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Metabolomics

Dietary Protein–Related Changes in Hepatic Transcription Correspond to Modifications in Hepatic Protein Expression in Growing Pigs¹

Peter Junghans, Thilo Kaehne, Manfred Beyer, Cornelia C. Metges and Manfred Schwerin^*,2

Research Units Nutritional Physiology "Oskar Kellner" and ^* Molecular Biology, Research Institute for the Biology of Farm Animals Dummerstorf, D-18196 Dummerstorf, Germany and Institute of Experimental Internal Medicine, Research Center Immunology, Otto-von-Guericke-University Magdeburg, D-39120 Magdeburg, Germany

²To whom correspondence should be addressed. E-mail: schwerin{at}fbn-dummerstorf.de.

	ABSTRACT

TOP ABSTRACT MATERIALS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
In a previous investigation we showed by expression profiling based on transcription analysis using differential display RT-PCR (DDRT-PCR) and real-time RT-PCR that a soy protein diet (SPI) significantly changes the hepatic transcription pattern compared with a casein diet (CAS). The present study was conducted to determine whether the transcriptional modulation is translated into protein expression. The hepatic mRNA abundance of four genes (EP24.16, LC3, NPAP60L, RFC2) that showed diet-related expression in previous DDRT-PCR experiments was analyzed by real-time RT-PCR. Two pigs that showed the most prominent SPI-related changes of transcription and two casein-fed pigs were selected and their hepatic protein pattern was studied comparatively by two-dimensional gel electrophoresis and peptide mass fingerprinting. The two-dimensional protein gel electrophoresis revealed a predominant SPI-associated upregulation of protein expression that corresponded to the results of the mRNA study. Of 380 diet-related protein spots displayed, 215 appeared exclusively or enlarged in the two SPI pigs; 10 of 39 diet-related expressed protein spots extracted could be identified by peptide mass fingerprinting and database search. Compared with the transcriptomics approach, the proteomics approach led in part to the identification of the same diet-associated expressed molecules (plasminogen, trypsin, phospholipase A₂, glutathione-S-transferase , retinal binding protein) or at least molecules belonging to the same metabolic pathways (protein and amino acid metabolism, oxidative stress response, lipid metabolism). The present results at the proteome level confirm SPI-related increased oxidative stress response and significant effects on protein biosynthesis already observed at the transcriptome level.

KEY WORDS: • protein diets • transcriptome • proteome • liver • swine

Understanding the basis of differences in nutrient effects on health and performance requires an appreciation not only of the traditional links between nutrition and metabolism but also of the less well-defined links between nutrition and gene expression (1). DNA-based as well as the more recently developed protein-based techniques offer new avenues to study the regulation of diet-associated gene expression.

Efficient methods such as Northern blots, RT-PCR, subtractive hybridization, DNA-micro/macro-arrays (2,3) and differential display (DD)² RT-PCR (4) are used to study gene expression at the RNA-level (transcriptome). The transcription of genomic DNA to produce mRNA is the first step in the process of protein synthesis, and differences in gene expression are responsible for both morphological and phenotypic differences as well as indicative of cellular response to environmental stimuli and perturbations. Unlike the genome, the transcriptome is highly dynamic and changes rapidly and dramatically in responses to perturbations and cellular demands. Knowledge of the regulation and extent of expression of a gene is central to the understanding of the activity and biological role of its encoded protein. Additionally, changes in the multigene patterns of expression can provide clues about regulatory mechanisms and cellular functions and biochemical pathways.

However, RNA-based measurements do not describe the final products of expression, the proteins. Protein-based methods are important because they describe translational and post-translational protein modifications and protein complexes. Because of their importance, many methods have been developed for monitoring protein levels, including Western blots, two-dimensional gels, methods based on protein or peptide chromatographic separation and MS detection (5,6), methods that use specific protein-fusion reporter constructs and colorimetric readouts (7), and methods based on characterization of actively translated, polysomal mRNA (8,9).

