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Record W4399427909 · doi:10.1038/s41467-024-49198-w

Protein translation rate determines neocortical neuron fate

2024· article· en· W4399427909 on OpenAlexaff
Екатерина Борисова, Andrew G. Newman, Marta Couce Iglesias, Rike Dannenberg, Theres Schaub, Bo Qin, Alexandra Rusanova, Marisa M. Brockmann, Janina Koch, Marieatou Daniels, Paul Turko, Olaf Jahn, David R. Kaplan, Marta Rosário, Takao Iwawaki, C.M.T. Spahn, Christian Rosenmund, David Meierhofer, Matthew L. Kraushar, Victor Tarabykin, Mateusz C. Ambrozkiewicz

Bibliographic record

VenueNature Communications · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersRussian Science FoundationFritz Thyssen StiftungStudienstiftung des Deutschen VolkesDeutsche Forschungsgemeinschaft
KeywordsTranslation (biology)NeuronNeocortexNeuroscienceComputer scienceBiologyBiochemistry

Abstract

fetched live from OpenAlex

The mammalian neocortex comprises an enormous diversity regarding cell types, morphology, and connectivity. In this work, we discover a post-transcriptional mechanism of gene expression regulation, protein translation, as a determinant of cortical neuron identity. We find specific upregulation of protein synthesis in the progenitors of later-born neurons and show that translation rates and concomitantly protein half-lives are inherent features of cortical neuron subtypes. In a small molecule screening, we identify Ire1α as a regulator of Satb2 expression and neuronal polarity. In the developing brain, Ire1α regulates global translation rates, coordinates ribosome traffic, and the expression of eIF4A1. Furthermore, we demonstrate that the Satb2 mRNA translation requires eIF4A1 helicase activity towards its 5'-untranslated region. Altogether, we show that cortical neuron diversity is generated by mechanisms operating beyond gene transcription, with Ire1α-safeguarded proteostasis serving as an essential regulator of brain development.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.728
Threshold uncertainty score0.327

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 head0.015
GPT teacher head0.338
Teacher spread0.323 · 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

Citations22
Published2024
Admission routes1
Has abstractyes

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