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SRP067527
Mus musculus
9 Downloadable Samples
Illumina HiSeq 2500
Description
Using a CML mouse model, we identified differences in gene expression between leukemic compared with non-leukemic LTHSC, including increased expression of the thrombopoietin (THPO) receptor MPL. LTHSC expressing high levels of MPL showed enhanced JAK/STAT signaling and proliferation in response to THPO in vitro, and increased leukemogenic capacity in vivo compared to LTHSC with low MPL expression. Although both G0 and S-phase subpopulations were increased in MPL expressing LTHSC, LSC capacity was restricted to quiescent cells. Inhibition of MPL expression in CML LTHSC resulted in reduced THPO-induced JAK/STAT signaling and leukemogenic potential. Similar observations were made with LTHSC from CML patients. MPL expressing LTHSC demonstrated reduced sensitivity to BCR-ABL TKI treatment but demonstrated increased sensitivity to JAK inhibitors. Our studies identify MPL expression levels as a key determinant of heterogeneous leukemia-initiating capacity and drug sensitivity of CML LTHSC, and suggest that MPL-expressing CML stem cells are critical targets for therapy. Overall design: To evaluate heterogeneity in LSC potential, donor LTHSC from SCL-tTA/BCR-ABL mice (200 cells/mouse) were transplanted into a cohort of congenic FVBN mice. Recipient mice were followed for engraftment of donor CML cells and development of CML. LTHSCs were isolated from leukemic and non-leukemic recipient mice and global gene expression was analyzed using RNA-Seq.
Publication Title
Heterogeneity of leukemia-initiating capacity of chronic myelogenous leukemia stem cells.
Sample Metadata Fields
Specimen part, Subject
View Samples- Mus musculus
- 6 Downloadable Samples
Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
The retinal projection neurons, retinal ganglion cells (RGCs), can be categorized into distinct morphological and functional subtypes and by the laterality of their projections. Here, we used a new method for purifying the sparse population of ipsilaterally projecting RGCs in mouse retina from their contralaterally-projecting counterparts during embryonic development through rapid retrograde labeling followed by fluorescence-activated cell sorting (FACS). Through microarray analysis, we have uncovered the distinct molecular signatures that define and distinguish ipsilateral and contralateral RGCs during the critical period of axonal outgrowth and decussation, with over three hundred genes differentially experienced within these two cell populations. Amongst the genes upregulated in ipsilateral RGCs are many that are known to be expresed in progenitors cells and mark immaturity," including Math5 (Atoh7), Sox2, and cyclin D2. Many of these differentially regulated genes were subsequently validated via in vivo expression analysis. Thus, the molecular signatures of ipsilateral and contralateral RGCs and the mechanisms that regulate their differentiation are more diverse than previously expected.
Publication Title
Ipsilateral and Contralateral Retinal Ganglion Cells Express Distinct Genes during Decussation at the Optic Chiasm.
Sample Metadata Fields
Specimen part
View Samples- Mus musculus
- 7 Downloadable Samples
- Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
miR-155 transgenic mice develop pre-B cell leukemia/lymphoma. Though some targets of miR-155 are known, understanding of the mechanism by which miR-155 overexpression drives malignant transformation is not known. MicroRNAs regulate multiple genes.
Publication Title
miR-155 targets histone deacetylase 4 (HDAC4) and impairs transcriptional activity of B-cell lymphoma 6 (BCL6) in the Eμ-miR-155 transgenic mouse model.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 11 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
Post-transplant molecularly defined Burkitt lymphomas are frequently MYC-negative and characterized by the 11q-gain/loss pattern.
Sample Metadata Fields
Sex, Age, Treatment
View Samples- Homo sapiens
- 11 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
We performed genomic and transcriptomic analysis of seven cases of molecular Burkitt lymphoma (mBL) developed in immunosuppressed patients who underwent solid organ transplantation. Interestingly, three cases (43%) were MYC-translocation-negative and revealed the 11q-gain/loss aberration recently identified in 3% of mBL developed in immunocompetent hosts.1 Based on array CGH data, minimal gain and loss regions of 11q (MGR/~4Mb and MLR/~13.5Mb, respectively) were defined and integrative genomic and transcriptomic analysis identified 35 differentially expressed genes, when compared with classic BL. All 16 MGR-dysregulated genes were upregulated, including cancer related USP2, CBL and PAFAH1B2. As expected, all 19 MGL-dysregulated genes were downregulated and two of them, TBRG1 and EI24, are potential tumor suppressor genes. Interestingly, the vast majority of dysregulated 11q23-q25 genes are involved in the MYC and TP53 networks. We hypothesize that the 11q-gain/loss aberration represents a molecular variant of t(8q24/MYC) and affects the same pathological pathways as the MYC oncogene.