Using DDRT-PCR and real-time PCR, we previously reported significant effects of a soy protein isolate (SPI diet) on hepatic mRNA abundance of genes involved mainly in oxidative stress response, regulation of protein biosynthesis and cell proliferation, and on neuronal signal transfer compared with a casein (CAS) diet (10) in growing pigs. The present study was conducted to explore whether the transcriptional modulation is translated into protein expression. Two-dimensional protein gel electrophoresis was applied to plot hepatic protein patterns of the two pigs showing the most prominent SPI-related changes of transcription compared with two CAS-fed control pigs. Peptide mass fingerprinting was used to characterize the diet-associated expressed proteins.

	MATERIALS AND METHODS

TOP ABSTRACT MATERIALS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
    Pigs and diets. Castrated German Landrace male pigs were fed semisynthetic diets based on either CAS (Deutsches Milchkontor GmbH, Hamburg, Germany) or SPI (SUPRO 1610, Interfood, Bad Homburg, Germany) as the sole protein source, and protein intake was restricted to 50% of the recommended amount. Both diets were isoenergetic and isonitrogenous, and provided 2.5 times the maintenance requirement of metabolizable energy [3 x 650 kJ metabolizable energy/(kg body weight^0.62 · d)]. The diets fed contained (g/kg dry matter) 420 maize starch, 90 protein (either casein or soy protein isolate), 200 sucrose, 150 fat (75 margarine, 75 lard), 70 cellulose, 80 minerals and vitamins. The composition of the minerals and vitamins was described previously (10). After weaning, pigs were fed a starter diet for 1 wk followed by a CAS-containing diet for 4 wk. After this period, 7 pigs, weighing 21 kg, were fed a SPI-containing diet for another 4 wk, whereas the remaining 7 pigs continued to consume the CAS-containing diet.

At the end of wk 8 of the experimental feeding period, pigs were killed 18–24 h after the last food intake to measure diet-associated gene expression without direct interference of meal intake. Tissue samples were collected, immediately frozen and stored at -80°C until RNA and protein analysis.

All procedures involving animal handling and treatment were approved by the Committee for Animal Use and Care of the Agricultural Department of Mecklenburg-Western Pommerania, Germany, according to the German Law for Animal Protection (TierSchG).

    Quantitative RT-PCR. Transcript levels in 7 pigs from each feeding group were compared directly. Total RNA was extracted from liver samples using the RNeasy Total RNA Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. Synthesis of first strand cDNA was performed with MMLV-RT (Promega, Madison, WI) and random hexamer primers using 2 µg total RNA.

Quantitative analysis of PCR products was carried out in the LightCycler (Roche, Mannheim, Germany) according to optimized PCR protocols using gene-specific primers essentially as described in (10) and LightCycler DNA Master SYBR Green I (Roche). For LightCycler reactions, a master-mix of the following reaction components was prepared to the indicated final concentration: 12.6 µL water, 2.4 µL MgCl₂ (4 mmol/L), 0.5 µL Forward Primer (0.6 µmol/L), 0.5 µL Reverse Primer (0.6 µmol/L), and 2.0 µL LightCycler DNA Master SYBR Green I (10X). LightCycler master-mix (18 µL) was placed in the LightCycler glass capillaries and 10 ng reverse-transcribed total RNA in 2 µL was added as the PCR template. For all assays, an external standard curve that was based on a single-stranded DNA molecule calculation was used. External DNA standard dilutions of each recombinant plasmid from single-stranded DNA (10¹, 10², 10³, 10⁴, 10⁵ and 10⁶ copies) were generated from the cloned RT-PCR products into the pUC18 vector (Pharmacia, Freiburg, Germany), linearized by a unique restriction digest.

Fluorescence signals, which were recorded on-line during amplification, were subsequently analyzed using the "Second Derivative Maximum" method of the LightCycler Data Analysis software. The copy numbers were calculated relative to the amount of total RNA. Copy number of the housekeeping gene GAPDH was measured to normalize for equal RNA amounts. The mRNA abundance was analyzed in three independent repeated analyses.

