Barbara Sitek

Detection of novel biomarkers of liver cirrhosis by proteomic analysis

Hepatic cirrhosis is a life‐threatening disease arising from different chronic liver disorders. One major cause for hepatic cirrhosis is chronic hepatitis C. Chronic hepatitis C is characterized by a highly variable clinical course, with at least 20% developing liver cirrhosis within 40 years. Only liver biopsy allows a reliable evaluation of the course of hepatitis C by grading inflammation and staging fibrosis, and thus serum biomarkers for hepatic fibrosis with high sensitivity and specificity are needed. To identify new candidate biomarkers for hepatic fibrosis, we performed a proteomic approach of microdissected cirrhotic septa and liver parenchyma cells. In cirrhotic septa, we detected an increasing expression of cell structure associated proteins, including actin, prolyl 4‐hydroxylase, tropomyosin, calponin, transgelin, and human microfibril–associated protein 4 (MFAP‐4). Tropomyosin, calponin, and transgelin reflect a contribution of activated stellate cells/myofibroblasts to chronic liver injury. The expression of tropomyosin, transgelin, and MFAP‐4, an extracellular matrix associated protein, were further evaluated by immunohistochemistry. Tropomyosin and MFAP‐4 demonstrated high serum levels in patients with hepatic cirrhosis of different causes. Conclusion: A quantitative analysis of MFAP‐4 serum levels in a large number of patients showed MFAP‐4 as novel candidate biomarker with high diagnostic accuracy for prediction of nondiseased liver versus cirrhosis [area under receiver operating characteristic curve (AUC) = 0.97, P < 0.0001] as well as stage 0 versus stage 4 fibrosis (AUC = 0.84, P < 0.0001), and stages 0 to 3 versus stage 4 fibrosis (AUC = 0.76, P < 0.0001). (HEPATOLOGY 2009.)

Label-free quantification in clinical proteomics

Nowadays, proteomic studies no longer focus only on identifying as many proteins as possible in a given sample, but aiming for an accurate quantification of them. Especially in clinical proteomics, the investigation of variable protein expression profiles can yield useful information on pathological pathways or biomarkers and drug targets related to a particular disease. Over the time, many quantitative proteomic approaches have been established allowing researchers in the field of proteomics to refer to a comprehensive toolbox of different methodologies. In this review we will give an overview of different methods of quantitative proteomics with focus on label-free proteomics and its use in clinical proteomics.

Study of Early Leaf Senescence in Arabidopsis thaliana by Quantitative Proteomics Using Reciprocal 14N/15N Labeling and Difference Gel Electrophoresis

Leaf senescence represents the final stage of leaf development and is associated with fundamental changes on the level of the proteome. For the quantitative analysis of changes in protein abundance related to early leaf senescence, we designed an elaborate double and reverse labeling strategy simultaneously employing fluorescent two-dimensional DIGE as well as metabolic 15N labeling followed by MS. Reciprocal 14N/15N labeling of entire Arabidopsis thaliana plants showed that full incorporation of 15N into the proteins of the plant did not cause any adverse effects on development and protein expression. A direct comparison of DIGE and 15N labeling combined with MS showed that results obtained by both quantification methods correlated well for proteins showing low to moderate regulation factors. Nano HPLC/ESI-MS/MS analysis of 21 protein spots that consistently exhibited abundance differences in nine biological replicates based on both DIGE and MS resulted in the identification of 13 distinct proteins and protein subunits that showed significant regulation in Arabidopsis mutant plants displaying advanced leaf senescence. Ribulose 1,5-bisphosphate carboxylase/oxygenase large and three of its four small subunits were found to be down-regulated, which reflects the degradation of the photosynthetic machinery during leaf senescence. Among the proteins showing higher abundance in mutant plants were several members of the glutathione S-transferase family class phi and quinone reductase. Up-regulation of these proteins fits well into the context of leaf senescence since they are generally involved in the protection of plant cells against reactive oxygen species which are increasingly generated by lipid degradation during leaf senescence. With the exception of one glutathione S-transferase isoform, none of these proteins has been linked to leaf senescence before.

Early leaf senescence is associated with an altered cellular redox balance in Arabidopsis cpr5/old1 mutants

Reactive oxygen species (ROS) are the inevitable by-products of essential cellular metabolic and physiological activities. Plants have developed sophisticated gene networks of ROS generation and scavenging systems. However, ROS regulation is still poorly understood. Here, we report that mutations in the Arabidopsis CPR5/OLD1 gene may cause early senescence through deregulation of the cellular redox balance. Genetic analysis showed that blocking stress-related hormonal signalling pathways, such as ethylene, salicylic acid, jasmonic acid, abscisic acid and sugar, did not affect premature cell death and leaf senescence. We took a bioinformatics approach and analysed publicly available transcriptome data of presymptomatic cpr5/old1 mutants. The results demonstrate that many genes in the ROS gene network show at least fivefold increases in transcripts in comparison with those of wild-type plants, suggesting that presymptomatic cpr5/old1 mutants are in a state of high-cellular oxidative stress. This was further confirmed by a comparative, relative quantitative proteomics study of Arabidopsis wild-type and cpr5/old1 mutant plants, which demonstrated that several Phi family members of glutathione s-transferases significantly increased in abundance. In summary, our genetic, transcriptomic and relative quantitative proteomics analyses indicate that CPR5 plays a central role in regulating redox balance in Arabidopsis.

