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A proteomics approach was used to identify liver proteins that displayed altered levels in mice following treatment with a candidate drug. Samples from livers of mice treated with candidate drug or untreated were prepared, quantified, labeled with CyDye DIGE Fluors, and subjected to two-dimensional electrophoresis. Following scanning and imaging of gels from three different isoelectric focusing intervals (3–10, 7–11, 6.2–7.5), automated spot handling was performed on a large number of gel spots including those found to differ more than 20% between the treated and untreated condition. Subsequently, differentially regulated proteins were subjected to a three-step approach of mass spectrometry using (a) matrix-assisted laser desorption/ionization time-of-flight mass spectrometry peptide mass fingerprinting, (b) post-source decay utilizing chemically assisted fragmentation, and (c) liquid chromatography–tandem mass spectrometry. Using this approach we have so far resolved 121 differentially regulated proteins following treatment of mice with the candidate drug and identified 110 of these using mass spectrometry. Such data can potentially give improved molecular insight into the metabolism of drugs as well as the proteins involved in potential toxicity following the treatment. The differentially regulated proteins could be used as targets for metabolic studies or as markers for toxicity.
It is estimated that about 50% of drugs in development fail during clinical trials because of deficiencies in their absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties.1 The cost of these failures is naturally very high. In addition, 6.7% of hospitalized patients still suffer serious adverse reactions to drugs that have successfully completed development and have been introduced to the market.1 Improved means of gathering ADME/Tox information earlier in drug development should thus benefit pharmaceutical manufacturers and, of course, patients.
The goal of this study was to evaluate whether a proteomics approach could provide greater molecular insight into the metabolism and toxicity in the livers of animals treated with a candidate drug. Usually, biased experimental ADME/Tox approaches are used to study the level or activity of certain enzymes such as Cytochrome P450s already known to be involved in xenobiotic metabolism. The toxicological studies used in many laboratories are also biased and focus on certain predetermined markers for toxicity or, alternatively, on more general cellular parameters such as membrane permeability. The identification of further proteins involved in ADME/Tox may open possibilities for the development of new complementary tests for ADME/Tox properties.
In this study we tested whether an unbiased proteomics approach would identify differential levels of liver proteins following treatment of mice with a candidate drug. Information from such an approach could help elucidate which proteins are involved in metabolism and toxicity and thereby increase the value of ADME/Tox studies in drug development.
A selected candidate drug was administered orally to C57BL/6 mice over a period of 5 consecutive days. Livers from treated mice and untreated littermates were surgically removed, snap frozen in liquid nitrogen, and stored at −70°C.
A 0.5-g sample of each liver (three treated livers and one pooled control consisting of three untreated livers) were rinsed in phosphate-buffered saline and homogenized in 5 mL lysis buffer (10 mM Tris-HCl, pH 8.5, 7 M urea, 2 M thiourea, 5 mM magnesium acetate, 4% CHAPS).
To remove interfering nonprotein material and concentrate the sample 10 × 100-μL homogenate was subjected to treatment with the 2-D Clean Up kit according to the kit instructions (Amersham Biosciences, Uppsala, Sweden) and resuspended in 10 mM Tris-HCl, pH 8.3, 7 M urea, 2 M thiourea, 5 mM magnesium acetate, 4% CHAPS. For quantification of the samples the 2-D Quant Kit was used according to the kit instructions (Amersham Biosciences).
The concentration of each protein sample was adjusted to 10 mg/mL and 100 μg of each sample was labeled with CyDye DIGE Fluor according to the kit instructions.2,3 Each sample was run in duplicate, and each duplicate stained with both CyDye DIGE Fluor Cy5 minimal dye and CyDye DIGE Fluor Cy3 minimal dye. In accordance with the DIGE (difference gel electrophoresis) recommended experimental design, a pooled internal standard containing all samples included in the experiment was prepared and labeled with CyDye DIGE Fluor Cy2 minimal dye. Two different labeled samples and one pooled standard were mixed prior to electrophoresis and separated on a single two-dimensional (2D) gel.2,3
Cup loading was used for all analytical gels. One hundred fifty micrograms of the mixed sample (50 μg each of the Cy2-, Cy3-, and Cy5-labeled sample on each gel) was applied to each 24-cm Immobiline DryStrip (Amersham Biosciences). Proteins from three different pH intervals were analyzed by running 24-cm Immobiline DryStrips 3–10 NL, 7–11 NL, and 6.2–7.5, respectively. After image analysis (see below) of all analytical gels, preparative gels containing 1 mg of sample were run. For preparative electrophoresis, in-gel rehydration of 1 mg of unlabeled pooled standard mix of all samples was performed in DeStreak Rehydration solution4 with 2% immobilized pH gradient buffer according to the user manual (Amersham Biosciences).
