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Mus musculus
12 Downloadable Samples
Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
External stimulations of cells by hormones, growth factors or cytokines activate signal transduction pathways that subsequently induce a rearrangement of cellular gene expression. The representation and analysis of changes in the gene response is complicated, and essentially consists of multiple layered temporal responses. In such situations, matrix factorization techniques may provide efficient tools for the detailed temporal analysis. Related methods applied in bioinformatics intentionally do not take prior knowledge into account. In signal processing, factorization techniques incorporating data properties like second-order spatial and temporal structures have shown a robust performance. However, large-scale biological data rarely imply a natural order that allows the definition of an autocorrelation function. We therefore develop the concept of graph-autocorrelation. We encode prior knowledge like transcriptional regulation, protein interactions or metabolic pathways as a weighted directed graph. By linking features along this underlying graph, we introduce a partial ordering of the samples to define an autocorrelation function. Using this framework as constraint to the matrix factorization task allows us to set up the fast and robust graph decorrelation (GraDe) algorithm. To analyze the alterations in the gene response in IL-6 stimulated primary mouse hepatocytes by GraDe, a time-course microarray experiment was performed. Extracted gene expression profiles show that IL-6 activates genes involved in cell cycle progression and cell division in a time-resolved manner. On the contrary, genes linked to metabolic and apoptotic processes are down-regulated indicating that IL-6 mediated priming rendered hepatocytes more responsive towards cell proliferation and reduces expenses for the energy household.
Publication Title
Knowledge-based matrix factorization temporally resolves the cellular responses to IL-6 stimulation.
Sample Metadata Fields
Specimen part, Treatment, Time
View Samples- Homo sapiens
- 24 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Smoking is a major risk factor for Urothelial carcinoma (UC). However the complex mechanisms, how smoking promotes carcinogenesis and tumour progression, remain obscure. A microarray based approached was therefore performed to detect the smoking derived gene expression alteration in non-malignant and malignant urothelial tissues from patients with superficial or invasive UC. Smoking enhanced cell migration and response to tissue damages. In non-malignant tissues smoking induced immune response and altered the cytoskeleton. In urothelial carcinoma, smoking altered extracellular and chromosome structures. Smoking affected tissues from patients with invasive carcinomamore strongly, up-regulating particularly growth factors and oncogenes in non-malignant tissue of patients with invasive but not with superficial carcinoma. In former smokers, comparable changes were seen in tissues form patients with invasive disease while they were minor or reversed in tissue of patients with superficial disease. Best but not complete tissue repair was suggestedfor non-malignant tissue from patients with superficial tumours.
Publication Title
New insights into the influence of cigarette smoking on urothelial carcinogenesis: smoking-induced gene expression in tumor-free urothelium might discriminate muscle-invasive from nonmuscle-invasive urothelial bladder cancer.
Sample Metadata Fields
Specimen part, Disease
View Samples- Mus musculus
- 10 Downloadable Samples
- Affymetrix Mouse Gene 1.0 ST Array (mogene10st)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
Pluripotency-related, valproic acid (VPA)-induced genome-wide histone H3 lysine 9 (H3K9) acetylation patterns in embryonic stem cells.
Sample Metadata Fields
Specimen part, Cell line, Treatment, Time
View Samples- Mus musculus
- 6 Downloadable Samples
- Affymetrix Mouse Gene 1.0 ST Array (mogene10st)
Description
Gene expression profiles of E14 embryonic stem cells (ESCs) before and after treatment with low levels of the histone deacetylase (HDAC) inhibitors valproic acid (VPA) and sodium butyrate (NaBu).
Publication Title
Pluripotency-related, valproic acid (VPA)-induced genome-wide histone H3 lysine 9 (H3K9) acetylation patterns in embryonic stem cells.
