Supplemental Material can be found at: http://jn.nutrition.org/content/suppl/2014/02/11/jn.113.18373 1.DCSupplemental.html

The Journal of Nutrition Genomics, Proteomics, and Metabolomics

The Undernourished Neonatal Mouse Metabolome Reveals Evidence of Liver and Biliary Dysfunction, Inflammation, and Oxidative Stress1–3 Geoffrey A. Preidis,4* Mignon A. Keaton,5 Philippe M. Campeau,6 Brooke C. Bessard,4 Margaret E. Conner,7,8 and Peter J. Hotez7,9,10 4 Section of Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, Baylor College of Medicine and Texas ChildrenÕs Hospital, Houston, TX; 5Metabolon, Inc., Durham, NC; Departments of 6Molecular and Human Genetics, 7Molecular Virology and Microbiology, and 8Pathology and Immunology, Baylor College of Medicine, Houston, TX; 9National School of Tropical Medicine and Section of Pediatric Tropical Medicine, Department of Pediatrics, Baylor College of Medicine and Texas ChildrenÕs Hospital, Houston, TX; and 10Sabin Vaccine Institute, Texas ChildrenÕs Hospital Center for Vaccine Development, Houston, TX

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Abstract Undernutrition contributes to half of all childhood deaths under the age of 5 y, and confers upon survivors a life-long predisposition to obesity, type 2 diabetes, and cardiovascular disease. Mechanisms underlying the link between early nutrient deprivation and noncommunicable diseases are unknown. Using outbred CD1 neonatal mice, we measured metabolic profile differences between conventionally reared mice given unrestricted access to nursing, the control group, and undernourished mice subjected to protein-calorie deprivation through timed separation from lactating mothers. After 11 d of undernutrition, urine, plasma, liver, ileal fluid, cecal fluid, and stool were harvested from 8 pools of 4 neonatal mice per group. The metabolome was identified using a multiplatform mass spectrometry-based approach, and random forest metrics were used to identify the most important metabolites that distinguished the undernourished from the control group. Our data reveal striking metabolic changes in undernourished mice consistent with the known mammalian response to starvation, including evidence of muscle and fat catabolism and increased reliance on the tricarboxylic acid cycle for energy. However, we also revealed evidence of liver and biliary injury, anomalies in bile acid metabolism, oxidative stress and inflammation, accelerated heme breakdown, and altered regulation of DNA methylation. Among the metabolites that most strongly distinguished the 2 groups were 2-hydroxyisobutyrate, increased 3-fold in plasma of undernourished mice (P = 2.19 3 10211); urobilinogen, increased 11-fold in urine of undernourished mice (P = 4.22 3 1027); deoxycholate, decreased 94% in stool of undernourished mice (P = 3.0 3 1024); and 12 different products of the enzyme g-glutamyltransferase, increased in all 6 compartments of undernourished mice. This model of the undernourished neonatal metabolome illustrates the wide range of pathways disrupted by undernutrition in early development, and suggests mechanistic links between early starvation and persistent metabolic diseases. J. Nutr. 144: 273–281, 2014.

Introduction Undernutrition is among the most pressing issues in global health today. The WHO estimates that 101 million children under the age of 5 y (16%) are underweight, and that the linear

1 Supported by institutional start-up funds (to G.A.P.) by Baylor College of Medicine and Texas ChildrenÕs Hospital, and in part by Public Health Service Grants National Institutes of Health AI24998. 2 Author disclosures: M. A. Keaton was an employee of Metabolon, Inc., where the metabolomics analyses were performed. G. A. Preidis, P. M. Campeau, B. C. Bessard, M. E. Conner, P. J. Hotez, no conflicts of interest. 3 Supplemental Figures 1 and 2 and Supplemental Tables 1–6 are available from the "Online Supporting Material" link in the online posting of the article and from the same link in the online table of contents at http://jn.nutrition.org. * To whom correspondence should be addressed. E-mail: [email protected]. edu.

growth of 165 million children (26%) is stunted by undernutrition (1). In most cases, undernutrition is due to a combination of food insecurity and the consequences of limited access to clean water and sanitation, namely, repeated or chronic intestinal infections and polyparasitism. Undernutrition amplifies the global burden of enteric and diarrheal diseases and is implicated in >50% of childhood deaths under the age of 5. The long-term effects of even brief periods of undernutrition in early childhood include stunted growth, decreased response to live oral vaccines, intellectual disability, and increased susceptibility to noncommunicable diseases including obesity, diabetes, and cardiovascular disease later in life (2). How undernutrition mediates the majority of these devastating effects remains unclear. If biomarkers of poor nutritional status could be identified from easily

ã 2014 American Society for Nutrition. Manuscript received August 7, 2013. Initial review completed August 29, 2013. Revision accepted December 9, 2013. First published online December 31, 2013; doi:10.3945/jn.113.183731.

