bioRxivMolecular BiologyPreprint
Integrated analysis reveals neuro-immune pathway in the central nervous system that supports SGLT2is protective effects in treatment of cardiac remodeling
Miao Yuan, Hanxue WuiD, Jiawei WangiD, Zihan Qiu, Kexin LiiD, Jiaxi XuiD, Dengfeng Gao
Abstract
BackgroundDespite growing evidence of sodium-dependent glucose transporters 2 inhibitors (SGLT2i) improving heart failure, their mechanisms remain unclear. This study explored the effects of empagliflozin (EM) on sympathetic nerve activity and potential neuroimmune pathways. MethodsSingle-nucleus RNA sequencing (snRNA-seq) was performed in hypothalamic tissue of a DOCA-salt mouse model to screen for cellular changes. Validation of relevant mechanisms in DOCA-salt and high-salt diet (HSD) mouse models. Additionally, clinical data from 1,189 hospitalized heart failure patients were analyzed to evaluate the translational relevance of the experimental findings. ResultsEM treatment was associated with protective effects against cardiac remodeling in both DOCA-salt and HSD mice. Both models exhibited sympatho-excitation and neuronal hyperactivity in pre-autonomic brain regions, which appeared to be blunted by EM co-treatment. snRNA-seq analysis suggested that cellular interplay among vascular cells, microglia, and inhibitory neurons might be involved in these processes. Specifically, high-salt intake was linked to peripheral immune activation, including lymphocytosis and elevated plasma interferon-{gamma}, which potentially mediated microglial state transitions. EM might counteract the disease-associated upregulation of protein ubiquitination. Clinically, SGLT2i use was significantly associated with reduced lymphocyte counts in specific patient subgroups. ConclusionsEmpagliflozin may attenuate cardiac remodeling caused by high sodium intake through neuroimmune effects by restoring microglial homeostasis and inhibiting sympathetic nerve activity.
- organism: mouse
- organism: human
- assay: scRNA-seq
- research focus: differential expression
- research focus: cell type annotation
- approach: statistical model
- Citations
- 0
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