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Record W4402852684 · doi:10.1101/2024.09.24.614810

Dynamic BMP signaling regulates sclerotome induction and lineage diversification in zebrafish

2024· preprint· en· W4402852684 on OpenAlexafffund
Linjun Xie, C. Roger, Katrinka M. Kocha, Emilio E. Méndez‐Olivos, Peng Huang

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicKruppel-like factors research
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersAlberta Children's Hospital Research InstituteNational Institutes of Health
KeywordsZebrafishDiversification (marketing strategy)Lineage (genetic)Cell biologyBiologyGeneticsBusinessGene

Abstract

fetched live from OpenAlex

ABSTRACT The sclerotome is an embryonic structure that gives rise to various supportive tissues, including the axial skeleton and connective tissues. Despite its significance, the mechanisms underlying sclerotome induction and diversification during embryonic development remain poorly understood. Sclerotome progenitors exhibit transient bmp4 expression and an active response to BMP signaling. Using BMP gain- and loss-of-function tools, we demonstrate that BMP signaling is both necessary and sufficient for sclerotome induction. Furthermore, through mosaic expression of a dominant-negative tool, we show that BMP signaling induces sclerotome fate in a cell-autonomous manner. Interestingly, different populations of sclerotome-derived cells have distinct BMP signaling requirements. Sclerotome-derived notochord-associated cells in the trunk lack BMP response, and sustained BMP signaling inhibits their differentiation into tenocytes. By contrast, sclerotome-derived fin mesenchymal cells in the fin fold require high levels of BMP signaling for proper morphogenesis. Our findings suggest that dynamic regulation of BMP signaling is crucial for the induction of the sclerotome and the subsequent diversification of sclerotome-derived lineages in zebrafish.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.106
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.001
Research integrity0.0010.001
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 head0.014
GPT teacher head0.244
Teacher spread0.230 · 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.

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

Citations0
Published2024
Admission routes2
Has abstractyes

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