A complementation method for functional analysis of mammalian genes
Bibliographic record
Abstract
Our progress in understanding mammalian gene function has lagged behind that of gene identification. New methods for mammalian gene functional analysis are needed to accelerate the process. In yeast, the powerful genetic shuffle system allows deletion of any chromosomal gene by homologous recombination and episomal expression of a mutant allele in the same cell. Here, we report a method for mammalian cells, which employs a helper-dependent adenoviral (HD-Ad) vector to synthesize small hairpin (sh) RNAs to knock-down the expression of an endogenous gene by targeting untranslated regions (UTRs). The vector simultaneously expresses an exogenous version of the same gene (wild-type or mutant allele) lacking the UTRs for functional analysis. We demonstrated the utility of the method by using PRPF3, which encodes the human RNA splicing factor Hprp3p. Recently, missense mutations in PRPF3 were found to cause autosomal-dominant Retinitis Pigmentosa, a form of genetic eye diseases affecting the retina. We knocked-down endogenous PRPF3 in multiple cell lines and rescued the phenotype (cell death) with exogenous PRPF3 cDNA, thereby creating a genetic complementation method. Because Ad vectors can efficiently transduce a wide variety of cell types, and many tissues in vivo, this method could have a wide application for gene function studies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".