A human iPS cell myogenic differentiation system permitting high-throughput drug screening.

Muscular dystrophy is a disease characterized by progressive muscle weakness and degeneration. There are currently no available treatments for most muscular diseases, such as muscular dystrophy. Moreover, current therapeutics are focused on improving the quality of life of patients by relieving the symptoms or stress caused by the disease. Although the causative genes for many muscular diseases have been identified, the mechanisms underlying their pathogenesis remain unclear. Patient-derived induced pluripotent stem cells (iPSCs) have become a powerful tool for understanding the pathogenesis of intractable diseases, as well as for phenotype screening, which can serve as the basis for developing new drugs. However, it is necessary to develop an efficient and reproducible myogenic differentiation system. Previously, we reported a tetracycline-inducible MyoD overexpression model of myogenic differentiation using human iPSCs (hiPSCs). However, this model has certain disadvantages that limit its use in various applications, such as a drug screening. In this study, we developed an efficient and reproducible myogenic differentiation system by further modifying our previous protocol. The new protocol achieves efficient differentiation of feeder-free hiPSCs to myogenic cells via small-scale culture in six-well microplates to large-scale culture in 384-well microplates for high-throughput applications.

An integrative in silico approach for discovering candidates for drug-targetable protein-protein interactions in interactome data.

BACKGROUND: Protein-protein interactions (PPIs) are challenging but attractive targets for small chemical drugs. Whole PPIs, called the 'interactome', have been emerged in several organisms, including human, based on the recent development of high-throughput screening (HTS) technologies. Individual PPIs have been targeted by small drug-like chemicals (SDCs), however, interactome data have not been fully utilized for exploring drug targets due to the lack of comprehensive methodology for utilizing these data. Here we propose an integrative in silico approach for discovering candidates for drug-targetable PPIs in interactome data. RESULTS: Our novel in silico screening system comprises three independent assessment procedures: i) detection of protein domains responsible for PPIs, ii) finding SDC-binding pockets on protein surfaces, and iii) evaluating similarities in the assignment of Gene Ontology (GO) terms between specific partner proteins. We discovered six candidates for drug-targetable PPIs by applying our in silico approach to original human PPI data composed of 770 binary interactions produced by our HTS yeast two-hybrid (HTS-Y2H) assays. Among them, we further examined two candidates, RXRA/NRIP1 and CDK2/CDKN1A, with respect to their biological roles, PPI network around each candidate, and tertiary structures of the interacting domains. CONCLUSION: An integrative in silico approach for discovering candidates for drug-targetable PPIs was applied to original human PPIs data. The system excludes false positive interactions and selects reliable PPIs as drug targets. Its effectiveness was demonstrated by the discovery of the six promising candidate target PPIs. Inhibition or stabilization of the two interactions may have potential therapeutic effects against human diseases.