Alpinetin promotes hair regeneration via activating hair follicle stem cells.

BACKGROUND: Alopecia affects millions of individuals globally, with hair loss becoming more common among young people.  Various traditional Chinese medicines (TCM) have been used clinically for treating alopecia, however, the effective compounds and underlying mechanism are less known. We sought to investigate the effect of Alpinetin (AP), a compound extracted from Fabaceae and Zingiberaceae herbs, in hair regeneration. METHODS: Animal model for hair regeneration was mimicked by depilation in C57BL/6J mice. The mice were then topically treated with 3 mg/ml AP, minoxidil as positive control (PC), or solvent ethanol as vehicle control (VC) on the dorsal skin. Skin color changes which reflected the hair growth stages were monitored and pictured, along with H&E staining and hair shaft length measurement. RNA-seq analysis combined with immunofluorescence staining and qPCR analysis were used for mechanism study. Meanwhile, Gli1CreERT2; R26RtdTomato and Lgr5EGFP-CreERT2; R26RtdTomato transgenic mice were used to monitor the activation and proliferation of Gli1+ and Lgr5+ HFSCs after treatment. Furthermore, the toxicity of AP was tested in keratinocytes and fibroblasts from both human and mouse skin to assess the safety. RESULTS: When compared to minoxidil-treated and vehicle-treated control mice, topical application of AP promoted anagen initiation and delayed catagen entry, resulting in a longer anagen phase and hair shaft length. Mechanistically, RNA-seq analysis combined with immunofluorescence staining of Lef1 suggested that Lgr5+ HFSCs in lower bulge were activated by AP via Wnt signaling. Other HFSCs, including K15+, Lef1+, and Gli1+ cells, were also promoted into proliferating upon AP treatment. In addition, AP inhibited cleaved caspase 3-dependent apoptosis at the late anagen stage to postpone regression of hair follicles. More importantly, AP showed no cytotoxicity in keratinocytes and fibroblasts from both human and mouse skin. CONCLUSION: This study clarified the effect of AP in promoting hair regeneration by activating HFSCs via Wnt signaling. Our findings may contribute to the development of a new generation of pilatory that is more efficient and less cytotoxic for treating alopecia.

Large-scale identification of human protein function using topological features of interaction network.

The annotation of protein function is a vital step to elucidate the essence of life at a molecular level, and it is also meritorious in biomedical and pharmaceutical industry. Developments of sequencing technology result in constant expansion of the gap between the number of the known sequences and their functions. Therefore, it is indispensable to develop a computational method for the annotation of protein function. Herein, a novel method is proposed to identify protein function based on the weighted human protein-protein interaction network and graph theory. The network topology features with local and global information are presented to characterise proteins. The minimum redundancy maximum relevance algorithm is used to select 227 optimized feature subsets and support vector machine technique is utilized to build the prediction models. The performance of current method is assessed through 10-fold cross-validation test, and the range of accuracies is from 67.63% to 100%. Comparing with other annotation methods, the proposed way possesses a 50% improvement in the predictive accuracy. Generally, such network topology features provide insights into the relationship between protein functions and network architectures. The source code of Matlab is freely available on request from the authors.

Identification of drug-target interaction from interactome network with 'guilt-by-association' principle and topology features.

MOTIVATION: Identifying drug-target protein interaction is a crucial step in the process of drug research and development. Wet-lab experiment are laborious, time-consuming and expensive. Hence, there is a strong demand for the development of a novel theoretical method to identify potential interaction between drug and target protein. RESULTS: We use all known proteins and drugs to construct a nodes- and edges-weighted biological relevant interactome network. On the basis of the 'guilt-by-association' principle, novel network topology features are proposed to characterize interaction pairs and random forest algorithm is employed to identify potential drug-protein interaction. Accuracy of 92.53% derived from the 10-fold cross-validation is about 10% higher than that of the existing method. We identify 2272 potential drug-target interactions, some of which are associated with diseases, such as Torg-Winchester syndrome and rhabdomyosarcoma. The proposed method can not only accurately predict the interaction between drug molecule and target protein, but also help disease treatment and drug discovery. CONTACTS: zhanchao8052@gmail.com or ceszxy@mail.sysu.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Large-scale identification of potential drug targets based on the topological features of human protein-protein interaction network.

Identifying potential drug target proteins is a crucial step in the process of drug discovery and plays a key role in the study of the molecular mechanisms of disease. Based on the fact that the majority of proteins exert their functions through interacting with each other, we propose a method to recognize target proteins by using the human protein-protein interaction network and graph theory. In the network, vertexes and edges are weighted by using the confidence scores of interactions and descriptors of protein primary structure, respectively. The novel network topological features are defined and employed to characterize protein using existing databases. A widely used minimum redundancy maximum relevance and random forests algorithm are utilized to select the optimal feature subset and construct model for the identification of potential drug target proteins at the proteome scale. The accuracies of training set and test set are 89.55% and 85.23%. Using the constructed model, 2127 potential drug target proteins have been recognized and 156 drug target proteins have been validated in the database of drug target. In addition, some new drug target proteins can be considered as targets for treating diseases of mucopolysaccharidosis, non-arteritic anterior ischemic optic neuropathy, Bernard-Soulier syndrome and pseudo-von Willebrand, etc. It is anticipated that the proposed method may became a powerful high-throughput virtual screening tool of drug target.