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School of Chemistry and Manchester Interdisciplinary Biocentre, University of Manchester, Manchester M1 7ND, United Kingdom
* To whom correspondence should be addressed. E-mail: roy.goodacre{at}manchester.ac.uk.
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ABSTRACT |
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 TOP ABSTRACT LITERATURE CITED |
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When the human genome sequencing projects drew their conclusions (1,2), what was surprising was that just as in the microbiologist's pet organism Escherichia coli (3), a vast number of genes had never been seen before in classical (molecular) genetics. For E. coli K-12, a jaw dropping 38% of the total 4288 open reading frames (ORFs)4 had never been observed or studied before; of the remaining 60%, many of these were given gene function by association with DNA sequence matches to other organisms' genes held in the genomic databases and not by direct functional analyses. Despite the continuing deluge of genome sequences (http://www.genomesonline.org/), including those for microorganisms that are yet to be cultured in the laboratory (4), this situation has not improved significantly. For a whole organism, typically 20–40% of the genes can not be ascribed a function by sequence analogy, largely because although the databases are peppered with As, Cs, Gs, and Ts, they do not contain accurate information about what these ORFs actually do.
Clearly something needs to be done to assign biochemical function to these so-called orphan genes and to validate them as molecular targets for therapeutic intervention. In addition, many diseases have no clear diagnoses, and even fewer have any prognostic tests. Therefore, the search for biomarkers from body fluids that can serve as indicators of disease progression or response to therapeutic intervention has also increased.
Perhaps the way we were taught science is to blame for our forgetting the biological phenotype and the now urgent need to bridge the genotype-to-phenotype gap. Our love affair with molecular biology has certainly blinkered us, until recently, into thinking only in terms of its central dogma. In this concept, it is generally considered that there is a linear flow of information within a cell that goes from gene to transcript to protein. Enzymes would then affect metabolic pathways and thereby lead to changes in the phenotype of the organism (Fig. 1 A). However, this traditional thinking is no longer accepted. The cellular processes are in reality intimately networked with many feedback loops and thus should be represented as dynamic protein complexes interacting with neighborhoods of metabolites (5). The construction, visualization, and understanding of these networks (6) certainly present big challenges for systems biology, as does a full understanding of the fluxes through metabolic neighborhoods and their control (7).
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Metabolomics has a special place within systems biology, functional genomics, and biomarker discovery (9,10). The theory of metabolic control analysis (MCA) (7,11), as well as practical evidence (12), shows that although changes in the levels of individual enzymes may be expected to have little effect on metabolic fluxes, they can and do have significant effects on the concentrations of a variety of individual metabolites. Furthermore, Figure 1 B highlights the fact that metabolites are further down the line from gene to function and so more closely reflect the activities of the cell at the phenotypic (functional) level. Indeed, it has been shown (13) that changes in the metabolome are expected to be amplified relative to changes in the transcriptome and the proteome, as changes in the metabolite pool are the downstream result of gene expression. A consideration of the number of transcripts, proteins, and metabolites in humans also suggests that metabolomics is "the way forward." The human genome contains 31,897 genes (http://eugenes.org/). The expression and alternative splicing of the mRNAs indicate that humans may be able to produce 106 different proteins (14)! By contrast, a recent prediction by Palsson suggests that there are only 2645 metabolites (15). Certainly 2645 is a more manageable number to measure accurately. Finally, because many diseases are a result of metabolic disorders, it would be sensible to measure metabolism directly.
Metabolome analyses: considerations and approaches
As with any emerging science, there are a plethora of approaches being developed and improved on to measure quantitative changes in a cell's metabolism caused by either abiotic, biotic, or genetic perturbation, typically as a function of time. Although this area is constantly evolving, a number of recent reviews have extensively summarized the current technological approaches that are being developed within the metabolomics field (10,16–21), and so only a brief synopsis of the pertinent points is given here.