    Protein 2D-gel electrophoresis. Pig liver samples (100 mg) were crushed under liquid nitrogen to a fine powder. The powder was dissolved in 1 mL lysis buffer [7 mol/L urea, 2 mol/L thiourea, 40 g/L chaps, 50 mmol/L dithiothreitol (DTT), 1.0 g/L SDS, 20 mL/L Pharmalyte pH 3–10]. After 30 min at room temperature, the samples were centrifuged at 100,000 x g for 20 min. The supernatants were diluted in equal amounts of rehydration buffer (7 mol/L urea, 10 g/L chaps, 50 mmol/L DTT and 10 mL/L Pharmalyte pH 3–10). A volume of 500 µL of each preparation were used to rehydrate an IPG-strip (24 cm, pH 3–7, Amersham Biosciences, Freiburg, Germany), respectively. After 12 h of rehydration, the strips were transferred to a dry-strip unit on a Multiphor-Electrophoresis apparatus (Amersham Biosciences). Isoelectric focusing was performed at constant power (10 µA/IPG-strip) at 500 V for 12 h and 3500 V for 90 h. Then the strips were equilibrated in 50 mmol/L TRIS/HCl pH 8.8, 6 mol/L urea, 300 mL/L glycerol, 20 mL/L SDS and 10 mmol/L DTT. After 10 min, the equilibration solution was changed to 50 mmol/L TRIS/HCl, pH 8.8, 6 mol/L urea, 300 mL/L glycerol, 20 mL/L SDS and 200 mmol/L iodoacetamide for another 10 min. The IPG-strips were transferred to the top of SDS-gradient gels (100–160 g/L) and embedded in low melting agarose. Gels for further comparison were run simultaneously in a Hoefer IsoDalt electrophoresis unit at constant power (20 mA/gel) overnight. After running the second dimension, the gel was fixed in ethanol (300 mL/L), acetic acid (100 mL/L) and water (600 mL/L) overnight. The gel was washed 4 times for 30 min in 20 mL/L ethanol and then stained in 200 nmol/L Ruthenium-batho-phenantroline sulfate for at least 5 h. Protein bands were visualized using a fluorescence-imager (Molecular Imager FX, BioRad) at 488 nm for excitation and a 520 nm long-pass filter for emission.

    Tryptic digestion and MS (peptide-mass-fingerprinting). Protein spots of interest were excised from the gels after computer-aided comparison (Image Master Software, Amersham Biosciences). In-gel digestion was performed using an adaptation of the method of Shevchenko et al. (11). Gel pieces were washed by repeated addition and removal of 0.1 mol/L NH₄HCO₃ and acetonitrile, respectively. Subsequently, the gel particles were dried down in a vacuum centrifuge and rehydrated by a freshly prepared digestion buffer containing 50 mmol/L NH₄HCO₃ and 12.5 ng per µL of trypsin (Boehringer Mannheim, modified, sequencing grade) and incubated at 37°C overnight. The peptides were extracted from the gel by repeated addition of a sufficient volume of 25 mmol/L NH₄HCO₃ and acetonitrile, respectively. The extraction was forced by sonification. All extracts were pooled and dried in a vacuum centrifuge.

For MS peptide mapping, the peptides were redissolved in 5 µL of 1.0 mL/L trifluoroacetic acid and purified on a 200-nL reversed-phase C18-nanocolumn. Peptides were eluted in 5 µL of 700 mL/L acetonitrile and subsequently cocrystallized with -cyano-4-hydroxycinnamic acid (20 g/L) in 70 mL/L acetonitrile on a SCOUT 384-MALDI-Target. MS was performed on a matrix-assisted laser desorption/ionization time-of-flight mass spectrometer (Reflex III, Bruker Daltonics, Germany) in reflector mode with external calibration. Annotation of the tryptic fragments was done using BioTools 2.0 software (Bruker Daltonics, Germany). Mascot-Software (Matrix Science, London, UK) was used for the database searches. Database searches resulted in a hit list of proteins. To determine whether a database hit represented a specific hit or a random event, the probability-based MOWSE Score were used. The MOWSE score is based on the scoring system described by Pappin et al. (12). The score is -10 x log (P), where P is the probability that the observed match is a random event. By default, the significance level is set at P < 0.05. That is, if the score for a particular match exceeds the significance level, there is less than a 1 in 20 chance that the observed match is a random event. We used only matches with protein scores that were significant.

