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Identification and Function of Hematopoietic Stem and Progenitor Cell Specific Micrornas.

2010· article· en· W2483627127 on OpenAlexaff
Bernhard Gentner, Alice Giustacchini, Francesco Boccalatte, Giulia Schira, Massimo Saini, Eric R. Lechman, Hidefumi Hiramatsu, John E. Dick, Luigi Naldini

Bibliographic record

VenueBlood · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsHaematopoiesisProgenitor cellBiologyStem cellBone marrowMyeloidHematopoietic stem cellCell biologyImmunologyMolecular biology

Abstract

fetched live from OpenAlex

Abstract Abstract 2631 Little is known about microRNA function in hematopoietic stem and progenitor cells (HSPC). Using a lentivector genetic reporter strategy to functionally detect miRNA activity in hematopoietic cells at single cell resolution, we identified several miRNAs which were specifically expressed in mouse and human HSC and early progenitors, defined according to cell surface phenotype and functional repopulation assays. One of these HSPC-specific miRNAs, miR-126, was further studied. We generated a stable miR-126 knockdown (kd) or forced its expression (“knock-in”, ki) in mouse HSPC using lentiviral vectors. Kd or ki cells were competitively transplanted with congenic, control vector-transduced cells, and hematopoietic chimerism was followed for >1 year in both primary and secondary recipients. miR-126 kd HSPC displayed enhanced myeloid and/or lymphoid contribution during the early phases of reconstitution, while they subsequently contributed similarly as the control cells. When this steady state bone marrow (BM) was transplanted into secondary recipients, we noted an even more pronounced over-contribution of miR-126 kd cells to hematopoiesis. In the long run, however, some secondary mice showed signs of exhaustion of miR-126 kd cells. These data suggest that miR-126 kd enhances hematopoiesis, likely at the stem/early progenitor level and in particular under stress conditions. On the other hand, forced expression of miR-126 (ki) resulted in an early competitive disadvantage in vivo, with progressively decreasing contribution to all hematopoietic lineages, paralleled by a nearly complete depletion of Kit+Sca+Lin- (KSL) miR-126 ki cells in the BM at 6 weeks after transplant. At 3 weeks post-transplant, when miR-126 ki KSL cells could still be detected, we found an increased proliferative index in these cells as judged by EdU incorporation in vivo, paralleled by a higher hematopoietic output respect to control cells at week 2–4 after transplant. These data suggest that miR-126 ki might favor HSC commitment at the cost of self-renewal. This phenotype was specific for miR-126 and not due to vector toxicity, as we demonstrate stable, long term overexpression of several control miRNAs in vivo. Moreover, miR-126 ki cells showed normal clonogenic activity in vitro. We then optimized a protocol to stably knock down miR-126 in human cord blood (huCB) HSPC, and validated this approach by demonstrating upregulation of previously described miR-126 targets including the beta subunit of phosphoinositide-3-kinase. Manipulation of miR-126 activity changed cell growth and differentiation of huCB, and we show altered activation of key signal transduction pathways upon miR-126 kd. Identification of additional miR-126 targets is ongoing using unbiased proteomic and transcriptomic approaches. In summary, these data suggest that a narrow range of miR-126 activity is required for robust and sustained HSC function, and that its manipulation may provide novel insights into stem cell biology. Disclosures: No relevant conflicts of interest to declare.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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)0.0010.001

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 head0.005
GPT teacher head0.197
Teacher spread0.191 · 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 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

Citations0
Published2010
Admission routes1
Has abstractyes

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