Analytical and Bioanalytical Chemistry

, Volume 379, Issue 7, pp 992–1003

Identification of the regulatory proteins in human pancreatic cancers treated with Trichostatin A by 2D-PAGE maps and multivariate statistical analysis

Authors

    • Department of Environmental and Life SciencesUniversity of Eastern Piedmont
  • Elisa Robotti
    • Department of Environmental and Life SciencesUniversity of Eastern Piedmont
  • Daniela Cecconi
    • Department of Industrial BiotechnologiesUniversity of Verona
  • Mahmoud Hamdan
    • Computational, Analytical and Structural SciencesGlaxoSmithKline
  • Aldo Scarpa
    • Department of Pathology, Section of Anatomical PathologyUniversity of Verona
  • Pier Giorgio Righetti
    • Department of Industrial BiotechnologiesUniversity of Verona
Paper in Forefront

DOI: 10.1007/s00216-004-2707-x

Cite this article as:
Marengo, E., Robotti, E., Cecconi, D. et al. Anal Bioanal Chem (2004) 379: 992. doi:10.1007/s00216-004-2707-x

Abstract

In this paper, principal component analysis (PCA) is applied to a spot quantity dataset comprising 435 spots detected in 18 samples belonging to two different cell lines (Paca44 and T3M4) of control (untreated) and drug-treated pancreatic ductal carcinoma cells. The aim of the study was the identification of the differences occurring between the proteomic patterns of the two investigated cell lines and the evaluation of the effect of the drug Trichostatin A on the protein content of the cells. PCA turned out to be a successful tool for the identification of the classes of samples present in the dataset. Moreover, the loadings analysis allowed the identification of the differentially expressed spots, which characterise each group of samples. The treatment of both the cell lines with Trichostatin A therefore showed an appreciable effect on the proteomic pattern of the treated samples. Identification of some of the most relevant spots was also performed by mass spectrometry.

Keywords

PCAChemometricsHuman pancreatic tumourTrichostatin AProtein identification

Introduction

Since each cell or biological fluid has a rich protein content (often comprising thousands of proteins of different structure and size), an effective method for achieving their separation is necessary. In the field of proteomics [1, 2], the separation of proteins is usually achieved by two-dimensional (2D) electrophoresis, a very powerful tool which performs two successive electrophoretic runs: the first run (through a pH gradient) separates the proteins with respect to their isoelectric point, while the second run (through a porosity gradient or a highly sieving, constant concentration gel) separates them according to their molecular mass. This technique produces a two-dimensional map, a so-called 2D-PAGE (polyacrylamide gel electrophoresis), with the proteins appearing as spots spread all over the gel matrix. A 2D-PAGE map may thus be considered as a “snapshot” of the protein content of the investigated cell at a given point of its life cycle.

In this new post-genomic and proteomic era, the investigation of the protein content of different cell types has become fundamental. In fact, the physiological state of a particular cell or tissue is related to its protein content, and the onset of a particular disease may cause differences in the proteins contained in the pathological tissue: these differences may consist of changes in the relative abundance or even in the appearance/disappearance of some proteins [311]. The comparison of 2D-PAGE maps belonging to healthy subjects with samples belonging to individuals affected by any pathology thus becomes a fundamental tool for both diagnostic and prognostic purposes [311]. The 2D-PAGE technique is also widely applied in the field of drug development [1215], especially for cancer: two-dimensional gel-electrophoresis may be used to investigate if the treatment with a particular drug has played the expected role on the protein content of the pathological cell and to evaluate which effect was produced (e.g. up- or down-regulation, appearance/disappearance of pathological chains).

Unfortunately, the comparison of 2D-PAGE maps is not a trivial problem; its difficulty is principally due to:
  • The high complexity of the sample, which can produce maps with thousands of spots

  • The complex sample pre-treatment, characterised by several purification/extraction steps, which may contribute to the appearance of maps with spurious spots due to accidental chemical modifications

  • The sometimes small differences which often occur in the 2D-PAGE maps of treated and reference samples, which are much more difficult to recognise in complex maps.

