Sphingosin-1-phosphate Receptor 1: a Potential Target to Inhibit Neuroinflammation and Restore the Sphingosin-1-phosphate Metabolism
Bibliographic record
Abstract
BACKGROUND: Recent evidence suggests that an extreme shift may occur in sphingosine metabolism in neuroinflammatory contexts. Sphingosine 1-phosphate (S1P)-metabolizing enzymes (SMEs) regulate the level of S1P. We recently found that FTY720, a S1P analogue, and SEW2871, a selective S1P receptor 1 (S1P1) agonist, provide protection against neural damage and memory deficit in amyloid beta (Aβ)-injected animals. This study aimed to evaluate the effects of these two analogues on the expression of SMEs as well as their anti-inflammatory roles. METHODS: Rats were treated with intracerebral lipopolysaccharide (LPS) or Aβ. Memory impairment was assessed by Morris water maze and the effects of drugs on SMEs as well as inflammatory markers, TNF- α and COX-II, were determined by immunoblotting. RESULTS: Aβ and LPS differentially altered the expression profile of SMEs. In Aβ-injected animals, FTY720 and SEW2871 treatments exerted anti-inflammatory effects and restored the expression profile of SMEs, in parallel to our previous findings. In LPS animals however, in spite of anti-inflammatory effects of the two analogues, only FTY720 restored the levels of SMEs and prevented memory deficit. CONCLUSION: The observed ameliorating effects of FTY720 and SEW7821 can be partly attributed to the interruption of the vicious cycle of abnormal S1P metabolism and neuro-inflammation. The close imitation of the FTY720 effects by SW2871 in Aβ-induced neuro-inflammation may highlight the attractive role of S1P1 as a potential target to restore S1P metabolism and inhibit inflammatory processes.
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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".