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Record W2255229856 · doi:10.1016/j.ccell.2015.12.011

miR-126 Regulates Distinct Self-Renewal Outcomes in Normal and Malignant Hematopoietic Stem Cells

2016· article· en· W2255229856 on OpenAlexafffund
Eric R. Lechman, Bernhard Gentner, Stanley W.K. Ng, Erwin M. Schoof, Peter van Galen, Theodore A. Kennedy, Silvia Nucera, Fabio Ciceri, Kerstin B. Kaufmann, Naoya Takayama, Stephanie M. Dobson, Aaron C. Trotman-Grant, Gabriela Krivdova, Janneke Elzinga, Amanda Mitchell, Björn Nilsson, Karin G. Hermans, Kolja Eppert, René Marke, Ruth Isserlin, Véronique Voisin, Gary D. Bader, Peter W. Zandstra, Todd R. Golub, Benjamin L. Ebert, Jun Lü, Mark D. Minden, Jean Wang, Luigi Naldini, John E. Dick

Bibliographic record

VenueCancer Cell · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsMcGill University Health CentreUniversity of TorontoPrincess Margaret Cancer CentreUniversity Health Network
FundersNational Cancer InstituteCanadian Institutes of Health ResearchMinistero della SaluteCanada Research ChairsTerry Fox FoundationEuropean CommissionOntario Ministry of Health and Long-Term CareOntario Genomics Institute
KeywordsHaematopoiesisStem cellBiologyCancer researchMalignant cellsCell biologyImmunologyGeneticsCancer

Abstract

fetched live from OpenAlex

To investigate miRNA function in human acute myeloid leukemia (AML) stem cells (LSC), we generated a prognostic LSC-associated miRNA signature derived from functionally validated subpopulations of AML samples. For one signature miRNA, miR-126, high bioactivity aggregated all in vivo patient sample LSC activity into a single sorted population, tightly coupling miR-126 expression to LSC function. Through functional studies, miR-126 was found to restrain cell cycle progression, prevent differentiation, and increase self-renewal of primary LSC in vivo. Compared with prior results showing miR-126 regulation of normal hematopoietic stem cell (HSC) cycling, these functional stem effects are opposite between LSC and HSC. Combined transcriptome and proteome analysis demonstrates that miR-126 targets the PI3K/AKT/MTOR signaling pathway, preserving LSC quiescence and promoting chemotherapy resistance.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.006
GPT teacher head0.220
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), 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

Citations266
Published2016
Admission routes2
Has abstractyes

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