Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Expression data from CCRF_CEM cell line treated with 7-(2-Thienyl)-7-deazaadenosine (AB61), tubercidin and actinomycin D

ABSTRACT: CCRF_CEM cell line was treated by AB61 which is potent cytotoxic compound, as the positive controls were used tubercidin and actinomycin D. To assay the biological activity of the AB61 more broadly we performed transcriptional microarray analysis. . CCRF_CEM cell line was treated by AB61, tubercidin and actinomycin D at 5xIC50 for 3 hours. Total RNA was extracted and hybridized on Affymetrix microarrays. We sought to obtain expressional profile to get differential expression of genes following the drug treatment.

ORGANISM(S): Homo sapiens

SUBMITTER: Petr Dzubak

PROVIDER: E-GEOD-62593 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Similar Datasets

Project description:Background: The combination of Proteasome inhibitor with Glucocorticoid is one of the most promising antileukemic treatments. Both agents act through pluripotent signal mediators. The two types of agents inhibit key signals that are considered crucial to their effects on malignant cells. Methodology/Principal Findings: Combined use of the two reagents on the lymphoblastic leukemia cell line CCRF-CEM has a range of effects on the viability that depend on the dose and the time used. Even though both reagents are capable of enhancing cell death on the CEM cell line, no combinatorial increase in cell death is detectable, until after the first 120 hours of treatment. In contrast, there are a number of combinatorial effects on the cell cycle phase distribution of treated cells, which indicates a potential for mutual signal disruption between glucocorticoid and proteasome inhibitor at multiple levels. Microarray analysis indicates that Prednisolone and MG132 elicit highly divergent, early-response molecular signatures on this cell line. We assayed levels of the antiapoptotic protein Mcl-1 as a potential model of late, downstream target regulation by both glucocorticoid and proteasome. Synchronized use of Prednisolone with MG132 results in temporary stabilization of Mcl-1 in CEM cells. Stabilization is not the result of a common mechanism of action on the genome, as Prednisolone and MG132 elicit nonreduntant molecular signatures, and it occurs also when the cells are treated at different timepoints with either agent. Conclusions/Significance: Our results show that this glucocorticoid-resistant ALL lymphoblast line is highly sensitive to proteasome inhibitor, and suggest that the proteasome inhibitor and the glucocorticoid regulate different direct target genes. This difference in immediate targets, when the two agents are applied in combination, may lead to negative or positive interference with mechanisms regulating viability of the leukemic lymphoblast. Signal interference between glucocorticoid Prednisolone and the proteasome inhibitor MG132 is expected to occur at more than one level, resulting in complex effects on intracellular signal transduction pathways. The net result depends on the development of individual downstream effects and interactions between key signal mediators. Elucidation of the conditions of interference between glucocorticoid and proteasome targets on leukemic cell fate is expected to improve effects of applied treatment combinations. Experimental setups consisted of the three following samples obtained after 4 h treatment: control, 10nM prednisolone, 1uM prednisolone, 10uM prednisolone, 100M-NM-<M prednisolone, 700M-NM-<M prednisolone, 200nM MG132, 2M-NM-<M MG132 and 20M-NM-<M MG132. Untreated cells were used as reference.

Project description:A part of current research has intensively been focused on the proliferation and metabolic processes governing biological systems. Since the advent of high throughput methodologies like microarrays, the load of genomic data has increased geometrically and along with that the need for computational methods which will interpret these data. In the present work we study in vitro the common proliferation and metabolic processes, which are combined to the common oncogenic pathways, as far as gene expression is concerned, between the T-cell acute lymphoblastic leukemia (CCRF-CEM) and the rhabdomyosarcoma (TE-671) cell lines. We present a computational approach, using cDNA microarrays, in order to identify commonalities between diverse biological systems. Our analysis predicted that JAK1, STAT1, PIAS2 and CDK4 are the driving forces in the two cell lines. This type of analysis can lead to the understanding of the common mechanisms that transform physiological cells to malignant, as well as it reveals a new holistic way to understandthe the dynamics of tumor onset as well as the mechanistics of oncogenic drivers. The present work is concerned with the common expressional profile of two cell lines: the T-cell acute lymphoblastic leukemia (CCRF-CEM) and the rhabdomyosarcoma (TE-671) cell lines. Our investigation was focused on the identification of genes that share a common expression profile between the two cell lines. Both cell lines are characterized by the fact that their differentiation has stopped at an early stage, before they mature to their final cell type. Normally, these cells would have matured and progressed into differentiated cells, constituting blood and muscle cells, respectively. At some unknown point, normal differentiation ceased for these cells and they became malignant. From that point on, to the first manifestation of symptoms of malignancy, there is a lack of knowledge regarding the mechanisms underlying oncogenesis. From these observations, the question whether two distinct cell types destined to fulfill different functions, manifest similar mechanisms of growth and progression due to their malignant character, arises. The present study was focused on the identification of the differential expression profiles underlying the two cell lines. A previous report studied the expression profile of seven ARMS cell lines possessing the PAX3-FKHR fusion gene, along with other cell lines of different tumor types (22). To our knowledge, this is the first time that a comparison between two totally different types of neoplasia, such as the CCRF-CEM and TE-671 cell lines, is attempted. These mechanisms are examined with purpose to identify common drivers that lead to the progression of tumor cells. We hereby propose a new computational approach for the investigation of common oncogenic drivers.

Dataset Information

Expression data from CCRF_CEM cell line treated with 7-(2-Thienyl)-7-deazaadenosine (AB61), tubercidin and actinomycin D

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets