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.

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.

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.

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.

Protein biomarkers in cancer: natural glycoprotein microarray approaches.

Protein glycosylation is the most versatile and common protein modification and plays important roles in various biological processes and disease progression. In this review, the development of microarray technology for protein glycosylation analysis is described. Three types are discussed: carbohydrate, lectin and natural glycoprotein microarrays. The advantages of microarray technology to study protein glycosylation are high-volume throughput coupled with a highly miniaturized platform. These techniques show great promise for detecting interactions that involve carbohydrates and as a screening tool to detect glycan patterns important for the early diagnosis of disease.

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.

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.

A novel phosphoprotein analysis scheme for assessing changes in premalignant and malignant breast cell lines using 2D liquid separations, protein microarrays and tandem mass spectrometry.

An analysis of phosphorylation changes that occur during cancer progression would provide insights into the molecular pathways responsible for a malignant phenotype. In this study we employed a novel coupling of 2D-liquid separations and protein microarray technology to reveal changes in phosphoprotein status between premalignant (AT1) and malignant (CA1a) cell lines derived from the human MCF10A breast cell lines. Intact proteins were first separated according to their isoelectric point and hydrophobicities, then arrayed on SuperAmine glass slides. Phosphoproteins were detected using the universal, inorganic phospho-sensor dye, ProQ Diamond. Using this dye, out of 140 spots that were positive for phosphorylation, a total of 85 differentially expressed spots were detected over a pH range of 7.2 to 4.0. Proteins were identified and their peptides sequenced by mass spectrometry. The strategy enabled the identification of 75 differentially expressed phosphoproteins, from which 51 phosphorylation sites in 27 unique proteins were confirmed. Interestingly, the majority of differentially expressed phosphorylated proteins observed were nuclear proteins. Three regulators of apoptosis, Bad, Bax and Acinus, were also differentially phosphorylated in the two cell lines. Further development of this strategy will facilitate an understanding of the mechanisms involved in malignancy progression and other disease-related phenotypes.

Early detection and biomarkers in pancreatic cancer.

Major advances in cancer control will be greatly aided by early detection for diagnosing and treating cancer in its preinvasive state before metastasis. Unfortunately, for pancreatic ductal adenocarcinoma (PDAC), which is the fourth leading cause of cancer-related death in the United States, effective early detection and screening are currently not available and tumors are typically diagnosed at a late stage, frequently after metastasis. Partly because of low sensitivity/specificity, existing biomarkers such as CA19-9 are not adequate as early detection markers of pancreatic cancer. Thus, a great need exists for new biomarkers for pancreatic cancer. This article focuses on recent developments in the identification of new serum protein biomarkers that are useful in the early detection of PDAC.

Differential expression of acidic proteins with progression in the MCF10 model of human breast disease.

A proteomic characterization of one premalignant (MCF10AT1) and two malignant (MCF10CA1a and MCF10 CA1d) human breast cancer cell lines has been performed using a combination of two-dimensional liquid separations and mass spectrometry. Chromatofocusing (CF) and NPS-RP-HPLC are combined with ESI-TOF-MS to resolve and detect intact proteins. Simultaneously, fractions are collected and digested for protein identification using MALDI-MS peptide mass fingerprinting. Following protein identification a small database was compiled for use in comparison between IDs and measured masses taking into account variables such as pI, hydrophobicity and potential modifications. Out of 239 mass bands detected between pH 4.6 and 6.0, 133 have been definitively associated with identified proteins and 67 show consistent up/down regulation in two malignant breast cancer cell lines relative to the precursor premalignant cell line. Of these, 8 are also altered in the premalignant MCF10AT1 cell line by treatment with estradiol. Differentially expressed proteins indicate significant changes to the cytoskeleton, cellular metabolism, and adaptation to environmental stressors in malignant cell lines.

Glycoprotein microarrays with multi-lectin detection: unique lectin binding patterns as a tool for classifying normal, chronic pancreatitis and pancreatic cancer sera.

Pancreatic cancer is the fourth leading cause of cancer-related death in the United States, with a 5-year survival rate of less than 4%. Effective early detection and screening are currently not available, and tumors are typically diagnosed at a late stage, frequently after metastasis. Existing clinical markers of pancreatic cancer lack specificity, as they are also found in inflammatory diseases of the pancreas and biliary tract. In the work described here, naturally occurring glycoproteins were enriched by using lectin affinity chromatography and then further resolved by nonporous reversed-phase chromatography. Glycoprotein microarrays were then printed and probed with a variety of lectins to screen glycosylation patterns in sera from normal, chronic pancreatitis, and pancreatic cancer patients. Ten normal, 8 chronic pancreatitis, and 6 pancreatic cancer sera were investigated. Data from the glycoprotein microarrays were analyzed using bioinformatics approaches including principal component analysis (PCA) and hierarchical clustering (HC). Both normal and chronic pancreatitis sera were found to cluster close together, although in two distinct groups, whereas pancreatic cancer sera were significantly different from the other two groups. Both sialylation and fucosylation increased as a function of cancer on several proteins including Hemopexin, Kininogen-1, Antithrombin-III, and Haptoglobin-related protein, whereas decreased sialylation was detected on plasma protease C1 inhibitor. Target alterations on glycosylations were verified by lectin blotting experiments and peptide mapping experiments using microLC-ESI-TOF. These altered glycan structures may have utility for the differential diagnosis of pancreatic cancer and chronic pancreatitis and identify critical differences between biological samples from patients with different clinical conditions.

