Induction of pluripotency in mouse somatic cells with lineage specifiers.

The reprogramming factors that induce pluripotency have been identified primarily from embryonic stem cell (ESC)-enriched, pluripotency-associated factors. Here, we report that, during mouse somatic cell reprogramming, pluripotency can be induced with lineage specifiers that are pluripotency rivals to suppress ESC identity, most of which are not enriched in ESCs. We found that OCT4 and SOX2, the core regulators of pluripotency, can be replaced by lineage specifiers that are involved in mesendodermal (ME) specification and in ectodermal (ECT) specification, respectively. OCT4 and its substitutes attenuated the elevated expression of a group of ECT genes, whereas SOX2 and its substitutes curtailed a group of ME genes during reprogramming. Surprisingly, the two counteracting lineage specifiers can synergistically induce pluripotency in the absence of both OCT4 and SOX2. Our study suggests a "seesaw model" in which a balance that is established using pluripotency factors and/or counteracting lineage specifiers can facilitate reprogramming.

In silico syndrome prediction for coronary artery disease in traditional chinese medicine.

Coronary artery disease (CAD) is the leading causes of deaths in the world. The differentiation of syndrome (ZHENG) is the criterion of diagnosis and therapeutic in TCM. Therefore, syndrome prediction in silico can be improving the performance of treatment. In this paper, we present a Bayesian network framework to construct a high-confidence syndrome predictor based on the optimum subset, that is, collected by Support Vector Machine (SVM) feature selection. Syndrome of CAD can be divided into asthenia and sthenia syndromes. According to the hierarchical characteristics of syndrome, we firstly label every case three types of syndrome (asthenia, sthenia, or both) to solve several syndromes with some patients. On basis of the three syndromes' classes, we design SVM feature selection to achieve the optimum symptom subset and compare this subset with Markov blanket feature select using ROC. Using this subset, the six predictors of CAD's syndrome are constructed by the Bayesian network technique. We also design Naïve Bayes, C4.5 Logistic, Radial basis function (RBF) network compared with Bayesian network. In a conclusion, the Bayesian network method based on the optimum symptoms shows a practical method to predict six syndromes of CAD in TCM.

Clinical data mining of phenotypic network in angina pectoris of coronary heart disease.

Coronary heart disease (CHD) is the leading causes of morbidity and mortality in China. The diagnosis of CHD in Traditional Chinese Medicine (TCM) was mainly based on experience in the past. In this paper, we proposed four MI-based association algorithms to analyze phenotype networks of CHD, and established scale of syndromes to automatically generate the diagnosis of patients based on their phenotypes. We also compared the change of core syndromes that CHD were combined with other diseases, and presented the different phenotype spectra.

[Prediction of network drug target based on improved model of bipartite graph valuation].

Network pharmacology, as a new developmental direction of drug discovery, was generating attention of more and more researchers. The key problem in drug discovery was how to identify the new interactions between drugs and target proteins. Prediction of new interaction was made to find potential targets based on the predicting model constructed by the known drug-protein interactions. According to the deficiencies of existing predicting algorithm based bipartite graph, a supervised learning integration method of bipartite graph was proposed in this paper. Firstly, the bipartite graph network was constructed based on the known interactions between drugs and target proteins. Secondly, the evaluation model for association between drugs and target proteins was created. Thirdly, the model was used to predict the new interactions between drugs and target proteins and confirm the new predicted targets. On the testing dataset, our method performed much better than three other predicting methods. The proposed method integrated chemical space, therapeutic space and genomic space, constructed the interaction network of drugs and target proteins, created the evaluation model and predicted the new interactions with good performance.

[Network-based local and global consistency of cardiovascular genes].

Drug targets discovery is one of the most important elements in new drug development, and a variety of methods have been developed recently from this point of view. This paper proposed a network-based local and global consistency for cardiovascular genes identification. Results were evaluated through the widely used database HPRD and DrugBank. Results showed that our algorithm can give reasonable candidate targets set. The method in this paper could be an impressive solution for targets searching.

Small-Molecule-Driven Direct Reprogramming of Mouse Fibroblasts into Functional Neurons.

Recently, direct reprogramming between divergent lineages has been achieved by the introduction of regulatory transcription factors. This approach may provide alternative cell resources for drug discovery and regenerative medicine, but applications could be limited by the genetic manipulation involved. Here, we show that mouse fibroblasts can be directly converted into neuronal cells using only a cocktail of small molecules, with a yield of up to >90% being TUJ1-positive after 16 days of induction. After a further maturation stage, these chemically induced neurons (CiNs) possessed neuron-specific expression patterns, generated action potentials, and formed functional synapses. Mechanistically, we found that a BET family bromodomain inhibitor, I-BET151, disrupted the fibroblast-specific program, while the neurogenesis inducer ISX9 was necessary to activate neuron-specific genes. Overall, our findings provide a "proof of principle" for chemically induced direct reprogramming of somatic cell fates across germ layers without genetic manipulation, through disruption of cell-specific programs and induction of an alternative fate.