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3 University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75390; 4 Department of Food Science and Technology, University of California, Davis, CA 95616; 5 Pfizer Corporation, Chesterfield, MO 63198; 6 Department of Chemistry, Analytical Chemistry and 7 Department of Food Science and Nutrition, University of Illinois, Urbana, IL 61801; and 8 Department of Pharmacy Practice, University of Illinois, Chicago, IL 60187
* To whom correspondence should be addressed. E-mail: mahady{at}uic.edu.
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ABSTRACT |
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 TOP ABSTRACT IntroductionLITERATURE CITED |
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Introduction |
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TOPABSTRACTIntroductionLITERATURE CITED |
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Over the past 5 y there have been significant advancements in the scientific and clinical assessment of the quality, safety, and efficacy of DS and DF, as indicated by the exponential growth in the published medical literature. In the United States, much of this progress has been a consequence of the establishment of the National Center for Complementary and Alternative Medicine, and its mandate by Congress to conduct and support basic and applied research and training. In addition, strong consumer interest in these products has also stimulated industry-based research in the field. Thus, the strong emphasis placed on scientific research on these products by federal and industrial interests has been a primary driving force in facilitating the important scientific advancements in these fields.
In terms of DS and DF research, the recognition of the health benefits attributable to them has led to considerable interest in how these complex mixtures of chemicals and proteins are responsible for much of the observed beneficial effects. Further, researchers evaluating the biological activities in experimental and human models have recognized that individual variation in response may be considerable and that the effects of DS and DF are pleiotropic in that multiple metabolic pathways are impacted. New proteomic and metabolomic methods and technologies are now emerging that offer exciting opportunities for identifying the multiple molecular targets for DS and DF and thus for determining mechanisms by which they influence the quality of life (6).
These opportunities will necessitate the characterization of phytochemical-gene-protein dynamics that requires the application of these new analytical technologies that enable scientists to more fully explore the regulation of RNA transcription (transcriptomics), the profile of proteins encoded by these genes (proteomics), and ultimately the metabolic consequences of such changes (metabolomics). Knowing the complete sequences of the genome and measuring changes in gene expression by microarray technologies constitute important first steps but do not ensure that biological function is understood. Tens of thousands of genes have been identified that yield, in turn, similar numbers of RNA transcripts. The level of complexity in gene expression increases when one considers that the proteome is comprised of an estimated 100,000 proteins when alternative splicing, RNA editing, and numerous options for posttranslational modifications (PTM) are taken into account. Furthermore, functioning proteins rarely act in isolation, thus making the elucidation of protein activation and the dynamics of multiple protein complexes imperative. Ultimately, it has been argued that the complete metabolic profile of small molecules characteristic of a cell's specific physiological or developmental state most closely mirrors the phenotype. This makes profiling of the metabolome an additional frontier to assimilate so that the complexities of the biochemical and biological functioning of an organism can be unraveled. Overarching all of these areas of functional genomics are the enormous tasks of integrating and interpreting the information gained from these types of profiling into an accessible body of knowledge, which makes the field of computational biology a critical partner. Identifying reliable biomarkers of health and disease, understanding individual variability in response to diet and lifestyle, and ultimately developing personalized nutrition and medical strategies will be a few of the benefits arising from progress made in all of these arenas of functional genomics.
The purpose of this symposium was to begin the process of communicating new innovative proteomic and metabolomic applications and methodologies for use by researchers in the nutrition and natural product communities. Proteomic profiling may encompass such analytical tools as protein microarrays, comparative 2-dimensional gel electrophoresis, MS, fluorescence analysis, structural crystallography, and nuclear magnetic spectrometry (NMR), approaches that can be a part of global or targeted strategies. This symposium highlighted 2 proteomic approaches, protein fingerprinting in complex mixtures with peptoid microarrays and top-down MS (TDMS) for annotation of gene products. Likewise, metabolomic profiling can employ such tools as MS and NMR to characterize not only a snapshot of the cell's fingerprint of small molecule metabolites but also the changing flux of metabolites in response to dietary changes of the host. An overview of methodologies used in metabolic profiling of natural products and an overview of the applications and challenges of metabolomics to human nutrition research were both presented.
