Proteomics and Restenosis
Santhi K. Ganesh, MD
and Elizabeth G. Nabel, MD
C
ONTENTSINTRODUCTION
PROTEOMICS METHODS
PROTEOMICS INRS SUMMARY
REFERENCES
10
INTRODUCTION
The human genome sequence is now known. With the completion of the human genome project, focus has shifted to the immense task of understanding the function of genes and their roles in disease processes. The proteome is defined as the complement of proteins expressed by the genome. The ultimate expression of the genome is the pro- teome. As proteins are the actual molecular workhorses that carry out gene function within cells, proteomic studies are functional genomic investigations. Investigation at the protein level is directly reflective of cellular function and alterations in disease states. Proteome analysis provides highly detailed information because protein expression and function is regulated through multiple mechanisms, including post- translational modification, compartmentalization, protein–protein interactions, varying degradation patterns. Novel advances in protein–analysis technologies have yielded powerful analytic tools that can now be applied to biomedical research. The methods used in proteomics are based on pairing of these tools with the human genome sequence.
These methods allow profiling of proteins and determination of protein modifications for the purposes of defining disease mechanisms and biomarker discovery. Several spe- cific proteins have known roles in vascular injury responses, many of which have been directly implicated in the development of restenosis. As functional genomics pushes the current thinking past individual gene function in disease processes, proteomics will be used to precisely define molecular functions and the interconnections between func- tional gene networks and pathways. Furthermore, the definition of protein-level interac- tions is expected to highlight novel disease mechanisms and provide further insight into altered cellular function in cardiovascular diseases.
From: Contemporary Cardiology: Essentials of Restenosis: For the Interventional Cardiologist Edited by: H. J. Duckers, E. G. Nabel, and P. W. Serruys © Humana Press Inc., Totowa, NJ
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PROTEOMICS Background
Several reasons exist for focusing on protein analysis. Expression profiles of mRNA within a cell do not necessarily reflect the amount of active protein within the cell.
Although amino acid sequence of a protein can be inferred from DNA sequence, the gene sequence does not describe alternative splice variants or post-translational modifi- cations that may be essential for any given protein’s function and activity. Furthermore, the genome sequence is static and does not adequately reflect dynamic cellular processes, and even mRNA expression provides only one level of information regarding the ultimate expression and function of a gene. The overall concept of proteomics and the rationale for its study are straightforward. However, investigation of the proteome presents significant technical challenges. The proteome is incrementally complex com- pared with the study of DNA or mRNA because proteins are made of 20 amino acids, as opposed to four nucleotides. Protein sequencing strategies are available, but these are not nearly as high-throughput as DNA sequencing or mRNA hybridization for iden- tification. The dynamic range of protein abundance is larger, up to 109 in the case of plasma (1) (Fig. 1). The study of low-abundance proteins is hampered by the lack of amplification methods. Additionally, the diversity of functional regulation of proteins can additionally make analysis more complex. Protein function is regulated through a variety of mechanisms, including alternative splicing of transcripts, contranslational and post-translational modification, compartmentalization within cells, protein–protein interactions and varying degradation patterns. Examination of protein–protein interac- tions alone demonstrates a high-degree of complexity (2) (Fig. 2). However, significant technical advances in proteomic instrumentation makes study of the proteome possible.
As proteomics aims to identify proteins as well as define the cellular and biochemical functions of identified proteins under various conditions, proteomics investigations are more complex but are accordingly highly revealing. Proper study design and informed application of the available technologies are essential to getting the most from any pro- teomics study (3). Using clearly defined experimental approaches, the knowledge that can now be obtained using proteomics is beyond what has been possible in the past and is leading to new understanding of cellular function and disease pathophysiology.
Examples of successful proteomic applications in cardiovascular research include the determination of downstream effectors of protein kinase C signaling during myocardial preconditioning and functional regulators of apoptosis in vascular smooth muscle cells (4,5). Functional post-translational modifications of contractile proteins in myocardial tissue have been defined and suggest mechanisms for myocardial pre- conditioning and stunning (6–8). As proteomic technologies develop and applications in cardiovascular research broaden, significant understanding of disease mechanisms, prediction of responses to pharmacological therapies and biomarker discovery will be possible (9).
