Gene expression profiles of peripheral blood samples from C57BL/6 mice exposed with ionizing radiation.
Biological pathway selection through Bayesian integrative modeling.
Sex, Specimen part, Treatment, Time
View SamplesStaphylococcus aureus causes a spectrum of human infection. Diagnostic delays and uncertainty lead to treatment delays and inappropriate antibiotic use. A growing literature suggests the hosts inflammatory response to the pathogen represents a potential tool to improve upon current diagnostics. The hypothesis of this study is that the host responds differently to S. aureus than to E. coli infection in a quantifiable way, providing a new diagnostic avenue. This study uses Bayesian sparse factor modeling and penalized binary regression to define peripheral blood gene-expression classifiers of murine and human S. aureus infection. The murine-derived classifier distinguished S. aureus infection from healthy controls and Escherichia coli-infected mice across a range of conditions (mouse and bacterial strain, time post infection) and was validated in outbred mice (AUC>0.97). A S. aureus classifier derived from a cohort of 95 human subjects distinguished S. aureus blood stream infection (BSI) from healthy subjects (AUC 0.99) and E. coli BSI (AUC 0.82). Murine and human responses to S. aureus infection share common biological pathways, allowing the murine model to classify S. aureus BSI in humans (AUC 0.84). Both murine and human S. aureus classifiers were validated in an independent human cohort (AUC 0.95 and 0.94, respectively). The approach described here lends insight into the conserved and disparate pathways utilized by mice and humans in response to these infections. Furthermore, this study advances our understanding of S. aureus infection; the host response to it; and identifies new diagnostic and therapeutic avenues.
Gene expression-based classifiers identify Staphylococcus aureus infection in mice and humans.
Race
View SamplesA 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.
Host gene expression classifiers diagnose acute respiratory illness etiology.
No sample metadata fields
View SamplesTranscript dynamics in the S. cerevisiae BF264-15D strains with genetically perturbed TFN or CDK-APC/C components. We compared the extents to which the programs of cell-cycle transcription were initiated during various cell-cycle arrests.
No associated publication
No sample metadata fields
View SamplesThis SuperSeries is composed of the SubSeries listed below.
Integrating factor analysis and a transgenic mouse model to reveal a peripheral blood predictor of breast tumors.
Specimen part
View SamplesAbstract from paper - Potti A, et al
A genomic strategy to refine prognosis in early-stage non-small-cell lung cancer.
No sample metadata fields
View SamplesWe analyzed gene expression in human peripheral blood mononuclear cells (PBMCs) from breast cancer patients, patients with benign breast abnormalities, healthy cancer-free individuals as well as patients with other types of cancer (gastrointestinal and brain cancers).
Integrating factor analysis and a transgenic mouse model to reveal a peripheral blood predictor of breast tumors.
Specimen part
View SamplesYeast cell cycle transcript dynamics in three S. cerevisiae strains grown at 30 degrees Celsius: cdc20 GALL-CDC20 (persistent mitotic CDK activity; CDK on), cdc8-ts (DNA replication checkpoint), GAL-cse4-353 (spindle assembly checkpoint), cdc8-ts cdc20 (DNA replication checkpoint, CDK on), and cdc8-ts cdc20, rad53-1 (DNA replication checkpoint without Rad53 activity, CDK on) in a BF264-15DU background. We compared transcript levels of genes previously shown to be periodically expressed in wild-type cells and in cells lacking all mitotic cyclins (clb1,2,3,4,5,6; CDK off).
Checkpoints couple transcription network oscillator dynamics to cell-cycle progression.
No sample metadata fields
View SamplesSignatures of Oncogenic Pathway Deregulation in Human Cancers.
Oncogenic pathway signatures in human cancers as a guide to targeted therapies.
No sample metadata fields
View SamplesSignatures of Oncogenic Pathway Deregulation in Human Cancers.
Oncogenic pathway signatures in human cancers as a guide to targeted therapies.
No sample metadata fields
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