Comprehensive analysis of proteins of pH fractionated samples using monolithic LC/MS/MS, intact MW measurement and MALDI-QIT-TOF MS.

A comprehensive platform that integrates information from the protein and peptide levels by combining various MS techniques has been employed for the analysis of proteins in fully malignant human breast cancer cells. The cell lysates were subjected to chromatofocusing fractionation, followed by tryptic digestion of pH fractions for on-line monolithic RP-HPLC interfaced with linear ion trap MS analysis for rapid protein identification. This unique approach of direct analysis of pH fractions resulted in the identification of large numbers of proteins from several selected pH fractions, in which approximately 1.5 microg of each of the pH fraction digests was consumed for an analysis time of ca 50 min. In order to combine valuable information retained at the protein level with the protein identifications obtained from the peptide level information, the same pH fraction was analyzed using nonporous (NPS)-RP-HPLC/ESI-TOF MS to obtain intact protein MW measurements. In order to further validate the protein identification procedures from the fraction digest analysis, NPS-RP-HPLC separation was performed for off-line protein collection to closely examine each protein using MALDI-TOF MS and MALDI-quadrupole ion trap (QIT)-TOF MS, and excellent agreement of protein identifications was consistently observed. It was also observed that the comparison to intact MW and other MS information was particularly useful for analyzing proteins whose identifications were suggested by one sequenced peptide from fraction digest analysis.

Screening of glycosylation patterns in serum using natural glycoprotein microarrays and multi-lectin fluorescence detection.

Protein glycosylation has been implicated in key biological processes including immunological recognition, cellular adhesion, protein folding, and signaling as well as disease progression. Although several methods are available to assess glycosylation of protein structures, none of them is able to screen complex biological samples at a global as well as an individual scale. A novel strategy presented here uses an all-liquid phase enrichment and prefractionation methodology coupled to glycoprotein microarray technology using a multiple lectin-based, biotin-streptavidin detection scheme. Selective detection of glycan structures was made possible by employing multiple lectins to screen glycoprotein standards as well as serum samples from normal subjects or patients with chronic pancreatitis or pancreatic cancer. Interestingly, in some instances, a greater degree of glycosylation was seen in proteins that were underexpressed based on the reversed-phase chromatogram alone. Studies with standard proteins established the limits of detection to be in the 2.5-5-fmol range. Studies on serum samples showed differences in glycosylation patterns, particularly with respect to sialylation, mannosylation, and fucosylation, in normal, pancreatitis, and cancer sera. By coupling glycoprotein enrichment and fractionation with a microarray platform, we have shown that naturally occurring glycoproteins from human serum can be screened and characterized for different glycan structures, thereby allowing one to do comparative studies that monitor individual glycosylation changes within a glycoproteome representing different biological states. This approach may be useful to identify potential biomarkers in cancer.

Flexibility in substrate recognition by thimet oligopeptidase as revealed by denaturation studies.

Thimet oligopeptidase (TOP) is a soluble metalloendopeptidase belonging to a family of enzymes including neurolysin and neprilysin that utilize the HEXXH metal-binding motif. TOP is widely distributed among cell types and is able to cleave a number of structurally unrelated peptides. A recent focus of interest has been on structure-function relationships in substrate selectivity by TOP. The enzyme's structural fold comprises two domains that are linked at the bottom of a deep substrate-binding cleft via several flexible loop structures. In the present study, fluorescence spectroscopy has been used to probe structural changes in TOP induced by the chemical denaturant urea. Fluorescence emission, anisotropy and collisional quenching data support a two-step unfolding process for the enzyme in which complete loss of the tertiary structure occurs in the second step. Complete loss of activity and loss of catalytic Zn(II) from the active site, monitored by absorption changes of the metal chelator 4-(2-pyridylazo)-resorcinol, are also connected with the second step. In contrast, the first unfolding event, which is linked to changes in the non-catalytic domain, leads to a sharp increase in kcat towards a 9-residue substrate and a sharp decrease in kcat for a 5-residue substrate. Thus a conformational change in TOP has been directly correlated with a change in substrate selectivity. These results provide insight into how the enzyme can process the range of structurally unrelated peptides necessary for its many physiological roles.