Polarity-Dependent Distribution of Angiomotin Localizes Hippo Signaling in Preimplantation Embryos

SummaryBackgroundIn preimplantation mouse embryos, the first cell fate specification to the trophectoderm or inner cell mass occurs by the early blastocyst stage. The cell fate is controlled by cell position-dependent Hippo signaling, although the mechanisms underlying position-dependent Hippo signaling are unknown.ResultsWe show that a combination of cell polarity and cell-cell adhesion establishes position-dependent Hippo signaling, where the outer and inner cells are polar and nonpolar, respectively. The junction-associated proteins angiomotin (Amot) and angiomotin-like 2 (Amotl2) are essential for Hippo pathway activation and appropriate cell fate specification. In the nonpolar inner cells, Amot localizes to adherens junctions (AJs), and cell-cell adhesion activates the Hippo pathway. In the outer cells, the cell polarity sequesters Amot from basolateral AJs to apical domains, thereby suppressing Hippo signaling. The N-terminal domain of Amot is required for actin binding, Nf2/Merlin-mediated association with the E-cadherin complex, and interaction with Lats protein kinase. In AJs, S176 in the N-terminal domain of Amot is phosphorylated by Lats, which inhibits the actin-binding activity, thereby stabilizing the Amot-Lats interaction to activate the Hippo pathway.ConclusionsWe propose that the phosphorylation of S176 in Amot is a critical step for activation of the Hippo pathway in AJs and that cell polarity disconnects the Hippo pathway from cell-cell adhesion by sequestering Amot from AJs. This mechanism converts positional information into differential Hippo signaling, thereby leading to differential cell fates

Urban and rural geographies of aging : a local spatial correlation analysis of aging population measures

The spatial distribution of aging populations is commonly measured with either the aging population ratio or the aging population density. Used in isolation, however, these measures may fail to detect aging communities in certain types of urban or rural setting. This study uses both indices simultaneously to identify types and locations of aging communities more accurately. We investigate the spatial distribution of these communities using a standard correlation analysis and bivariate local spatial statistic analysis. Empirical analysis of geospatial data of the Aichi Prefecture in Japan suggests that using both indices allows us to capture different types of aging communities in diverse contexts (e.g. depopulated rural areas, pockets of aging communities in urban areas, and growing concentrations of aging population in the suburbs). The analysis uses data sets aggregated at different areal scales, confirming the generally stable nature of the outcome, despite some scale sensitivity