Clinical Proteomics
Clinical proteomics is a new field of applied proteomics, primarily used in the con- text of biomarker determination. Beyond the identification and characterization of indi- vidual protein biomarkers, newer proteomics tools, such as mass spectrometry (MS), have shown early promise for novel biomarker assessments. One such example is the use of surface enhanced laser desporption-ionziation time-of-flight MS for diagnostic proteomics, in which the mass spectrum resulting from analysis of complex body
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Fig. 1. Dynamic range of 70 proteins in the human plasma proteome (1). Protein abundance is plot- ted on a log scale and spans over eleven orders of magnitude. Classical plasma proteins are clustered on the left. Proteins expressed in states of tissue leakage, such as enzymes and troponins, are clus- tered in the center. Cytokines, which are known to be of relatively low abundance in plasma, are clus- tered on the right. Reproduced with permission (1).
fluids, such as serum, is used as a biomarker for disease in itself. That is, the spectral signals serve as a biomarker, regardless of not knowing individual protein identities that make up the mass spectral signals. In surface enhanced laser desporption-ionziation technology, the sample is subjected to interaction with a specialized surface, on which proteins are captured by the process of adsorption, electrostatic interaction or affinity chromatography on a solid-phase chip surface (10).
The proteins are then ionized and detected, using laser desorption/ionization. From this, a mass spectrum is reported. Analysis of multiple spectra is achieved using advanced computational methods to determine spectral patterns distinctive of disease states. The strength of this approach lies in the ability to simultaneously assay many proteins in a complex mixture. In this manner, proteomic diagnostics is potentially an effective method to evaluate groups of proteins in unison for biomarker purposes. The use of sets of proteins as opposed to a single protein marker is expected to enhance the sensitivity and specificity for disease-state identification. This approach has been suc- cessfully applied in pilot studies in the field of cancer diagnostics. Validation in larger Food and Drug Administration-sponsored trials is currently ongoing (11–14). The appli- cation of this type of pattern recognition approach is just beginning in the field of car- diovascular research and has significant potential to aid in the prediction or diagnosis of the occurrence of specific vascular injury responses.
METHODS Proteomics Tools
The tools used in proteomics are based on well-established analytic methods that have been recently adapted for use in proteomic investigations. Publication of the draft
human genome sequence resulted in an explosion of proteomic databases and tools with which to identify proteins based on sequence data. In combination, these two advances have enabled proteomic approaches in biomedical research. The key steps in proteomics are the separation of complex mixtures of proteins and the identification of proteins using MS. The typical workflow of a proteomic investigation starts with sepa- ration of a complex protein sample, such as that derived from a tissue or cells. Two- dimensional gel electrophoresis (2DGE) and liquid chromatography are two well-established protein separation strategies and have been scaled to high-throughput applications with use of robotics and automated multidimensional separation strategies.
Once proteins are separated, individual protein spots, in the case of gels or fractions and in the case of chromatographic separation are digested with an enzyme, such as trypsin, which will cleave at predictable locations within a protein. This results in peptide frag- ments that have a predictable mass.
The peptides are then analyzed with MS to determine mass and matched against a database created from an in silico translation of the genome sequence, considering the enzyme used to cleave proteins (Fig. 3). Mass-based matches made in this manner are often sufficient to unambiguously identify a protein. In the case where the protein identity is still not clearly determined, possibly owing to post-translational or other modifications that may alter the exact mass of a peptide, tandem MS can be performed.
In this method, peptides are broken into sequential ions, from which direct amino acid
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Fig. 2. Yeast interactome map of proteins based on early yeast two-hybrid measurements in Saccaromyces cerevisiae (2). A high degree of interconnectedness has been observed in maps of protein–protein interac- tions, illustrating the complexity of the proteome. Reproduced with permission (2).
