Metabolomics in the study of aging and caloric restriction

Neurotrophic effects of intermittent fasting, calorie restriction and exercise: a review and annotated bibliography

In the last decades, important progress has been achieved in the understanding of the neurotrophic effects of intermittent fasting (IF), calorie restriction (CR) and exercise. Improved neuroprotection, synaptic plasticity and adult neurogenesis (NSPAN) are essential examples of these neurotrophic effects. The importance in this respect of the metabolic switch from glucose to ketone bodies as cellular fuel has been highlighted. More recently, calorie restriction mimetics (CRMs; resveratrol and other polyphenols in particular) have been investigated thoroughly in relation to NSPAN. In the narrative review sections of this manuscript, recent findings on these essential functions are synthesized and the most important molecules involved are presented. The most researched signaling pathways (PI3K, Akt, mTOR, AMPK, GSK3β, ULK, MAPK, PGC-1α, NF-κB, sirtuins, Notch, Sonic hedgehog and Wnt) and processes (e.g., anti-inflammation, autophagy, apoptosis) that support or thwart neuroprotection, synaptic plasticity and neurogenesis are then briefly presented. This provides an accessible entry point to the literature. In the annotated bibliography section of this contribution, brief summaries are provided of about 30 literature reviews relating to the neurotrophic effects of interest in relation to IF, CR, CRMs and exercise. Most of the selected reviews address these essential functions from the perspective of healthier aging (sometimes discussing epigenetic factors) and the reduction of the risk for neurodegenerative diseases (Alzheimer's disease, Huntington's disease, Parkinson's disease) and depression or the improvement of cognitive function.

Reducing subspace models for large-scale covariance regression

We develop an envelope model for joint mean and covariance regression in the large p, small n setting. In contrast to existing envelope methods, which improve mean estimates by incorporating estimates of the covariance structure, we focus on identifying covariance heterogeneity by incorporating information about mean-level differences. We use a Monte Carlo EM algorithm to identify a low-dimensional subspace that explains differences in both means and covariances as a function of covariates, and then use MCMC to estimate the posterior uncertainty conditional on the inferred low-dimensional subspace. We demonstrate the utility of our model on a motivating application on the metabolomics of aging. We also provide R code that can be used to develop and test other generalizations of the response envelope model.

SHR/NCrl rats as a model of ADHD can be discriminated from controls based on their brain, blood, or urine metabolomes

Attention-Deficit Hyperactivity Disorder (ADHD) is one of the most common neurodevelopmental disorder characterized by inattention, impulsivity, and hyperactivity. The neurobiological mechanisms underlying ADHD are still poorly understood, and its diagnosis remains difficult due to its heterogeneity. Metabolomics is a recent strategy for the holistic exploration of metabolism and is well suited for investigating the pathophysiology of diseases and finding molecular biomarkers. A few clinical metabolomic studies have been performed on peripheral samples from ADHD patients but are limited by their access to the brain. Here, we investigated the brain, blood, and urine metabolomes of SHR/NCrl vs WKY/NHsd rats to better understand the neurobiology and to find potential peripheral biomarkers underlying the ADHD-like phenotype of this animal model. We showed that SHR/NCrl rats can be differentiated from controls based on their brain, blood, and urine metabolomes. In the brain, SHR/NCrl rats displayed modifications in metabolic pathways related to energy metabolism and oxidative stress further supporting their importance in the pathophysiology of ADHD bringing news arguments in favor of the Neuroenergetic theory of ADHD. Besides, the peripheral metabolome of SHR/NCrl rats also shared more than half of these differences further supporting the importance of looking at multiple matrices to characterize a pathophysiological condition of an individual. This also stresses out the importance of investigating the peripheral energy and oxidative stress metabolic pathways in the search of biomarkers of ADHD.

