Multidimensional protein identification technology analysis highlights mitoxantrone‐induced expression modulations in the primary prostate cancer cell proteome

Chemotherapeutic agents as they are used today have limited effectiveness against prostate cancer, but may potentially be used in new combinations with more efficacious results. Mitoxantrone, used for palliation of prostate cancer, has recently been found by our group to improve the susceptibility of primary prostate cancer cells to killing through the Fas‐mediated death pathway. Here we used a shotgun proteomics approach to first profile the entire prostate cancer proteome and then identify specific factors involved in this mitoxantrone response. Peptides derived from primary prostate cancer cells treated with or without 100 nM mitoxantrone were analyzed by multidimensional protein identification technology (MudPIT). Strict limits and data filtering hierarchies were applied to identify proteins with high confidence. We identified 1498 proteins belonging to the prostate cancer proteome, 83 of which were significantly upregulated and 27 of which were markedly downregulated following mitoxantrone treatment. These proteins perform diverse functions, including ceramide production, tumour suppression, and oxidative reduction. Detailed proteomic analyses of prostate cancer cells and their response to mitoxantrone will further our understanding of its mechanisms of action. Identification of proteins influenced by treatment with mitoxantrone or other compounds may lead to the development of more effective drug combinations against prostate cancer.     

A proteomic investigation of isogenic radiation resistant prostate cancer cell lines

To model the problem of radiation resistance in prostate cancer, cell lines mimicking a clinical course of conventionally fractionated or hypofractionated radiotherapy have been generated. Proteomic analysis of radiation resistant and radiosensitive DU145 prostate cancer cells detected 4410 proteins. Over 400 proteins were differentially expressed across both radiation resistant cell lines and pathway analysis revealed enrichment in epithelial to mesenchymal transition, glycolysis and hypoxia. From the radiation resistant protein candidates, the cell surface protein CD44 was identified in the glycolysis and epithelial to mesenchymal transition pathways and may serve as a potential therapeutic target.     

Comparative proteomic analysis to discover potential therapeutic targets in human multiple myeloma

To clarify the molecular mechanisms that participate in the formation of multiple myeloma (MM) and to detect any tumor-related biomarkers, we performed proteomic analysis of cellular protein extracts from MM cells and normal plasma cells. Plasma cells from nine patients with newly diagnosed MM and nine healthy donors were purified by using anti-CD138 based immunomagnetic bead-positive selection. The protein profiles of purified MM and normal plasma cells were compared using 2-DE. We identified a total of 43 differentially expressed proteins, and confirmed with Western blotting six proteins. The altered proteins were analyzed using the software program Pathway Studio and the biological network can be accessed via (http://life-health.jnu.edu.cn/pathway/pathway.html). Further functional studies showed that annexin A1 knock down modestly induces lethality alone and potentiates the effects of dexamethasone on both dexamethasone-sensitive and dexamethasone-resistant MM cells. By correlating the proteomic data with these functional studies, the current results provide not only new insights into the pathogenesis of MM but also direct implications for the development of novel anti-MM therapeutic strategies and could lead to the discovery of potential therapeutic targets. Future molecular and functional studies would provide novel insights into the roles of these dysregulated proteins in the molecular etiology of MM.     

Quantitative mass spectrometry for colorectal cancer proteomics

This review documents the uses of quantitative MS applied to colorectal cancer (CRC) proteomics for biomarker discovery and molecular pathway profiling. Investigators are adopting various labeling and label-free MS approaches to quantitate differential protein levels in cells, tumors, and plasma/serum. We comprehensively review recent uses of this technology to examine mouse models of CRC, CRC cell lines, their secretomes and subcellular fractions, CRC tumors, CRC patient plasma/serum, and stool samples. For biomarker discovery these approaches are uncovering proteins with potential diagnostic and prognostic utility, while in vitro cell culture experiments are characterizing proteomic and phosphoproteomic responses to disrupted signaling pathways due to mutations or to inhibition of drugable enzymes.     

Proteomic profiling of yeast- and hyphal-specific responses of Candida albicans to the antifungal agent, HWY-289

