Comparison of software tools to improve the detection of carcinogen induced changes in the rat liver proteome by analyzing SELDI-TOF-MS spectra

A common animal model of chemical hepatocarcinogenesis was used to demonstrate the potential identification of carcinogenicity related protein signatures/biomarkers. Therefore, an animal study in which rats were treated with the known liver carcinogen N-nitrosomorpholine (NNM) or the corresponding vehicle was evaluated. Histopathological investigation as well as SELDI-TOF-MS analysis was performed. SELDI-TOF-MS is an affinity-based mass spectrometry method in which subsets of proteins from biological samples are selectively adsorbed to a chemically modified surface. The proteins are subsequently analyzed with respect to their mass-charge ratios (m/z) by a time of flight (TOF) mass spectrometry (MS) approach. As data preprocessing of SELDI-TOF-MS spectra is essential, baseline correction, normalization, peak detection, and alignment of raw spectra were performed using either the Ciphergen ProteinChip Software 3.1 or functions implemented in the library PROcess of the BioConductor Project. Baseline correction and normalization algorithms of both tools lead to comparable results, whereas results after peak detection and alignment steps differed. Variability between technical and biological replicates was investigated. A linear mixed model with factors experimental group and time point was applied for each protein peak, taking into account the different correlation structure of technical and biological replicates. Alternatively, only median intensity values of technical replicates were used. Results of both models were similar and correlated well with those of the histopathological evaluation of the study. In conclusion, statistical analyses lead to comparable results, whereas parameter settings for preprocessing proved to be crucial.     

Prevalidation of potential protein biomarkers in toxicology using iTRAQ reagent technology

Today, toxicoproteomics still relies mainly on 2-DE followed by MS for detection and identification of proteins, which might characterize a certain state of disease, indicate toxicity or even predict carcinogenicity. We utilized the classical 2-DE/MS approach for the evaluation of early protein biomarkers which are predictive for chemically induced hepatocarcinogenesis in rats. We were able to identify statistically significantly deregulated proteins in N-nitrosomorpholine exposed rat liver tissue. Based on literature data, biological relevance in the early molecular process of hepatocarcinogenicity could be suggested for most of these potential biomarkers. However, in order to ensure reliable results and to create the prerequisites necessary for integration in routine toxicology studies in the future, these protein expression patterns need to be prevalidated using independent technology platforms. In the current study, we evaluated the usefulness of iTRAQ reagent technology (Applied Biosystems, Framingham, USA), a recently introduced MS-based protein quantitation method, for verification of the 2-DE/MS biomarkers. In summary, the regulation of 26 2-DE/MS derived protein biomarkers could be verified. Proteins like HSP 90-beta, annexin A5, ketohexokinase, N-hydroxyarylamine sulfotransferase, ornithine aminotransferase, and adenosine kinase showed highly comparable fold changes using both proteomic quantitation strategies. In addition, iTRAQ analysis delivered further potential biomarkers with biological relevance to the processes of hepatocarcinogenicity: e.g. placental form of glutathione S-transferase (GST-P), carbonic anhydrase, and aflatoxin B1 aldehyde reductase. Our results show both the usefulness of iTRAQ reagent technology for biomarker prevalidation as well as for identification of further potential marker proteins, which are indicative for liver hepatocarcinogenicity.     

Quantitative proteomic analysis of rat liver for carcinogenicity prediction in a 28-day repeated dose study

The potential of quantitative proteomic analysis to predict carcinogenicity of chemical compounds was investigated. Using 2D-DIGE, we analyzed the effects of 63 chemical compounds on protein expression in the rat liver after 28 daily doses. Types of carcinogens were categorized depending on the species and organ specificity. The carcinogen characteristic proteins for each classification were identified by Welch's t value. For evaluation of the predictive concordance we used support vector machines. The rat hepatic carcinogen-specific classification gave higher concordance than the other classification. The generalization performance was measured by leave-one-out cross-validation. For genotoxic and non-genotoxic compounds, a concordance of 79.3 and 76.5%, respectively, was obtained by the top 30 ranked proteins with Welch's t value. Furthermore, we found that the increase of the expression level of the stress response proteins as the common feature of poorly predicted chemical compounds in the leave-20%-out cross-validation. Quantitative proteomics could be promising technique for developing biomarker panels that can be used for carcinogenicity prediction. The list of proteins identified in this study and the zoomed gel images of the top ranked proteins in statistic analysis are provided in Supplementary Data.     

