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Record W1988547264 · doi:10.1158/0008-5472.can-05-1041

Leukemogenic MLL Fusion Proteins Bind across a Broad Region of the <i>Hox a9</i> Locus, Promoting Transcription and Multiple Histone Modifications

2005· article· en· W1988547264 on OpenAlexaff
Thomas A. Milne, Mary Ellen Martin, Hugh W. Brock, Robert K. Slany, Jay L. Hess

Bibliographic record

VenueCancer Research · 2005
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsUniversity of British Columbia
FundersNational Cancer Institute
KeywordsBiologyFusion proteinHox geneHistone methyltransferaseHistoneChromosomal translocationTranscription factorHistone methylationHistone H3Molecular biologyGeneticsGeneDNA methylationGene expression

Abstract

fetched live from OpenAlex

Chromosome translocations involving the mixed lineage leukemia gene MLL are associated with aggressive acute leukemias in both children and adults. Leukemogenic MLL fusion proteins delete the MLL SET domain Lys(4) methyltransferase activity and fuse MLL to 1 of >40 different translocation partners. Some MLL fusion proteins involve nuclear proteins that are transcriptional activators, whereas others have transcriptional activating activity but instead dimerize the truncated MLL molecule. Both types of MLL fusion proteins enforce persistent expression of Hox a9 and Meis1, which is pivotal for leukemogenesis through mechanisms that remain obscure. Here, we show that nuclear and dimerizable forms of MLL bind with a similar pattern to the Hox a9 locus that overlaps the distribution of wild-type MLL and deregulate transcription of three isoforms of Hox a9. Induction of MLL fusion protein activity is associated with increased levels of histone acetylation and Lys(4) methylation at Hox target genes. In addition, the MLL-ENL-ER protein, but not dimerized MLL, also induces dimethylation of histone H3 at Lys(79), suggesting alternative mechanisms for transcriptional activation.

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)0.001
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 score0.068
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.084
GPT teacher head0.397
Teacher spread0.312 · 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".

Quick stats

Citations182
Published2005
Admission routes1
Has abstractyes

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