Phosphorylation and glycosylation isoforms of bovine κ-casein variant E in homozygous Swedish Red cow milk detected by liquid chromatography-electrospray ionization mass spectrometry

Direct Glycan Analysis of Biological Samples and Intact Glycoproteins by Integrating Machine Learning-Driven Surface-Enhanced Raman Scattering and Boronic Acid Arrays

Frequent monitoring of glycan patterns is a critical step in studying glycan-mediated cellular processes. However, the current glycan analysis tools are resource-intensive and less suitable for routine use in standard laboratories. We developed a novel glycan detection platform by integrating surface-enhanced Raman spectroscopy (SERS), boronic acid (BA) receptors, and machine learning tools. This sensor monitors the molecular fingerprint spectra of BA binding to cis-diol-containing glycans. Different types of BA receptors could yield different stereoselective reactions toward different glycans and exhibit unique vibrational spectra. By integration of the Raman spectra collected from different BA receptors, the structural information can be enriched, eventually improving the accuracy of glycan classification and quantification. Here, we established a SERS-based sensor incorporating multiple different BA receptors. This sensing platform could directly analyze the biological samples, including whole milk and intact glycoproteins (fetuin and asialofetuin), without tedious glycan release and purification steps. The results demonstrate the platform's ability to classify milk oligosaccharides with remarkable classification accuracy, despite the presence of other non-glycan constituents in the background. This sensor could also directly quantify sialylation levels of a fetuin/asialofetuin mixture without glycan release procedures. Moreover, by selecting appropriate BA receptors, the sensor exhibits an excellent performance of differentiating between α2,3 and α2,6 linkages of sialic acids. This low-cost, rapid, and highly accessible sensor will provide the scientific community with an invaluable tool for routine glycan screening in standard laboratories.

Effects of protein genetic variants on their phosphorylation levels, milk composition, milk proteome, and milk coagulation ability in Chinese Holstein bovine milk

Milk samples were collected from 3625 Chinese Holstein cows to assess the effects of κ-casein (κ-CN) and β-lactoglobulin (β-LG) genetic variants on its milk coagulation properties. The results show that Chinese Holstein cows have a higher frequency of the κ-CN AA and AB variants, and β-LG of the AB and AA variants. Of these, κ-CN B variants, the β-LG AA and BB variants were more frequent in milk showing good coagulation. The effects of the genetic variants on milk composition, milk proteome, and protein phosphorylation sites were studied. The results showed that higher concentrations of protein and dry matter were found in κ-CN BE variant. Moreover, large variations in milk proteome among different κ-CN and β-LG variants were observed. Highly phosphorylated for κ-CN, especially Ser97, was observed in cows with the κ-CN BE variant, but no effect of β-LG variants on phosphorylation site was found. Of the various factors examined, variation of κ-CN phosphorylation sites Ser97 may be the most important in affecting casein structure and milk coagulation ability. Some milk protein contents were found to be negative factors for milk coagulation. In summary, this study showed that κ-CN genetic variants contained different milk compositions and phosphorylation site Ser97 influenced milk coagulation.