Disrupted minor intron splicing is prevalent in Mendelian disorders
Bibliographic record
Abstract
BACKGROUND: Splicing is crucial for proper gene expression, and is predominately executed by the major spliceosome. Conversely, 722 introns in 699 human minor intron-containing genes (MIGs) are spliced by the minor spliceosome. Splicing of these minor introns is disrupted in diseases caused by pathogenic variants in the minor spliceosome, ultimately leading to the aberrant expression of a subset of these MIGs. However, the effect of variants in minor introns and MIGs on diseases remains unexplored. METHODS: Variants in MIGs and associated clinical manifestations were identified using ClinVar. The HPO database was then used to curate the related symptoms and affected organ systems. RESULTS: We found pathogenic variants in 211 MIGs, which commonly resulted in intellectual disability, seizures and microcephaly. This revealed a subset of MIGs whose aberrant splicing may contribute to the pathogenesis of minor spliceosome-related diseases. Moreover, we identified 51 pathogenic variants in minor intron splice sites that reduce the splice site strength and can induce alternative splicing. CONCLUSION: These findings highlight that disrupted minor intron splicing has a broader impact on human diseases than previously appreciated. The hope is that this knowledge will aid in the development of therapeutic strategies that incorporate the minor intron splicing pathway.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".