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GSE39965
Homo sapiens
14 Downloadable Samples
Affymetrix Human Exon 1.0 ST Array [transcript (gene) version (huex10st), Affymetrix Human Gene 1.0 ST Array (hugene10st)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
Distinct signal transduction pathways downstream of the (P)RR revealed by microarray and ChIP-chip analyses.
Sample Metadata Fields
Cell line
View Samples- Homo sapiens
- 8 Downloadable Samples
- Affymetrix Human Gene 1.0 ST Array (hugene10st)
Description
Within the overall project, we performed a set of microarray and chromatin-immunoprecipitation (ChIP)-chip experiments using siRNA against the (pro)renin receptor ((P)RR), stable overexpression of PLZF, the PLZF translocation inhibitor genistein and the specific V-ATPase inhibitor bafilomycin to dissect transcriptional pathways downstream of the (P)RR.
Publication Title
Distinct signal transduction pathways downstream of the (P)RR revealed by microarray and ChIP-chip analyses.
Sample Metadata Fields
Cell line
View Samples- Homo sapiens
- 6 Downloadable Samples
- Affymetrix Human Gene 1.0 ST Array (hugene10st)
Description
Within the overall project, we performed a set of microarray and chromatin-immunoprecipitation (ChIP)-chip experiments using siRNA against the (pro)renin receptor ((P)RR), stable overexpression of PLZF, the PLZF translocation inhibitor genistein and the specific V-ATPase inhibitor bafilomycin to dissect transcriptional pathways downstream of the (P)RR.
Publication Title
Distinct signal transduction pathways downstream of the (P)RR revealed by microarray and ChIP-chip analyses.
Sample Metadata Fields
Cell line
View Samples- Homo sapiens
- 2886 Downloadable Samples
- Affymetrix Human Genome U133A 2.0 Array (hgu133a2)
Description
Consider the problem of designing a panel of complex biomarkers to predict a patient's health or disease state when one can pair his or her current test sample, called a target sample, with the patient's previously acquired healthy sample, called a reference sample. As contrasted to a population averaged reference, this reference sample is individualized. Automated predictor algorithms that compare and contrast the paired samples to each other could result in a new generation of test panels that compare to a person's healthy reference to enhance predictive accuracy. This study develops such an individualized predictor and illustrates the added value of including the healthy reference for design of predictive gene expression panels. The objective is to predict each subject's state of infection, e.g., neither exposed nor infected, exposed but not infected, pre-acute phase of infection, acute phase of infection, post-acute phase of infection. Using gene microarray data collected in a large-scale serially sampled respiratory virus challenge study, we quantify the diagnostic advantage of pairing a person's baseline reference with his or her target sample.
Publication Title
An individualized predictor of health and disease using paired reference and target samples.
Sample Metadata Fields
Specimen part, Subject, Time
View Samples- Homo sapiens
- 268 Downloadable Samples
- Affymetrix Human Genome U133A 2.0 Array (hgu133a2)
Description
The transcriptional responses of human hosts towards influenza viral pathogens are important for understanding virus-mediated immunopathology. Despite great advances gained through studies using model organisms, the complete temporal host transcriptional responses in a natural human system are poorly understood. In a human challenge study using live influenza (H3N2/Wisconsin) viruses, we conducted a clinically uninformed (unsupervised) factor analysis on gene expression profiles and established an ab initio molecular signature that strongly correlates to symptomatic clinical disease. This is followed by the identification of 42 biomarkers whose expression patterns best differentiate early from late phases of infection. In parallel, a clinically informed (supervised) analysis revealed over-stimulation of multiple viral sensing pathways in symptomatic hosts and linked their temporal trajectory with development of diverse clinical signs and symptoms. The resultant inflammatory cytokine profiles were shown to contribute to the pathogenesis because their significant increase preceded disease manifestation by 36 hours. In subclinical asymptomatic hosts, we discovered strong transcriptional regulation of genes involved in inflammasome activation, genes encoding virus interacting proteins, and evidence of active anti-oxidant and cell-mediated innate immune response. Taken together, our findings offer insights into influenza virus-induced pathogenesis and provide a valuable tool for disease monitoring and management in natural environments.
Publication Title
Temporal dynamics of host molecular responses differentiate symptomatic and asymptomatic influenza a infection.
Sample Metadata Fields
Specimen part
View Samples- Homo sapiens
- 113 Downloadable Samples
- Affymetrix Human Genome U133A 2.0 Array (hgu133a2)
Description
Diagnosis of acute respiratory viral infection is currentlybased on clinical symptoms and pathogen detection. Use of host peripheral blood gene expression data to classify individuals with viral respiratory infection represents a novel means of infection diagnosis.
Publication Title
Gene expression signatures diagnose influenza and other symptomatic respiratory viral infections in humans.
Sample Metadata Fields
Subject, Time
View Samples- Homo sapiens
- 647 Downloadable Samples
- Affymetrix Human Genome U133A 2.0 Array (hgu133a2)
Description
Diagnosis of influenza A infection is currently based on clinical symptoms and pathogen detection. Use of host peripheral blood gene expression data to classify individuals with influenza A virus infection represents a novel approach to infection diagnosis
Publication Title
A host transcriptional signature for presymptomatic detection of infection in humans exposed to influenza H1N1 or H3N2.
Sample Metadata Fields
Specimen part, Subject, Time
View Samples- Mus musculus, Homo sapiens
- 321 Downloadable Samples
- Affymetrix Mouse Genome 430 2.0 Array (mouse4302), Affymetrix Human Genome U133A 2.0 Array (hgu133a2)
Description
Staphylococcus 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.
Publication Title
Gene expression-based classifiers identify Staphylococcus aureus infection in mice and humans.
Sample Metadata Fields
Race
View Samples- Homo sapiens
- 277 Downloadable Samples
- Affymetrix Human Genome U133A 2.0 Array (hgu133a2)
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.
Publication Title
Host gene expression classifiers diagnose acute respiratory illness etiology.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 42 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Respiratory viral infections follow an unpredictable clinical course in young children ranging from a common cold to respiratory failure. The transition from mild to severe disease occurs rapidly and is difficult to predict. The pathophysiology underlying disease severity has remained elusive. There is an urgent need to better understand the immune response in this disease to come up with biomarkers that may aid clinical decision making. In a prospective study, flow cytometric and genome-wide gene expression analyses were performed on blood samples of 26 children with a diagnosis of severe, moderate or mild Respiratory Syncytial Virus (RSV) infection. Differentially expressed genes were validated using Q-PCR in a second cohort of 80 children during three consecutive winter seasons. FACS analyses were also performed in the second cohort and on recovery samples of severe cases in the first cohort. Severe RSV infection was associated with a transient but marked decrease in CD4+ T, CD8+ T, and NK cells in peripheral blood. Gene expression analyses in both cohorts identified Olfactomedin4 (OLFM4) as a fully discriminative marker between children with mild and severe RSV infection, giving a PAM cross-validation error of 0%. Patients with an OLFM4 gene expression level above -7.5 were 6 times more likely to develop severe disease, after correction for age at hospitalization and gestational age. In conclusion, by combining genome-wide expression profiling of blood cell subsets with clinically well-annotated samples, OLFM4 was identified as a biomarker for severity of pediatric RSV infection.
Publication Title
Olfactomedin 4 Serves as a Marker for Disease Severity in Pediatric Respiratory Syncytial Virus (RSV) Infection.
Sample Metadata Fields
Specimen part, Disease
View Samples