Proteomic Differences Between Hepatocellular Carcinoma and Nontumorous Liver Tissue Investigated by a Combined Gel-based and Label-free Quantitative Proteomics Study

Proteomics-based clinical studies have been shown to be promising strategies for the discovery of novel biomarkers of a particular disease. Here, we present a study of hepatocellular carcinoma (HCC) that combines complementary two-dimensional difference in gel electrophoresis (2D-DIGE) and liquid chromatography (LC-MS)-based approaches of quantitative proteomics. In our proteomic experiments, we analyzed a set of 14 samples (7 × HCC versus 7 × nontumorous liver tissue) with both techniques. Thereby we identified 573 proteins that were differentially expressed between the experimental groups. Among these, only 51 differentially expressed proteins were identified irrespective of the applied approach. Using Western blotting and immunohistochemical analysis the regulation patterns of six selected proteins from the study overlap (inorganic pyrophosphatase 1 (PPA1), tumor necrosis factor type 1 receptor-associated protein 1 (TRAP1), betaine-homocysteine S-methyltransferase 1 (BHMT)) were successfully verified within the same sample set. In addition, the up-regulations of selected proteins from the complements of both approaches (major vault protein (MVP), gelsolin (GSN), chloride intracellular channel protein 1 (CLIC1)) were also reproducible. Within a second independent verification set (n = 33) the altered protein expression levels of major vault protein and betaine-homocysteine S-methyltransferase were further confirmed by Western blots quantitatively analyzed via densitometry. For the other candidates slight but nonsignificant trends were detectable in this independent cohort. Based on these results we assume that major vault protein and betaine-homocysteine S-methyltransferase have the potential to act as diagnostic HCC biomarker candidates that are worth to be followed in further validation studies.

Analysis of the Pancreatic Tumor Progression by a Quantitative Proteomic Approach and Immunhistochemical Validation.

To increase the knowledge about the development of pancreatic ductal adenocarcinoma, (PDAC) detailed analysis of the tumor progression is required. To identify proteins differentially expressed in the pancreatic intraepithelial neoplasia (PanIN), the precursor lesions of PDAC, we conducted a quantitative proteome study on microdissected PanIN cells. Proteins from 1000 microdissected cells were subjected to a procedure combining fluorescence dye saturation labeling with high resolution two-dimensional gel electrophoresis (2-DE). Differentially regulated protein spots were identified using protein lysates from PDAC tissues as a reference proteome followed by nanoLC-ESI-MS/MS. Thirty-seven single lesions of different PanIN grade (PanIN 1A/B, PanIN 2, PanIN 3) from nine patients were analyzed. Their protein expression was compared with each other, with PDAC cells and with normal ductal cells. The differential expression of differentially regulated protein spots was validated by means of immunohistochemistry using tissue microarrays. Of 2500 protein spots, 86 were found to be significantly regulated (p < 0.05, ratio > 1.6) during PanIN progression. Thirty-one nonredundant proteins were identified by mass spectrometry. Immunohistochemistry revealed that the differential expression of the selected candidate proteins major vault protein (MVP), anterior gradient 2 (AGR 2) and 14-3-3 sigma, annexin A4, and S100A10 could be successfully validated in PanIN lesions. The highly sensitive and robust proteome analysis revealed differentially regulated proteins involved in pancreatic tumor progression. The analysis of normal preneoplastic and neoplastic pancreatic tissue establishes a basis for identification of candidate biomarkers in PanIN progression that can be detected in pancreatic juice and in serum or are candidates for in vivo imaging approaches.