Proteomics-protein fingerprinting of complex mixtures
The term proteome was defined in 1995 as the entire protein complement expressed by a genome. Thus, proteomics is the systematic analysis of "all" proteins present within a target cell or tissue at a given time. These analyses result in the characterization of the structure and function of such proteins and should include the sequences, cellular location, PTM, and splice variants (7). The Human Genome Project demonstrated that there are fewer protein-coding genes in the human genome than there are proteins in the human proteome, implying that protein diversity cannot be fully characterized solely by gene expression analysis (7). This fact makes proteomics a particularly useful tool for characterizing novel biomarkers.
Strategies for discovering novel biomarkers. Various technologies and strategies exist for discovering biomarkers, but 1 newer approach is proteome profiling by MS (8,9). This approach is extremely sensitive and can be used to screen serum samples for scarce protein biomarkers. Many proteins are often secreted or otherwise released into the blood in various disease states, and MS is a powerful method for identifying the presence of such molecules. Currently the major hindrance to this approach seems to be reproducibility. Ongoing work by various laboratories includes the development of an alternative approach to biomarker discovery and detection that incorporates aspects of both of these approaches, using chemical microarrays to identify the presence of proteins in complex mixtures of biological samples (8,9).
Chemical microarrays are similar to DNA microarrays, but instead of DNA being spotted onto the arrays, compounds can be covalently linked to glass slides using any number of different chemistries and screened for those that bind specific proteins (10,11). Proteins are typically labeled with a dye or probed with labeled secondary antibodies specific for those proteins. In this manner, tools for chemical genetics or active compounds in DF and DS can be identified. Chemical microarrays displaying thousands of peptoids (12), molecules with properties similar to peptides, have recently been developed (13) and are resistant to enzymatic degradation (14). When an individual protein is hybridized to a peptoid microarray and visualized, many spots on the array light up to some degree. Each of the peptoids on the arrays binds different proteins with varying affinities, in effect producing a unique and reproducible binding pattern for each protein. This was true even when proteins were hybridized to slides in the presence of 1000-fold excess (by weight) of E. coli lysate (12). Preliminary data suggest that these arrays and techniques will be useful for biomarker development.
TDMS of proteins
Along with its many utilities, human proteomics presents some unique challenges because of PTM, polymorphisms (cSNP), and transcript editing (15). These challenges have been efficiently addressed previously by TDMS utilizing a custom 8.5-T quadrupole FTMS (15–17). TDMS is uniquely useful for the accurate, sensitive, and routine characterization of protein PTM. This is because TDMS measures the whole protein; thus, all variations affecting primary sequence can be detected as they occur in combination (15,16).
Over the past year a 12-T LTQ FTMS has been constructed and used to perform online FTMS analysis and fitted with a TriVersa nanospray source (15). This configuration enables rapid online MS and MS/MS while collecting fractions for follow-up offline analyses. This platform has provided improved throughput for yeast proteins compared with the previous studies using offline MS. Recently, a dual on-/offline multidimensional characterization by automated TDMS (MudCAT) has been developed, enabling faster, more precise protein identification than shown previously. Application of MudCAT to proteins isolated from primary leukocytes and HeLa cell lines yields the greatest number of identifications to date for human proteins. Given extensive offline work, as described in previous publications (17), proteins may often be identified using accurate intact mass tags, requiring no MS/MS for identification.
