Distinct Impairments Characterizing Different ADNP Mutants Reveal Aberrant Cytoplasmic-Nuclear Crosstalk
Bibliographic record
Abstract
(1) Background: Activity-dependent neuroprotective protein (ADNP) is essential for neuronal structure and function. Multiple de novo pathological mutations in ADNP cause the autistic ADNP syndrome, and they have been further suggested to affect Alzheimer's disease progression in a somatic form. Here, we asked if different ADNP mutations produce specific neuronal-like phenotypes toward better understanding and personalized medicine. (2) Methods: We employed CRISPR/Cas9 genome editing in N1E-115 neuroblastoma cells to form neuron-like cell lines expressing ADNP mutant proteins conjugated to GFP. These new cell lines were characterized by quantitative morphology, immunocytochemistry and live cell imaging. (3) Results: Our novel cell lines, constitutively expressing GFP-ADNP p.Pro403 (p.Ser404* human orthologue) and GFP-ADNP p.Tyr718* (p.Tyr719* human orthologue), revealed new and distinct phenotypes. Increased neurite numbers (day 1, in culture) and increased neurite lengths upon differentiation (day 7, in culture) were linked with p.Pro403*. In contrast, p.Tyr718* decreased cell numbers (day 1). These discrete phenotypes were associated with an increased expression of both mutant proteins in the cytoplasm. Reduced nuclear/cytoplasmic boundaries were observed in the p.Tyr718* ADNP-mutant line, with this malformation being corrected by the ADNP-derived fragment drug candidate NAP. (4) Conclusions: Distinct impairments characterize different ADNP mutants and reveal aberrant cytoplasmic-nuclear crosstalk.
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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 | 0.000 | 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 | 0.001 |
| 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".