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Record W2975600638 · doi:10.7554/elife.50226

An RNAi screen unravels the complexities of Rho GTPase networks in skin morphogenesis

2019· article· en· W2975600638 on OpenAlexfundno aff
Mélanie Laurin, Nicholas C. Gomez, John M. Levorse, Ataman Sendoel, Megan Sribour, E. Fuchs

Bibliographic record

VenueeLife · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSkin and Cellular Biology Research
Canadian institutionsnot available
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Cancer InstituteNational Institutes of HealthHuman Frontier Science ProgramCanadian Institutes of Health ResearchCancer Research SocietyBurroughs Wellcome FundHoward Hughes Medical Institute
KeywordsMorphogenesisCell biologyBiologyCytoskeletonInteractomeRNA interferenceMorphogenHair follicleGTPaseProteomicsCellGeneticsGene

Abstract

fetched live from OpenAlex

During mammalian embryogenesis, extensive cellular remodeling is needed for tissue morphogenesis. As effectors of cytoskeletal dynamics, Rho GTPases and their regulators are likely involved, but their daunting complexity has hindered progress in dissecting their functions. We overcome this hurdle by employing high throughput in utero RNAi-mediated screening to identify key Rho regulators of skin morphogenesis. Our screen unveiled hitherto unrecognized roles for Rho-mediated cytoskeletal remodeling events that impact hair follicle specification, differentiation, downgrowth and planar cell polarity. Coupling our top hit with gain/loss-of-function genetics, interactome proteomics and tissue imaging, we show that RHOU, an atypical Rho, governs the cytoskeletal-junction dynamics that establish columnar shape and planar cell polarity in epidermal progenitors. Conversely, RHOU downregulation is required to remodel to a conical cellular shape that enables hair bud invagination and downgrowth. Our findings underscore the power of coupling screens with proteomics to unravel the physiological significance of complex gene families.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.316

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 head0.013
GPT teacher head0.272
Teacher spread0.259 · 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

Citations23
Published2019
Admission routes1
Has abstractyes

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