CRISPR-Cas9 human gene replacement and phenomic characterization in <i>Caenorhabditis elegans</i> to understand the functional conservation of human genes and decipher variants of uncertain significance
Bibliographic record
Abstract
Our ability to sequence genomes has vastly surpassed our ability to interpret the genetic variation we discover. This presents a major challenge in the clinical setting, where the recent application of whole exome and whole genome sequencing has uncovered thousands of genetic variants of uncertain significance. Here, we present a strategy for targeted human gene replacement and phenomic characterization based on CRISPR-Cas9 genome engineering in the genetic model organism Caenorhabditis elegans that will facilitate assessment of the functional conservation of human genes and structure-function analysis of disease-associated variants with unprecedented precision. We validate our strategy by demonstrating that direct single-copy replacement of the C. elegans ortholog (daf-18) with the critical human disease-associated gene Phosphatase and Tensin Homolog (PTEN) is sufficient to rescue multiple phenotypic abnormalities caused by complete deletion of daf-18, including complex chemosensory and mechanosenory impairments. In addition, we used our strategy to generate animals harboring a single copy of the known pathogenic lipid phosphatase inactive PTEN variant (PTEN-G129E) and showed that our automated in vivo phenotypic assays could accurately and efficiently classify this missense variant as loss-of-function. The integrated nature of the human transgenes allows for analysis of both homozygous and heterozygous variants and greatly facilitates high-throughput precision medicine drug screens. By combining genome engineering with rapid and automated phenotypic characterization, our strategy streamlines identification of novel conserved gene functions in complex sensory and learning phenotypes that can be used as in vivo functional assays to decipher variants of uncertain significance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".