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accession-icon GSE71127
A transcriptome-based classifier to identify developmental toxicants by stem cell testing: design, validation, and optimization for histone deacetylase inhibitors
  • organism-icon Homo sapiens
  • sample-icon 82 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Test systems to identify developmental toxicants are urgently needed. A combination of human stem cell technology and transcriptome analysis was used here to provide proof-of-concept that toxicants with a related mode of action can be identified, and grouped for read-across. We chose a test system of developmental toxicity, related to the generation of neuroectoderm from pluripotent stem cells (UKN1), and exposed cells for six days to benchmark concentration (BMC) of histone deacetylase inhibitors (HDACi) valproic acid, trichostatin-A, vorinostat, belinostat, panobinostat and entinostat. To provide insight into their toxic action, we identified HDACi consensus genes, assigned them to superordinate biological processes, and mapped them to a human transcription factor network constructed from hundreds of transcriptome data sets. We also tested a heterogeneous group of mercurials (methylmercury, thimerosal, mercury(II)chloride, mercury(II)bromide, 4-chloromercuribenzoic acid, phenylmercuric acid) (BMCs). Microarray data were compared at the highest non-cytotoxic concentration for all 12 toxicants. A support vector machine (SVM)-based classifier predicted all HDACi correctly. For validation, the classifier was applied to legacy data sets of HDACi, and for each exposure situation, the SVM predictions correlated with the developmental toxicity. Finally, optimization of the classifier based on 100 probe sets showed that eight genes (F2RL2, TFAP2B, EDNRA, FOXD3, SIX3, MT1E, ETS1, LHX2) are sufficient to separate HDACi from mercurials. Our data demonstrate, how human stem cells and transcriptome analysis can be combined for mechanistic grouping and prediction of toxicants. Extension of this concept to mechanisms beyond HDACi would allow prediction of human developmental toxicity hazard of unknown compounds with the UKN1 test system.

Publication Title

A transcriptome-based classifier to identify developmental toxicants by stem cell testing: design, validation and optimization for histone deacetylase inhibitors.

Sample Metadata Fields

Sex, Specimen part

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accession-icon GSE141253
Development of a neural rosette formation assay (RoFA) to identify neurodevelopmental toxicants and to characterize their transcriptome disturbances
  • organism-icon Homo sapiens
  • sample-icon 148 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

The first in vitro tests for developmental toxicity made use of rodent cells. Newer teratology tests, e.g. developed during the ESNATS project, use human cells and measure mechanistic endpoints (such as transcriptome changes). However, the toxicological implications of mechanistic parameters are hard to judge, without functional/morphological endpoints. To address this issue, we developed a new version of the human stem cell-based test STOP-tox(UKN). For this purpose, the capacity of the cells to self-organize to neural rosettes was assessed as functional endpoint: pluripotent stem cells were allowed to differentiate to neuroepithelial cells for six days in the presence or absence of toxicants. Then, both transcriptome changes were measured (standard STOP-tox(UKN)), and cells were allowed to form rosettes. After optimization of staining methods, an imaging algorithm for rosette quantification was implemented and used for an automated rosette formation assay (RoFA). Neural tube toxicants (like valproic acid), which are known to disturb human development at stages when rosette-forming cells are present, were used as positive controls. Established toxicants led to distinctly different tissue organization and differentiation stages. RoFA outcome and transcript changes largely correlated concerning (i) the concentration-dependence, (ii) the time-dependence, and (iii) the set of positive hits identified amongst 24 potential toxicants. Using such comparative data, a prediction model for the RoFA was developed. The comparative analysis was also used to identify gene dysregulations that are particularly predictive for disturbed rosette formation. This ‘RoFA predictor gene set’ may be used for a simplified and less costly setup of the STOP-tox(UKN) assay.

Publication Title

Development of a neural rosette formation assay (RoFA) to identify neurodevelopmental toxicants and to characterize their transcriptome disturbances.