    Statistical analysis. For all analyses, the SAS/STAT package (13) was used. Means of the quantitative RT-PCR values for all genes of the two diet groups were compared using Student’s t test.

	RESULTS

TOP ABSTRACT MATERIALS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
    Quantification of diet-associated mRNA abundance of genes involved in the control of protein biosynthesis. In pigs fed the soy protein diet, the mean hepatic transcription levels of the EP24.16, LC3, NPAP60L and RFC2 were 140–230% of the CAS group (all P < 0.008; Fig. 1). Interestingly, at the transcription level, the response to the soy protein diet was extremely variable among individual pigs. Thus, mRNA abundance of individual pigs of the SPI group varied from values comparable to those of the CAS group to > 300% increased concentrations (copy number/10 ng RNA). Compared with the mean of the casein group, pigs 1 and 3 of the SPI group had the most elevated transcript levels of the genes analyzed (EP24.16: 260 and 249%; LC3: 140 and 155%; NPAP60L: 169 and 166%; RFC2: 249 and 335%, respectively), and they were selected for protein analysis in comparison to pigs 1 and 2 of the CAS group.

View larger version (14K): [in this window] [in a new window]   FIGURE 1 Effect of protein diets based either on casein (CAS) or soy protein isolate (SPI) on hepatic mRNA abundance (value x factor given below gene name) of genes involved in regulation of protein biosynthesis in pigs (RFC2: replication factor C2, NPAP60L: nuclear pore-associated protein, EP24.16: endopeptidase 24.16, LC3: light chain 3 polypeptide). Values are mean ± SD, n = 7. **Different from CAS, P < 0.01; ***different from CAS, P < 0.001.

View larger version (70K): [in this window] [in a new window]   FIGURE 2 Two-dimensional protein gel electrophoresis of liver samples of two German Landrace pigs fed casein (CAS) or soy protein isolate (SPI) diets. The six spots identified were isolated and characterized by peptide mass fingerprinting. Results of the database comparison are given.

	DISCUSSION

TOP ABSTRACT MATERIALS AND METHODS RESULTS DISCUSSION LITERATURE CITED

In a previous study in growing pigs using DDRT-PCR and real-time PCR, a SPI diet had complex effects on hepatic mRNA expression profile compared with a CAS diet (10). Present results of quantitative RT-PCR confirm these findings. Hepatic mRNA abundance of four genes (EP24.16, LC3, NPAP60L, RFC2) was significantly different between the diet groups, thus verifying SPI-associated modification of the expression of genes involved in protein biosynthesis.

Replication factor C2 (RFC2), an auxiliary factor for DNA polymerases and , is a multiprotein complex consisting of five different polypeptides and is required at the replication fork for loading the essential processivity factor proliferating cell nuclear antigen onto the 3'-ends of nascent DNA strands during chromosomal DNA replication (23,24).

The NPAPL60 represents a subunit of the nuclear pore complexes involved in the nuclear envelope (25,26). The NPAP60L is thought to be involved in nuclear protein import and export (27).

Endopeptidase 24.16 (EP24.16) is a thiol- and metal-dependent oligopeptidase that is involved in the metabolic inactivation of bioactive peptides and is found in multiple intracellular compartments in mammalian cells (28). The upregulation of the EP24.16 strongly indicates an SPI-diet–associated increase in peptide degradation.

