The mRNA molecules expressed in cow’s milk are important molecular biomarkers for different physiological and pathological conditions in cattle. The prediction of the quantity that a specific mRNA type could be expressed in cow’s milk is a challenging theoretical task. The current study presents for the first time several different Machine Learning models to predict the mRNA expression using the mRNA secondary structure fragments. This unique methodology is based on a dataset of experimental mRNA expression data. Each mRNA molecule has a specific secondary structure represented as a string that can be used to read all the possible mRNA secondary structure fragments. This information is used as input for the Machine Learning methods from Weka software in order to obtain classification models that can predict low, medium and high expression of new mRNA types in the cow’s milk. The mRNA expression levels have been measured with High Throughput Screening techniques. The initial features included the counting of the mRNA secondary structure fragments for each expressed mRNA. The model features were transformed in frequencies and the expression levels were converted into low and high classes. In order to reduce the high number of possible features, a feature selection method has been applied. Thus, the best classification model was obtained with BayesNet method and is based on 24 features and 4067 cases. The model has the true positive rate for the low mRNA expression class of 0.78 (average true positive rate of 0.66). Further studies are needed improve the current results, using datasets with different feature sets and more advanced Machine Learning methods.

The annual nitrogen loss from the livestock production sector poses a significant threat to the global natural environment. Therefore, it is urgent to focus on improving the nutrient utilization efficiency of ruminants and promoting the sustainable development of livestock production. Twelve 60-day-old Ganxi goats with similar body weights were selected and randomly assigned to two dietary treatment groups. The control group was fed only a basal diet, while the treatment group was supplemented with 32 mg/d of Swollenin. The experiment lasted for 30 days. At the end of the experimental period, the goats were euthanized, and their intestinal contents were collected, rapidly frozen, and stored at −80 °C for subsequent metagenomic and metabolomic analyses. In the Swollenin group, we observed changes in gut microbiota structure and significantly enhanced feed conversion efficiency compared to the control group. Notably, genera such as Bacteroides, Ruminococcus, and Bifidobacterium exhibited significantly higher abundance. Following Swollenin supplementation, the gene abundance associated with the secondary bile acid biosynthesis pathway in the intestinal tract of young goats was significantly higher. The levels of primary bile acids (BAs), including taurocholic acid, glycocholic acid, taurochenodeoxycholic acid, and glycochenodeoxycholic acid, were significantly lower, while the concentrations of secondary BAs such as ursodeoxycholic acid and deoxycholic acid were significantly higher. The abundance of nitrogen-fixing and nitrogen-assimilating genes in the gut of young goats in the Swollenin group was significantly higher. Furthermore, co-occurrence network analysis revealed a strong correlation between bile acid metabolism and nitrogen metabolism pathways. These results suggest that nutritional regulation may serve as a preventive strategy to optimize the symbiotic development of animals and their gut microbiota, ultimately improving nitrogen utilization.

Metabolites present in the hemolymph of honey bees play a crucial role in modulating the metamorphic process within the species. However, the precise alterations in metabolite composition, along with the associated variances and regulatory pathways implicated during the larvae-to-pupae metamorphosis of honey bees, remain incompletely elucidated. In this investigation, we gathered hemolymph samples from honey bee larvae at three distinct physiological stages-feeding, prepupal, and pupae and subjected them to metabolite analysis utilizing the liquid chromatography-mass spectrometry (LC-MS) technique. Employing principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), we identified significant differential metabolites and conducted a metabolic pathway analysis on those differentially up-regulated during the prepupal and pupae stages. Notably, metabolites up-regulated in the hemolymph of prepupal stage larvae primarily governed glucose metabolism and fat digestion and absorption, while those in pupae stage larvae were involved in regulating chitin and lipopolysaccharide precursor formation, as well as the biosynthesis of phenylalanine, tyrosine, and tryptophan. These findings bear significant implications for advancing our comprehension of the metamorphic processes in honey bees.