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Record W4225463548 · doi:10.1242/dmm.049359

Rapid assessment of the temporal function and phenotypic reversibility of neurodevelopmental disorder risk genes in <i>Caenorhabditis elegans</i>

2022· article· en· W4225463548 on OpenAlexafffund
Lexis D. Kepler, Troy A. McDiarmid, Catharine H. Rankin

Bibliographic record

VenueDisease Models & Mechanisms · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsPhenotypeCaenorhabditis elegansBiologyGeneGeneticsLoss functionFunction (biology)Computational biology

Abstract

fetched live from OpenAlex

Recent studies have indicated that some phenotypes caused by decreased function of select neurodevelopmental disorder (NDD) risk genes can be reversed by restoring gene function in adulthood. However, few of the hundreds of risk genes have been assessed for adult phenotypic reversibility. We developed a strategy to rapidly assess the temporal requirements and phenotypic reversibility of NDD risk gene orthologs using a conditional protein degradation system and machine-vision phenotypic profiling in Caenorhabditis elegans. We measured how degrading and re-expressing orthologs of EBF3, BRN3A and DYNC1H1 at multiple periods throughout development affect 30 morphological, locomotor, sensory and learning phenotypes. We found that phenotypic reversibility was possible for each gene studied. However, the temporal requirements of gene function and degree of rescue varied by gene and phenotype. This work highlights the critical need to assess multiple windows of degradation and re-expression and a large number of phenotypes to understand the many roles a gene can have across the lifespan. This work also demonstrates the benefits of using a high-throughput model system to prioritize NDD risk genes for re-expression studies in other organisms.

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 score0.444
Threshold uncertainty score0.641

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 head0.008
GPT teacher head0.207
Teacher spread0.199 · 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

Citations6
Published2022
Admission routes2
Has abstractyes

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