Views
11 months ago

2008 Scientific Report

  • Text
  • Report
  • Institute
  • Protein
  • Michigan
  • Signaling
  • Tumor
  • Molecular
  • Laboratory

Van Andel Research

Van Andel Research Institute | Scientific Report Research Interests C-reactive protein C-reactive protein (CRP) is a crucial component of the body’s innate immune system. CRP is involved in the recognition and removal of pathogens and dying cells and in the signaling that controls inflammation. While CRP is crucial to the maintenance of health, recent research has demonstrated a possible involvement of CRP in the development of diseases associated with inflammation. A more complete understanding of CRP functions in normal and disease-associated inflammation could have valuable therapeutic implications. We have used a novel method developed in our laboratory, called Antibody Array Interaction Mapping (AAIM), to uncover possible additional roles for CRP in inflammation and disease. A protein’s function is determined in part by its interactions with other proteins, and identifying and measuring changes in those interactions are keys to understanding protein functions. Current methods of detecting protein-protein interactions—such as immunoprecipitation, mass spectrometry, yeast two-hybrid assay, and protein arrays—are not suitable for measuring changes over multiple samples and may require purified proteins instead of native, biological samples. AAIM complements these methods by allowing quantitative, high-throughput comparisons of protein-protein interaction levels in biological samples. We produce multiple, identical arrays containing antibodies targeting a variety of proteins that might interact with each other. A native, nondenatured biological sample such as serum is incubated on each array, and proteins in the sample are captured by the antibodies according to their specificities. After unbound proteins are washed away, each array is probed with a detection antibody that corresponds to one of the capture antibodies, and the detection antibodies localize on the array wherever their targets are found. The pattern of binding of the detection antibodies can reveal potential protein-protein interactions. Using this tool, we have discovered several novel protein-protein interactions in human serum, including previously unknown interactions between CRP and other inflammation-related proteins. The finding of a subset of CRP circulating in complex with inflammatory mediators suggests previously unrecognized functions or sites of action for CRP. An intriguing aspect of this bound form of CRP is that it appears to be conformationally different than the freely circulating form. The bound CRP is structurally altered in a way that produces potent biological effects distinct from those of normal CRP. We have shown a biological context for the bound form of CRP; now we are seeking to determine how the functions of this bound CRP differ from those of free CRP and how abnormal levels of bound CRP might be involved in inflammation-related pathologies. We also are characterizing the components of circulating multiprotein complexes involving CRP and characterizing the details of those interactions. AAIM has been a valuable tool for the discovery and ongoing study of these multiprotein complexes, especially using monoclonal antibodies with defined specificities for various regions and forms of CRP. Other proteomics methods, performed in the collaboration with the Mass Spectrometry and Proteomics lab at VARI, facilitate this work. Glycosylation in pancreatic cancer The development of biomarkers for the accurate and early diagnosis of pancreatic cancer has been challenging. Many of the candidate biomarkers are either elevated in other conditions or only in later-stage disease, leading to unacceptably low specificity and sensitivity. A common molecular feature of pancreatic cancer is alteration of the carbohydrate structures (glycans) that are attached to certain proteins. Glycan alterations can appear at a higher rate than changes in protein abundance, and certain glycan structures may be unique to particular disease states, even at early stages of cancer development. Thus, the detection of particular glycans on specific proteins may form the basis of improved pancreatic cancer biomarkers. 26

VARI | 2008 The key to developing improved markers is the ability to reproducibly measure specific glycans on specific proteins. Many of the carbohydrate structures on proteins in normal and cancer tissues have been characterized using mass spectrometry and enzymatic methods. Those methods are valuable for defining structures, but they do not have the precision or throughput necessary to look at changes in levels between samples, which is necessary to assess biomarker potential. A new method developed in our laboratory provides the means to obtain more detailed information on glycan variation. We use lectins— proteins that bind specific glycan structures—and glycan-binding antibodies to probe the levels of particular glycans on the proteins captured by antibody arrays. This method provides the important feature of allowing comparison between samples of the levels of particular glycans on specific proteins so that we can assess their diagnostic potential. A product based on this technology is now available from GenTel Biosciences (Madison, WI). The class of proteins called mucins shows particularly high levels of glycan alteration in pancreatic cancer. Mucins are longchain, heavily glycosylated proteins on epithelial cell surfaces that have roles in cell protection, interaction with the extracellular space, and regulation of extracellular signaling. Altered carbohydrates on mucins can affect critical processes in cancer such as cell migration or extracellular signaling to the immune system. We have extensively characterized the glycan variations on mucins secreted into the blood of pancreatic cancer patients. In some cases, the levels of certain mucin glycans are altered in cancer patients more often than the levels of the core proteins (Figure 1a). As a result, detection of the glycans performed better as a biomarker than detection of the core proteins (Figure 1b). The efficient analysis of many samples and glycan structures was made possible by the ability to run dozens of samples on a single microscope slide. A device based on that technology, which partitions microscope slides for efficient sample processing, is available from The Gel Company (San Francisco, CA). Our work shows the promise of this approach and points to key directions for further developing biomarkers of pancreatic cancer. Our research now focuses on the goals of identifying the protein carriers of cancer-associated glycans, of identifying the most important cancer-associated glycans and the reagents to detect them, and of applying these discoveries to pancreatic cancer diagnostics (Figure 1c). Figure 1 In addition, we are seeking to better understand the origins of glycan alterations and the functional contribution of these molecules to pancreatic cancer development and progression. Figure 1. Pancreatic cancer biomarker development. a) Comparison of glycan versus protein detection. The level of the MUC5ac core protein in serum samples from cancer patients and healthy subjects, determined using monoclonal antibody (mAb) sandwich assays, is indicated along the vertical axis. The level of glycan CA 19-9 on MUC5ac, determined using a mAb to capture MUC5ac and another antibody to detect CA 19-9 on the captured protein, is indicated along the horizontal axis. b) Receiver-operator characteristic curve analysis comparing the biomarker performance of core protein versus glycan detection. Each curve gives the sensitivity (rate of true positive detection) and the specificity (rate of true negative detection) for discriminating cancer subjects from control subjects at various thresholds of discrimination. “AUC” is area-under-the-curve, indicating the total discriminating ability of each marker. c) Cluster analysis. The glycan measurements along the vertical axis were taken in the samples indicated along the horizontal axis; the color of each square is the level of each measurement (see the color bar). The rows and columns were ordered (clustered) by similarity, showing consistently increased levels in the cancer patients. 27

Publications by Year