Upon secretion, transforming growth factor (TGF) β is maintained in a sequestered state in extracellular matrix as a latent form. The latent TGFβ is considered as a molecular sensor that releases active TGFβ in response to the perturbations of the extracellular matrix at the situations of mechanical stress, wound repair, tissue injury, and inflammation. The biological implication of the temporal discontinuity of TGFβ storage in the matrix and its activation is obscure. Here, using several animal models in which latent TGFβ is activated in vascular matrix in response to injury of arteries, we show that active TGFβ controls the mobilization and recruitment of (messenchymal stem cells) MSCs to participate in tissue repair and remodeling. MSCs were mobilized into the peripheral blood in response to vascular injury and recruited to the injured sites where they gave rise to both endothelial cells for reendothelialization and myofibroblastic cells to form thick neointima. TGFβ were activated in the vascular matrix in both rat and mouse models of mechanical injury of arteries. Importantly, the active TGFβ released from the injured vessels is essential to induce the migration of MSCs, and cascade expression of monocyte chemotactic protein-1 (MCP-1) stimulated by TGFβ amplifies the signal for migration. Moreover, sustained high levels of active TGFβ were observed in peripheral blood, and at the same time points following injury, Sca1+CD29+CD11b−CD45− MSCs, in which 91% are nestin+ cells, were mobilized to peripheral blood and recruited to the remodeling arteries. Intravenously injection of recombinant active TGFβ1 in uninjured mice rapidly mobilized MSCs into circulation. Further, inhibitor of TGFβ type I receptor (TβRI) blocked the mobilization and recruitment of MSCs to the injured arteries. Thus, TGFβ is an injury-activated messenger essential for the mobilization and recruitment of MSCs to participate in tissue repair/remodeling.

The web-server 4mCPred, is accessible at http://server.malab.cn/4mCPred/index.jsp.

Enhancers are cis elements that play an important role in regulating gene expression by enhancing it. Recent study of modifications revealed that enhancers are a large group of functional elements with many different subgroups, which have different biological activities and regulatory effects on target genes. As powerful auxiliary tools, several computational methods have been proposed to distinguish enhancers from other regulatory elements, but only one method has been considered to clustering them into subgroups. In this study, we developed a predictor (called EnhancerPred) to distinguish between enhancers and nonenhancers and to determine enhancers’ strength. A two-step wrapper-based feature selection method was applied in high dimension feature vector from bi-profile Bayes and pseudo-nucleotide composition. Finally, the combination of 104 features from bi-profile Bayes, 1 feature from nucleotide composition and 9 features from pseudo-nucleotide composition yielded the best performance for identifying enhancers and nonenhancers, with overall Acc of 77.39%. The combination of 89 features from bi-profile Bayes and 10 features from pseudo-nucleotide composition yielded the best performance for identifying strong and weak enhancers, with overall Acc of 68.19%. The process and steps of feature optimization illustrated that it is necessary to construct a particular model for identifying strong enhancers and weak enhancers.