Exogenous L-fucose attenuates depression induced by chronic unpredictable stress: Implicating core fucosylation has an antidepressant potential
Bibliographic record
Abstract
Core fucosylation is one of the most essential modifications of the N -glycans, catalyzed by α1,6-fucosyltransferase (Fut8), which transfers fucose from guanosine 5′-diphosphate (GDP)-fucose to the innermost N -acetylglucosamine residue of N -glycans in an α1-6 linkage. Our previous studies demonstrated that lipopolysaccharide (LPS) can induce a more robust neuroinflammatory response in Fut8 homozygous knockout (KO) ( Fut8 −/− ) and heterozygous KO ( Fut8 +/− ) mice contrasted to the wild-type ( Fut8 +/+ ) mice. Exogenous administration of L-fucose suppressed LPS-induced neuroinflammation. Numerous studies indicate that neuroinflammation plays a vital role in the development of depression. Here, we investigated whether core fucosylation regulates depression induced by chronic unpredictable stress (CUS), a well-established model for depression. Our results showed that Fut8 +/− mice exhibited depressive-like behaviors and increased neuroinflammation earlier than Fut8 +/+ mice. Administration of L-fucose significantly reduced CUS-induced depressive-like behaviors and pro-inflammatory cytokine levels in Fut8 +/− mice. The L-fucose treatment produced antidepressant effects by attenuating the complex formation between gp130 and the interleukin-6 (IL-6) receptor and the JAK2/STAT3 signaling pathway. Notably, L-fucose treatment increased dendritic spine density and postsynaptic density protein 95 (PSD-95) expression, which were suppressed in CUS-induced depression. Furthermore, the effects of L-fucose on the CUS-induced depression were also observed in Fut8 +/+ mice. Our results clearly demonstrate that L-fucose ameliorates neuroinflammation and synaptic defects in CUS-induced depression, implicating that core fucosylation has significant anti-neuroinflammatory activity and an antidepressant potential.
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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) | 0.001 | 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) | 0.001 | 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".