Tea consumption has been identified to have an anti-obesity effect. Whether it is associated with gut microbiota modulation is investigated in this study. Phenolic profiles of infusions of green tea, oolong tea and black tea were comprehensively compared first, by utilizing ultra-performance liquid chromatography-electrospray ionization-quadrupole time-of-flight mass spectrometry (UPLC-ESI-Q-TOFMS). Subsequently, high-fat-diet induced obese C57BL/6J mice were orally administered these three types of tea infusions for 13 weeks to evaluate their anti-obesity and gut microbiota modulatory effects. In general, 8 phenolic acids, 12 flavanols, 9 flavonols, 2 alkaloids and 1 amino acid were identified from the three types of tea infusions. Though they possess diverse phenolic compounds, no significant differences in the prevention of the development of obesity in high-fat-fed mice were discovered among the three types of tea. Based on high-throughput MiSeq sequencing and multivariate statistical analysis, it was revealed that tea infusion consumption substantially increased diversity and altered the structure of gut microbiota. The linear discriminant analysis effect size algorithm identified 30 key phylotypes in response to high-fat diet and tea, including Alistipes, Rikenella, Lachnospiraceae, Akkermansia, Bacteroides, Allobaculum, Parabacteroides, etc. Moreover, Spearman's correlation analysis indicated that these key phylotypes might have a close association with the obesity related indexes of the host. This study provides detailed information regarding the impact of tea consumption on gut microbiota, which may be helpful in understanding the anti-obesity mechanisms of tea.

Target-based screening is one of the major approaches in drug discovery. Besides the intended target, unexpected drug off-target interactions often occur, and many of them have not been recognized and characterized. The off-target interactions can be responsible for either therapeutic or side effects. Thus, identifying the genome-wide off-targets of lead compounds or existing drugs will be critical for designing effective and safe drugs, and providing new opportunities for drug repurposing. Although many computational methods have been developed to predict drug-target interactions, they are either less accurate than the one that we are proposing here or computationally too intensive, thereby limiting their capability for large-scale off-target identification. In addition, the performances of most machine learning based algorithms have been mainly evaluated to predict off-target interactions in the same gene family for hundreds of chemicals. It is not clear how these algorithms perform in terms of detecting off-targets across gene families on a proteome scale. Here, we are presenting a fast and accurate off-target prediction method, REMAP, which is based on a dual regularized one-class collaborative filtering algorithm, to explore continuous chemical space, protein space, and their interactome on a large scale. When tested in a reliable, extensive, and cross-gene family benchmark, REMAP outperforms the state-of-the-art methods. Furthermore, REMAP is highly scalable. It can screen a dataset of 200 thousands chemicals against 20 thousands proteins within 2 hours. Using the reconstructed genome-wide target profile as the fingerprint of a chemical compound, we predicted that seven FDA-approved drugs can be repurposed as novel anti-cancer therapies. The anti-cancer activity of six of them is supported by experimental evidences. Thus, REMAP is a valuable addition to the existing in silico toolbox for drug target identification, drug repurposing, phenotypic screening, and side effect prediction. The software and benchmark are available at https://github.com/hansaimlim/REMAP.

Background Although cisplatin-based chemotherapy has been used as the first-line treatment for ovarian cancer (OC), tumor cells develop resistance to cisplatin during treatment, causing poor prognosis in OC patients. Studies have demonstrated that overactivation of the phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/AKT/mTOR) pathway is involved in tumor chemoresistance and that overexpression of microRNA-497 (miR497) may overcome OC chemotherapy resistance by inhibiting the mTOR pathway. However, the low transcriptional efficiency and unstable chemical properties of miR497 limit its clinical application. Additionally, triptolide (TP) was confirmed to possess a superior killing effect on cisplatin-resistant cell lines, partially through inhibiting the mTOR pathway. Even so, the clinical applications of TP are restricted by serious systemic toxicity and weak water solubility. Results Herein, whether the combined application of miR497 and TP could further overcome OC chemoresistance by synergically suppressing the mTOR signaling pathway was investigated. Bioinspired hybrid nanoparticles formed by the fusion of CD47-expressing tumor exosomes and cRGD-modified liposomes (miR497/TP-HENPs) were prepared to codeliver miR497 and TP. In vitro results indicated that the nanoparticles were efficiently taken up by tumor cells, thus significantly enhancing tumor cell apoptosis. Similarly, the hybrid nanoparticles were effectively enriched in the tumor areas and exerted significant anticancer activity without any negative effects in vivo. Mechanistically, they promoted dephosphorylation of the overactivated PI3K/AKT/mTOR signaling pathway, boosted reactive oxygen species (ROS) generation and upregulated the polarization of macrophages from M2 to M1 macrophages. Conclusion Overall, our findings may provide a translational strategy to overcome cisplatin-resistant OC and offer a potential solution for the treatment of other cisplatin-resistant tumors. Graphical Abstract