Neuronal NMNAT2 Overexpression Does Not Achieve Significant Neuroprotection in Experimental Autoimmune Encephalomyelitis/Optic Neuritis
Bibliographic record
Abstract
Optic neuritis, inflammation, and demyelination of the optic nerve (ON), is one of the most common clinical manifestations of multiple sclerosis; affected patients suffer persistent visual symptoms due to ON degeneration and secondary retinal ganglion cell (RGC) death. The mouse experimental autoimmune encephalomyelitis (EAE) model replicates optic neuritis and significant RGC soma and axon loss. Nicotinamide mononucleotide adenylyltransferases (NMNATs) are NAD + -synthetic enzymes that have been shown to be essential for axon integrity, activation of which significantly delays axonal Wallerian degeneration. NMNAT2, which is enriched in axons, has been proposed as a promising therapeutic target for axon injury-induced neurodegeneration. We therefore investigated whether activation of NMNAT2 can be used as a gene therapy strategy for neuroprotection in EAE/optic neuritis. To avoid the confounding effects in inflammatory cells, which play important roles in EAE initiation and progression, we used an RGC-specific promoter to drive the expression of the long half-life NMNAT2 mutant in mouse RGCs in vivo . However, optical coherence tomography in vivo retina imaging did not reveal significant protection of the ganglion cell complex, and visual function assays, pattern electroretinography, and optokinetic response also showed no improvement in mice with NMNAT2 overexpression. Postmortem histological analysis of retina wholemounts and semithin sections of ON confirmed the in vivo results: NMNAT2 activation in RGCs does not provide significant neuroprotection of RGCs in EAE/optic neuritis. Our studies suggest that a different degenerative mechanism than Wallerian degeneration is involved in autoimmune inflammatory axonopathy and that NMNAT2 may not be a major contributor to this mechanism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".