PeptideWitch-A Software Package to Produce High-Stringency Proteomics Data Visualizations from Label-Free Shotgun Proteomics Data.

PeptideWitch is a python-based web module that introduces several key graphical and technical improvements to the Scrappy software platform, which is designed for label-free quantitative shotgun proteomics analysis using normalised spectral abundance factors. The program inputs are low stringency protein identification lists output from peptide-to-spectrum matching search engines for 'control' and 'treated' samples. Through a combination of spectral count summation and inner joins, PeptideWitch processes low stringency data, and outputs high stringency data that are suitable for downstream quantitation. Data quality metrics are generated, and a series of statistical analyses and graphical representations are presented, aimed at defining and presenting the difference between the two sample proteomes.

Specific Sialoforms Required for the Immune Suppressive Activity of Human Soluble CD52.

Human CD52 is a small glycopeptide (12 amino acid residues) with one N-linked glycosylation site at asparagine 3 (Asn3) and several potential O-glycosylation serine/threonine sites. Soluble CD52 is released from the surface of activated T cells and mediates immune suppression via its glycan moiety. In suppressing activated T cells, it first sequesters the pro-inflammatory high mobility group Box 1 (HMGB1) protein, which facilitates its binding to the inhibitory sialic acid-binding immunoglobulin-like lectin-10 (Siglec-10) receptor. We aimed to identify the features of CD52 glycan that underlie its bioactivity. Analysis of native CD52 purified from human spleen revealed extensive heterogeneity in N-glycosylation and multi-antennary sialylated N-glycans with abundant polyLacNAc extensions, together with mainly di-sialylated O-glycosylation type structures. Glycomic (porous graphitized carbon-ESI-MS/MS) and glycopeptide (C8-LC-ESI-MS) analysis of recombinant soluble human CD52-immunoglobulin Fc fusion proteins revealed that CD52 bioactivity was correlated with a high abundance of tetra-antennary α-2,3/6 sialylated N-glycans. Removal of α-2,3 sialylation abolished bioactivity, which was restored by re-sialylation with α-2,3 sialyltransferases. When glycoforms of CD52-Fc were fractionated by anion exchange MonoQ-GL chromatography, bioactive fractions displayed mainly tetra-antennary, α-2,3 sialylated N-glycan structures and a lower relative abundance of bisecting GlcNAc structures compared to non-bioactive fractions. In addition, O-glycan core type-2 di-sialylated structures at Ser12 were more abundant in bioactive CD52 fractions. Understanding the structural features of CD52 glycan required for its bioactivity will aid its development as an immunotherapeutic agent.

Alpinia zerumbet (Pers.): Food and Medicinal Plant with Potential In Vitro and In Vivo Anti-Cancer Activities.

BACKGROUND/AIM: Plants play an important role in anti-cancer drug discovery, therefore, the current study aimed to evaluate the biological activity of Alpinia zerumbet (A. zerumbet) flowers. METHODS: The phytochemical and biological criteria of A. zerumbet were in vitro investigated as well as in mouse xenograft model. RESULTS: A. zerumbet extracts, specially CH2Cl2 and MeOH extracts, exhibited the highest potent anti-tumor activity against Ehrlich ascites carcinoma (EAC) cells. The most active CH2Cl2 extract was subjected to bioassay-guided fractionation leading to isolatation of the naturally occurring 5,6-dehydrokawain (DK) which was characterized by IR, MS, 1H-NMR and 13C-NMR. A. zerumbet extracts, specially MeOH and CH2Cl2 extracts, exhibited significant inhibitory activity towards tumor volume (TV). Furthermore, A. zerumbet extracts declined the high level of malonaldehyde (MDA) as well as elevated the levels of superoxide dismutase (SOD) and catalase (CAT) in liver tissue homogenate. Moreover, DK showed anti-proliferative action on different human cancer cell lines. The recorded IC50 values against breast carcinoma (MCF-7), liver carcinoma (Hep-G2) and larynx carcinoma cells (HEP-2) were 3.08, 6.8, and 8.7 µg/mL, respectively. CONCLUSION: Taken together, these findings open the door for further investigations in order to explore the potential medicinal properties of A. zerumbet.

The effect of streptozotocin-induced hyperglycemia on N-and O-linked protein glycosylation in mouse ovary.