Proteotyping of polymorphisms. With 27,461 validated (frequency >1%) codings, nonsynonymous cSNP on 10,995 genes according to dbSNP (http://www.ncbi.nlm.nih.gov/), cSNP occur on a great many proteins but often elude detection by traditional proteomic methods. Given that TDMS observes the entire protein sequence, it readily detects cSNP, wherein acyl-CoA binding protein, known to harbor 2 validated cSNP, is observed to be heterozygous at a single locus using offline TDMS/MS, with apparently nonequivalent allele expression ratios. With offline TDMS, 3 proteins were observed to be heterozygous, among which a 63-kDa protein was identified and determined to be heterozygous by TDMS with validation by traditional genotyping. Further, using online MudCAT analysis, allele quantification was achieved for 3 unique individuals with heterozygous genotype at the locus encoding Charcot-Leyden crystal protein. The allele expression ratios for these humans proved to be consistent across individuals.
Application of MudCAT to leukocyte and HeLa proteomics.
Application of MudCAT with no offline MS/MS to leukocytes produces 528 unique masses observed with 144 unique masses targeted for online MS/MS. Utilizing intact mass tags and online MS/MS identifications, 99 protein forms (from 56 genes) were identified with complete characterization of 38 forms. Given the limited number of proteins identified previously from neutrophils (
60% of total leukocyte population, 286 proteins) using multidimensional protein identification technology and 2D-PAGE (18), it was determined that the leukocyte proteome has fewer expressed proteins than the >1000 proteins found in breast cancer cell lines using multidimensional protein identification techniques (19).
Human proteomic efforts have enabled the generation of identification lists for TDMS in HeLa and leukocytes consisting of 158 (from 120 genes) and 133 protein forms (from 77 genes), with an aggregate of 261 protein forms from 162 genes. By use of these lists, protein forms may be rapidly identified using intact mass tags, enabling rapid proteomic screening by MudCAT for PTM and cSNP while allowing further targeting of new protein forms by on-/offline TDMS.
These powerful proteomics tools will make critical contributions to characterization of the subtle changes in the proteome resulting from variability in the consumption of DF and DS. Published uses of proteomic applications to DS and DF research are listed in Table 1.
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Metabolomics describes the qualitative and quantitative analysis of all low-molecular-weight metabolites of a cell or organism and their dynamics in biological systems. Thus, it is frequently reasoned that metabolic information reflects biological endpoints more accurately than transcript or protein analysis. As a result, the science of metabolomics has become increasingly popular in supporting research programs designed to 1) enhance the nutritional quality of crops and vegetables and 2) assess the impact of dietary changes on human health. To fully contribute to such aims, an ideal metabolomics methodology would allow a complete and quantitative global analysis of metabolite content and of metabolic flux in any given biological system. At present, however, technical approaches to metabolic profiling allow identification of only a subset of previously described metabolites. Current options for data acquisition in metabolomics essentially center on different variants of NMR or MS (31).
Metabolomics strategies exploiting such technologies are characterized, at least in part, by 2 different conceptual approaches (31) defined herein as "targeted" and "nontargeted." Nontargeted approaches aim to provide a hypothesis-free global overview of readily detectable metabolites. The ranges and biochemical identities of metabolites measured in a given nontargeted metabolic profiling experiment are typically dependent on the data acquisition technology adopted.
Targeted approaches use optimized measurements designed for specific classes of metabolites or pathways. Consequently, they offer advantages in terms of improved quantification and interpretability of acquired data. Strategies can be developed for assessing discrete classes of metabolites including, but not restricted to, organic acids, sugars, free amino acids, and lipids. Indeed, metabolomic "subdisciplines" are now developing as technologies for assessing different metabolite classes improve. For example, lipidomics, profiling technologies focused on lipids, represents an increasingly well-developed and influential aspect of metabolomics (32; www.lipidmaps.org).
As is the case in transcriptomics and proteomics, scientific advances will continue as metabolomics attempts to incorporate technical approaches that drive improvements in analytical technologies, informatics, and standardized approaches to data reporting and dissemination (33,34). However, it is also reasonable to argue that technical strategies be defined by the biological question of interest and that current technologies, when judiciously applied, serve current needs.
For the purposes of this overview on the relevance of new profiling technologies to nutraceutical and DS research, we consider targeted metabolic profiling approaches to 1) targeted profiling in product characterization; 2) markers for product intake and consumption; and 3) targeted profiling in assessing impact of product intake and consumption.
