MCP Tips for better browsing
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

Originally published In Press as doi:10.1074/mcp.M600296-MCP200 on February 11, 2007.
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Supplemental Data
Right arrow All Versions of this Article:
M600296-MCP200v1
6/6/953    most recent
Right arrow Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow Glossary
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Lane, C. S.
Right arrow Articles by Patterson, L. H.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Lane, C. S.
Right arrow Articles by Patterson, L. H.
Social Bookmarking
Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati  
What's this?

Molecular & Cellular Proteomics 6:953-962, 2007.
© 2007 by The American Society for Biochemistry and Molecular Biology, Inc.

Research

Comparative Cytochrome P450 Proteomics in the Livers of Immunodeficient Mice Using 18O Stable Isotope Labeling*,S

Catherine S. Lane{ddagger},§, Yuqin Wang{ddagger}, Richard Betts{ddagger}, William J. Griffiths{ddagger}, and Laurence H. Patterson||

From {ddagger} The School of Pharmacy, University of London, London WC1N 1AX, United Kingdom and the || Institute of Cancer Therapeutics, University of Bradford, West Yorkshire BD7 1DP, United Kingdom


   ABSTRACT
TOP
 ABSTRACT
EXPERIMENTAL PROCEDURES
 RESULTS AND DISCUSSION
 REFERENCES
 
Quantitative changes in cytochrome P450 (CYP) proteins involved in drug metabolism as a consequence of drug treatment are important parameters in predicting the fates and pharmacological consequences of xenobiotics and drugs. In this study we undertook comparative P450 proteomics using liver from control and 1,4-bis-2-(3,5-dichloropyridyloxybenzene) (TCPOBOP)-dosed mice. The method involved separation of microsomal proteins by SDS-PAGE, trypsin digestion, and postdigest 18O/16O labeling followed by nano-LC-MS/MS for peptide identification and LC-MS for relative quantification. Seventeen P450 proteins were identified from mouse liver of which 16 yielded data sufficient for relative quantification. All the P450s detected were unambiguously identified except the highly homologous CYP2A4/2A5. With the exception of CYP2A12, -2D10, and -2F2, the levels of all the P450s quantified were affected by treatment with TCPOBOP (3 mg/kg). CYP1A2, -2A4/5, -2B10, -2B20, -2C29, -2C37, -2C38, -3A11, and -39A1 were up-regulated, and CYP2C40, -2E1, -3A41, and -27A1 down-regulated. The response of CYP2B20 to stimulation has not been distinguished previously from that of CYP2B10 because of the poor discrimination between these two proteins (they share 87% sequence identity). Differential response to chemical stimulation by closely related members of the CYP2C subfamily was also observed.

The cytochromes P450 (P450s or CYPs1) are a superfamily of mixed function oxidases, members of which are present in virtually all living organisms. P450s are characterized into families and subfamilies by their sequence similarities. Currently there are over 360 families and more than 3100 sequenced and named enzymes; these numbers are continuing to increase. In human there are 57 putatively functional P450 genes, whereas in mouse there are 102 such genes.2 P450s are thought to have evolved, in part, as a protective adaptive response against the toxic effects of environmental chemicals (1). They are the most important drug-metabolizing enzymes in mammals and in humans are responsible for the phase I metabolism of 70–80% of all clinically used drugs (2). In addition to their detoxification role, P450s can also be responsible for the conversion of chemical toxins and procarcinogens to their toxic or carcinogenic forms (3). The ability of P450s to activate chemical toxins has been exploited in cancer chemotherapy where several anticancer drugs, including cyclophosphamide, ifosfamide, dacarbazine, and AQ4N are known to be metabolically activated by P450 isoforms to their respective cytotoxic species (4). A method that would enable not only the non-selective identification of the P450 enzymes present in diseased tissue but also the quantification of expression levels relative to that of surrounding normal tissue would provide invaluable information regarding the metabolic fate of the selected drugs and hence the outcome of therapy.

Traditional approaches to the analysis of P450 enzymes rely on immunoblotting, activity assays, and the detection of P450 mRNA. These techniques have significant limitations: not only do they require preselection of the P450 enzymes to be investigated so that if an unknown or unexpected P450 were present in a sample it would be overlooked, but they are unable to provide reliable quantitative information. Immunoblotting, although being very sensitive, relies on the availability of enzyme-specific antibodies; this specificity can be confounded by the high degree of sequence homology between members of the same subfamily. After preselection of the P450s to be investigated, each enzyme must be identified in turn. Because different P450 enzymes exhibit a broad and often overlapping substrate specificity, activity assays that are designed to interrogate the activity of a P450 enzyme may not be totally enzyme-specific; in addition, multiple analysis techniques are invariably required because different assays must be developed for different target substrates. Measurement at the expression level is fraught with uncertainty because the presence and abundance of a particular type of mRNA does not necessarily infer the presence and abundance of the corresponding protein (5–7).

Over the last 10 years, MS-based proteomics has become an indispensable tool for the analysis of proteins in biological samples. Quantification in MS-based proteomics is most effectively achieved by the use of stable isotope dilution, which makes use of the fact that two analytes differing only in stable isotope composition will be chemically almost identical yet can be differentiated in a mass spectrometer due to their mass differences. Several analytical approaches exist for quantification using stable isotopes; broadly these approaches fall into four categories. Stable isotopes can be incorporated (i) metabolically, as in the SILAC (stable isotope labeling by amino acids in cell culture) approach (8), or (ii) chemically, as in the iTRAQ (9) and ICAT (10) methods. (iii) Isotopically labeled synthetic peptides/proteins that are specific to the protein(s) of interest can be spiked into samples prior to or in combination with enzymatic digestion (AQUA/QCAT (11, 12)), or (iv) stable isotopes can be incorporated enzymatically using trypsin, Glu-C, or Lys-N (13–15). Although methods i, ii, and iv provide relative quantitative data, this can be put on an absolute scale for defined proteins by the additional incorporation of method iii.