Rapamycin Treatment Ameliorates Age-Related Accumulation of Toxic Metabolic Intermediates in Brains of the Ts65Dn Mouse Model of Down Syndrome and Aging

Down syndrome (DS), caused by trisomy of chromosome 21, is the most common genetic cause of intellectual disability. Individuals with DS exhibit changes in neurochemistry and neuroanatomy that worsen with age, neurological delay in learning and memory, and predisposition to Alzheimer's disease. The Ts65Dn mouse is the best characterized model of DS and has many features reminiscent of DS, including developmental anomalies and age-related neurodegeneration. The mouse carries a partial triplication of mouse chromosome 16 containing roughly 100 genes syntenic to human chromosome 21 genes. We hypothesized that there would be differences in brain metabolites with trisomy and age, and that long-term treatment with rapamycin, mechanistic target of rapamycin (mTOR) inhibitor and immunosuppressant, would correct these differences. Using HPLC coupled with electrochemical detection, we identified differences in levels of metabolites involved in dopaminergic, serotonergic, and kynurenine pathways in trisomic mice that are exacerbated with age. These include homovanillic acid, norepinephrine, and kynurenine. In addition, we demonstrate that prolonged treatment with rapamycin reduces accumulation of toxic metabolites (such as 6-hydroxymelatonin and 3-hydroxykynurenine) in aged mice.

A window into extreme longevity; the circulating metabolomic signature of the naked mole-rat, a mammal that shows negligible senescence

Mouse-sized naked mole-rats (Heterocephalus glaber), unlike other mammals, do not conform to Gompertzian laws of age-related mortality; adults show no age-related change in mortality risk. Moreover, we observe negligible hallmarks of aging with well-maintained physiological and molecular functions, commonly altered with age in other species. We questioned whether naked mole-rats, living an order of magnitude longer than laboratory mice, exhibit different plasma metabolite profiles, which could then highlight novel mechanisms or targets involved in disease and longevity. Using a comprehensive, unbiased metabolomics screen, we observe striking inter-species differences in amino acid, peptide, and lipid metabolites. Low circulating levels of specific amino acids, particularly those linked to the methionine pathway, resemble those observed during the fasting period at late torpor in hibernating ground squirrels and those seen in longer-lived methionine-restricted rats. These data also concur with metabolome reports on long-lived mutant mice, including the Ames dwarf mice and calorically restricted mice, as well as fruit flies, and even show similarities to circulating metabolite differences observed in young human adults when compared to older humans. During evolution, some of these beneficial nutrient/stress response pathways may have been positively selected in the naked mole-rat. These observations suggest that interventions that modify the aging metabolomic profile to a more youthful one may enable people to lead healthier and longer lives.

Deciphering hallmark processes of aging from interaction networks

BACKGROUND

Aging is broadly considered to be a dynamic process that accumulates unfavourable structural and functional changes in a time dependent fashion, leading to a progressive loss of physiological integrity of an organism, which eventually leads to age-related diseases and finally to death.

SCOPE OF REVIEW

The majority of aging-related studies are based on reductionist approaches, focusing on single genes/proteins or on individual pathways without considering possible interactions between them. Over the last few decades, several such genes/proteins were independently analysed and linked to a role that is affecting the longevity of an organism. However, an isolated analysis on genes and proteins largely fails to explain the mechanistic insight of a complex phenotype due to the involvement and integration of multiple factors.

MAJOR CONCLUSIONS

Technological advance makes it possible to generate high-throughput temporal and spatial data that provide an opportunity to use computer-based methods. These techniques allow us to go beyond reductionist approaches to analyse large-scale networks that provide deeper understanding of the processes that drive aging.

GENERAL SIGNIFICANCE

In this review, we focus on systems biology approaches, based on network inference methods to understand the dynamics of hallmark processes leading to aging phenotypes. We also describe computational methods for the interpretation and identification of important molecular hubs involved in the mechanistic linkage between aging related processes. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

Metabolomics Reveals Dynamic Metabolic Changes Associated with Age in Early Childhood

Objectives

A detailed understanding of the metabolic processes governing rapid growth in early life is still lacking. The aim of this study was to investigate the age-related metabolic changes in healthy children throughout early childhood.