Sample Metadata Fields

Sex, Specimen part, Cell line, Treatment

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accession-icon GSE37464
Pleiotropic Effects of the Trichloroethylene-Associated P81S VHL Mutation on Metabolism, Apoptosis and ATM-Mediated DNA Damage Response
  • organism-icon Mus musculus
  • sample-icon 10 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.1 ST Array (mogene11st)

Description

Gene expression data from VHL teratomas comparing genes differentially expressed based on apoptotic response to tumor microenvironment.

Publication Title

Pleiotropic effects of the trichloroethylene-associated P81S VHL mutation on metabolism, apoptosis, and ATM-mediated DNA damage response.

Sample Metadata Fields

Specimen part

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accession-icon GSE41005
HSF1 mediated Gene regulation in T cells at normal (37C) and febrile (40C) temperatures
  • organism-icon Mus musculus
  • sample-icon 8 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

HSF1 is a major transcriptional regulator of heat shock responses. Many cells activate HSF1 in response to heat shock temperatures (>42oC) and other cellular stress causing agents. Unlike other cell types, T cells activate HSF1 in response to T cell activation or when exposed to febrile (40oC) temperatures, suggesting a role for HSF1 beyond the heat-shock response.

Publication Title

Heat shock transcription factor 1 is activated as a consequence of lymphocyte activation and regulates a major proteostasis network in T cells critical for cell division during stress.

Sample Metadata Fields

Specimen part, Treatment

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accession-icon SRP007825
Small RNA sequencing in normal and psoriatic skin
  • organism-icon Homo sapiens
  • sample-icon 66 Downloadable Samples
  • Technology Badge IconIllumina Genome Analyzer IIx

Description

We report the application of Illumina small RNA sequencing to normal human skin, as well as uninvolved and involved psoriatic skin. By obtaining over 600 million qualified reads from 20 healthy controls and 47 psoriasis biopsies (uninvolved/involved), we have generated a complete small RNA profile in normal and diseased human skin, with particular emphasis on miRNAs. We report the discovery of 284 putative novel miRNAs as well as 98 differentially expressed miRNAs in psoriatic skin. Overall design: miRNA discovery and expression profiling in 67 normal and psoriatic human skin biopsies

Publication Title

Deep sequencing of small RNAs from human skin reveals major alterations in the psoriasis miRNAome.

Sample Metadata Fields

Specimen part, Disease, Disease stage, Subject

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accession-icon SRP150269
Nonalcoholic fatty liver, but not nonalcoholic steatohepatitis is a protective factor to tetrachloroethylene-associated kidney effects in male C57BL/6J mice
  • organism-icon Mus musculus
  • sample-icon 78 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

Purpose: We investigated the tetrachloroethylene associated changes in kidney transcriptomes among healthy mice, nonalcoholic fatty liver disease mice, and nonalcoholic steatohepatitis mice. Overall design: Male C57BL/6J mice were fed a low-fat diet (4% fat), high-fat diet (31% fat), or methionine/choline/folate deficient diet. Following an 8-week diet, mice were administered either a single dose of tetrachloroethylene (PERC, 300 mg/kg/d in 5% Alkamuls-EL620 in saline, 5 mL/kg) and euthanized at 24 hours post dose, or five consecutive daily doses of PERC or vehicle (n=8/diet/treatment) and euthanized at 4hours post dose. The harvested kidneys were subjected to mRNA sequencing using Illumina Hiseq 2500. Jac-NASH-063 was excluded from analysis because it did not have a good yield.

Publication Title

Modulation of Tetrachloroethylene-Associated Kidney Effects by Nonalcoholic Fatty Liver or Steatohepatitis in Male C57BL/6J Mice.

Sample Metadata Fields

Cell line, Treatment, Subject

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accession-icon GSE68878
Gene expression profiles of normal B-cell subsets from sternal bone marrow
  • organism-icon Homo sapiens
  • sample-icon 38 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Exon 1.0 ST Array [CDF: huex10st_Hs_ENSG_18.0 (huex10st)

Description

Mononuclear cells were isolated from the sternal bone marrow and prepared for multiparametric flow cytometry using an optimized and validated protocol. B-cell subsets of PreBI, PreBII, Immature, Naive, Memory and Plasma cells were isolated and a al of 38 gene expression profiles were generated using the HuEx-1_0-st-v2-micro array chip from Affymetrix to characterize the gene expression in the individual subpopulations.

