Glycosylation modifies the physicochemical properties and protein binding functions of glycoconjugates. These modifications are biosynthesized in the endoplasmic reticulum and Golgi apparatus by a series of enzymatic transformations that are under complex control. As a result, mature glycans on a given site are heterogeneous mixtures of glycoforms. This gives rise to a spectrum of adhesive properties that strongly influences interactions with binding partners and resultant biological effects. In order to understand the roles glycosylation plays in normal and disease processes, efficient structural analysis tools are necessary. In the field of glycomics, liquid chromatography/mass spectrometry (LC/MS) is used to profile the glycans present in a given sample. This technology enables comparison of glycan compositions and abundances among different biological samples, i.e. normal versus disease, normal versus mutant, etc. Manual analysis of the glycan profiling LC/MS data is extremely time-consuming and efficient software tools are needed to eliminate this bottleneck. In this work, we have developed a tool to computationally model LC/MS data to enable efficient profiling of glycans. Using LC/MS data deconvoluted by Decon2LS/DeconTools, we built a list of unique neutral masses corresponding to candidate glycan compositions summarized over their various charge states, adducts and range of elution times. Our work aims to provide confident identification of true compounds in complex data sets that are not amenable to manual interpretation. This capability is an essential part of glycomics work flows. We demonstrate this tool, GlycReSoft, using an LC/MS dataset on tissue derived heparan sulfate oligosaccharides. The software, code and a test data set are publically archived under an open source license.

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BackgroundThe objective of this study was to establish a culture system and elucidate the unique characteristics of a bovine mammary epithelial cell line in vitro.MethodologyMammary tissue from a three year old lactating dairy cow (ca. 100 d relative to parturition) was used as a source of the epithelial cell line, which was cultured in collagen-coated tissue culture dishes. Fibroblasts and epithelial cells successively grew and extended from the culturing mammary tissue at the third day. Pure epithelial cells were obtained by passages culture.Principal FindingsThe strong positive immunostaining to cytokeratin 18 suggested that the resulting cell line exhibited the specific character of epithelial cells. Epithelial cells cultured in the presence of 10% FBS, supraphysiologic concentrations of insulin, and hydrocortisone maintained a normal diploid chromosome modal number of 2n = 60. Furthermore, they were capable of synthesizing β-casein (CSN2), acetyl-CoA carboxylase-α (ACACA) and butyrophilin (BTN1A1). An important finding was that frozen preservation in a mixture of 90% FBS and 10% DMSO did not influence the growth characteristics, chromosome number, or protein secretion of the isolated epithelial cell line.ConclusionsThe obtained mammary epithelial cell line had normal morphology, growth characteristics, cytogenetic and secretory characteristics, thus, it might represent an useful tool for studying the function of Chinese Holstein dairy cows mammary epithelial cell (CMECs).

Despite the publication of several software tools for analysis of glycopeptide tandem mass spectra, there remains a lack of consensus regarding the most effective and appropriate methods. In part, this reflects problems with applying standard methods for proteomics database searching and false discovery rate calculation. While the analysis of small post-translational modifications (PTMs) may be regarded as an extension of proteomics database searching, glycosylation requires specialized approaches. This is because glycans are large and heterogeneous by nature causing glycopeptides to exist as multiple glycosylated variants. Thus, the mass of the peptide cannot be calculated directly from that of the intact glycopeptide. In addition, the chemical nature of the glycan strongly influences product ion patterns observed for glycopeptides. As a result, glycopeptidomics requires specialized bioinformatics methods. We summarize the recent progress towards a consensus for effective glycopeptide tandem mass spectrometric analysis.