Targeted profiling in product characterization. Quality traits in important crops and vegetables are related to metabolic composition (35), whereas metabolic changes underpin plant development and responses to applied stresses. There already exists well-reviewed research on the value of metabolomics in investigating carotenoid and flavonoid chemistry in tomato (36). However, here we present a less-reviewed example to highlight the relevance of assessing metabolic engineering strategies for manipulating metabolite composition in important plants.
The Commonwealth Scientific and Industrial Research Organization of Australia has utilized transgenic expression and RNA inhibitory silencing of enzymes involved in the benzylisoquinoline pathway to modulate expression of morphine and pharmaceutically important products such as thebaine (37). Metabolic profiling indicated that overexpression of codeinone reductase, which catalyzes the penultimate step in morphine synthesis, not only resulted in the intended increase in morphine and codeine levels but was accompanied by an unexpected increase in levels of the upstream intermediate thebaine. Details regarding regulation of the cellular and subcellular localization of the morphinan pathway still remain unclear, and no definite reasons for this secondary effect, as identified by metabolic profiling, can be established at present. RNAi silencing of the same enzyme was also characterized by unexpected changes in benzylisoquinoline metabolism. Although the intended decrease in morphine and codeine was observed, secondary accumulation of the biosynthetic precursor (S)-reticuline (as well as methylated derivatives), several enzymatic steps upstream of codeinone synthesis, was not predicted (37). This accumulation may indicate a feedback mechanism preventing intermediates from general benzylisoquinoline metabolism from entering the morphine-specific branch. Both findings highlight the value of targeted metabolic profiling in identifying the potential for secondary effects on metabolic engineering.
Markers for product intake and consumption. Phenolic compounds in tea have long been associated with health benefits. Although these are relatively well established in animal models, the situation in humans is a little more ambiguous. This is largely because of uncertainties in providing effective markers for tea consumption and an uneasy reliance on subject questionnaires. To address this problem, Luo et al. (38) developed a method for measuring green tea polyphenolics utilizing an electrochemical-based detection technology. This method has been proven to be a reliable indicator for levels of tea-derived epigallocatechin-3-gallate and epicatechin-3-gallate in human plasma. It therefore allows an improved approach to correlating putative health benefits with tea composition. The method also, of course, allows a means to characterize polyphenolic levels in tea. This has also been a focus of Zini et al. (39), who have also established an LC-MS method that was used to characterize green and black tea. This methodology allowed quantification of numerous phenolic classes as well as purine alkaloids. According to Zini et al. (39), green tea has greater levels of flavan-3-ols, which are associated with greater health benefits.
Targeted profiling in assessing impact of product intake and consumption. The possible contribution of lipid profiling to assessments of dietary or drug impact on health is illustrated here by a recent study on the effect of the PPAR activator rosiglitazone on metabolic regulation in a diabetic mouse model (40). An analytical platform developed by Lipomics Technologies that allows quantitative measurement of over 500 lipid metabolites in blood and tissue samples revealed that rosiglitazone treatment resulted in fat accumulation in the liver and tissue toxicity. Yet cholesterol ester and triglyceride levels were decreased in blood, suggesting, quite incorrectly, a decrease in de novo lipid synthesis. Clearly, comprehensive profiling technology may contribute more to understanding of all aspects of lipid regulation, an understanding that is prerequisite in the development of new drugs and health-oriented dietary approaches.
Lipids remain an extremely important component of the metabolome, and further developments in data acquisition technologies can only be of marked benefit. Recognizing this, the U.S. National Institute of General Medical Sciences has, since 2003, sponsored a LIPIDMAPS consortium of 16 academic research institutes and 2 private enterprises to fully map metabolic pathways in the macrophage cell. Because macrophages play a significant role in the formation of atherosclerotic lesions, understanding and defining changes in macrophage biology in terms of changes in lipid metabolism will prove invaluable to drug discovery. Indeed, many well-established drugs such as aspirin, ibuprofen, naproxen, Celebrex, and statins work by modulating lipid metabolism. Because many DF and DS such as vitamin C, garlic, ginger, glucosamine, turmeric, and willow bark also have significant antiinflammatory activities, the development of fully mapped pathways will significantly facilitate the research progress in this field.
