Prelamin A and ZMPSTE24 in premature and physiological aging
Bibliographic record
Abstract
As human longevity increases, understanding the molecular mechanisms that drive aging becomes ever more critical to promote health and prevent age-related disorders.Premature aging disorders or progeroid syndromes can provide critical insights into aspects of physiological aging.A major cause of progeroid syndromes which result from mutations in the genes LMNA and ZMPSTE24 is disruption of the final posttranslational processing step in the production of the nuclear scaffold protein lamin A. LMNA encodes the lamin A precursor, prelamin A and ZMPSTE24 encodes the prelamin A processing enzyme, the zinc metalloprotease ZMPSTE24.Progeroid syndromes resulting from mutations in these genes include the clinically related disorders Hutchinson-Gilford progeria syndrome (HGPS), mandibuloacral dysplasia-type B, and restrictive dermopathy.These diseases have features that overlap with one another and with some aspects of physiological aging, including bone defects resembling osteoporosis and atherosclerosis (the latter primarily in HGPS).The progeroid syndromes have ignited keen interest in the relationship between defective prelamin A processing and its accumulation in normal physiological aging.In this review, we examine the hypothesis that diminished processing of prelamin A by ZMPSTE24 is a driver of physiological aging.We review features a new mouse (Lmna L648R/L648R ) that produces solely unprocessed prelamin A and provides an ideal model for examining the effects of its accumulation during aging.We also discuss existing data on the accumulation of prelamin A or its variants in human physiological aging, which call out for further validation and more rigorous experimental approaches to determine if prelamin A contributes to normal aging.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".