Previously we have demonstrated the use of SDS-PAGE in combination with reversed phase LC coupled to electrospray (ES) MS/MS for the identification of multiple P450 proteins (16, 17). One of the main advantages of this approach is the molecular mass-based separation provided by SDS-PAGE, which allows proteins only from the molecular mass region of the P450s to be selected for analysis. The objective of the current study was to combine this qualitative approach with stable isotope labeling to develop a method that would allow the relative quantification of P450s in healthy and cancerous tissue. Metabolic labeling approaches are not feasible for the analysis of human samples. Amino acid-specific labeling methods, such as ICAT, are restricted to the analysis of amino acid-specific peptides. Such a procedure would inherently limit differentiation between P450 enzymes with high sequence similarity. The iTRAQ approach (when performed on the peptide level) is not compatible with the use of gel electrophoresis because the peptide-reactive group utilized in the tagging chemistry also reacts with other amine-containing moieties (for example, acrylamide, Tris buffer, and ammonium bicarbonate). AQUA and QCAT, although providing absolute quantification, restrict analysis to one protein per peptide; in addition, they suffer from the same limitation as Western blotting in that it is necessary to preselect the proteins to be analyzed.

Differential labeling with 18O has been applied to the quantitative analysis of proteins digested with trypsin in solution (14, 18) and has recently been shown to be compatible with gel-digested proteins (19). The technique has several advantages over other isotopic labeling techniques. All tryptic peptides can be labeled, and all peptides increase by the same mass. Because peptides are enzymatically labeled, side reactions that are inherent to chemical labeling experiments are avoided. In this study we used immobilized trypsin and 18O-labeled water for postdigest labeling of tryptic peptides after extraction from SDS-PAGE gels. Using this method, two atoms of 18O are incorporated into the carboxyl terminus of tryptic peptides. The use of immobilized trypsin, which can subsequently be removed from the sample, allows for higher trypsin concentrations without overwhelming samples with intact soluble trypsin and its autolysis products; the high concentration of trypsin compensates for the slower 18O incorporation rates for some peptides. Initially validation experiments were performed using recombinant P450s and human liver microsomes applied to SDS-PAGE gels in known amounts. The efficiency of the labeling reaction was assessed using recombinant CYP3A4 applied to SDS-PAGE gels. Comparative P450 proteomics was then performed using an animal model in which human tumors are grown on immunodeficient mice (20). The compound 1,4-bis-2-(3,5-dichloropyridyloxybenzene) (TCPOBOP) has been shown to induce profound expression of human P450s in the animal model (20). Although we saw no evidence of this in the current study, in future work we will exploit 18O labeling to assess the induction of P450s in these mice in response to a panel of other P450-inducing agents. Relative quantification was performed using liver from control and TCPOBOP-dosed mice. Two labeling experiments were performed: in the first (forward labeling) experiment the treated sample was labeled with 18O, and in the second (reverse labeling) experiment the untreated sample was labeled with 18O. This approach should ensure that no bias is introduced into the results from peptides that may be resistant to trypsin-catalyzed 18O exchange. Finally the proteomics results for two CYP proteins, one found to be up-regulated by TCPOBOP and the other found to be down-regulated, were confirmed by Western blot analysis.


   EXPERIMENTAL PROCEDURES
TOP
 ABSTRACT
 EXPERIMENTAL PROCEDURES
RESULTS AND DISCUSSION
 REFERENCES
 
Materials—
Human recombinant P450 enzymes CYP2E1 and CYP3A4 were purchased from PanVera (Invitrogen). Immobilized trypsin, immobilized trypsin buffer (containing 50 mM Tris-HCl, pH 8, 50 mM CaCl2), and [18O]water (≥97% 18O content) were from the ProlyticaTM 18O labeling kit manufactured by Stratagene (La Jolla, CA); extra [18O]water (95% 18O content) was purchased from Sigma. Anti-CYP1A2 and anti-CYP2E1 primary antibodies were from Abcam (Cambridge, UK).

Animal Work—
Nu-Nu mice were maintained and all experiments were undertaken in accordance with criteria outlined in a license granted under the Animals (Scientific Procedures) Act 1986 and approved by the ethics committee of the University of Bradford. Xenograft tumor (DLD-1 human colon adenocarcinoma) were transplanted subcutaneously in the flanks of 6–8-week-old female Nu-Nu mice and allowed to develop for 3–4 weeks. TCPOBOP was administered intraperitoneally in 10% DMSO, arachis oil at 3 mg/kg with the mice sacrificed 4 days later. Control mice were administered 10% DMSO, arachis oil only. Livers were taken from TCPOBOP-treated and control mice. As hepatic microsomal P450 content is at the nmol/mg of protein level, it is not necessary to pool liver samples. Further as mice were bred to be genetically identical, no benefit is to be gained by analyzing microsomal proteins from a population of mice of the same genotype.

Preparation of Microsomes—
Human liver microsomes used for validation of the technique were prepared previously (16). Mouse liver microsomes were prepared using differential centrifugation as follows. Tissue from control and TCPOBOP-treated mice were washed with cold isotonic saline (0.9% sodium chloride, 4 °C) to remove blood. Connective tissue was excised. Liver was homogenized using an Ultra Turax T25 (Janke and Kunkel, IKA Labortecnik, Staufen, Germany) in buffer containing 50 mM Tris-HCl (pH 7.2, 4 °C), 0.25 M sucrose, 1 mM EDTA, 100 mM sodium chloride, 0.1 M DTT, and one Complete protease inhibitor tablet (Roche Diagnostics)/50 ml of buffer. An initial centrifugation at 2400 x g for 10 min was used to sediment the cell debris, nuclei, and unbroken cells. The supernatant was centrifuged at 12,000 x g for 20 min at 4 °C. Supernatant from this step was centrifuged at 180,000 x g for 60 min at 4 °C. The resultant microsomal pellet was suspended in buffer containing 0.1 M Tris-HCl (pH 7.4), 15% glycerol, 1 mM EDTA, and one Complete protease inhibitor tablet/50 ml of buffer and then centrifuged again at 180,000 x g for 1 h. The final pellet was suspended in buffer containing 0.1 M Tris-HCl (pH 7.4), 15% glycerol, 1 mM EDTA, and one Complete protease inhibitor tablet/50 ml and stored at –80 °C.

