Canagliflozin, an inhibitor of sodium glucose co-transporter (SGLT) 2, has been shown to reduce body weight during the treatment of type 2 diabetes mellitus (T2DM). In this study, we sought to determine the role of canagliflozin in body weight loss and liver injury in obesity. C57BL/6J mice were fed a high-fat diet to simulate diet-induced obesity (DIO). Canagliflozin (15 and 60 mg/kg) was administered to DIO mice for 4 weeks. Orlistat (10 mg/kg) was used as a positive control. The body weight, liver weight, liver morphology, total cholesterol (TC) and triglyceride (TG) levels were examined. Signaling molecules, including diacylgycero1 acyltransferase-2 (DGAT2), peroxisome proliferation receptor alpha-1 (PPARα1), PPARγ1, PPARγ2 mRNA levels and the protein expression of SGLT2 were evaluated. Canagliflozin reduced body weight, especially the high-dose canagliflozin, and resulted in increased body weight loss compared with orlistat. Moreover, canagliflozin reduced the liver weight and the ratio of liver weight to body weight, lowered the serum levels of TC and TG, and ameliorated liver steatosis. During the canagliflozin treatment, SGLT2, DGAT2, PPARγ1 and PPARγ2 were inhibited, and PPARα1 was elevated in the liver tissues. This finding may explain why body weight was reduced and secondary liver injury was ameliorated in response to canagliflozin. Together, the results suggest that canagliflozin may be a potential anti-obesity strategy.

Converting peanut protein biomass waste into environmentally friendly meat substitutes by a high-moisture extrusion process can help solve both resource and waste problems and be “double green”. A multiscale method combined with some emerging techniques such as atomic force microscopy-based infrared spectroscopy and X-ray microscopy was used to make the whole extrusion process visible to show the process of forming a meat-like fibrous structure using two-dimensional and three-dimensional perspectives. The results showed that the protein molecules underwent dramatic structural changes and unfolded in the extruder barrel, which created favorable conditions for molecular rearrangement in the subsequent zones. It was confirmed that the meat-like fibrous structure started to form at the junction of the die and the cooling zone and that this structure was caused by the phase separation and rearrangement of protein molecules in the cooling zone. Moreover, the interactions between hydrogen bonds and disulfide bonds formed in the cooling zone maintained the meat-like fibrous structure with an α-helix > β-sheet > β-turn > random coil. Of the two main peanut proteins, arachin played a greater role in forming the fibrous structure than conarachin, especially those subunits of arachin with a molecular weight of 42, 39, and 22 kDa.

In patients with advanced HBV-related HCC treated with sorafenib, a high baseline HBV load was an adverse prognostic factor for survival. However, survival was significantly improved with the use of antiviral therapy.

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The bioactive peptide has wide functions, such as lowering blood glucose levels and reducing inflammation. Meanwhile, computational methods such as machine learning are becoming more and more important for peptide functions prediction. Most of the previous studies concentrate on the single-functional bioactive peptides prediction. However, the number of multi-functional peptides is on the increase; therefore, novel computational methods are needed. In this study, we develop a method MLBP (Multi-Label deep learning approach for determining the multi-functionalities of Bioactive Peptides), which can predict multiple functions including anti-cancer, anti-diabetic, anti-hypertensive, anti-inflammatory and anti-microbial simultaneously. MLBP model takes the peptide sequence vector as input to replace the biological and physiochemical features used in other peptides predictors. Using the embedding layer, the dense continuous feature vector is learnt from the sequence vector. Then, we extract convolution features from the feature vector through the convolutional neural network layer and combine with the bidirectional gated recurrent unit layer to improve the prediction performance. The 5-fold cross-validation experiments are conducted on the training dataset, and the results show that Accuracy and Absolute true are 0.695 and 0.685, respectively. On the test dataset, Accuracy and Absolute true of MLBP are 0.709 and 0.697, with 5.0 and 4.7% higher than those of the suboptimum method, respectively. The results indicate MLBP has superior prediction performance on the multi-functional peptides identification. MLBP is available at https://github.com/xialab-ahu/MLBP and http://bioinfo.ahu.edu.cn/MLBP/.