Heterogeneous Nuclear Ribonucleoprotein A1 Knockdown Alters Constituents of Nucleocytoplasmic Transport
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Changes in nuclear morphology, alterations to the nuclear pore complex (NPC), including loss, aggregation, and dysfunction of nucleoporins (Nups), and nucleocytoplasmic transport (NCT) abnormalities have become hallmarks of neurodegenerative diseases. Previous RNA sequencing data utilizing knockdown of heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) identified enrichment for pathways and changes in RNAs related to nuclear morphology and showed differential expression of key nuclear targets. This suggests that dysfunction of hnRNP A1, which is observed in neurodegenerative diseases, may contribute to abnormalities in nuclear morphology, NPC, and NCT. METHODS: We performed knockdown of hnRNP A1 in Neuro-2A cells, a neuronal cell line, to examine nuclear morphology, NPC, and NCT. RESULTS: First, we examined nuclear morphology using Lamin B, wherein we observed increased nuclear envelope abnormalities in cells with hnRNP A1 knockdown as compared to control. To quantify changes in Lamin B, we designed and validated an automated computer-based model, which quantitatively confirmed our observations. Next, we investigated the impact of hnRNP A1 knockdown on components of the NPC and NCT. In line with the previous literature, we found changes in Nups, including altered distribution and reduced protein expression, as well as disrupted NCT. Finally, we validated our findings in multiple sclerosis (MS) brains, a disease with a significant neurodegenerative component caused by hnRNP A1 dysfunction, where neuronal nuclear envelope alterations were significantly increased as compared to controls. CONCLUSIONS: Together, these data implicate hnRNP A1 as an important contributor to nuclear morphology, Nup expression and distribution, and NCT and suggest that hnRNP A1 dysfunction may lead to defects in these processes in neurodegenerative diseases.
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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) | 0.000 | 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) | 0.002 | 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".