Metabolites by their nature are generally labile species, chemically very diverse, and often present in a very wide dynamic range. In addition, if one is analyzing a living system, the enzymes are still active, and so metabolite turnover will continue [for example, in yeast this has been estimated to occur in seconds (22)]. Therefore, it is important to quench metabolism or else the system will equilibrate, and the metabolite information will be compromised. Ideally metabolism should be stopped immediately. For unicellular organisms, this can be achieved by spraying the sample into very cold (<–40°C) buffered methanol (23). By contrast, for tissues from animals or plants, it is usual to snap freeze these samples in liquid nitrogen (N2), after which mechanical disruption is employed to release metabolites (24).
The above quenching methods are, however, realistic only in a laboratory setting. For the analysis of human biofluids (e.g., blood, urine, tears, breath, and saliva), it may not always be possible to spray these immediately into cold methanol. If a patient is referred to a clinical biochemical laboratory at a local hospital, then immediate cessation of metabolic turnover from a freshly drawn blood sample is possible. By contrast, if the patient has been to see a doctor or nurse in a general practitioner's clinic, then it may be hours before the serum can be separated from the blood sample and frozen. The latter scenario is of course closer to reality for mass screening, and as part of the HUSERMET consortium (http://www.husermet.org/), this stability is being assessed and will form part of a wider program to investigate and then define the human serum metabolome in health and disease.
After metabolism has been quenched, most metabolomics analyses that use chromatography require that the metabolites be extracted. A variety of methods are being used (17,23,25), and the majority are based on acid, alkali, or ethanolic extraction. In addition, Folch-based extractions (26) using methanol:chloroform:H2O are also employed to separate polar metabolites from lipophilic ones before analysis.
Although there are no universally accepted metabolomics strategies, it is possible to summarize the most popular ones (16,27,28); these are detailed in Table 1 and include metabolite target analysis, metabolite profiling, metabolomics, metabolite flux analysis, and metabolic fingerprinting (and footprinting). Table 1 includes a brief description of what these 5 strategies aim to achieve and which analytical approaches are commonly used. Clearly, to perform detailed metabolomics analyses, one must be suitably tooled up. Although this is certainly costly in terms of equipment and manpower, the actually running costs per sample are generally lower and of higher throughput than a typical microarray or proteomics experiment. This is especially true for the metabolic fingerprinting methods, which take in the order of a few seconds to minutes to generate information-rich metabolic fingerprints from each sample.
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All of the biochemical analyses that are used for metabolomics generate data torrents; when multiple time-course measurements are made to measure any dynamics, these become data floods, and when one thinks spatially (e.g., location within the liver), then tsunamis of data are easily produced! And it is perhaps no coincidence that one refers to the information overload in terms of natural disasters! As the French philosopher Jules Henri Poincaré (1854–1912) said in La Science et l‘Hypothèse: "Science is built up with facts, as a house is with stones. But a collection of facts is no more a science than a heap of stones is a house." Therefore, in addition to strategies for data storage, curation, and retrieval of metabolomics data, robust statistical analyses are needed to turn these data into information and hence understand changes in cellular metabolism.
Each metabolomics experiment contains 2 general data sets. The first are the metabolite data themselves and the second a summary of the experimental setup. The latter metadata (data about the data) are just as important as the metabolite data and need to be captured and stored so that database mining is easily achieved (e.g., one might want to retrieve all the GC-MS serum profiles from diabetic females who are nonsmokers, nonpregnant, aged 21–30, with a BMI of 20–25). With reference to data storage, curation, and retrieval, the reader is referred to Table 2, which summarizes the main sorts of database (29) and the current international efforts in progress to address the structure and contents of these metabolite databases.
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Unsupervised algorithms are used when one wants to answer a question such as "Are the metabolite profiles from the same patient taken throughout the day different?" In this case one wants an algorithm that will cluster the metabolite data into groups (31). For MVA this optimization procedure computes dimensionality reduction (30,32), where a large body of metabolite data (x-data) are summarized by means of a few parameters with minimal loss of information. The most used approaches are principal components (PCA) and hierarchical cluster analyses (HCA). After clustering (or what is also called explanatory analysis), the ordination plots or dendrograms then have to be interpreted. Alternative approaches include nested algorithms (e.g., PCA followed by HCA), soft independent modeling of class analogy (SIMCA) and k-nearest neighbors (kNN).
