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SRP059039
Homo sapiens
206 Downloadable Samples
IlluminaHiSeq2500
Description
Diarrhea remains a major cause of death in children. Current diagnostic methods largely rely on stool culture and suffer from low sensitivity and inadequate specificity, often leading to inappropriate treatment. The objective of the present study was to use RNA sequencing (RNAseq) analysis to determine blood transcriptional profiles specific for several common pathogenic bacteria and viruses that cause diarrhea in children. We collected whole blood samples from children in Mexico having diarrhea associated with a single pathogen and without systemic complications. Our RNAseq data suggested that the blood signatures can differentiate children with diarrhea from healthy children either with or without bacterial colonization. Moreover, we detected different expression profiles from bacterial and viral infection, demonstrating for the first time the use of RNAseq to identify the etiology of infectious diarrhea. Overall design: 255 whole blood samples from 246 children including children with diarrhea caused by rotavirus (n=60 total; 5 repeated; 55 unique), E.coli (n=55), Salmonella (n=36), Shigella (n=37), adenovirus (n=8), norovirus (n=7), and control children (n=52 total; 4 repeated; 48 unique).
Publication Title
Shared and organism-specific host responses to childhood diarrheal diseases revealed by whole blood transcript profiling.
Sample Metadata Fields
No sample metadata fields
View Samples- Mus musculus
- 48 Downloadable Samples
Sentrix MouseRef-8 Expression BeadChip (Target ID), Rosetta/Merck Mouse TOE 75k Array 1 microarray
Description
A major task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription, and phenotypic information. Here we validated our method through the characterization of transgenic and knockout mouse models of candidate genes that were predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being novel, resulted in significant changes in obesity related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F2 intercross studies allows high confidence prediction of causal genes, and identification of involved pathways and networks.
Publication Title
Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.
Sample Metadata Fields
No sample metadata fields
View Samples- Mus musculus
- 16 Downloadable Samples
Sentrix MouseRef-8 Expression BeadChip (Target ID)
Description
A major task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription, and phenotypic information. Here we validated our method through the characterization of transgenic and knockout mouse models of candidate genes that were predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being novel, resulted in significant changes in obesity related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F2 intercross studies allows high confidence prediction of causal genes, and identification of involved pathways and networks.
Publication Title
Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.
Sample Metadata Fields
No sample metadata fields
View Samples- Mus musculus
- 14 Downloadable Samples
- Sentrix MouseRef-8 Expression BeadChip (Target ID)
Description
A major task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription, and phenotypic information. Here we validated our method through the characterization of transgenic and knockout mouse models of candidate genes that were predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being novel, resulted in significant changes in obesity related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F2 intercross studies allows high confidence prediction of causal genes, and identification of involved pathways and networks.
Publication Title
Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.
Sample Metadata Fields
No sample metadata fields
View Samples- Mus musculus
- 10 Downloadable Samples
- Sentrix MouseRef-8 Expression BeadChip (Target ID)
Description
A major task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription, and phenotypic information. Here we validated our method through the characterization of transgenic and knockout mouse models of candidate genes that were predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being novel, resulted in significant changes in obesity related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F2 intercross studies allows high confidence prediction of causal genes, and identification of involved pathways and networks.
Publication Title
Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.
Sample Metadata Fields
No sample metadata fields
View Samples- Mus musculus
- 8 Downloadable Samples
- Sentrix MouseRef-8 Expression BeadChip (Target ID)
Description
A major task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription, and phenotypic information. Here we validated our method through the characterization of transgenic and knockout mouse models of candidate genes that were predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being novel, resulted in significant changes in obesity related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F2 intercross studies allows high confidence prediction of causal genes, and identification of involved pathways and networks.
Publication Title
Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.