Sample Metadata Fields
Specimen part, Cell line, Treatment
View Samples- Mus musculus
- 4 Downloadable Samples
- Affymetrix Mouse Gene 1.0 ST Array (mogene10st)
Description
Gene expression profiles of E14 embryonic stem cells (ESCs) before and after treatment with low levels of the histone deacetylase (HDAC) inhibitor valproic acid (VPA).
Publication Title
Pluripotency-related, valproic acid (VPA)-induced genome-wide histone H3 lysine 9 (H3K9) acetylation patterns in embryonic stem cells.
Sample Metadata Fields
Specimen part, Cell line, Treatment
View Samples- Homo sapiens
- 2 Downloadable Samples
- Affymetrix Human Gene 2.0 ST Array (hugene20st)
Description
Transcripts upregulated or downregulated by HOXB7-MEK signaling were identified for use on the microarray using the Affymetrix GeneChip WT PLUS Reagent Kit in comparison with HOXB7-knockdown S2-013 cells that were transfected with rescue-HOXB7 plasmid and treated with MEK inhibitor, and HOXB7-knockdown S2-013 cells that were transfected with rescue-HOXB7 plasmid but not treated with MEK inhibitor.
Publication Title
The transcription factor HOXB7 regulates ERK kinase activity and thereby stimulates the motility and invasiveness of pancreatic cancer cells.
Sample Metadata Fields
Specimen part
View Samples
GSE23591- Homo sapiens
- 39 Downloadable Samples
- Affymetrix Human Gene 1.0 ST Array (hugene10st)
Description
Primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of Janus-activated kinase (JAK) / signal transducer and activator of transcription (STAT) signaling pathway. Due to complex non-linear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. To untangle these features, we used dynamic pathway modeling that employs model development and calibration based on extensive quantitative data generation. Quantitative data were collected on JAK/STAT pathway signaling components in two lymphoma-derived cell lines, MedB-1 and L1236, representative of PMBL and cHL, respectively. We showed that the amounts of STAT5 and STAT6 are higher whereas the amount of SHP1 is lower in the two lymphoma cell lines compared to B cells from healthy donors. Distinctively, L1236 cells harbor more JAK2 and less SHP1 molecules per cell than MedB-1 or control cells. In our experimental setting interleukin-13 (IL13) stimulation levels remained constant over time. In MedB-1 cells surface IL13 receptor alpha 2 had a strong IL13-sequestering/decoy function. In both lymphoma cell lines we observed IL13-induced activation of interleukin-4 receptor alpha, JAK2 and STAT5, but not of STAT6, which was highly phosphorylated even without stimulus. Furthermore, the known STAT-inducible negative regulators CISH and SOCS3 were up-regulated within 2 hours in MedB-1 but not in L1236 cells. Global transcription profiling revealed 11 early and 16 sustained common genes up-regulated by IL13 in both lymphoma cell lines. Based on this detailed information we established two individual mathematical models, MedB-1 and L1236 model, which were able to describe the respective experimental data. Sensitivity analysis of the model identified six possible therapeutic targets able to reduce gene expression levels in L1236 cells and three in MedB-1 cells. By inhibition of STAT5 phosphorylation we successfully validated one of the predicted targets demonstrating the potential of the approach in guiding target identification for highly deregulated signaling networks in cancer cells. We established mathematical models of the JAK/STAT pathway in two lymphoma cell types (PMBL and cHL), able to reproduce experimental data and to predict possible therapeutic targets.
Publication Title
Dynamic mathematical modeling of IL13-induced signaling in Hodgkin and primary mediastinal B-cell lymphoma allows prediction of therapeutic targets.
Sample Metadata Fields
Cell line, Time
View Samples- Mus musculus
- 34 Downloadable Samples
Illumina HiSeq 2500
Description
To provide further insight to the signaling pathways deregulated by SPOP mutation and determine the relevance of these models to human prostate cancer, we performed RNA-seq on SPOP mutant organoids and controls. RNA-seq reads mapped to human and mouse SPOP confirmed appropriate expression of the F133V transgenic transcript without overexpression compared to endogenous mouse Spop. Quantification of gene expression was performed via RSEQtools using GENCODE as reference gene–annotation set. Both SPOPmut and SPOPwt were done in the same run. S0 was done in first run; S1 and S2 were done in second run. S3, S4 and S5 were done in third run. S5mut and S5wt were excluded from differentially expressed genes analysis, due to the different mouse line. Overall design: Differentially expressed genes between mouse SPOPmut organoids and control by RNA-seq.
