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Serum metabolomics in animal models and human disease

James, Emma L.; Parkinson, Eric K.

Current Opinion in Clinical Nutrition and Metabolic Care: September 2015 - Volume 18 - Issue 5 - p 478–483
doi: 10.1097/MCO.0000000000000200
ASSESSMENT OF NUTRITIONAL STATUS AND ANALYTICAL METHODS: Edited by Kristina Norman and Dwight E. Matthews
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Purpose of review The aim of this study is to highlight some recent uses of serum metabolomics in human and animal studies. The main themes are the importance of understanding the underlying variation in human metabolism and the use of serum metabolomics in disease profiling.

Recent findings Several studies have attempted to use serum metabolomics to develop noninvasive biomarkers of disease and/or track the consequences of nutritional and genetic interventions. Many advances have been made with common changes being identified in ageing, the menopause and cancer but several problems of interpretation have emerged from these studies. These include the small sample sizes in most human studies and the differences between human and rodent metabolomes. However, a metabolic screen of over 1000 ‘healthy’ humans (the Humsermet project) has highlighted many variables that may be used to refine the interpretation and design of previous and future human studies alike, in addition to data mining.

Summary Some common serum metabolome alterations have been identified but many inconsistencies remain. The construction of a human serum metabolome database should be informative in the design of future human and animal model studies.

Centre for Clinical and Diagnostic Oral Sciences, Institute of Dentistry, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, Turner Street, London, UK

Correspondence to Emma L. James, Centre for Clinical and Diagnostic Oral Sciences, Blizard Institute, 4 Newark Street, London E1 2AT, UK. Tel: +44 0 207 882 2390; e-mail: emma.james@qmul.ac.uk

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INTRODUCTION

Serum metabolomics

The metabolome, the collective term for all of the intermediates and waste products of the biochemical reactions that occur within a biological sample, represents the metabolic processes that underpin specific diseases, enables quantification of nutrient or toxin exposure and links genetic makeup to molecular mechanisms in drug and nutrient metabolism (also known as metabolomics) (see reviews [1,2]). The metabolome is increasingly being recognized as a rich source of holistic information about the health of an organism, as evidenced by the large increase in metabolomics studies published in the last 4 years (Fig. 1).

FIGURE 1

FIGURE 1

Although it is possible to collect metabolomics data from many different compartments (e.g. blood, saliva, urine, soft tissue and conditioned cell culture media), serum is clinically particularly useful as it is readily available and contains molecules that represent ‘short term’ biomarkers, so it is reflective of the current state of an organism – this can be useful for tracking metabolism over a set time course in animal models or humans.

In the search for novel biomarkers and insights into biological processes a common approach to employ is an untargeted screen to detect as many molecules as possible, coupled with multivariate analysis such as principal components analysis (PCA) to make a comparison between a control and test group(s) without having prior knowledge as to which molecules to compare. Candidate molecules identified using databases such as the Human Metabolome Database (HMDB) [3] can then be further assessed quantitatively using labelled standards in what is known as targeted metabolomics (Fig. 2).

FIGURE 2

FIGURE 2

Box 1

Box 1

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USE IN ANIMAL MODELS

Animal models offer the ability to manipulate genes of interest, or induce disease in a controlled fashion and investigate the metabolic implications on a whole organism level.

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Ageing

Wilson et al.[4▪] studied the caspase 2 null mouse which displays premature ageing. In common with aged wild type mice, young caspase 2 null mice showed an increase in several saturated fatty acids and a lower abundance of glucose and mannose-6-phosphate relative to their wild type counterparts. However, most of the serum metabolites they found significantly associated with ageing were different from human ageing [5] and in the conditioned medium of cultured senescent human cells [6]. Nevertheless eicosapentaenoic acid, EPA; C20:5n3 was elevated in all three studies and so these common findings are encouraging.

