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accession-icon SRP120630
APT1 regulates the asymmetric partitioning of Notch and Wnt signaling during cell division
  • organism-icon Homo sapiens
  • sample-icon 9 Downloadable Samples
  • Technology Badge IconNextSeq 500

Description

Asymmetric cell division results in two distinctly fated daughter cells to generate cellular diversity. A major molecular hallmark of an asymmetric division is the unequal partitioning of cell-fate determinant proteins. We have previously established that growth factor signaling promotes protein depalmitoylation to foster polarized protein localization, which in turns drives migration and metastasis. Here, we report protein palmitoylation as a key mechanism for the asymmetric partitioning of the cell-fate determinants Numb (Notch antagonist) and ß-catenin (canonical Wnt regulator) through the activity of a depalmitoylating enzyme, APT1. Using point mutants, we show specific palmitoylated residues on proteins, such as Numb, are required for asymmetric localization. Furthermore, by live-cell imaging, we show that reciprocal interactions between APT1 and CDC42 regulate the asymmetric localization of Numb and ß-catenin to the plasma membrane. This in turn restricts Notch and Wnt transcriptional activity to one daughter cell. Moreover, we show altering APT1 expression changes the transcriptional signatures to those resembling that of Notch and ß-catenin in MDA-MB-231 cells. We also show loss of APT1 depletes the population of CD44+/CD24lo/ALDH+ tumorigenic cells in colony formation assays. Together, the findings of this study demonstrate that palmitoylation, via APT1, is a major mechanism of asymmetric cell division regulating Notch and Wnt-associated protein dynamics, gene expression, and cellular functions. Overall design: Gene expression by RNAseq of MDA-MB-231 triple receptor negative breast cancer cells expressing scramble control vector, shAPT1 knockdown, and APT1wt performed in triplicate. Total of 9 samples were analyzed.

Publication Title

The depalmitoylase APT1 directs the asymmetric partitioning of Notch and Wnt signaling during cell division.

Sample Metadata Fields

Specimen part, Cell line, Treatment, Subject

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accession-icon GSE38614
Hierarchical regulation in a KRAS pathway-dependent transcriptional network revealed by a reverse-engineering approach
  • organism-icon Rattus norvegicus
  • sample-icon 18 Downloadable Samples
  • Technology Badge Icon Affymetrix Rat Gene 1.0 ST Array (ragene10st)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Reverse engineering a hierarchical regulatory network downstream of oncogenic KRAS.

Sample Metadata Fields

Cell line, Treatment

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accession-icon GSE38584
Hierarchical regulation in a KRAS pathway-dependent transcriptional network revealed by a reverse-engineering approach (7TF and control)
  • organism-icon Rattus norvegicus
  • sample-icon 16 Downloadable Samples
  • Technology Badge Icon Affymetrix Rat Gene 1.0 ST Array (ragene10st)

Description

RAS mutations are highly relevant for progression and therapy response of human tumours, but the genetic network that ultimately executes the oncogenic effects is poorly understood. Here we used a reverse-engineering approach in an ovarian cancer model to reconstruct KRAS oncogene-dependent cytoplasmic and transcriptional networks from perturbation experiments based on gene silencing and pathway inhibitor treatments. We measured mRNA and protein levels in manipulated cells by microarray, RT-PCR and Western Blot analysis, respectively. The reconstructed model revealed complex interactions among the transcriptional and cytoplasmic components, some of which were confirmed by double pertubation experiments. Interestingly, the transcription factors decomposed into two hierarchically arranged groups. To validate the model predictions we analysed growth parameters and transcriptional deregulation in the KRAS-transformed epithelial cells. As predicted by the model, we found two functional groups among the selected transcription factors. The experiments thus confirmed the predicted hierarchical transcription factor regulation and showed that the hierarchy manifests itself in downstream gene expression patterns and phenotype.

Publication Title

Reverse engineering a hierarchical regulatory network downstream of oncogenic KRAS.

Sample Metadata Fields

Cell line, Treatment

View Samples
accession-icon GSE38585
Hierarchical regulation in a KRAS pathway-dependent transcriptional network revealed by a reverse-engineering approach (RAS-ROSE and ROSE with siRNA)
  • organism-icon Rattus norvegicus
  • sample-icon 2 Downloadable Samples
  • Technology Badge Icon Affymetrix Rat Gene 1.0 ST Array (ragene10st)

Description

RAS mutations are highly relevant for progression and therapy response of human tumours, but the genetic network that ultimately executes the oncogenic effects is poorly understood. Here we used a reverse-engineering approach in an ovarian cancer model to reconstruct KRAS oncogene-dependent cytoplasmic and transcriptional networks from perturbation experiments based on gene silencing and pathway inhibitor treatments. We measured mRNA and protein levels in manipulated cells by microarray, RT-PCR and Western Blot analysis, respectively. The reconstructed model revealed complex interactions among the transcriptional and cytoplasmic components, some of which were confirmed by double pertubation experiments. Interestingly, the transcription factors decomposed into two hierarchically arranged groups. To validate the model predictions we analysed growth parameters and transcriptional deregulation in the KRAS-transformed epithelial cells. As predicted by the model, we found two functional groups among the selected transcription factors. The experiments thus confirmed the predicted hierarchical transcription factor regulation and showed that the hierarchy manifests itself in downstream gene expression patterns and phenotype.

Publication Title

Reverse engineering a hierarchical regulatory network downstream of oncogenic KRAS.

Sample Metadata Fields

Cell line, Treatment

View Samples
accession-icon GSE46184
Breast Cancer Gene Expression Data from Hamburg Series
  • organism-icon Homo sapiens
  • sample-icon 73 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

Gene expression profiling of surgical biopsies from 74 breast cancer patients of different subtypes from Hamburg dataset.

