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accession-icon GSE197751
Cigarette smoke extract disturbs mitochondria-regulated airway epithelial cell responses to pneumococci
  • organism-icon Homo sapiens
  • sample-icon 18 Downloadable Samples
  • Technology Badge Icon Affymetrix Clariom S Human array (clariomshuman)

Description

Chronic obstructive pulmonary disease (COPD) is a heterogenous respiratory disease mainly caused by smoking. Respiratory infections constitute a major risk factor for acute worsening of COPD symptoms or COPD exacerbation. Mitochondrial functionality, which is crucial for the execution of physiologic functions of metabolically active cells, is impaired in airway epithelial cells (AECs) of COPD patients as well as smokers. However, the potential contribution of mitochondrial dysfunction in AECs to progression of COPD, infection-triggered exacerbations in AECs and a potential mechanistic link between mitochondrial and epithelial barrier dysfunction is unknown to date. In this study, we used an in vitro COPD exacerbation model based on AECs exposed to cigarette smoke extract (CSE) followed by infection with Streptococcus pneumoniae (Sp). The levels of oxidative stress, as an indicator of mitochondrial stress were quantified upon CSE and Sp. The expression of proteins associated with mitophagy, mitochondrial content and biogenesis as well as mitochondrial fission and fusion was quantified upon CSE and Sp. Transcriptional AEC profiling was performed to identify the potential changes in innate immune pathways and correlate them with mitochondrial function. We found that CSE exposure substantially altered mitochondrial function in AECs by suppressing mitochondrial complex protein levels, reducing mitochondrial membrane potential and increasing mitochondrial stress and mitophagy. Moreover, CSE-induced mitochondrial dysfunction correlated with reduced enrichment of genes involved in apical junctions and innate immune responses to Sp, particularly type I interferon responses. Together, our results demonstrated that CSE-induced mitochondrial dysfunction may contribute to impaired innate immune responses to Sp and may thus trigger COPD exacerbation.

Publication Title

Cigarette Smoke Extract Disturbs Mitochondria-Regulated Airway Epithelial Cell Responses to Pneumococci.

Sample Metadata Fields

Specimen part, Cell line, Treatment

View Samples
accession-icon GSE90620
Carbon sources tune antibiotic susceptibility in Pseudomonas aeruginosa via TCA cycle control
  • organism-icon Pseudomonas aeruginosa pao1
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Pseudomonas aeruginosa Array (paeg1a)

Description

This study examines the mechanisms underlying fumarate- and glyoxylate-mediated changes in tobraymcyin sensitivity in PAO1 cells

Publication Title

Carbon Sources Tune Antibiotic Susceptibility in Pseudomonas aeruginosa via Tricarboxylic Acid Cycle Control.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE4188
Drosophila whole testis gene expression
  • organism-icon Drosophila melanogaster
  • sample-icon 13 Downloadable Samples
  • Technology Badge Icon Affymetrix Drosophila Genome Array (drosgenome1)

Description

Whole testes were dissected from adult males. RNA from five or six biological replicates were generated and the expression profiles were determined using Affymetrix Drosophila Genechip 1 arrays. Comparisons between the bgcn- and Os+bgcn- groups allowed for the identification of stem cell genes.

Publication Title

Novel regulators revealed by profiling Drosophila testis stem cells within their niche.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE15245
Prediction of acute multiple sclerosis relapses by transcription levels of peripheral blood cells
  • organism-icon Homo sapiens
  • sample-icon 90 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

Background: The ability to predict the spatial frequency of relapses in multiple sclerosis (MS) would enable treating physicians to decide when to intervene more aggressively and to plan clinical trials more accurately. Methods: In the current study our objective was to determine if subsets of genes can predict the time to the next acute relapse in patients with MS. Data-mining and predictive modeling tools were utilized to analyze a gene-expression dataset of 94 non-treated patients; 62 patients with definite MS and 32 patients with clinically isolated syndrome (CIS). The dataset included the expression levels of 10,594 genes and annotated sequences corresponding to 22,215 gene-transcripts that appear in the microarray. Results: We designed a two stage predictor. The first stage predictor was based on the expression level of 10 genes, and predicted the time to next relapse with a resolution of 500 days (error rate 0.079, p< 0.001). If the predicted relapse was to occur in less than 500 days, a second stage predictor based on an additional different set of 9 genes was used, resulting in a prediction with a resolution of 50 days as to the timing of the next relapse. The error rate of this predictor was 2.3 fold lower than the error rate of random predictions (error rate = 0.35, p<0.001). The predictors were further evaluated and found effective not only in untreated patients but were also valid for MS patients which subsequently received immunomodulatory treatments after the initial testing (the error rate of the first level predictor was < 0.18 with p<0.001 for all the patient groups). Conclusions: We conclude that gene expression analysis is a valuable tool that can be used in clinical practice to predict future MS disease activity. Similar approach can be also useful for dealing with other autoimmune diseases that characterized by relapsing-remitting nature

Publication Title

Prediction of acute multiple sclerosis relapses by transcription levels of peripheral blood cells.

