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Record W2766330359 · doi:10.1038/s41467-017-01326-5

Evolution of AF6-RAS association and its implications in mixed-lineage leukemia

2017· article· en· W2766330359 on OpenAlexafffund
Matthew J. Smith, Elizabeth Ottoni, Noboru Ishiyama, Marilyn Goudreault, André Haman, Claus Meyer, Monika Tucholska, Geneviève M. C. Gasmi-Seabrook, Serena Menezes, Rob C. Laister, Mark D. Minden, Rolf Marschalek, Anne‐Claude Gingras, Trang Hoang, Mitsuhiko Ikura

Bibliographic record

VenueNature Communications · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Degradation and Inhibitors
Canadian institutionsOntario Institute for Cancer ResearchUniversity of TorontoLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalPrincess Margaret Cancer CentreUniversité de MontréalInstitute for Research in Immunology and Cancer
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsDeutsche ForschungsgemeinschaftPrincess Margaret Hospital FoundationCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchCancer Research Society
KeywordsChromosomal translocationEffectorBiologyCarcinogenesisLeukemiaGeneEpigeneticsGeneticsCell biologyCancer research

Abstract

fetched live from OpenAlex

Elucidation of activation mechanisms governing protein fusions is essential for therapeutic development. MLL undergoes rearrangement with numerous partners, including a recurrent translocation fusing the epigenetic regulator to a cytoplasmic RAS effector, AF6/afadin. We show here that AF6 employs a non-canonical, evolutionarily conserved α-helix to bind RAS, unique to AF6 and the classical RASSF effectors. Further, all patients with MLL-AF6 translocations express fusion proteins missing only this helix from AF6, resulting in exposure of hydrophobic residues that induce dimerization. We provide evidence that oligomerization is the dominant mechanism driving oncogenesis from rare MLL translocation partners and employ our mechanistic understanding of MLL-AF6 to examine how dimers induce leukemia. Proteomic data resolve association of dimerized MLL with gene expression modulators, and inhibiting dimerization disrupts formation of these complexes while completely abrogating leukemogenesis in mice. Oncogenic gene translocations are thus selected under pressure from protein structure/function, underscoring the complex nature of chromosomal rearrangements.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.626
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
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 head0.014
GPT teacher head0.300
Teacher spread0.286 · 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 designObservational
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

Citations32
Published2017
Admission routes2
Has abstractyes

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