Identification of HSP90 as Potential Biomarker of Biliary Atresia Using Two-Dimensional Electrophoresis and Mass Spectrometry

Biliary atresia (BA) is a devastating cholestatic liver disease targeting infants. Current diagnosis depends on surgical exploration of the biliary tree. The aim of the present study was to identify potential biomarkers for the diagnosis of biliary atresia (BA). Two-dimensional electrophoresis was utilized for the identification of proteins that were differentially expressed in liver biopsies of 20 BA patients and 12 infants with non-BA neonatal cholestasis (NC) as controls. Using mass spectrometry, we identified 15 proteins with expressions significantly altered. Out of the 15 proteins identified, heat shock protein (HSP) 90 was the most significantly altered and was down-regulated in BA samples compared to NC samples using immunoblotting analysis. Our findings suggest that HSP90 might be a potential biomarker for the diagnosis of BA and may be used for monitoring further development and therapy for BA. This study demonstrated that a comprehensive strategy of proteomic identification combined with further validation should be adopted in biomarker discovery.     

Tissue proteomics for cancer biomarker development: laser microdissection and 2D-DIGE

Novel cancer biomarkers are required to achieve early diagnosis and optimized therapy for individual patients. Cancer is a disease of the genome, and tumor tissues are a rich source of cancer biomarkers as they contain the functional translation of the genome, namely the proteome. Investigation of the tumor tissue proteome allows the identification of proteomic signatures corresponding to clinico-pathological parameters, and individual proteins in such signatures will be good biomarker candidates. Tumor tissues are also a rich source for plasma biomarkers, because proteins released from tumor tissues may be more cancer specific than those from non-tumor cells. Two-dimensional difference gel electrophoresis (2D-DIGE) with novel ultra high sensitive fluorescent dyes (CyDye DIGE Fluor satulation dye) enables the efficient protein expression profiling of laser-microdissected tissue samples. The combined use of laser microdissection allows accurate proteomic profiling of specific cells in tumor tissues. To develop clinical applications using the identified biomarkers, collaboration between research scientists, clinicians and diagnostic companies is essential, particularly in the early phases of the biomarker development projects. The proteomics modalities currently available have the potential to lead to the development of clinical applications, and channeling the wealth of produced information towards concrete and specific clinical purposes is urgent.     

Focal elevation of liver microsomal epoxide hydrolase in early preneoplastic stages and its behaviour in the further course of hepatocarcinogenesis

Treatment of rats with N-nitrosomorpholine (NNM) for 7 weeks led to a focal increase in liver microsomal epoxide hydrolase (EH) as early as 2 weeks after withdrawal of the carcinogen. This treatment also leads to hyperplastic nodules and liver tumors, but much later. At the same early time point, ATPase activity was decreased in the same islands. Most of these areas already had increased γ-glutamyltranspeptidase activity. The increase in EH at this early time point was more distinct than the decrease in ATPase which has thus far been considered a suitable marker of the earliest stages in hepatocarcinogenesis. The focal increase in EH was also observed in all benign hepatomas, but not in any of the hepatocellular carcinomas investigated so far.     

Multidimensional protein fractionation of blood proteins coupled to data-independent nanoLC–MS/MS analysis

In order to exploit human blood as a source of protein disease biomarkers, robust analytical methods are needed to overcome the inherent molecular complexity of this bio-fluid. We present the coupling of label-free SAX chromatography and IMAC to a data-independent nanoLC–MS/MS (nanoLC–MS^E) platform for analysis of blood plasma and serum proteins. The methods were evaluated using protein standards added at different concentrations to two groups of samples. The results demonstrate that both techniques enable accurate protein quantitation using low sample volumes and a minimal number of fractions. Combining both methods, 883 unique proteins were identified, of which 423 proteins showed high reproducibility. The two approaches resulted in identification of unique molecular signatures with an overlap of approximately 30%, thus providing complimentary information on sub-proteomes. These methods are potentially useful for systems biology, biomarker discovery, and investigation of phosphoproteins in blood.     