Metabolomic applications to nutrition research
Life sciences are actively advancing biomedicine from a paradigm of diagnosing and treating disease to assessing and maintaining health. In such a perspective, assessment of health and diet will be critical to success. Assessing nutritional health will require accurate, comprehensive analyses of personal metabolic status and will use the tools of metabolomics to accurately establish the metabolic status of both diseased and healthy individuals. Individual metabolite analyses have long been a cornerstone of biomarkers of risk for disease, and it is a logical next step to transform biomarker measurements to metabolomic analyses as the cornerstone of assessors of health.
Metabolomics has been included in the NIH roadmap as a core technology in the overall initiative to guide the development of and assist in delivering the solutions to human metabolic diseases (41). The ultimate goal of this technology is to gain an understanding of the effects of exogenous compounds such as DS and DF on human metabolic regulation. However, the application of metabolomics specifically to the field of nutrition faces numerous challenges before it can realize its potential. First, the metabolome has to be defined in practical terms. The metabolome is not definable in the same sense as the genome. Metabolites change constantly in every cell and body fluid; hence, the metabolome will always be a biological concept that is only pragmatically definable. Nonetheless, all of our cells and biofluids take considerable effort to maintain homeostasis so that the actual variations in any given metabolite pool are typically minor relative to the abundance of the metabolites themselves. The basic molecules of human metabolism, i.e., those that all humans have in relatively constant amounts and without which life is not possible, can be considered the endogenous metabolome. Defining the molecules that constitute this core metabolite pool is an immediate priority of the field of metabolomics. In addition to the endogenous metabolome, there are tens to hundreds of thousands of nonnutrient molecules in the flora and fauna consumed as food that are absorbed into blood, metabolized, and released into body fluids such as urine and saliva. In addition, our resident microflora also produce metabolites that can contribute to and alter the metabolome of human biofluids. The first step in building a detailed knowledge of human metabolism using metabolomics, therefore, is simply to begin the process of chronicling the molecules and data banking this information in accessible databases. The Human Metabolome Project has established the first draft and is well on the way to cataloguing the endogenous metabolome (41).
When the technologies of high-throughput metabolite analytics and the databases of endogenous metabolites and their abundances are sufficiently in place, metabolomics will be able to address the key elements needed to improve human diet and health: 1) assessment, 2) phenotypic annotation, and 3) guided intervention. Metabolomics is the logical approach to assess dysfunction and metabolic imbalances caused by dietary components (41). In relating metabolism to health, it is vital to capture the full range of human metabolism to ensure that all aspects of health are addressed in unbiased platforms. Selected biomarkers are possible for disease diagnostics but will never accurately capture the full range of human metabolism or the discouraging diversity of dysfunctions that different individuals are likely to encounter. Metabolomics will be indispensable in relating varying metabolite patterns within individuals in the population to the varying phenotypes of these individuals. A sole focus on the endogenous metabolome can still be used in studies of nonessential nutrients and contaminating components in the diet because it is primarily through their effects on the pathways of endogenous metabolism that these exogenous compounds exert their effects. Metabolomics furthermore is suited to explore the complex relation between nutrition and metabolism and to investigate the roles that dietary components play in many aspects of health and disease (41). Metabolomics is already being used to guide genome and proteome annotations (42). In addition, metabolomics will be used to explore homeostatic control and determine how the human metabolic balance is disturbed by nutritional deficiencies or excesses of dietary components (41).
The potential value of metabolomics to understanding nutrition has already been demonstrated in studies of endogenous lipids (40) and in studies that have examined the metabolism of nonnutrient compounds that may be beneficial although not essential (Table 2), and even the interaction between the intestinal microflora and intestinal metabolism (43). Intervention brings assessment and phenotype understanding to its logical application, improving individual phenotypes according to the health wishes of the individual. Although it remains the least studied, intervention is the whole point of the exercise. Knowing the health status, however detailed, is practically useless if it cannot be improved through nutritional intervention.
