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accession-icon GSE15659
Microarray analysis of FoxP3-expressing or non-expressing subsets of human CD4+ T cells
  • organism-icon Homo sapiens
  • sample-icon 5 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Gene expression profiles of subsets of CD4+ T cells according to their expression of FoxP3 and CD45RA were compared.

Publication Title

Functional delineation and differentiation dynamics of human CD4+ T cells expressing the FoxP3 transcription factor.

Sample Metadata Fields

Specimen part

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accession-icon GSE42834
Human whole blood microarray study to compare patients with tuberculosis, sarcoidosis, pneumonia, and lung cancer
  • organism-icon Homo sapiens
  • sample-icon 16 Downloadable Samples
  • Technology Badge IconIllumina HumanHT-12 V4.0 expression beadchip

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Transcriptional blood signatures distinguish pulmonary tuberculosis, pulmonary sarcoidosis, pneumonias and lung cancers.

Sample Metadata Fields

Sex, Specimen part, Disease, Disease stage, Treatment, Race, Subject

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accession-icon GSE42830
Human whole blood microarray study to compare patients with tuberculosis, sarcoidosis, pneumonia, and lung cancer (training)
  • organism-icon Homo sapiens
  • sample-icon 16 Downloadable Samples
  • Technology Badge IconIllumina HumanHT-12 V4.0 expression beadchip

Description

This study used whole blood transcriptional signatures from patients with tuberculosis compared to those with similar pulmonary diseases, sarcoidosis, pneumonia and primary lung cancer. TB and sarcoidosis had similar signatures that were distinct from pneumonia and lung cancer.

Publication Title

Transcriptional blood signatures distinguish pulmonary tuberculosis, pulmonary sarcoidosis, pneumonias and lung cancers.

Sample Metadata Fields

Sex, Specimen part, Disease, Disease stage, Race

View Samples
accession-icon SRP195418
Transcriptome Signature of Cellular Senescence
  • organism-icon Homo sapiens
  • sample-icon 31 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 4000, Illumina HiSeq 2500

Description

Abstract: Cellular senescence, an integral component of aging and cancer, arises in response to diverse triggers, including telomere attrition, macromolecular damage, and signaling from activated oncogenes. At present, senescent cells are identified by the combined presence of multiple traits, such as senescence-associated protein expression and secretion, DNA damage, and ß-galactosidase activity; unfortunately, these traits are neither exclusively nor universally present in senescent cells. To identify robust shared markers of senescence, we have performed RNA-sequencing analysis across 8 diverse models of senescence triggered in human diploid fibroblasts (WI-38, IMR-90) and endothelial cells (HUVEC, HAEC) by replicative exhaustion, exposure to ionizing radiation or doxorubicin, and expression of the oncogene HRASG12V. The intersection of the altered transcriptomes revealed 47 RNAs consistently elevated and 26 RNAs consistently reduced across all senescence models, including many protein-coding mRNAs and some long noncoding RNAs. We propose that these shared transcriptome profiles will enable the identification of senescent cells in vivo, the investigation of their roles in aging and malignancy, and the development of strategies to target senescent cells therapeutically. Overall design: Transcriptomic analysis of various cell line models of senescence and their respective controls

Publication Title

Transcriptome signature of cellular senescence.

Sample Metadata Fields

Specimen part, Cell line, Treatment, Subject

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accession-icon GSE14632
DNA microarray analysis of the anti-inflammatory effects of PDTC on IL-6 signaling in HepG2 cells
  • organism-icon Homo sapiens
  • sample-icon 20 Downloadable Samples
  • Technology Badge IconIllumina humanRef-8 v2.0 expression beadchip

Description

Interleukin-6 (IL-6) is a proinflammatory cytokine that exerts a wide range of cellular, physiological and pathophysiological responses. Pyrrolidine dithiocarbamate (PDTC) antagonizes the cellular responsiveness to IL-6 through impairment in STAT3 activation and downstream signaling. Here, a transcriptional profiling was conducted as a basis for understanding the biological properties of PDTC in human HepG2 hepatocarcinoma cells. A global comparison of mRNA identified a highly significant difference of dysregulated gene expression transduced by PDTC versus IL-6 in HepG2 cells. Through an unbiased pathway analysis method, we have uncovered the mammalian target of rapamycin (mTOR) pathway together with rapid and dynamic alterations in REDD1 (regulated in development and DNA damage response 1) expression as one of the underlying molecular mechanisms responsible for IL-6 resistance to PDTC. Quantitative PCR and Western blot analyses validated the microarray data by showing the reciprocal pattern of REDD1 expression and subsequent mTOR inhibition after stimulation with PDTC relative to IL-6.

Publication Title

Impact of pyrrolidine dithiocarbamate and interleukin-6 on mammalian target of rapamycin complex 1 regulation and global protein translation.

Sample Metadata Fields

Cell line

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accession-icon GSE873
Muscle, normal extraocular, profile
  • organism-icon Homo sapiens
  • sample-icon 5 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

Molecular definition of human extraocular muscles (EOM). Human EOM were compared with limb (quadriceps femoris) muscle.