Methods

Healthy children from a birth cohort were enrolled in this study from birth through 4 years of age. Urinary metabolites were assessed at 6 months, and 1, 2, 3, and 4 yr of age by using ¹H-nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical analysis including principal components analysis (PCA) and partial least-squares discriminant analysis (PLS-DA). Metabolic pathway analysis was performed using the MetPA web tool.

Results

A total of 105 urine samples from 30 healthy children were collected and analyzed. Metabolites contributing to the discrimination between age groups were identified by using supervised PLS-DA (Q² = 0.60; R² = 0.66). A significantly higher urinary trimethylamine N-oxide (TMAO) and betaine level was found in children aged 6 months. Urinary glycine and glutamine levels declined significantly after 6 months of age and there was a concomitant compensatory increase in urinary creatine and creatinine. Metabolic pathway analysis using MetPA revealed similar nitrogen metabolism associated energy production across all ages assessed. Pathways associated with amino acid metabolism were significantly different between infants aged 6 months and 1 year, whereas pathways associated with carbohydrate metabolism were significantly different between children at ages 2 and 3 years.

Conclusions

Urine metabolomics ideally represents dynamic metabolic changes across age. Urinary metabolic profiles change significantly within the first year of life, which can potentially provide crucial information about infant nutrition and growth.

A systems-level "misunderstanding": the plasma metabolome in Huntington's disease

Objective

Huntington’s disease (HD) is a rare neurodegenerative disease caused by the expansion of an N-terminal repeat in the huntingtin protein. The protein is expressed in all cells in the body; hence, peripheral tissues, such as blood, may recapitulate processes in the brain. The plasma metabolome may provide a window into active processes that influence brain health and a unique opportunity to noninvasively identify processes that may contribute to neurodegeneration. Alterations in metabolic pathways in brain have been shown to profoundly impact HD. Therefore, identification and quantification of critical metabolomic perturbations could provide novel biomarkers for disease onset and disease progression.

Methods

We analyzed the plasma metabolomic profiles from 52 premanifest (PHD), 102 early symptomatic HD, and 140 healthy controls (NC) using liquid chromatography coupled with a highly sensitive electrochemical detection platform.

Results

Alterations in tryptophan, tyrosine, purine, and antioxidant pathways were identified, including many related to energetic and oxidative stress and derived from the gut microbiome. Multivariate statistical modeling demonstrated mutually distinct metabolomic profiles, suggesting that the processes that determine onset were likely distinct from those that determine progression. Gut microbiome-derived metabolites particularly differentiated the PHD metabolome, while the symptomatic HD metabolome was increasingly influenced by metabolites that may reflect mutant huntingtin toxicity and neurodegeneration.

Interpretation

Understanding the complex changes in the delicate balance of the metabolome and the gut microbiome in HD, and how they relate to disease onset, progression, and phenotypic variability in HD are critical questions for future research.

Hypothesis: the sound of the individual metabolic phenotype? Acoustic detection of NMR experiments

We present here an innovative hypothesis and report preliminary evidence that the sound of NMR signals could provide an alternative to the current representation of the individual metabolic fingerprint and supply equally significant information. The NMR spectra of the urine samples provided by four healthy donors were converted into audio signals that were analyzed in two audio experiments by listeners with both musical and non-musical training. The listeners were first asked to cluster the audio signals of two donors on the basis of perceived similarity and then to classify unknown samples after having listened to a set of reference signals. In the clustering experiment, the probability of obtaining the same results by pure chance was 7.04% and 0.05% for non-musicians and musicians, respectively. In the classification experiment, musicians scored 84% accuracy which compared favorably with the 100% accuracy attained by sophisticated pattern recognition methods. The results were further validated and confirmed by analyzing the NMR metabolic profiles belonging to two other different donors. These findings support our hypothesis that the uniqueness of the metabolic phenotype is preserved even when reproduced as audio signal and warrants further consideration and testing in larger study samples.

