The proteome of normal pancreatic juice.

OBJECTIVES: The aims of this study were to characterize the proteome of normal pancreatic juice, to analyze the effect of secretin on the normal proteome, and to compare these results with published data from patients with pancreatic cancer. METHODS: Paired pancreatic fluid specimens (before and after intravenous secretin stimulation) were obtained during endoscopic pancreatography from 3 patients without significant pancreatic pathology. Proteins were identified and quantified by mass spectrometry-based protein quantification technology. The human RefSeq (NCBI) database was used to compare the data in samples from patients without pancreatic disease with published data from 3 patients with pancreatic cancer. RESULTS: A total of 285 proteins were identified in normal pancreatic juice. Ninety had sufficient amino acid sequences identified to characterize the protein with a high level of confidence. All 90 proteins were present before and after secretin administration but with altered relative concentrations, usually by 1 to 2 folds, after stimulation. Comparison with 170 published pancreatic cancer proteins yielded an overlap of only 42 proteins. CONCLUSIONS: Normal pancreatic juice contains multiple proteins related to many biological processes. Secretin alters the concentration but not the spectrum of these proteins. The pancreatic juice proteome of patients without pancreatic disease and that of patients with pancreatic cancer differ markedly.

Proteomic Characterization of Cerebrospinal Fluid from Ataxia-Telangiectasia (A-T) Patients Using a LC/MS-Based Label-Free Protein Quantification Technology.

Cerebrospinal fluid (CSF) has been used for biomarker discovery of neurodegenerative diseases in humans since biological changes in the brain can be seen in this biofluid. Inactivation of A-T-mutated protein (ATM), a multifunctional protein kinase, is responsible for A-T, yet biochemical studies have not succeeded in conclusively identifying the molecular mechanism(s) underlying the neurodegeneration seen in A-T patients or the proteins that can be used as biomarkers for neurologic assessment of A-T or as potential therapeutic targets. In this study, we applied a high-throughput LC/MS-based label-free protein quantification technology to quantitatively characterize the proteins in CSF samples in order to identify differentially expressed proteins that can serve as potential biomarker candidates for A-T. Among 204 identified CSF proteins with high peptide-identification confidence, thirteen showed significant protein expression changes. Bioinformatic analysis revealed that these 13 proteins are either involved in neurodegenerative disorders or cancer. Future molecular and functional characterization of these proteins would provide more insights into the potential therapeutic targets for the treatment of A-T and the biomarkers that can be used to monitor or predict A-T disease progression. Clinical validation studies are required before any of these proteins can be developed into clinically useful biomarkers.

Serum proteomic analysis of diet-induced steatohepatitis and metabolic syndrome in the Ossabaw miniature swine.

We recently developed a nutritional model of steatohepatitis and metabolic syndrome in Ossabaw pigs. Here we describe changes in the serum proteome of pigs fed standard chow (control group; n = 7), atherogenic diet (n = 5), or modified atherogenic diet (M-ath diet group; n = 6). Pigs fed atherogenic diet developed metabolic syndrome and mildly abnormal liver histology, whereas pigs fed M-ath diet exhibited severe metabolic syndrome and liver injury closely resembling human nonalcoholic steatohepatitis (NASH). Using a label-free mass spectrometry-based proteomics approach, we identified 1,096 serum proteins, 162 of which changed significantly between any two diet groups (false discovery rate <5%). Biological classification of proteins with significant changes revealed functions previously implicated in development of NASH in humans, including immune system regulation and inflammation (orosomucoid 1, serum amyloid P component, paraoxonase 1, protein similar to alpha-2-macroglobulin precursor, beta-2-microglobulin, p101 protein, and complement components 2 and C8G), lipid metabolism (apolipoproteins C-III, E, E precursor, B, and N), structural and extracellular matrix proteins (transthyretin and endopeptidase 24.16 type M2), and coagulation [carboxypeptidase B2 (plasma)]. Several proteins with significant differential expression in pigs were also identified in our recent human proteomics study as changing significantly in serum from patients across the spectrum of nonalcoholic fatty liver disease, including apolipoproteins C-III and B, orosomucoid 1, serum amyloid P component, transthyretin, paraoxonase 1, and a protein similar to alpha-2-macroglobulin precursor. This serum proteomic analysis provides additional information about the pathogenesis of NASH and further characterizes our large animal model of diet-induced steatohepatitis and metabolic syndrome in Ossabaw pigs.

