Loss of Lipocalin 10 Exacerbates Diabetes-Induced Cardiomyopathy via Disruption of Nr4a1-Mediated Anti-Inflammatory Response in Macrophages
Bibliographic record
Abstract
Metabolic disorders (i.e., hyperglycemia, hyperlipidemia, and hyperinsulinemia) cause increased secretion of inflammatory cytokines/chemokines, leading to gradual loss of cardiac resident macrophage population and increased accumulation of inflammatory monocytes/macrophages in the heart. Such self-perpetuating effect may contribute to the development of cardiomyopathy during diabetes. Recent meta-analysis data reveal that lipocalin 10 (Lcn10) is significantly downregulated in cardiac tissue of patients with heart failure but is increased in the blood of septic patients. However, the functional role of Lcn10 in cardiac inflammation triggered by metabolic disorders has never been investigated. In this study, we demonstrate that the expression of Lcn10 in macrophages was significantly decreased under multiple metabolic stress conditions. Furthermore, Lcn10-null macrophages exhibited pro-inflammatory phenotype in response to inflammation stimuli. Next, using a global Lcn10-knockout (KO) mouse model to induce type-2 diabetes (T2D), we observed that loss of Lcn10 promoted more pro-inflammatory macrophage infiltration into the heart, compared to controls, leading to aggravated insulin resistance and impaired cardiac function. Similarly, adoptive transfer of Lcn10-KO bone marrow cells into X-ray irradiated mice displayed higher ratio of pro-/anti-inflammatory macrophages in the heart and worsened cardiac function than those mice received wild-type (WT) bone marrows upon T2D conditions. Mechanistically, RNA-sequencing analysis showed that Nr4a1, a nuclear receptor known to have potent anti-inflammatory effects, is involved in Lcn10-mediated macrophage activation. Indeed, we found that nuclear translocation of Nr4a1 was disrupted in Lcn10-KO macrophages upon stimulation with LPS + IFNγ. Accordingly, treatment with Cytosporone B (CsnB), an agonist of Nr4a1, attenuated the pro-inflammatory response in Lcn10-null macrophages and partially improved cardiac function in Lcn10-KO diabetic mice. Together, these findings indicate that loss of Lcn10 skews macrophage polarization to pro-inflammatory phenotype and aggravates cardiac dysfunction during type-2 diabetes through the disruption of Nr4a1-mediated anti-inflammatory signaling pathway in macrophages. Therefore, reduction of Lcn10 expression observed in diabetic macrophages may be responsible for the pathogenesis of diabetes-induced cardiac dysfunction. It suggests that Lcn10 might be a potential therapeutic factor for diabetic heart failure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".