Human platelets as a platform to monitor metabolic biomarkers using stable isotopes and LC-MS

BACKGROUND

Intracellular metabolites such as CoA thioesters are modulated in a number of clinical settings. Their accurate measurement from surrogate tissues such as platelets may provide additional information to current serum and urinary biomarkers.

METHODS

Freshly isolated platelets from healthy volunteers were treated with rotenone, propionate or isotopically labeled metabolic tracers. Using a recently developed LC-MS-based methodology, absolute changes in short-chain acyl-CoA thioesters were monitored, as well as relative metabolic labeling using isotopomer distribution analysis.

RESULTS

Consistent with in vitro experiments, isolated platelets treated with rotenone showed decreased intracellular succinyl-CoA and increased β-hydroxybutyryl-CoA, while propionate treatment resulted in increased propionyl-CoA. In addition, isotopomers of the CoAs were readily detected in platelets treated with the [(13)C]- or [(13)C(15)N]-labeled metabolic precursors.

CONCLUSION

Here, we show that human platelets can provide a powerful ex vivo challenge platform with potential clinical diagnostic and biomarker discovery applications.

Chronic caloric restriction partially protects against age-related alteration in serum metabolome

Calorie restriction (CR) remains the most robust metabolic intervention to extend lifespan and improve healthspan in several species. Using global and targeted mass spectrometry-based metabolomics approaches, here we show that chronic CR prevents age-related changes in specific metabolic signatures. Global metabolomic analysis using ultra-performance liquid chromatography-tandem mass spectrometry detected more than 7,000 metabolites in sera from ad-libitum-fed young, aged, and aged C57BL/6 mice maintained on 40 % CR. Multivariate statistical analysis of mass spectrometry data revealed a clear separation among the young, aged, and aged-CR mice demonstrating the potential of this approach for producing reliable metabolic profiles that discriminate based on age and diet. We have identified 168 discriminating features with high statistical significance (p ≤ 0.001) and validated and quantified three of these metabolites using targeted metabolite analysis. Calorie restriction prevented the age-related alteration in specific metabolites, namely lysophosphatidylcholines (16:1 and 18:4), sphingomyelin (d18:1/12:0), tetracosahexaenoic acid, and 7α-dihydroxy-4-cholesten-3-one, in the serum. Pathway analysis revealed that CR impacted the age-related changes in metabolic byproducts of lipid metabolism, fatty acid metabolism, and bile acid biosynthesis. Our data suggest that metabolomics approach has the potential to elucidate the metabolic mechanism of CR's potential anti-aging effects in larger-scale investigations.

Structural characterization of plasma metabolites detected via LC-electrochemical coulometric array using LC-UV fractionation, MS, and NMR

Liquid chromatography (LC) separation combined with electrochemical coulometric array detection (EC) is a sensitive, reproducible, and robust technique that can detect hundreds of redox-active metabolites down to the level of femtograms on column, making it ideal for metabolomics profiling. EC detection cannot, however, structurally characterize unknown metabolites that comprise these profiles. Several aspects of LC-EC methods prevent a direct transfer to other structurally informative analytical methods, such as LC-MS and NMR. These include system limits of detection, buffer requirements, and detection mechanisms. To address these limitations, we developed a workflow based on the concentration of plasma, metabolite extraction, and offline LC-UV fractionation. Pooled human plasma was used to provide sufficient material necessary for multiple sample concentrations and platform analyses. Offline parallel LC-EC and LC-MS methods were established that correlated standard metabolites between the LC-EC profiling method and the mass spectrometer. Peak retention times (RT) from the LC-MS and LC-EC system were linearly related (r(2) = 0.99); thus, LC-MS RTs could be directly predicted from the LC-EC signals. Subsequent offline microcoil-NMR analysis of these collected fractions was used to confirm LC-MS characterizations by providing complementary, structural data. This work provides a validated workflow that is transferrable across multiple platforms and provides the unambiguous structural identifications necessary to move primary mathematically driven LC-EC biomarker discovery into biological and clinical utility.

