Early defect of transforming growth factor β1 formation in Huntington’s disease
Bibliographic record
Abstract
A defective expression or activity of neurotrophic factors, such as brain- and glial-derived neurotrophic factors, contributes to neuronal damage in Huntington's disease (HD). Here, we focused on transforming growth factor-β (TGF-β(1) ), a pleiotropic cytokine with an established role in mechanisms of neuroprotection. Asymptomatic HD patients showed a reduction in TGF-β(1) levels in the peripheral blood, which was related to trinucleotide mutation length and glucose hypometabolism in the caudate nucleus. Immunohistochemical analysis in post-mortem brain tissues showed that TGF-β(1) was reduced in cortical neurons of asymptomatic and symptomatic HD patients. Both YAC128 and R6/2 HD mutant mice showed a reduced expression of TGF-β(1) in the cerebral cortex, localized in neurons, but not in astrocytes. We examined the pharmacological regulation of TGF-β(1) formation in asymptomatic R6/2 mice, where blood TGF-β(1) levels were also reduced. In these R6/2 mice, both the mGlu2/3 metabotropic glutamate receptor agonist, LY379268, and riluzole failed to increase TGF-β(1) formation in the cerebral cortex and corpus striatum, suggesting that a defect in the regulation of TGF-β(1) production is associated with HD. Accordingly, reduced TGF-β(1) mRNA and protein levels were found in cultured astrocytes transfected with mutated exon 1 of the human huntingtin gene, and in striatal knock-in cell lines expressing full-length huntingtin with an expanded glutamine repeat. Taken together, our data suggest that serum TGF-β(1) levels are potential biomarkers of HD development during the asymptomatic phase of the disease, and raise the possibility that strategies aimed at rescuing TGF-β(1) levels in the brain may influence the progression of HD.
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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) | 0.001 | 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".