Selecting targets for therapeutic validation through differential protein expression using chromatography-mass spectrometry

The identification of potential targets for therapeutic intervention can be accomplished on a systematic basis by a variety of techniques that include quantitative analysis of gene-specific mRNA levels and expressed proteins in normal and diseased cells. Differences in the expression levels of nucleic acid and protein gene products could suggest protein drug targets that are directly causative of disease, or reveal biochemical pathways that could be modulated by therapeutic molecules. Any effort based on mRNA or protein expression level comparisons could be confounded by a number of factors: level in steady-state may not be correlated with actual encoded protein levels; differentially expressed protein levels might be a result of disease process, and not causative of the process, and therapeutic intervention based on such a difference will be unproductive and the differential expression of mRNA or protein may be the result of biological variation unrelated to the disease process under study. In order to address these possibly confounding factors, it is necessary to validate potential targets by establishing their firm association with disease, and their minimal distribution in non-diseased tissues of any type. This requirement suggests that emphasis on true and reproducible quantitation of protein expression levels in a variety of samples will be an effective and highly efficient method of generating drug targets with a high degree of utility. To achieve this aim, we have established an industrial-scale proteomics-based discovery platform consisting of cell biology, protein chemistry, and mass spectrometry technical groups together with bioinformatics groups. The analytical method used for quantitation employs isotope labeling for differential analysis (ICATTM, Applied Biosystems, Inc.). With this technique, tryptic peptides are generated from labeled proteins that have been specifically captured from various subcellular locations or protein families. The resulting peptides are identified and quantified by mass spectrometry. To evaluate this approach on a large-scale, we have applied it to a study of continuous cell lines derived from human pancreatic adenocarcinomas. We have been able to establish processes for target discovery for small molecule drug targets as well as therapeutic antibody target identification for cell surface proteins. In addition, we have developed a process for identification of serum markers of this disease based upon standardized fractionation procedures. The results of these analyses will be presented together with the some of the issues from both the wet and dry (computational) lab that need to be addressed in such an undertaking.     

A multiplexed quantitative strategy for membrane proteomics: opportunities for mining therapeutic targets for autosomal dominant polycystic kidney disease

Toward multiplexed, comprehensive, and robust quantitation of the membrane proteome, we report a strategy combining gel-assisted digestion, iTRAQ (isobaric tags for relative and absolute quantitation) labeling, and LC-MS/MS. Quantitation of four independently purified membrane fractions from HeLa cells gave high accuracy (<8% error) and precision (<12% relative S.D.), demonstrating a high degree of consistency and reproducibility of this quantitation platform. Under stringent identification criteria (false discovery rate = 0%), the strategy efficiently quantified membrane proteins; as many as 520 proteins (91%) were membrane proteins, each quantified based on an average of 14.1 peptides per integral membrane protein. In addition to significant improvements in signal intensity for most quantified proteins, most remarkably, topological analysis revealed that the biggest improvement was achieved in detection of transmembrane peptides from integral membrane proteins with up to 19 transmembrane helices. To the best of our knowledge, this level of coverage exceeds that achieved previously using MS and provides superior quantitation accuracy compared with other methods. We applied this approach to the first proteomics delineation of phenotypic expression in a mouse model of autosomal dominant polycystic kidney disease (ADPKD). By characterizing kidney cell plasma membrane from wild-type versus PKD1 knock-out mice, 791 proteins were quantified, and 67 and 37 proteins showed > or =2-fold up-regulation and down-regulation, respectively. Some of these differentially expressed membrane proteins are involved in the mechanisms underlying major abnormalities in ADPKD, including epithelial cell proliferation and apoptosis, cell-cell and cell-matrix interactions, ion and fluid secretion, and membrane protein polarity. Among these proteins, targeting therapeutics to certain transporters/receptors, such as epidermal growth factor receptor, has proven effective in preclinical studies of ADPKD; others are known drug targets in various diseases. Our method demonstrates how comparative membrane proteomics can provide insight into the molecular mechanisms underlying ADPKD and the identification of potential drug targets, which may lead to new therapeutic opportunities to prevent or retard the disease.     

