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ORIGINAL ARTICLE
Year : 2014  |  Volume : 51  |  Issue : 2  |  Page : 159-162
 

Proteomics analysis of human brain glial cell proteome by 2D gel


1 Department of Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 Department of Biology, Science and Research Branch, Islamic Azad University; Iran National Science Foundation, Tehran, Iran
3 Department of Genetics, Tehran Medical Branch, Islamic Azad University, Tehran, Iran

Date of Web Publication7-Aug-2014

Correspondence Address:
M Hashemi
Department of Genetics, Tehran Medical Branch, Islamic Azad University, Tehran
Iran
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Source of Support: Iran National Science Foundation (INSF), Tehran, Iran, Conflict of Interest: None


DOI: 10.4103/0019-509X.138271

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 » Abstract 

Introduction: Proteomics is increasingly employed in both neurological and oncological research, and applied widely in every area of neuroscience research including brain cancer. Astrocytomas are the most common glioma and can occur in most parts of the brain and occasionally in the spinal cord. Patients with high-grade astrocytomas have a life expectancy of <1 year even after surgery, chemotherapy, and radiotherapy. Materials and Methods: We extracted proteins from tumors and normal brain tissues and then evaluated the protein purity by Bradford test and spectrophotometry method. In this study, we separated proteins by the two-dimensional (2DG) gel electrophoresis method, and the spots were analyzed and compared using statistical data. Results: On each analytical 2D gel, an average of 800 spots was observed. In this study, 164 spots exhibited up-regulation of expression level, whereas the remaining 179 spots decreased in astrocytoma tumor relative to normal tissue. Results demonstrate that functional clustering and principal component analysis (PCA) has considerable merits in aiding the interpretation of proteomic data. Proteomics is a powerful tool in identifying multiple proteins that are altered following a neuropharmacological intervention in a disease of the central nervous system (CNS). Conclusion: 2-D gel and cluster analysis have important roles in the diagnostic management of astrocytoma patients, providing insight into tumor biology. The application of proteomics to CNS research has invariably been very successful in yielding large amounts of data.


Keywords: Cluster, 2D-DIGE, glioma, proteomics and astrocytoma


How to cite this article:
Pooladi M, Abad S, Hashemi M. Proteomics analysis of human brain glial cell proteome by 2D gel. Indian J Cancer 2014;51:159-62

How to cite this URL:
Pooladi M, Abad S, Hashemi M. Proteomics analysis of human brain glial cell proteome by 2D gel. Indian J Cancer [serial online] 2014 [cited 2019 Sep 22];51:159-62. Available from: http://www.indianjcancer.com/text.asp?2014/51/2/159/138271



 » Introduction Top


Proteomics is increasingly employed in both neurological and oncological research to provide insight into the molecular basis of disease but rarely has a coherent, novel pathophysiological insight emerged. [1],[2] Proteomics analysis is now applied widely in every area of neuroscience research including brain cancer. [3],[4] Proteomic measurements provide a wealth of information complementary to the transcriptomics data [5] because biological systems comprise protein components resulting from transcriptional and post-transcriptional control, post-translational modifications, and shifts in proteins among the different cellular compartments. [6],[7] The recent progress of oncoproteomics has opened new routes for the discovery of cancer-related biomarkers. Diagnostic oncoproteomics is the application of proteomic techniques in the diagnosis of malignancies. Early detection of cancer has the potential to reduce mortality dramatically. [8] Protein separation and comparison by two-dimensional poly-acrylamide gel electrophoresis (2D-PAGE) is used to determine the quantity of a particular protein. [9],[10],[11] High-resolution 2D electrophoresis can resolve up to 5,000 proteins simultaneously (~2,000 proteins routinely), and detect and quantify <1 ng protein per spot. [12] Thus, data mining is an important tool for a first exploration of proteomics results as the amount of data increases with the increasing sensitivity and availability of the technique. [13],[14]

