Wedgwood Kyle C A, Richardson Sarah J, Morgan Noel G, Tsaneva-Atanasova Krasimira
Centre for Biomedical Modelling and Analysis, University of Exeter Exeter, UK.
University of Exeter Medical School, University of Exeter Exeter, UK.
Front Physiol. 2016 Dec 27;7:633. doi: 10.3389/fphys.2016.00633. eCollection 2016.
Type 1 diabetes (T1D) is an auto-immune disease characterized by the selective destruction of the insulin secreting beta cells in the pancreas during an inflammatory phase known as insulitis. Patients with T1D are typically dependent on the administration of externally provided insulin in order to manage blood glucose levels. Whilst technological developments have significantly improved both the life expectancy and quality of life of these patients, an understanding of the mechanisms of the disease remains elusive. Animal models, such as the NOD mouse model, have been widely used to probe the process of insulitis, but there exist very few data from humans studied at disease onset. In this manuscript, we employ data from human pancreases collected close to the onset of T1D and propose a spatio-temporal computational model for the progression of insulitis in human T1D, with particular focus on the mechanisms underlying the development of insulitis in pancreatic islets. This framework allows us to investigate how the time-course of insulitis progression is affected by altering key parameters, such as the number of the CD20+ B cells present in the inflammatory infiltrate, which has recently been proposed to influence the aggressiveness of the disease. Through the analysis of repeated simulations of our stochastic model, which track the number of beta cells within an islet, we find that increased numbers of B cells in the peri-islet space lead to faster destruction of the beta cells. We also find that the balance between the degradation and repair of the basement membrane surrounding the islet is a critical component in governing the overall destruction rate of the beta cells and their remaining number. Our model provides a framework for continued and improved spatio-temporal modeling of human T1D.
1型糖尿病（T1D）是一种自身免疫性疾病，其特征是在称为胰岛炎的炎症阶段胰腺中分泌胰岛素的β细胞被选择性破坏。T1D患者通常依赖于外部提供胰岛素来控制血糖水平。虽然技术发展显著提高了这些患者的预期寿命和生活质量，但对该疾病机制的了解仍然难以捉摸。动物模型，如非肥胖糖尿病（NOD）小鼠模型，已被广泛用于探究胰岛炎的过程，但在疾病发作时对人类进行研究的数据非常少。在本手稿中，我们利用在T1D发病时收集的人类胰腺数据，提出了一个时空计算模型，用于人类T1D中胰岛炎的进展，特别关注胰岛炎在胰岛中发展的潜在机制。这个框架使我们能够研究通过改变关键参数，如炎症浸润中存在的CD20 + B细胞数量，胰岛炎进展的时间进程会受到怎样的影响，最近有人提出该参数会影响疾病的侵袭性。通过对我们的随机模型的重复模拟分析，该模型跟踪胰岛内β细胞的数量，我们发现胰岛周围空间中B细胞数量的增加会导致β细胞更快地被破坏。我们还发现，胰岛周围基底膜降解与修复之间的平衡是控制β细胞总体破坏率及其剩余数量的关键因素。我们的模型为持续改进人类T1D的时空建模提供了一个框架。
