Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China.
Commun Biol. 2024 Oct 5;7(1):1267. doi: 10.1038/s42003-024-06955-3.
Cellular bioenergetics and mitochondrial dynamics are crucial for the secretion of insulin by pancreatic beta cells in response to elevated levels of blood glucose. To elucidate the interactions between energy production and mitochondrial fission/fusion dynamics, we combine live-cell mitochondria imaging with biophysical-based modeling and graph-based network analysis. The aim is to determine the mechanism that regulates mitochondrial morphology and balances metabolic demands in pancreatic beta cells. A minimalistic differential equation-based model for beta cells is constructed that includes glycolysis, oxidative phosphorylation, calcium dynamics, and fission/fusion dynamics, with ATP synthase flux and proton leak flux as main regulators of mitochondrial dynamics. The model shows that mitochondrial fission occurs in response to hyperglycemia, starvation, ATP synthase inhibition, uncoupling, and diabetic conditions, in which the rate of proton leakage exceeds the rate of mitochondrial ATP synthesis. Under these metabolic challenges, the propensities of tip-to-tip fusion events simulated from the microscopy images of the mitochondrial networks are lower than those in the control group and prevent the formation of mitochondrial networks. The study provides a quantitative framework that couples bioenergetic regulation with mitochondrial dynamics, offering insights into how mitochondria adapt to metabolic challenges.
细胞生物能量学和线粒体动力学对于胰腺β细胞响应升高的血糖水平分泌胰岛素至关重要。为了阐明能量产生与线粒体分裂/融合动力学之间的相互作用，我们将活细胞线粒体成像与基于生物物理的建模和基于图的网络分析相结合。目的是确定调节胰腺β细胞中线粒体形态和平衡代谢需求的机制。构建了一个针对β细胞的基于最小二乘微分方程的模型，其中包括糖酵解、氧化磷酸化、钙动力学和分裂/融合动力学，ATP 合酶通量和质子泄漏通量作为线粒体动力学的主要调节剂。该模型表明，线粒体分裂发生在高血糖、饥饿、ATP 合酶抑制、解偶联和糖尿病等情况下，其中质子泄漏的速率超过线粒体 ATP 合成的速率。在这些代谢挑战下，模拟来自线粒体网络显微镜图像的尖端到尖端融合事件的倾向低于对照组，从而防止线粒体网络的形成。该研究提供了一个定量框架，将生物能量调节与线粒体动力学相结合，深入了解线粒体如何适应代谢挑战。