Gliomas are the most common human primary brain tumors, [15],[16],[17],[18] are highly invasive and heterogeneous, and respond poorly to treatment. [15],[19] Most gliomas (>80%) appear to arise from astrocytomas, the most abundant type of glial cells, and are known as astrocytomas. [5],[19] Patients with high-grade (grade IV) astrocytomas have a life expectancy of <1 year even after surgery, chemotherapy, and radiation therapy. [20] The grade IV glioma, commonly known as glioblastoma multiforme (GBM) is the most aggressive and lethal type with an average life expectancy of one year or less from the time of diagnosis of the disease. [21],[22],[23] Diagnosis of GBM is triggered by the onset of symptoms and is based on cerebral imaging and histological examination. [24],[25] Identifying groups of individuals or objects similar to each other but different from individuals or objects in other groups can be intellectually satisfying, profitable, or sometimes both. Although both cluster analysis and discriminant analysis classify objects (or cases) into categories, discriminant analysis requires you to know group membership for the cases used to derive the classification rule. [3],[14]

In the present study, we investigated change in protein expression in human brain astrocytoma tumor to get an understanding of data and specific software molecular diagnosis of astrocytoma. Here, proteins of tumoral and normal brain tissues were extracted and evaluated by proteomic tools (2D gel). After providing cluster and principal component analysis (PCA) of spots, their alterations are monitored using statistical data and specific software. Using different proteomic approaches, multiple differentially expressed astrocytoma proteins were identified, a few of which could be investigated further as potential surrogate markers for astrocytoma tumors.


 » Materials and Methods Top


Patient samples

For this study, all individuals filled a written informed consent form. Astrocytoma tumors were surgically removed at hospitals in Tehran. The tumors were classified by a team of neurophathologists according to the guidelines of the World Health Organization (WHO) classification of tumors of the central nervous system.

Tissue and samples preparation

Tissue samples of both tumoral and normal brain tissue were snap-frozen immediately after operation in liquid nitrogen and stored at -80° until used for proteomic analysis. To obtain tissue extracts, the samples were broken into suitable pieces and were homogenized in lysis buffer II consisting of lysis buffer I {7M urea, 2M thiourea, 4% [w/v] CHAPS (3-[(3-cholamidopropyl) dimethylammonio]-1-propanesulfonate), 0.2% [w/v] 100 × Bio-Lyte 3/10}, dithiothreitol (DTT), and 1 mM ampholyte and protease inhibitor on ice. Cell lysis was completed by subsequent sonication (4 × 30 pulses). The samples were then centrifuged 20,000g at 4°C for 30 minutes to remove insoluble debris. The supernatants were combined with acetone 100% and centrifuged at 15,000g, and then the supernatants were decanted and removed (3_times). Acetone 100% was added to the protein precipitant and kept at -20° overnight. The samples were then centrifuged again at 15,000g and the precipitant incubated 1 hour at room temperature. The protein samples were dissolved in rehydration buffer [8M urea, 1%[w/v] CHAPS, DTT, ampholyte pH (4) and protease inhibitor]. Protein concentrations were determined using the Bradford test and spectrophotometry method, and the protein extracts were then separated and used for 2D gel electrophoresis.

2DG electrophoresis

The isoelectric focusing for first-dimensional electrophoresis was performed using 18 cm, pH3-10 immobilized pH gradient (IPG) strips. The samples were diluted in a solution containing rehydration buffer, IPG buffer, and DTT to reach a final protein amount of 500 μg per strip. The strips were subsequently subjected to voltage gradient as described in the instructions of the manufacturer. Once focused, the IPG strips were equilibrated twice for 15 minutes in equilibration buffer I [50 mM tris-Hcl (pH: 8.8), 6M urea, 30% glycerol, 2% sodium dodecyl sulfate (SDS), and DTT] and equilibration buffer II. The second-dimension SDS-PAGE was carried out using 12% PAGEs. Following SDS-PAGE, the gels were stained using the Coomassie Blue method overnight.

Image analysis

The gel images were analyzed by Progenesis SameSpots software to identify spots differentially expressed between tumor and control samples based on their volume and density. The spots were carefully matched individually and only spots that showed a definite difference were defined as altered.

Data statistics analysis

The Student's t-test was used to rank proteins found altered in astrocytoma tumor compared to normal tissue according to statistical probability. The t-test was chosen to create a hierarchy because it is easily understood by many different target audiences and is currently a common practice in the majority of proteomics analyses. Protein clustering analyses were performed on the list of proteins deemed significantly altered in astrocytoma tumors (P < 0.05).