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Fig. 3. Typical proteomic workflow for peptide mass fingerprinting. Proteins are isolated from the cells or tissue of interest and separated. In this example, two-dimensional gel electrophoresis is the separation method. Individual protein spots are then cut out of the gel, enzymatically digested into peptides of which mass is determined using mass spectrometry. Reproduced with permission (42).
sequence is determined. This method can provide unequivocal protein identification as well as additional information, such as the existence of a specific post-translation modification at a specific amino acid. These proteomic findings must then be inter- preted in the context of known or predicted biological function. Adaptations of these techniques for higher throughput and more sophisticated proteome investigations are an area of rapid technological development. Although gene expression profiling more commonly refers to transcript analyses, methods to quantify as well as identify proteins offer the tantalizing potential to use protein expression as a new gene expres- sion method (15).
Interpretation of Proteomics Data
Proteomic data analysis requires similar considerations as in the interpretation of microarrays, with special attention to reproducibility and the statistical issue of multiple hypothesis testing. Reproducibility of assays, such as 2DGE can be problematic, and meaningful comparisons between two samples may be challenging. One weakness of all proteomic techniques is the limited detection of low-abundance proteins. This is a problem common to any large-scale analysis and is seen in transcript profiling studies as well. However, the wide dynamic range of protein abundance presents a larger challenge in proteomics. Coverage of the proteome is additionally complicated by the various properties of proteins. Hydrophobicity, extreme charge states or isoelectric mobility, location within membranes, and the presence of certain post-translational modifications can diminish the ability to detect and identify certain proteins. New fluo- rescent dye systems for 2DGE now allow for the analysis of two samples on one gel, providing a robust means for controlling for technical factors during the 2DGE.
Additionally, these fluorescent dyes have improved sensitivity over standard protein- detection methods. Other advances in automation, microfluidics and nanotechnology are expected to significantly assist the widespread application of proteomics technol- ogy as well. Finally, any findings of a proteomic study, much like a microarray tran- script profiling study, must be followed by functional validation of the specific findings. This is a necessary step to both ensure the validity and functional signifi- cance of any findings.
Combined investigations of transcript and protein expression show special promise for determining gene function because cross-validation can be per- formed across both sets of genomic data. Integrated global quantitative analy- ses of biological information from multiple levels provide detailed insight into the operation of a system (16). Modeling of detailed cell signaling and other pathways is assisted, and further examination of the model can then be per- formed to understand the properties of the system. This approach has been applied in experimental systems of yeast, where galactose utilization has been analyzed under various conditions and after various perturbations, such as gene inactivation, investigations were performed to determine mRNA expression, protein expression, protein–protein interactions, and protein–DNA interac- tions. This investigation yielded a detailed map of galactose utilization path- ways, regulatory networks, and relationship to other biochemical pathways (16).
These types of integrated approaches hold promise in functional genomic inves- tigations as a systems biology approach to understanding the human genome as well and hold particular promise for understanding the perturbations in systems that cause disease.
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PROTEOMICS IN RESTENOSIS
The application of proteomics to understand vascular injury responses will shed light on vascular homeostatic and disease mechanisms that lead to restenosis. Several distinct proteins are already known to have important roles in potentiating inflammatory and pro- liferative mechanisms. Examples are 12-lipoxygenase products, transforming growth fac- tor-β, leukocyte β 2-integrins, M-cerebral spinal fluid, vascular cell adhesion molecule-1, tumor necrosis factor-α, Interleukin-8, Interferon-γ and the nuclear factor-κB pathways (17–30). Several of these proteins that have well-understood inflammatory functions are additionally known to be mitogenic signals, triggering the exit of vascular smooth muscle cells from G0/G1and entry into the cell cycle. Several specific genes in cell-cycle regula- tory pathways also have defined roles in the development of restenosis. Proteomics offers the advantage of being able to fill in such functional pathways, by both demonstrating protein expression and defining the state of individual proteins (31). For example, phos- phorylation of specific cell cycle proteins, such as p27Kip1, is known to regulate its sta- bility and function (32). Proteomics is the only approach that can adequately address the question of what post-translational modifications to proteins occur on a global basis after vascular injury, and what may be the functional consequences of these.
