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Record W3092710087 · doi:10.1016/j.celrep.2020.108257

LRIG1-Mediated Inhibition of EGF Receptor Signaling Regulates Neural Precursor Cell Proliferation in the Neocortex

2020· article· en· W3092710087 on OpenAlexafffund
Danielle Jeong, Daniela Lozano Casasbuenas, Archana Gengatharan, Kyshona M. Edwards, Armen Saghatelyan, David R. Kaplan, Freda D. Miller, Scott A. Yuzwa

Bibliographic record

VenueCell Reports · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeurogenesis and neuroplasticity mechanisms
Canadian institutionsCanada Research ChairsUniversité LavalHospital for Sick ChildrenUniversity of Toronto
FundersCanada First Research Excellence FundCanadian Institutes of Health ResearchHospital for Sick ChildrenUniversity of Toronto
KeywordsNeural stem cellNeocortexCell biologyBiologyCell growthStem cellPrecursor cellEpidermal growth factorCellReceptorNeurosphereNeuroscienceAdult stem cellEmbryonic stem cellBiochemistry

Abstract

fetched live from OpenAlex

Here, we ask how neural stem cells (NSCs) transition in the developing neocortex from a rapidly to a slowly proliferating state, a process required to maintain lifelong stem cell pools. We identify LRIG1, known to regulate receptor tyrosine kinase signaling in other cell types, as a negative regulator of cortical NSC proliferation. LRIG1 is expressed in murine cortical NSCs as they start to proliferate more slowly during embryogenesis and then peaks postnatally when they transition to give rise to a portion of adult NSCs. Constitutive or acute loss of Lrig1 in NSCs over this developmental time frame causes stem cell expansion due to increased proliferation. LRIG1 controls NSC proliferation by associating with and negatively regulating the epidermal growth factor receptor (EGFR). These data support a model in which LRIG1 dampens the stem cell response to EGFR ligands within the cortical environment to slow their proliferation as they transition to postnatal adult NSCs.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.028
GPT teacher head0.224
Teacher spread0.195 · 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

Citations24
Published2020
Admission routes2
Has abstractyes

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