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.

BackgroundPromoter is an important sequence regulation element, which is in charge of gene transcription initiation. In prokaryotes, σ70 promoters regulate the transcription of most genes. The promoter recognition has been a crucial part of gene structure recognition. It’s also the core issue of constructing gene transcriptional regulation network. With the successfully completion of genome sequencing from an increasing number of microbe species, the accurate identification of σ70 promoter regions in DNA sequence is not easy.ResultsIn order to improve the prediction accuracy of sigma70 promoters in prokaryote, a promoter recognition model 70ProPred was established. In this work, two sequence-based features, including position-specific trinucleotide propensity based on single-stranded characteristic (PSTNPss) and electron-ion potential values for trinucleotides (PseEIIP), were assessed to build the best prediction model. It was found that 79 features of PSTNPSS combined with 64 features of PseEIIP obtained the best performance for sigma70 promoter identification, with a promising accuracy and the Matthews correlation coefficient (MCC) at 95.56% and 0.90, respectively.ConclusionThe jackknife tests showed that 70ProPred outperforms the existing sigma70 promoter prediction approaches in terms of accuracy and stability. Additionally, this approach can also be extended to predict promoters of other species. In order to facilitate experimental biologists, an online web server for the proposed method was established, which is freely available at http://server.malab.cn/70ProPred/.Electronic supplementary materialThe online version of this article (10.1186/s12918-018-0570-1) contains supplementary material, which is available to authorized users.

Posttranslational modifications (PTMs) play an important role in regulating protein folding, activity and function and are involved in almost all cellular processes. Identification of PTMs of proteins is the basis for elucidating the mechanisms of cell biology and disease treatments. Compared with the laboriousness of equivalent experimental work, PTM prediction using various machine-learning methods can provide accurate, simple and rapid research solutions and generate valuable information for further laboratory studies. In this review, we manually curate most of the bioinformatics tools published since 2008. We also summarize the approaches for predicting ubiquitination sites and glycosylation sites. Moreover, we discuss the challenges of current PTM bioinformatics tools and look forward to future research possibilities.

Enhancers are cis-acting elements that play major roles in upregulating eukaryotic gene expression by providing binding sites for transcription factors and their complexes. Because enhancers are highly cell/tissue specific, lack common motifs, and are far from the target gene, the systematic and precise identification of enhancer regions in DNA sequences is a big challenge. In this study, we developed an enhancer prediction method called EnhancerPred2.0 by combining position-specific trinucleotide propensity (PSTNP) information with the electron-ion interaction potential (EIIP) values for trinucleotides, to predict enhancers and their subgroups. To obtain the optimal combination of features, F-score values were used in a two-step wrapper-based feature selection method, which was applied in a high dimensional feature vector from PSTNP and EIIP. Finally, 126 optimized features from PSTNP combined with 32 optimized features from EIIP yielded the best performance for identifying enhancers and non-enhancers, with an overall accuracy (Acc) of 88.27% and a Matthews correlation coefficient (MCC) of 0.77. Additionally, 198 features from PSTNP combined with 37 features from EIIP yielded the best performance for identifying strong and weak enhancers, with an overall Acc of 98.05% and a MCC of 0.96. Rigorous jackknife tests indicated that EnhancerPred2.0 was significantly better than the existing enhancer prediction methods in both overall accuracy and stability.

Gene regulatory network (GRN) is the important mechanism of maintaining life process, controlling biochemical reaction and regulating compound level, which plays an important role in various organisms and systems. Reconstructing GRN can help us to understand the molecular mechanism of organisms and to reveal the essential rules of a large number of biological processes and reactions in organisms. Various outstanding network reconstruction algorithms use specific assumptions that affect prediction accuracy, in order to deal with the uncertainty of processing. In order to study why a certain method is more suitable for specific research problem or experimental data, we conduct research from model-based, information-based and machine learning-based method classifications. There are obviously different types of computational tools that can be generated to distinguish GRNs. Furthermore, we discuss several classical, representative and latest methods in each category to analyze core ideas, general steps, characteristics, etc. We compare the performance of state-of-the-art GRN reconstruction technologies on simulated networks and real networks under different scaling conditions. Through standardized performance metrics and common benchmarks, we quantitatively evaluate the stability of various methods and the sensitivity of the same algorithm applying to different scaling networks. The aim of this study is to explore the most appropriate method for a specific GRN, which helps biologists and medical scientists in discovering potential drug targets and identifying cancer biomarkers.

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