Enhanced glycolytic metabolism supports transmigration of brain-infiltrating macrophages in multiple sclerosis
Bibliographic record
Abstract
The migration of leukocytes into the CNS drives the neuropathology of multiple sclerosis (MS). This penetration likely utilizes energy resources that remain to be defined. Using the experimental autoimmune encephalomyelitis (EAE) model of MS, we determined that macrophages within the perivascular cuff of post-capillary venules are highly glycolytic as manifested by strong expression of lactate dehydrogenase A (LDHA) that converts pyruvate to lactate. These macrophages expressed prominent levels of monocarboxylate transporter-4 (MCT-4) specialized in secreting lactate from glycolytic cells. The functional relevance of glycolysis was confirmed by siRNA-mediated knockdown of LDHA and MCT-4, which decreased lactate secretion and macrophage transmigration. MCT-4 was in turn regulated by EMMPRIN (CD147) as determined through co-expression/co-immunoprecipitation studies, and siRNA-mediated EMMPRIN silencing. The functional relevance of MCT-4/EMMPRIN interaction was affirmed by lower macrophage transmigration in culture using the MCT-4 inhibitor, α-cyano-4-hydroxy-cinnamic acid (CHCA), a cinnamon derivative. CHCA also reduced leukocyte infiltration and the clinical severity of EAE. Relevance to MS was corroborated by the strong expression of MCT-4, EMMPRIN and LDHA in perivascular macrophages in MS brains. These results detail the metabolism of macrophages for transmigration from perivascular cuffs into the CNS parenchyma and identifies CHCA and diet as potential modulators of neuro-inflammation in MS.
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 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 | 0.000 |
| 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".