Another example of the importance of understanding post-translational modification of proteins is the discovery of the role of advanced glycosylation end products (AGE) after arterial injury. In the setting of arterial injury, AGE formation and deposition is stimulated (33). AGE bind to a multiligand receptor (RAGE) and trigger augmentation of processes known to be central in neointimal lesion development, including inflam- matory responses, cellular migration and proliferation (34). RAGE is a multiligand member of the immunoglobulin superfamily of cell surface molecules, interacts with distinct molecules implicated in homeostasis, development, and inflammation, and cer- tain diseases, such as diabetes. RAGE is expressed at low levels in adult tissues in home- ostasis, but highly upregulated at sites of vascular pathology (35–37). The glycosylated proteins that are ligands to RAGE are the product of extensive post-translational mod- ification that are thought to play a significant role in the vasculopathy observed in dia- betics. Binding of RAGE by a ligand triggers activation of key cell signaling pathways, such as p21 (ras), MAP kinases, NF-κB, and cdc42/rac, thereby reprogramming cellular properties (38–40). Furthermore, in this study, RAGE expression was demonstrated to modulate the expression of genes known to have important roles in vascular homeosta- sis, such as tenascin C, an extracellular matrix protein with antiadhesive effects, and MMP12, an enzyme capable of degrading arterial elastin. Blockade of RAGE-depend- ent cellular activation suppressed transcripts for these genes and diminished neointimal hyperplasia. Because post-translational modification of proteins is understood to be an acute response mechanism, proteomic applications in this type of investigation would clarify acute responses. Downstream targets of activated cell signaling pathways can also be filled in using a global approach. In the example provided, further understand- ing of the importance of AGE and related protein modifications in the mechanism restenosis may also help to explain clinical observations, such as the finding that restenosis is more prevalent among diabetics (41).
SUMMARY
The development of proteomics offers exciting and novel approaches to examine disease processes at a molecular level. Proteomics is already beginning to unravel many
of the complexities of gene function in homeostatic as well as pathological conditions.
The application of proteomics to understanding responses to vascular injury and the development of restenosis will be highly enhanced by the technological advances that continue to be made in this field. As proteomics technology continues to improve, it will be possible in the near future to obtain a more comprehensive picture of the patho- logical vascular injury responses that culminate in the development of restenosis.
REFERENCES
1. Anderson NL, Anderson NG. The human plasma proteome: history, character, and diagnostic prospects. Mol Cell Proteomics 2002;1:845–867.
2. Hasty J, McMillen D, Collins JJ. Engineered gene circuits. Nature 2002;420:224–230.
3. Arrell DK, Neverova I, Van Eyk JE. Cardiovascular proteomics: evolution and potential. Circ Res 2001;88:763–773.
4. Ping P, Zhang J, Pierce WM Jr, Bolli R. Functional proteomic analysis of protein kinase C epsilon signaling complexes in the normal heart and during cardioprotection. Circ Res 2001;88:59–62.
5. Taurin S, Seyrantepe V, Orlov SN, et al. Proteome analysis and functional expression identify mor- talin as an antiapoptotic gene induced by elevation of [Na+]i/[K+]i ratio in cultured vascular smooth muscle cells. Circ Res 2002;91:915–922.
6. Arrell DK, Neverova I, Fraser H, Marban E, Van Eyk JE. Proteomic analysis of pharmacologically preconditioned cardiomyocytes reveals novel phosphorylation of myosin light chain 1. Circ Res 2001;89:480–487.
7. Foster DB, Noguchi T, VanBuren P, Murphy AM, Van Eyk JE. C-terminal truncation of cardiac troponin I causes divergent effects on ATPase and force: implications for the pathophysiology of myocardial stunning. Circ Res 2003;93:917–924.
8. Buscemi N, Foster DB, Neverova I, Van Eyk JE. p21-activated kinase increases the calcium sensitivity of rat triton-skinned cardiac muscle fiber bundles via a mechanism potentially involving novel phos- phorylation of troponin I. Circ Res. 2002;91:509–516.