SDS-PAGE—
SDS-PAGE was performed according to the manufacturer's instructions using the NuPAGE® electrophoresis system from Invitrogen. Samples were diluted with NuPAGE sample buffer, NuPAGE reducing agent, and water and heated at 70 °C for 10 min. They were then diluted to contain 20 mM iodoacetamide and incubated at room temperature in the dark for 30 min. Samples (25 µg of protein, determined by Bradford Assay (21), for the mouse liver microsomes; other amounts as described under "Results") were resolved on NuPAGE 4–12% bis-Tris precast 1.0-mm 10-well gels using NuPAGE MOPS SDS running buffer. Samples to be compared were run in lanes adjacent to each other. Gels were stained with SimplyBlueTM SafeStain (Invitrogen) and destained with water.

Western Blot Analysis—
Western blotting was undertaken according to the method described previously by Hawkins et al. (22) using anti-CYP1A2 (ab22717, mouse monoclonal antibody (d15 (16VII F10F12)) to CYP1A2) and anti-CYP2E1 (ab28146, rabbit polyclonal antibody to CYP2E1) primary antibodies (Abcam) at a final dilution of 1:5000.

In-gel Tryptic Digestion and Peptide Extraction—
Gel bands were excised from SDS-PAGE gels with a razor blade. For mouse microsomal samples, the molecular mass region between ~45 and 62 kDa was divided into seven to eight pairs of bands (in a separate study performed on these mouse liver microsomal samples, P450s were exclusively identified in the molecular mass range of 45–62 kDa, data not shown). Bands were washed in water and completely destained using 50 mM ammonium bicarbonate in 50% acetonitrile. Bands were cut into ~1-mm3 pieces and dried initially with acetonitrile and then in a SpeedVac for 30 min. Digestion was carried out using sequencing grade modified trypsin (40 ng/µl) (Promega, Southampton, UK) in 50 mM ammonium bicarbonate. Sufficient trypsin solution was added to swell the gel pieces, which were kept on ice for 30 min and then covered with 50 mM ammonium bicarbonate and incubated at 37 °C overnight. Peptides were extracted from the gel pieces, with ultrasonication, using sequential washings with a solution of 70% acetonitrile alternated with acetonitrile drying steps. Extracted peptides were dried to 5–10 µl in a SpeedVac and stored at –80 °C for postdigest labeling.

Postdigest 18O Labeling—
Immobilized trypsin (Stratagene) (2 µl/sample) was washed several times with a 10x volume of trypsin buffer. The trypsin was centrifuged between washes at 6000 x g and finally resuspended in 10 µl of trypsin buffer/sample. Immobilized trypsin (10 µl/sample) was added to the extracted peptides, and the mixture was dried completely in a SpeedVac. Peptides and immobilized trypsin were redissolved in 8 µl of [16O]water or [18O]water and 2 µl of acetonitrile and shaken for 4 h at room temperature. The reaction was quenched by the addition of 1 µl of formic acid. Samples were centrifuged at 14,000 x g to allow removal of the immobilized trypsin, dried to ~5 µl in a SpeedVac, and stored at –80 °C. Immediately prior to analysis, samples were diluted with 0.1% formic acid in water or [18O]water, and pairs for comparison were combined. Samples were filtered, using filter paper and gel-loading pipette tips, and analyzed by LC-ES-MS/MS and LC-ES-MS.

LC-ES-MS/MS and LC-ES-MS—
LC was performed using a Micromass CapLC system with autosampler (Waters, Manchester, UK), which uses a 10-port zero dead volume valve to enable fast sample loading onto a precolumn (1-mm x 300-µm PepMapTM C18 guard column, 5 µm, 100 Å, Dionex, Camberley, Surrey, UK) at a flow rate of 15 µl/min delivered isocratically with solvent C (0.1% formic acid in water) by auxiliary pump C. Sample was washed on the guard column for 3 min with solvent C before the 10-port valve was switched to allow transfer of the sample onto the analytical column (Waters Symmetry® C18, 3.5 µm, 75-µm x 150-mm NanoEaseTM column), which was equilibrated with 96% solvent A (0.1% formic acid in 5% acetonitrile), 4% solvent B (0.1% formic acid in 95% acetonitrile) at a flow rate of ~200 nl/min. Three min after sample loading the proportion of solvent B was increased linearly to 35% over 37 min and then to 90% over 5 min, maintained at 90% solvent B for 7 min (wash phase), and then re-equilibrated at 96% solvent A, 4% solvent B for 11 min. The column effluent was continuously directed into a Micromass Q-TOF GlobalTM mass spectrometer (Waters), which was operated in the positive ion ES mode and either in MS-only mode for relative quantification over the m/z range of 450–1800 (1.0-s scan time, 0.1-s interscan time) or with repeat injections in the data-dependent analysis (DDA) mode for peptide identification. DDA uses an initial "survey scan" that identifies the four most abundant multiply charged ions (tryptic peptides usually appear as 2+ and/or 3+ ions), which are then fragmented by MS/MS, which provides sequence information, before another survey scan is performed, and the cycle is repeated throughout the chromatographic run. DDA was performed using a 1-s MS survey scan (m/z range, 450–1800) followed by 1-s MS/MS scans (0.1-s interscan time) on up to four different precursor ions (intensity threshold, 10 counts/s). In DDA mode, MS/MS spectrum acquisition (in the m/z range of 50–1800) was allowed for up to a total of 4.4 s on each precursor ion or stopped when the signal intensity fell below 3 counts/s, and a new MS to MS/MS cycle was started. Precursors were excluded from any further MS/MS fragmentation for 45 s to minimize repeated identification of the same peptide; singly charged ions were also excluded as precursors for MS/MS. Each sample was analyzed twice by LC-ES-MS/MS for peptide identification and twice by LC-ES-MS for peptide quantification. In the DDA mode the Q-TOF instrument can spend up to 17.6 s performing MS/MS scans for every 1.1 s spent performing MS survey scans; therefore relatively little MS data are collected per LC peak. It was therefore necessary to analyze each sample in a separate MS-only analysis to allow peptide quantification. Peptides were identified from their MS/MS spectra in DDA files (see "Protein Identification"), and then the corresponding peaks in the MS-only files were located on the basis of mass, charge, and retention times. Mass chromatograms were constructed, and the spectra were averaged at half-peak height (unless interfering peaks were present in which case spectra were averaged from the part of the peak where interferences were absent). Spectra were smoothed (Savitsky Golay, 2 x 4 channels) and centroided (80% centroid top), and centroid intensities were recorded. For each peptide reported, quantification was performed on at least two replicates, and the average replicate value was taken and used for statistical analysis (four replicates when forward and reverse labeling experiments are considered).