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Cancer

LaConti et al.[7] have recently published a study identifying a serum metabolic signature of 50 ions detected by ultra performance liquid chromatography time of flight mass spectrometry (UPLC TOF MS) that distinguishes between precancerous lesions, advanced pancreatic cancer and normal pancreas in a well established mouse model, although early stage lesions were less reliably detected. One of the metabolites identified, citrate, has also been reported to be elevated in another serum study of pancreatic cancer in rats, and the current study demonstrated that this rise in citrate was due to increased citrate synthase expression. However, despite being able to separate disease from healthy controls based on serum metabolites with either MS or 1H NMR, older studies of humans with pancreatic cancer using similar techniques do not show the same specific elevation in citrate [8].

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USE IN MONITORING INTERVENTIONS

As well as identifying biomarkers, metabolomics can be used to monitor the effectiveness and mechanisms involved in potential treatments. Using ovariectomized mice as a model of postmenopausal osteoporosis, Chen et al.[9] investigated the effectiveness of increasing vitamin D2 levels and maintaining bone density by feeding mice mushrooms that had been irradiated with UV to increase their vitamin D content. Using NMR followed by spectral deconvolution and peak identification to analyze the serum of sham, ovariectomized, ovariectomized + nonirradiated mushrooms and ovariectomized + irradiated mushrooms, Chen et al. demonstrated that the metabolic profile of the ovariectomized mice on the irradiated mushroom diet was distinct from that of the other groups. Using PCA they were able to see that this separation was caused mostly by amino acids and energy metabolites previously shown to be associated with the bone forming cell osteoblasts. The mice on the irradiated mushroom diet also showed increased levels of osteocalcin in their serum, a molecule secreted by osteoblasts, and reduced levels of PYD which is a product of collagen breakdown associated with the bone resorbing cell osteoclasts. This suggests a direct link between the level of vitamin D2 and amount of bone maintenance via inhibition of collagen breakdown and promotion of osteoblasts.

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THE HUMAN METABOLOME

Equating mechanisms and biomarkers derived from highly controlled experiments in rats or mice to what we observe in humans, who aside from being a different species are also heterogeneous in regards to the plethora of factors that affect serum metabolite levels, is a challenge. In the two biomarker studies mentioned above, the markers found in the animal models did not all show up in human studies. Is this because the animal models are not a robust representation of the conditions in humans? After all the metabolites are the footprint of the biological process, therefore if the same signature is not present between species it might indicate there are different mechanisms at play. Alternatively, is it because the human studies simply do not have enough power, given the variability in the sample, to detect the signature of the disease over the background of other confounding factors? Although a database of human serum metabolites, including those found in ‘normal’ levels exists [10] recent work by Dunn et al.[11▪▪] has begun to address the specific issue of variability.

The Husermet project (http://http://www.husermet.org/) used nontargeted GC–MS and UPLC–MS to study the hydrophilic and lipophilic metabolic complement of serum samples in 1200 healthy UK patients. This large study reported that the variation in serum metabolites ranged from less than 5% to more than 200% due to differences in sex, age, BMI, blood pressure (BP) and smoking, although the variation in the serum metabolome was less than in the urine metabolome published previously, some of which could be linked to lifestyle or drug use. For example, highly variable metabolites such as caffeine could be linked to its variable consumption, N-methylpyrrolidinine may be linked to its use in drug vehicles, salicylic acid possibly linked to aspirin use or smoking, trehalose could be linked to food additives and oxidized longer chain fatty acids, acyl carnitines, and/or two γ-glutamyl dipeptides (isoleucine and leucine) could be linked to variations in oxidative stress.

Major sources of variability were sex (e.g. caffeine and 2-aminomalonic acid being higher in females) and age, several metabolites increased with age including citrate, which we have recently reported to accumulate in the extracellular environment of DNA-damaged and senescent cells [6]. BMI was also linked to metabolite variation, a range of amino acids varied with BMI and short chain organic acids such as acetate, certain diacylglycerides, sphingolipids, lyso-glycerophospholipids and fatty acids showed a decrease in concentration with increased BMI, and other metabolites showed a female-specific decrease and glycerol-3-phosphate a male-specific decrease. Additionally increased BP was linked to a decrease in methionine disulphide whereas methionine increased. Among many other correlations were increases in citrulline and lactate, as well as correlations with urate, triacylglycerides, dipeptides, glycerophosphocholines and 4-hydroxyphenyl lactic acid. Several amino acids (aspartate, histidine and lysine), glycerol-3-phosphate and a number of fatty acids, citrate, lactate biotin and inositol decreased in smokers when compared with nonsmokers and even exsmokers.

