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Record W2947301741 · doi:10.1242/dev.173310

Kirrel2 is differentially required in populations of olfactory sensory neurons for the targeting of axons in the olfactory bulb

2019· article· en· W2947301741 on OpenAlexafffund
Neelima Vaddadi, Katrine Iversen, Reesha Raja, Alina Phen, Alexandra Brignall, Émilie Dumontier, Jean‐François Cloutier

Bibliographic record

VenueDevelopment · 2019
Typearticle
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchNovo Nordisk Fonden
KeywordsBiologyOlfactory bulbSensory systemNeuroscienceOlfactory systemOlfactory ensheathing gliaAnatomyCentral nervous system

Abstract

fetched live from OpenAlex

The formation of olfactory maps in the olfactory bulb (OB) is crucial for the control of innate and learned mouse behaviors. Olfactory sensory neurons (OSNs) expressing a specific odorant receptor project axons into spatially conserved glomeruli within the OB and synapse onto mitral cell dendrites. Combinatorial expression of members of the Kirrel family of cell adhesion molecules has been proposed to regulate OSN axonal coalescence; however, loss-of-function experiments have yet to establish their requirement in this process. We examined projections of several OSN populations in mice that lacked either Kirrel2 alone, or both Kirrel2 and Kirrel3. Our results show that Kirrel2 and Kirrel3 are dispensable for the coalescence of MOR1-3-expressing OSN axons to the most dorsal region (DI) of the OB. In contrast, loss of Kirrel2 caused MOR174-9- and M72-expressing OSN axons, projecting to the DII region, to target ectopic glomeruli. Our loss-of-function approach demonstrates that Kirrel2 is required for axonal coalescence in subsets of OSNs that project axons to the DII region and reveals that Kirrel2/3-independent mechanisms also control OSN axonal coalescence in certain regions of the OB.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.636
Threshold uncertainty score0.421

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 head0.268
GPT teacher head0.295
Teacher spread0.027 · 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 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

Citations20
Published2019
Admission routes2
Has abstractyes

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