Project description:This project was done in collaboration with Dr. Richard Cummings to examine sialylation of CD44v6 and its role in clearance of neutrophils from inflamed intestinal epithelium. Specifically, we have employed a functional approach using membrane preparations from interferon gamma-stimulated intestinal epithelial cells to generate a monoclonal antibody, designated GM35, which blocks neutrophil transepithelial migration through the promotion of neutrophil adhesion at the apical surface of the intestinal epithelium. Protein biochemistry, sequencing, confocal microscopic analysis, and immunoprecipitation studies all identify the protein ligand for this antibody as CD44v6. However, selective inhibition of O- or N-linked glycosylation reveal that the antibody is specific for an O-linked glycotope and glycoarray analysis of the GM35 antibody by Core H of the Consortium for Functional Glycomics reveal that this antibody binds with high affinity and specificity to a carbohydrate epitope consistent in structure with sLeA. Inhibition of O-linked glycosylation attenuated both GM35 binding and its functional effects as did specific cleavage of sialic acid residues from the cell surface, using neuraminidase, although the functional effects of cleavage were smaller and harder to assess. CD44v6 has previously been studied as a marker of inflammation in Inflammatory Bowel Disease, and GM35 staining is clearly upregulated by interferon gamma in T84 and HT29 intestinal epithelial cells. The specific enzymes governing the sialylation of CD44v6 in intestinal epithelium have yet to be determined. Analysis of a panel of intestinal epithelial cells including T84, HT29, and Caco-2 cells revealed that cell-type specific regulation of both splicing and glycosylation is essential for the regulation of the expression of the GM35 epitope. And, forcible expression of CD44 in Caco-2 cells, which are negative for CD44v6 at baseline, results only in upregulation of previously expressed isoforms of CD44, but not in GM35 expression. Based on western blot and immunofluorescent staining, GM35 expression is greatest in interferon gamma-treated T84 cells. Thus, we analyzed RNA samples for the expression of glycosylation specific genes in interferon gamma-stimulated T84 cells relative to that of both non-stimulated T84 cells and GM35-negative Caco-2 cells in order to identify glycosylation-specific targets contributing to the interferon gamma-dependent upregulation of sialylated CD44v6 in intestinal epithelial cells.

Project description:This a model from the article: Sequential polarization and imprinting of type 1 T helper lymphocytes by interferon-gamma and interleukin-12. Schulz EG, Mariani L, Radbruch A, Höfer T. Immunity.2009;30(5):666-8. 19409816, Abstract: Differentiation of naive T lymphocytes into type I T helper (Th1) cells requires interferon-gamma and interleukin-12. It is puzzling that interferon-gamma induces the Th1 transcription factor T-bet, whereas interleukin-12 mediates Th1 cell lineage differentiation. We use mathematical modeling to analyze the expression kinetics of T-bet, interferon-gamma, and the IL-12 receptor beta2 chain (IL-12Rbeta2) during Th1 cell differentiation, in the presence or absence of interleukin-12 or interferon-gamma signaling. We show that interferon-gamma induced initial T-bet expression, whereas IL-12Rbeta2 was repressed by T cell receptor (TCR) signaling. The termination of TCR signaling permitted upregulation of IL-12Rbeta2 by T-bet and interleukin-12 signaling that maintained T-bet expression. This late expression of T-bet, accompanied by the upregulation of the transcription factors Runx3 and Hlx, was required to imprint the Th cell for interferon-gamma re-expression. Thus initial polarization and subsequent imprinting of Th1 cells are mediated by interlinked, sequentially acting positive feedback loops of TCR-interferon-gamma-Stat1-T-bet and interleukin-12-Stat4-T-bet signaling. The original model was created by: Edda G. Schulz schulz@drfz.de Theoretical Biophysics, Institute of Biology, Humboldt Universität, Invalidenstrasse 42, 10115 Berlin, Germany. This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.For more information see the terms of use.To cite BioModels Database, please use Le Novère N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

Project description:Rateitschak2012 - Interferon-gamma (IFNγ) induced STAT1 signalling (PC_IFNg100) This model is described in the article: Parameter identifiability and sensitivity analysis predict targets for enhancement of STAT1 activity in pancreatic cancer and stellate cells. Rateitschak K, Winter F, Lange F, Jaster R, Wolkenhauer O. PLoS Comput. Biol. 2012; 8(12): e1002815 Abstract: The present work exemplifies how parameter identifiability analysis can be used to gain insights into differences in experimental systems and how uncertainty in parameter estimates can be handled. The case study, presented here, investigates interferon-gamma (IFNγ) induced STAT1 signalling in two cell types that play a key role in pancreatic cancer development: pancreatic stellate and cancer cells. IFNγ inhibits the growth for both types of cells and may be prototypic of agents that simultaneously hit cancer and stroma cells. We combined time-course experiments with mathematical modelling to focus on the common situation in which variations between profiles of experimental time series, from different cell types, are observed. To understand how biochemical reactions are causing the observed variations, we performed a parameter identifiability analysis. We successfully identified reactions that differ in pancreatic stellate cells and cancer cells, by comparing confidence intervals of parameter value estimates and the variability of model trajectories. Our analysis shows that useful information can also be obtained from nonidentifiable parameters. For the prediction of potential therapeutic targets we studied the consequences of uncertainty in the values of identifiable and nonidentifiable parameters. Interestingly, the sensitivity of model variables is robust against parameter variations and against differences between IFNγ induced STAT1 signalling in pancreatic stellate and cancer cells. This provides the basis for a prediction of therapeutic targets that are valid for both cell types. This model is hosted on BioModels Database and identified by: BIOMD0000000585. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:Glycobiology plays a central role in prostate cancer (PCa), as evidenced by the aberrant glycosylation in PCa-derived glycoproteins. Understanding the protein glycosylation changes underling PCa onset and progression may open opportunities for the discovery of novel biomarkers for PCa detection and stratification as well as targets for PCa metastasis. Advances in mass spectrometry-based glycomics and glycoproteomics have opened up exciting avenues for deep characterisation of the immensely, yet poorly understood complex glycoproteome. In this study, we have used integrated glycomics and glycoproteomics to explore the protein, cell and tumour-grade specific N- and O- glycosylation signatures in surgically-removed fresh PCa tissues grouped into five PCa disease grades and tissues from benign prostatic hyperplasia patients (BPH). Porous graphitised carbon–liquid chromatography (PGC–LC)-MS/MS analysis was used to profile the N-and O-glycome, revealing PCa grade specific N and O glycosylation changes, including oligomannosidic and paucimannosidic, bisecting-GlcNAc and highly branched tri- and tetra-antennary fucosylated and sialylated N-glycans as well as sialylated core 1 and 2 type O-glycans. High resolution LC-MS/MS was then employed to capture the micro and macroheterogeneity of 1,085 N-glycosites (>7,400 unique N-glycopeptides) from 540 N-glycoproteins and 360 O-glycosites (>500 unique O-glycopeptides) from 178 O-glycoproteins and reveal protein and cell specific N- and O- glycosylation changes associated with PCa progression. We also showed PCa grade-specific increase in the expression of oligosaccharyltransferase (OST) subunits that correlate with changes in the site occupancy of N-glycoproteins. In conclusion, this study shows that integrated glycomics and glycoproteomics can provide unprecedented insight into key molecular features and site-specific glycan heterogeneity contributing to the highly diverse tumour-microenvironment and allow us to deconstruct the regulation of the protein, cell and tumour-grade-specific glycosylation associated with PCa onset and development.

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