Protein Identification—
Peak list (.pkl) files were created from data acquired in the DDA mode using Micromass MassLynx software version 4.0 (Waters). The parameters for creating .pkl files were as follows. All MS/MS files were selected, spectra were smoothed (Savitsky Golay, 2 x 3 channels) and centroided (80% centroid top; minimum peak width at half-height, 4 channels), and text output was generated. Peptide and protein identifications were performed using the Mascot search engine public server (Matrix Science) (23). Database searching was restricted to tryptic peptides of mammalian proteins using the most current Swiss-Prot database. Carboxyl-terminal double 18O, methionine oxidation, and carbamidomethylcysteine were selected as variable modifications; one missed cleavage was allowed. Precursor and MS/MS tolerances were <0.5 Da monoisotopic mass. The level of confidence for peptide identifications was based on the Mascot assignment of "identity" and "homology"; for peptide identifications with scores corresponding to homology, data were checked manually. Protein identifications were based on the detection of unique peptides. Where protein identifications were based on one peptide only, data were always checked manually and only accepted when criteria outlined by the Molecular and Cellular Proteomics document on protein identification were met (e.g. see Kapp at www.mcponline.org/misc/PariReport_PP.shtml).


   RESULTS AND DISCUSSION
TOP
 ABSTRACT
 EXPERIMENTAL PROCEDURES
 RESULTS AND DISCUSSION
REFERENCES
 
The strategy used for quantification of differential P450 expression from SDS-PAGE gels using 18O labeling is summarized in Fig. 1. Before applying the technique to unknown samples it was necessary to analyze control samples to validate the procedure. Differentially labeled samples are combined immediately before LC-ES-MS analysis, but sample loss could potentially occur at several steps prior to this, including band cutting, peptide extraction, drying, and during removal of immobilized trypsin after the labeling reaction. The efficiency of the labeling reaction had to be determined because any residual unlabeled (16O) or singly labeled (16O18O) peptide from the 18O-labeled sample will lead to quantification errors when mixed with the unlabeled sample. As a further control, when applying the technique to liver microsomes from TCPOBOP-treated and control mice two labeling experiments were performed: in the first (forward labeling) experiment the treated sample was labeled with 18O, and in the second (reverse labeling) experiment the untreated sample was labeled with 18O. This approach should ensure that no bias is introduced into the results from peptides that may be resistant to trypsin-catalyzed 18O exchange.


Figure 1
View larger version (16K):
[in this window]
[in a new window]

  		FIG. 1. The 18O labeling strategy. Sample pairs for comparison are separated by SDS-PAGE, and bands are selected for analysis. After in-gel digestion with trypsin, a postdigest is performed in either 18O-labeled water or normal isotopic water. Sample pairs are then combined and analyzed by nano-LC-ES-MS/MS for peptide identification and nano-LC-ES-MS for relative quantification.

 
The Efficiency of the 18O Labeling Reaction—
Recombinant human CYP3A4 was used to evaluate the efficiency of the 18O labeling reaction. For every SDS-PAGE run, one lane was loaded with 5 pmol of recombinant CYP3A4. The CYP3A4 protein band was subjected to in-gel tryptic digestion in parallel with the sample bands, and the extracted CYP3A4 peptides were labeled with 18O. This sample was analyzed to determine the extent of the labeling reaction every time the procedure was performed. Over the course of three labeling experiments, eight CYP3A4 peptides were identified with labeling efficiencies (expressed as (I4/(I0 + I2 + I4)) x 100 where I0, I2, and I4 represent the intensities of the first isotopic peaks of the 16O-, single 18O-, and double 18O-labeled peptides, respectively; see Fig. 2) ranging from 88 to 98% (Table I). For each CYP3A4 peptide identified, the percentage of unlabeled peptide is less than 2%. Any singly 18O-labeled peptide will cause an increase in the intensity of the third isotopic peak of the unlabeled peptide, and this can be used to mathematically correct for non-quantitative labeling (see "Calculation of 16O/18O Ratios and Protein Quantification").


Figure 2
View larger version (25K):
[in this window]
[in a new window]

  		FIG. 2. Mass spectrum showing the isotope pattern produced by CYP3A4 peptide LQEEIDAVLPNK labeled with 18O. Because the labeling reaction is not 100% efficient, some unlabeled and single 18O-labeled peptides may be present. The efficiency of the 18O labeling reaction is expressed as (I4/(I0 + I2 + I4)) x 100 where I0, I2, and I4 represent the intensities of the first isotopic peaks of the 16O-, single 18O-, and double 18O-labeled peptides, respectively. In this case labeling efficiency is 86%.

 

View this table:
[in this window]
[in a new window]

  		TABLE I Labeling efficiencies for CYP3A4 peptides extracted from SDS-PAGE gels and labeled with 18O

 
Calculation of 16O/18O Ratios and Protein Quantification—
To allow easy comparison between forward and reverse labeling experiments, individual peptide ratios were calculated according to the method of Ross et al. (9). Peak intensities from the first isotopic peaks of the 16O- and 18O-labeled peptides were inserted into the following formula: intensity(treated)/(intensity(control) + intensity(treated)). All values therefore fall between 0 and 1 with a peptide with unchanged expression (1:1 ratio) having a value of 0.5. Peptide ratios were grouped into proteins and averaged to obtain protein-level ratios for the P450s quantified.

Recalling that paired sets of isotope clusters are separated by 4 Da (Fig. 3), corrections to the peak intensity of the first isotopic peak of the double 18O-labeled peptide were performed as follows. The sequences of the tryptic peptides were used to calculate the theoretical isotope peak distribution using Micromass MassLynx software. The predicted intensity of the fifth isotope peak of the unlabeled (16O) peptide was subtracted from the intensity of the first isotopic peak of the double 18O-labeled peptide. Second to correct for any incomplete incorporation of 18O into the carboxyl terminus of the labeled peptides, the difference between the measured intensity of the third isotope peak of the unlabeled peptide and its predicted intensity was added to the intensity of the first isotopic peak of the double 18O-labeled peptide. This second correction could not be applied when the measured intensity of the third isotope peak of the unlabeled peptide was less than the predicted intensity. This was most frequently true when I0 was much larger than I4. For these cases the question arises as to whether it is appropriate to make the corrections described above. To assess this, uncorrected mean ratios and corresponding standard deviations for the P450s quantified in the validation experiments and in the mouse liver forward and reverse labeling experiments were calculated and compared statistically with the corrected ratios using the variance ratio F-test and Student's two-sampled t test. In all cases (30 proteins quantified using more than one peptide over three labeling experiments) the means were not statistically different (Student's two-sampled t test, p values of 0.2–1). It was therefore concluded that the corrections described above were unnecessary. Correspondingly uncorrected ratios were used for all the analyses described in this study.