Soluble polymer-based isotopic labeling (SoPIL): a new strategy to discover protein biomarkers?

Much attention has been given to protein biomarker discovery in the field of proteomics in the past few years. Proteomic strategies for biomarker discovery normally include the identification of proteins that alter during the progression of a particular disease state in high throughput. To perform these studies requires the ability to measure changes of low-abundance proteins in highly complex mixtures from different biological states. Soluble polymer-based isotope labeling (SoPIL) is a new proteomics strategy that targets specific classes of proteins for isotopic labeling, efficient isolation and accurate quantitation by mass spectrometry. The method exploits the features of homogenous solution-phase reaction, simple solid-phase extraction and characteristic cell-permeable nanoparticles. Recent applications demonstrate that the SoPIL reagents are ideal for quantitative proteomics and phosphoproteomics, and could have the potential to discover disease markers in the most physiologically relevant settings.     

Soluble polymer-based isotopic labeling (SoPIL): a new strategy to discover protein biomarkers?

Much attention has been given to protein biomarker discovery in the field of proteomics in the past few years. Proteomic strategies for biomarker discovery normally include the identification of proteins that alter during the progression of a particular disease state in high throughput. To perform these studies requires the ability to measure changes of low-abundance proteins in highly complex mixtures from different biological states. Soluble polymer-based isotope labeling (SoPIL) is a new proteomics strategy that targets specific classes of proteins for isotopic labeling, efficient isolation and accurate quantitation by mass spectrometry. The method exploits the features of homogenous solution-phase reaction, simple solid-phase extraction and characteristic cell-permeable nanoparticles. Recent applications demonstrate that the SoPIL reagents are ideal for quantitative proteomics and phosphoproteomics, and could have the potential to discover disease markers in the most physiologically relevant settings.     

Amino acid-coded tagging approaches in quantitative proteomics

To improve the efficiency, accuracy, reproducibility, throughput and proteome coverage of mass spectrometry-based quantitative approaches, both in vitro and in vivo tagging of particular amino acid residues of cellular proteins have been introduced to assist mass spectrometry for global-scale comparative studies of differentially expressed proteins/modifications between different biologically relevant cell states or cells at different pathological states. The basic features of these methods introduce pair-wise isotope signals of each individual peptide containing a particular type of tagged amino acid (amino acid-coded mass tagging) that originated from different cell states. In this review, the applications of major amino acid-coded mass tagging-based quantitative proteomics approaches, including isotope-coded affinity tag, isobaric tags for relative and absolute quantification (iTRAQ?) and stable isotope labeling by amino acids in cell culture are summarized in the context of their respective strengths/weakness in identifying those differentially expressed or post-translational modified proteins regulated by particular cellular stress on a genomic scale in a high-throughput manner. Importantly, these gel-free, in-spectra quantitative mechanisms have been further explored to identify/characterize large-scale protein–protein interactions involving various functional pathways. Taken together, the information about quantitative proteome changes, including multiple regulated proteins and their interconnected relationships, will provide an important insight into the molecular mechanisms, where novel targets for diagnosis and therapeutic intervention will be identified.     

Quantitative proteomic analysis of membrane proteins involved in astroglial differentiation of neural stem cells by SILAC labeling coupled with LC-MS/MS

Membrane proteins play a critical role in the process of neural stem cell self-renewal and differentiation. Here, we apply the SILAC (stable isotope labeling by amino acids in cell culture) approach to quantitatively compare the membrane proteome of the self-renewing and the astroglial differentiating cells. High-resolution analysis on a linear ion trap-Orbitrap instrument (LTQ-Orbitrap) at sub-ppm mass accuracy resulted in confident identification and quantitation of more than 700 distinct membrane proteins during the astroglial differentiation. Of the 735 quantified proteins, seven cell surface proteins display significantly higher expression levels in the undifferentiated state membrane compared to astroglial differentiating membrane. One cell surface protein transferrin receptor protein 1 may serve as a new candidate for NSCs surface markers. Functional clustering of differentially expressed proteins by Ingenuity Pathway Analysis revealed that most of overexpressed membrane proteins in the astroglial differentiation neural stem cells are involved in cellular growth, nervous system development, and energy metabolic pathway. Taken together, this study increases our understanding of the underlying mechanisms that modulate complex biological processes of neural stem cell proliferation and differentiation.     

