Late lactation is frequently accompanied by changes in goat milk composition and volatile-related quality, which may reduce its commercial value. This study investigated whether Astragalus polysaccharide (APS), a plant-derived bioactive polysaccharide from Astragalus membranaceus (Fisch.) Bunge, is associated with milk quality responses distinct from those induced by probiotic supplementation. Late lactation Saanen dairy goats were assigned to probiotic or APS supplementation for 30 days, and milk samples were analyzed using conventional composition testing, electronic nose (E-nose) profiling, proteomics, untargeted metabolomics, and lipidomics. APS supplementation reduced milk urea nitrogen by 6.20 mg/dL and decreased milk fat percentage, while electronic nose (E-nose) profiling revealed a distinct sensor-level volatile fingerprint dominated by W5S, W2W, and W1S responses. At the molecular level, APS generated a broader metabolomic response than probiotic supplementation, with 904 versus 587 differential metabolites, and was associated with a milk proteomic signature featuring higher ATP citrate lyase abundance (2.52-fold). Lipidomics further showed selective remodeling of milk lipid subclasses, including increased phosphatidylethanolamine, phosphatidylcholine, and ceramide abundance by approximately 1.4-fold, 1.5-fold, and nearly 5-fold, respectively. Together, these milk-based data indicate that APS supplementation was associated with a distinct quality signature involving nitrogen-use indicators, volatile-related sensor profiles, lipid-related protein features, and lipid subclass redistribution. The findings support APS as a potential non-microbial nutritional strategy for modulating late lactation goat milk quality.
Commercial cheeses exhibit distinct compositional profiles shaped by milk substrate, production practices, and geographical terroir. However, the relative contribution of geographic origin versus milk source to cheese metabolomes remains poorly quantified, particularly in unstandardized commercial products. This explor...
Guo-Xuan Hang, Jia-Qi Sun, L. Kwok et al.· Journal of Dairy Science· 0 citations
Background The overuse of antimicrobial growth promoters (AGPs) in poultry farming has raised global concerns about antimicrobial resistance and food safety, prompting the search for effective natural alternatives. Moringa is rich in bioactive compounds with antimicrobial and immunomodulatory properties; however, its e...
F. Lei, Chun-Pan Zhao, Fei Wang et al.· Frontiers in Microbiology· 0 citations
A combined "microbe-miRNA" signature comprising the low-abundance genus Ruminobacter, the miRNA 7_14677, and chi-miR-361-5p was identified and exhibited high diagnostic accuracy not only between CON and SARA but also between SARA and SARA-T, as well as between pooled healthy (CON+SARA-T) and SARA animals.
Guanghao Xu, Yi-Yang Sun, Shi-Xi Liu et al.· Journal of Dairy Science· 0 citations
Long-term storage of Lapsang Souchong (LS) black tea is traditionally believed to improve its aged flavor and functional quality, but the multi-level changes underlying this process remain insufficiently characterized. In this study, LS black tea samples from eight production years (2013–2023, corresponding to 10 to 0...
Shu-Lin Zheng, Ao-Ge Gong, Li-Rong Zuo et al.· Frontiers in Plant Science· 0 citations
This study aimed to explore the generational succession rules of rectal microbes and metabolites in crossbred dairy cows, and clarify the regulatory mechanism underlying generational differentiation of milk quality traits, so as to provide a theoretical basis for improving milk quality and realizing precision breeding...
Xing-Yuan Wang, Wei Huang, Y. Sha et al.· Microorganisms· 0 citations
Dairy and meat goats exhibit breed-specific physiological differences that are associated with changes in their gastrointestinal microbiome and plasma metabolome. This study aimed to characterize differences in the fecal microbiome and whole plasma metabolome of growing dairy and meat goats. Twenty Alpine (n = 10; 64...
S. Ikuejamoye-Omotore, Z. Estrada-Reyes, R. Murray et al.· Journal of Animal Science· 0 citations
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.