Sample Metadata Fields
No sample metadata fields
View Samples- Rattus norvegicus
- 54 Downloadable Samples
- Affymetrix Rat Gene 1.0 ST Array (ragene10st)
Description
Neural progenitor cells (NPCs) have regenerative capabilities that are activated during inflammation. By measuring the global transcriptome and performing functional studies, we aimed at elucidating if and how NPCs from the non-germinal niche of the spinal cord differ from germinal niche NPCs, here represented by the subventricular zone (SVZ) NPCs. Moreover, we investigated how these cells are affected by chronic inflammation modeled by Experimental Autoimmune Encephalomyelitis (EAE). NPCs were isolated and propagated from the SVZ and cervical, thoracic and caudal regions of the spinal cord from healthy rats and rats subjected to EAE. Using Affymetrix microarray analyses, the global transcriptome was measured in the different NPC populations both in undifferentiated and differentiated cultures. These analyses were paralleled by differentiation studies and quantitative RT-PCR of differentiation-specific genes.
Publication Title
Change of fate commitment in adult neural progenitor cells subjected to chronic inflammation.
Sample Metadata Fields
Specimen part, Disease, Disease stage
View Samples- Drosophila melanogaster
- 53 Downloadable Samples
- Affymetrix Drosophila Genome Array (drosgenome1)
Description
An important but largely unmet challenge in understanding the mechanisms that govern formation of specific organs is to decipher the complex and dynamic genetic programs exhibited by the diversity of cell types within the tissue of interest. Here, we use an integrated genetic, genomic and computational strategy to comprehensively determine the molecular identities of distinct myoblast subpopulations within the Drosophila embryonic mesoderm at the time that cell fates are initially specified. A compendium of gene expression profiles was generated for primary mesodermal cells purified by flow cytometry from appropriately staged wild-type embryos and from twelve genotypes in which myogenesis was selectively and predictably perturbed. A statistical meta-analysis of these pooled datasetsbased on expected trends in gene expression and on the relative contribution of each genotype to the detection of known muscle genesprovisionally assigned hundreds of differentially expressed genes to particular myoblast subtypes. Whole embryo in situ hybridizations were then used to validate the majority of these predictions, thereby enabling true positive detection rates to be estimated for the microarray data. This combined analysis reveals that myoblasts exhibit much greater gene expression heterogeneity and overall complexity than was previously appreciated. Moreover, it implicates the involvement of large numbers of uncharacterized, differentially expressed genes in myogenic specification and subsequent morphogenesis. These findings also underscore a requirement for considerable regulatory specificity for generating diverse myoblast identities. Finally, to illustrate how the developmental functions of newly identified myoblast genes can be efficiently surveyed, a rapid RNA interference assay that can be scored in living embryos was developed and applied to selected genes. This integrated strategy for examining embryonic gene expression and function provides a substantially expanded framework for further studies of this model developmental system.
Publication Title
An integrated strategy for analyzing the unique developmental programs of different myoblast subtypes.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 20 Downloadable Samples
- Affymetrix Human Gene 1.0 ST Array (hugene10st)
Description
LCM was perfomed on adjacent tumor and stromal cells to identify differentially expressed genes in triple negative breast cancer.
Publication Title
Refinement of Triple-Negative Breast Cancer Molecular Subtypes: Implications for Neoadjuvant Chemotherapy Selection.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 16 Downloadable Samples
- Affymetrix Human Gene 1.0 ST Array (hugene10st)
Description
One of the most likely risks astronauts on long duration space missions face is exposure to ionizing radiation associated with highly energetic and charged heavy (HZE) particles. Since access to medical expertise on such a mission is limited at best, early diagnosis and mitigation of such exposure is critical. In order to accurately determine the dosage within 1 hour post-exposure, dose-dependent biomarkers are needed. Therefore, we performed a dose-course transcriptional analysis for radiation exposure at 0, 0.3, 1.5, and 3.0 Gy with corresponding time point at 1 hour (hr) post-exposure using Affymetrix GeneChip Human Gene 1.0 ST v1 Array chips. The analysis of our data suggests a set of sensitive genetic biomarkers specific to each radiation level as well as generic radiation response biomarkers. Upregulated biomarkers can then be used within lab-on-a-chip (LOC) systems to detect exposure to ionizing radiation.
Publication Title
Transcriptional profile of immediate response to ionizing radiation exposure.
Sample Metadata Fields
Specimen part, Time
View Samples