By contrast, for supervised learning algorithms, one generally wants to place a new metabolite pattern into a class that one has already encountered. For example "Does this serum sample come from someone who has prostate cancer or not?" For these cases there must be some gold standard data of metabolic profiles taken from patients with prostate cancer and healthy matched controls. With supervised learning, then, one knows the desired responses (y-data, or traits or classes; in this case prostate cancer or not) associated with each of the metabolite data inputs (x-data). The goal of supervised analyses is to find a mathematical transformation (model) that will correctly associate all or some of the inputs with the target traits (16,33,34). This is usually achieved by minimizing the error between the known target and the model's response (output), with artificial neural networks (ANNs), discriminant algorithms (DAs), and partial least squares (PLS) currently being the most popular (33,35,36).
The example trait to be predicted above is categorical (e.g., disease vs. healthy), but one may of course want a quantitative prediction to be made. For example, the Gleason grade for prostate cancer is important, as this would dictate therapeutic action. ANNs and PLS are ideally suited for quantification (33,37) and so will also play a role for predictions of the severity of disease.
Finally with respect to supervised learning, there also exist special types of algorithms that affect explanatory analyses; that is to say, the mathematical transformation from input to output data is transparent. These inductive reasoning methods include decision tree approaches in which predictive segregation (branching) of the data produces decision boundaries allowing the discovery of which metabolites are important. CART (classification and regression trees) (38), FuRES (fuzzy rule-building expert system) (39), and C4.5/C5 (40) are the most popular decision tree algorithms. In addition, there are a range of evolutionary computation (EC) algorithms (41) that also affect inductive reasoning, and include genetic algorithms (GAs) (42,43), genetic programming (GP) (44–47), and genomic computing (GC) (48). These are based on the concepts of Darwinian selection and are programmed to evolve the desired mapping between input and output variables; again these elucidate which metabolites are important.
All of the methods used must be validated properly, and this is an important step within good modeling practice (GMP) (49). Brown et al. (50) have recently described a metabolomics data analysis pipeline. In this process, one goes from the design of good experiments through instrumental optimization, data storage, and manipulation, the chemometric data-processing methods in common use, and the necessary means of validation and cross-validation for giving conclusions that are credible (50). This pipeline is likely to help in the validation of the model in terms of its biological relevance (especially when inductive algorithms are used), which can be tested by a complementary approach using transcriptomics and proteomics.
Superorganisms and their biological complexity
Thanks to the genome sequence projects, the number of genes in the human has been calculated to be a modest 31,897 (http://eugenes.org/), a relatively small number when one considers that yeast has 7,547, thale cress (Arabidopsis thaliana) contains 29,388 ORFs, and a measly little worm (Caenorhabditis elegans) has 23,399. Indeed, Mus musculus (the mouse) has 
6,000 genes more than humans, and one may wonder if we really are that complex after all!
Gill et al. have used small subunit ribosomal DNA sequences to estimate that the human intestinal microflora are composed of between 1013 and 1014 microorganisms (10 times the number of our own cells, comprising >1000 bacterial species). The metagenome of this so-called microbiome has at least 100 times as many genes as our own genome (51). The microbiome can be thought of as an additional organ, which is estimated to weigh 1 kg in an adult human and is mutalistic to humans (and the commensal microflora that inhabit the host) (52). In mice and rats, it has been shown that Lactobacillus species have antidiabetic effects on noninsulin-dependent diabetes mellitus (53,54). Ordovas and Mooser have suggested that the microbiome may play an important role in maintaining human health (55), and it is likely therefore that the intestinal microflora (as well as those bacteria found on the skin, in airways, and in the urogenital tract) play a vital role in our well-being.
