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Record W2921663494 · doi:10.1016/j.stem.2019.02.002

RAL GTPases Drive Intestinal Stem Cell Function and Regeneration through Internalization of WNT Signalosomes

2019· article· en· W2921663494 on OpenAlexaff
Joel Johansson, Máté Nászai, Michael C. Hodder, Karen Pickering, Bryan W. Miller, Rachel A. Ridgway, Yachuan Yu, Pascal Peschard, Saskia M. Brachmann, Andrew D. Campbell, Julia B. Cordero, Owen J. Sansom

Bibliographic record

VenueCell stem cell · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicInvertebrate Immune Response Mechanisms
Canadian institutionsMcGill University
FundersRoyal SocietyCancer Research UKWellcome TrustUniversity of GlasgowNovartis
KeywordsBiologyWnt signaling pathwayCell biologyRegeneration (biology)Stem cellLGR5GTPaseSmall GTPaseProgenitor cellFrizzledSignal transduction

Abstract

fetched live from OpenAlex

Ral GTPases are RAS effector molecules and by implication a potential therapeutic target for RAS mutant cancer. However, very little is known about their roles in stem cells and tissue homeostasis. Using Drosophila, we identified expression of RalA in intestinal stem cells (ISCs) and progenitor cells of the fly midgut. RalA was required within ISCs for efficient regeneration downstream of Wnt signaling. Within the murine intestine, genetic deletion of either mammalian ortholog, Rala or Ralb, reduced ISC function and Lgr5 positivity, drove hypersensitivity to Wnt inhibition, and impaired tissue regeneration following damage. Ablation of both genes resulted in rapid crypt death. Mechanistically, RALA and RALB were required for efficient internalization of the Wnt receptor Frizzled-7. Together, we identify a conserved role for RAL GTPases in the promotion of optimal Wnt signaling, which defines ISC number and regenerative potential.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.193
Teacher spread0.183 · 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 source (direct Gemma or distilled Codex), 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

Citations65
Published2019
Admission routes1
Has abstractno

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