with confirmed candidemia as well as patients with other acute viral, bacterial, and non-infectious illnesses and derived pathogen class-specific gene expression classifiers.
2021-06-08 | GSE176261 | GEO
Project description:Improved diagnostics are necessary to differentiate between multipe potential etiologies of acute illness in a hospitalized patient. The peripheral blood host gene expression response can act as a supplementary diagnostic tool and better inform the full host immune response to a pathogen. We performed RNA sequencing on peripheral blood from 48 hospitalized patients with confirmed candidemia as well as patients with other acute viral, bacterial, and non-infectious illnesses and derived pathogen class-specific gene expression classifiers.
2021-06-08 | GSE176260 | GEO
Project description:Host gene expression profiling of fungal (candidemia), bacterial, and viral infection, and non-infectious illness [discovery cohort]
| PRJNA735653 | ENA
Project description:Host gene expression profiling of fungal (candidemia), bacterial, and viral infection, and non-infectious illness
| PRJNA735648 | ENA
Project description:Tuberculosis biomarker validation cohort
| PRJNA616117 | ENA
Project description:A pressing clinical challenge is identifying the etiologic basis of acute respiratory illness. Without reliable diagnostics, the uncertainty associated with this clinical entity leads to a significant, inappropriate use of antibacterials. Use of host peripheral blood gene expression data to classify individuals with bacterial infection, viral infection, or non-infection represents a complementary diagnostic approach. Patients with respiratory tract infection along with ill, non-infected controls were enrolled through the emergency department or undergraduate student health services. Whole blood was obtained to generate gene expression profiles. These profiles were then used to generate signatures of bacterial acute respiratory infection, viral acute respiratory infection, and non-infectious illness. 273 subjects were ascertained for this analysis. This included 88 patients with non-infectious illness, 115 with viral acute respiratory infection, and 70 with bacterial acute respiratory infection. Samples were obtained at the time of enrollment, which was at initial clinical presentation. Total RNA was extracted from human blood using the PAXgene Blood RNA Kit. Microarray data were generated using the GeneChip Human Genome U133A 2.0 Array. Microarrays were generated in two microarray batches with seven overlapping samples giving rise to 280 total microarray experiments.
2016-01-20 | E-GEOD-63990 | biostudies-arrayexpress
Project description:A pressing clinical challenge is identifying the etiologic basis of acute respiratory illness. Without reliable diagnostics, the uncertainty associated with this clinical entity leads to a significant, inappropriate use of antibacterials. Use of host peripheral blood gene expression data to classify individuals with bacterial infection, viral infection, or non-infection represents a complementary diagnostic approach. Patients with respiratory tract infection along with ill, non-infected controls were enrolled through the emergency department or undergraduate student health services. Whole blood was obtained to generate gene expression profiles. These profiles were then used to generate signatures of bacterial acute respiratory infection, viral acute respiratory infection, and non-infectious illness.
2016-01-20 | GSE63990 | GEO
Project description:Background: Septic shock heterogeneity has important implications for the conduct of clinical trials and individual patient management. We previously addressed this heterogeneity by indentifying 3 putative subclasses of children with septic shock based on a 100-gene expression signature corresponding to adaptive immunity and glucocorticoid receptor signaling. Herein we attempted to prospectively validate the existence of these gene expression-based subclasses in a validation cohort. Methods: Gene expression mosaics were generated from the 100 class-defining genes for 82 individual patients in the validation cohort. Patients were classified into 1 of 3 subclasses (“A”, “B”, or “C”) based on color and pattern similarity relative to reference mosaics generated from the original derivation cohort. Separate classifications were conducted by 21 individual clinicians and a computer-based algorithm. After subclassification the clinical database was mined for clinical phenotyping. Results: In the final consensus subclassification generated by clinicians, subclass A patients had a higher illness severity, as measured by illness severity scores and maximal organ failure, relative to subclasses B and C. The k coefficient across all possible inter-evaluator comparisons was 0.633. Similar observations were made based on the computer-generated subclassification. Patients in subclass A were also characterized by repression of a large number of genes having functional annotations related to zinc biology. Conclusions: We have validated the existence of subclasses of children with septic shock based on a biologically relevant, 100-gene expression signature. The subclasses can be indentified by clinicians without formal bioinformatics training, at a clinically relevant time point, and have clinically relevant phenotypic differences.
2011-01-13 | GSE26378 | GEO
Project description:Background: Septic shock heterogeneity has important implications for the conduct of clinical trials and individual patient management. We previously addressed this heterogeneity by indentifying 3 putative subclasses of children with septic shock based on a 100-gene expression signature corresponding to adaptive immunity and glucocorticoid receptor signaling. Herein we attempted to prospectively validate the existence of these gene expression-based subclasses in a validation cohort. Methods: Gene expression mosaics were generated from the 100 class-defining genes for 82 individual patients in the validation cohort. Patients were classified into 1 of 3 subclasses (“A”, “B”, or “C”) based on color and pattern similarity relative to reference mosaics generated from the original derivation cohort. Separate classifications were conducted by 21 individual clinicians and a computer-based algorithm. After subclassification the clinical database was mined for clinical phenotyping. Results: In the final consensus subclassification generated by clinicians, subclass A patients had a higher illness severity, as measured by illness severity scores and maximal organ failure, relative to subclasses B and C. The k coefficient across all possible inter-evaluator comparisons was 0.633. Similar observations were made based on the computer-generated subclassification. Patients in subclass A were also characterized by repression of a large number of genes having functional annotations related to zinc biology. Conclusions: We have validated the existence of subclasses of children with septic shock based on a biologically relevant, 100-gene expression signature. The subclasses can be indentified by clinicians without formal bioinformatics training, at a clinically relevant time point, and have clinically relevant phenotypic differences. Expression data from 82 children with septic shock and 21 normal controls were generated using whole blood-derived RNA samples representing the first 24 hours of admission to the pediatric intensive care unit. The controls were used for normalization. Subsequently, we used the expression data from 100 class defining genes to validate the existence of pediatric septic shock subclasses having phenotypic differences.
2011-01-13 | E-GEOD-26378 | biostudies-arrayexpress
Project description:Focusing on host response to infection, we utilized a murine model to develop a blood gene expression signature that accurately classified mice with candidemia and distinguished candidemia from S. aureus bacteremia. Validation of the signature was achieved in an independent cohort of mice.
2010-02-25 | GSE20524 | GEO