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Record W2765920731 · doi:10.1242/jcs.207456

Mind the (sr)GAP – roles of Slit–Robo GAPs in neurons, brains and beyond

2017· review· en· W2765920731 on OpenAlexfundno aff
Bethany Lucas, Jeff Hardin

Bibliographic record

VenueJournal of Cell Science · 2017
Typereview
Languageen
FieldNeuroscience
TopicAxon Guidance and Neuronal Signaling
Canadian institutionsnot available
FundersInstitute of GeneticsNational Institute of General Medical SciencesNational Institutes of HealthUniversity of Wisconsin-Madison
KeywordsBiologyGTPaseSlitCell biologyNeuroscienceEffectorCell migrationGTPase-activating proteinGene familyGene duplicationCellGeneGene expressionGeneticsSignal transductionG protein

Abstract

fetched live from OpenAlex

The Slit-Robo GTPase-activating proteins (srGAPs) were first identified as potential Slit-Robo effectors that influence growth cone guidance. Given their N-terminal F-BAR, central GAP and C-terminal SH3 domains, srGAPs have the potential to affect membrane dynamics, Rho family GTPase activity and other binding partners. Recent research has clarified how srGAP family members act in distinct ways at the cell membrane, and has expanded our understanding of the roles of srGAPs in neuronal and non-neuronal cells. Gene duplication of the human-specific paralog of srGAP2 has resulted in srGAP2 family proteins that may have increased the density of dendritic spines and promoted neoteny of the human brain during crucial periods of human evolution, underscoring the importance of srGAPs in the unique sculpting of the human brain. Importantly, srGAPs also play roles outside of the nervous system, including during contact inhibition of cell movement and in establishing and maintaining cell adhesions in epithelia. Changes in srGAP expression may contribute to neurodevelopmental disorders, cancer metastasis and inflammation. As discussed in this Review, much remains to be discovered about how this interesting family of proteins functions in a diverse set of processes in metazoans and the functional roles srGAPs play in human disease.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.124
GPT teacher head0.374
Teacher spread0.250 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations43
Published2017
Admission routes1
Has abstractyes

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