Martínez-López J A, Lindqvist A, Lopez-Pascual A, Harder A, Chen P, Ngara M, Shcherbina L, Siffo S, Cowan E, Baira S M, Kryvokhyzha D, Karagiannopoulos A, Chriett S, Skene N G, Prasad R B, Lancien M, Johnson P F, Eliasson P, Eliasson L, Louvet C, Spégel P, Muñoz-Manchado A B, Sandberg R, Hjerling-Leffler J, Wierup N
Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.
Department of Engineering, Universidad Loyola, Seville, Spain.
Nat Commun. 2025 Oct 27;16(1):9475. doi: 10.1038/s41467-025-65060-z.
Perturbed secretion of insulin and other pancreatic islet hormones is the main cause of type 2 diabetes (T2D). The islets harbor five cell types that are potentially altered differently by T2D. Whole-islet transcriptomics and single-cell RNA-sequencing (scRNAseq) studies have revealed differentially expressed genes without reaching consensus. Here, we demonstrate that further insights into T2D disease mechanisms can be obtained by network-based analysis of scRNAseq data from individual cell types. We developed differential gene coordination network analysis (dGCNA) and analyzed islet SmartSeq2 scRNAseq data from 16 T2D and 16 non-T2D individuals. dGCNA reveals T2D-induced cell type-specific networks of dysregulated genes with remarkable ontological specificity, thus allowing for a comprehensive and unbiased functional classification of genes involved in T2D. In beta cells eleven networks of genes are detected, revealing that mitochondrial electron transport chain, glycolysis, cytoskeleton organization, cell proliferation, unfolded protein response and three networks of beta cell transcription factors are perturbed, whereas exocytosis, lysosomal regulation and insulin translation programs are instead enhanced in T2D. Furthermore, we validated the ability of dGCNA to reveal disease mechanisms and predict the functional context of genes by showing that TMEM176A/B regulates beta cell microfilament organization and that CEPBG is an important regulator of the unfolded protein response. In addition, when comparing beta- and alpha cells, we found substantial differences, reproduced across independent datasets, confirming cell type-specific alterations in T2D. We conclude that analysis of networks of differentially coordinated genes provides detailed insight into cell type-specific gene function and T2D pathophysiology.
胰岛素及其他胰岛激素分泌紊乱是2型糖尿病（T2D）的主要病因。胰岛包含五种细胞类型，T2D可能对它们产生不同程度的影响。全胰岛转录组学和单细胞RNA测序（scRNAseq）研究已揭示了差异表达基因，但尚未达成共识。在此，我们证明通过对来自单个细胞类型的scRNAseq数据进行基于网络的分析，可以进一步深入了解T2D的发病机制。我们开发了差异基因协调网络分析（dGCNA），并分析了来自16名T2D患者和16名非T2D个体的胰岛SmartSeq2 scRNAseq数据。dGCNA揭示了T2D诱导的细胞类型特异性失调基因网络，具有显著的本体特异性，从而能够对参与T2D的基因进行全面且无偏倚的功能分类。在β细胞中检测到11个基因网络，表明线粒体电子传递链、糖酵解、细胞骨架组织、细胞增殖、未折叠蛋白反应以及三个β细胞转录因子网络受到干扰，而在T2D中胞吐作用、溶酶体调节和胰岛素翻译程序反而增强。此外，我们通过证明TMEM176A/B调节β细胞微丝组织以及CEPBG是未折叠蛋白反应的重要调节因子，验证了dGCNA揭示疾病机制和预测基因功能背景的能力。此外，在比较β细胞和α细胞时，我们发现了在独立数据集中重现的显著差异，证实了T2D中细胞类型特异性的改变。我们得出结论，对差异协调基因网络的分析为细胞类型特异性基因功能和T2D病理生理学提供了详细的见解。