Data-independent acquisition and quantification of extracellular matrix from human lung in chronic inflammation-associated carcinomas

Early events associated with chronic inflammation and cancer involve significant remodeling of the extracellular matrix (ECM), which greatly affects its composition and functional properties. Using lung squamous cell carcinoma (LSCC), a chronic inflammation-associated cancer (CIAC), we optimized a robust proteomic pipeline to discover potential biomarker signatures and protein changes specifically in the stroma. We combined ECM enrichment from fresh human tissues, data-independent acquisition (DIA) strategies, and stringent statistical processing to analyze “Tumor” and matched adjacent histologically normal (“Matched Normal”) tissues from patients with LSCC. Overall, 1802 protein groups were quantified with at least two unique peptides, and 56% of those proteins were annotated as “extracellular.” Confirming dramatic ECM remodeling during CIAC progression, 529 proteins were significantly altered in the “Tumor” compared to “Matched Normal” tissues. The signature was typified by a coordinated loss of basement membrane proteins and small leucine-rich proteins. The dramatic increase in the stromal levels of SERPINH1/heat shock protein 47, that was discovered using our ECM proteomic pipeline, was validated by immunohistochemistry (IHC) of “Tumor” and “Matched Normal” tissues, obtained from an independent cohort of LSCC patients. This integrated workflow provided novel insights into ECM remodeling during CIAC progression, and identified potential biomarker signatures and future therapeutic targets.     

Identification and Chronological Analysis of Genomic Signatures in Influenza A Viruses

An increase in the availability of data on the influenza A viruses (IAV) has enabled the identification of the potential determinants of IAV host specificity using computational approaches. In this study, we proposed an alternative approach, based on the adjusted Rand index (ARI), for the evaluation of genomic signatures of IAVs and their ability to distinguish hosts they infected. Our experiments showed that the host-specific signatures identified using the ARI were more characteristic of their hosts than those identified using previous measures. Our results provided updates on the host-specific genomic signatures in the internal proteins of the IAV based on the sequence data as of February 2013 in the National Center for Biotechnology Information (NCBI). Unlike other approaches for signature recognition, our approach considered not only the ability of signatures to distinguish hosts (according to the ARI), but also the chronological relationships among proteins. We identified novel signatures that could be mapped to known functional domains, and introduced a chronological analysis to investigate the changes in host-specific genomic signatures over time. Our chronological analytical approach provided results on the adaptive variability of signatures, which correlated with previous studies’ findings, and indicated prospective adaptation trends that warrant further investigation.     

LCM纯化的鼻咽癌间质和正常鼻咽间质的定量蛋白质组学研究

间质在肿瘤发生发展中的作用越来越受到重视．为寻找与鼻咽癌( nasopharyngeal carcinoma,NPC )发生发展相关的特异性间质蛋白,采用激光捕获显微切割技术( laser capture microdissection,LCM )纯化鼻咽癌间质和正常鼻咽黏膜间质,荧光差异双向凝胶电泳( fluorescent two-dimensional difference gel electrophoresis 2-D,DIGE )结合质谱技术分离鉴定间质相关蛋白．Western blot及免疫组织化学技术验证了其中3个差异蛋白(CapG、L-plastin和S100A9),证实了2D-DIGE结果的可靠性．建立了LCM 纯化的鼻咽癌间质和正常鼻咽间质的荧光差异蛋白表达图谱,高通量筛选与肿瘤发生相关的间质蛋白,共得到34个有统计学意义的蛋白质点,质谱鉴定得到20个差异蛋白．研究结果提示：这些差异表达的蛋白质将有助于阐明鼻咽癌细胞和周围间质的关系．对间质蛋白功能的进一步研究,将有助于解析间质在肿瘤发生中的作用机制,并为从间质途径寻找肿瘤治疗靶标提供新思路．     

Integrated pipeline for mass spectrometry-based discovery and confirmation of biomarkers demonstrated in a mouse model of breast cancer

Despite their potential to impact diagnosis and treatment of cancer, few protein biomarkers are in clinical use. Biomarker discovery is plagued with difficulties ranging from technological (inability to globally interrogate proteomes) to biological (genetic and environmental differences among patients and their tumors). We urgently need paradigms for biomarker discovery. To minimize biological variation and facilitate testing of proteomic approaches, we employed a mouse model of breast cancer. Specifically, we performed LC-MS/MS of tumor and normal mammary tissue from a conditional HER2/Neu-driven mouse model of breast cancer, identifying 6758 peptides representing >700 proteins. We developed a novel statistical approach (SASPECT) for prioritizing proteins differentially represented in LC-MS/MS datasets and identified proteins over- or under-represented in tumors. Using a combination of antibody-based approaches and multiple reaction monitoring-mass spectrometry (MRM-MS), we confirmed the overproduction of multiple proteins at the tissue level, identified fibulin-2 as a plasma biomarker, and extensively characterized osteopontin as a plasma biomarker capable of early disease detection in the mouse. Our results show that a staged pipeline employing shotgun-based comparative proteomics for biomarker discovery and multiple reaction monitoring for confirmation of biomarker candidates is capable of finding novel tissue and plasma biomarkers in a mouse model of breast cancer. Furthermore, the approach can be extended to find biomarkers relevant to human disease.     