Usually, the comparison of 2D-PAGE maps is performed by means of some specific softwares (e.g. Melanie III or PDQuest) [1619], which exploit the following three-step method:
  • The 2D-PAGE images to be compared are aligned, so that all images are reduced to the same size. This step needs the choice of at least two positively identified spots in all the maps; the maps are then matched to each other on the basis of the position of these two spots

  • The spots present on each map are independently revealed

  • The maps are matched to each other in order to identify the common information (spots present in all the maps) and the different one (spots detected only on some of the samples). If the comparison is performed on a set of replicate maps this step produces a “synthetic” map which summarises the common information and contains only the spots present in all the compared maps.

The great amount of information produced by the comparison of 2D maps can be investigated by modern multivariate techniques like principal component analysis (PCA) [2022], classification methods [23, 24] and multidimensional scaling (MDS) [25].

Our research group has developed a new method based on both fuzzy logic and classification methods for the comparison of the proteomic pattern of classes of 2D-PAGE maps [26, 27]. This method has also been applied together with MDS for the study of 2D maps from control and diseased individuals [28]. Different proteomics patterns have also been investigated by the use of three-way principal component analysis [29].

PCA has been applied to the comparison of 2D-PAGE maps on the basis of the spot volume since the mid-1980s by Anderson et al. [30] in the USA and Tarroux et al. [31] in France. Recently, it has been applied to the study of DNA and RNA fragments of several biological systems [3235] and to the characterisation of proteomic patterns of different classes of tissues [3641]. Another recent application of PCA is for the characterisation of the anticancer activity of bohemine, a new omoleucine-derived synthetic cyclin-dependent kinase inhibitor, by Kovarova et al. [42].

In this paper, PCA is applied to a dataset comprising 18 samples belonging to two different cell lines (Paca44 and T3M4) of pancreatic human cancer before and after the treatment with a new drug (Trichostatin A). This approach focuses on the evaluation of the efficacy of the drug (reflected in a difference in the protein content of control and treated samples) and to the identification of the differences occurring between the samples (control/treated samples and Paca44/T3M4 cell lines). Some of the proteins responsible for the identified differences in the control and treated Paca44 samples were also characterised by mean of mass spectrometry with the matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) technique.

Theory

Principal component analysis

Principal component analysis [2022] is a multivariate statistical method, which allows the representation of the original dataset in a new reference system characterised by new variables called principal components (PCs). Each PC has the property of explaining the maximum possible amount of residual variance contained in the original dataset: the first PC explains the maximum amount of variance contained in the overall dataset, while the second one explains the maximum residual variance. The PCs are then calculated hierarchically, so that experimental noise and random variations are contained in the last PCs. The PCs, which are expressed as linear combinations of the original variables are orthogonal to each other and can be used for an effective representation of the system under investigation with a lower number of variables than in the original case. The co-ordinates of the samples in the new reference system are called scores while the coefficient of the linear combination describing each PC (i.e. the weights of the original variables on each PC) are called loadings. The graphical representation of scores by means of PCs allows the identification of groups of samples showing a similar behaviour (samples close to one another in the graph) or different characteristics (samples far from each other). By looking at the corresponding loading plot, it is possible to identify the variables, which are responsible for the analogies or the differences detected for the samples in the score plot. From this point of view, PCA is a very powerful visualisation tool, which allows the representation of multivariate datasets by means of only few PCs identified as the most relevant.

Cluster analysis

Cluster analysis techniques allow one to investigate the relationships between the objects or the variables of a dataset in order to recognise the existence of groups. The most commonly used approaches belong to the class of agglomerative hierarchical methods [43], in which the objects are grouped (linked together) on the basis of a measure of their similarity. The most similar objects or groups of objects are linked first. The result of such analyses is a graph, called a dendrogram, in which the objects (x-axis) are connected at decreasing levels of similarity (y-axis). The results of hierarchical clustering methods depend on the specific measure of similarity and on the linking method.