Publication Title

Definition of the unique human extraocular muscle allotype by expression profiling.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE471
Expression profiling of extraocular muscles
  • organism-icon Rattus norvegicus
  • sample-icon 16 Downloadable Samples
  • Technology Badge Icon Affymetrix Rat Genome U34 Array (rgu34a)

Description

The extraocular muscles (EOM) are anatomically and physiologically distinct from other skeletal muscles. EOM are preferentially affected in mitochondrial myopathies, but spared in Duchenne's muscular dystrophy. The anatomical and pathophysiological properties of EOM have been attributed to their unique molecular makeup: an allotype. We used expression profiling to define molecular features of the EOM allotype. We found 346 differentially expressed genes in rat EOM compared with tibialis anterior, based on a twofold difference cutoff. Genes required for efficient, fatigue-resistant, oxidative metabolism were increased in EOM, whereas genes for glycogen metabolism were decreased. EOM also showed increased expression of genes related to structural components of EOM such as vessels, nerves, mitochondria, and neuromuscular junctions. Additionally, genes related to specialized functional roles of EOM such as the embryonic and EOM-specific myosin heavy chains and genes for muscle growth, development, and/or regeneration were increased. The EOM expression profile was validated using biochemical, structural, and molecular methods. Characterization of the EOM expression profile begins to define gene transcription patterns associated with the unique anatomical, metabolic, and pathophysiological properties of EOM.

Publication Title

Expression profiling reveals metabolic and structural components of extraocular muscles.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE93551
LncRNA OIP5-AS1/cyrano suppresses GAK expression to control mitosis
  • organism-icon Homo sapiens
  • sample-icon 16 Downloadable Samples
  • Technology Badge IconIllumina HumanHT-12 V4.0 expression beadchip

Description

Long noncoding RNAs (lncRNAs) have been found to regulate the expression of mRNAs with which they share partial complementarity. We sought to identify the mechanism through which the lncRNA OIP5-AS1, which is abundant in the cytoplasm, suppressed cell proliferation. Silencing of OIP5-AS1 in human cervical carcinoma cells revealed the appearance of many aberrant (monopolar, multipolar, misaligned) mitotic spindles. By biotin-oligomer affinity pulldown, proteomic, and bioinformatic analyses, we identified a subset of human cell cycle regulatory proteins encoded by mRNAs that were capable of interacting with OIP5-AS1. Further investigation revealed that GAK mRNA, which encodes a cyclin G-associated kinase important for mitotic progression, was a prominent target of OIP5-AS1. The interaction between these two transcripts led to a reduction in GAK mRNA stability and GAK protein abundance, as determined in cells in which OIP5-AS1 levels were increased or decreased. Importantly, the aberrant mitotic cell division seen after silencing OIP5-AS1 was partly rescued if GAK was simultaneously silenced. These findings indicate that the abnormal mitoses seen after silencing OIP5-AS1 was caused by an untimely rise in GAK levels and suggest that OIP5-AS1 suppresses cell proliferation at least in part by reducing GAK levels

Publication Title

LncRNA OIP5-AS1/cyrano suppresses GAK expression to control mitosis.

Sample Metadata Fields

Specimen part, Disease, Disease stage, Cell line, Treatment

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accession-icon GSE43830
Long Noncoding RNA MALAT1 Controls Cell Cycle Progression by Regulating the Expression of Oncogenic Transcription Factor B-MYB
  • organism-icon Homo sapiens
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Exon 1.0 ST Array [probe set (exon) version (huex10st)

Description

Transcriptome analysis of control and MALAT1 lncRNA-depleted RNA samples from human diploid lung fibroblasts [WI38]

Publication Title

Long noncoding RNA MALAT1 controls cell cycle progression by regulating the expression of oncogenic transcription factor B-MYB.

Sample Metadata Fields

Specimen part, Cell line

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accession-icon SRP069768
Senescence-associated lncRNAs: senescence-associated long noncoding RNAs.
  • organism-icon Homo sapiens
  • sample-icon 2 Downloadable Samples
  • Technology Badge IconIlluminaGenomeAnalyzerII

Description

Noncoding RNAs include small transcripts, such as microRNAs and piwi-interacting RNAs, and a wide range of long noncoding RNAs (lncRNAs). Although many lncRNAs have been identified, only a small number of lncRNAs have been characterized functionally. Here, we sought to identify lncRNAs differentially expressed during replicative senescence. We compared lncRNAs expressed in proliferating, early-passage, 'young' human diploid WI-38 fibroblasts [population doubling (PDL) 20] with those expressed in senescent, late-passage, 'old' fibroblasts (PDL 52) by RNA sequencing (RNA-Seq). Numerous transcripts in all lncRNA groups (antisense lncRNAs, pseudogene-encoded lncRNAs, previously described lncRNAs and novel lncRNAs) were validated using reverse transcription (RT) and real-time, quantitative (q)PCR. Among the novel senescence-associated lncRNAs (SAL-RNAs) showing lower abundance in senescent cells, SAL-RNA1 (XLOC_023166) was found to delay senescence, because reducing SAL-RNA1 levels enhanced the appearance of phenotypic traits of senescence, including an enlarged morphology, positive ß-galactosidase activity, and heightened p53 levels. Our results reveal that the expression of known and novel lncRNAs changes with senescence and suggests that SAL-RNAs play direct regulatory roles in this important cellular process. Overall design: RNA was extracted from both young and senescent WI-38 cells and used for total RNA-Seq.

Publication Title

Senescence-associated lncRNAs: senescence-associated long noncoding RNAs.

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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