Figure 3
View larger version (20K):
[in this window]
[in a new window]

  		FIG. 3. Mass spectrum showing the isotopic pattern produced by analysis of a pair of identical peptides (CYP2C37 tryptic peptide CLVEELR, carbamidomethylcysteine), one unlabeled and the other labeled with two 18O atoms at the carboxyl terminus. Relative quantification can be performed by comparison of the abundances of the unlabeled and doubly 18O-labeled peptides. In this case, the labeled peptide is ~5 times as abundant as the unlabeled peptide.

 
Proof of Concept for the Quantification of Differential P450 Expression from SDS-PAGE Gels Using 18O Labeling—
To evaluate the use of 18O labeling for the quantification of P450 proteins from SDS-PAGE gels a series of samples were analyzed. Recombinant CYP3A4 and CYP2E1 were separately applied to an SDS-PAGE gel. CYP3A4 was applied to two lanes run in parallel at 5 pmol/lane, whereas CYP2E1 was applied to two separate lanes also run in parallel at 5 and 2 pmol, respectively (Table II). In addition, a sample of human liver microsomes was applied to a further two lanes at the level of 25 and 10 µg (total protein), and bands in the P450 molecular weight region were analyzed (Fig. 4). Student's t tests were performed to assess the probability that the population mean ratio for the experimental values is equal to the theoretical mean ratio. The results are summarized in Table II. The standard deviations for all measurements are less than 0.1 (seven of the 10 P450s have standard deviations of less than 0.05), which indicates a high degree of precision. For six of the 10 P450s assessed, the probabilities show that the experimental mean ratio is statistically very similar to the theoretical ratio (p values of 0.2–0.7). For recombinant CYP2E1 and microsomal CYP1A2, -2A6, and -3A4 the p values are 0.017, 0.046, 0.003, and 0.017, respectively. These values indicate that the probabilities that the population mean ratios are the same as the theoretical values are low. This can be attributed to non-systematic error during the procedure. In real terms, the values are close: none of the experimental mean values differ from the theoretical values by more than 0.1; for every protein quantified, experimental mean ratios agree with theoretical values by more than 70%; for eight of 10 of the proteins quantified agreement is ≥90% (Table II). Given the multistep procedure used, these figures indicate that the technique is reliable and reproducible for the relative quantification of P450s from SDS-PAGE gels.


View this table:
[in this window]
[in a new window]

  		TABLE II Proof of concept for the quantification of differential P450 expression from SDS-PAGE gels using 18O labeling

Recombinant CYP2E1, recombinant CYP3A4, and a sample of human liver microsomes were analyzed by SDS-PAGE in known amounts, and the ratios were evaluated.

 

Figure 4
View larger version (22K):
[in this window]
[in a new window]

  		FIG. 4. SDS-PAGE gel analysis of recombinant CYP3A4 and human liver microsomes showing sample pairs selected for the validation of the 18O labeling method for relative quantification of differentially expressed P450 proteins. Lanes 1 and 8, molecular mass markers; Lane 2, recombinant CYP3A4, 5 pmol; Lane 3, recombinant CYP3A4, 5 pmol; Lane 4, recombinant CYP2E1, 5 pmol; Lane 5, recombinant CYP2E1, 2 pmol; Lane 6, human liver microsomes, 25 µg of total protein; Lane 7, human liver microsomes, 10 µg of total protein. The molecular mass region of the microsomal samples selected for comparative proteomic analysis is indicated with a box.

 
Relative Quantification of Cytochrome P450 Proteins in the Livers of Immunodeficient Mice Using 18O Stable Isotope Labeling—
Mice, which represent a highly controllable experimental model system, are extensively used as animal models for studying human disease. In this study the effect of administering TCPOBOP on P450 expression in immunodeficient mice were investigated. Animal models, in which human tumors are grown on immunodeficient mice, have been widely used to evaluate the response of human tumors to anticancer drugs. Although no human P450s were identified in this study, this represents a starting point for future studies of anticancer drugs on immunodeficient mice. This model represented an ideal system on which to perform comparative P450 proteomics using 18O labeling. Mouse liver microsomes from control and TCPOBOP-dosed mice were separated by SDS-PAGE (Fig. 5), and protein bands from the P450 molecular mass region (45–62 kDa) were excised and subjected to in-gel digestion with trypsin. We have demonstrated in a separate study on these mouse liver microsomes that P450s are exclusively identified in the 45–62-kDa molecular mass range. Extracted peptides were treated with immobilized trypsin in normal isotopic water (control) or [18O]water (TCPOBOP-dosed) to allow incorporation of 18O at both carboxyl-terminal oxygens in the dosed sample. Differentially labeled sample pairs were combined and analyzed by LC-ES-MS/MS for peptide identification and LC-ES-MS for relative quantification. The reverse labeling experiment was also performed in which peptides from the control sample were labeled with 18O. For the reverse labeling experiment, a slightly wider range of molecular masses was excised from the gel as well as one band at ~90 kDa (Fig. 5); this was to identify housekeeping proteins, which could be used for normalization purposes.


Figure 5
View larger version (30K):
[in this window]
[in a new window]

  		FIG. 5. SDS-PAGE analysis of liver microsomes from control and TCPOBOP-treated mice grafted with a DLD-1 human tumor xenograft. Lane 1, molecular mass markers; Lane 2, liver microsomes from control mouse (25 µg of total protein); Lane 3, liver microsomes from TCPOBOP-treated mouse (25 µg of total protein); Lane 4, CYP3A4, 5 pmol. Molecular mass regions from microsomes selected for comparative proteomics with 18O labeling are indicated with boxes. Dashed lines represent areas that were selected for the reverse but not the original labeling experiment. The P450 molecular mass region (45–62 kDa) was divided into seven pairs of bands for the original labeling experiment and eight pairs of bands for the reverse experiment.