Pestivirus type 1 polyprotein or light chain 3 polypeptide (LC3) gene is a structural protein and encodes the light chain 3 of the microtubule-associated proteins 1A (MAP1A) and 1B (MAP1B) (29,30). Microtubule-associated proteins regulate microtubule stability. MAP1A stabilizes microtubules in postnatal axons. Phosphorylated MAP1B may play a role in the cytoskeletal changes that accompany neurite extension (31).

Although changes in the patterns of gene expression at the transcript level can provide clues about diet-related regulatory mechanisms and cellular functions as well as biochemical pathways, RNA-based measurements do not describe the final products of expression, the proteins. Protein-based methods are important because they describe translational and post-translational protein modifications and protein complexes. However, applicability of protein-based techniques in farm animals is often limited compared with their use in humans and mice due to the "poor" peptide mass database. At present, there are >50,000 and 25,000 database entries for humans and mice, respectively, whereas only 1072 entries for pigs were available in the MSDB database (14).

The present study was conducted to compare diet-related hepatic gene expression patterns at the transcriptome level with those at the proteome level. Two pigs showing the most prominent SPI-related changes of hepatic transcription were selected and hepatic protein patterns of these two pigs chronically fed protein-restricted diets based on SPI and two control pigs fed protein-restricted diets based on CAS were studied comparatively using two-dimension-protein gel electrophoresis and peptide mass fingerprinting.

Two-dimensional gel electrophoresis in combination with MS or chromatography is a well-established and widely used tool to screen alterations at the protein level in tissues under normal and pathophysiologic conditions or under the influence of different external conditions such as the nutritional environment (15,32,33).

Analysis of two-dimension protein gels indicated a predominant SPI-associated upregulation of gene expression that corresponds to previous findings on transcriptome level (10). In the SPI pigs, 215 of the 380 diet-related protein spots displayed appeared exclusively or were enlarged. In a mammal overlapping search, only a small proportion of the extracted diet-related expressed protein spots (11 of 39 spots) could be identified because the low number of entries in the porcine peptide database hampered identification of protein spots. However, comparative image analysis of silver-stained protein gels of the SPI pigs with the CAS pigs showed diet-related modification of some of the same proteins (plasminogen, trypsin, phospholipase A₂, glutathione-S-transferase , retinal binding protein) or at least proteins belonging to the same metabolic pathways (protein and amino acid metabolism, oxidative stress response, lipid metabolism) compared with the transcriptomics approach.

Finally, knowing the protein inventory of a cell and post-translational protein modifications and their integration with genomics and transcriptomics will allow better understanding of disease mechanisms or nutrient effects. The observed high coincidence of diet-affected changes at the transcriptome and proteome levels provides evidence that most of the genes that exhibit an altered mRNA abundance are related to changes in protein abundance (34); it also suggests that data obtained at the transcriptome level may be highly informative for characterizing nutritional physiologic effects. The present results confirm at the proteome level the SPI-related increased oxidative stress response and decreased protein biosynthesis already observed at the transcriptome level.

It remains to be determined whether and how long the observed diet-dependent changes in expression profiles persist after termination of the dietary challenge, and whether there is a physiologic response that maintains whole-body oxidative/antioxidative balance.

	ACKNOWLEDGMENTS

 
The authors thank A. Jugert and J. Bittner for technical assistance in real-time PCR experiments.

	FOOTNOTES

 
¹ Funded in part by grants from the Schaumann-Stiftung.

³ Abbreviations used: CAS, casein; DD, differential display; DTT, dithiothreitol; EP24.16, endopeptidase 24.16; LC3, light chain 3 polypeptide; MAP1A, microtubule-associated protein 1A; NPAPL60, nuclear pore-associated protein 60L; RFC2, replication factor C2; SPI, soy protein isolate.

Manuscript received 11 June 2003. Initial review completed 11 July 2003. Revision accepted 29 September 2003.