Experimental

The dataset comprised 18 2D maps, which were divided into four classes:
  • Four replicate 2D maps of a Paca44 cell line pool

  • Five replicate 2D maps of a T3M4 cell line pool

  • Four replicate 2D maps of a Paca44 cell pool treated for 48 h with Trichostatin A

  • Five replicate 2D maps of a T3M4 cell pool treated for 48 h with Trichostatin A.

Because we used pools of cell lines (grown under the same conditions), four to five replicates of 2D maps for each sample were deemed amply sufficient for reducing variability due to experimental errors. This strategy is common practice in today’s proteome analysis. Figure 1 represents an example, for each class, of the experimental 2D maps obtained.
Fig. 1

2D-PAGE maps of the real samples of pancreatic human cancer: examples of control (untreated) Paca44 cells, treated Paca44 cells, control (untreated) T3M4 cells and treated T3M4 cells

Software

Principal component analysis was performed with UNSCRAMBLER (Camo Inc., version 7.6, Norway). Cluster analysis was performed with STATISTICA (Statsoft Inc., version 5.1, USA). Graphical representations were performed with both UNSCRAMBLER and STATISTICA. The 2D-PAGE maps were scanned with a GS-710 densitometer (Bio-Rad Laboratories, Hercules, CA, USA) and analysed with the software PDQuest Version 6.2 (Bio-Rad Laboratories, Hercules, CA, USA).

Chemicals and materials

Urea, thiourea, 3-[(cholamidopropyl)dimethylammonium]-1-propane-sulfonate (CHAPS), iodoacetamide (IAA), tributylphosphine (TBP) and sodium dodecyl sulfate (SDS) were obtained from Fluka Chemie (Buchs, Switzerland). Bromophenol blue and agarose were from Pharmacia-LKB (Uppsala, Sweden). Acrylamide, N′,N′-methylenebisacrylamide, ammonium persulfate, TEMED, the Protean IEF Cell, the GS-710 Densitometer and the 17-cm-long, immobilised pH 3–10 linear gradient strips were from Bio-Rad Laboratories (Hercules, CA, USA). Ethanol, methanol and acetic acid were from Merck (Darmstadt, Germany). Trichostatin A (TSA) was obtained from Sigma–Aldrich Ltd. (St. Louis, MO, USA). A 3.3 mM solution of TSA in absolute ethanol was prepared and stored at −80°C until use.

Cell treatment with TSA

Paca44 and T3M4 cells were grown in RPMI 1640 supplemented with 20 mM glutamine and 10% (v/v) FBS (BioWhittaker, Italy) and were incubated at 37°C with 5% (v/v) CO2. Subconfluent cells were treated with 0.2 mM TSA for 48 h.

Cell lysis

Protein extraction from cells was performed with lysis buffer (40 mM Tris, 1% v/v NP40, 1 mM Na3VO4, 1 mM NaF, 1 mM PMSF, protease inhibitor cocktail). Cells were left in lysis buffer for 30 min in ice. After centrifugation at 14,000×g at 4°C for removal of particulate material, the protein solution was collected and stored at −80°C until used.

Two-dimensional gel electrophoresis

Seventeen-centimeter-long, pH 3–10 immobilized pH gradient strips (IPG; Bio-Rad Labs., Hercules, CA, USA) were rehydrated for 8 h with 450 μL of 2D solubilizing solution (7 M urea, 2 M thiourea, 5 mM tributylphosphine, 40 mM Tris and 20 mM iodoacetamide) containing 2 mg mL−1 of total reduced/alkylated protein from sample cells. Isoelectric focussing (IEF) was carried out with a Protean IEF Cell (Biorad, Hercules, CA, USA) with a low initial voltage and then by applying a voltage gradient up to 10,000 V with a limiting current of 50 μA. The total product time×voltage applied was 70,000 Vh for each strip, and the temperature was set at 20°C. For the second dimension, the IPGs strips were equilibrated for 26 min by rocking in a solution of 6 M urea, 2% w/v SDS, 20% v/v glycerol, 375 mM Tris-HCl, pH 8.8. The IPG strips were then laid on an 8–18% T gradient SDS-PAGE with 0.5% w/v agarose in the cathode buffer (192 mM glycine, 0.1% w/v SDS and Tris to pH 8.3). The anodic buffer was a solution of 375 mM Tris HCl, pH 8.8. The electrophoretic run was performed by setting a current of 2 mA for each gel for 2 h, then 5 mA/gel for 1 h, 10 mA/gel for 20 h and 20 mA/gel until the end of the run. During the whole run the temperature was set at 11°C. Gels were stained overnight with colloidal Coomassie blue (0.1% w/v Coomassie Brilliant Blue G, 34% v/v methanol, 3% v/v phosphoric acid and 17% w/v ammonium sulphate); destaining was performed with a solution of 5% v/v acetic acid until a clear background was achieved.