激光捕获显微切割技术结合18O标记定量蛋白质组技术在胃癌标志物筛查中的应用研究

建立一种更加精确地分离鉴定胃癌特异肿瘤标志物的定量蛋白质组学技术．首先采用激光捕获显微切割技术(LCM)纯化胃腺癌细胞及胃黏膜良性上皮细胞,将裂解的样本总蛋白经过1D SDS-PAGE预分离,然后采用¹⁸O/¹⁶O分别标记两种样本酶切后的多肽混合物．结合纳升级液相色谱(Nano-HPLC-MS/MS)定量地鉴定胃癌细胞和胃黏膜良性上皮细胞的差异表达蛋白．共筛选出78个差异表达蛋白,其中42个蛋白质在胃癌组织中表达上调,36个蛋白质下调．Western blot 技术验证了其中几个差异蛋白(moesin, periostin, annexin A2, annexin A4)的表达,与蛋白质组学研究的结果一致．LCM技术结合¹⁸O稳定同位素标记的定量蛋白质组学技术,为研究胃癌发生机制、筛选胃癌的分子标志物提供了新的思路,亦为诸如胃癌等复杂体系蛋白质的分离鉴定提供了新的技术选择．     

Proteomic profiling of pancreatic cancer for biomarker discovery

Pancreatic cancer is a uniformly lethal disease that is difficult to diagnose at early stage and even more difficult to cure. In recent years, there has been a substantial interest in applying proteomics technologies to identify protein biomarkers for early detection of cancer. Quantitative proteomic profiling of body fluids, tissues, or other biological samples to identify differentially expressed proteins represents a very promising approach for improving the outcome of this disease. Proteins associated with pancreatic cancer identified through proteomic profiling technologies could be useful as biomarkers for the early diagnosis, therapeutic targets, and disease response markers. In this article, we discuss recent progress and challenges for applying quantitative proteomics technologies for biomarker discovery in pancreatic cancer.     

Quantitative analysis of surface plasma membrane proteins of primary and metastatic melanoma cells

Plasma membrane proteins play critical roles in cell-to-cell recognition, signal transduction and material transport. Because of their accessibility, membrane proteins constitute the major targets for protein-based drugs. Here, we described an approach, which included stable isotope labeling by amino acids in cell culture (SILAC), cell surface biotinylation, affinity peptide purification and LC-MS/MS for the identification and quantification of cell surface membrane proteins. We applied the strategy for the quantitative analysis of membrane proteins expressed by a pair of human melanoma cell lines, WM-115 and WM-266-4, which were derived initially from the primary and metastatic tumor sites of the same individual. We were able to identify more than 100 membrane and membrane-associated proteins from these two cell lines, including cell surface histones. We further confirmed the surface localization of histone H2B and three other proteins by immunocytochemical analysis with confocal microscopy. The contamination from cytoplasmic and other nonmembrane-related sources is greatly reduced by using cell surface biotinylation and affinity purification of biotinylated peptides. We also quantified the relative expression of 62 identified proteins in the two types of melanoma cells. The application to quantitative analysis of membrane proteins of primary and metastatic melanoma cells revealed great potential of the method in the comprehensive identification of tumor progression markers as well as in the discovery of new protein-based therapeutic targets.     