9. Patterson SD, Aebersold RH. Proteomics: the first decade and beyond. Nat Genet 2003;33:311–323.
10. Issaq HJ, Conrads TP, Prieto DA, Tirumalai R, Veenstra TD. SELDI-TOF MS for diagnostic pro- teomics. Anal Chem 2003;75:148A–155A.
11. Petricoin EF, Ardekani AM, Hitt BA, et al. Use of proteomic patterns in serum to identify ovarian cancer. Lancet 2002;359:572–577.
12. Adam BL, Qu Y, Davis JW, et al. Serum protein fingerprinting coupled with a pattern-matching algo- rithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Res 2002;62:3609–3614.
13. Yanagisawa K, Shyr Y, Xu BJ, et al. Proteomic patterns of tumour subsets in non-small-cell lung cancer. Lancet 2003;362:433–439.
14. Pusch W, Flocco MT, Leung SM, Thiele H, Kostrzewa M. Mass spectrometry-based clinical pro- teomics. Pharmacogenomics 2003;4:463–476.
15. Pasa-Tolic L, Lipton MS, Masselon CD, et al. Gene expression profiling using advanced mass spec- trometric approaches. J Mass Spectrom 2002;37:1185–1198.
16. Ideker T, Thorsson V, Ranish JA, et al. Integrated genomic and proteomic analyses of a systemati- cally perturbed metabolic network. Science 2001;292:929–934.
17. Hayashi S, Watanabe N, Nakazawa K, et al. Roles of P-selectin in inflammation, neointimal forma- tion, and vascular remodeling in balloon-injured rat carotid arteries. Circulation 2000;102:1710–1717.
18. Kumar A, Hoover JL, Simmons CA, Lindner V, Shebuski RJ. Remodeling and neointimal formation in the carotid artery of normal and P-selectin-deficient mice. Circulation 1997;96:4333–4342.
19. Gu JL, Pei H, Thomas L, et al. Ribozyme-mediated inhibition of rat leukocyte-type 12-lipoxygenase prevents intimal hyperplasia in balloon-injured rat carotid arteries. Circulation 2001;103:1446–1452.
20. Smith JD, Bryant SR, Couper LL, et al. Soluble transforming growth factor-beta type II receptor inhibits negative remodeling, fibroblast transdifferentiation, and intimal lesion formation but not endothelial growth. Circ Res 1999;84:1212–1222.
21. Ward MR, Agrotis A, Kanellakis P, Hall J, Jennings G, Bobik A. Tranilast prevents activation of transforming growth factor-beta system, leukocyte accumulation, and neointimal growth in porcine coronary arteries after stenting. Arterioscler Thromb Vasc Biol 2002;22:940–948.
182 Part II / Genetic Basis of Restenosis
10_Nabel 6/15/07 4:50 PM Page 182
22. Horvath C, Welt FG, Nedelman M, Rao P, Rogers C. Targeting CCR2 or CD18 inhibits experimental in-stent restenosis in primates: inhibitory potential depends on type of injury and leukocytes targeted.
Circ Res 2002;90:488–494.
23. Simon DI, Dhen Z, Seifert P, Edelman ER, Ballantyne CM, Rogers C. Decreased neointimal forma- tion in Mac-1(−/−) mice reveals a role for inflammation in vascular repair after angioplasty. J Clin Invest 2000;105:293–300.
24. Hojo Y, Ikeda U, Katsuki T, et al. Chemokine expression in coronary circulation after coronary angioplasty as a prognostic factor for restenosis. Atherosclerosis 2001;156:165–170.
25. Zohlnhofer D, Richter T, Neumann F, et al. Transcriptome analysis reveals a role of interferon- gamma in human neointima formation. Mol Cell 2001;7:1059–1069.
26. Libby P, Ganz P. Restenosis revisited—new targets, new therapies. N Engl J Med 1997;337:418–419.
27. Collins T, Read MA, Neish AS, Whitley MZ, Thanos D, Maniatis T. Transcriptional regulation of endothelial cell adhesion molecules: NF-kappa B and cytokine-inducible enhancers. Faseb J 1995;9:899–909.