Protein identification by mass spectrometry

In situ digestion and extraction of peptides

The spots of interest were carefully excised from the gel with a razor blade, placed in Eppendorf tubes, and destained by washing three times for 20 min in 50% v/v acetonitrile, 2.5 mM Tris, pH 8.5. The gel pieces were dehydrated at room temperature and covered with 10 μL of trypsin (0.04 mg mL−1) in Tris buffer (2.5 mM, pH 8.5) and left at 37°C overnight. The spots were crushed and peptides were extracted in 15 μL of 50% v/v acetonitrile, 1% v/v formic acid. The extraction was conducted in an ultrasonic bath for 15 min. The sample was centrifuged at 8,000×g for 2 min, and the supernatant was collected.

MALDI-TOF analysis

The extracted peptides were loaded onto the target plate by mixing 1 μL of each solution with the same volume of a matrix solution, prepared fresh every day by dissolving 10 mg mL−1 cyano-4-hydroxycinnamic acid in acetonitrile/ethanol (1:1 v:v), and allowed to dry. Measurements were performed by using a TofSpec 2E MALDI-TOF instrument (Micromass, Manchester, UK), operated in reflectron mode, with an accelerating voltage of 20 kV. Peptide masses were searched against SWISS-PROT, TrEMBL and NCBInr databases by utilizing the ProteinLynx program from Micromass, Profound from Prowl, and Mascot from Matrix Science.

Results and discussion

Protein pattern analysis with the PDQuest software

The 2D gels of all the samples (Paca44 control and treated with TSA, T3M4 control and treated with TSA) were scanned with a GS-710 densitometer (Bio-Rad), and analysed with the software PDQuest. A match-set was created from the protein patterns of the 18 replicates 2D maps. A standard gel was generated out of the image with the highest spot number. Spot quantities of all gels were normalized to remove non-expression-related variations in spot intensity, so the raw quantity of each spot in a gel was divided by the total quantity of all the spots in that gel that have been included in the standard. The results were evaluated in terms of spot optical density (OD). The analysis with the PDQuest allowed two types of comparisons: between the two different cell lines (Paca44 versus T3M4), and between the control and TSA-treated cell lines (control versus TSA-treated) in order to detect protein variations that were at least two-fold. The Student’s t-test analysis allowed the identification of 60 spots up-regulated (with a significance level α of 0.05) and 45 spots down-regulated (with α=0.05) in the T3M4 cell line with respect to the PaCa44 cell line; and 11 spots up-regulated (with a significance level α of 0.05) and two spots down-regulated in TSA-treated cell lines in respect to the control samples.

Figures 2a and b show the results of comparison between the two different cell lines. In Fig. 2a the 45 spots with a higher optical density than in Paca44 (which were thus less intense in the T3M4 cell line) are marked in red, whereas in Fig. 2b the 60 spots with a higher optical density than in T3M4 are marked in red.
Fig. 2a–d

Results of a comparison between the two different cell lines: a the 45 spots with lower optical density than in T3M4 (thus more intense in Paca44 cell line) are marked in red; b the 60 spots of lower intensity than in T3M4 are marked in red. c and d show the results of comparison between the control and TSA-treated cell lines: in panel c the two spots down-regulated in TSA-treated cell lines (more intense in the control) are marked in red, while in panel d the 11 spots up-regulated in TSA-treated cell lines are marked in red

Figures 2c and d show the results of comparison between the control and TSA-treated cell lines. In Fig. 2c the two spots down-regulated in TSA-treated cell lines (which were thus more intense in the control) are marked in red, while in Fig. 2d the 11 spots up-regulated in TSA-treated cell lines are marked in red.