 
Seventeen P450 proteins were identified from mouse liver (Table III and Supplemental Table 1) of which 16 yielded data sufficient for relative quantification. Of these, 11 were quantified using more than one unique tryptic peptide (Table III and Supplemental Table 1). All the P450s detected were unambiguously identified with the exception of the highly homologous CYP2A4/2A5, which share 98% sequence identity, and CYP2B9/2B19 for which quantification using the one peptide identified was not possible due to interfering mass spectral peaks. However, the approach was able to discriminate between such highly homologous P450s as CYP2B10 and -2B20 (87% sequence identity) and CYP2C29, -2C37, and -2C38 (71% sequence identity).


View this table:
[in this window]
[in a new window]

  		TABLE III Relative quantification of microsomal P450 proteins from the liver of immunodeficient mice following TCPOBOP treatment using stable isotope labeling of tryptic peptides with [18O]water

The accuracy of single peptide identifications were manually verified according to the accepted rules of gas-phase ion chemistry (see Kapp at www.mcponline.org/misc/PariReport_PP.shtml).

 
The relative levels of most of the P450s quantified were affected by treatment with TCPOBOP (Table III and Fig. 6). Nine of the P450s quantified were up-regulated (CYP1A2, -2A4/5, -2B10, -2B20, -2C29, -2C37, -2C38, -3A11, and -39A1), four were down-regulated (CYP2C40, -2E1, -3A41, and -27A1), and only three were unaffected (CYP2A12, -2D10, and -2F2). The results for the up-regulated P450s CYP1A2, -2A4/5, -2B10, -2C29, -2C37, -2C38, and -3A11 are in line with those in the literature regarding P450 expression changes in response to TCPOBOP. Strong up-regulation of CYP2B10 mRNA and protein expression after TCPOBOP treatment is well documented (24–26); our results show an up-regulation of 17-fold (Table III). CYP3A11 mRNA has been shown to be up-regulated by TCPOBOP (27, 28) as have CYP2A5 mRNA (26) and CYP1A, -2A, -2B, and -2C proteins by Western blot and mRNA (25). On the basis of Western blot and metabolism experiments the CYP1A and -2A proteins found to be up-regulated were characterized as CYP1A2, -2A4, and -2A5, which agree with the findings of present study (25). Members of the mouse CYP3A subfamily were shown by immunoblot to be up-regulated upon TCPOBOP treatment, but these did not include CYP3A41 (28). CYP3A41 was found to be down-regulated in the present study. Konstandi et al. (29) report that mouse liver CYP2A5 activity was found to be induced by TCPOBOP by about 2.5-fold, which is very similar to the 2.3-fold up-regulation of CYP2A4/5 shown in our analyses (Table III). This is the first time that up-regulation of CYP2B20 and CYP39A1 has been shown. CYP2B20 shares 87% sequence identity with CYP2B10; the responses of these two proteins have not previously been distinguished from one another. Down-regulation of P450s by TCPOBOP in mice has not been reported previously, although there is some evidence of down-regulation of CYP2E1 mRNA in response to TCPOBOP treatment (25). Of particular significance is the result that CYP2C29, -2C37, and -2C38 are up-regulated, whereas CYP2C40 is down-regulated. Because of the poor discrimination between these closely related proteins (they share 61% sequence identity), differential response to chemical stimulation has not previously been detected using conventional techniques. However, Jenkins et al. (30) using ICAT methodology have been able to differentiate between CYP2C29 and -2C40.


Figure 6
View larger version (9K):
[in this window]
[in a new window]

  		FIG. 6. The effect of TCPOBOP treatment on the expression of cytochrome P450 proteins in mouse liver.

 
To further confirm the proteomics data presented in this study, Western blot analysis using anti-CYP1A2 and anti-CYP2E1 primary antibodies was performed on liver microsomal proteins from TCPOBOP-treated and control mice (Supplemental Fig. 1). CYP1A2 was clearly up-regulated in liver of the TCPOBOP-treated mouse, and CYP2E1 was down-regulated in this mouse.

When making quantitative proteomics comparisons between two samples it is necessary to normalize for the total protein content of each sample. In this study, the protein concentrations of the microsomes were determined by Bradford assay (21), and 25 µg of total protein was analyzed for each sample. However, the perturbations of the P450s quantified were so large that it was desirable to confirm normalization by alternative means. Techniques such as Western and Northern blotting traditionally use ‘housekeeping proteins‘ such as glyceraldehyde-3-phosphate dehydrogenase, ß-actin, and ß-tubulin to correct for protein loading and factors such as transfer efficiency (31). No housekeeping proteins were identified in the forward labeling experiment, and for this purpose, a wider molecular weight region of the SDS-PAGE gel was selected for analysis in the reverse labeling experiment (Fig. 5). ß-Actin was identified (Table III). Quantification was performed using five peptides, and a protein mean ratio of 0.56 was calculated, equivalent to a 1.3-fold change after TCPOBOP treatment. This value is very close to the no change value of 1 expected and serves as confirmation of normalization by microsomal protein content.

The standard deviation values shown in Table III are generally less than 0.1, indicating a high degree of precision (9). The only exceptions are CYP2C29 (0.103, forward labeling experiment) and CYP2F2 (0.17, forward labeling experiment). These values can be explained as follows. The P450 molecular weight region of the SDS-PAGE gel was cut into several pairs of bands for analysis (Fig. 5); each one of these gel slices contained many proteins. Where protein bands were split between gel slices some anomalous results were observed with peptide ratios differing between gel slices; for this reason, large standard deviations were recorded. This was the case for CYP2C29 and CYP2F2 in the forward labeling experiment. It was reasoned that taking the mean value of the peptide ratios should approximate to the actual ratio. This was proven to be the case when the reverse labeling experiment was performed: on this occasion CYP2C29 and CYP2F2 were identified from one gel band only with mean ratios very similar to those from the forward labeling experiment (Table III).

