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Record W4320486061 · doi:10.26508/lsa.202201810

Dual function of Rab1A in secretion and autophagy: hypervariable domain dependence

2023· article· en· W4320486061 on OpenAlexfundno aff
Valeriya Gyurkovska, Rakhilya Murtazina, Sarah F. Zhao, Sojin Shikano, Yukari Okamoto, Nava Segev

Bibliographic record

VenueLife Science Alliance · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular transport and secretion
Canadian institutionsnot available
FundersNational Institute of Neurological Disorders and StrokeNational Institute of General Medical SciencesNational Institutes of HealthUniversity of Alberta
KeywordsAutophagyRabCell biologySecretionGTPaseEndocytosisBiologyIntracellularChemistryReceptorApoptosisBiochemistry

Abstract

fetched live from OpenAlex

We currently understand how the different intracellular pathways, secretion, endocytosis, and autophagy are regulated by small GTPases. In contrast, it is unclear how these pathways are coordinated to ensure efficient cellular response to stress. Rab GTPases localize to specific organelles through their hypervariable domain (HVD) to regulate discrete steps of individual pathways. Here, we explored the dual role of Rab1A/B (92% identity) in secretion and autophagy. We show that although either Rab1A or Rab1B is required for secretion, Rab1A, but not Rab1B, localizes to autophagosomes and is required early in stress-induced autophagy. Moreover, replacing the HVD of Rab1B with that of Rab1A enables Rab1B to localize to autophagosomes and regulate autophagy. Therefore, Rab1A-HVD is required for the dual functionality of a single Rab in two different pathways: secretion and autophagy. In addition to this mechanistic insight, these findings are relevant to human health because both the pathways and Rab1A/B were implicated in diseases ranging from cancer to neurodegeneration.

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 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 score0.698
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.235
Teacher spread0.223 · 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

Citations26
Published2023
Admission routes1
Has abstractyes

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