Publication Title

Long Noncoding RNA Expression during Human B-Cell Development.

Sample Metadata Fields

Specimen part, Subject

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accession-icon GSE69033
Long noncoding RNA expression during human B-cell development
  • organism-icon Homo sapiens
  • sample-icon 30 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Exon 1.0 ST Array [CDF: huex10st_Hs_ENSG_18.0 (huex10st)

Description

Long noncoding RNAs (lncRNAs) have emerged as important regulators of diverse cellular processes, but their roles in the developing immune system are poorly understood. In this study, we analysed lncRNA expression during human B-cell development by array-based expression profiling of eleven distinct flow-sorted B-cell subsets, comprising pre-B1, pre-B2, immature, naive, memory, and plasma cells from bone marrow biopsies (n=7), and naive, centroblast, centrocyte, memory, and plasmablast cells from tonsil tissue samples (n=6), respectively. A remapping strategy was used to assign the array probes to 37630 gene-level probe sets, reflecting the most recent updates in genomic and transcriptomic databases, which enabled expression profiling of 19579 long noncoding RNAs, comprising 3947 antisense RNAs, 5277 lincRNAs, 7625 pseudogenes, and 2730 additional lncRNAs. As a first step towards inferring the functions of the identified lncRNAs in developing B-cells, we analysed their co-expression with well-characterized protein-coding genes, a method known as guilt by association. By using weighted gene co-expression network analysis, we identified 272 lincRNAs, 471 antisense RNAs, 376 pseudogene RNAs, and 64 lncRNAs within seven sub-networks associated with distinct stages of B-cell development, such as early B-cell development, B-cell proliferation, affinity maturation of antibody, and terminal differentiation. These data provide an important resource for future studies on the functions of lncRNAs in development of the adaptive immune response, and the pathogenesis of B-cell malignancies that originate from distinct B-cell subpopulations.

Publication Title

Long Noncoding RNA Expression during Human B-Cell Development.

Sample Metadata Fields

Specimen part, Subject

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accession-icon GSE44294
Dysregulation of Macrophage Activation Profiles by Engineered Nanoparticles
  • organism-icon Mus musculus
  • sample-icon 18 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430A 2.0 Array (mouse430a2)

Description

To investigate how the phenotype of macrophages that have engulfed engineered nanoparticles (ENPs) differs from normal macrophages, we conducted Affymetrix microarray studies to identify the gene regulatory pathways affected by the ENPs. To mimic potential occupational exposure scenarios, the experimental design involved pretreatment of mouse primary bone marrow macrophages with the ENPs (25 mg/ml) for 24 hr, followed by removal of residual ENPs and challenging the macrophages with the TLR4 ligand and surrogate bacterial stimulus, lipopolysachharide (LPS) for 4 hr. The 4 hr challenge time was chosen based on preliminary studies which showed many of the proinflammatory gene expression responses peak between 2-6 hr after LPS treatment.

Publication Title

Dysregulation of macrophage activation profiles by engineered nanoparticles.

Sample Metadata Fields

Specimen part, Treatment

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accession-icon GSE13005
Macrophage response to silica nanoparticles
  • organism-icon Mus musculus
  • sample-icon 21 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430A 2.0 Array (mouse430a2)

Description

Using a macrophage cell line, we demonstrate the ability of amorphous silica particles to stimulate inflammatory protein secretion and induce cytotoxicity. Whole genome microarray analysis of early gene expression changes induced by 10nm and 500nm particles showed that the magnitude of change for the majority of genes correlated more tightly with particle surface area than either particle mass or number. Gene expression changes that were size-specific were also identified, however the overall biological processes represented by all gene expression changes were nearly identical, irrespective of particle diameter. Our results suggest that on an equivalent nominal surface area basis, common biological modes of action are expected for nano- and supranano-sized silica particles.

Publication Title

Macrophage responses to silica nanoparticles are highly conserved across particle sizes.

Sample Metadata Fields

No sample metadata fields

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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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