B4GALT6 regulates astrocyte activation during CNS inflammation (INM8P.360)
Bibliographic record
Abstract
Abstract Astrocytes play complex roles in neuroinflammation. Thus, it is important to characterize the mechanisms regulating astrocyte function, as well as potential targets for the therapeutic modulation of astrocyte activity. Here we report that during the chronic-progressive phase of EAE, astrocytes promote disease pathogenesis by a lipid-dependent signaling pathway. In line with previous studies, depletion of reactive astrocytes during the acute phase of chronic-progressive EAE exacerbated disease. However, depletion of astrocytes in the chronic phase ameliorated the disease. Hence, to understand this dichotomy we compared gene expression profiles of astrocytes from mice in acute and chronic stages of EAE. One gene associated with the chronic stage was B4GALT6, which codes for the β-1,4-galactosyltransferase 6 that catalyzes the synthesis of lactosylceramide (LacCer). We observed that LacCer levels are upregulated in the CNS during chronic EAE, and that LacCer synthesized by the astrocytes acts in an autocrine manner to trigger transcriptional programs that promote the recruitment and activation of infiltrating monocytes and microglia, and neurodegeneration. We also detected increased B4GALT6 expression and LacCer levels in MS lesions. Finally, the inhibition of LacCer synthesis suppressed CNS innate immunity and neurodegeneration, and interfered with the activation of human astrocytes. Thus, B4GALT6 is a potential therapeutic target for MS and neuroinflammatory 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) | 0.002 | 0.001 |
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".