Protocadherin 12 deficiency alters morphogenesis and transcriptional profile of the placenta.

Protocadherins are transmembrane proteins exhibiting homophilic adhesive activities through their extracellular domain. Protocadherin 12 (Pcdh12) is expressed in angiogenic endothelial cells, mesangial cells of kidney glomeruli, and glycogen cells of the mouse placenta. To get insight into the role of this protein in vivo, we analyzed PCDH12-deficient mice and investigated their placental phenotype. The mice were alive and fertile; however, placental and embryonic sizes were reduced compared with wild-type mice. We observed defects in placental layer segregation and a decreased vascularization of the labyrinth associated with a reduction in cell density in this layer. To understand the molecular events responsible for the phenotypic alterations observed in Pcdh12(-/-) placentas, we analyzed the expression profile of embryonic day 12.5 mutant placentas compared with wild-type placentas, using pangenomic chips: 2,289 genes exhibited statistically significant changes in expressed levels due to loss of PCDH12. Functional grouping of modified genes was obtained by GoMiner software. Gene clusters that contained most of the differentially expressed genes were those involved in tissue morphogenesis and development, angiogenesis, cell-matrix adhesion and migration, immune response, and chromatin remodeling. Our data show that loss of PCDH12 leads to morphological alterations of the placenta and to notable changes in its gene expression profile. Specific genes emerging from the microarray screen support the biological modifications observed in PCDH12-deficient placentas.

A stochastic differential equation model for transcriptional regulatory networks.

BACKGROUND: This work explores the quantitative characteristics of the local transcriptional regulatory network based on the availability of time dependent gene expression data sets. The dynamics of the gene expression level are fitted via a stochastic differential equation model, yielding a set of specific regulators and their contribution. RESULTS: We show that a beta sigmoid function that keeps track of temporal parameters is a novel prototype of a regulatory function, with the effect of improving the performance of the profile prediction. The stochastic differential equation model follows well the dynamic of the gene expression levels. CONCLUSION: When adapted to biological hypotheses and combined with a promoter analysis, the method proposed here leads to improved models of the transcriptional regulatory networks.