Gene expression propionic patients, carriers, and controls
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Kimberly Chapman
PROVIDER: E-GEOD-42816 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:Gene expression in LCLs from PA patients, their parents, and HapMap sex and age match controls at low glucose (9 mg/dL) and normal glucose growth conditions.
2013-01-01 | GSE42816 | GEO
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Project description:Variation in gene expression is a fundamental aspect of human phenotypic variation. Several studies have analyzed gene expression levels in populations of different continental ancestry, and concluded that there is variation across populations at a fraction of expressed genes. Here we analyze gene expression levels in African American cell lines, which differ from previously analyzed cell lines in that samples from this population have variable proportions of continental ancestry. We show that for most genes examined, gene expression varies with genetic ancestry. Experiment Overall Design: Lymphoblastoid cell lines (LCL) for 60 HapMap CEU, 60 HapMap YRI, and 82 AFA from the Human Variation Panel were obtained from Coriell Cell Repositories. LCLs were grown in culture, total RNA was extracted and hybridized to Affymetrix HG-FOCUS arrays.
2009-01-14 | E-GEOD-10824 | biostudies-arrayexpress
Project description:Noncoding variants play a central role in the genetics of complex traits, but we still lack a full description of the main molecular pathways through which they act. Here we used molecular data to quantify the contribution of cis-acting genetic effects at each major stage of gene regulation from chromatin to proteins, within a population sample of Yoruba lymphoblastoid cell lines (LCLs). We performed 4sU metabolic labeled transcripts in 65 YRI LCLs to identify genetic variants that affect transcription rates. As expected, we found an important contribution of genetic variation via chromatin, contributing â¼65% of eQTLs (expression Quantitative Trait Loci). The remaining eQTLs, which are not asso- ciated with chromatin-level variation, are highly enriched in transcribed regions, and hence may affect expression through co- or post-transcriptional processes. International HapMap lymphoblastoid cell lines (LCLs) derived from YRI (Yoruba in Ibadan, Nigeria); We adapted the 4sU labelling method from (PMID 21516085). Briefly, cell cultures were grown to log phase in volumes sufficient to yield about 300 ng of 4sU-labeled RNA. Cells were incubated with 4sU for the required length of time (0, 30, or 60 minutes), then washed, pelleted, and frozen. Total RNA was extracted, and 4sU-labeled RNA was separated from total RNA using a bead-based biotin-streptavidin purification protocol. We sequenced metabolic labeled transcripts in 65 YRI LCLs 30 minutes and 60 minutes after incubation.
2016-03-29 | E-GEOD-75220 | biostudies-arrayexpress
Project description:Obesity-induced insulin resistance of the liver is characterised by increased gluconeogenesis, which contributes to elevated blood glucose levels in individuals with type 2 diabetes. Research into how fatty acids induce insulin resistance has commonly focused on the induction of insulin resistance. We hypothesise that by shifting focus to the reversal of an insulin resistant phenotype, novel insights can be made into the mechanisms by which insulin resistance can be overcome. Using global gene and lipid expression profiling, we aimed to identify biological pathways altered in parallel with restoration of palmitate-induced deregulation of glucose production using metformin and sodium salicylate. FAO hepatoma cells were treated with palmitate (0.075mM, 48h) with or without metformin (0.25mM) and sodium salicylate (2mM) in the final 24h of palmitate treatment, and effects on glucose production were determined. Microarray followed by gene set enrichment analysis was performed to investigate pathway regulation. A lipidomic analysis (HPLC-MS/MS) and measurement of secreted bile acids and cholesterol were performed. Reversal of palmitate-induced impairment of glucose production by metformin and sodium salicylate was characterised by down-regulated expression of metabolic pathways regulating acetyl-CoA to cholesterol and bile acid biosynthesis. Total levels of intracellular and secreted cholesterol and bile acids were not different between impaired and restored glucose production. Total intracellular levels of diacylgycerol, triacylglycerol and cholesterol esters increased with palmitate (impaired glucose production), however, these were not further altered with metformin and sodium salicylate (restored glucose production). Six individual lipid species containing 18:0 and 18:1 side-chains were reduced by metformin and sodium salicylate. Widespread lipid metabolism changes induced by the reversal of palmitate-induced deregulation of glucose production with metformin and sodium salicylate were identified. While cholesterol and bile acid levels remained unchanged, the flux through these pathways may in part explain these findings. The identification of lipid species containing 18:0 and 18:1 side chains being regulated alongside changes to glucose production may indicate potential mediators of glucose production and insulin resistance. Three-condition experiment, Vehicle, Palmitate (PA) and Palmitate (PA) + Metformin (Met) + Sodium Sailcylate (NaS) with biological replicates: 8 Vehicle, 20 PA and 20 PA+Met+NaS , independently grown and harvested. One replicate per array.
