Arachidonic acid-derived lipid mediators in multiple sclerosis pathogenesis: fueling or dampening disease progression?
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system (CNS), characterized by neuroinflammation, demyelination, and neurodegeneration. Considering the increasing prevalence among young adults worldwide and the disabling phenotype of the disease, a deeper understanding of the complexity of the disease pathogenesis is needed to ultimately improve diagnosis and personalize treatment opportunities. Recent findings suggest that bioactive lipid mediators (LM) derived from ω-3/-6 polyunsaturated fatty acids (PUFA), also termed eicosanoids, may contribute to MS pathogenesis. For example, disturbances in LM profiles and especially those derived from the ω-6 PUFA arachidonic acid (AA) have been reported in people with MS (PwMS), where they may contribute to the chronicity of neuroinflammatory processes. Moreover, we have previously shown that certain AA-derived LMs also associated with neurodegenerative processes in PwMS, suggesting that AA-derived LMs are involved in more pathological events than solely neuroinflammation. Yet, to date, a comprehensive overview of the contribution of these LMs to MS-associated pathological processes remains elusive. MAIN BODY: This review summarizes and critically evaluates the current body of literature on the eicosanoid biosynthetic pathway and its contribution to key pathological hallmarks of MS during different disease stages. Various parts of the eicosanoid pathway are highlighted, namely, the prostanoid, leukotriene, and hydroxyeicosatetraenoic acids (HETEs) biochemical routes that include specific enzymes of the cyclooxygenases (COXs) and lipoxygenases (LOX) families. In addition, cellular sources of LMs and their potential target cells based on receptor expression profiles will be discussed in the context of MS. Finally, we propose novel therapeutic approaches based on eicosanoid pathway and/or receptor modulation to ultimately target chronic neuroinflammation, demyelination and neurodegeneration in MS. SHORT CONCLUSION: The eicosanoid pathway is intrinsically linked to specific aspects of MS pathogenesis. Therefore, we propose that novel intervention strategies, with the aim of accurately modulating the eicosanoid pathway towards the biosynthesis of beneficial LMs, can potentially contribute to more patient- and MS subtype-specific treatment opportunities to combat MS.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".