All of these observations, made in a large cohort of healthy individuals, provide an invaluable reference point for other publications and future work in which a more targeted approach has been taken, or when the study contains a much smaller number of patients. Importantly, the authors explain how they were able to correct for intrarun drift in spectra, as is inevitable when running such large numbers of samples on different days. Further expansion of these observations in a robust and reproducible manner, such as described by Dunn et al., is vital to maximize the usefulness of targeted studies in smaller groups of patients. The use of big data open access repositories, such as MetaboLights and a project to map the metabolome similar to the Human Genome Project, which has proved so useful in understanding genetic variation, will greatly enhance the interpretation of serum metabolomics studies.

Arguably as important as the actual observations are the calculations made by Dunn et al. using this large dataset to build a model capable of classifying additional data accurately. The authors concluded that in their particular study, comparing all ‘healthy’ individuals, 600 samples would have been enough to accurately build a predictive model from the data; however, in studies comparing a healthy group with a control group where variability would likely be increased the ideal sample number would be higher.

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CLINICAL TESTING

Although there are many potential challenges to consider when analyzing data, the relatively noninvasive nature of serum metabolomics has revolutionized clinical studies, allowing researchers to study biochemical processes in humans like never before. The ability to take multiple samples over a short space of time has led to informative studies on the temporal patterns in metabolism of drugs and foods in humans.

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Nutrition and obesity

One recent example is the work of Liu et al.[12] studying the metabolic signature associated with insulin resistance in the serum of healthy versus obese young men at several time points during an oral glucose tolerance test. Using targeted GCMS and UPLC MS they confirmed and expanded upon previous studies showing that there are significant differences between fasting levels of free fatty acids in the serum of obese and nonobese men on similar diets, and that levels of branched chain amino acids (BCAA) and palmitic acid in serum after a meal correlate with insulin resistance. Although, it is worth considering that a study by Xie et al.[13▪] found that there are sex differences in the serum BCAA profiles of obese patients.

Wittenbecher et al.[14] used a targeted approach to screen over 2000 people as a part of a nested cohort study to investigate the link between red meat and type 2 diabetes. By adjusting for factors such as BMI, age, sex and lifestyle, the authors were able to eliminate several sources of bias and narrow down the relationship link to serum ferritin, low glycine and altered hepatic-derived lipid concentrations. Although the authors identified several areas for improvement within their study, which may have resulted in more molecules being flagged up, the comprehensive analysis and large study size make this a very robust contribution to the wider knowledge.

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Aging, cardiovascular disease and the menopause

In a study of over 10 000 females Auro et al.[15▪▪] reported that menopause status associated with amino acids increased glutamine, tyrosine and isoleucine, along with serum cholesterol and atherogenic lipoproteins and additionally in a subset of women with glycine and total, monounsaturated, and omega-7 fatty acid and omega-9 fatty acid. The authors concluded that the increase in these amino acids in addition to certain lipids was related to the menopause in women and might impact in numerous pathways associated with diabetes and cardiovascular diseases. Interestingly, the authors also noted age-related increases in omega-3 polyunsaturated fatty acids and citrate both of which are independent markers of human ageing and cellular senescence [5,6].

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Cancer

Several recent studies have used serum metabolomics as a first step towards developing more accurate noninvasive tests for cancer and although there has been limited consensus in the cancer signatures obtained some of this might subsequently be rectified by taking into account confounding factors such as age, BMI, sex and smoking history. However, the study by Kumar et al.[16▪] did confirm earlier reports that sarcosine when assessed by 1H NMR as well as three new metabolites could well help refine the notoriously unreliable prostate specific antigen test for prostate cancer. Also two studies (Zamani et al. 2014 [17], Zhu et al. 2014 [18]) identified alterations in bile acid metabolism as being indicative of colon cancer, and one study [18] suggested that this pathway may be able to distinguish malignancies from premalignancies. Elevated levels of alanine and glycine have now been reported in both prostate cancer [16▪] and colon cancer [18] and elevated alanine has also been reported previously in the saliva of oral premalignancies, suggesting that this amino acid could be a useful noninvasive marker of neoplasia or cancer. However, further work is needed to verify this as it was not identified as a biomarker of oral premalignancy in the recent larger study of Gupta et al.[19]. The recent results are summarized in Table 1.

