Novel mutations in the<i>TRIM37</i>gene in Mulibrey Nanism
Bibliographic record
Abstract
Mulibrey nanism is an autosomal recessive prenatal-onset growth disorder of unknown pathogenesis. The main clinical features are pre- and postnatal growth failure, characteristic dysmorphic craniofacial features, heart disease, and hepatomegaly. Five truncating mutations in the TRIM37 gene have previously been reported in Mulibrey nanism patients. The TRIM37 protein encodes a novel protein of unknown function. It contains a tripartite motif (TRIM, also denoted the RING-B-box-Coiled-coil or RBCC domain) and a TRAF (tumor necrosis factor-receptor associated factor) domain. TRIM37 localizes to peroxisomes classifying Mulibrey nanism as a peroxisomal disorder. Here we have characterized the genomic structure of the TRIM37 gene, which has 24 exons spanning approximately 109 kb of genomic DNA. Further, we report six novel disease-associated mutations, five of which predict a truncated protein: c.745C>T (p.Gln249X), c.1411C>T (p.Arg471X), c.2056C>T (p.Arg686X), and an 8.6 kb genomic deletion (c.1314+507_1668-207del resulting in p.Arg439fsX4). The sixth mutation (c.965G>T) is the first missense mutation (p.Gly322Val) associated with Mulibrey nanism. It affects the TRAF domain of TRIM37 and results in altered subcellular localization of the mutant TRIM37 protein, further suggesting that it is pathogenic.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".