Sodium dodecyl sulfate–polyacrylamide gel electrophoresis was performed overnight using lab-cast 12.5% Laemmli gels on LF glass plates run on the Ettan DALTtwelve electrophoresis system according to the manufacturer’s protocol (Amersham Biosciences; see also refs. 5 and 6). Deep Purple (Amersham Biosciences) was used for poststaining of the gels.
Scanning was performed on Typhoon 9410 Variable Mode Imager using 520BP40 (Cy2), 580BP30 (Cy3), 670BP30 (Cy5), and 560LP (Deep Purple) emission filters. The resolution was set to 100 μm.
Images were analyzed using DeCyder Differential Analysis Software v5.0 in both the “differential in-gel analysis” module and the “biological variation analysis” module. For statistical analysis the Student’s t-test p value was set to 0.01 and with an average ratio of spots greater than or equal to 1.5 times (for 3–10 NL gels) or 1.2 times (for 7–11 and 6.2–7.5 gels) intensity. Individual images were created for the different Cy2-, Cy3-, and Cy5-labeled gels and spots detected using DeCyder differential in-gel analysis module. The spot maps were then imported to DeCyder biological variation analysis module where gels were matched.
One preparative 3–10 NL, 7–11 NL, and 6.2–7.5 gel of each type was added to each of the experimental studies and pick lists that included all spots of interest (i.e., those differentially regulated following treatment with the candidate drug) were created for automated spot handling and matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) analyses [peptide mass fingerprinting (PMF) and chemically assisted fragmentation (CAF)]. Concerning pH 3–10 NL, two parallel preparative gels were added to the study, one used for protein identification using MALDI-PMF and MALDI-CAF, and one gel for liquid chromatography–tandem mass spectrometry (LC-MS/MS) analysis.
Selected proteins were subjected to fully automated spot handling in the Ettan Spot Handling Workstation (Amersham Biosciences). The method selected included spot picking, digestion, extraction of tryptic peptides, and spotting on Ettan MALDI target slides which were automatically run over night.
In the automated procedure gel plugs were cut by a 2-mm picking head, and washed twice in 50% methanol/50 mM ammonium bicarbonate and once in 75% acetonitrile before drying. For digestion, 10 μL trypsin solution (0.02 μg/mL; sequencing grade, Ettan Chemicals) was added before incubation at 37°C for 2 h. Extraction was performed in two steps by addition of 50% acetonitrile and 0.1% trifluoroacetic acid (Ettan Chemicals). The pooled extract was finally dried prior to a two-step spotting procedure in matrix (5 mg/mL recrystallized α-cyano-4-hydroxy-cinnamic acid, LaserBio Labs, Sophia Antipolis Cedex, France). In the final step before MALDI-TOF (time-of-flight) analysis, a tenth of the dissolved sample was mixed with the matrix layer on the target, saving the remaining part of the sample for CAF-MS or LC-MS/MS analyses.
