Predicting drug-target interactions using matrix factorization with self-paced learning and dual similarity information.

BACKGROUND: Drug repositioning (DR) refers to a method used to find new targets for existing drugs. This method can effectively reduce the development cost of drugs, save time on drug development, and reduce the risks of drug design. The traditional experimental methods related to DR are time-consuming, expensive, and have a high failure rate. Several computational methods have been developed with the increase in data volume and computing power. In the last decade, matrix factorization (MF) methods have been widely used in DR issues. However, these methods still have some challenges. (1) The model easily falls into a bad local optimal solution due to the high noise and high missing rate in the data. (2) Single similarity information makes the learning power of the model insufficient in terms of identifying the potential associations accurately. OBJECTIVE: We proposed self-paced learning with dual similarity information and MF (SPLDMF), which introduced the self-paced learning method and more information related to drugs and targets into the model to improve prediction performance. METHODS: Combining self-paced learning first can effectively alleviate the model prone to fall into a bad local optimal solution because of the high noise and high data missing rate. Then, we incorporated more data into the model to improve the model's capacity for learning. RESULTS: Our model achieved the best results on each dataset tested. For example, the area under the receiver operating characteristic curve and the precision-recall curve of SPLDMF was 0.982 and 0.815, respectively, outperforming the state-of-the-art methods. CONCLUSION: The experimental results on five benchmark datasets and two extended datasets demonstrated the effectiveness of our approach in predicting drug-target interactions.

Exploring active ingredients and mechanisms of Coptidis Rhizoma-ginger against colon cancer using network pharmacology and molecular docking.

BACKGROUND: Colon cancer is the most prevalent and rapidly increasing malignancy globally. It has been suggested that some of the ingredients in the herb pair of Coptidis Rhizoma and ginger (Zingiber officinale), a traditional Chinese medicine, have potential anti-colon cancer properties. OBJECTIVE: This study aimed to investigate the molecular mechanisms underlying the effects of the Coptidis Rhizoma-ginger herb pair in treating colon cancer, using an integrated approach combining network pharmacology and molecular docking. METHODS: The ingredients of the herb pair Coptidis Rhizoma-ginger, along with their corresponding protein targets, were obtained from the Traditional Chinese Medicine System Pharmacology and Swiss Target Prediction databases. Target genes associated with colon cancer were retrieved from the GeneCards and OMIM databases. Then, the protein targets of the active ingredients in the herb pair were identified, and the disease-related overlapping targets were determined using the Venn online tool. The protein-protein interaction (PPI) network was constructed using STRING database and analyzed using Cytoscape 3.9.1 to identify key targets. Then, a compound-target-disease-pathway network map was constructed. The intersecting target genes were subjected to Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses for colon cancer treatment. Molecular docking was performed using the Molecular Operating Environment (MOE) software to predict the binding affinity between the key targets and active compounds. RESULTS: Besides 1922 disease-related targets, 630 targets associated with 20 potential active compounds of the herb pair Coptidis Rhizoma-ginger were collected. Of these, 229 intersection targets were obtained. Forty key targets, including STAT3, Akt1, SRC, and HSP90AA1, were further analyzed using the ClueGO plugin in Cytoscape. These targets are involved in biological processes such as miRNA-mediated gene silencing, phosphatidylinositol 3-kinase (PI3K) signaling, and telomerase activity. KEGG enrichment analysis showed that PI3K-Akt and hypoxia-inducible factor 1 (HIF-1) signaling pathways were closely related to colon cancer prevention by the herb pair Coptidis Rhizoma-ginger. Ten genes (Akt1, TP53, STAT3, SRC, HSP90AA1, JAK2, CASP3, PTGS2, BCl2, and ESR1) were identified as key genes for validation through molecular docking simulation. CONCLUSIONS: This study demonstrated that the herb pair Coptidis Rhizoma-ginger exerted preventive effects against colon cancer by targeting multiple genes, utilizing various active compounds, and modulating multiple pathways. These findings might provide the basis for further investigations into the molecular mechanisms underlying the therapeutic effects of Coptidis Rhizoma-ginger in colon cancer treatment, potentially leading to the development of novel drugs for combating this disease.

In Vitro Characterization of Polysaccharides from Fresh Tea Leaves in Simulated Gastrointestinal Digestion and Gut Microbiome Fermentation.

Tea plants have a long cultivation history in the world, but there are few studies on polysaccharides from fresh tea leaves. In this study, tea polysaccharides (TPSs) were isolated from fresh tea leaves. Then, we investigated the characteristics of TPSs during in vitro simulated digestion and fermentation; moreover, the effects of TPSs on gut microbiota were explored. The results revealed that saliva did not significantly affect TPSs' molecular weight, monosaccharide composition, and reducing sugar content, indicating that TPSs cannot be digested in the oral cavity. However, TPSs were partially decomposed in the gastrointestinal tract after gastric and intestinal digestion, resulting in the release of a small amount of free glucose monosaccharides. Our in vitro fermentation experiments demonstrated that TPSs are degraded by gut microbiota, leading to short-chain fatty acid (SCFA) production and pH reduction. Moreover, TPSs increased the abundance of Bacteroides, Lactobacillus, and Bifidobacterium but reduced that of Escherichia, Shigella, and Enterococcus, demonstrating that TPSs can regulate the gut microbiome. In conclusion, TPSs are partially decomposed by gut microbiota, resulting in the production of SCFAs and the regulation of gut microbiota composition and function. Therefore, TPSs may be used to develop a prebiotic supplement to regulate the gut microbiome and improve host health.

Improved Computational Drug-Repositioning by Self-Paced Non-Negative Matrix Tri-Factorization.

