Forward genetic screen in human podocytes identifies diphthamide biosynthesis genes as regulators of adhesion
Bibliographic record
Abstract
Podocyte function is tightly linked to the complex organization of its cytoskeleton and adhesion to the underlying glomerular basement membrane. Adhesion of cultured podocytes to a variety of substrates is reported to correlate with podocyte health. To identify novel genes that are important for podocyte function, we designed an in vitro genetic screen based on podocyte adhesion to plates coated with either fibronectin or soluble Fms-like tyrosine kinase-1 (sFLT1)/Fc. A genome-scale pooled RNA interference screen on immortalized human podocytes identified 77 genes that increased adhesion to fibronectin, 101 genes that increased adhesion to sFLT1/Fc, and 44 genes that increased adhesion to both substrates when knocked down. Multiple shRNAs against diphthamide biosynthesis protein 1−4 (DPH1−DPH4) were top hits for increased adhesion. Immortalized human podocyte cells stably expressing these hairpins displayed increased adhesion to both substrates. We then used CRISPR-Cas9 to generate podocyte knockout cells for DPH1, DPH2, or DPH3, which also displayed increased adhesion to both fibronectin and sFLT1/Fc, as well as a spreading defect. Finally, we showed that Drosophila nephrocyte-specific knockdown of Dph1, Dph2, and Dph4 resulted in altered nephrocyte function. In summary, we report here a novel high-throughput method to identify genes important for podocyte function. Given the central role of podocyte adhesion as a marker of podocyte health, these data are a rich source of candidate regulators of glomerular disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".