Table 1

Table 1

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CONCLUSION

In conclusion, serum metabolomics has already begun to give us better insight into the complex effects of disease and treatment on whole biological systems. The biggest challenges are in study design, collecting relevant metadata on factors known to impact on metabolite profiles and analysis of the data which takes into account those known factors in order to derive meaningful information about the condition of interest. So far any consensus between the serum metabolomes of human conditions and the animal models designed to investigate them has been limited, although the publication of the Husermet project [11▪▪] should perhaps help standardize these studies in the future.

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Acknowledgements

The authors apologize in advance to the authors whose papers have been omitted due to space constraints.

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Financial support and sponsorship

This study is supported by Kenny Linton and the Blizard Institute ‘Molecular and Cellular Biology of Medicine’ Theme. Emma James is funded by a James Paget Award and the Institute of Dentistry, Queen Mary University of London.

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Conflicts of interest

There are no conflicts of interest.

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REFERENCES AND RECOMMENDED READING

Papers of particular interest, published within the annual period of review, have been highlighted as:

  • ▪ of special interest
  • ▪▪ of outstanding interest
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REFERENCES

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4▪. Wilson CH, Shalini S, Filipovska A, et al. Age-related proteostasis and metabolic alterations in Caspase-2-deficient mice. Cell Death Dis 2015; 6:e1597.

An interesting model of ageing that links some serum metabolites elevated in human ageing with specific mechanisms.

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This is the first attempt to characterize the variation in metabolites in healthy human serum, and will be a useful reference for the interpretation of existing and future studies.

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This study expands on work that previously identified BCAAs as a biomarker of obesity risk and finds the signature is gender specific.

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15▪▪. Auro K, Joensuu A, Fischer K, et al. A metabolic view on menopause and ageing. Nat Commun 2014; 5.

This article describes a very large study of over 26 000 people and provides important information on how the human metabolome changes with age in both sexes and following the menopause in females. Novel amino acid biomarkers of cardiovascular disease in postmenopausal women are also identified.

16▪. Kumar D, Gupta A, Mandhani A, Sankhwar SN. Metabolomics-derived prostate cancer biomarkers: fact or fiction? J Proteome Res 2015; 14:1455–1464.

Although this article examined a small sample size it used a clinically tractable technique to re-examine and expand the use of serum metabolomics in the reliable detection of prostate cancer, which is currently a major health problem worldwide.

17. Zamani Z, Arjmand M, Vahabi F, et al. A metabolic study on colon cancer using 1H nuclear magnetic resonance spectroscopy. Biochem Res Int 2014; 2014:7.
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20. Zang X, Jones CM, Long TQ, et al. Feasibility of detecting prostate cancer by ultraperformance liquid chromatography–mass spectrometry serum metabolomics. J Proteome Res 2014; 13:3444–3454.
21▪. Mondul AM, Moore SC, Weinstein SJ, et al. Metabolomic analysis of prostate cancer risk in a prospective cohort: the alpha-tocolpherol, beta-carotene cancer prevention (ATBC) study. Int J Cancer 2015; [Epub ahead of print].

    A highly powered study of prostate cancer that has identified numerous candidate metabolites associated with prostate cancer risk.

    22▪. Cross AJ, Moore SC, Boca S, et al. A prospective study of serum metabolites and colorectal cancer risk. Cancer 2014; 120:3049–3057.

    This very large study of colon cancer is suitably powered and although largely negative may provide an important resource once the relevant variables are understood.

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      Keywords:

      biomarkers; cancer; metabolomics; serum; study design

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