Drug repositioning (DR) is a strategy to find new targets for existing drugs, which plays an important role in reducing the costs, time, and risk of traditional drug development. Recently, the matrix factorization approach has been widely used in the field of DR prediction. Nevertheless, there are still two challenges: 1) Learning ability deficiencies, the model cannot accurately predict more potential associations. 2) Easy to fall into a bad local optimal solution, the model tends to get a suboptimal result. In this study, we propose a self-paced non-negative matrix tri-factorization (SPLNMTF) model, which integrates three types of different biological data from patients, genes, and drugs into a heterogeneous network through non-negative matrix tri-factorization, thereby learning more information to improve the learning ability of the model. In the meantime, the SPLNMTF model sequentially includes samples into training from easy (high-quality) to complex (low-quality) in the soft weighting way, which effectively alleviates falling into a bad local optimal solution to improve the prediction performance of the model. The experimental results on two real datasets of ovarian cancer and acute myeloid leukemia (AML) show that SPLNMTF outperforms the other eight state-of-the-art models and gets better prediction performance in drug repositioning. The data and source code are available at: https://github.com/qi0906/SPLNMTF.

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology.

As the raw material for tea making, the quality of tea leaves directly affects the quality of finished tea. The quality of fresh tea leaves is mainly assessed by manual judgment or physical and chemical testing of the content of internal components. Physical and chemical methods are more mature, and the test results are more accurate and objective, but traditional chemical methods for measuring the biochemical indexes of tea leaves are time-consuming, labor-costly, complicated, and destructive. With the rapid development of imaging and spectroscopic technology, spectroscopic technology as an emerging technology has been widely used in rapid non-destructive testing of the quality and safety of agricultural products. Due to the existence of spectral information with a low signal-to-noise ratio, high information redundancy, and strong autocorrelation, scholars have conducted a series of studies on spectral data preprocessing. The correlation between spectral data and target data is improved by smoothing noise reduction, correction, extraction of feature bands, and so on, to construct a stable, highly accurate estimation or discrimination model with strong generalization ability. There have been more research papers published on spectroscopic techniques to detect the quality of tea fresh leaves. This study summarizes the principles, analytical methods, and applications of Hyperspectral imaging (HSI) in the nondestructive testing of the quality and safety of fresh tea leaves for the purpose of tracking the latest research advances at home and abroad. At the same time, the principles and applications of other spectroscopic techniques including Near-infrared spectroscopy (NIRS), Mid-infrared spectroscopy (MIRS), Raman spectroscopy (RS), and other spectroscopic techniques for non-destructive testing of quality and safety of fresh tea leaves are also briefly introduced. Finally, in terms of technical obstacles and practical applications, the challenges and development trends of spectral analysis technology in the nondestructive assessment of tea leaf quality are examined.

Antiobesity and anti-inflammation effects of Hakka stir-fried tea of different storage years on high-fat diet-induced obese mice model via activating the AMPK/ACC/CPT1 pathway.

BACKGROUND: As a typical representative of metabolic syndrome, obesity is also one of the extremely dangerous factors of cardiovascular diseases. Thus, the prevention and treatment of obesity has gradually become a global campaign. There have been many reports that green tea is effective in preventing obesity, but as a kind of green tea with regional characteristics, there have been no reports that Hakka stir-fried tea (HT) of different storage years has a weight loss effect. AIMS: The aim was to investigate the effect of HT in diet-induced obese mice. METHODS: The mice were divided into five groups as follows: the control group received normal diet; the obese model group received high-fat diet; and HT2003, HT2008, and HT2015 groups, after the induction of obesity via a high-fat diet, received HT of different storage years treatment for 6 weeks, respectively. RESULTS: It was observed that HT decreased the levels of serum and liver triglyceride; the ratio of liver to body weight; accumulation of epididymal, perirenal, and mesenteric fat; the degree of hepatic steatosis; and adipocyte hypertrophy, with the concomitant reduction of body weight. Moreover, HT decreased the expression levels of proinflammatory cytokines tumor necrosis factor α (TNF α), inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2) and reduced fatty acid synthase (FAS) activity in liver tissue of obese mice. In addition, HT treatment also increased the phosphorylation of AMP-activated protein kinase (AMPK) and its direct downstream proteins, acetyl coenzyme A carboxylase (ACC), and carnitine palmitoyltransferase I (CPT-1), which participate in FAS pathway. CONCLUSIONS: These findings demonstrate that HT treatment has a potential protection on high-fat diet-induced obesity mice via activating the AMPK/ACC/CPT1 pathway, and to a certain extent, it has nothing to do with the storage time of three kinds of HT.

Active extracts of black tea (Camellia Sinensis) induce apoptosis of PC-3 prostate cancer cells via mitochondrial dysfunction.

Cancer of the prostate gland is the most common invasive malignancy and the second leading cause of cancer-related death in human males. Many studies have shown that black tea reduces the risk of several types of cancer. We studied the effects of active extracts of black tea and the black tea polyphenols theaflavins (TFs), on the cellular proliferation and mitochondria of the human prostate cancer cell line PC-3. Our studies revealed that Yinghong black tea extracts (YBT), Assam black tea extracts (ABT) and TFs inhibited cell proliferation in a dose-dependent manner. We also showed that TFs, YBT and ABT affected the morphology of PC-3 cells and induced apoptosis or even necrosis in PC-3 cells. In addition, it was observed that the samples significantly caused loss of the mitochondrial membrane potential, release of cytochrome c from the intermembrane space into the cytosol, decrease of the ATP content and activation of caspase-3 compared with the control. Taken together, these findings suggest that black tea could act as an effective anti-proliferative agent in PC-3 cells, and TFs, YBT and ABT induced apoptosis of PC-3 cells through mitochondrial dysfunction.