Cell-based computational model of early ovarian development in mice.
Cell-based computational model of early ovarian development in mice.
复制标题
小鼠早期卵巢发育的细胞计算模型。
DOI：
10.1093/biolre/iox089
复制
发表时间：
2017
影响因子：
3.6
通讯作者：
Watanabe,KarenH
中科院分区：
文献类型：
--
作者：
Wear,HannahM;Eriksson,Annika;Yao,HumphreyHung-Chang;Watanabe,KarenH
尽管它对生殖的重要性，早期卵巢发育的某些机制仍然是一个谜。为了提高我们的理解，我们构建了第一个基于细胞的小鼠卵巢发育计算模型，该模型分为两个阶段：第一阶段从胚胎第5.5天（E5.5）到E12.5;第二阶段从E12.5到出生后第2天。我们使用该模型研究了四种机制：在第一阶段，（i）原始生殖细胞（PGCs）是否在迁移过程中经历有丝分裂;（ii）从后肠分泌KIT配体的机制是否类似于诱导细胞-细胞信号传导或以静态方式分泌;在第二阶段，（iii）细胞粘附的变化导致生殖细胞巢破裂;以及（iv）卵巢皮质中原始卵泡的定位是否是由于颗粒细胞的增殖。我们发现，前三个假设的组合产生的结果与实验图像和PGC丰度数据一致。第四种假设的结果与实验图像不匹配，这表明卵泡定位涉及更详细的过程。模型的阶段I和阶段II很好地再现了实验观察到的细胞计数和形态。敏感性分析确定接触能、有丝分裂率、KIT趋化强度和I期扩散率以及II期卵母细胞死亡率为对模型预测影响最大的参数。结果表明，计算模型可以用来理解未知的机制，产生新的假设，并作为一种教育工具。
Despite its importance to reproduction, certain mechanisms of early ovarian development remain a mystery. To improve our understanding, we constructed the first cell-based computational model of ovarian development in mice that is divided into two phases: Phase I spans embryonic day 5.5 (E5.5) to E12.5; and Phase II spans E12.5 to postnatal day 2. We used the model to investigate four mechanisms: in Phase I, (i) whether primordial germ cells (PGCs) undergo mitosis during migration; and (ii) if the mechanism for secretion of KIT ligand from the hindgut resembles inductive cell–cell signaling or is secreted in a static manner; and in Phase II, (iii) that changes in cellular adhesion produce germ cell nest breakdown; and (iv) whether localization of primordial follicles in the cortex of the ovary is due to proliferation of granulosa cells. We found that the combination of the first three hypotheses produced results that aligned with experimental images and PGC abundance data. Results from the fourth hypothesis did not match experimental images, which suggests that more detailed processes are involved in follicle localization. Phase I and Phase II of the model reproduce experimentally observed cell counts and morphology well. A sensitivity analysis identified contact energies, mitotic rates, KIT chemotaxis strength, and diffusion rate in Phase I and oocyte death rate in Phase II as parameters with the greatest impact on model predictions. The results demonstrate that the computational model can be used to understand unknown mechanisms, generate new hypotheses, and serve as an educational tool.