A Taybi-Linder syndrome-related RTTN variant impedes neural rosette formation in human cortical organoids
Bibliographic record
Abstract
Taybi-Linder syndrome (TALS) is a rare autosomal recessive disorder characterized by severe microcephaly with abnormal gyral pattern, severe growth retardation and bone abnormalities. It is caused by pathogenic variants in the RNU4ATAC gene. Its transcript, the small nuclear RNA U4atac, is involved in the excision of ~850 minor introns. Here, we report a patient presenting with TALS features but no pathogenic variants were found in RNU4ATAC, instead the homozygous RTTN c.2953A>G variant was detected by whole-exome sequencing. After deciphering the impact of the variant on the RTTN protein function at centrosome in engineered RTTN-depleted RPE1 cells and patient fibroblasts, we analysed neural stem cells (NSC) derived from CRISPR/Cas9-edited induced pluripotent stem cells and revealed major cell cycle and mitotic abnormalities, leading to aneuploidy, cell cycle arrest and cell death. In cortical organoids, we discovered an additional function of RTTN in the self-organisation of NSC into neural rosettes, by observing delayed apico-basal polarization of NSC. Altogether, these defects contributed to a marked delay of rosette formation in RTTN-mutated organoids, thus impeding their overall growth and shedding light on mechanisms leading to microcephaly.
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) | 0.000 | 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) | 0.001 | 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".