Biomarker discovery from pancreatic cancer secretome using a differential proteomic approach

Quantitative proteomics can be used as a screening tool for identification of differentially expressed proteins as potential biomarkers for cancers. Candidate biomarkers from such studies can subsequently be tested using other techniques for use in early detection of cancers. Here we demonstrate the use of stable isotope labeling with amino acids in cell culture (SILAC) method to compare the secreted proteins (secretome) from pancreatic cancer-derived cells with that from non-neoplastic pancreatic ductal cells. We identified 145 differentially secreted proteins (>1.5-fold change), several of which were previously reported as either up-regulated (e.g. cathepsin D, macrophage colony stimulation factor, and fibronectin receptor) or down-regulated (e.g. profilin 1 and IGFBP-7) proteins in pancreatic cancer, confirming the validity of our approach. In addition, we identified several proteins that have not been correlated previously with pancreatic cancer including perlecan (HSPG2), CD9 antigen, fibronectin receptor (integrin beta1), and a novel cytokine designated as predicted osteoblast protein (FAM3C). The differential expression of a subset of these novel proteins was validated by Western blot analysis. In addition, overexpression of several proteins not described previously to be elevated in human pancreatic cancer (CD9, perlecan, SDF4, apoE, and fibronectin receptor) was confirmed by immunohistochemical labeling using pancreatic cancer tissue microarrays suggesting that these could be further pursued as potential biomarkers. Lastly the protein expression data from SILAC were compared with mRNA expression data obtained using gene expression microarrays for the two cell lines (Panc1 and human pancreatic duct epithelial), and a correlation coefficient (r) of 0.28 was obtained, confirming previously reported poor associations between RNA and protein expression studies.     

An Integrated Approach Based on Multiplexed Protein Array and iTRAQ Labeling for In-Depth Identification of Pathways Associated to IVF Outcome

The emergence of high-throughput protein quantification methodologies has enabled the comprehensive characterization by longitudinal and cross-sectional studies of biological fluids under physiological and pathological conditions. In particular, the simultaneous investigation of cytokines and growth factors signaling pathways and their associated downstream effectors by integrated multiplexed approaches offers a powerful strategy to gain insights into biological networks and processes in living systems. A growing body of research indicates that bioactive molecules of human reproductive fluids, including human follicular fluid (hFF), may affect oocyte quality, fertilization and embryo development, thus potentially influencing the physiopathology of pregnancy-related conditions.In this work, an iTRAQ labeling strategy has been complemented with a multiplexed protein array approach to analyze hFFs with the aim to investigate biological processes and pathways related to in vitro fertilization (IVF) outcome. The iTRAQ labeling strategy lead to the quantification of 89 proteins, 30 of which were differentially expressed in hFFs with successful compared to unsuccessful IVF outcome. The targeted study, based on multiplexed antibody protein arrays, allowed the simultaneous quantification of 27 low abundance proteins, including growth factors, chemokines and cytokines endowed with pro- and anti-inflammatory activity. A significant number of differentially regulated proteins were involved in biological functions related to blood coagulation, acute phase response signaling and complement system. Overall, the present results provide an integrated overview of protein changes in hFFs associated to IVF outcome, thus improving current knowledge in reproductive medicine and fertility research.     

Amino acid-coded tagging approaches in quantitative proteomics

To improve the efficiency, accuracy, reproducibility, throughput and proteome coverage of mass spectrometry-based quantitative approaches, both in vitro and in vivo tagging of particular amino acid residues of cellular proteins have been introduced to assist mass spectrometry for global-scale comparative studies of differentially expressed proteins/modifications between different biologically relevant cell states or cells at different pathological states. The basic features of these methods introduce pair-wise isotope signals of each individual peptide containing a particular type of tagged amino acid (amino acid-coded mass tagging) that originated from different cell states. In this review, the applications of major amino acid-coded mass tagging-based quantitative proteomics approaches, including isotope-coded affinity tag, isobaric tags for relative and absolute quantification (iTRAQ) and stable isotope labeling by amino acids in cell culture are summarized in the context of their respective strengths/weakness in identifying those differentially expressed or post-translational modified proteins regulated by particular cellular stress on a genomic scale in a high-throughput manner. Importantly, these gel-free, in-spectra quantitative mechanisms have been further explored to identify/characterize large-scale protein-protein interactions involving various functional pathways. Taken together, the information about quantitative proteome changes, including multiple regulated proteins and their interconnected relationships, will provide an important insight into the molecular mechanisms, where novel targets for diagnosis and therapeutic intervention will be identified.     

