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E-GEOD-9648 - Transcription profiling of human Jurkat T leukemia cells to detect anticancer metabolites discovered by computational metabolomics

Released on 20 October 2008, last updated on 2 May 2014
Homo sapiens
Samples (2)
Array (1)
Protocols (7)
CoMet, a fully automated Computational Metabolomics method to predict changes in metabolite levels in cancer cells compared to normal references has been developed and applied to Jurkat T leukemia cells with the goal of testing the following hypothesis: up or down regulation in cancer cells of the expression of genes encoding for metabolic enzymes leads to changes in intracellular metabolite concentrations that contribute to disease progression. Nine metabolites predicted to be lowered in Jurkat cells with respect to normal lymphoblasts were examined: riboflavin, tryptamine, 3-sulfino-L-alanine, menaquinone, dehydroepiandrosterone, α-hydroxystearic acid, hydroxyacetone, seleno-L-methionine and 5,6-dimethylbenzimidazole. All, alone or in combination, exhibited antiproliferative activity. Of eleven metabolites predicted to be increased or unchanged in Jurkat cells, only two (bilirubin and androsterone) exhibited significant antiproliferative activity. These results suggest that cancer cell metabolism may be regulated to reduce the intracellular concentration of certain antiproliferative metabolites, resulting in uninhibited cellular growth and have the implication that many other endogenous metabolites with important roles in carcinogenesis are awaiting discovery. Experiment Overall Design: The first step of the CoMet approach consists of the classification of each enzyme-coding human gene into four possible groups: G1) upregulated in cancer cells, G2) downregulated in cancer cells, G3) expressed in both, normal and cancer cells, at levels that are statistically indistinguishable, and G4) not expressed in both, normal and cancer cells. We used two types of data for the classification: the log base 2 signal intensities and the presence calls of the corresponding probe sets, as reported by the Affymetrix Microarray Suite Software 5.0 (MAS 5.0). First, an “off” status is provisionally assigned to each gene in each of the two studied conditions (normal and cancer) if the mean fraction of presence calls labeled as “marginal” or “absent” in the corresponding probe sets is at least 80%; otherwise, an “on” status is assigned. Then, each gene is temporarily classified into the G1, G2, G3 or G4 group, according to its on/off status in normal and cancer conditions. Finally, genes in the temporary G3 or G4 groups are transferred to the G1 or G2 groups if they fulfill the following criterion for differential expression: the signal intensities in normal and cancer samples exhibit a statistically significant difference in at least 40% of the corresponding probe sets, as evaluated by an ANOVA two-tailed test with P < 0.005. Experiment Overall Design: RNA extraction, amplification and microarray data processing. Total RNA was extracted from cell lines using Trizol (Invitrogen) and processed using the RiboAmp OA or HS kit (Arcturus) in conjunction with the IVT Labeling Kit from Affymetrix, to produce an amplified, biotin-labeled mRNA suitable for hybridizing to GeneChip Probe Arrays (Affymetrix). Labeled mRNA was hybridized to GeneChip Human Genome U133 Plus 2.0 Arrays in the GeneChip Hybridization oven 640, further processed with the GeneChip Fluidics Station 450 and scanned with the GeneChip Scanner. Affymetrix .CEL files were processed using the Affymetrix Expression Console (EC) Software Version 1.1. Files were processed using the default MAS5 3’ expression workflow which includes scaling all probes to a target intensity (TGT) of 500. Spiked in report controls used were AFFX-BioB, AFFX-BioC, AFFX-BioDn, and AFFX-CreX. Affymetrix .CEL files for three normal lymphoblast samples used as a normal reference to compare Jurkat cells expression data were directly retrieved from the Gene Expression Omnibus (, samples GSM113678, GSM113802 and GSM113803 of untreated GM15851 cells from the Series GSE5040).
Experiment types
transcription profiling by array, unknown experiment type
Identification of metabolites with anticancer properties by computational metabolomics. Adrian K Arakaki, Roman Mezencev, Nathan J Bowen, Ying Huang, John F McDonald, Jeffrey Skolnick. Mol Cancer 7:57 (2008)
Investigation descriptionE-GEOD-9648.idf.txt
Sample and data relationshipE-GEOD-9648.sdrf.txt
Raw data (1)
Processed data (1)
Array designA-AFFY-44.adf.txt
R ExpressionSetE-GEOD-9648.eSet.r