Virulence of Candida albicans is attributable to its unique dimorphic transition from nonpathogenic yeast cells to pathogenic hyphal cells. We previously discovered a novel antifungal agent, known as HWY-289. To characterize the mechanism underlying HWY-289 antifungal activity, we performed 2-DE to identify proteins that were differentially expressed during yeast-to-hyphal transition and in response to HWY-289. Twenty-four differentially expressed protein spots were identified in HWY-289-treated yeast. Most differentially expressed proteins were involved in carbohydrate-derived energy metabolism, cellular detoxification, and antioxidant defenses. Two proteins were involved in cell cycle regulation and DNA processing, and both were downregulated by HWY-289, suggesting that this agent might promote cell death by weakening cellular defense systems. HWY-289 inhibited yeast-to-hyphal transition in a dose-dependent manner. 2-DE analysis of hyphae uncovered several proteins that were induced during yeast-to-hyphal transition. Of these, aconitase and phosphatidylinositol transfer protein were downregulated by HWY-289, suggesting that they mediate the antifungal effects of HWY-289. Finally, RT-PCR analysis revealed that HWY-289 induced expression of three RAS-related genes (CcCST20, CaHST7, and CaCPH1) in yeast cells, but suppressed their expression in hyphae. Thus, the antifungal action of HWY-289 may be attributable to its ability to disrupt prohyphal RAS signaling.     

A new approach to prediction of radiotherapy of bladder cancer cells in small dataset analysis

Bladder cancer is a common urologic cancer. Radiotherapy plays an increasingly important role in treatment bladder cancer due to radiotherapy preserves normal bladder function. However, the five-year survival rate after radiotherapy for bladder cancer patients is 30–50%. Some biological proteins influence the outcome of radiotherapy. One or two specific proteins may not be sufficient to predict the effect of radiotherapy, analyzing multiple oncoproteins and tumor suppressor proteins may help the prediction. At present, no effective technique has been used to predict the outcome of radiotherapy by multiple protein expression file from a very limited number of patients. The bootstrap technique provides a new approach to improve the accuracy of prediction the outcome of radiotherapy in small dataset analysis. In this study, 13 proteins in each cell line from individual patient were measured and then cell viability was determined after cells irradiated with 5, 10, 20, or 30 Gy of cobalt-60. The modeling results showed that when the number of training data increased, the learning accuracy of the prediction the outcome of radiotherapy was enhanced stably, from 55% to 85%. Using this technique to analyze the outcome of radiotherapy related to protein expression profile of individual cell line provides an example to help patients choosing radiotherapy for treatment.     

Proteome of human endometrium: Identification of differentially expressed proteins in proliferative and secretory phase endometrium

Purpose: To exploit the potential of proteomics to identify and study additional yet‐unidentified important proteins present in human endometrium. Experimental design: The proteome of human endometrium would be established using 2‐DE and MALDI and the data analyzed to identify differential protein expression in the proliferative and secretory phase of the menstrual cycle using PDQuest software and MALDI. Results: In the present work, 2‐DE of human endometrium protein led to the resolution of over 200 spots. Subsequent MALDI analysis of 215 spots allowed the identification of 194 proteins. A total of 57 out of the 215 spots were found to be differentially expressed, out of which 49 could be identified using MALDI. These differentially expressed proteins included structural proteins, molecular chaperones, signaling proteins, metabolic proteins, proteins related to immunity, RNA biogenesis, protein biosynthesis and others. The differential expressions of seven representative proteins in secretory and proliferative phase endometrium tissue were confirmed by immunoblot analysis. Conclusion and clinical relevance: This study establishes the 2‐D proteome of human endometrium represented by 194 identified protein spots. The present data provides an important clue towards determining the function of these proteins with respect to endometrium related diseases.     

Proteomic and other analyses to determine the functional consequences of deregulated kallikrein-related peptidase (KLK) expression in prostate and ovarian cancer

Rapidly developing proteomic tools are improving detection of deregulated kallikrein-related peptidase (KLK) expression, at the protein level, in prostate and ovarian cancer, as well as facilitating the determination of functional consequences downstream. MS-driven proteomics uniquely allows for the detection, identification, and quantification of thousands of proteins in a complex protein pool, and this has served to identify certain KLKs as biomarkers for these diseases. In this review, we describe applications of this technology in KLK biomarker discovery and elucidate MS-based techniques that have been used for unbiased, global screening of KLK substrates within complex protein pools. Although MS-based KLK degradomic studies are limited to date, they helped to discover an array of novel KLK substrates. Substrates identified by MS-based degradomics are reported with improved confidence over those determined by incubating a purified or recombinant substrate and protease of interest, in vitro. We propose that these novel proteomic approaches represent the way forward for KLK research, in order to correlate proteolysis of biological substrates with tissue-related consequences, toward clinical targeting of KLK expression and function for cancer diagnosis, prognosis, and therapies.     

Highlights of the first BSPR London Regional Meeting and Fifth Imperial Proteomics Day

Now that the genomics revolution is tailing off proteomics promises an even more radical transformation of biological and medical research. Ultimately, the clues to the mysteries of biological processes will lye within the proteins present at a given time. Proteomics, the characterization of complex protein mixtures, builds on a wide range of expertise from protein chemistry to mass spectrometry and bioinformatics. Each step from sample preparation, high resolution protein separation to protein identification is underpinned by the latest technological developments in protein separation and mass spectrometry and supported by powerful computational image and data analysis algorithms which requires large scale data management and storage facilities. The success of proteomics depends on further developments in technology which need a wide range of scientific expertise. This meeting, the first BSPR London Regional Meeting, brought together scientists from a broad background who share a common interest in novel solutions for the characterization of complex protein mixtures.     