Cerebrospinal fluid metabolome in mood disorders-remission state has a unique metabolic profile

Targeted metabolomics provides an approach to quantify metabolites involved in specific molecular pathways. We applied an electrochemistry-based, targeted metabolomics platform to define changes in tryptophan, tyrosine, purine and related pathways in the depressed and remitted phases of major depressive disorder (MDD). Biochemical profiles in the cerebrospinal fluid of unmedicated depressed (n = 14; dMDD) or remitted MDD subjects (n = 14; rMDD) were compared against those in healthy controls (n = 18; HC). The rMDD group showed differences in tryptophan and tyrosine metabolism relative to the other groups. The rMDD group also had higher methionine levels and larger methionine-to-glutathione ratios than the other groups, implicating methylation and oxidative stress pathways. The dMDD sample showed nonsignificant differences in the same direction in several of the metabolic branches assessed. The reductions in metabolites associated with tryptophan and tyrosine pathways in rMDD may relate to the vulnerability this population shows for developing depressive symptoms under tryptophan or catecholamine depletion.

Applications of mass spectrometry to metabolomics and metabonomics: detection of biomarkers of aging and of age-related diseases

Every 5 years or so new technologies, or new combinations of old ones, seemingly burst onto the science scene and are then sought after until they reach the point of becoming commonplace. Advances in mass spectrometry instrumentation, coupled with the establishment of standardized chemical fragmentation libraries, increased computing power, novel data-analysis algorithms, new scientific applications, and commercial prospects have made mass spectrometry-based metabolomics the latest sought-after technology. This methodology affords the ability to dynamically catalogue and quantify, in parallel, femtomole quantities of cellular metabolites. The study of aging, and the diseases that accompany it, has accelerated significantly in the last decade. Mutant genes that alter the rate of aging have been found that increase lifespan by up to 10-fold in some model organisms, and substantial progress has been made in understanding fundamental alterations that occur at both the mRNA and protein level in tissues of aging organisms. The application of metabolomics to aging research is still relatively new, but has already added significant insight into the aging process. In this review we summarize these findings. We have targeted our manuscript to two audiences: mass spectrometrists interested in applying their technical knowledge to unanswered questions in the aging field, and gerontologists interested in expanding their knowledge of both mass spectrometry and the most recent advances in aging-related metabolomics.

Blood-based protein biomarkers for diagnosis and classification of neurodegenerative diseases: current progress and clinical potential

Biomarker research is a rapidly advancing field in medicine. Recent advances in genomic, genetic, epigenetic, neuroscientific, proteomic, and metabolomic knowledge and technologies have opened the way to thriving research. In the most general sense, a biomarker refers to any useful characteristic that can be measured and used as an indicator of a normal biologic process, a pathogenic process, or a pharmacologic response to a therapeutic agent. Despite the extensive resources concentrated on this area, there are very few biomarkers currently available that qualify and are satisfactorily validated for mental disorders, and there is still a major lack of biomarkers for typifying neurodegenerative disorders such as Alzheimer's disease and Parkinson's disease. This article provides an overview of this field of research and focuses on recent advances in biomarker research in Alzheimer's disease and Parkinson's disease.

