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Systems Analysis Reveals Regulators of Apoptosis, Cell Cycle, Signal Transduction and Transcription as Novel Direct Targets of the Acute Promyelocytic Leukemia Fusion Protein NuMA-RAR α

2006· article· en· W2550207442 on OpenAlexaff
Mahadeo A. Sukhai, Mariam Thomas, Yali Xuan, Patrícia P. Reis, Rikki R. Bharadwaj, Suzanne Kamel‐Reid

Bibliographic record

VenueBlood · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinoids in leukemia and cellular processes
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoOntario Institute for Cancer Research
Fundersnot available
KeywordsBiologySignal transductionAcute promyelocytic leukemiaRetinoidFusion proteinCell biologyTranscription factorFusion geneRetinoic acidTransgeneGeneCancer researchMolecular biologyGenetics

Abstract

fetched live from OpenAlex

Abstract We used the hCG-NuMA-RARα transgenic model of acute promyelocytic leukemia (APL) (Sukhai et al, 2004) to determine the downstream genetic targets of NuMA-RARα, one of five APL-associated fusion proteins (X-RARα). X-RARα retain the C-terminal domains of retinoic acid receptor (RAR) α, and are thought to interfere with retinoid signaling pathways and thus inhibit neutrophil differentiation. However, X-RARα have a wider range of DNA binding specificities compared to RARα, and may deregulate novel downstream targets that play critical roles in APL. Our group previously identified PPARγ signaling as being one such pathway deregulated by X-RARα (Kamel-Reid et al, 2003). We sought to identify additional, nonretinoid signaling, target genes of NuMA-RARα by using a combined experimental and in silico approach. Affymetrix oligonucleotide array analysis (mouse 430A arrays) was performed on RNA harvested from bone marrow cultures established from wild-type and transgenic mice. Genes deemed to be significantly deregulated and of biological relevance were validated by quantitative real-time RT-PCR. In our analysis, we identified 260 significantly over-expressed and 278 significantly under-expressed genes in transgenic bone marrow cultures, compared to wild-type. As NuMA-RARα is an aberrant transcriptional repressor, we focused on under-expressed genes: 251/278 were putative direct targets of NuMA-RARα. 82/251 were retinoid response genes; 169/251 were novel targets, external to retinoid signaling pathways. 150/251 had known function. Genes involved in regulation of apoptosis, cell cycle, signal transduction and transcription were over-represented within this dataset, in comparison to their frequency within the mouse genome. NuMA-RARα deregulated a number of myeloid transcription factors, including PU.1 (11.5-fold under-expression compared to wild-type), members of the C/EBP (3.5–5.0 fold under-expression) and GATA families (8.5–11.0-fold over-expression), as well as c-Myb and AML1. Binding sites for these transcription factors were overrepresented within the promoters of NuMA-RARα-deregulated genes. NuMA-RARα also deregulated a set of genes involved in cell cycle regulation and DNA damage response, including Gadd45α (1.9-fold under-expressed) and Dusp1 (3.8-fold under-expressed), and components of signal transduction pathways, including Jak2 (20.0-fold under-expressed). By mapping the chromosomal addresses of deregulated genes onto the murine genome, we determined that changes in chromosome copy number may not be responsible for deregulation. Finally, our comparison with other, previously published, microarray analyses indicated that NuMA-RARα shared some common target genes with other leukemia fusion genes, and that gene deregulation caused by NuMA-RARα could give rise to a molecular phenotype reminiscent of hematopoietic stem cells. Several genes identified as candidate downstream targets in these analyses were also previously identified as putative secondary events in APL mouse models. NuMA-RARα therefore has specific effects on the transcriptome, distinct from retinoid signaling, and from other leukemia fusion genes. Future functional studies are required to determine the cooperative relationship between these novel target genes and NuMA-RARα.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.003
GPT teacher head0.185
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2006
Admission routes1
Has abstractyes

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