Quantitative proteomic analysis of a paired human liver healthy versus carcinoma cell lines with the same genetic background to identify potential hepatocellular carcinoma markers

To comprehensively measure global changes in protein expression associated with human hepatocellular carcinoma (HCC), comparative proteomic analysis of two cell lines derived from the healthy and carcinoma tissue of a same donor respectively was conducted using quantitative amino acid-coded mass tagging /stable isotope labeling with amino acids in cell culture-based LC-MS/MS approach. Among a total of 501 proteins precisely quantified, the expressions of 128 proteins were significantly altered including 70 proteins up-regulated and 58 down-regulated in HCC cells. According to their previously characterized functions, the differentially expressed proteins were found associated with nine functional categories including glycolysis, stress response, cell communication, cell cycle, apoptosis/death, etc. For example, multiple enzymes involving glycolysis pathway were found differentially regulated in HCC cells, illustrating the critical participation of glycolysis in the HCC transformation. The accuracy of certain differentially expressed proteins identified through the amino acid-coded mass tagging-based quantification was validated in the paired cell lines, and later their pathological correlations were examined in multiple clinical pairs of normal versus tumor tissues from HCC specimen by using a variety of biological approaches including Western blotting and in situ immunoassays. These consistencies suggested that multiple proteins such as HSP27, annexin V, glyceraldehyde-3-phosphate dehydrogenase, nucleolin and elongation factor Tu could be the biomarkers candidates for diagnosis of HCC.     

Proteomics cataloging analysis of human expressed prostatic secretions reveals rich source of biomarker candidates

Expressed prostatic secretions (EPS) contain proteins of prostate origin that may reflect the health status of the prostate and be used as diagnostic markers for prostate diseases including prostatitis, benign prostatic hyperplasia, and prostate cancer. Despite their importance and potential applications, a complete catalog of EPS proteins is not yet available. We, therefore, undertook a comprehensive analysis of the EPS proteome using 2‐D micro‐LC combined with MS/MS. Using stringent filtering criteria, we identified a list of 114 proteins with at least two unique‐peptide hits and an additional 75 proteins with only a single unique‐peptide hit. The proteins identified include kallikrein 2 (KLK2), KLK3 (prostate‐specific antigen), KLK11, and nine cluster of differentiation (CD) molecules including CD10, CD13, CD14, CD26, CD66a, CD66c, CD 143, CD177, and CD224. To our knowledge, this list represents the first comprehensive characterization of the EPS proteome, and it provides a candidate biomarker list for targeted quantitative proteomics analysis using a multiple reaction monitoring (MRM) approach. To help prioritize candidate biomarkers, we constructed a protein–protein interaction network of the EPS proteins using Cytoscape (www.cytoscape.org), and overlaid the expression level changes from the Oncomine database onto the network.     

Degradative proteomics and disease mechanisms

Protein degradation is a fundamental biological process, which is essential for the maintenance and regulation of normal cellular function. In humans and animals, proteins can be degraded by a number of mechanisms: the ubiquitin-proteasome system, autophagy and intracellular proteases. The advances in contemporary protein analysis means that proteomics is increasingly being used to explore these key pathways and as a means of monitoring protein degradation. The dysfunction of protein degradative pathways has been associated with the development of a number of important diseases including cancer, muscle wasting disorders and neurodegenerative diseases. This review will focus on the role of proteomics to study cellular degradative processes and how these strategies are being applied to understand the molecular basis of diseases arising from disturbances in protein degradation.     

Proteomic profiling reveals comprehensive insights into adrenergic receptor-mediated hypertrophy in neonatal rat cardiomyocytes

