A living cell-based fluorescent reporter for high-throughput screening of anti-tumor drugs

Suppression of cellular O-linkedβ-N-acetylglucosaminylation(O-Glc NAcylation)can repress proliferation and migration of various cancer cells,which opens a new avenue for cancer therapy.Based on the regulation of insulin gene transcription,we designed a cell-based fluorescent reporter capable of sensing cellular O-Glc NAcylation in HEK293 T cells.The fluorescent reporter mainly consists of a reporter(green fluorescent protein(GFP)),an internal reference(red fluorescent protein),and an operator(neuronal differentiation 1),which serves as a"sweet switch"to control GFP expression in response to cellular OGlc NAcylation changes.The fluorescent reporter can efficiently sense reduced levels of cellular OGlc NAcylation in several cell lines.Using the fluorescent reporter,we screened 120 natural products and obtained one compound,sesamin,which could markedly inhibit protein O-Glc NAcylation in He La and human colorectal carcinoma-116 cells and repress their migration in vitro.Altogether,the present study demonstrated the development of a novel strategy for anti-tumor drug screening,as well as for conducting gene transcription studies.

On essential topics of BYY harmony learning： Current status, challenging issues, and gene analysis applications

As a supplementary of [Xu L. Front. Electr. Electron. Eng. China, 2010, 5（3）： 281-328], this paper outlines current status of efforts made on Bayesian Ying- Yang （BYY） harmony learning, plus gene analysis appli- cations. At the beginning, a bird＇s-eye view is provided via Gaussian mixture in comparison with typical learn- ing algorithms and model selection criteria. Particularly, semi-supervised learning is covered simply via choosing a scalar parameter. Then, essential topics and demand- ing issues about BYY system design and BYY harmony learning are systematically outlined, with a modern per- spective on Yin-Yang viewpoint discussed, another Yang factorization addressed, and coordinations across and within Ying-Yang summarized. The BYY system acts as a unified framework to accommodate unsupervised, su- pervised, and semi-supervised learning all in one formu- lation, while the best harmony learning provides novelty and strength to automatic model selection. Also, mathe- matical formulation of harmony functional has been ad- dressed as a unified scheme for measuring the proximity to be considered in a BYY system, and used as the best choice among others. Moreover, efforts are made on a number of learning tasks, including a mode-switching factor analysis proposed as a semi-blind learning frame- work for several types of independent factor analysis, a hidden Markov model （HMM） gated temporal fac- tor analysis suggested for modeling piecewise stationary temporal dependence, and a two-level hierarchical Gaus- sian mixture extended to cover semi-supervised learning, as well as a manifold learning modified to facilitate au- tomatic model selection. Finally, studies are applied to the problems of gene analysis, such as genome-wide asso- ciation, exome sequencing analysis, and gene transcrip- tional regulation.