2013-11-08 | E-GEOD-52204 | biostudies-arrayexpress
Project description:We developed a method that perturbs transcription factor followed by monitoring genome-wide allelic expression measurements in living cells. As a model for this novel approach, we chose the factor NFκB. We performed TNF-α induction coupled to inhibition of NFκB in LCLs followed by Allelic Expression (AE) analysis on Illumina HumanOmni5-Quad BeadChips. Samples used included two HapMap trios: one from the CEU (GM12891, GM12892, and GM12878) and one from the YRI (GM19239, GM19238, and GM19240) population. NF-κB knockdown was validated using RT-PCR of known gene targets for NFkB. Differential AE was assessed on Illumina HumanOmni5-Quad BeadChips for each experimental condition: (1) inhibition with Helenanin followed 1 hour later by activation by TNF-α (TN); (2) activation by TNF-α (tnfalpha); (3) DMSO; (4) no treatment (notmt).
2014-11-03 | E-GEOD-61254 | biostudies-arrayexpress
Project description:Lymphoblastoid cell lines (LCLs), originally collected as renewable sources of DNA, are now being used as a model system to study genotype-phenotype relationships in human cells. These cell lines have been used to search for genetic variants that are associated with drug response as well as with more basic cellular traits such as RNA levels. In setting out to extend such studies by searching for genetic variants contributing to drug response, we observed that phenotypes in LCLs were, in our lab and others, significantly affected by experimental confounders (i.e. in vitro growth rate, metabolic state, and relative levels of the Epstein-Barr virus used to transform the cells). As we did not find any SNPs associated with genome-wide significance to drug response, we evaluated whether incorporating RNA expression levels (and eQTLs) in the analysis could increase power to detect such effects. As previously shown, cis-acting eQTLs were detectable for a sizeable fraction of RNAs and baseline levels of many RNAs predicted response to several drugs. However, we found only limited evidence that SNPs influenced drug response through their effect on expression of RNA. Efforts to use LCLs to map genes underlying cellular traits will require great care to control experimental confounders, unbiased methods for integrating and interpreting such multi-dimensional data, and much larger sample sizes than have been applied to date. Experiment Overall Design: We studied 269 cell lines densely genotyped by the International HapMap Project [31]. Cell lines were cultured and characterized at baseline for a variety of cellular phenotypes including growth rate, ATP levels, mitochondrial DNA copy number, EBV copy number, and measures of B-cell relevant cell surface receptors and cytokine levels. Each cell line was exposed in 384-well plates to a range of doses for each of seven drugs selected based on their divergent mechanisms of action and importance in clinical use for treatment of B-cell diseases, focusing on anti-cancer agents: 5-fluorouracil (5FU), methotrexate (MTX), simvastatin, SAHA, 6-mercaptopurine (6MP), rapamycin, and bortezomib. Drug response was measured using Celltiter Glo, an ATP-activated intracellular luminescent marker that, when compared to mock-treated control wells, can represent relative levels of cellular viability and metabolic activity. RNA was collected at baseline and RNA transcript levels were measured genome-wide on the Affymetrix platform. Baseline characterization and plating for drug response experiments was performed using batches of 90 cell lines from each HapMap analysis panel (CEU, JPT / CHB, and YRI) on each of three experiment days. The order of cell lines within each panel was randomized to avoid inducing artificial intra-familial correlation. Each drug was tested at a range of doses around the expected IC50 as reported for the drug by the NCI DTP; each dose of drug was tested in two wells per plate and on two separate plates. These replicate measurements for each cell line allowed assessment of intra-experimental variation. To evaluate day-to-day (i.e. inter-experimental) variation in all traits, a subset of 90 cell lines (30 from each of the three HapMap panels) was grown from a fresh aliquot and the entire experiment was repeated. To evaluate the effect of technical error on measured RNA levels, a set of 22 RNAs previously expression profiled (using Illumina HumanChip) at Wellcome Trust Sanger Institute (WTSI) (generously provided by Emmanouil T. Dermatsakis) was included in expression profiling at the Broad on Affymetrix arrays. Data can be downloaded from the Broad Institute web site: (http://www.broad.mit.edu/~yelensky/cell_lines_paper/). Please see Materials and Methods for details of QC, normalization, etc.
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