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Record W3162592790 · doi:10.1242/dev.191122

HoxD transcription factors define monosynaptic sensory-motor specificity in the developing spinal cord

2021· article· en· W3162592790 on OpenAlexfundno aff
Fumiyasu Imai, Mike Adam, S. Steven Potter, Yutaka Yoshida

Bibliographic record

VenueDevelopment · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsnot available
FundersNational Institutes of HealthDalhousie UniversityNational Institute of Neurological Disorders and StrokeUniversität Basel
KeywordsNeuroscienceSensory systemBiologySpinal cordAxonMotor neuronTranscription factorSensory neuronProprioceptionDendrite (mathematics)Axon guidanceAnatomyGeneGenetics

Abstract

fetched live from OpenAlex

The specificity of monosynaptic connections between proprioceptive sensory neurons and their recipient spinal motor neurons depends on multiple factors, including motor neuron positioning and dendrite morphology, axon projection patterns of proprioceptive sensory neurons in the spinal cord, and the ligand-receptor molecules involved in cell-to-cell recognition. However, with few exceptions, the transcription factors engaged in this process are poorly characterized. Here, we show that members of the HoxD family of transcription factors play a crucial role in the specificity of monosynaptic sensory-motor connections. Mice lacking Hoxd9, Hoxd10 and Hoxd11 exhibit defects in locomotion but have no obvious defects in motor neuron positioning or dendrite morphology through the medio-lateral and rostro-caudal axes. However, we found that quadriceps motor neurons in these mice show aberrant axon development and receive inappropriate inputs from proprioceptive sensory axons innervating the obturator muscle. These genetic studies demonstrate that the HoxD transcription factors play an integral role in the synaptic specificity of monosynaptic sensory-motor connections in the developing spinal cord.

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 score0.598
Threshold uncertainty score0.416

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 head0.051
GPT teacher head0.311
Teacher spread0.260 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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