New frontiers to cure Alport syndrome: COL4A3 and COL4A5 gene editing in podocyte-lineage cells
Bibliographic record
Abstract
Alport syndrome (AS) is an inherited genetic disorder characterized by range of alterations from glomerular basement membrane abnormalities up to end-stage renal disease. Pathogenic variants in the collagen α3, α4, and α5 encoding genes are causative both of the autosomal dominant and of the X-linked forms of AS. Podocytes are the only renal cells that are able to produce the COL(IV)a3-a4a5 heterotrimer. We have previously demonstrated how it is possible to isolate podocyte-lineage cells from urine of patients, providing an easily accessible cellular model closer to the podocytes' physiological conditions. Taking advantage of disease-relevant cell lines, we employed a two-plasmid approach in order to achieve a beneficial and stable variant-specific correction using CRISPR/Cas9 genome editing. One plasmid carries a Donor DNA and a reporter system mCherry/GFP to track the activity of Cas9 in cells. The other plasmid carries a self-cleaving SpCas9 and the variant-specific sgRNA. We have analyzed two stable podocyte-lineage cell lines, harboring a variant in the X-linked COL4A5 (p.(Gly624Asp)) and in the autosomal COL4A3 gene (p.(Gly856Glu)). We have achieved reversion of variants greater than 40% with undesired insertions/deletions lower than 15%. Overall, we have demonstrated a new gene therapy approach directly on patients' cells, key players of Alport pathogenesis, and we have reverted COL4 causative variants towards the wild type state. These results, in combination with preclinical models, could open new frontiers in the management and the treatment of the disorder.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".