Proteomic changes at 8 weeks after infection are associated with chronic liver pathology in experimental schistosomiasis

Chronic Schistosoma mansoni infection can present as a moderate or severe disease, termed intestinal or hepatosplenic schistosomiasis, respectively. Similarly, either moderate splenomegaly or hypersplenomegaly syndrome develops in CBA/J mice by 20 weeks of infection and is similar to intestinal or hepatosplenic schistosomiasis respectively. Using this mouse model and two-dimensional differential in gel electrophoresis, the liver proteomic signatures of uninfected mice and mice infected for 6, 8, 12, or 20 weeks were compared, and significant protein spots identified using mass spectrometry. We found the greatest number of changes at 12 weeks suggesting that this period represents the peak time of change. Pathway analysis identified specific proteins and pathways that correlated to the pathological changes indicative of severe disease, and these pathways were involved as early as 8 weeks after infection. These findings provide insight into the development of severe liver pathology in schistosomiasis and may aid in developing biomarkers for hepatosplenic schistosomiasis.     

Pareto Optimization Identifies Diverse Set of Phosphorylation Signatures Predicting Response to Treatment with Dasatinib

Multivariate biomarkers that can predict the effectiveness of targeted therapy in individual patients are highly desired. Previous biomarker discovery studies have largely focused on the identification of single biomarker signatures, aimed at maximizing prediction accuracy. Here, we present a different approach that identifies multiple biomarkers by simultaneously optimizing their predictive power, number of features, and proximity to the drug target in a protein-protein interaction network. To this end, we incorporated NSGA-II, a fast and elitist multi-objective optimization algorithm that is based on the principle of Pareto optimality, into the biomarker discovery workflow. The method was applied to quantitative phosphoproteome data of 19 non-small cell lung cancer (NSCLC) cell lines from a previous biomarker study. The algorithm successfully identified a total of 77 candidate biomarker signatures predicting response to treatment with dasatinib. Through filtering and similarity clustering, this set was trimmed to four final biomarker signatures, which then were validated on an independent set of breast cancer cell lines. All four candidates reached the same good prediction accuracy (83%) as the originally published biomarker. Although the newly discovered signatures were diverse in their composition and in their size, the central protein of the originally published signature — integrin β4 (ITGB4) — was also present in all four Pareto signatures, confirming its pivotal role in predicting dasatinib response in NSCLC cell lines. In summary, the method presented here allows for a robust and simultaneous identification of multiple multivariate biomarkers that are optimized for prediction performance, size, and relevance.     

Peptide location fingerprinting reveals modification‐associated biomarker candidates of ageing in human tissue proteomes

Although dysfunctional protein homeostasis (proteostasis) is a key factor in many age‐related diseases, the untargeted identification of structurally modified proteins remains challenging. Peptide location fingerprinting is a proteomic analysis technique capable of identifying structural modification‐associated differences in mass spectrometry (MS) data sets of complex biological samples. A new webtool (Manchester Peptide Location Fingerprinter), applied to photoaged and intrinsically aged skin proteomes, can relatively quantify peptides and map statistically significant differences to regions within protein structures. New photoageing biomarker candidates were identified in multiple pathways including extracellular matrix organisation (collagens and proteoglycans), protein synthesis and folding (ribosomal proteins and TRiC complex subunits), cornification (keratins) and hemidesmosome assembly (plectin and integrin α6β4). Crucially, peptide location fingerprinting uniquely identified 120 protein biomarker candidates in the dermis and 71 in the epidermis which were modified as a consequence of photoageing but did not differ significantly in relative abundance (measured by MS1 ion intensity). By applying peptide location fingerprinting to published MS data sets, (identifying biomarker candidates including collagen V and versican in ageing tendon) we demonstrate the potential of the MPLF webtool for biomarker discovery.     