Pretreatment metabotype as a predictor of response to sertraline or placebo in depressed outpatients: a proof of concept

The purpose of this study was to determine whether the baseline metabolic profile (that is, metabotype) of a patient with major depressive disorder (MDD) would define how an individual will respond to treatment. Outpatients with MDD were randomly assigned to sertraline (up to 150 mg per day) (N=43) or placebo (N=46) in a double-blind 4-week trial. Baseline serum samples were profiled using the liquid chromatography electrochemical array; the output was digitized to create a 'digital map' of the entire measurable response for a particular sample. Response was defined as ≥50% reduction baseline to week 4 in the 17-item Hamilton Rating Scale for Depression total score. Models were built using the one-out method for cross-validation. Multivariate analyses showed that metabolic profiles partially separated responders and non-responders to sertraline or to placebo. For the sertraline models, the overall correct classification rate was 81% whereas it was 72% for the placebo models. Several pathways were implicated in separation of responders and non-responders on sertraline and on placebo including phenylalanine, tryptophan, purine and tocopherol. Dihydroxyphenylacetic acid, tocopherols and serotonin were common metabolites in separating responders and non-responders to both drug and placebo. Pretreatment metabotypes may predict which depressed patients will respond to acute treatment (4 weeks) with sertraline or placebo. Some pathways were informative for both treatments whereas other pathways were unique in predicting response to either sertraline or placebo. Metabolomics may inform the biochemical basis for the early efficacy of sertraline.

3-Hydroxykynurenine and clinical symptoms in first-episode neuroleptic-naive patients with schizophrenia

One branch of the tryptophan catabolic cascade is the kynurenine pathway, which produces neurotoxic [3-hydroxykynurenine (3-OHKY), quinolinic acid] and neuroinhibitory (kynurenic acid) compounds. Kynurenic acid acts as a competitive antagonist at the glycine site of N-methyl-d-asparate receptors at high concentrations and as a non-competitive antagonist on the α7-nicotinic acetylcholine receptor at low concentrations. Kynurenine compounds also influence cognitive functions known to be disrupted in schizophrenia. Alterations in tryptophan metabolism are therefore of potential significance for the pathophysiology of this disorder. In this paper, tryptophan metabolites were measured from plasma using high-pressure liquid chromatography coupled with electrochemical coulometric array detection, and relationships were tested between these metabolic signatures and clinical symptoms for 25 first-episode neuroleptic-naive schizophrenia patients. Blood samples were collected and clinical and neurological symptoms were rated at baseline and again at 4 wk following initiation of treatment. Level of 3-OHKY and total clinical symptom scores were correlated when patients were unmedicated and neuroleptic-naive, and this relationship differed significantly from the correlation observed for patients 4 wk after beginning treatment. Baseline psychosis symptoms were predicted only by neurological symptoms. Moreover, baseline 3-OHKY predicted clinical change at 4 wk, with the lowest concentrations of 3-OHKY being associated with the greatest improvement in symptoms. Taken together, our findings suggest a neurotoxic product of tryptophan metabolism, 3-OHKY, predicts severity of clinical symptoms during the early phase of illness and before exposure to antipsychotic drugs. Baseline level of 3-OHKY may also predict the degree of clinical improvement following brief treatment with antipsychotics.

Metabolomic changes in autopsy-confirmed Alzheimer's disease

BACKGROUND

Metabolomics, the global science of biochemistry, provides powerful tools to map perturbations in the metabolic network and enables simultaneous quantification of several metabolites to identify metabolic perturbances that might provide insights into disease.

METHODS

In this pilot study, we took a targeted electrochemistry-based metabolomics approach where liquid chromatography followed by coulometric array detection enables quantification of over 30 metabolites within key neurotransmitter pathways (dopamine and serotonin) and pathways involved in oxidative stress.

RESULTS

Using samples from postmortem ventricular cerebrospinal fluid (15 Alzheimer's disease [AD] and 15 nondemented subjects with autopsy-confirmed diagnoses) and by using regression models, correlations, Wilcoxon rank-sum tests, and t-tests we identified alterations in tyrosine, tryptophan, purine, and tocopherol pathways in patients with AD. Reductions in norepinephrine and its related metabolites were also seen, consistent with previously published data.

