Integrating transcriptomics, metabolomics, and network pharmacology to investigate multi-target effects of sporoderm-broken spores of Ganoderma lucidum on improving HFD-induced diabetic nephropathy rats
Bibliographic record
Abstract
Diabetes mellitus (DM) is a major metabolic disease endangering global health, with diabetic nephropathy (DN) as a primary complication lacking curative therapy. Sporoderm-broken spores of Ganoderma lucidum (GLP), an herbal medicine, has been used for the treatment of metabolic disorders. In this study, DN was induced in Sprague-Dawley rats using streptozotocin (STZ) and a high-fat diet (HFD), and the protective mechanisms of GLP were investigated through transcriptomic, metabolomic, and network pharmacology (NP) analyses. Our results demonstrated that GLP intervention ameliorated renal damage and inflammation levels in DN rats. Integrative metabolomic and transcriptomic analysis revealed that GLP treatment modulated glucose and cellular energy metabolisms by regulating relevant genes. GLP significantly suppressed the inflammations by impacting glucose and energy metabolism-related gene expression ( Igfbp1 and Angptl4 ) and enhanced metabolic biomarkers of 4-Aminocatechol. In addition, NP analysis further indicated that GLP may efficiently alleviate DN via immune-related pathways. In conclusion, this study provides supportive evidence of the anti-inflammatory effects of GLP supplements, highlighting their potential for promising clinical applications in treating DN. • Revealing an integrated landscape of transcriptomic and metabolomic profiling in diabetic nephropathy. • GLP suppressed the inflammations through downregulating key glucose and energy metabolism-related genes expression ( Igfbp1 and Angptl4 ). • GLP may treat DN efficiently via immune-related signalling pathways from network pharmacology. • Providing a theoretical insight of anti-immunity mechanisms for GLP treatment in future clinical applications.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".