This interaction of the microbiome with humans suggests that the human be considered as a superorganism (56), where we are in fact a human-microbe hybrid (57). Eckburg et al. have shown that the diversity in the microbiome is huge and that there is significant intersubject variability (58). The womb is sterile, and so babies are born with gnotobiotic gastrointestinal tracts, and the new soon-to-be superorganism needs to acquire its commensal microflora (59). These bacteria are mainly maternally acquired, and this process is largely achieved in the first year of life. We may be born 100% human but will die 90% bacterial—a truly complex organism!
It was stated earlier that, from human genome sequence analyses, there are predicted to be 2645 metabolites in the human metabolic network (15). This number will inevitably need revising when one considers the likelihood that one will detect prokaryotic-derived metabolites in humans. Indeed, Nicholson et al. have detected microbial metabolites from the intestinal microflora in human serum and urine using nuclear magnetic resonance (NMR) spectroscopy (60). There are likely to be more examples forthcoming from these and other researchers, and this added layer of complexity needs careful consideration. Figure 2 shows a metabolic network from a superorganism, where secreted metabolites from humans may be metabolically changed and reabsorbed across the intestinal cell wall. Thus, not only will environmental effects (vide infra) have impact on the hundreds of functionally specialized cell types found in humans (61), but understanding both the interactions among many different organisms in a single complex system and their perturbation to environmental changes is very important, especially when this is highly person specific (58).
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One environmental factor that is very important is what happens to the health of an individual when he or she eats. The link between nutrition and health is obvious, and the role of bioactive food components in the protection against disease is also well documented (e.g., 62–64).
The area of nutrigenomics has recently emerged where the aim is to generate a picture of how gene expression changes when a human is exposed to various nutrients (65–68). This approach may allow the discovery of bioprotective foods. Metabolomics is expected to play a pivotal role here (http://www.nugo.org/metabolomics) because nutrition by definition is aimed at maintaining cellular and organism homeostasis. In addition, it is known that some metabolic diseases can be prevented by nutrition (69), and food and drink contain many metabolites; thus, measuring metabolites directly would be sensible. Within this context nutrigenomics can be expanded and should include defining tissue, cellular, or biofluid-specific nutritional metabolomes (70–73).
The homeostasis of human metabolism is affected by a combination of intrinsic and extrinsic factors (74). The intrinsic factors include body composition, tissue turnover, resting metabolic rate, age, human genotype, health status, reproductive status, and diurnal cycle. The extrinsic factors include diet (in terms of nutrients and nonnutrients), drugs (prescribed and lifestyle/leisure activities), physical activity, microbiome, and stress (Fig. 3). Metabolomics would aid in the interpretation of disease processes because a baseline healthy metabolome under different nutritional conditions could be defined leading to personalized metabolic assessment (75). Controlled perturbation from this baseline would lead to an understanding of how to keep human metabolism in homeostasis by tailoring nutritional intake (69).
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Metabolomics is the functional analysis method aimed at acquiring robust and reproducible quantitative information on intracellular and extracellular metabolites. It is gaining increasing interest across a wide variety of disciplines including functional genomics, integrative and systems biology, nutrigenomics, pharmacogenomics, and biomarker discovery for disease prognoses, diagnoses, and therapy monitoring.
Man can be thought of as a superorganism, and the health of this human-microbe hybrid will be affected by intrinsic properties such as human genetics, diurnal cycles, and age and by extrinsic factors such as lifestyle choices (food, drink, and drug intake) and the acquisition of a stable "healthy" gut microbiome. Alterations in this superorganism will be manifested in the metabolite complement as revealed by its serum and urine samples. The unraveling of this metabolic compartmentalization in this complex ecosystem will certainly be a challenge for systems biology approaches and necessary for defining human health at the molecular level. Understanding how to keep human metabolism in healthy homeostasis will lead to lifestyle choices regarding nutritional intake (you are what you eat!) and will allow the tailoring of immediate and future personalized healthcare.
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FOOTNOTES |
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2 Author Disclosure: No relationships to disclose.
3 The author received financial support from the Engineering and Biological Systems committee of the UK BBSRC.
4 Abbreviations used: HCA, hierarchical cluster analysis; MCA, metabolic control analysis; MVA, multivariate analysis; ORF, open reading frames; PCA, principal components analysis.
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