Epicatechin (EC) has significant antiinflammation, antioxidation, and anticancer activities. It also provides a new alternative treatment for mastitis, which can result in great economic losses in the dairy industry if left untreated. The purpose of this study was to investigate the anti-inflammatory effects of EC on mastitis and the underlying mechanism using in vivo and in vitro systems. The use of ELISA and immunohistochemistry assays showed that EC treatment at 1.5, 7.5, 15, and 30 mg/mL decreased protein expression of inflammatory mediators, including cyclooxygenase-2 and inducible nitric oxide synthase; inflammatory cytokines, which were composed of IL-1β, TNF-α, and IL-6 in lipopolysaccharide (LPS)-stimulated bovine mammary epithelial cell line (MAC-T); and mouse mammary gland, together with reduced filtration of T lymphocytes in the mouse mammary gland. Furthermore, EC treatment reduced LPS-induced phosphorylation levels of p65 and inhibitor of NF-κB, and blocked nuclear translocation of p65 as revealed by western blot and immunofluorescence test in MAC-T cells and the mouse mammary gland. Epicatechin also attenuated LPS-induced phosphorylation levels of mitogen-activated protein kinase members (i.e., p38, c-Jun N-terminal kinase 1/2 and extracellular regulated protein kinases 1/2). Using RNA-seq and tandem mass tag analyses, upregulation of TMEM35A and TMPO proteins was disclosed in MAC-T cells cotreated with LPS and EC. Although clustered regularly interspaced short palindromic repeats/Cas9-based knockdown of TMEM35A and TMPO attenuated abundance of phosphorylated (p)-p65, p-p38, TNF-α, and iNOS, overexpression of TMEM35A reversed EC-mediated effects in TMPO knockdown cells. Moreover, interaction between TMEM35A and TMPO was detected using the co-immunoprecipitation method. In conclusion, our data demonstrated that EC inhibited LPS-induced inflammatory response in MAC-T cells and the mouse mammary gland. Importantly, TMEM35A mediated the transmembrane transport of EC, and the interaction between TMEM35A and TMPO inhibited MAPK and NF-κB pathways.

Food-derived peptides are usually safe natural drug candidates that can potentially inhibit the angiotensin-converting enzyme (ACE). The wet experiments used to identify ACE inhibitory peptides (ACEiPs) are time-consuming and costly, making it important and urgent to reduce the scope of experimental validation through bioinformatics methods. Here, we construct an ACE inhibitory peptide predictor (ACEiPP) using optimized amino acid descriptors (AADs) and long- and short-term memory neural networks. Our results show that combined-AAD models exhibit more efficient feature transformation ability than single-AAD models, especially the training model with the optimal descriptors as the feature inputs, which exhibits the highest predictive ability in the independent test (Acc = 0.9479 and AUC = 0.9876), with a significant performance improvement compared to the existing three predictors. The model can effectively characterize the structure–activity relationship of ACEiPs. By combining the model with database mining, we used ACEiPP to screen four ACEiPs with multiple reported functions. We also used ACEiPP to predict peptides from 21,249 food-derived proteins in the Database of Food-derived Bioactive Peptides (DFBP) and construct a library of potential ACEiPs to facilitate the discovery of new anti-ACE peptides.