A recurrent de novo missense mutation in UBTF causes developmental neuroregression
Bibliographic record
Abstract
UBTF (upstream binding transcription factor) exists as two isoforms; UBTF1 regulates rRNA transcription by RNA polymerase 1, whereas UBTF2 regulates mRNA transcription by RNA polymerase 2. Herein, we describe 4 patients with very similar patterns of neuroregression due to recurrent de novo mutations in UBTF (GRCh37/hg19, NC_000017.10: g.42290219C > T, NM_014233.3: c.628G > A) resulting in the same amino acid change in both UBTF1 and UBTF2 (p.Glu210Lys [p.E210K]). Disease onset in our cohort was at 2.5 to 3 years and characterized by slow progression of global motor, cognitive and behavioral dysfunction. Notable early features included hypotonia with a floppy gait, high-pitched dysarthria and hyperactivity. Later features included aphasia, dystonia, and spasticity. Speech and ambulatory ability were lost by the early teens. Magnetic resonance imaging showed progressive generalized cerebral atrophy (supratentorial > infratentorial) with involvement of both gray and white matter. Patient fibroblasts showed normal levels of UBTF transcripts, increased expression of pre-rRNA and 18S rRNA, nucleolar abnormalities, markedly increased numbers of DNA breaks, defective cell-cycle progression, and apoptosis. Expression of mutant human UBTF1 in Drosophila neurons was lethal. Although no loss-of-function variants are reported in the Exome Aggregation Consortium (ExAC) database and Ubtf-/- is early embryonic lethal in mice, Ubtf+/- mice displayed only mild motor and behavioral dysfunction in adulthood. Our data underscore the importance of including UBTF E210K in the differential diagnosis of neuroregression and suggest that mainly gain-of-function mechanisms contribute to the pathogenesis of the UBTF E210K neuroregression syndrome.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".