A direct interaction between transforming growth factor (TGF)-betas and amyloid-beta protein affects fibrillogenesis in a TGF-beta receptor-independent manner.
Bibliographic record
Abstract
Transforming growth factor-beta (TGF-beta) receptor-mediated signaling has been proposed to mediate both the beneficial and deleterious roles for this cytokine in amyloid-beta protein (Abeta) function. In order to assess receptor dependence of these events, we used PC12 cell cultures, which are devoid of TGF-beta receptors. Surprisingly, TGF-beta potentiated the neurotoxic effects of the 40-residue Abeta peptide, Abeta-(1-40), in this model suggesting that there may be a direct, receptor-independent interaction between TGF-beta and Abeta-(1-40). Surface plasmon resonance confirmed that TGF-beta binds with high affinity directly to Abeta-(1-40) and electron microscopy revealed that TGF-beta enhances Abeta-(1-40) oligomerization. Immunohistochemical examination of mouse brain revealed that hippocampal CA1 and dentate gyrus, two regions classically associated with Abeta-mediated pathology, lack TGF-beta Type I receptor immunoreactivity, thus indicating that TGF-beta receptor-mediated signaling would not be favored in these regions. Our observations not only provide for a unique, receptor-independent mechanism of action for TGF-beta, but also help to reconcile the literature interpreting the role of TGF-beta in Abeta function. These data support a critical etiological role for this mechanism in neuropathological amyloidoses.
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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".