Plasma and CSF neurofilament light chain are stabilized in response to mutant huntingtin lowering in the brain of Huntington disease mice
Bibliographic record
Abstract
Abstract Background Therapeutic approaches aimed at lowering levels of toxic mutant huntingtin (mHTT) in the brain can reverse disease phenotypes in animal models of Huntington disease (HD) and are currently being evaluated in clinical trials. Sensitive and dynamic response biomarkers are needed to assess the efficacy of such candidate therapies. Neurofilament light chain (NfL) is a biomarker of neurodegeneration that increases in cerebrospinal fluid (CSF) and blood with HD progression. However, it remains unknown whether NfL in biofluids may be useful as a response biomarker for assessing the efficacy of disease-modifying therapies for HD. Methods Longitudinal plasma and cross-sectional CSF samples were collected from the YAC128 transgenic mouse model of HD and wild type littermate control mice throughout the natural history of disease. Additionally, biofluids were collected from YAC128 mice following intracerebroventricular administration of an antisense oligonucleotide targeting the mutant HTT transgene (HTT ASO) at ages either before or after the onset of disease phenotypes. NfL concentrations in plasma and CSF were quantified using ultrasensitive single-molecule array technology. Results Plasma and CSF NfL concentrations were significantly elevated in YAC128 compared to wild type littermate control mice from 9 months of age. Treatment of YAC128 mice with either 15 or 50 µg HTT ASO resulted in dose-dependent, allele-selective reduction of mHTT throughout the brain at a 3-month interval, which was sustained with high dose HTT ASO treatment up 6 months. Lowering of mHTT in the brain with HTT ASO initiated prior to the onset of regional brain atrophy and HD-like motor deficits in YAC128 mice had minimal effect on plasma NfL at either dose but resulted in a dose-dependent reduction of CSF NfL. In contrast, initiating mHTT lowering in the brain after the onset of neuropathological and behavioural phenotypes in YAC128 mice resulted in a dose-dependent stabilization of NfL increases in both plasma and CSF. Conclusions Our data provides evidence that the response of NfL in biofluids is influenced by the magnitude of mHTT lowering in the brain and the timing of intervention, suggesting that NfL may represent a promising exploratory response biomarker for 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 | 0.001 |
| 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".