@article{ZHOU2026, 
author = {Lianyu ZHOU and Xinghang FEI and Huachun YIN and Zhenle ZANG and Xin ZHENG and Xiaotang FAN and Song LI and Hui YANG},
title = {Hypothalamic neural communication characteristics in 3 mouse models based on sequencing analysis: Attenuated inhibitory regulation with dominant roles of glutamate, NO, and electrical synapse signaling},
year = {2026},
journal = {Journal of Army Medical University},
volume = {48},
number = {6},
pages = {687-700},
keywords = {obesity animal models, hypothalamus, single-nucleus RNA sequencing, neuronal communication, ligand-receptor interactions},
url = {https://www.sciopen.com/article/10.16016/j.2097-0927.202512099},
doi = {10.16016/j.2097-0927.202512099},
abstract = {ObjectiveTo compare the hypothalamic cellular composition and neuron-neuron communication networks across 3 etiologically distinct mouse models of obesity, high-fat diet (HFD), leptin-deficient (ob/ob), and leptin receptor-deficient (db/db) so as to identify universal mechanistic clues that can be validated across etiologies and searching for etiology-specific targets applicable to stratified treatment.MethodsSix-week-old male C57 mice were fed with HFD continuously until 18 weeks of age to establish a HFD model. In parallel, 18-week-old male ob/ob mice and db/db mice were subjected as genetic obesity models, and age-matched male C57 mice served as controls. Three mice were included per group (n=3), totaling 12 mice, were euthanized, and the hypothalamic tissues were meticulously dissected under a stereomicroscope. Single-nucleus transcriptome sequencing (snRNA-seq) was performed using the 10x Genomics v3. 1 platform. The obtained sequencing data were aligned with Cell Ranger and processed in Seurat for quality control, dimensionality reduction, and clustering. Subsequently, neuron-neuron communication networks were inferred based on NeuronChat and its nervous system-specific ligand-receptor database, systematically comparing changes in differential communication networks, signal output/input strength, information flow, and interaction quantity between each obesity model and C57 controls.ResultsDimensionality reduction and clustering of hypothalamic snRNA-seq data from C57, HFD, ob/ob, and db/db mice identified several major cell populations, including excitatory neurons, inhibitory neurons, and multiple non-neuronal cell types, with cells of the same type showing model-dependent shifts in uniform manifold approximation and projection (UMAP) space. Neuronal subclustering resolved 7 GABAergic and 10 glutamatergic subtypes, indicating pronounced neuronal heterogeneity in the hypothalamus. Across the 3 obese models, GABAergic inhibitory incoming signaling and the neurexin-neuroligin (Nrxn-Nlgn) pathway were broadly downregulated. Meanwhile, each model exhibited a distinct communication-network signature: the HFD model was characterized by enhanced electrical synapses mediated by gap junction protein alpha-1 (Gja1-Gja1) and reduced Nrxn-Nlgn output; the db/db model showed dominant nitric oxide-soluble guanylate cyclase signaling (NO-Gucy1a2) together with enrichment of prodynorphin (Pdyn)-related pathways; the ob/ob model was primarily marked by enhanced glutamate receptor interactions. Pairwise comparisons among models further validated these patterns: HFD relatively enriched Gja1-related signaling and certain metabotropic glutamate receptor interactions; db/db relatively enriched NO-Gucy1a2 and synaptic adhesion-related pathways; ob/ob relatively enriched in ionotropic/metabotropic glutamate receptor interactions.ConclusionDistinct neural communication network patterns exist in the hypothalamus across obesity models of different etiologies: diet-induced obesity preferentially exhibits enhanced electrical synapse-related pathways, leptin receptor-deficient model is primarily characterized by increased NO-related signaling, and the leptin-deficient model is centrally distinguished by enhanced glutamatergic excitatory interactions. Notably, attenuated inhibitory regulation emerges as a common feature across models, providing novel insights into understanding the pathological mechanisms underlying obesity development.}
}