Principal component analysis

The differential analysis performed by PDQuest on the 18 2D maps allowed the identification of 435 spots. The matching procedure produced a dataset comprising 435 variables (the optical density of each matched spot) and 18 objects (the 18 samples), thus giving a data matrix of dimensions 18×435. All variables were autoscaled before performing PCA. The autoscaling procedure transforms the variables so that they all have a null average value and a unit variance: this last feature is fundamental, since it allows all the variables to bring the same amount of information to the overall dataset. In the present case, the autoscaling procedure is particularly suitable: it gives the small and large spots the same relevance, thus enhancing the detection of the differences between the four classes of 2D maps, which are often to be searched for among the smallest spots (less abundant proteins) rather than among the largest ones (more abundant proteins). Since PCA was performed on a singular matrix, with more variables than samples, a NIPALS algorithm was used for PCs calculation. The results of PCA are given in Table 1. The first three PCs explain more than 58% of the total variance contained in the original dataset and were considered for the successive analysis.
Table 1

Results of PCA performed on the overall dataset: percentage of explained variance and percentage of cumulative explained variance

 

Explained variance (%)

Cumulative explained variance (%)

PC1

37.59

37.59

PC2

12.90

50.49

PC3

8.27

58.75

PC4

5.42

64.17

PC5

4.70

68.87

PC6

4.33

73.20

PC7

3.47

76.67

Figure 3 represents the score plots of the first three principal components. The first score plot (Fig. 3a) shows the samples’ co-ordinates on PC2 and PC1: the four classes of samples appear completely separated along the two PCs. In fact, at large positive values along PC1, there are the samples belonging to the cell line Paca44, while the samples belonging to the second cell line, T3M4, are grouped at large negative scores on the same PC. The information about the cell type differences is then explained by the first PC. The information about the differences occurring between control and drug-treated samples is instead explained by the second PC: the drug-treated samples of both cell lines in fact appear at large negative scores on PC2 and the control samples at large positive scores on the same PC. However, PC2 is dominated by the relative down-regulation of the spots in the Paca44 cell line, since the samples belonging to this cell line show the largest variations of the scores on the second PC. Thus PC2 describes the TSA general effect, with a larger contribution due to the Paca44 cell line. The first two PCs are thus able to account for the differences occurring between the four classes investigated; however, PC3 also explains a significant amount of variance, which is worthy of being interpreted. The score plot of PC3 versus PC1 (Fig. 3b) shows the control samples belonging to the T3M4 cell line at large positive scores on PC3 together with the TSA-treated ones of the Paca44 cell line; the other two groups of samples (TSA-treated samples of the T3M4 cells, and control samples of Paca44 cells) being grouped at large negative scores on the same PC. However, as for PC2, the third PC is dominated by the relative down-regulation of the spots in the T3M4 cell line, since the samples belonging to this cell line show the largest change of the scores on PC3. The third PC therefore mainly describes information about the effect of TSA on the T3M4 cell line (i.e. complementary information with respect to that accounted for by PC2). Figure 4 reports the cell survival of the two cell lines which present a similar sensitivity to a 48-h treatment with Trichostatin A. With respect to this further information, the third PC mainly accounts for the sensitivity of the T3M4 cell line to the treatment with TSA.
Fig. 3a, b

Score plots of the first three PCs: a PC2 versus PC1 and b PC3 versus PC1

Fig. 4

Cell survival after a 48-h treatment with Trichostatin A for Paca44 and T3M4 cell lines

From the previous considerations, it is possible to state that the first three PCs allow a synthetic and exhaustive representation of the investigated dataset:
  • PC1 explains the information related to the two cell lines

  • PC2 carries the information about the TSA effect, mainly for the Paca44 cell line

  • PC3 carries the information about the sensitivity to TSA, mainly for the T3M4 cell line.

