Effects of spliceosomal mutations on brain patterning and morphogenesis
Bibliographic record
Abstract
The major spliceosome consists of U1, U2, U5, and U4/U6 small nuclear ribonucleoprotein (snRNPs), and each snRNP has distinct and sequential roles during the mRNA splicing process. Mutations in the core components of the spliceosome are associated with brain and neurological defects. The goal of this study is to examine the role of three core spliceosomal components, SNRPB, SF3B4, and EFTUD2, in brain patterning and morphogenesis, and whether or not they are required in the neural tube or neural crest cells for brain development. Previously, we identified brain defects associated with the mutation of these spliceosomal components in the neural tube and neural crest cells. Structures derived from the midbrain and hindbrain region, such as the diencephalon, pons, and cerebellum, were found to be absent in E12.5 and E14.5 mutant embryos. In this study, we aim to use mutant mouse models to identify shared transcripts and pathways disrupted by mutations in these spliceosomal genes. Through RNA‐seq analysis, we identified a number of mis‐expressed RNA‐binding proteins that may contribute to the malformationis found in these mutants. We plan to examine level and expression of these RNA binding proteins in wild type and mutant embryos to evaluate how they contribute to the phenotypes found. Furthermore, to assess the potential clinical application of our findings, we will use human embryonic stem cells (hESC) to generate human neural crest cells with mutatioin in these splicing factors; shared transcripts and pathways identified in the mouse model will be compared to those found in induced human neural crest cells. Distinct spliceosomophathies are difficult to diagnose due to phenotypic overlaps. The significance of this work lies in its potential to identify a disrupted pathway shared by mutations in various splicing factors, a therapeutic alternative across multiple spliceosomopathies.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".