Post-translational modification of proteins namely glycosylation influences cellular behavior, structural properties and interactions including during ovarian follicle development and atresia. However, little is known about protein glycosylation changes occurring in diabetes mellitus in ovarian tissues despite the well-known influence of diabetes on the outcome of successful embryo implantation. In our study, the use of PGC chromatography-ESI mass spectrometry in negative ion mode enabled the identification of 138 N-glycans and 6 O-glycans on the proteins of Streptozotocin-induced (STZ) diabetic mouse ovarian tissues (n = 3). Diabetic mouse ovaries exhibited a relative decrease in sialylation, fucosylation and, to a lesser extent, branched N-linked glycan structures, as well as an increase in oligomannose structures on their proteins, compared with nondiabetic mouse ovaries. Changes in N-glycans occurred in the diabetic liver tissue but were more evident in diabetic ovarian tissue of the same mouse, suggesting an organ-specific effect of diabetes mellitus on protein glycosylation. Although at a very low amount, O-GalNAc glycans of mice ovaries were present as core type 1 and core type 2 glycans; with a relative increase in the NeuGc:NeuAc ratio as the most significant difference between control and diabetic ovarian tissues. STZ-treated mice also showed a trend towards an increase in TNF-α and IL1-B inflammatory cytokines, which have previously been shown to influence protein glycosylation.

Human disease glycomics: technology advances enabling protein glycosylation analysis - part 2.

INTRODUCTION: The changes in glycan structures have been attributed to disease states for several decades. The surface glycosylation pattern is a signature of physiological state of a cell. In this review we provide a link between observed substructural glycan changes and a range of diseases. Areas covered: We highlight biologically relevant glycan substructure expression in cancer, inflammation, neuronal diseases and diabetes. Furthermore, the alterations in antibody glycosylation in a disease context are described. Expert commentary: Advances in technologies, as described in Part 1 of this review have now enabled the characterization of specific glycan structural markers of a range of disease states. The requirement of including glycomics in cross-disciplinary omics studies, such as genomics, proteomics, epigenomics, transcriptomics and metabolomics towards a systems glycobiology approach to understanding disease mechanisms and management are highlighted.

Human disease glycomics: technology advances enabling protein glycosylation analysis - part 1.

INTRODUCTION: Protein glycosylation is recognized as an important post-translational modification, with specific substructures having significant effects on protein folding, conformation, distribution, stability and activity. However, due to the structural complexity of glycans, elucidating glycan structure-function relationships is demanding. The fine detail of glycan structures attached to proteins (including sequence, branching, linkage and anomericity) is still best analysed after the glycans are released from the purified or mixture of glycoproteins (glycomics). The technologies currently available for glycomics are becoming streamlined and standardized and many features of protein glycosylation can now be determined using instruments available in most protein analytical laboratories. Areas covered: This review focuses on the current glycomics technologies being commonly used for the analysis of the microheterogeneity of monosaccharide composition, sequence, branching and linkage of released N- and O-linked glycans that enable the determination of precise glycan structural determinants presented on secreted proteins and on the surface of all cells. Expert commentary: Several emerging advances in these technologies enabling glycomics analysis are discussed. The technological and bioinformatics requirements to be able to accurately assign these precise glycan features at biological levels in a disease context are assessed.

Genetically and Phenotypically Distinct Pseudomonas aeruginosa Cystic Fibrosis Isolates Share a Core Proteomic Signature.

The opportunistic pathogen Pseudomonas aeruginosa is among the main colonizers of the lungs of cystic fibrosis (CF) patients. We have isolated and sequenced several P. aeruginosa isolates from the sputum of CF patients and compared them with each other and with the model strain PAO1. Phenotypic analysis of CF isolates showed significant variability in colonization and virulence-related traits suggesting different strategies for adaptation to the CF lung. Genomic analysis indicated these strains shared a large set of core genes with the standard laboratory strain PAO1, and identified the genetic basis for some of the observed phenotypic differences. Proteomics revealed that in a conventional laboratory medium PAO1 expressed 827 proteins that were absent in the CF isolates while the CF isolates shared a distinctive signature set of 703 proteins not detected in PAO1. PAO1 expressed many transporters for the uptake of organic nutrients and relatively few biosynthetic pathways. Conversely, the CF isolates expressed a narrower range of transporters and a broader set of metabolic pathways for the biosynthesis of amino acids, carbohydrates, nucleotides and polyamines. The proteomic data suggests that in a common laboratory medium PAO1 may transport a diverse set of "ready-made" nutrients from the rich medium, whereas the CF isolates may only utilize a limited number of nutrients from the medium relying mainly on their own metabolism for synthesis of essential nutrients. These variations indicate significant differences between the metabolism and physiology of P. aeruginosa CF isolates and PAO1 that cannot be detected at the genome level alone. The widening gap between the increasing genomic data and the lack of phenotypic data means that researchers are increasingly reliant on extrapolating from genomic comparisons using experimentally characterized model organisms such as PAO1. While comparative genomics can provide valuable information, our data suggests that such extrapolations may be fraught with peril.