The loadings of the three significant PCs provide information on the spots responsible for the regulatory effect (i.e. they can allow the identification of the differences occurring between the 2D-PAGE maps of the four groups of samples). For this purpose, the loading plots of these three components are reported in Figs. 5, 6 and 7, in which the two central maps report the spots as circles centred in the (x,y)-position revealed by PDQuest analysis. The red-coloured spots have a large positive loading on the correspondent PC, whereas the blue ones identify the spots with large negative loadings. Thus the spots are represented in a colour scale in which the increasing red or blue tone is proportional to its loading. The colour of the spots changes from those which show a small influence (small positive or negative loading, light red or light blue) towards those which show a large influence (large positive or negative loadings, dark blue or red); the spots marked as a black circle do not have a relevant loading on the first three PCs. Figure 5 shows the loadings of the spots on PC1: the red-coloured circles identify those spots showing a larger optical density in the Paca44 cells or spots, which are identified in this cell line but not in the other one; the blue-coloured circles identify the spots that are more intense in the T3M4 cell line or those which are present in this cell line but missing in Paca44 cells. The two examples of real samples at top of Fig. 5 are characterised by large optical densities of the red-coloured spots and small values of the blue-coloured ones (Paca44). At the bottom of Fig. 5 there are two examples of real samples of the T3M4 cell line, characterised by large optical densities of the blue-coloured spots and small values of the red-coloured ones.
Fig. 5

Loading plots of PC1 (coloured maps) with two examples of 2D-PAGE maps of real samples (Paca44 control and treated) characterised by large values of the red-coloured spots (top) and two examples of 2D-PAGE maps of real samples (T3M4 control and treated) characterised by large values of the blue-coloured spots (bottom)

Fig. 6

Loading plots of PC2 (coloured maps) with two examples of 2D-PAGE maps of real samples (Paca44 and T3M4 control) characterised by large values of the red-coloured spots (top) and two examples of 2D-PAGE maps of real samples (Paca44 and T3M4 treated) characterised by large values of the blue-coloured spots (bottom)

Fig. 7

Loading plots of PC3 (coloured maps) with two examples of 2D-PAGE maps of real samples (Paca44 treated and T3M4 control) characterised by large values of the red-coloured spots (top) and two examples of 2D-PAGE maps of real samples (Paca44 control and T3M4 treated) characterised by large values of the blue-coloured spots (bottom)

Figure 6 represents the loadings plots of the second component: the red-coloured spots identify spots characterised by a larger optical density in the control samples or missing in the TSA-treated ones, whereas the blue-coloured spots represent the opposite situation [i.e. spots with a larger density in the treated samples or missing in the control ones (of both cell lines)]. Figure 6 (top and bottom) represents two examples of control real samples of the two cell lines (characterised by large values of the red-coloured spots) and two examples of TSA-treated real samples of the two cell lines (characterised by large values of the blue-coloured spots).

The loading plots of the third PC are represented in Fig. 7: the red-coloured circles identify those spots showing a larger optical density in the control T3M4 cells and the TSA-treated Paca44 cells or spots which are absent in the other two classes of samples; the blue-coloured circles instead show the opposite behaviour: they represent the spots which show a larger optical density in the control Paca44 cells and the TSA-treated T3M4 cells or absent in the other two classes. In this case too, an example of a real sample of each class is presented: the top figure represents two examples of real samples characterised by large values of the red-coloured spots, whereas the bottom figure reports two examples of real samples characterised by large values of the blue-coloured spots.

