Evidence of A Negative Feedback Network Between TDP-43 and miRNAs Dependent on TDP-43 Nuclear Localization
Bibliographic record
Abstract
TAR DNA-binding protein 43 (TDP-43) is a DNA/RNA-binding protein that is integral to RNA processing. Among these functions is a critical role in microRNA (miRNA) biogenesis through interactions with the DROSHA and DICER complexes. It has been previously shown that there is a general reduction in miRNA levels within the spinal cord and spinal motor neurons of amyotrophic lateral sclerosis (ALS) patients. In addition, the most common pathological feature of ALS is re-distribution of TDP-43 from the nucleus to the cytoplasm where it forms cytoplasmic inclusions. Among miRNAs dysregulated in ALS, several are known to regulate TDP-43 expression. In this study, we demonstrate that TDP-43 is in a regulatory negative feedback network with miR-181c-5p and miR-27b-3p that is dependent on its nuclear localization within HEK293T cells. Further, we show that cellular stress which induces a redistribution of TDP-43 from the nucleus to the cytoplasm correlates with the reduced production of miR-27b-3p and miR-181c-5p. This suggests that reduced nuclear TDP-43 disrupts a negative feedback network between itself and miRNAs. These findings provide a further understanding of altered miRNA biogenesis as a key pathogenic process in ALS.
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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 | 0.000 |
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".