(1) Background: The use of antibiotics affects the composition of gut microbiota. Studies have suggested that the colonization of gut microbiota in early life is related to later food allergies. Still, the relationship between altered intestinal microbiota in adulthood and food allergies is unclear. (2) Methods: We established three mouse models to analyze gut microbiota dysbiosis’ impact on the intestinal barrier and determine whether this effect can increase the susceptibility to and severity of food allergy in later life. (3) Results: The antibiotic-induced gut microbiota dysbiosis significantly reduced Lachnospiraceae, Muribaculaceae, and Ruminococcaceae, and increased Enterococcaceae and Clostridiales. At the same time, the metabolic abundance was changed, including decreased short-chain fatty acids and tryptophan, as well as enhanced purine. This change is related to food allergies. After gut microbiota dysbiosis, we sensitized the mice. The content of specific IgE and IgG1 in mice serum was significantly increased, and the inflammatory response was enhanced. The dysbiosis of gut microbiota caused the sensitized mice to have more severe allergic symptoms, ruptured intestinal villi, and a decrease in tight junction proteins (TJs) when re-exposed to the allergen. (4) Conclusions: Antibiotic-induced gut microbiota dysbiosis increases the susceptibility and severity of food allergies. This event may be due to the increased intestinal permeability caused by decreased intestinal tight junction proteins and the increased inflammatory response.

Traditional food allergen identification mainly relies on in vivo and in vitro experiments, which often needs a long period and high cost. The artificial intelligence (AI)-driven rapid food allergen identification method has solved the above mentioned some drawbacks and is becoming an efficient auxiliary tool. Aiming to overcome the limitations of lower accuracy of traditional machine learning models in predicting the allergenicity of food proteins, this work proposed to introduce deep learning model—transformer with self-attention mechanism, ensemble learning models (representative as Light Gradient Boosting Machine (LightGBM) eXtreme Gradient Boosting (XGBoost)) to solve the problem. In order to highlight the superiority of the proposed novel method, the study also selected various commonly used machine learning models as the baseline classifiers. The results of 5-fold cross-validation showed that the area under the receiver operating characteristic curve (AUC) of the deep model was the highest (0.9578), which was better than the ensemble learning and baseline algorithms. But the deep model need to be pre-trained, and the training time is the longest. By comparing the characteristics of the transformer model and boosting models, it can be analyzed that, each model has its own advantage, which provides novel clues and inspiration for the rapid prediction of food allergens in the future.

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Background Ginsenosides accumulation responses to temperature are critical to quality formation in cold-dependent American ginseng. However, the studies on cold requirement mechanism relevant to ginsenosides have been limited in this species. Methods Two experiments were carried out: one was a multivariate linear regression analysis between the ginsenosides accumulation and the environmental conditions of American ginseng from different sites of China and the other was a synchronous determination of ginsenosides accumulation, overall DNA methylation, and relative gene expression in different tissues during different developmental stages of American ginseng after experiencing different cold exposure duration treatments. Results Results showed that the variation of the contents as well as the yields of total and individual ginsenosides Rg1, Re, and Rb1 in the roots were closely associated with environmental temperature conditions which implied that the cold environment plays a decisive role in the ginsenoside accumulation of American ginseng. Further results showed that there is a cyclically reversible dynamism between methylation and demethylation of DNA in the perennial American ginseng in response to temperature seasonality. And sufficient cold exposure duration in winter caused sufficient DNA demethylation in tender leaves in early spring and then accompanied the high expression of flowering gene PqFT in flowering stages and ginsenosides biosynthesis gene PqDDS in green berry stages successively, and finally, maximum ginsenosides accumulation occurred in the roots of American ginseng. Conclusion We, therefore, hypothesized that cold-induced DNA methylation changes might regulate relative gene expression involving both plant development and plant secondary metabolites in such cold-dependent perennial plant species.

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