Tasneem H. Patwa

Plasma glycoprotein profiling for colorectal cancer biomarker identification by lectin glycoarray and lectin blot

Colorectal cancer (CRC) remains a major worldwide cause of cancer-related morbidity and mortality largely due to the insidious onset of the disease. The current clinical procedures utilized for disease diagnosis are invasive, unpleasant, and inconvenient; hence, the need for simple blood tests that could be used for the early detection of CRC. In this work, we have developed methods for glycoproteomics analysis to identify plasma markers with utility to assist in the detection of colorectal cancer (CRC). Following immunodepletion of the most abundant plasma proteins, the plasma N -linked glycoproteins were enriched using lectin affinity chromatography and subsequently further separated by nonporous silica reversed-phase (NPS-RP)-HPLC. Individual RP-HPLC fractions were printed on nitrocellulose coated slides which were then probed with lectins to determine glycan patterns in plasma samples from 9 normal, 5 adenoma, and 6 colorectal cancer patients. Statistical tools, including principal component analysis, hierarchical clustering, and Z-statistics analysis, were employed to identify distinctive glycosylation patterns. Patients diagnosed with colorectal cancer or adenomas were shown to have dramatically higher levels of sialylation and fucosylation as compared to normal controls. Plasma glycoproteins with aberrant glycosylation were identified by nano-LC-MS/MS, while a lectin blotting methodology was used to validate proteins with significantly altered glycosylation as a function of cancer progression. The potential markers identified in this study for diagnosis to distinguish colorectal cancer from adenoma and normal include elevated sialylation and fucosylation in complement C3, histidine-rich glycoprotein, and kininogen-1. These potential markers of colorectal cancer were subsequently validated by lectin blotting in an independent set of plasma samples obtained from 10 CRC patients, 10 patients with adenomas, and 10 normal subjects. These results demonstrate the utility of this strategy for the identification of N -linked glycan patterns as potential markers of CRC in human plasma, and may have the utility to distinguish different disease states.

GLYCOPROTEIN ANALYSIS USING PROTEIN MICROARRAYS AND MASS SPECTROMETRY

Protein glycosylation plays an important role in a multitude of biological processes such as cell-cell recognition, growth, differentiation, and cell death. It has been shown that specific glycosylation changes are key in disease progression and can have diagnostic value for a variety of disease types such as cancer and inflammation. The complexity of carbohydrate structures and their derivatives makes their study a real challenge. Improving the isolation, separation, and characterization of carbohydrates and their glycoproteins is a subject of increasing scientific interest. With the development of new stationary phases and molecules that have affinity properties for glycoproteins, the isolation and separation of these compounds have advanced significantly. In addition to detection with mass spectrometry, the microarray platform has become an essential tool to characterize glycan structure and to study glycosylation-related biological interactions, by using probes as a means to interrogate the spotted or captured glycosylated molecules on the arrays. Furthermore, the high-throughput and reproducible nature of microarray platforms have been highlighted by its extensive applications in the field of biomarker validation, where a large number of samples must be analyzed multiple times. This review covers a brief survey of the other experimental methodologies that are currently being developed and used to study glycosylation and emphasizes methodologies that involve the use of microarray platforms. This review describes recent advances in several options of microarray platforms used in glycoprotein analysis, including glycoprotein arrays, glycan arrays, lectin arrays, and antibody/lectin arrays. The translational use of these arrays in applications related to characterization of cells and biomarker discovery is also included.

The identification of phosphoglycerate kinase-1 and histone H4 autoantibodies in pancreatic cancer patient serum using a natural protein microarray.

Protein microarrays have been used to explore whether a humoral response to pancreatic cancer‐specific tumor antigens has utility as a biomarker of pancreatic cancer. To determine if such arrays can be used to identify novel autoantibodies in the sera from pancreatic cancer patients, proteins from a pancreatic adenocarcinoma cell line (MIAPACA) were resolved by 2‐D liquid‐based separations, and then arrayed on nitrocellulose slides. The slides were probed with serum from a set of patients diagnosed with pancreatic cancer and compared with age‐ and sex‐matched normal subjects. To account for patient‐to‐patient variability, we used a rank‐based non‐parametric statistical testing approach in which proteins eliciting significant differences in the humoral response in cancer compared with control samples were identified. The prediction analysis for microarrays classification algorithm was used to explore the classification power of the proteins found to be differentially expressed in cancer and control sera. The generalization error of the classification analysis was estimated using leave‐one‐out cross‐validation. A serum diagnosis of pancreatic cancer in this set was predicted with 86.7% accuracy, with a sensitivity and specificity of 93.3 and 80%, respectively. Candidate autoantibody biomarkers identified using this approach were studied for their classification power by performing a humoral response experiment on recombinant proteins using an independent sample set of 238 serum samples. Phosphoglycerate kinase‐1 and histone H4 were noted to elicit a significant differential humoral response in cancer sera compared with age‐ and sex‐matched sera from normal patients and patients with chronic pancreatitis and diabetes. This work demonstrates the use of natural protein arrays to study the humoral response as a means to search for the potential markers of cancer in serum.

