Proteomic and metabolomic analyses reveal the novel targets of spermine for alleviating diabetic cardiomyopathy in type II diabetic mice
Bibliographic record
Abstract
Diabetic cardiomyopathy (DCM) is one of the most serious complications of diabetes. Recent cardiology studies suggest that spermine has a cardioprotective effect. Here, we used proteomic and metabolomic analyses to reveal the underlying research targets in a type II diabetic (T2D) mouse model treated with spermine. Left ventricular tissues from nine mice (Control group, three; T2D group, three; T2D+SP group, three) were excised and analyzed. Quantitative analysis of the global proteome and metabolome was performed using the 4D label-free technique and untargeted metabolomics, respectively, and differentially expressed proteins (DEPs) and metabolites were used to perform bioinformatic analyses. A total of 169 DEPs were identified in T2D/Control group, including 115 upregulated and 54 downregulated proteins. Furthermore, 16 DEPs were identified in T2D+SP/T2D group, where these DEPs were found highly enriched in the cellular, metabolic processes, biological regulation, response to stimulus, and immune system process. The results of association analysis between proteomics and metabolomics showed that SP could affect the production of 51 metabolites by regulating the expression of 16 DEPs in the T2D+SP/T2D group. We also found that PRKG1 was closely related to the expressions of 10 overlapping metabolites between db/db and SP-treated mice. Our findings provide insights into the underlying mechanisms for DCM and suggest the potential applicability of utilizing spermine on protecting against DCM-associated cardiac function deterioration.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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".