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.

BackgroundGambogic acid (GA) is a major active ingredient of gamboge, a widely used traditional Chinese medicine that has been reported to be a potent cytotoxic agent against some malignant tumors. Many studies have shown that the NF-kappa B signaling pathway plays an important role in anti-apoptosis and the drug resistance of tumor cells during chemotherapy. In this study, the effects and mechanisms of GA and the NF-kappa B inhibitor celastrol on oral cancer cells were investigated.MethodsThree human oral squamous cell carcinoma cell lines, Tca8113, TSCC and NT, were treated with GA alone, celastrol alone or GA plus celastrol. Cytotoxicity was assessed by MTT assay. The rate of apoptosis was examined with annexin V/PI staining as well as transmission electronic microscopy in Tca8113 cells. The level of constitutive NF-kappa B activity in oral squamous cell carcinoma cell lines was determined by immunofluorescence assays and nuclear extracts and electrophoretic mobility shift assays (EMSAs) in vitro. To further investigate the role of NF-kappa B activity in GA and celastrol treatment in oral squamous cell carcinoma, we used the dominant negative mutant SR-IκBα to inhibit NF-kappa B activity and to observe its influence on the effect of GA.ResultsThe results showed that GA could inhibit the proliferation and induce the apoptosis of the oral squamous cell carcinoma cell lines and that the NF-kappa B pathway was simultaneously activated by GA treatment. The minimal cytotoxic dose of celastrol was able to effectively suppress the GA-induced NF-kappa B pathway activation. Following the combined treatment with GA and the minimal cytotoxic dose of celastrol or the dominant negative mutant SR-IκBα, proliferation was significantly inhibited, and the apoptotic rate of Tca8113 cells was significantly increased.ConclusionThe combination of GA and celastrol has a synergistic antitumor effect. The effect can be primarily attributed to apoptosis induced by a decrease in NF-kappa B pathway activation. The NF-kappa B signaling pathway plays an important role in this process. Therefore, combining GA and celastrol may be a promising modality for treating oral squamous cell carcinoma.

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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

Florfenicol (FF) is a broad-spectrum antibiotic used increasingly in aquaculture, livestock, and poultry to treat diseases. To avoid using labor-intensive instrumental methods to detect residues of FF in food and food products, a simple and convenient indirect competitive enzyme-linked immunosorbent assay (ic-ELISA) method for florfenicol's major metabolite, florfenicol amine (FFA), was developed using a polyclonal antibody prepared in this study. FFA was covalently attached to carrier protein as immunogen by using the glutaraldehyde method. The antibodies obtained were characterized by an ELISA method and showed excellent specificity and sensitivity with the 50% inhibition values (IC 50) of 3.34 microg/L for FFA in PBS buffer. In the ELISA, sample extractions were performed by ethyl acetate/ammonium hydroxide (90 + 10, v/v) following combined acid hydrolysis of FF and its known metabolites. The limits of detection (LOD) calculated from the analysis of 20 known negative swine muscle, chicken muscle, and fish samples were 3.08, 3.3, and 3.86 microg/kg (mean + 3 SD), respectively. Recoveries of FFA fortified at the levels of 5, 50, 100, and 300 microg/kg ranged from 64.6 to 124.7%, with coefficients of variation of 11.3-25.8% over the range of FFA concentrations studied. Validation of the ELISA method with FFA-fortified swine muscle at the levels of 10, 50, 100, and 200 microg/kg was carried out using GC, resulting in a similar correlation in swine muscle ( r = 0.97). The results suggest that this ELISA is a specific, accurate, and sensitive method, which is suitable for use as a screening method to detect residues of FFA in animal edible tissues.

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