Publication Title
Post-transplant molecularly defined Burkitt lymphomas are frequently MYC-negative and characterized by the 11q-gain/loss pattern.
Sample Metadata Fields
Sex, Age, Treatment
View Samples- Homo sapiens
- 15 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Transient expression of two factors, or from Oct4 alone, resulted in efficient generation of human iPSCs. The reprogramming strategy described revealed a potential transcriptional signature for human iPSCs yet retaining the gene expression of donor cells in human reprogrammed cells free of viral and transgene interference.
Publication Title
Transcriptional signature and memory retention of human-induced pluripotent stem cells.
Sample Metadata Fields
Sex, Specimen part
View Samples- Mus musculus
- 9 Downloadable Samples
- Affymetrix Mouse Gene 1.0 ST Array (mogene10st)
Description
The Ets transcription factor, ERG, plays a central role in definitive hematopoiesis and its overexpression in acute myeloid leukemia is associated with a stem cell signature and bad prognosis. However, little is known about the underlying mechanism by which ERG causes leukemia. Therefore we sought to identify ERG targets that participate in development of leukemia by integration of expression arrays and Chromatin immunoprecipitation.
Publication Title
Genome-scale expression and transcription factor binding profiles reveal therapeutic targets in transgenic ERG myeloid leukemia.
Sample Metadata Fields
No sample metadata fields
View Samples- Saccharomyces cerevisiae
- 80 Downloadable Samples
- Affymetrix Yeast Genome 2.0 Array (yeast2)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
Dynamics of oscillatory phenotypes in Saccharomyces cerevisiae reveal a network of genome-wide transcriptional oscillators.
Sample Metadata Fields
No sample metadata fields
View Samples- Saccharomyces cerevisiae
- 49 Downloadable Samples
- Affymetrix Yeast Genome 2.0 Array (yeast2)
Description
Genetic and environmental factors influence the phenotype of an organism. Time is rarely considered when studying changes in cellular phenotype. Time-resolved microarray data revealed genome-wide transcriptional changes in cells oscillating with ~2 and ~4 h periods. We mapped the global patterns of transcriptional oscillations into a 3-dimensional map to represent different cellular phenotypes of oscillation period. This map shows the dynamic nature of transcripts through time and concentration space, and that they are ordered and coupled to each other. Although cells differed in oscillation periods, transcripts involved in certain processes were conserved in a deterministic way. This ordered timing of biological process may allow cells to grow energetically efficient. Decreased glucose levels in the media were found to increase the redox cycles of yeast strain CEN.PK113-7D. Glucose may have acted as signaling molecules for timing longer catabolic processes in the cell population. As oscillation period lengthened, the peak to trough ratio of transcripts increased and the percent of cells in the unbudded (G0/G1) phase of the cell cycle increased. Gene transcripts appear to be coordinated with metabolic functions and the cell cycle.
Publication Title
Dynamics of oscillatory phenotypes in Saccharomyces cerevisiae reveal a network of genome-wide transcriptional oscillators.
Sample Metadata Fields
No sample metadata fields
View Samples- Saccharomyces cerevisiae
- 31 Downloadable Samples
- Affymetrix Yeast Genome 2.0 Array (yeast2)
Description
Genetic and environmental factors influence the phenotype of an organism. Time is rarely considered when studying changes in cellular phenotype. Time-resolved microarray data revealed genome-wide transcriptional changes in cells oscillating with ~2 and ~4 h periods. We mapped the global patterns of transcriptional oscillations into a 3-dimensional map to represent different cellular phenotypes of oscillation period. This map shows the dynamic nature of transcripts through time and concentration space, and that they are ordered and coupled to each other. Although cells differed in oscillation periods, transcripts involved in certain processes were conserved in a deterministic way. This ordered timing of biological process may allow cells to grow energetically efficient. Decreased glucose levels in the media were found to increase the redox cycles of yeast strain CEN.PK113-7D. Glucose may have acted as signaling molecules for timing longer catabolic processes in the cell population. As oscillation period lengthened, the peak to trough ratio of transcripts increased and the percent of cells in the unbudded (G0/G1) phase of the cell cycle increased. Gene transcripts appear to be coordinated with metabolic functions and the cell cycle.
Publication Title
Dynamics of oscillatory phenotypes in Saccharomyces cerevisiae reveal a network of genome-wide transcriptional oscillators.
Sample Metadata Fields
No sample metadata fields
View Samples