Arithmetic cluster analysis was performed on two groups. Arithmetic cluster analysis employs correlation analysis to define if alterations in the levels of one individual protein are associated with alterations in the levels of a second protein across all samples (astrocytoma and normal tissues). Arithmetic correlation algorithms are integral to the Progensis SameSpots software (Nonlinear Dynamics v 3.0, 2008). Multiple areas on correlation coefficients between protein features were calculated by Progenesis SameSpots and the information visually represented in the form of a dendrogram.


 » Results Top


2D gel was used to identify proteins expressed in astrocytoma tumor and nontumor samples. The spots were separated by isoelectric point and molecular weights. On average, a total of 800 spots were separated in analytical gels and spots were subjected to automated difference in gel analysis using Progenesis SameSpots software version 3.1 (Nonlinear Dynamics Ltd. The representative set of overlaid 2D-DIGE (DIGE: Difference gel electrophoresis) images is given.

The first-dimension analysis was performed with a broad pH range (PH: 3-10), and IPG using strips of 18 cm. The total number of protein features was matched and analyzed between gels in the control group and tumor group; 343 spots (around 49% of the entire detected spots) were matched across all the gels. In software analysis, a total of 343 differentially expressed spots satisfied the statistical parameters (t-test and one-way ANOVA; P < 0.05).

The relative differences in expression among 146 spots were observed (fold >2). A total of 343 spots showed statistically significant differences (student's t-test; P < 0.05) in gel, of which 164 spots exhibited up regulation in expression level, whereas the remaining 179 spots were decreased in astrocytoma tumor relative to normal tissue. Up regulation is shown as red and down regulation as blue in imaging gel [Figure 1]. Of the 164 up regulated spots, 95 spots were between 1.1 and 2 fold, 48 spots were between two and four fold, and 21 spots exhibited over four fold increase in expression level [Figure 1]. Of the 179 down regulated spots, 111 spots were between 1.1 and 2 fold, 55 spots were between two and four, and 13 spots exhibited over four fold reduction in expression level [Figure 1].
Figure 1: Categorization of change in protein expression (up and downregulation) shown in Figure of the 48% up regulated spots (red) and the 52% downregulated spots (blue)

Click here to view


Cluster analysis

The total spots were matched between the tumor and the normal tissues. Spots were subjected to automated difference in gel analysis using Progenesis SameSpots software version 3.1 (Nonlinear Dynamics Ltd. Arithmetic cluster analysis was performed on this list of 343 spot proteins. Arithmetic cluster analysis explores how one individual protein level correlates with a second individual protein level across different samples. Protein levels that are tightly correlated suggest that the proteins might be coregulated or involved in the same biological pathway. A clear cluster analysis (dendrogram) with several distinct subgroups of proteins was generated [Figure 2].
Figure 2: Arithmetic cluster analysis; protein dendrogram of 343 proteins differentially altered (P < 0/05) in astrocytoma tumors from two groups (up and downregulated); this dendrogram clearly indicates the cluster of 164 spot proteins found upreglated (right branches, in red) and 179 spot proteins found downregulated (left branch, in blue) in astrocytoma tumor

Click here to view


Two main groups reflected the 164 spot proteins that increased (red) and 179 spot proteins that decreased (blue) in expression level in astrocytoma relative to normal tissue.

The total up regulated protein spots showed two main subgroups (subgroups I and II); subgroup II involved two branches [red in [Figure 2]], and the total down regulated spot proteins showed two main subgroups, (subgroup I and II); subgroup I involved two branches [blue in [Figure 2]].

PCA was performed on all the spot proteins and it showed two main groups (up and down regulated) [Figure 3].
Figure 3: Principal component analysis was performed on all the spot proteins; the PCA of 164 spot proteins was up reglated (right figure) and 179 spot proteins were down regulated (left figure) in astrocytoma tumor

Click here to view



 » Discussion Top


Proteomics has been adopted in numerous clinical fields of research and has generated an enormous amount of data. [1] This is a powerful tool for identifying multiple proteins that are altered following a neuropharmacological intervention in a disease of the CNS. [3],[26] Interpretative strategies are poorly developed in proteomics compared to other research fields in which similar data-handling challenges are faced. [2],[3] Clinical neuroproteomics aims to advance our understanding of disease and injury affecting the central and peripheral nervous systems through the study of protein expression and discovery of protein biomarkers to facilitate diagnosis and treatment. [27],[28]

