The heterozygous R155C VCP mutation: Toxic in humans! Harmless in mice?
Bibliographic record
Abstract
Heterozygous missense mutations in the human VCP gene cause inclusion body myopathy associated with Paget disease of bone and fronto-temporal dementia (IBMPFD) and amyotrophic lateral sclerosis (ALS). The exact molecular mechanisms by which VCP mutations cause disease manifestation in different tissues are incompletely understood. In the present study, we report the comprehensive analysis of a newly generated R155C VCP knock-in mouse model, which expresses the ortholog of the second most frequently occurring human pathogenic VCP mutation. Heterozygous R155C VCP knock-in mice showed decreased plasma lactate, serum albumin and total protein concentrations, platelet numbers, and liver to body weight ratios, and increased oxygen consumption and CD8+/Ly6C + T-cell fractions, but none of the typical human IBMPFD or ALS pathologies. Breeding of heterozygous mice did not yield in the generation of homozygous R155C VCP knock-in animals. Immunoblotting showed identical total VCP protein levels in human IBMPFD and murine R155C VCP knock-in tissues as compared to wild-type controls. However, while in human IBMPFD skeletal muscle tissue 70% of the total VCP mRNA was derived from the mutant allele, in R155C VCP knock-in mice only 5% and 7% mutant mRNA were detected in skeletal muscle and brain tissue, respectively. The lack of any obvious IBMPFD or ALS pathology could thus be a consequence of the very low expression of mutant VCP. We conclude that the increased and decreased fractions of the R155C mutant VCP mRNA in man and mice, respectively, are due to missense mutation-induced, divergent alterations in the biological half-life of the human and murine mutant mRNAs. Furthermore, our work suggests that therapy approaches lowering the expression of the mutant VCP mRNA below a critical threshold may ameliorate the intrinsic disease pathology.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".