PMF was performed on Ettan MALDI-ToF Pro7 (Amersham Biosciences). Using ProFound8 data acquisition, spectrum processing and database searches were performed in automatic mode with internal calibration using trypsin autolysis peaks.9
To further improve the identification rate, Ettan CAF MALDI Sequencing Kit was used on proteins not successfully identified by PMF according to the instructions from the manufacturer (Amersham Biosciences). This technique in conjugation with Sonar10 enables peptide sequence data to be acquired by easy fragmentation of the CAF-labeled tryptic peptides using MALDI-PSD.11
The few spots from the 3–10 NL DryStrip run that were still not identified using MALDI-MS (PMF and CAF) were subjected to LC-MS/MS analysis. The tandem mass spectrometric analysis was performed on Finnigan LTQ linear ion trap mass spectrometer fitted with a BioBasic C18 column (100 × 0.1 mm; Thermo Electron, San Jose, CA) running at 1 μL/min flow rate. A fast gradient profile enabled a total analysis time of 20 min during which approximately 5500 scans were acquired per sample using data-dependent mode. The spectra were then processed automatically by SEQUEST to get unambiguous identification based on peptide sequence contained in the product ion spectrum.12
Quantification of samples that had been subjected to 2D Clean Up Kit showed protein concentrations of 10.02 mg/mL for treated sample 1; 9.67 mg/mL for treated sample 2; 10.23 mg/mL for treated sample 3; and 10.40 mg/mL for pooled untreated control, i.e., 1/100 of the prepared sample was protein. Excellent recovery was achieved because this sample was diluted 10 times during the experimental procedure, and an organ such as the liver should contain about 10% protein. Following sample preparation, the samples were labeled with CyDye DIGE Fluor minimal dyes. The complete workflow is outlined in Figure 11.
Cy2 was used throughout the study for the pooled internal standard whereas treated and untreated samples were labeled with Cy3 and Cy5. A duplicate of each sample was labeled using both Cy3 and Cy5 minimal dyes.
After analytical 2D gel electrophoresis and image analysis, over 2500 gel spots were shown to be resolved on each gel (Fig. 22).). DeCyder software was used to match the different gels to each other and identify gel spots that were up- or down-regulated more than 20%. From the 3–10 NL, 7–11 NL, and 6.2–7.5 gels, 30, 30, and 61 spots, respectively, were found to be differentially regulated (Table 11).). Thus, in total, 121 proteins were found to be up-or down-regulated more than 20%.
When the 3–10 NL gels were compared with the 7–11 NL gels it was noticed that a number of additional regulated spots (12 spots, 10 of them identified using MS) were resolved on the 7–11 NL gel. By visual inspection of the gels it was also obvious that the 7–11 NL gel resolved the basic part of the 3–10 NL gel to a higher degree. In contrast, the 3–10 NL gel covers a broader part of the pI range. The aforementioned 10 identified regulated spots on the 7–11 NL gel represented 6 different gene products/proteins. Out of these, hydroxymethylglutaryl-CoA synthase was also identified from the 3–10 NL gel; although theoretically based on location on the 3–10 NL gel, 1–3 additional proteins in the 3–10 NL gel could possibly also have been resolved on the 7–11 NL gel.
In the range 6–7.5 on the 3–10 NL gel, approximately 5–10 spots were found to be differentially regulated (see Figs. 2A and CC).). When the 6.2–7.5 gel was analyzed, however, 61 protein spots were found to be differentially regulated following treatment with the candidate drug. Thus, the narrow-range gel proved to be a very successful tool in further identifying differentially regulated proteins. Since an equal amount of sample was loaded on all three types of gels, the improved result is most likely due to the outstanding resolution of protein spots on a 6.2–7.5 Immobiline DryStrip gel. Although the narrow-range gels resolved a significantly higher number of proteins spots compared with the 3–10 NL gels, it should be noted that only two spots of hydroxymethylglutaryl-CoA synthase overlapped between these pH ranges. One might have expected that 3–8 more spots from the 3–10 NL gel would also have been resolved on the 6.2–7.5 gel. It is unclear why these additional spots were not resolved.
These gels overlap in the pI range 7.0–7.5. A high degree of overlap was, as expected, observed between spots from the basic part of the 6.2–7.5 gel and the acidic part of the 7–11 NL gel; about 10 spots from the 7–11 NL gel could have been expected to be resolved on the 6.2–7.5 gel. Out of these 10 spots (6 distinct gene products), 7 (4 distinct gene products) were found to be identified also on the 6.2–7.5 gel. In addition, the 6.2–7.5 gels resolved a number of additional proteins in the overlapping region, again indicating the power of using overlapping narrow-range Immobiline DryStrips.
These results indicate that for a “complete” view of the proteome, a range of different strips that resolve proteins within different pI ranges and that include high-resolution narrow-range Immobiline DryStrips should be utilized. Such an approach will, to a large extent, facilitate the unambiguous identification of proteins with changed abundance using DeCyder software.