The conclusions derived by means of PCA show a very good agreement with those obtained by PDQuest analysis of the 2D-PAGE maps. The spots identified by PDQuest as the most characterising ones were also identified by means of PCA; in this last case, however, a larger number of spots were identified. Analysis of 2D-PAGE maps by dedicated software usually allows the identification of only those spots which exhibit at least a two-fold variation in the protein content. PCA is a robust tool which allows the detection of variations lower than the classical two-fold, since the changes due to the natural variability of the experimental steps are explained by the last PCs, which are not taken into account. The total information obtained by PCA is then larger than that obtained by dedicated software; for example, in the present case, the existence of the three patterns identified by PC1–PC3 (Figs. 5, 6 and 7,) could not be achieved by conventional PDQuest analysis.

Cluster analysis

Since, as just pointed out, the first three principal components are able to separate the four classes of samples present in the dataset and to account for the reasons of the differences occurring between them, they are used to perform a cluster analysis to verify how the samples are grouped by means of the first three PCs. The cluster analysis was performed by calculating a dendrogram with the Ward method; the distances were computed using the Euclidean distance. Figure 8 reports the obtained dendrogram; the ordinate label (Dleg/Dmax)×100 is a percentage dissimilarity scale expressing the linking distance (Dleg) of the groups of objects as a fraction of the maximum possible distance (Dmax). The samples are separated into two main groups, the first comprising the samples belonging to Paca44 cells, and the other given by the samples belonging to T3M4 cells. The two groups are then separated into two sub-groups each, at a normalised distance of more than 40%: both the cell lines considered are correctly separated in control and treated samples. The dendrogram obtained by considering the first three PCs is then able to correctly separate the four classes of samples, thus confirming the conclusions just derived by means of PCA.
Fig. 8

Dendrogram calculated on the basis of the first three PCs (Ward method, Euclidean distances)

Mass spectrometry

Mass spectrometry analysis was performed only on the Paca44 cell line as a result of the small size of the samples belonging to the T3M4 cell line. Some of the differentially expressed spots between control and treated Paca44 samples were identified by MALDI-TOF, as reported in Table 2. The identified spots are represented in Fig. 9 as black squares, and the SSP is indicated near each spot.
Table 2

Summary of the identified proteins from Paca44 cell line 2D gels. For spot numbers, refer to Fig. 2

Spot SSP

 

2D gel

Databank

Exp. Mr (Da)

Exp. pI

Theor. Mr.

Theor. pI

Z-Score

MOWSE- score

Protein name

Accession number

Coverage (%)

No. of peptides

Variation

2211

≈35,000

≈4.5

24,504

4.7

2.37

8.28 E12

Tropomyosin alpha four chain (Tropomyosin 4)

P07226

73.4

25

Decreased 2

2502

≈75,000

≈4.2

46,466

4.3

2.38

1.81 E19

Calreticulin precursor (CRP55) (Calregulin)

P27797

62

25

Decreased 2

2213

~35,000

≈4.9

32,798

4.7

2.36

4.83 E14

Tropomyosin alpha three chain (Tropomyosin 3)

P12324

65

31

Decreased 3

3103

≈26,000

≈5.4

19,582

4.9

2.41

1.09 E9

Translationally controlled tumor protein (TCTP)

P13693

47

14

Decreased 3

8305

≈37,000

≈9

37,406 and 35,899

9.0 and 8.6

2.4

5.69 E10 and 1.17 E10

Heterogeneous nuclear ribonucleoproteins A2/B1 and Glyceraldehyde 3-phosphate dehydrogenase, liver (EC 1.2.1.12)

P22626 and P04406

54 and 48

16 and 16

Decreased 2

3507

≈55,000

≈5.6

51,736 and 46,142

5.0 and 5.0

2.35

3.38 E12 and 4.19 E9

ATP synthase beta chain, mitochondrial precursor (EC 3.6.3.14) and protein disulfide isomerase A6 precursor (EC 5.3.4.1)

P06576 and Q15084

41 and 41

16 and 13

Decreased 3

3503

≈55,000

≈5.4

51,736 and 49,671

5.0 and 4.8

2.32

4.24 E11 and 1.20 E10

ATP synthase beta chain, mitochondrial precursor (EC 3.6.3.14) and Tubulin beta-1 chain