CONCLUSIONS

These data support further investigation of metabolomics in larger samples of clinical AD as well as in those with preclinical disease for use as biomarkers.

Web-enabled and improved software tools and data are needed to measure nutrient intakes and physical activity for personalized health research

Food intake, physical activity (PA), and genetic makeup each affect health and each factor influences the impact of the other 2 factors. Nutrigenomics describes interactions between genes and environment. Knowledge about the interplay between environment and genetics would be improved if experimental designs included measures of nutrient intake and PA. Lack of familiarity about how to analyze environmental variables and ease of access to tools and measurement instruments are 2 deterrents to these combined studies. This article describes the state of the art for measuring food intake and PA to encourage researchers to make their tools better known and more available to workers in other fields. Information presented was discussed during a workshop on this topic sponsored by the USDA, NIH, and FDA in the spring of 2009.

Opioid use affects antioxidant activity and purine metabolism: preliminary results

OBJECTIVE

More must be learned about metabolic and biochemical alterations that contribute to the development and expression of drug dependence. Experimental opioid administration influences mechanisms and indices of oxidative stress, such as antioxidant compounds and purine metabolism. We examined perturbations of neurotransmitter-related pathways in opioid dependence (OD).

METHODS

In this preliminary study, we used a targeted metabolomics platform to explore whether biochemical changes were associated with OD by comparing OD individuals (n = 14) and non-drug users (n = 10).

RESULTS

OD patients undergoing short-term methadone detoxification showed altered oxidation-reduction activity, as confirmed by higher plasma levels of alpha- and gamma-tocopherol and increased GSH/GSSG ratio. OD individuals had also altered purine metabolism, showing increased concentration of guanine and xanthosine, with decreased guanosine, hypoxanthine and hypoxanthine/xanthine and xanthine/xanthosine ratios. Other drug use in addition to opioids was associated with partly different biochemical changes.

CONCLUSIONS

This is a preliminary investigation using metabolomics and showing multiple peripheral alterations of metabolic pathways in OD. Further studies should explore the metabolic profile of conditions of opioid abuse, withdrawal and long-term abstinence in relation to agonist and antagonist treatment and investigate biochemical signatures of opioid substances and medications.

1H NMR metabolomics study of age profiling in children

Metabolic profiling of urine provides a fingerprint of personalized endogenous metabolite markers that correlate to a number of factors such as gender, disease, diet, toxicity, medication, and age. It is important to study these factors individually, if possible to unravel their unique contributions. In this study, age-related metabolic changes in children of age 12 years and below were analyzed by (1)H NMR spectroscopy of urine. The effect of age on the urinary metabolite profile was observed as a distinct age-dependent clustering even from the unsupervised principal component analysis. Further analysis, using partial least squares with orthogonal signal correction regression with respect to age, resulted in the identification of an age-related metabolic profile. Metabolites that correlated with age included creatinine, creatine, glycine, betaine/TMAO, citrate, succinate, and acetone. Although creatinine increased with age, all the other metabolites decreased. These results may be potentially useful in assessing the biological age (as opposed to chronological) of young humans as well as in providing a deeper understanding of the confounding factors in the application of metabolomics.

Utility of separation science in metabolomic studies

Metabolomics, or alternately metabonomics, an emerging field of biochemical research, is a complementary technique to genomics, transcriptomics, and proteomics. Direct quantitative measurements of metabolite expressions in urine, serum, plasma, and tissue are essential for the study of biological processes in normal and disease states. Since the number of metabolites in a biological sample is large separation science plays an important role in metabolomic research. This review describes the role of GC, HPLC, CE, and supercritical fluid chromatography in metabolomic research. The application of each technique is discussed and its advantages and limitations are discussed with selective illustrative examples.

