Disease-associated astrocyte epigenetic memory promotes CNS pathology
Bibliographic record
Abstract
Summary Astrocytes play important roles in the central nervous system (CNS) physiology and pathology. Indeed, astrocyte subsets defined by specific transcriptional activation states contribute to the pathology of neurologic diseases, including multiple sclerosis (MS) and its pre-clinical model experimental autoimmune encephalomyelitis (EAE) 1–8 . However, little is known about the stability of these disease-associated astrocyte subsets, their regulation, and whether they integrate past stimulation events to respond to subsequent challenges. Here, we describe the identification of an epigenetically controlled memory astrocyte subset which exhibits exacerbated pro-inflammatory responses upon re-challenge. Specifically, using a combination of single-cell RNA sequencing (scRNA-seq), assay for transposase-accessible chromatin with sequencing (ATAC–seq), chromatin immunoprecipitation with sequencing (ChIP–seq), focused interrogation of cells by nucleic acid detection and sequencing (FIND-seq), and cell-specific in vivo CRISPR/Cas9-based genetic perturbation studies we established that astrocyte memory is controlled by the metabolic enzyme ATP citrate lyase (ACLY), which produces acetyl coenzyme A (acetyl-CoA) used by the histone acetyltransferase p300 to control chromatin accessibility. ACLY + p300 + memory astrocytes are increased in acute and chronic EAE models; the genetic targeting of ACLY + p300 + astrocytes using CRISPR/Cas9 ameliorated EAE. We also detected responses consistent with a pro-inflammatory memory phenotype in human astrocytes in vitro ; scRNA-seq and immunohistochemistry studies detected increased ACLY + p300 + astrocytes in chronic MS lesions. In summary, these studies define an epigenetically controlled memory astrocyte subset that promotes CNS pathology in EAE and, potentially, MS. These findings may guide novel therapeutic approaches for MS and other neurologic diseases.
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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) | 0.002 | 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".