Protein arrays as tools for serum autoantibody marker discovery in cancer

Protein array technology has begun to play a significant role in the study of protein–protein interactions and in the identification of antigenic targets of serum autoantibodies in a variety of autoimmune disorders. More recently, this technology has been applied to the identification of autoantibody signatures in cancer.The identification of tumour-associated antigens (TAAs) recognised by the patient's immune response represents an exciting approach to identify novel diagnostic cancer biomarkers and may contribute towards a better understanding of the molecular mechanisms involved. Circulating autoantibodies have not only been used to identify TAAs as diagnostic/prognostic markers and potential therapeutic targets, they also represent excellent biomarkers for the early detection of tumours and potential markers for monitoring the efficacy of treatment. Protein array technology offers the ability to screen the humoral immune response in cancer against thousands of proteins in a high throughput technique, thus readily identifying new panels of TAAs. Such an approach should not only aid in improved diagnostics, but has already contributed to the identification of complex autoantibody signatures that may represent disease subgroups, early diagnostics and facilitated the analysis of vaccine trials.     

A novel application for Cocoacrisp protein as a biomarker for experimental pulmonary fibrosis

Pulmonary fibrosis is a debilitating disease affecting up to 2 million people worldwide, with a median survival rate of only 3 years after diagnosis. The aim of this study was to evaluate a potential protein biomarker (Cocoacrisp, CC) to identify the onset of pulmonary fibrosis. A model of fibrosis was induced via intratracheal instillation of bleomycin, and samples were collected during the early phase of the disease. Immunohistochemical identification of CC was carried out in lung tissue from the bleomycin model. Quantification by image analysis showed CC levels were doubled (p?<0.0003), after a single bleomycin dose, but not after double instillation. Microscopic analysis revealed that CC signal was primarily detected on the alveolar surface. The secretion of the novel protein CC during the early stages of bleomycin-induced injury may have the potential to be utilized as a clinical biomarker for the early stages of fibrosis, particularly as it may be detectable in bronchoalveolar lavage fluid.     

Prediction of non-genotoxic carcinogenesis in rats using changes in gene expression following acute dosing

Non-genotoxic carcinogenicity of chemicals is currently routinely evaluated in 2-year rodent bioassays. Therefore, the development of early biomarkers for non-genotoxic carcinogenesis would result in substantial savings in time and expense. The current study investigates whether early changes in gene expression may be developed as markers for cancer. Animals were treated for 1 or 5 days with either non-genotoxic carcinogens (NGTCs) or non-carcinogens and gene expression was analyzed by quantitative PCR (qPCR). We tested two gene signatures previously reported to detect non-genotoxic carcinogens. Using one gene signature it was confirmed that 3/3 non-genotoxic carcinogens and 2/2 non-carcinogens are correctly identified with data from 1 or 5 days of dosing. In contrast an alternative signature correctly identified 0/3 and 2/3 non-genotoxic carcinogens at 1 and 5 days of treatment, respectively and 2/2 non-carcinogens at both time-points. Additionally, we evaluated a novel panel of putative biomarker genes, from the literature, many of which have roles in cell growth and division, including myc, cdc2 and mcm6. These genes were significantly induced by non-genotoxic carcinogens and not by non-carcinogens. Using the average fold-induction across this panel, 2/3 non-genotoxic carcinogens were detected at both 1 and 5 days. These data support the idea that acute changes in gene expression may provide biomarkers for non-genotoxic carcinogenesis but also highlight interesting differences in the sensitivities of distinct gene signatures.     

Proteomic sensitivity to dietary manipulations in rainbow trout

Changes in dietary protein sources due to substitution of fish meal by other protein sources can have metabolic consequences in farmed fish. A proteomics approach was used to study the protein profiles of livers of rainbow trout that have been fed two diets containing different proportions of plant ingredients. Both diets control (C) and soy (S) contained fish meal and plant ingredients and synthetic amino acids, but diet S had a greater proportion of soybean meal. A feeding trial was performed for 12 weeks at the end of which, growth and protein metabolism parameters were measured. Protein growth rates were not different in fish fed different diets; however, protein consumption and protein synthesis rates were higher in the fish fed the diet S. Fish fed diet S had lower efficiency of retention of synthesised protein. Ammonia excretion was increased as well as the activities of hepatic glutamate dehydrogenase and aspartate amino transferase (ASAT). No differences were found in free amino acid pools in either liver or muscle between diets. Protein extraction followed by high-resolution two-dimensional electrophoresis, coupled with gel image analysis, allowed identification and expression of hundreds of protein. Individual proteins of interest were then subjected to further analysis leading to protein identification by trypsin digest fingerprinting. During this study, approximately 800 liver proteins were analysed for expression pattern, of which 33 were found to be differentially expressed between diets C and S. Seventeen proteins were positively identified after database searching. Proteins were identified from diverse metabolic pathways, demonstrating the complex nature of gene expression responses to dietary manipulation revealed by proteomic characterisation.