Identification of Proteomic Differences between Squamous Cell Carcinoma of the Lung and Bronchial Epithelium

Proteins that exhibit different expression levels in normal and malignant lung cells are good candidate biomarkers to improve early diagnosis and intervention. We used a quantitative approach and compared the proteome of microdissected cells from normal human bronchial epithelium and squamous cell carcinoma tumors of histopathological grades G2 and G3. DIGE analysis and subsequent MS-based protein identification revealed that 32 non-redundant proteins were differentially regulated between the respective tissue types. These proteins are mainly involved in energy pathways, cell growth or maintenance mechanisms, protein metabolism, and the regulation of DNA and RNA metabolism. The expression of some of these proteins was analyzed by immunohistochemistry using tissue microarrays containing tissue specimen of 55 patients, including normal bronchial epithelium, squamous cell carcinomas, adenocarcinomas, and large cell carcinomas. The results of the immunohistochemical studies correlated with the proteome study data and revealed that particularly HSP47 and a group of cytokeratins (i.e. cytokeratins 6a, 16, and 17) are significantly co-regulated in squamous cell carcinoma. Furthermore cytokeratin 17 showed significantly higher abundance in G2 grade compared with G3 grade squamous cell carcinomas in both the gel-based and the immunohistochemical analysis. Therefore this protein might be used as a marker for stratification between different tumor grades.

Comparison of label-free and label-based strategies for proteome analysis of hepatoma cell lines.

Within the past decade numerous methods for quantitative proteome analysis have been developed of which all exhibit particular advantages and disadvantages. Here, we present the results of a study aiming for a comprehensive comparison of ion-intensity based label-free proteomics and two label-based approaches using isobaric tags incorporated at the peptide and protein levels, respectively. As model system for our quantitative analysis we used the three hepatoma cell lines HepG2, Hep3B and SK-Hep-1. Four biological replicates of each cell line were quantitatively analyzed using an RPLC-MS/MS setup. Each quantification experiment was performed twice to determine technical variances of the different quantification techniques. We were able to show that the label-free approach by far outperforms both TMT methods regarding proteome coverage, as up to threefold more proteins were reproducibly identified in replicate measurements. Furthermore, we could demonstrate that all three methods show comparable reproducibility concerning protein quantification, but slightly differ in terms of accuracy. Here, label-free was found to be less accurate than both TMT approaches. It was also observed that the introduction of TMT labels at the protein level reduces the effect of underestimation of protein ratios, which is commonly monitored in case of TMT peptide labeling. Previously reported differences in protein expression between the particular cell lines were furthermore reproduced, which confirms the applicability of each investigated quantification method to study proteomic differences in such biological systems. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

A practical data processing workflow for multi-OMICS projects☆

Multi-OMICS approaches aim on the integration of quantitative data obtained for different biological molecules in order to understand their interrelation and the functioning of larger systems. This paper deals with several data integration and data processing issues that frequently occur within this context. To this end, the data processing workflow within the PROFILE project is presented, a multi-OMICS project that aims on identification of novel biomarkers and the development of new therapeutic targets for seven important liver diseases. Furthermore, a software called CrossPlatformCommander is sketched, which facilitates several steps of the proposed workflow in a semi-automatic manner. Application of the software is presented for the detection of novel biomarkers, their ranking and annotation with existing knowledge using the example of corresponding Transcriptomics and Proteomics data sets obtained from patients suffering from hepatocellular carcinoma. Additionally, a linear regression analysis of Transcriptomics vs. Proteomics data is presented and its performance assessed. It was shown, that for capturing profound relations between Transcriptomics and Proteomics data, a simple linear regression analysis is not sufficient and implementation and evaluation of alternative statistical approaches are needed. Additionally, the integration of multivariate variable selection and classification approaches is intended for further development of the software. Although this paper focuses only on the combination of data obtained from quantitative Proteomics and Transcriptomics experiments, several approaches and data integration steps are also applicable for other OMICS technologies. Keeping specific restrictions in mind the suggested workflow (or at least parts of it) may be used as a template for similar projects that make use of different high throughput techniques. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

Analysis of disease-associated protein expression using quantitative proteomics—fibulin-5 is expressed in association with hepatic fibrosis.

Hepatic fibrosis and cirrhosis are major health problems worldwide. Until now, highly invasive biopsy remains the diagnostic gold standard despite many disadvantages. To develop noninvasive diagnostic assays for the assessment of liver fibrosis, it is urgently necessary to identify molecules that are robustly expressed in association with the disease. We analyzed biopsied tissue samples from 95 patients with HBV/HCV-associated hepatic fibrosis using three different quantification methods. We performed a label-free proteomics discovery study to identify novel disease-associated proteins using a subset of the cohort (n = 27). Subsequently, gene expression data from all available clinical samples were analyzed (n = 77). Finally, we performed a targeted proteomics approach, multiple reaction monitoring (MRM), to verify the disease-associated expression in samples independent from the discovery approach (n = 68). We identified fibulin-5 (FBLN5) as a novel protein expressed in relation to hepatic fibrosis. Furthermore, we confirmed the altered expression of microfibril-associated glycoprotein 4 (MFAP4), lumican (LUM), and collagen alpha-1(XIV) chain (COL14A1) in association to hepatic fibrosis. To our knowledge, no tissue-based quantitative proteomics study for hepatic fibrosis has been performed using a cohort of comparable size. By this means, we add substantial evidence for the disease-related expression of the proteins examined in this study.