Characterization of global yeast quantitative proteome data generated from the wild-type and glucose repression saccharomyces cerevisiae strains: the comparison of two quantitative methods

The quantitative proteomic analysis of complex protein mixtures is emerging as a technically challenging but viable systems-level approach for studying cellular function. This study presents a large-scale comparative analysis of protein abundances from yeast protein lysates derived from both wild-type yeast and yeast strains lacking key components of the Snf1 kinase complex. Four different strains were grown under well-controlled chemostat conditions. Multidimensional protein identification technology followed by quantitation using either spectral counting or stable isotope labeling approaches was used to identify relative changes in the protein expression levels between the strains. A total of 2388 proteins were relatively quantified, and more than 350 proteins were found to have significantly different expression levels between the two strains of comparison when using the stable isotope labeling strategy. The stable isotope labeling based quantitative approach was found to be highly reproducible among biological replicates when complex protein mixtures containing small expression changes were analyzed. Where poor correlation between stable isotope labeling and spectral counting was found, the major reason behind the discrepancy was the lack of reproducible sampling for proteins with low spectral counts. The functional categorization of the relative protein expression differences that occur in Snf1-deficient strains uncovers a wide range of biological processes regulated by this important cellular kinase.     

Influence of Ki-ras-driven oncogenic transformation on the protein network of murine fibroblasts

Ki-ras gene mutations that specifically occur in codons 12, 13 and 61 are involved in the carcinogenesis of acute myeloid leukemia, melanoma and different carcinomas. In order to define potential mutation-specific therapeutic targets, stable transfectants of NIH3T3 cells carrying different Ki-ras4B gene mutations were generated. Wild type Ki-ras transformants, mock transfectants and parental cells served as controls. These in vitro model systems were systematically analyzed for their protein expression pattern using two-dimensional gel electrophoresis followed by mass spectrometry and/or protein sequencing. Using this approach, a number of target molecules that are differentially but coordinately expressed in the ras transfectants were identified next to other proteins that exhibit a distinct regulation pattern in the different cell lines analyzed. The differentially expressed proteins predominantly belong to the families of cytoskeletal proteins, heat shock proteins, annexins, metabolic enzymes and oxidoreductases. Their validation was assessed by real-time quantitative RT-PCR and/or Western blot analysis. Our results suggest that the Ki-ras-transformed cells represent a powerful tool to study Ki-ras gene mutation-driven protein expression profiles. In addition, this approach allows the discovery of ras-associated cellular mechanisms, which might lead to the identification of physiological targets for pharmacological interventions of the treatment of Ki-ras-associated human tumors.     

Determination of an Angiotensin II-regulated Proteome in Primary Human Kidney Cells by Stable Isotope Labeling of Amino Acids in Cell Culture (SILAC)

Angiotensin II (AngII), the major effector of the renin-angiotensin system, mediates kidney disease progression by signaling through the AT-1 receptor (AT-1R), but there are no specific measures of renal AngII activity. Accordingly, we sought to define an AngII-regulated proteome in primary human proximal tubular cells (PTEC) to identify potential AngII activity markers in the kidney. We utilized stable isotope labeling with amino acids (SILAC) in PTECs to compare proteomes of AngII-treated and control cells. Of the 4618 quantified proteins, 83 were differentially regulated. SILAC ratios for 18 candidates were confirmed by a different mass spectrometry technique called selected reaction monitoring. Both SILAC and selected reaction monitoring revealed heme oxygenase-1 (HO-1) as the most significantly up-regulated protein in response to AngII stimulation. AngII-dependent regulation of the HO-1 gene and protein was further verified in PTECs. To extend these in vitro observations, we overlaid a network of significantly enriched gene ontology terms from our AngII-regulated proteins with a dataset of differentially expressed kidney genes from AngII-treated wild type mice and AT-1R knock-out mice. Five gene ontology terms were enriched in both datasets and included HO-1. Furthermore, HO-1 kidney expression and urinary excretion were reduced in AngII-treated mice with PTEC-specific AT-1R deletion compared with AngII-treated wild-type mice, thus confirming AT-1R-mediated regulation of HO-1. Our in vitro approach identified novel molecular markers of AngII activity, and the animal studies demonstrated that these markers are relevant in vivo. These interesting proteins hold promise as specific markers of renal AngII activity in patients and in experimental models.     