Sample Metadata Fields

Sex, Age, Specimen part, Disease, Disease stage

View Samples
accession-icon GSE14833
Expression data from different stages of hematopoietic cells development
  • organism-icon Mus musculus
  • sample-icon 46 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

18 different population of cells in different developmental stages in hematopoietic hierarchy have been purifyed by FACS analyses from wild type C57Bl6 mice and subjected to Micrroarray Affymetrix mouse 430.2 platform

Publication Title

CCAAT/enhancer binding protein alpha (C/EBP(alpha))-induced transdifferentiation of pre-B cells into macrophages involves no overt retrodifferentiation.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE53606
Identification of candidate genes for processing quality for good chapatti (flat unleavened bread)
  • organism-icon Triticum aestivum
  • sample-icon 35 Downloadable Samples
  • Technology Badge Icon Affymetrix Wheat Genome Array (wheat)

Description

Gene-specific two-way ANOVA analysis for identification of candidate genes for processing quality, seed development, and interaction (quality x development)

Publication Title

Genome-wide transcriptome study in wheat identified candidate genes related to processing quality, majority of them showing interaction (quality x development) and having temporal and spatial distributions.

Sample Metadata Fields

Specimen part

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accession-icon GSE47495
Transcriptional profiling of left ventricle and peripheral blood cells in rats with post-myocardial infarction
  • organism-icon Rattus norvegicus
  • sample-icon 34 Downloadable Samples
  • Technology Badge Icon Affymetrix Rat Gene 1.0 ST Array (ragene10st)

Description

Myocardial infarction (MI) often results in left ventricular (LV) remodeling followed by heart failure (HF). It is of great clinical importance to understand the molecular mechanisms that trigger transition from compensated LV injury to HF and to identify relevant diagnostic biomarkers. In this study, we performed transcriptional profiling of LVs in rats with a wide range of experimentally induced infarct sizes and of peripheral blood mononuclear cells (PBMCs) in animals that developed HF.

Publication Title

Transcriptional profiling of left ventricle and peripheral blood mononuclear cells in a rat model of postinfarction heart failure.

Sample Metadata Fields

Specimen part

View Samples
accession-icon GSE32330
Expression data from C/EBP alpha induced transdifferentiation of pre-B cells into macrophages
  • organism-icon Mus musculus
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

Earlier work has shown that pre-B cells can be converted into macrophages by the transcription factor C/EBP? at very high frequencies. Using this system we have now performed a systematic analysis of the question whether during transdifferentiation the cells transiently reactivate progenitor restricted genes or even retrodifferentiate. A transcriptome analysis of transdifferentiating cells showed that most genes are continuously up or downregulated, acquiring a macrophage phenotype within 5 days. In addition, we observed the transient reactivation of a subset of immature myeloid markers, as well as low levels of the progenitor markers Kit and Flt3 and a few lineage inappropriate genes. However, we were unable to observe the re-expression of cell surface marker combinations that characterize hematopoietic stem and progenitor cells (HSPCs), including c-Kit and Flt3. This was the case even when C/EBPalpha was activated in pre-B cells under culture conditions that favor HSPC growth or when the transcription factor was activated in a time limited fashion. Together, our findings are consistent with the notion that the conversion from pre-B cells to macrophages is mostly direct and does not involve overt retrodifferentiation.

Publication Title

CCAAT/enhancer binding protein alpha (C/EBP(alpha))-induced transdifferentiation of pre-B cells into macrophages involves no overt retrodifferentiation.

Sample Metadata Fields

Specimen part, Time

View Samples
accession-icon GSE56032
Comparative transcriptional profiling of developing wheat grains with contrasting levels of minerals
  • organism-icon Triticum aestivum
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Wheat Genome Array (wheat)

Description

Transcriptional comparison of developing grains between two wheat genotypes with contrasting levels of minerals in grain, using Affymetrix GeneChip Wheat Genome Array.

Publication Title

Comparative transcriptional profiling of two wheat genotypes, with contrasting levels of minerals in grains, shows expression differences during grain filling.

Sample Metadata Fields

Specimen part

View Samples
accession-icon SRP111831
Transcriptional Profiling of the Chicken Tracheal Response to Virulent Mycoplasma gallisepticum Strain Rlow
  • organism-icon Gallus gallus
  • sample-icon 35 Downloadable Samples
  • Technology Badge IconNextSeq 500

Description

The goal of this study was to provide a global assessment of the host''s response to M. gallisepticum over a 7-day time course Overall design: Differential gene expression was assessed between Rlow infected and Hayflick''s only ''control chickens'' on days 1, 3, 5, and 7 (5 infected per day, 4 controls on day 1, 5 controls per days 3, 5, and 7), 39 total chickens assessed.

Publication Title

Transcriptional Profiling of the Chicken Tracheal Response to Virulent Mycoplasma gallisepticum Strain R<sub>low</sub>.

Sample Metadata Fields

Subject, Time

View Samples

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)

fund-icon Fund the CCDL

Developed by the Childhood Cancer Data Lab

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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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