Co‐localization of tau and <scp>TDP</scp> ‐43 after extracellular vesicle delivery to cells
Bibliographic record
Abstract
Perturbations in the metabolism of microtubule‐associated protein tau (tau) underlie the pathology of a broad array of dementias, including chronic traumatic encephalopathy, amyotrophic lateral sclerosis (ALS) with cognitive impairment (ALSci) and approximately half of the dementias associated with frontotemporal lobar degeneration. We recently observed significantly increased hippocampal tau pathology in rats injected with pseudophosphorylated human tau (2N4R tau T175D ) co‐expressing an ALS‐associated TAR DNA‐binding protein 43 (TDP‐43) mutant (TDP‐43 M337V ) when compared to wild‐type rats. To understand this mechanism, we examined whether the extracellular vesicles (EVs) derived from wild‐type TDP‐43 (wtTDP‐43) or tau‐expressing cells could transfer expression of these proteins to recipient cells, and whether co‐localization of these proteins occurs. mCherry‐ wt TDP‐43 or EGFP‐tau constructs were expressed in HEK293 or SH‐SY5Y cells. The secretome and EV fractions contained wt TDP‐43 or 2N4R tau protein and RNA, and could transfer proteins into nontransfected cells. Co‐localization was also detected in the cytosol of recipient cells. In silico modeling of tau and TDP‐43 interactions suggests hydrogen bonding underlies this interaction. These studies further our understanding of the interaction between tau and TDP‐43 by demonstrating their ability to co‐aggregate and in providing a mechanism by which cell–cell transfer of either protein via extracellular vesicles can lead to these synergistic interactions.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".