Statistical analyses were performed to compare the two (forward and reverse) labeling experiments. Variance ratio F-tests and Student's two-sampled t tests were performed on the results for the eight P450s for which quantification was performed using two or more peptides for both the forward and the reverse labeling experiments (Table III). The p values, which represent the probability that the population mean ratios for both the forward and reverse labeling experiments are equal, are shown in Table III. The p values are high for all but CYP2A4/5. It was not possible to distinguish between CYP2A4 and CYP2A5, and so this could be the cause of the apparent discrepancy between the two labeling experiments. Percentage agreements were also calculated and are high (>70%) for all P450s that were quantified in both labeling experiments.

To date, there have been few proteomics studies to quantify cytochrome P450s. Jenkins et al. (30) used ICAT reagents for relative and absolute quantification of hepatic P450s induced in response to phenobarbital or 3-methylcholanthrene. ICAT reagents specifically label cysteine residues and rely on MS/MS of cysteine-containing peptides to identify and LC-MS to quantify proteins. This is not an ideal approach to differentiate between isozymes with a high degree of amino acid sequence similarity, such as P450 subfamily members. An alternative label-free approach to quantification was suggested by Alterman et al. (32); this involved the use of unlabeled internal standard tryptic peptides and MALDI-based peptide mass fingerprinting. This method was validated for a mixture of three recombinant proteins, but whether it can be applied to biological samples containing a range of P450 proteins of varying abundance is open to question.

In summary, the method reported in this communication allows identification and relative quantification of a wide range of P450 isoforms. The technique has exquisite discriminatory powers, allowing distinction between such highly homologous isozymes as CYP2B10 and CYP2B20 (87% sequence identity). The method takes advantage of SDS-PAGE to separate membrane proteins and can potentially use every tryptic peptide of unique amino acid sequence for protein identification and quantification. The method of labeling is enzymatic rather than chemical and has the advantage of being SDS-PAGE-compatible and avoids the introduction of derivatization artifacts. The method has been successfully applied to the analysis of differential P450 expression in the livers of control and TCPOBOP-treated mice grafted with human tumors. Changes in liver P450 expression after treatment with TCPOBOP are consistent with those found in the literature.


   ACKNOWLEDGMENTS
 
We thank Cathrin Seibert and Kersti Karu for sample preparation.


  FOOTNOTES
 
Received, August 4, 2006, and in revised form, February 7, 2007.

Published, MCP Papers in Press, February 11, 2007, DOI 10.1074/mcp.M600296-MCP200

1 The abbreviations used are: CYP, cytochrome P450; TCPOBOP, 1,4-bis-2-(3,5-dichloropyridyloxybenzene); iTRAQ, isobaric tagging for relative and absolute quantitation; AQUA, absolute quantification; ES, electrospray; bis-Tris, bis(2-hydroxyethyl)iminotris(hydroxymethyl)methane HCl; DDA, data-dependent analysis; QCAT, absolute quantification of multiple peptides concatenated into a synthetic gene. Back

2 D. R. Nelson, personal communication. Back

* This work was supported in part by The School of Pharmacy and United Kingdom Biotechnology and Biological Sciences Research Council Grants BB/C515771/1 and BB/C511356/1. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. Back

S The on-line version of this article (available at http://www.mcponline.org) contains supplemental material. Back

§ Holds a C. W. Maplethorpe Postdoctoral Fellowship of the University of London. Back

To whom correspondence should be addressed. Tel.: 44-207753-5876; Fax: 44-207753-5964; E-mail: william.griffiths{at}pharmacy.ac.uk


   REFERENCES
TOP
 ABSTRACT
 EXPERIMENTAL PROCEDURES
 RESULTS AND DISCUSSION
 REFERENCES
 

  1. Lewis, D. F. V. (1996) Cytochromes P450: Structure, Function and Mechanism, 1st Ed., Taylor and Francis, London

  2. Evans, W. E., and Relling, M. V. (1999) Pharmacogenomics: translating functional genomics into rational therapeutics. Science 286, 487 –491[Abstract/Free Full Text]

  3. Wolf, C. R. (1990) Metabolic factors in cancer susceptibility. Cancer Surv. 9, 437 –474[Medline]

  4. Patterson, L. H., and Murray, G. I. (2002) Tumour cytochrome P450 and drug activation. Curr. Pharm. Des. 8, 1335 –1347[CrossRef][Medline]

  5. Anderson, L., and Seilhamer, J. (1997) A comparison of selected mRNA and protein abundances in human liver. Electrophoresis 18, 533 –537[CrossRef][Medline]

  6. McFadyen, M. C. E., Rooney, P. H., Melvin, W. T., and Murray, G. I. (2003) Quantitative analysis of the Ah receptor/cytochrome P450 CYP1B1/CYP1A1 signalling pathway. Biochem. Pharmacol. 65, 1663 –1674[CrossRef][Medline]

  7. Gygi, S. P., Rochon, Y., Franza, B. R., and Aebersold, R. (1999) Correlation between protein and mRNA abundance in yeast. Mol. Cell. Biol. 19, 1720 –1730[Abstract/Free Full Text]

  8. Ong, S. E., Blagoev, B., Kratchmarova, I., Kristensen, D. B., Steen, H., Pandey, A., and Mann, M. (2002) Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics 1, 376 –386[Abstract/Free Full Text]

  9. Ross, P. L., Huang, Y. N., Marchese, J. N., Williamson, B., Parker, K., Hattan, S., Khainovski, N., Pillai, S., Dey, S., Daniels, S., Purkayastha, S., Juhasz, P., Martin, S., Bartlet-Jones, M., He, F., Jacobson, A., and Pappin, D. J. (2004) Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol. Cell. Proteomics 3, 1154 –1169[Abstract/Free Full Text]

  10. Gygi, S. P., Rist, B., Gerber, S. A., Turecek, F., Gelb, M. H., and Aebersold, R. (1999) Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat. Biotechnol. 17, 994 –999[CrossRef][Medline]

  11. Gerber, S. A., Rush, J., Stemman, O., Kirschner, M. W., and Gygi, S. P. (2003) Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc. Natl. Acad. Sci. U. S. A. 100, 6940 –6945[Abstract/Free Full Text]

  12. Beynon, R. J., Doherty, M. K., Pratt, J. M., and Gaskell, S. J. (2005) Multiplexed absolute quantification in proteomics using artificial QCAT proteins of concatenated signature peptides. Nat. Methods 2, 587 –589[CrossRef][Medline]