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Data analysis, visualization, and annotation. The field of metabolomics is envisioned to produce and interpret massive quantities of parallel data; however, the tools to handle such immense datasets are not yet in place. The first challenges to analyzing metabolomics data statistically are a direct and logical extension of the probabilistic statistics familiar to all nutritionists, avoiding type 1 and type 2 errors. Tools to analyze datasets and avoid making dozens or hundreds of type 1 errors are particularly necessary when considering biologically important differences in metabolites within individuals, where variation may be small. This problem has been addressed from several perspectives, for example, using conservative mathematical corrections leading to the inevitable acceptance of a substantially higher rate of type 2 errors or using biological information to guide the statistical analyses according to clusters of metabolites that are functionally related (38).
The second set of problems relate primarily to informing the scientist of the basic informational content of the data, as large data sets are not easily interpreted by single investigators. Eventually, automated interpretation models will be the toolsets developed to deal with such studies. Nonetheless there is an immediate need for tools that simply provide a more intuitive picture of experimental outcomes.
With the capabilities of displaying individual metabolites as entire pathway ensembles now possible, the next step is to relate the metabolic fluctuations to their broader phenotypical implications. For example, elevated levels of sterol intermediates in plasma may suggest that high cholesterol biosynthesis as an underlying cause of hypercholesterolemia and nutritional reductions in those same intermediates should correlate with a phenotypic lowering of plasma cholesterol. Converting changes in metabolite quantities in plasma to actual predicted alterations in metabolic pathways from various tissues through the plasma compartment is possible for certain specific metabolites and pathways. However, to date, no mathematical toolset has been developed that is capable of merging all metabolites with phenotype. This process of annotating metabolism according to nutritional and genetic inputs and health outputs as discrete measurable phenotypes is an obvious goal of the next generation of nutritional sciences.
Currently, new innovative proteomic and metabolomic applications and methodologies are available for use by scientists in the nutrition and natural product communities. In terms of proteomics, the development of alternative approaches to biomarker discovery and detection using chemical microarrays to identify the presence of proteins in complex mixtures of biological samples is an important advancement. This same technology can be applied to determining the impact of DF and DS on newly discovered biomarkers. Along with its many utilities, human proteomics presents some unique challenges because of PTM, polymorphisms, and transcript editing. To overcome some of these issues, TDMS has proven to be uniquely useful for the characterization of protein PTM because it measures the whole protein; thus, all variations affecting primary sequence can be detected as they occur in combination. These techniques can be readily applied to determine the impact of DF and DS on PTM (Table 1).
Although the field of proteomics is relatively well established, the science of metabolomics is just beginning to assemble its critical toolsets. The goal of combining innovative experimental designs, comprehensive and quantitative metabolite analytical platforms, computational biology protocols for data handling and manipulation, and automated biological annotations of metabolites to pathways is not yet achieved. Nonetheless, the promise of this new science to contribute to the understanding of DF and DS and their impact on human health is unquestionably attractive (Table 2). As DF and DS research continues to progress, opportunities to use these new and innovative research technologies and methodologies will be critical for the advancement of science and improvements in human health.
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FOOTNOTES |
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2 Author disclosures: J. Astle, J. T. Ferguson, J. B. German, G. G. Harrigan, N. L. Kelleher, T. Kodadek, B. A. Parks, M. J. Roth, K. W. Singletary, C. D. Wenger, and G. B. Mahady, no conflicts of interest.
9 Current address: Monsanto Company, St. Louis, MO 63167.
10 Abbreviations used: cSNP, polymorphism; DF, dietary factor; DS, dietary supplement; MudCAT, multidimensional characterization by automated TDMS; NMR, nuclear magnetic spectrometry; PTM, posttranslational modification; TDMS, top-down MS.
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