Serum proteomics and biomarker discovery across the spectrum of nonalcoholic fatty liver disease.

UNLABELLED: Nonalcoholic fatty liver disease (NAFLD), ranging from relatively benign simple steatosis to progressive nonalcoholic steatohepatitis (NASH) and fibrosis, is an increasingly common chronic liver disease. Liver biopsy is currently the only reliable tool for staging the subtypes of NAFLD; therefore, noninvasive serum biomarkers for evaluation of liver disease and fibrosis are urgently needed. We performed this study to describe changes in the serum proteome and identify biomarker candidates in serum samples from 69 patients with varying stages of NAFLD (simple steatosis, NASH, and NASH with advanced bridging [F3/F4] fibrosis) and 16 obese controls. Using a label-free mass spectrometry-based approach we identified over 1,700 serum proteins with a peptide identification (ID) confidence level of >75%, 605 of which changed significantly between any two patient groups (false discovery rate <5%). Importantly, expression levels of 55 and 15 proteins changed significantly between the simple steatosis and NASH F3/F4 group and the NASH and NASH F3/F4 group, respectively. Classification of proteins with significant changes showed involvement in immune system regulation and inflammation, coagulation, cellular and extracellular matrix structure and function, and roles as carrier proteins in the blood. Further, many of these proteins are synthesized exclusively by the liver and could potentially serve as diagnostic biomarkers for identifying and staging NAFLD. CONCLUSION: This proteomic analysis reveals important information regarding the pathogenesis/progression of NAFLD and NASH and demonstrates key changes in serum protein expression levels between control subjects and patients with different stages of fatty liver. Future validation of these potential biomarkers is needed such that these proteins may be used in place of liver biopsy to facilitate diagnosis and treatment of patients with NAFLD.

LC/MS-based quantitative proteomic analysis of paraffin-embedded archival melanomas reveals potential proteomic biomarkers associated with metastasis.

BACKGROUND: Melanoma metastasis status is highly associated with the overall survival of patients; yet, little is known about proteomic changes during melanoma tumor progression. To better understand the changes in protein expression involved in melanoma progression and metastasis, and to identify potential biomarkers, we conducted a global quantitative proteomic analysis on archival metastatic and primary melanomas. METHODOLOGY AND FINDINGS: A total of 16 metastatic and 8 primary cutaneous melanomas were assessed. Proteins were extracted from laser captured microdissected formalin fixed paraffin-embedded archival tissues by liquefying tissue cells. These preparations were analyzed by a LC/MS-based label-free protein quantification method. More than 1500 proteins were identified in the tissue lysates with a peptide ID confidence level of >75%. This approach identified 120 significant changes in protein levels. These proteins were identified from multiple peptides with high confidence identification and were expressed at significantly different levels in metastases as compared with primary melanomas (q-Value<0.05). CONCLUSIONS AND SIGNIFICANCE: The differentially expressed proteins were classified by biological process or mapped into biological system networks, and several proteins were implicated by these analyses as cancer- or metastasis-related. These proteins represent potential biomarkers for tumor progression. The study successfully identified proteins that are differentially expressed in formalin fixed paraffin-embedded specimens of metastatic and primary melanoma.

Label-free mass spectrometry-based protein quantification technologies in proteomic analysis.

Major technological advances have made proteomics an extremely active field for biomarker discovery and validation in recent years. These improvements have lead to an increased emphasis on larger scale, faster and more efficient methods for protein biomarker discoveries in human tissues, cells and biofluids. However, most current proteomic methodologies for biomarker discovery and validation are not highly automated and generally labour intensive and expensive. Improved automation as well as software programs capable of handling a large amount of data are essential in order to reduce the cost of discovery and increase the throughput. In this review, we will discuss and describe the label-free mass spectrometry-based protein quantification technologies and a case study utilizing one of these methods for biomarker discovery.

A multiple reaction monitoring method for absolute quantification of the human liver alcohol dehydrogenase ADH1C1 isoenzyme.