	LITERATURE CITED

TOP ABSTRACT MATERIALS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 

1. Hesketh, J. E., Vasconcelos, M. H. & Bermano, G. (1998) Regulatory signals in messenger RNA: determinants of nutrient-gene interaction and metabolic compartmentation. Br. J. Nutr. 80:307-321.[Medline]

2. Ferguson, J. A., Boles, T. C., Adams, C. P. & Walt, D. R. (1996) A fiber-optic DNA biosensor microarray for the analysis of gene expression. Nat. Biotechnol. 14:1681-1684.[Medline]

3. Bowtell, D. D. (1999) Options available—from start to finish—for obtaining expression data by microarray. Nat. Genet. 21:25-32.[Medline]

4. Liang, P. & Pardee, A. B. (1992) Differential display of eukaryotic messenger RNA by means of the polymerase chain reaction. Science (Washington, DC) 257:967-971.[Abstract/Free Full Text]

5. Mann, M. (1999) Quantitative proteomics?. Nat. Biotechnol. 17:954-955.[Medline]

6. Oda, Y., Huang, K., Cross, F. R., Cowburn, D. & Chait, B. T. (1999) Accurate quantification of protein expression and site-specific phosphorylation. Proc. Natl. Acad. Sci. U.S.A. 96:6591-6596.[Abstract/Free Full Text]

7. Ross-Macdonald, P., Sheenan, A., Roeder, G. S. & Snyder, M. (1997) A multipurpose transposon system for analysing protein production, localization, and function in Saccharomyces cerevisae. Proc. Natl. Acad. Sci. U.S.A. 94:190-195.[Abstract/Free Full Text]

8. Zong, Q., Schummer, M., Hood, L. & Morris, D. R. (1999) Messenger RNA translation state: the second dimension of high-throughput expression screening. Proc. Natl. Acad. Sci. U.S.A. 96:10632-10636.[Abstract/Free Full Text]

9. Diehn, M., Eisen, M. B., Botstein, D. & Brown, P. O. (2000) Large-scale identification of secreted and membrane-associated gene products using DNA microarrays. Nat. Genet. 25:58-62.[Medline]

10. Schwerin, M., Dorroch, U., Beyer, M., Swalve, H., Metges, C. C. & Junghans, P. (2002) Dietary protein modifies hepatic gene expression associated with oxidative stress responsiveness in growing pigs. FASEB J. 16:1322-1324.[Abstract/Free Full Text]

11. Shevchenko, A., Wilm, M., Vorm, O. & Mann, M. (1996) Mass spectrometric sequencing of proteins from silver stained polyacrylamide gels. Anal. Chem. 68:850-858.[Medline]

12. Pappin, D.J.C., Hojrup, P. & Bleasby, A. J. (1993) Rapid identification of proteins by peptide-mass fingerprinting. Curr. Biol. 3:327-332.[Medline]

13. SAS Institute Inc. (1999) SAS/STAT User’s Guide, Version 8 1999 SAS Institute Cary, NC.

14. http://www.matrixscience.com: MSDB database 2000, last updating May 05, 2003 Matrix Science London, UK .

15. Stierum, R., Burgemeister, R., Van Helvoort, A., Peijnenburg, A., Schutze, K., Seidelin, M., Vang, O. & Van Ommen, B. (2001) Functional food ingredients against colorectal cancer. An example project integrating functional genomics, nutrition and health. Nutr. Metab. Cardiovasc. Dis. 11(suppl. 4):94-98.[Medline]

16. German, J. B., Roberts, M. A., Fay, L. & Watkins, S. (2002) Metabolomics and individual metabolic assessment: the next great challenge for nutrition. J. Nutr. 132:2486-2487.[Free Full Text]

17. Van Ommen, B. & Stierum, R. (2002) Nutrigenomics: exploiting systems biology in the nutrition and health arena. Curr. Opin. Biotechnol. 13:517-521.[Medline]

18. Scheel, J., Von Brevern, M. C., Horlein, A., Fischer, A., Schneider, A. & Bach, A. (2002) Yellow pages to the transcriptome. Pharmacogenomics 3:791-807.[Medline]

19. Daniel, H. (2002) Genomics and proteomics: importance for the future of nutrition research. Br. J. Nutr. 87(suppl. 2):S305-S311.