Myocardial adrenergic receptors (ARs) play important roles in cardiac hypertrophy. However, the detailed molecular mechanism of AR-mediated cardiac hypertrophy remains elusive to date. To gain full insight into how ARs are involved in the regulation of cardiac hypertrophy, protein expression profiling was performed with comparative proteomics approach on neonatal rat cardiomyocytes. Forty-six proteins were identified as differentially expressed in hypertrophic cardiomyocytes induced by AR stimulation. To better understand the biological significance of the obtained proteomic data, we utilized the ingenuity pathway analysis tool to construct biological networks and analyze function and pathways that might associate with AR-mediated cardiac hypertrophy. Pathway analysis strongly suggested that ROS may be involved in the development of AR-mediated cardiac hypertrophy, which was then confirmed by further experimentation. The results showed that a marked increase in ROS production was detected in AR-mediated cardiac hypertrophy and blocking of ROS production significantly inhibited AR-mediated cardiac hypertrophy. We further proved that the ROS production was through NADPH oxidase or the mitochondrial electron transport chain and this ROS accumulation resulted in activation of extracellular signal-regulated kinase 1/2 leading to AR-mediated cardiac hypertrophy. These experimental results support the hypothesis, from the ingenuity pathway analysis, that AR-mediated cardiac hypertrophy is associated with the dysregulation of a complicated oxidative stress-regulatory network. In conclusion, our results provide a basis for understanding the detailed molecular mechanisms of AR-mediated cardiac hypertrophy.     

Proteomics studies of pancreatic cancer

Pancreatic cancer is the fourth leading cause of cancer death in the United States, with 4% survival 5 years after diagnosis. Biomarkers are desperately needed to improve earlier, more curable cancer diagnosis and to develop new effective therapeutic targets. The development of quantitative proteomics technologies in recent years offers great promise for understanding the complex molecular events of tumorigenesis at the protein level, and has stimulated great interest in applying the technology for pancreatic cancer studies. Proteomic studies of pancreatic tissues, juice, serum/plasma, and cell lines have recently attempted to identify differentially expressed proteins in pancreatic cancer to dissect the abnormal signaling pathways underlying oncogenesis, and to detect new biomarkers. It can be expected that the continuing evolution of proteomics technology with better resolution and sensitivity will greatly enhance our capability in combating pancreatic cancer.     

Quantitative proteomics for identification of cancer biomarkers

Quantitative proteomics can be used for the identification of cancer biomarkers that could be used for early detection, serve as therapeutic targets, or monitor response to treatment. Several quantitative proteomics tools are currently available to study differential expression of proteins in samples ranging from cancer cell lines to tissues to body fluids. 2-DE, which was classically used for proteomic profiling, has been coupled to fluorescence labeling for differential proteomics. Isotope labeling methods such as stable isotope labeling with amino acids in cell culture (SILAC), isotope-coded affinity tagging (ICAT), isobaric tags for relative and absolute quantitation (iTRAQ), and (18) O labeling have all been used in quantitative approaches for identification of cancer biomarkers. In addition, heavy isotope labeled peptides can be used to obtain absolute quantitative data. Most recently, label-free methods for quantitative proteomics, which have the potential of replacing isotope-labeling strategies, are becoming popular. Other emerging technologies such as protein microarrays have the potential for providing additional opportunities for biomarker identification. This review highlights commonly used methods for quantitative proteomic analysis and their advantages and limitations for cancer biomarker analysis.     

Wavelet-based procedures for proteomic mass spectrometry data processing

Proteomics aims at determining the structure, function and expression of proteins. High-throughput mass spectrometry (MS) is emerging as a leading technique in the proteomics revolution. Though it can be used to find disease-related protein patterns in mixtures of proteins derived from easily obtained samples, key challenges remain in the processing of proteomic MS data. Multiscale mathematical tools such as wavelets play an important role in signal processing and statistical data analysis. A wavelet-based algorithm for proteomic data processing is developed. A MATLAB implementation of the software package, called WaveSpect0, is presented including processing procedures of step-interval unification, adaptive stationary discrete wavelet denoising, baseline correction using splines, normalization, peak detection, and a newly designed peak alignment method using clustering techniques. Applications to real MS data sets for different cancer research projects in Vanderbilt Ingram Cancer Center show that the algorithm is efficient and satisfactory in MS data mining.     

Urinary extracellular vesicles for biomarker source to monitor polycystic kidney disease

Extracellular vesicles (EVs) are bilayered lipid vesicles, 50–1000 nm in diameter and secreted by most types of cells. They contain many proteins, mRNAs, miRNAs, and lipids that reflect the pathophysiological state of the cells they originate from, and are therefore considered to be a rich source of potential biomarkers. In this issue (Pocsfalvi, G. et al., Proteomics Clin. Appl. 2015, 9, 552–567), Pocsfalvi et al. conducted pioneering investigations to determine whether changes in the protein content of EVs occur during progression of autosomal dominant polycystic kidney disease (ADPKD), a common genetic disorder that predominantly affects the kidneys. Most significantly, iTRAQ-based quantitative proteomics showed that cytoskeleton-regulating and Ca²⁺-binding proteins are differentially expressed in urinary EVs of ADPKD patients. Impressively, these proteins are involved in biological processes that are closely related to the pathogenic state of tubular epithelial cells in ADPKD, demonstrating the possibility to monitor the status of patients using urinary EVs.