Preparative gels were run and matched to the analytical gels using DeCyder Differential Analysis software. The pick list was thereby generated and then transfered to the spot handling workstation which was used to pick, digest, and spot all 121 differentially regulated proteins. In addition, 532 and 40 protein spots shown not to be differentially regulated following drug treatment from two types of these gels (3–10 NL and 7–11 NL, respectively) were also processed by the automated spot handling procedure. We focused our attention, however, on the identification of the 30+30+61 differentially regulated spots using a combination of PMF, CAF derivatization, and LC-MS/MS. Table 11 illustrates the workflow and shows the methods used and outcome of protein identification for the 3–10 NL, 7–11 NL and 6.2–7.5 experiments.
MALDI-TOF analysis identified 24/30 (75%) of the differentially regulated proteins from the 3–10 NL and 7–11 NL gels and 52/61 (85%) proteins from the 6.2–7.5 gel, giving a total 100/121 (83%) of the proteins (see Figs. 33 and 44 for representative mass spectra).
By employing CAF-derivatization and MALDI-PSD analysis, another 4 out of 6 proteins from the 3–10 NL gel, 3 out of 6 proteins from the 7–11 gel, and 1 out of 9 proteins from the 6.2–7.5 gel could be successfully identified. Thus, out of the remaining 21 proteins, 8 (38%) were successfully identified using CAF. Figure 55 shows a representative sequence obtained by CAF.
Finally, from the spots picked from the 3–10 NL gel, LC-MS/MS analysis was performed on the two remaining proteins not identified by PMF or CAF. Both proteins could be identified, providing a 100% success rate for the 3–10 NL experiment (see Fig. 66 for sequence information for one of these proteins). In the 7–11 NL and 6.2–7.5 experiments, LC-MS/MS analysis was not performed. In summary, 110 out of the 121 regulated spots were identified (Tables 22–4), representing 91% of the spots.
Using a highly automated proteomics approach, 121 differentially regulated proteins were identified following treatment with the candidate drug. The information that can be obtained with this approach should lead to the discovery of useable biomarkers that signal drug toxicity and/or altered metabolism, which, in turn, can be used to identify candidate drugs that should be excluded early in drug development campaigns.
The study shows the value of combining different pH-range Immobiline DryStrips to separate CyDye DIGE Fluor-labeled proteins, since every new strip included in the study enabled the discovery of additional differentially regulated proteins. By adding a narrow-range (pH 6.2–7.5 and 7–11) strip, many more differentially regulated proteins were resolved compared with the previous 3–10 NL analysis, indicating the power of adding narrow-range strips to the 2D DIGE study. The 3–10 NL Immobiline DryStrip, however, is more suitable for obtaining an overview of the complete pI range, and by using the new 3–11 NL Immobiline DryStrip an even wider overview should be obtained. The study also shows the power of combining different mass spectrometry approaches in the effort to reach a very high success rate.
The identified proteins can be grouped in different functional categories that may be involved in the xenobiotic metabolism of drugs and in toxicity (Fig. 77).). Concerning future analyses, the function of the 110 identified proteins will be studied in order to understand more about their biological roles. Analyses will also be conducted using additional narrow-range strips to resolve even more proteins. It should be noted that phase I xenobiotic metabolism of drugs takes place primarily in the endoplasmic reticulum and that many of the responsible proteins (such as Cytochrome P450s) are membrane bound. Therefore subcellular fractionation, and for example the addition of further detergents during 2D gel electrophoresis, might be added to this study to identify such proteins. However, the present study indicates that the 2D-DIGE approach will prove very useful for discovery of new markers signaling toxicity and also for proteins involved in xenobiotic metabolism.
CAF, Cy, CyDye, DeCyder, Ettan, DeStreak, Immobiline, and Typhoon are trademarks of Amersham Biosciences Limited. Finnigan and LTQ are trademarks of the Thermo Electron Corporation. GE and GE Monogram are trademarks of the General Electric Company. Amersham and Amersham Biosciences are trademarks of Amersham Plc. Amersham Biosciences Ltd, a General Electric Company, going to market as GE Healthcare.