Originally, at the core of proteomics was 2D gel electrophoresis which permitted the separation of thousands of protein variants (spots). [14],[29] The approximately 21,000 genes in the human genome encode for well over 100,000 proteins, for which an astonishing 200 to 300 distinguished types of post-translation modification are estimated, illustrating the wealth of information in the proteome. [2],[27]

High-resolution 2D electrophoresis can resolve up to 5,000 proteins simultaneously (~2,000 proteins routinely), and detect and quantify <1 ng of protein per spot. Today's 2D electrophoresis technology with IPGs has largely overcome the former limitations of carrier ampholyte-based 2D electrophoresis with respect to reproducibility, handling, resolution, and separation of very acidic or basic proteins. [28],[29] The proteomic patterns showed high intensity spots in the acidic region (312 spots) and weak spots in the basic region (31 spots).

In this study, we seprated proteins between 10 and 100 KDa with the result that 16% protein spots were <25 KDa (54 spots), 17% protein spots between 26 and 50 KDa (58 spots), 38% protein spots between 51 and 75 KDa (132 spots), and 29% protein spots >75 KDa (99 spots).

2D-DIGE is a very powerful technique allowing controls and experimental samples to be run on the same gel together with an internal reference. Using the software (for example, SameSpots or Decyder), gel images can be analyzed to identify statistically significant differences in protein expression between different samples on a limited number of gels. [12],[14],[16] Biostatistics is essential to ensure the collection of robust and meaningful data and that the results withstand the most rigorous stoical analyses at the level of the resulting clinical/analytical matrix. This includes the determination of both false-positive and false-negative rates which are critical for evaluating the success of the biomarker. [27] Bioinformatics analysis of molecular weights, isoelectric point, mass-to-charge ratio, and ion intensity data obtained using proteomics is complicated due to many factors, such as technical and biological variability and also chemical and electronic noise that can result in baseline shifts; these may influence the data and show differences between sample groups that have no true biological meaning. [30] For example, differential analysis of glioblastoma multiforme proteome, [1] such as differential protein expression in low-grade astrocytomas and human brain tissue [19] or serum proteome alterations in human GBM. [22]

Because the goal of this cluster analysis is to form similar groups of judges, we have to decide on the criterion to be used for measuring similarity or distance. Distance is a measure of how far apart two objects are, whereas similarity measures how similar two objects are.

In addition, many proteins are implicated in numerous biological processes. Distinct but complementary to protein functional analysis, arithmetic cluster analysis is a useful approach for highlighting subgroups of proteins that are associated on the basis of numerical correlation across different biological samples. [3],[31] For example, glial fibrillary acidic protein (GFAP) is a cell-specific marker and plays an essential role in neuron/astrocyte interactions during CNS development, which can be used to distinguish astrocytes from other glial cells. When the CNS is injured by trauma or disease, astrocytes proliferate rapidly. Studies have reported decreased GFAP expression in high-grade gliomas compared with low-grade astrocytomas, suggesting that GFAP could serve as a specific marker for low-grade astrocytomas. [1],[19],[32]

The effect of the clustering algorithm was also important but a significant interaction was observed between clustering algorithm and data set. Additionally, it was interesting to note the high correlation between clusters, reflecting the quality of the replication grouping, and a biologically relevant grouping of the samples. Identification and validation of the protein markers splitting the two lineages is under way. [13] The best entry criterion for cluster analysis remains debatable; however, there are currently no general standards of practice in the proteomics field. Choices of entry criteria include significant level of fold change values according to t-test (P < 0.05 or 0.01). [3],[22] ANOVA analysis previously performed on this data set showed a similar result. [13] Indeed, differences observed for significant differentially expressed proteins (between fold <2, fold 2-4, and fold >2) in astrocytoma were, respectively, biological function, variation and differentially expressed. [3],[13],[22]

Consequently, the biological differences detected by both ANOVA and clustering were higher than technical or biological variability, which validates our analysis. [13]


 » Conclusion Top


Our results have shown that multiple protein spots associated with different biological disease are differentially expressed in astrocytoma patients. 2D gel and cluster analysis have important roles in the diagnosis management of astrocytoma patients and in providing insight into tumor biology. The application of proteomics to CNS research has invariably been very successful in yielding large amounts of data. However, the output of many of these studies including those from our laboratory is often limited to lists of proteins found increased or decreased in the disease state.

 
 » References Top

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