20. Kellner, R. (2000) Proteomics. Concepts and perspectives. Fresenius J. Anal. Chem. 366:517-524.[Medline]

21. Dreger, M. (2003) Proteome analysis at the level of subcellular structures. Eur. J. Biochem. 270:589-599.[Medline]

22. Yokoyama, S. (2003) Protein expression systems for structural genomics and proteomics. Curr. Opin. Chem. Biol. 7:39-43.[Medline]

23. Noskov, V. N., Araki, H. & Sugino, A. (1998) The RFC2 gene, encoding the third-largest subunit of the replication factor C complex, is required for an S-phase checkpoint in Saccharomyces cerevisiae. Mol. Cell. Biol. 18:4914-4923.[Abstract/Free Full Text]

24. Reynolds, N., Fantes, P. A. & MacNeill, S. A. (1999) A key role for replication factor C in DNA replication checkpoint function in fission yeast. Nucleic Acids Res. 15:462-469.

25. Trichet, V., Shkolny, D., Dunham, I., Beare, D. & McDermid, H. E. (1999) Mapping and complex expression pattern of the human NPAP60L nucleoporin gene. Cytogenet. Cell Genet. 85:221-226.[Medline]

26. Ganeshan, R. & Parnaik, V. K. (2000) Phosphorylation of NPA58, a rat nuclear pore-associated protein, correlates with its mitotic distribution. Exp. Cell. Res. 261:199-208.[Medline]

27. Muller, D., Thieke, K., Burgin, A., Dickmanns, A. & Eilers, M. (2000) Cyclin E-mediated elimination of p27 requires its interaction with the nuclear pore-associated protein mNPAP60. EMBO J. 19:2168-2180.[Medline]

28. Garrido, P. A., Vandenbulcke, F., Ramjaun, A. R., Vincent, B., Checler, F., Ferro, E. & Beaudet, A. (1999) Confocal microscopy reveals thimet oligopeptidase (EC 3.4.24.15) and neurolysin (EC 3.4.24.16) in the classical secretory pathway. DNA Cell. Biol. 18:323-331.[Medline]

29. Lien, L. L., Feener, C. A., Fischbach, N. & Kunkel, L. M. (1994) Cloning of human microtubule-associated protein 1B and the identification of a related gene on chromosome 15. Genomics 22:273-280.[Medline]

30. Fukuyama, R. & Rapoport, S. I. (1995) Brain-specific expression of human microtubule-associated protein 1A (MAP1A) gene and its assignment to human chromosome 15. J. Neurosi. Res. 40:820-825.[Medline]

31. Fink, J. K., Jones, S. M., Esposito, C. & Wilkowski, J. (1996) Human microtubule-associated protein 1a (MAP1A) gene: genomic organization, cDNA sequence, and developmental- and tissue-specific expression. Genomics 35:577-585.[Medline]

32. Hanash, S. M., Bobek, M. P., Rickman, D. S., Williams, T., Rouillard, J. M., Kuick, R. & Puravs, E. (2002) Integrating cancer genomics and proteomics in the post-genome era. Proteomics 2:69-75.[Medline]

33. Paulson, L., Martin, P., Persson, A., Nilsson, C. L., Ljung, E., Westman-Brinkmalm, A., Eriksson, P. S., Blennow, K. & Davidsson, P. (2003) Comparative genome- and proteome analysis of cerebral cortex from MK-801-treated rats. J. Neurosci. Res. 71:526-533.[Medline]

34. Lockhart, D. J. & Winzeler, E. A. (2000) Genomics, gene expression and DNA arrays. Nature (Lond.) 405:827-836.[Medline]

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