Cellular rejuvenation protects neurons from inflammation mediated cell death
Bibliographic record
Abstract
Abstract In multiple sclerosis (MS), the invasion of the central nervous system by peripheral immune cells is followed by the activation of resident microglia and astrocytes. This cascade of events results in demyelination, which triggers neuronal damage and death. The molecular signals in neurons responsible for this damage are not yet fully characterized. In MS, retinal ganglion cell neurons (RGCs) of the central nervous system (CNS) undergo axonal injury and cell death. This phenomenon is mirrored in the experimental autoimmune encephalomyelitis (EAE) mouse model of MS. To understand the molecular landscape, we isolated RGCs from mice subjected to the EAE protocol. RNA-sequencing and ATAC-sequencing analyses were performed. Pathway analysis of the RNA-sequencing data revealed that RGCs displayed a molecular signature, similar to aged neurons, showcasing features of senescence. Single-nucleus RNA-sequencing analysis of neurons from human MS patients revealed a comparable senescence-like phenotype., which was supported by immunostaining RGCs in EAE mice. These changes include alterations to the nuclear envelope, modifications in chromatin marks, and accumulation of DNA damage. Transduction of RGCs with an Oct4 - Sox2 - Klf4 transgene to convert neurons in the EAE model to a more youthful epigenetic and transcriptomic state enhanced the survival of RGCs. Collectively, this research uncovers a previously unidentified senescent-like phenotype in neurons under pathological inflammation and neurons from MS patients. The rejuvenation of this aged transcriptome improved visual acuity and neuronal survival in the EAE model supporting the idea that age rejuvenation therapies and senotherapeutic agents could offer a direct means of neuroprotection in autoimmune disorders.
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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".