  13. Schnolzer, M., Jedrzejewski, P., and Lehmann, W. D. (1996) Protease-catalyzed incorporation of 18O into peptide fragments and its application for protein sequencing by electrospray and matrix-assisted laser desorption/ionization mass spectrometry. Electrophoresis 17, 945 –953[CrossRef][Medline]

  14. Yao, X., Freas, A., Ramirez, J., Demirev, P. A., and Fenselau, C. (2001) Proteolytic 18O labeling for comparative proteomics: model studies with two serotypes of adenovirus. Anal. Chem. 73, 2836 –2842[Medline]

  15. Rao, K. C., Carruth, R. T., and Miyagi, M. (2005) Proteolytic 18O labeling by peptidyl-Lys metalloendopeptidase for comparative proteomics. J. Proteome Res. 4, 507 –514[CrossRef][Medline]

  16. Lane, C. S., Nisar, S., Griffiths, W. J., Fuller, B. J., Davidson, B. R., Hewes, J., Welham, K. J., and Patterson, L. H. (2004) Identification of cytochrome P450 enzymes in human colorectal metastases and the surrounding liver: a proteomic approach. Eur. J. Cancer 40, 2127 –2134[CrossRef][Medline]

  17. Nisar, S., Lane, C. S., Wilderspin, A. F., Welham, K. J., Griffiths, W. J., and Patterson, L. H. (2004) A proteomic approach to the identification of cytochrome P450 isoforms in male and female rat liver by nanoscale liquid chromatography-electrospray ionization-tandem mass spectrometry. Drug Metab. Dispos. 32, 382 –386[Abstract/Free Full Text]

  18. Heller, M., Mattou, H., Menzel, C., and Yao, X. (2003) Trypsin catalyzed 16O-to-18O exchange for comparative proteomics: tandem mass spectrometry comparison using MALDI-TOF, ESI-QTOF, and ESI-ion trap mass spectrometers. J. Am. Soc. Mass Spectrom. 14, 704 –718[CrossRef][Medline]

  19. Bantscheff, M., Dumpelfeld, B., and Kuster, B. (2004) Femtomole sensitivity post-digest 18O labeling for relative quantification of differential protein complex composition. Rapid Commun. Mass Spectrom. 18, 869 –876[CrossRef][Medline]

  20. Smith, G., Harrison, D. J., East, N., Rae, F., Wolf, H., and Wolf, C. R. (1993) Regulation of cytochrome P450 gene expression in human colon and breast tumour xenografts. Br. J. Cancer 68, 57 –63[Medline]

  21. Bradford, M. M. (1976) A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 72, 248 –254[CrossRef][Medline]

  22. Hawkins, L. M., Prybylowski, K., Chang, K., Moussan, C., Stephenson, F. A., and Wenthold, R. J. (2004) Export from the endoplasmic reticulum of assembled N-methyl-D-aspartic acid receptors is controlled by a motif in the c terminus of the NR2 subunit. J. Biol. Chem. 279, 28903 –28910[Abstract/Free Full Text]

  23. Perkins, D. N., Pappin, D. J., Creasy, D. M., and Cottrell, J. S. (1999) Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20, 3551 –3567[CrossRef][Medline]

  24. Honkakoski, P., Moore, R., Gynther, J., and Negishi, M. (1996) Characterization of phenobarbital-inducible mouse Cyp2b10 gene transcription in primary hepatocytes. J. Biol. Chem. 271, 9746 –9753[Abstract/Free Full Text]

  25. Smith, G., Henderson, C. J., Parker, M. G., White, R., Bars, R. G., and Wolf, C. R. (1993) 1,4-Bis[2-(3,5-dichloropyridyloxy)]benzene, an extremely potent modulator of mouse hepatic cytochrome P-450 gene expression. Biochem. J. 289, 807 –813[Medline]

  26. Cai, Y., Konishi, T., Han, G., Campwala, K. H., French, S. W., and Wan, Y. J. (2002). The role of hepatocyte RXR {alpha} in xenobiotic-sensing nuclear receptor-mediated pathways. Eur. J. Pharm. Sci. 15, 89 –96[CrossRef][Medline]

  27. Wei, P., Zhang, J., Dowhan, D. H., Han, Y., and Moore, D. D. (2002) Specific and overlapping functions of the nuclear hormone receptors CAR and PXR in xenobiotic response. Pharmacogenomics J. 2, 117 –126[CrossRef][Medline]

  28. Anakk, S., Kalsotra, A., Kikuta, Y., Huang, W., Zhang, J., Staudinger, J. L., Moore, D. D., and Strobel, H. W. (2004) CAR/PXR provide directives for Cyp3a41 gene regulation differently from Cyp3a11. Pharmacogenomics J. 4, 91 –101[CrossRef][Medline]

  29. Konstandi, M., Marselos, M., Radon-Camus, A. M., Johnson, E., and Lang, M. A. (1998) The role of stress in the regulation of drug metabolizing enzymes in mice. Eur. J. Drug Metab. Pharmacokinet. 23, 483 –490[Medline]

  30. Jenkins, R. E., Kitteringham, N. R., Hunter, C. L., Webb, S., Hunt, T. J., Elsby, R., Watson, R. B., Williams, D., Pennington, S. R., and Park, B. K. (2006) Relative and absolute quantitative expression profiling of cytochromes P450 using isotope-coded affinity tags. Proteomics 6, 1934 –1947[CrossRef][Medline]

  31. Ferguson, R. E., Carroll, H. P., Harris, A., Maher, E. R., Selby, P. J., and Banks, R. E. (2005) Housekeeping proteins: a preliminary study illustrating some limitations as useful references in protein expression studies. Proteomics 5, 566 –571[CrossRef][Medline]

  32. Alterman, M. A., Kornilayev, B., Duzhak, T., and Yakovlev, D. (2005) Quantitative analysis of cytochrome p450 isozymes by means of unique isozyme-specific tryptic peptides: a proteomic approach. Drug Metab. Dispos. 33, 1399 –1407[Abstract/Free Full Text]


Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati    What's this?



This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Supplemental Data
Right arrow All Versions of this Article:
M600296-MCP200v1
6/6/953    most recent
Right arrow Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow Glossary
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Lane, C. S.
Right arrow Articles by Patterson, L. H.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Lane, C. S.
Right arrow Articles by Patterson, L. H.
Social Bookmarking
Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit