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Record W2037269347 · doi:10.1182/blood-2011-12-397844

Posttranslational regulation of self-renewal capacity: insights from proteome and phosphoproteome analyses of stem cell leukemia

2012· article· en· W2037269347 on OpenAlexafffund
Matthias Trost, Martin Sauvageau, Olivier Hérault, Paul Déléris, Christelle Pomiès, Jalila Chagraoui, Nadine Mayotte, Sylvain Meloche, Guy Sauvageau, Pierre Thibault

Bibliographic record

VenueBlood · 2012
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsHôpital Maisonneuve-RosemontUniversité de MontréalInstitute for Research in Immunology and Cancer
FundersCanadian Institutes of Health Research
KeywordsStem cellProteomeBiologyLeukemiaMyeloid leukemiaProteomicsCell biologyMolecular biologyCancer researchBiochemistryGeneticsGene

Abstract

fetched live from OpenAlex

We recently generated 2 phenotypically similar Hoxa9+Meis1 overexpressing acute myeloid leukemias that differ by their in vivo biologic behavior. The first leukemia, named FLA2, shows a high frequency of leukemia stem cells (LSCs; 1 in 1.4 cells), whereas the second, FLB1, is more typical with a frequency of LSCs in the range of 1 per several hundred cells. To gain insights into possible mechanisms that determine LSC self-renewal, we profiled and compared the abundance of nuclear and cytoplasmic proteins and phosphoproteins from these leukemias using quantitative proteomics. These analyses revealed differences in proteins associated with stem cell fate, including a hyperactive p38 MAP kinase in FLB1 and a differentially localized Polycomb group protein Ezh2, which is mostly nuclear in FLA2 and predominantly cytoplasmic in FLB1. Together, these newly documented proteomes and phosphoproteomes represent a unique resource with more than 440 differentially expressed proteins and 11 543 unique phosphopeptides, of which 80% are novel and 7% preferentially phosphorylated in the stem cell-enriched leukemia.

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 score0.142
Threshold uncertainty score0.534

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 head0.038
GPT teacher head0.279
Teacher spread0.242 · 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

Citations27
Published2012
Admission routes2
Has abstractyes

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