MAP3K4 Controls the Chromatin Modifier HDAC6 during Trophoblast Stem Cell Epithelial-to-Mesenchymal Transition.

The first epithelial-to-mesenchymal transition (EMT) occurs in trophoblast stem (TS) cells during implantation. Inactivation of the serine/threonine kinase MAP3K4 in TS cells (TSKI4 cells) induces an intermediate state of EMT, where cells retain stemness, lose epithelial markers, and gain mesenchymal characteristics. Investigation of relationships among MAP3K4 activity, stemness, and EMT in TS cells may reveal key regulators of EMT. Here, we show that MAP3K4 activity controls EMT through the ubiquitination and degradation of HDAC6. Loss of MAP3K4 activity in TSKI4 cells results in elevated HDAC6 expression and the deacetylation of cytoplasmic and nuclear targets. In the nucleus, HDAC6 deacetylates the promoters of tight junction genes, promoting the dissolution of tight junctions. Importantly, HDAC6 knockdown in TSKI4 cells restores epithelial features, including cell-cell adhesion and barrier formation. These data define a role for HDAC6 in regulating gene expression during transitions between epithelial and mesenchymal phenotypes.

TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks.

BACKGROUND: Several methods have been developed for analyzing genome-scale models of metabolism and transcriptional regulation. Many of these methods, such as Flux Balance Analysis, use constrained optimization to predict relationships between metabolic flux and the genes that encode and regulate enzyme activity. Recently, mixed integer programming has been used to encode these gene-protein-reaction (GPR) relationships into a single optimization problem, but these techniques are often of limited generality and lack a tool for automating the conversion of rules to a coupled regulatory/metabolic model. RESULTS: We present TIGER, a Toolbox for Integrating Genome-scale Metabolism, Expression, and Regulation. TIGER converts a series of generalized, Boolean or multilevel rules into a set of mixed integer inequalities. The package also includes implementations of existing algorithms to integrate high-throughput expression data with genome-scale models of metabolism and transcriptional regulation. We demonstrate how TIGER automates the coupling of a genome-scale metabolic model with GPR logic and models of transcriptional regulation, thereby serving as a platform for algorithm development and large-scale metabolic analysis. Additionally, we demonstrate how TIGER's algorithms can be used to identify inconsistencies and improve existing models of transcriptional regulation with examples from the reconstructed transcriptional regulatory network of Saccharomyces cerevisiae. CONCLUSION: The TIGER package provides a consistent platform for algorithm development and extending existing genome-scale metabolic models with regulatory networks and high-throughput data.