Stage-specific regulation of the WNT/ß-catenin pathway enhances differentiation of hESCs into hepatocytes.

BACKGROUND & AIMS: Hepatocytes differentiated from human embryonic stem cells (hESCs) have the potential to overcome the shortage of primary hepatocytes for clinical use and drug development. Many strategies for this process have been reported, but the functionality of the resulting cells is incomplete. We hypothesize that the functionality of hPSC-derived hepatocytes might be improved by making the differentiation method more similar to normal in vivo hepatic development. METHODS: We tested combinations of growth factors and small molecules targeting candidate signaling pathways culled from the literature to identify optimal conditions for differentiation of hESCs to hepatocytes, using qRT-PCR for stage-specific markers to identify the best conditions. Immunocytochemistry was then used to validate the selected conditions. Finally, induction of expression of metabolic enzymes in terminally differentiated cells was used to assess the functionality of the hESC-derived hepatocytes. RESULTS: Optimal differentiation of hESCs was attained using a 5-stage protocol. After initial induction of definitive endoderm (stage 1), we showed that inhibition of the WNT/ß-catenin pathway during the 2nd and 3rd stages of differentiation was required to specify first posterior foregut, and then hepatic gut cells. In contrast, during the 4th stage of differentiation, we found that activation of the WNT/ß-catenin pathway allowed generation of proliferative bipotent hepatoblasts, which then were efficiently differentiated into hepatocytes in the 5th stage by dual inhibition of TGF-ß and NOTCH signaling. CONCLUSION: Here, we show that stage-specific regulation of the WNT/ß-catenin pathway results in improved differentiation of hESCs to functional hepatocytes.

Supervised inference of gene-regulatory networks.

BACKGROUND: Inference of protein interaction networks from various sources of data has become an important topic of both systems and computational biology. Here we present a supervised approach to identification of gene expression regulatory networks. RESULTS: The method is based on a kernel approach accompanied with genetic programming. As a data source, the method utilizes gene expression time series for prediction of interactions among regulatory proteins and their target genes. The performance of the method was verified using Saccharomyces cerevisiae cell cycle and DNA/RNA/protein biosynthesis gene expression data. The results were compared with independent data sources. Finally, a prediction of novel interactions within yeast gene expression circuits has been performed. CONCLUSION: Results show that our algorithm gives, in most cases, results identical with the independent experiments, when compared with the YEASTRACT database. In several cases our algorithm gives predictions of novel interactions which have not been reported.

A parallel genetic algorithm for single class pattern classification and its application for gene expression profiling in Streptomyces coelicolor.

BACKGROUND: Identification of coordinately regulated genes according to the level of their expression during the time course of a process allows for discovering functional relationships among genes involved in the process. RESULTS: We present a single class classification method for the identification of genes of similar function from a gene expression time series. It is based on a parallel genetic algorithm which is a supervised computer learning method exploiting prior knowledge of gene function to identify unknown genes of similar function from expression data. The algorithm was tested with a set of randomly generated patterns; the results were compared with seven other classification algorithms including support vector machines. The algorithm avoids several problems associated with unsupervised clustering methods, and it shows better performance then the other algorithms. The algorithm was applied to the identification of secondary metabolite gene clusters of the antibiotic-producing eubacterium Streptomyces coelicolor. The algorithm also identified pathways associated with transport of the secondary metabolites out of the cell. We used the method for the prediction of the functional role of particular ORFs based on the expression data. CONCLUSION: Through analysis of a time series of gene expression, the algorithm identifies pathways which are directly or indirectly associated with genes of interest, and which are active during the time course of the experiment.