Publication Title

Prognostic relevance of glycosylation-associated genes in breast cancer.

Sample Metadata Fields

Sex, Specimen part

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accession-icon SRP079238
Chromatin remodelling factor SMARCD2 (BAF60B) regulates transcriptional networks controlling early and late differentiation of neutrophil granulocytes
  • organism-icon Mus musculus
  • sample-icon 64 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 1500

Description

Differentiation of haematopoietic stem cells followsa hierarchical program of transcription-factor regulated events. Early myeloid cell differentiation is dependent on PU.1 and CEBPA (CCAAT/enhancer binding protein alpha), late myeloid differentiation is orchestrated by CEBPE (CCAAT/enhancer binding protein epsilon). The influence of SWI/SNF (SWItch/Sucrose Non-Fermentable) chromatin remodelling factors as novel master regulators of haematopoietic differentiation is only beginning to be explored. Here, we identified three homozygous loss-of-function mutations in SMARCD2 (SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily d, member 2), a member of the SWI/SNF complex, in three unrelated pedigrees. We find that SMARCD2-deficient hematopoiesis results in dysfunctional neutrophil granulocytes, characterized by specific granule deficiency, myelodysplasia, and an excess of blast cells. We can show that SMARCD2 controls early steps in the differentiation of myeloid-erythroid progenitor cells in mice and zebra fish. In vitro SMARCD2 interacts with the transcription factor CEBPE. Furthermore, we find that SMARCD2 controls expression of neutrophil proteins stored in specific granules and leads to transcriptional and chromatin changes in AML cells. Hence, we identify SMARCD2 as a key factor controlling myelopoiesis and as a potential tumour suppressor in leukemia. Overall design: We analyzed CD45.2+ Lin- Mac+/low Sca1+ cKit+ (LSK) cells from Smarcd2 wild-type, heterozygous and mutant foetal livers in at least 5 replicates Additionally, we analysed three different progenitor populations from Smarcd2 wild-type and homozygous knock-out foetal livers: CD45+Lin-Sca-1-CD177+CD34lowCD16/32 (FCGR)low(MEP) CD45+Lin-Sca-1-CD177+CD34+CD16/32(FCGR)int (CMP) CD45+Lin-Sca-1-CD177+CD34+CD16/32(FCGR)high (GMP)

Publication Title

Chromatin-remodeling factor SMARCD2 regulates transcriptional networks controlling differentiation of neutrophil granulocytes.

Sample Metadata Fields

Sex, Specimen part, Cell line, Subject

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accession-icon GSE39079
Foam cell specific LXR ligand
  • organism-icon Homo sapiens
  • sample-icon 29 Downloadable Samples
  • Technology Badge IconIllumina HumanHT-12 V3.0 expression beadchip

Description

OBJECTIVE:

Publication Title

Foam cell specific LXRα ligand.

Sample Metadata Fields

Sex, Specimen part, Cell line

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accession-icon GSE42069
Three human cell types respond to multi-walled carbon nanotubes and titanium dioxide nanobelts with cell-specific transcriptomic and proteomic expression
  • organism-icon Homo sapiens
  • sample-icon 86 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A 2.0 Array (hgu133a2)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Three human cell types respond to multi-walled carbon nanotubes and titanium dioxide nanobelts with cell-specific transcriptomic and proteomic expression patterns.

Sample Metadata Fields

Specimen part, Cell line, Treatment, Time

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accession-icon GSE42068
Three human cell types respond to multi-walled carbon nanotubes and titanium dioxide nanobelts with cell-specific transcriptomic and proteomic expression [THP-1 cells]
  • organism-icon Homo sapiens
  • sample-icon 30 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A 2.0 Array (hgu133a2)

Description

To identify key biological pathways that define toxicity or biocompatibility after nanoparticle exposure, three human cell types were exposed in vitro to two high aspect ratio nanoparticles for 1 hr or 24 hr and collected for global transcriptomics.

Publication Title

Three human cell types respond to multi-walled carbon nanotubes and titanium dioxide nanobelts with cell-specific transcriptomic and proteomic expression patterns.

Sample Metadata Fields

Specimen part, Cell line, Treatment, Time

View Samples
accession-icon GSE42066
Three human cell types respond to multi-walled carbon nanotubes and titanium dioxide nanobelts with cell-specific transcriptomic and proteomic expression [Caco-2/HT29-MTX cells]
  • organism-icon Homo sapiens
  • sample-icon 29 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A 2.0 Array (hgu133a2)

Description

To identify key biological pathways that define toxicity or biocompatibility after nanoparticle exposure, three human cell types were exposed in vitro to two high aspect ratio nanoparticles for 1 hr or 24 hr and collected for global transcriptomics.

Publication Title

Three human cell types respond to multi-walled carbon nanotubes and titanium dioxide nanobelts with cell-specific transcriptomic and proteomic expression patterns.

Sample Metadata Fields

Specimen part, Cell line, Treatment, Time

View Samples
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refine.bio is a repository of uniformly processed and normalized, ready-to-use transcriptome data from publicly available sources. refine.bio is a project of the Childhood Cancer Data Lab (CCDL)

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Cite refine.bio

Casey S. Greene, Dongbo Hu, Richard W. W. Jones, Stephanie Liu, David S. Mejia, Rob Patro, Stephen R. Piccolo, Ariel Rodriguez Romero, Hirak Sarkar, Candace L. Savonen, Jaclyn N. Taroni, William E. Vauclain, Deepashree Venkatesh Prasad, Kurt G. Wheeler. refine.bio: a resource of uniformly processed publicly available gene expression datasets.
URL: https://www.refine.bio

Note that the contributor list is in alphabetical order as we prepare a manuscript for submission.

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