Although significant progress has been made in protein quantification using mass spectrometry during recent years, absolute protein quantification in complex biological systems remains a challenging task in proteomics. The use of stable isotope-labeled standard peptide is the most commonly used strategy for absolute quantification, but it might not be suitable in all instances. Here we report an alternative strategy that employs a stable isotope-labeled intact protein as an internal standard to absolutely quantify the alcohol dehydrogenase (ADH) expression level in a human liver sample. In combination with a new targeted proteomics approach employing the method of multiple reaction monitoring (MRM), we precisely and quantitatively measured the absolute protein expression level of an ADH isoenzyme, ADH1C1, in human liver. Isotope-labeled protein standards are predicted to be particularly useful for measurement of highly homologous isoenzymes such as ADHs where multiple signature peptides can be examined by MRM in a single experiment.

Searching for potential biomarkers of cisplatin resistance in human ovarian cancer using a label-free LC/MS-based protein quantification method.

Platinum-based chemotherapy, such as cisplatin, is the primary treatment for ovarian cancer. However, drug resistance has become a major impediment to the successful treatment of ovarian cancer. To date, the molecular mechanisms of resistance to platinum-based chemotherapy remain unclear. In this study, we applied an LC/MS-based protein quantification method to examine the global protein expression of two pairs of ovarian cancer cell lines, A2780/A2780-CP (cisplatin-sensitive/cisplatin-resistant) and 2008/2008-C13*5.25 (cisplatin-sensitive/cisplatin-resistant). We identified and quantified over 2000 proteins from these cell lines and 760 proteins showed significant expression changes with a false discovery rate of less than 5% between paired groups. Based on the results we obtained, we suggest several potential pathways that may be involved in cisplatin resistance in human ovarian cancer. This study provides not only a new proteomic platform for large-scale quantitative protein analysis, but also important information for discovery of potential biomarkers of cisplatin resistance in ovarian cancer. Furthermore, these results may be clinically relevant for diagnostics, prognostics, and therapeutic improvement for ovarian cancer treatment.

Tissue heterogeneity of the mammalian mitochondrial proteome.

The functionality of the mitochondrion is primarily determined by nuclear encoded proteins. The mitochondrial functional requirements of different tissues vary from a significant biosynthetic role (liver) to a primarily energy metabolism-oriented organelle (heart). The purpose of this study was to compare the mitochondrial proteome from four different tissues of the rat, brain, liver, heart, and kidney, to provide insight into the extent of mitochondrial heterogeneity and to further characterize the overall mitochondrial proteome. Mitochondria were isolated, solubilized, digested, and subjected to quantitative liquid chromatography-mass spectroscopy. Of the 16,950 distinct peptides detected, 8,045 proteins were identified. High-confidence identification threshold was reached by 1,162 peptides, which were further analyzed. Of these 1,162 proteins, 1,149 were significantly different in content (P and q values < 0.05) between at least 2 tissues, whereas 13 were not significantly different between any tissues. Confirmation of the mitochondrial origin of proteins was determined from the literature or via NH(2)-terminal mitochondrial localization signals. With these criteria, 382 proteins in the significantly different groups were confirmed to be mitochondrial, and 493 could not be confirmed to be mitochondrial but were not definitively localized elsewhere in the cell. A total of 145 proteins were assigned to the rat mitochondrial proteome for the first time via their NH(2)-terminal mitochondrial localization signals. Among the proteins that were not significantly different between tissues, three were confirmed to be mitochondrial. Most notable of the significantly different proteins were histone family proteins and several structural proteins, including tubulin and intermediate filaments. The mitochondrial proteome from each tissue had very specific characteristics indicative of different functional emphasis. These data confirm the notion that mitochondria are tuned by the nucleus for specific functions in different tissues.

SUM: a new way to incorporate mismatch probe measurements.

Affymetrix's high-density oligonucleotide arrays offer an exciting technology in biomedical research. With more and more statistical involvement in every step of the process, there has been a constant effort to make sure that the expression data are appropriately extracted in the first place. According to Affymetrix GeneChip technology, each gene is represented by 11-20 oligo probe pairs; the challenge is how to extract one meaningful number, expression, from the 11-20 pairs of numbers. More specifically, there is first a need to differentiate the components of specific binding, nonspecific binding, and optical background noise in both PM and MM probes, and then an expression measure that is proportional to the true abundance of transcripts is to be derived. A new method, SUM, which sums up PM and MM values and then follows a process similar to that of RMA, is considered. The performance of SUM is investigated and compared to the three most popular methods, MAS5, dChip, and RMA. The assessments are based on a well-controlled experiment dataset that is publicly available. The results show that in several respects the performance of SUM is comparable to that of RMA and dChip, and all three of these methods show some advantages over MAS5. There is some evidence showing that SUM has higher differential sensitivity than other methods in certain situations.

