Presence and distribution of progerin in HGPS cells is ameliorated by drugs that impact on the mevalonate and mTOR pathways
Bibliographic record
Abstract
Hutchinson-Gilford progeria syndrome (HGPS) is a rare, premature ageing syndrome in children. HGPS is normally caused by a mutation in the LMNA gene, encoding nuclear lamin A. The classical mutation in HGPS leads to the production of a toxic truncated version of lamin A, progerin, which retains a farnesyl group. Farnesyltransferase inhibitors (FTI), pravastatin and zoledronic acid have been used in clinical trials to target the mevalonate pathway in HGPS patients to inhibit farnesylation of progerin, in order to reduce its toxicity. Some other compounds that have been suggested as treatments include rapamycin, IGF1 and N-acetyl cysteine (NAC). We have analysed the distribution of prelamin A, lamin A, lamin A/C, progerin, lamin B1 and B2 in nuclei of HGPS cells before and after treatments with these drugs, an FTI and a geranylgeranyltransferase inhibitor (GGTI) and FTI with pravastatin and zoledronic acid in combination. Confirming other studies prelamin A, lamin A, progerin and lamin B2 staining was different between control and HGPS fibroblasts. The drugs that reduced progerin staining were FTI, pravastatin, zoledronic acid and rapamycin. However, drugs affecting the mevalonate pathway increased prelamin A, with only FTI reducing internal prelamin A foci. The distribution of lamin A in HGPS cells was improved with treatments of FTI, pravastatin and FTI + GGTI. All treatments reduced the number of cells displaying internal speckles of lamin A/C and lamin B2. Drugs targeting the mevalonate pathway worked best for progerin reduction, with zoledronic acid removing internal progerin speckles. Rapamycin and NAC, which impact on the MTOR pathway, both reduced both pools of progerin without increasing prelamin A in HGPS cell nuclei.
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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 | 0.000 | 0.000 |
| 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".