No evidence that human GIGYF2 interacts with growth factor receptor-bound protein 10 (GRB10): implication for human disease
Bibliographic record
Abstract
Abstract GIGYF2 (Growth factor receptor bound protein 10 (GRB10)-interacting GYF (glycine-tyrosine-phenylalanine) protein 2) reduces mRNA stability and translation via microRNAs, ribosome quality control, and several RNA-binding proteins. GIGYF2 was first identified in mouse cell lines as an interacting partner with Growth factor receptor-bound protein 10 (GRB10), which binds to the insulin receptor and insulin-like growth-factor receptor 1. Mutations in the human GIGYF2 gene were reported in autism spectrum disorder and Parkinson’s disease. In mouse models, mutations in the gene encoding GIGYF2 exhibited disease phenotypes. It was, therefore, thought that the GIGYF2-associated disease in humans is caused by defective GRB10 signaling. We show here that GIGYF2 does not interact with GRB10 in human cell lines, as determined by proximity ligation and co-immunoprecipitation assays. Size exclusion chromatography assay further confirmed these findings. The lack of interaction is explained by the absence of the critical GYF-domain binding PPGΦ sequence in the human GRB10 protein. These results are in contrast with the current understanding that a GIGYF2/GRB10 complex is associated with human disease via IR and IGF-1R signaling and underscore alternative mechanisms responsible for the observed phenotypes related to mutations in the human GIGYF2 gene.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".