Publication Title
SPOP Mutation Drives Prostate Tumorigenesis In Vivo through Coordinate Regulation of PI3K/mTOR and AR Signaling.
Sample Metadata Fields
Specimen part, Subject
View Samples- Homo sapiens
- 34 Downloadable Samples
- Affymetrix Human Transcriptome Array 2.0 (hta20)
Description
substantial number of people at risk to develop type 2 diabetes could not improve insulin sensitivity by physical training intervention. We studied the mechanisms of this impaired exercise response in 20 middle-aged individuals who performed a controlled eight weeks cycling and walking training at 80 % individual VO2max. Participants identified as non-responders in insulin sensitivity (based on Matsuda index) did not differ in pre-intervention parameters compared to high responders. The failure to increase insulin sensitivity after training correlates with impaired up-regulation of mitochondrial fuel oxidation genes in skeletal muscle, and with the suppression of the upstream regulators PGC1 and AMPK2. The muscle transcriptome of the non-responders is further characterized by an activation of TGF and TGF target genes, which is associated with increases in inflammatory and macrophage markers. TGF1 as inhibitor of mitochondrial regulators and insulin signaling is validated in human skeletal muscle cells. Activated TGF1 signaling down-regulates the abundance of PGC1, AMPK2, mitochondrial transcription factor TFAM, and of mitochondrial enzymes. Thus, increased TGF activity in skeletal muscle can attenuate the improvement of mitochondrial fuel oxidation after training and contribute to the failure to increase insulin sensitivity.
Publication Title
TGF-β Contributes to Impaired Exercise Response by Suppression of Mitochondrial Key Regulators in Skeletal Muscle.
Sample Metadata Fields
Specimen part
View Samples- Homo sapiens
- 88 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
The biology underlying nodal metastasis is poorly understood. Transcriptome profiling has helped to characterize both primary tumors seeding nodal metastasis and the metastasis themselves. The interpretation of these data, however, is not without ambiguities. Here we profiled the transcriptomes of 17 papillary thyroid cancer (PTC) nodal metastases, associated primary tumors and primary tumors from N0 patients. We also included patient-matched normal thyroid and lymph node samples as controls to address some limits of previous studies. We found that the transcriptomes of patient-matched primary tumors and metastases were more similar than of unrelated metastases/primary pairs, a result also reported in other organ systems, and that part of this similarity reflected patient background. We found that the comparison of patient-matched primary tumors and metastases was heavily confounded by the presence of lymphoid tissues in the metastasis samples. An original data adjustment procedure was developed to circumvent this problem. It revealed a differential expression of stroma-related gene expression signatures also regulated in other organ systems. The comparison of N0 vs. N+ primary tumors uncovered a signal irreproducible across independent PTC datasets. This signal was also detectable when comparing the normal thyroid tissues adjacent to N0 and N+ tumors, suggesting a cohort specific bias also likely to be present in previous studies with similar statistical power. Classification of N0 vs. N+ yielded an accuracy of 63%, but additional statistical controls not presented in previous studies, revealed that this is likely to occur by chance alone. To address this issue, we used large datasets from The Cancer Genome Atlas and showed that N0 vs. N+ classification rates could not be reached randomly for most cancers. Yet, it was significant, but of limited accuracy (<70%) for thyroid, breast and head and neck cancers.
Publication Title
Revisiting the transcriptional analysis of primary tumours and associated nodal metastases with enhanced biological and statistical controls: application to thyroid cancer.
Sample Metadata Fields
Sex
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