P06576 and P07437

38 and 42

15 and 14

Decreased 2

5104

≈20,000

≈6.2

16,310 and 17,160

5.5 and 5.7

1.91 and 2.01

2.22 E9 and 5.89 E7

ARP2/3 complex 16 kDa subunit (P16-ARC) and Stathmin (Phosphoprotein p19) (pp19) (Oncoprotein 18)

O15511 and P16949

86 and 77

14 and 24

Increased 8

4003

≈18,000

≈5.8

16,040

5.5

2.34

 

Deduced protein product shows significant homology to coactosin

NCBInr: AAA88022.1

57

17

Increased 2

8015

≈20,000

≈8

16,363

8.5

1.98

1.92 E9

UEV protein (ubiquitin-conjugating E2 enzyme variant)

NCBInr: AAH28673

75

13

Increased 2

7006

≈13,500

≈6.5

13,654

6.4

2.38

1.02 E6

Hint Protein

P49773

59

6

Increased 3

Matching peptides verus total number of peptides submitted to database search, sequence coverage and score resulted for peptide fingerprinting matching are listed. Protein accession number, theoretical pI and Mr were obtained from the SWISS-PROT and NCBI databases. MOWSE and Z-Scores (output of the identification softwares ProteinProbe and ProFound, respectively) are measures of the statistical significance of the identification hits

Fig. 9

Spots identified by MS analysis: the number near each spot identifies the SSP number

Biological significance of some interesting identified proteins

Among the proteins which were identified by MALDI-TOF analysis, of particular interest are the down-regulated translationally controlled tumour protein (TCTP) as well as the up-regulated protein stathmin (OP18). Their roles will be briefly discussed below.

Translationally controlled tumour protein (TCTP), a three-fold down-regulated polypeptide, seems to be involved in tumour reversion, that is, in the process by which some cancer cells lose their malignant phenotype. In a recent study, Tuynder et al. [44] showed that TCTP is strongly down-regulated in the reversion processes of human leukemia and breast cancer cell lines.

Stathmin (Oncoprotein 18, OP18) was eight-fold up-regulated by the TSA treatment. Stathmin is a p53-regulated member of a novel class of microtubule-destabilizing proteins known to promote microtubule depolymerization during interphase and late mitosis [45]. Thus, high levels of stathmin could induce growth arrest at the G2 to mitotic boundary [46, 47]. This suggests a cell cycle arrest at the G2 phase of Paca44 cell treated with TSA. Due to its effect of inhibiting cell proliferation via a mitotic block, the up-regulation of stathmin reported here appears to be consistent with the antitumoural activity of TSA.

Conclusions

Principal component analysis is applied here to a dataset comprising by 18 samples belonging to control (untreated) and drug-treated pancreatic human cancer cells; the samples belong to two different cell lines: Paca44 and T3M4. PCA turned out to be a successful tool for the identification of the classes of samples present in the dataset; moreover, the loadings analysis allowed the identification of the regulatory spots, which characterise each group of samples. Thus, the treatment of both cell lines with Trichostatin A showed an appreciable effect on the proteomic pattern of the control samples. The separation of the samples into four groups by mean of the first three PCs was also confirmed by cluster analysis. The conclusion driven by PCA resulted in good agreement with those obtained from the application of the differential analysis provided by PDQuest.

The MALDI-TOF analysis performed on the Paca44 cell line allowed the identification of some of the spots differentially expressed in control versus treated Paca44 samples. The biological significance of some of the proteins differentially expressed upon TCA treatment is discussed.

Acknowledgements

Supported by grants from AIRC (Associazione Italiana Ricerca sul Cancro, Milano, Italy), FIRB 2001 (No. RBNF01KJHT), MIUR (Ministero dell’Istruzione, dell’Università e della Ricerca, Rome, Italy; COFIN 2003), Fondazione Cassa di Risparmio di Verona and the European Community, Grant No. QLG2-CT-2001-01903 and No. QLG-CT-2002-01196.

Copyright information

© Springer-Verlag 2004