Quantitative proteomic signature of liver cancer cells: tissue transglutaminase 2 could be a novel protein candidate of human hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common diseases worldwide, with extremely poor prognosis due to failure in diagnosing it early. Alpha-fetoprotein (AFP) is the only available biomarker for HCC diagnosis; however, its use in the early detection of HCC is limited, especially because about one-third of patients afflicted with HCC have normal levels of serum AFP. Thus, identifying additional biomarkers that may be used in combination with AFP to improve early detection of HCC is greatly needed. A quantitative proteomic analysis approach using stable isotope labeling with amino acids in cell culture (SILAC) combined with LTQ-FT-MS/MS identification was used to explore differentially expressed protein profiles between normal (HL-7702) and cancer (HepG2 and SK-HEP-1) cells. A total of 116 proteins were recognized as potential markers that could distinguish between HCC and normal liver cells. Certain proteins, such as AFP, intercellular adhesion molecule-1 (ICAM-1), IQ motif containing GTPase activating protein 2 (IQGAP2), claudin-1 (CLDN1) and tissue transglutaminase 2 (TGM2), were validated both in multiple cell lines and in 61 specimens of clinical HCC cases. TGM2 was overexpressed in some of the AFP-deficient HCC cells (SK-HEP-1 and Bel-7402) and in about half of the tumor tissues with low levels of serum AFP (17/32, AFP-negative HCC). Trace amounts of TGM2 were found to be expressed in the samples with high serum AFP (26/29, AFP-positive HCC). Moreover, TGM2 expression in liver tissues showed an inverse correlation with the level of serum AFP in HCC patients. Notably, TGM2 existed in the supernatant of the AFP-deficient SK-HEP-1, SMMC-7721 and HLE cells, and it was found to be induced in AFP-producing cells (HepG2) by specific siRNA silence assay. Serum TGM2 levels of 109 HCC patients and 42 healthy controls were further measured by an established ELISA assay; the levels were significantly higher in HCC patients, and they correlated with the histological grade and tumor size. These data suggest that TGM2 may serve as a novel histological/serologic candidate involved in HCC, especially for the individuals with normal serum AFP. These novel findings may provide important clues to identify new biomarkers of HCC and indirectly improve early detection of the disease.     

An experimental strategy for the identification of AMPylation targets from complex protein samples

AMPylation is a posttranslational modification (PTM) that has recently caught much attention in the context of bacterial infections as pathogens were shown to secrete Fic proteins that AMPylate Rho GTPases and thus interfere with host cell signaling processes. Although Fic proteins are widespread and found in all kingdoms of life, only a small number of AMPylation targets are known to date. A major obstacle to target identification is the limited availability of generic strategies allowing sensitive and robust identification of AMPylation events. Here, we present an unbiased MS‐based approach utilizing stable isotope‐labeled ATP. The ATP isotopes are transferred onto target proteins in crude cell lysates by in vitro AMPylation introducing specific reporter ion clusters that allow detection of AMPylated peptides in complex biological samples by MS analysis. Applying this strategy on the secreted Fic protein Bep2 of Bartonella rochalimae, we identified the filamenting protein vimentin as an AMPylation target that was confirmed by independent assays. Vimentin represents a new class of target proteins and its identification emphasizes our method as a valuable tool to systematically uncover AMPylation targets. Furthermore, the approach can be generically adapted to study targets of other PTMs that allow incorporation of isotopically labeled substrates.     

Advances in quantitative proteomics via stable isotope tagging and mass spectrometry

The high-throughput identification and accurate quantification of proteins are essential components of proteomic strategies for studying cellular functions and processes. Techniques that are largely based on stable isotope protein or peptide labeling and automated tandem mass spectrometry are increasingly being applied in quantitative proteomic studies. Over the past year, significant progress has been made toward improving and diversifying these technologies with respect to the methods for stable isotope labeling, process automation and data processing and analysis. Advances in stable isotope protein labeling and recent biological studies that used stable isotope based quantitative proteomics techniques are reviewed.