Enhanced Detection of Autoantibodies on Protein Microarrays Using a Modified Protein Digestion Technique

High-throughput studies to determine differential immune (humoral) response to diseases are becoming of increasing interest because the information they provide can help in early diagnosis as well as monitoring of therapeutics. Protein microarrays are a high-throughput and convenient technology that can be applied to the study of the humoral response. Proteins can be arrayed on slides and then probed with serum from different classes of patients to observe differences that may exist among autoantibodies that reflect differences in disease states. However, such studies may be difficult to interpret due to the weak overall signal response of such protein microarrays. We propose that this weak signal response is due to the physical positioning of the disease proteins that renders them sterically hindered from binding partners in the serum. In this study, we hypothesize that reducing the complexity and size of the disease proteins by chemical digestion using cyanogen bromide (CNBr) may enhance the overall signal from the humoral response and facilitate visualization of disease-specific responses in various classes of serum. A modified protein microarray methodology using CNBr digestion is presented here. The new workflow was applied to a set of 10 serum samples from healthy subjects, 10 from patients with chronic pancreatitis and 10 from patients diagnosed with pancreatic cancer and the results were compared to results obtained in the absence of CNBr digestion. CNBr digestion allowed the identification of 10 additional autoantibodies that responded to serum, 5 of which were unique to pancreatitis and cancer sera. This new methodology may increase the sensitivity of microarray studies measuring autoantibodies in serum.

Analysis of protein glycosylation and phosphorylation using liquid phase separation, protein microarray technology, and mass spectrometry.

Protein glycosylation and phosphorylation are very common posttranslational modifications. The alteration of these modifications in cancer cells is closely related to the onset and progression of cancer and other disease states. In this protocol, strategies for monitoring the changes in protein glycosylation and phosphorylation in serum or tissue cells on a global scale and specifically characterizing these alterations are included. The technique is based on lectin affinity enrichment for glycoproteins, all liquid-phase two-dimensional fractionation, protein microarray, and mass spectrometry technology. Proteins are separated based on pI in the first dimension using chromatofocusing (CF) or liquid isoelectric focusing (IEF) followed by the second-dimension separation using nonporous silica RP-HPLC. Five lectins with different binding specificities to glycan structures are used for screening glycosylation patterns in human serum through a biotin streptavidin system. Fluorescent phosphodyes and phosphospecific antibodies are employed to detect specific phosphorylated proteins in cell lines or human tissues. The purified proteins of interest are identified by peptide sequencing. Their modifications including glycosylation and phosphorylation could be further characterized by mass-spectrometry-based approaches. These strategies can be used in biological samples for large-scale glycoproteome/phosphoproteome screening as well as for individual protein modification analysis.

The identification of auto-antibodies in pancreatic cancer patient sera using a naturally fractionated Panc-1 cell line

The immunogenic nature of cancer can be explored to distinguish pancreatic cancer from related non-cancer conditions. We describe a liquid-based microarray approach followed by statistical analysis and confirmation for discovery of auto-immune biomarkers for pancreatic cancer. Proteins from the Panc-1 pancreatic cancer cell line were fractionated using a 2-D liquid separation method into over 1052 fractions and spotted onto nitrocellulose coated glass slides. The slides were hybridized with 37 pancreatic cancer sera, 24 chronic pancreatitis sera and 23 normal sera to detect elevated levels of reactivity against the proteins in spotted fractions. The response data obtained from protein microarrays was first analyzed by Wilcoxon Rank-Sum Tests to generate two lists of fractions that positively responded to the cancer sera and showed p-values less than 0.02 in the pairwise comparison between cancer specimens and normal and chronic pancreatitis specimens. The top 3 fractions with the lowest correlations were combined in receiver operating characteristic analyses. The area-under-the-curve (AUC) values are 0.813 and 0.792 for cancer vs. normal and cancer vs. pancreatitis respectively. Outlier-Sum statistics were then applied to the microarray data to determine the existence of outliers exclusive in cancer sera. The selected fractions were identified by LC-MS/MS. We further confirmed the occurrence of outliers with three proteins among cancer samples in a confirmation experiment using a separate dataset of 165 serum samples containing 48 cancer sera and 117 non-cancer controls. Phosphoglycerate kinase 1 (PGK1) elicited greater reactivity in 20.9% (10 in 48) of the samples in the cancer group, while no outlier was present in the non-cancer groups.

All-Liquid Separations, Protein Microarrays, and Mass Spectrometry to Interrogate Serum Proteomes: An Application to Serum Glycoproteomics

Disease-related changes in serum proteins are reasonable targets for early detection particularly due to the noninvasive approach in obtaining samples. Glycoproteins specifically have been implicated in a variety of disease types ranging from immune diseases to cancers. High-throughput screening methods that can assess glycosylation states of all serum proteins in normal and diseased sample groups can facilitate early detection as well as shed light on disease progression mechanisms. Outlined here is a combination of liquid separation, protein microarray, and mass spectrometry approach to highlight candidate proteins involved in diseases through glycosylation mechanisms.

Identification of differentially expressed spatial clusters using humoral response microarray data

The antibody microarray is a powerful chip-based technology for profiling hundreds of proteins simultaneously and is used increasingly nowadays. To study humoral response in pancreatic cancers, Patwa et al. (2007) developed a two-dimensional liquid separation technique and built a two-dimensional antibody microarray. However, identifying differential expression regions on the antibody microarray requires the use of appropriate statistical methods to fairly assess the large amounts of data generated. In this paper, we propose a permutation-based test using spatial information of the two-dimensional antibody microarray. By borrowing strength from the neighboring differentially expressed spots, we are able to detect the differential expression region with very high power controlling type I error at 0.05 in our simulation studies. We also apply the proposed methodology to a real microarray dataset.