28. Shi C, Zhang X, Chen Z, Robinson MK, Simon DI. Leukocyte integrin Mac-1 recruits toll/interleukin-1 receptor superfamily signaling intermediates to modulate NF-kappaB activity. Circ Res 2001;
89(10):859–865.
29. Bourcier T, Sukhova G, Libby P. The nuclear factor kappa-B signaling pathway participates in dys- regulation of vascular smooth muscle cells in vitro and in human atherosclerosis. J Biol Chem 1997;
272(25):15,817–15,824.
30. Breuss JM, Cejna M, Bergmeister H, Kadl A, Baumgartl G, Steurer S, et al. Activation of nuclear factor-kappa B significantly contributes to lumen loss in a rabbit iliac artery balloon angioplasty model. Circulation 2002;105(5):633–638.
31. Elia AE, Cantley LC, Yaffe MB. Proteomic screen finds pSer/pThr-binding domain localizing Plk1 to mitotic substrates. Science 2003;299(5610):1228–1231.
32. Boehm M, Yoshimoto T, Crook MF, Nallamshetty S, True A, Nabel GJ, et al. A growth factor- dependent nuclear kinase phosphorylates p27(Kip1) and regulates cell cycle progression. EMBO J 2002;21(13):3390–3401.
33. Schmidt AM, Hasu M, Popov D, Zhang JH, Chen J, Yan SD, et al. Receptor for advanced glycation end products (AGEs) has a central role in vessel wall interactions and gene activation in response to circulating AGE proteins. Proc Natl Acad Sci USA 1994;91(19): 8807–8811.
34. Sakaguchi T, Yan SF, Yan SD, Belov D, Rong LL, Sousa M, et al. Central role of RAGE-dependent neointimal expansion in arterial restenosis. J Clin Invest 2003;111(7):959–972.
35. Park L, Raman KG, Lee KJ, Lu Y, Ferran LJ, Jr., Chow WS, et al. Suppression of accelerated dia- betic atherosclerosis by the soluble receptor for advanced glycation endproducts. Nat Med 1998;4(9):
1025–1031.
36. Bucciarelli LG, Wendt T, Qu W, Lu Y, Lalla E, Rong LL, et al. RAGE blockade stabilizes established atherosclerosis in diabetic apolipoprotein E-null mice. Circulation 2002;106(22):2827–2835.
37. Wendt T, Bucciarelli L, Qu W, Lu Y, Yan SF, Stern DM, et al. Receptor for advanced glycation end- products (RAGE) and vascular inflammation: insights into the pathogenesis of macrovascular com- plications in diabetes. Curr Atheroscler Rep 2002;4(3):228–237.
38. Taguchi A, Blood DC, del Toro G, Canet A, Lee DC, Qu W, et al. Blockade of RAGE-amphoterin signalling suppresses tumour growth and metastases. Nature 2000;405(6784):354–360.
39. Hofmann MA, Drury S, Fu C, Qu W, Taguchi A, Lu Y, et al. RAGE mediates a novel proinflamma- tory axis: a central cell surface receptor for S100/calgranulin polypeptides. Cell 1999;97(7):889–901.
40. Schmidt AM, Yan SD, Brett J, Mora R, Nowygrod R, Stern D. Regulation of human mononuclear phagocyte migration by cell surface-binding proteins for advanced glycation endproducts. J Clin Invest 1993;91(5):2155–2168.
41. Mathew V, Gersh BJ, Williams BA, Laskey WK, Willerson JT, Tilbury RT, et al. Outcomes in patients with diabetes mellitus undergoing percutaneous coronary intervention in the current era: a report from the Prevention of REStenosis with Tranilast and its Outcomes (PRESTO) trial. Circulation 2004;109(4):476–480.
42. Ganesh SK, Nabel EG. Genomics and Gene Transfer. In: Fuster V, Topol E, Nabel E (eds).
Atherosclerosis and Coronary Artery Disease, Lippincott Williams Wilkens, Philadelphia, 2004, pp.
647–661.