Gas chromatography/time-of-flight mass spectrometry based metabonomic approach to differentiating hypertension- and age-related metabolic variation in spontaneously hypertensive rats

Metabonomics is a systematic approach to the study of in vivo metabolic profiles and therefore allows deep insight into and a better understanding of the pathogenesis of disease. To characterize the development of hypertension, a hypertensive animal model, the spontaneously hypertensive rat (SHR), and its normotensive control, the Wistar Kyoto (WKY) rat, were investigated and their blood plasma analyzed using the high-throughput metabolomic tool, gas chromatography/time-of-flight mass spectrometry (GC/TOFMS). A total of 187 peaks were quantitatively determined after deconvolution, and 78 of them were identified. Principal components analysis (PCA) and projection to latent structure partial least-squares discriminant analysis (PLS-DA) were used to process the GC/TOFMS data. The resulting mathematical models were further validated by cross-validation. Plasma compositional differences of many identified compounds showed hypertension-related variation between SHR and WKY rats, and age-related changes from 10 to 18 weeks for both the SHR and WKY rats. These compositional changes involved compounds such as hexadecanoic acid, linoleic acid, oleic acid, stearic acid, 3-hydroxybutyric acid, citric acid, threonic acid, tyrosine, tryptophan, threonine, phenylalanine, serine, ornithine, methionine, 3-hydroxyproline, creatinine, erythrose, myo-inositol, D-methylglucopyranoside, tocopherol, sitosterol, and nonesterified cholesterol. Significantly elevated free fatty acids (FFA) were observed in SHR relative to those in WKY rats, and their levels increased as the SHR aged from 10 to 18 weeks. The close correlation between FFA and hypertension suggests that FFA are potential biomarker candidates for hypertension and they may play an important role in the development of hypertension in SHR. It is also indicated that GC/TOFMS-based metabonomics is a powerful approach to identifying potential biomarkers and investigating the pathological processes of hypertension and the physiological developments of aging.

Ageing in a eusocial insect: molecular and physiological characteristics of life span plasticity in the honey bee

Commonly held views assume that ageing, or senescence, represents an inevitable, passive, and random decline in function that is strongly linked to chronological age. In recent years, genetic intervention of life span regulating pathways, for example, in Drosophila as well as case studies in non-classical animal models, have provided compelling evidence to challenge these views.Rather than comprehensively revisiting studies on the established genetic model systems of ageing, we here focus on an alternative model organism with a wild type (unselected genotype) characterized by a unique diversity in longevity - the honey bee.Honey bee (Apis mellifera) life span varies from a few weeks to more than 2 years. This plasticity is largely controlled by environmental factors. Thereby, although individuals are closely related genetically, distinct life histories can emerge as a function of social environmental change.Another remarkable feature of the honey bee is the occurrence of reverted behavioural ontogeny in the worker (female helper) caste. This behavioural peculiarity is associated with alterations in somatic maintenance functions that are indicative of reverted senescence. Thus, although intraspecific variation in organismal life span is not uncommon, the honey bee holds great promise for gaining insights into regulatory pathways that can shape the time-course of ageing by delaying, halting or even reversing processes of senescence. These aspects provide the setting of our review.We will highlight comparative findings from Drosophila melanogaster and Caenorhabditis elegans in particular, and focus on knowledge spanning from molecular- to behavioural-senescence to elucidate how the honey bee can contribute to novel insights into regulatory mechanisms that underlie plasticity and robustness or irreversibility in ageing.

Systematic screens for human disease genes, from yeast to human and back

Systematic screens for human disease genes have emerged in recent years, due to the wealth of information provided by genome sequences and large scale datasets. Here we review how integration of genomic data in yeast and human is helping to elucidate the genetic basis of mitochondrial diseases. The identification of nearly all yeast mitochondrial proteins and many of their functional interactions provides insight into the role of mitochondria in cellular processes. This information enables prioritization of the candidate genes underlying mitochondrial disorders. In an iterative fashion, the link between predicted human candidate genes and their disease phenotypes can be experimentally tested back in yeast.