<i>N</i>-Glycosylated <i>Ganoderma lucidum</i> immunomodulatory protein improved anti-inflammatory activity <i>via</i> inhibition of the p38 MAPK pathway
Bibliographic record
Abstract
The global health emergency generated by coronavirus disease-2019 has prompted the search for immunomodulatory agents. There are many potential natural products for drug discovery and development to tackle this disease. One of these candidates is the Ganoderma lucidum fungal immunomodulatory protein (FIP-glu). In the present study, we clarify the influences of N-linked glycans on the improvement of anti-inflammatory activity and the potential mechanisms of action. Four proteins, including FIP-glu (WT) and its mutants N31S, T36N and N31S/T36N, were successfully expressed in P. pastoris, of which T36N and N31S/T36N were glycoproteins. After treatment with peptide-N-glycosidase F, the results of SDS-PAGE and Western blot showed that the glycan moiety was removed completely, indicating that the glycan moiety was N-linked. This was also demonstrated by UPLC-qTOF-MS. The cytotoxicity assay showed that N-linked glycans decreased the cytotoxicity of WT; while, the RT-qPCR assay showed that N-glycosylated WT regulated the mRNA expression of IL-6 and TGF-β1. The Western blot results showed that N-glycosylated WT reduced the phosphorylation level of p38 MAPK. In conclusion, our findings revealed a novel mechanism by which N-glycosylation of FIP-glu improved its anti-inflammatory activity through the regulation of the expression of inflammatory cytokines in RAW264.7 via inhibition of p38 MAPK phosphorylation. It was proved that N-glycosylation significantly improved the functional properties of FIP-glu, providing theoretical and technical support for expanding the application of FIPs in the food and pharmaceutical industries.
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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 | 0.000 |
| 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".