Royal jelly (RJ) is a natural bee product that contains a variety of biologically active ingredients and has antitumor, antiallergic, antibacterial and immune-regulating effects. Inflammatory bowel disease (IBD) is a chronic inflammatory disease of the intestine that can cause abdominal pain and diarrhea. With this study, we aimed to explore the protective effect of RJ on DSS-induced colitis in mice. The physiochemical parameters (water, protein, 10-hydroxy-2-decenoic acid, total sugar, starch, ash and acidity) of the RJ samples used in this study met the requirements of the international and Chinese national standards. Treatment with RJ improved symptoms and colonic cell apoptosis and decreased intestinal permeability by increasing the expression of tight-junction protein, goblet cells and their secretion mucin, MUC2, in DSS-induced ulcerative colitis mice. RJ also reduced the expression of proinflammatory cytokine IL-6 and increased the expression of anti-inflammatory cytokine IL-10 and sIgA. DSS resulted in an increase in the relative abundance of Parabacteroides, Erysipelotrichaceae, Proteobacteria (Gammaproteobacteria, Enterobacteriales and Enterobacteriaceae) and Escherichia Shigella in the colon and a decrease in the relative abundance of Muribaculum. In the RJ treatment group, the relative abundance of the above intestinal flora was improved by treatment with 2.0 g/kg RJ. These results suggested that RJ alleviated DSS-induced colitis by improving the colonic mucosal barrier.

Increasing evidence suggests that royal jelly (RJ) has exceptional biological properties, and that major royal jelly proteins (MRJPs) are the key active factors in RJ. The objective of this study was to compare the difference in the protein content between RJ and MRJPs using non-labeled, quantitative proteomics technology, and to investigate the adjustment features and mechanisms of MRJPs on murine immune functions and the composition of intestinal flora in cyclophosphamide-treated mice. Results showed that, during the process of extracting MRJPs, the ratio of the protein types in the main protein and other proteins decreased significantly, except for MRJP1 and MRJP7, which demonstrated that an enriching effect of MRJP1 and MRJP7 was present during the extraction process. Cyclophosphamide-induced mice were orally administered MRJPs. Results showed that the middle-dose group, which received 0.25 g/(kg·bw) of royal jelly main protein, demonstrated a clear impact on the development of the spleen and liver, the quantity of peripheral blood leukocytes, immunoglobulin content, immune factor level, and the proliferation ability of spleen lymphocytes. A 16S rRNA high-throughput sequencing technology analysis showed that MRJPs could improve the component and richness of intestinal flora and raise the immunity of mice. The above-mentioned results indicated that the application of MRJPs is very likely to have an advantage effect on murine immune functions.

To shorten the complex and time-consuming process of the identification method of the traditional food angiotensin-I-converting enzyme (ACE-I) inhibitory peptides, we propose AHTPeptideFusion based on a segmented fusion with the protein language model and deep learning. The statistical analysis found that hydrophobic amino acids, N-terminal valine is a dominant amino acid in the activity of ACE-I inhibitory peptides. In 12 machine learning (ML) algorithms, the transformer outperformed the other 11 models, with the best performance in predicting short and medium peptides. In the external dataset, AHTPeptideFusion fused by transformer and random forest (RF) showed excellent performance (accuracy > 0.9) in predicting ACE-I inhibitory peptides with lengths ranging from 2 to 15 amino acid residues and different activity distributions, and the reliability and accuracy of AHTPeptideFusion was demonstrated by synthetic peptide and ACE-I inhibition experiments. In addition, hydrogen bonding and electrostatic interaction between 4 synthetic peptides and active residues of ACE-I were found by molecular docking. To further explore the ACE-I inhibitory peptides from animal-derived foods, we established an automated pipeline consisting of the trinity of proteomics, virtual enzymatic digestion and AHTPeptideFusion, and tapped the ACE-I inhibitory peptide released from royal jelly after digestion in the gastrointestinal tract. In conclusion, this computational pipeline will become a powerful screening tool for active peptides from animal-derived foods, which can help food scientists accelerate the mining and design of active peptides from animal-derived foods. Overall, AHTPeptideFusion will be a powerful ACE-I inhibitor peptide prediction tool, it can help food scientists accelerate the mining and design of ACE-I inhibitory peptides.