Phencyclidine-induced changes in rat cortical gene expression identified by microarray analysis: implications for schizophrenia.

Acute phencyclidine induces schizophrenia-like symptoms in healthy humans and psychotic episodes in schizophrenics. Although phencyclidine is known as a N-methyl d-aspartate receptor antagonist (NMDA-R), the molecular events underlying the behavioral symptoms remain largely unknown. Statistical analysis of oligonucleotide microarray data was used to identify phencyclidine-induced alterations in rat cortical gene expression. Acute phencyclidine produced a statistically significant change in 477 genes in rat prefrontal cortex (PFC), a brain area associated with cognitive dysfunction in schizophrenics. Real-time quantitative PCR (RTQ-PCR) confirmed a subset of these changes ranging from -59% to 255% (smallest confirmation: -19%). Subsequent time-course and dose-response studies using RTQ-PCR confirmed and extended the original microarray results. At the molecular level, genes altered by phencyclidine are related to diverse biological processes including stress, inflammatory response, growth and development, neural plasticity and signal transduction. Further analysis, aimed at assessing the relevance of our results to schizophrenia, revealed dysregulation of genes related to: (i) thalamocortical projections, (ii) neurotransmission and neuromodulation, (iii) thyroid hormone activity, (iv) oligodendrocyte linage, (v) brain lipid metabolism, (vi) sleep architecture and (viii) the velocardiofacial syndrome.

At what scale should microarray data be analyzed?

INTRODUCTION: The hybridization intensities derived from microarray experiments, for example Affymetrix's MAS5 signals, are very often transformed in one way or another before statistical models are fitted. The motivation for performing transformation is usually to satisfy the model assumptions such as normality and homogeneity in variance. Generally speaking, two types of strategies are often applied to microarray data depending on the analysis need: correlation analysis where all the gene intensities on the array are considered simultaneously, and gene-by-gene ANOVA where each gene is analyzed individually. AIM: We investigate the distributional properties of the Affymetrix GeneChip signal data under the two scenarios, focusing on the impact of analyzing the data at an inappropriate scale. METHODS: The Box-Cox type of transformation is first investigated for the strategy of pooling genes. The commonly used log-transformation is particularly applied for comparison purposes. For the scenario where analysis is on a gene-by-gene basis, the model assumptions such as normality are explored. The impact of using a wrong scale is illustrated by log-transformation and quartic-root transformation. RESULTS: When all the genes on the array are considered together, the dependent relationship between the expression and its variation level can be satisfactorily removed by Box-Cox transformation. When genes are analyzed individually, the distributional properties of the intensities are shown to be gene dependent. Derivation and simulation show that some loss of power is incurred when a wrong scale is used, but due to the robustness of the t-test, the loss is acceptable when the fold-change is not very large.

Assessing the variability in GeneChip data.

INTRODUCTION: Oligonucleotide and cDNA microarray experiments are now common practice in biological science research. The goal of these experiments is generally to gain clues about the functions of genes by measuring how their expression levels rise and fall in response to changing experimental conditions. Measures of gene expression are affected, however, by a variety of factors. This paper introduces statistical methods to assess the variability of Affymetrix GeneChip data due to randomness. METHODS: The variation of Affymetrix's GeneChip signal data are quantified at both chip level and individual gene level, respectively, by the agreement study method and variance components method. Three agreement measurement methods are introduced to assess the variability among chips. Variation sources for gene expression data are decomposed into four categories: systematic experiment variation, treatment effect, biological variation, and chip variation. The focus of this paper is on evaluating and comparing the last two kinds of variations. RESULTS: Measurement of agreement and variance components methods were applied to an experimental data, and the calculation and interpretation were exemplified. The variability between biological samples were shown to exist and were assessed at both the chip level and individual gene level. Using the variance components method, it was found that the biological and chip variation are roughly comparable. The Statistical Analysis System (SAS) program for doing the agreement studies can be obtained from the correspondence author.