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the total amount of sample per neonatal mouse was minimal, material from 4 pups (2 males and 2 females from a single litter) was pooled to create each data point; the fifth undernourished mouse in each litter was not used. Similarly, each control litter was divided into 2 groups of 5 pups; 4 of the 5 pups were pooled to create each data point. Thus, a total of 32 undernourished pups from 8 litters and 32 control pups from 4 litters were used to obtain the 8 pooled samples per group. Whole blood was mixed with an equal volume of 9 mmol/L ethylenediamine tetracetic acid in phosphate-buffered saline for anticoagulation, and centrifuged at 1000 x g for 15 min, after which the plasma layer was removed. All samples were immediately sealed in cryo-tubes, flash-frozen in liquid nitrogen, and stored at 280°C until analysis. All protocols were approved by the Baylor College of Medicine Institutional Animal Care and Use Committee. Metabolomic profiling. At the time of analysis, samples were extracted and prepared as previously described (10,11). Briefly, extracted samples were divided into aliquots for analysis on GC/MS and liquid chromatography/MS/MS platforms. Using a fully automated system, raw spectrometry data were extracted, quality control measures were imposed, and peaks were identified by comparison to library entries of 2500 purified standards (12). Equivalent mass volumes were analyzed for all samples. Additionally, normalization of urine metabolite concentrations was performed using sample osmolality measured with an osmometer. Complete lists of all metabolites that differed between the 2 groups (P < 0.10) in each body compartment are presented in Supplemental Tables 1–6. A subset of biomarkers, the plasma amino acids, was quantitatively validated by ion exchange HPLC on the Biochrom 30+ Physiological High-Performance System (Biochrom US), according to the manufacturerÕs protocol. Statistical analysis. Raw global biochemical profiling data were found to be log-normal. WelchÕs 2-sample t tests were performed on logtransformed, median-scaled, minimum-value imputed data using Array Studio (OmicSoft Corporation) to identify biochemicals that differed significantly between experimental groups. Q values were computed to estimate the false-positive rate attributable to multiple comparisons (13). Heat maps were generated based on fold-change of the median scale, minimum-imputed means, and P value.

Methods Animal model. Five-d-old male and female outbred CD1 mice (Charles River Laboratories) were pooled and randomly reassigned, 10 pups per lactating dam, to 1 of 2 groups. Undernutrition was induced by separating from dams the same 5 randomly selected pups per litter for 4 h on day 5 of life, 8 h on day 6 of life, and 12 h on days 7–15 of life. Meanwhile, pups randomly assigned to the control group were maintained in standard litters of 10 without separation. All mice were housed in a pathogen-free facility, with irradiated rodent unpurified diet and drinking water, in a temperature-controlled 12-h light-dark cycle. Fasting occurred during the ‘‘light’’ cycle, when mice are typically sleeping. During their 12 h per day with dams, undernourished mice nursed ad libitum with their nonundernourished littermates; energy intake was neither controlled nor quantified. Undernourished mice were observed to sleep nearly the entire time they were separated. All mice in both groups were weighed, and therefore handled, daily. CD1 mice are typically weaned at 3 wk and routinely live >2 y in the laboratory environment. With respect to body weight, gender differences first become apparent around week 4 of life; thus, size differences were not yet detected at the conclusion of this experiment. Sample collection. At 16 d of age, 11 d after commencing calorie deprivation, 6 samples per mouse were harvested immediately following the 12-h overnight feed, to reduce the likelihood that findings would be due to acutely fed versus acutely food-deprived state differences. Before sample harvest, mice were weighed and measured from nose to rump. Urine was collected by suprapubic massage. Mice were exsanguinated and whole liver was removed, along with complete luminal contents of the ileum, cecum, and colon; the latter served as a proxy for stool because stool is not reproducibly expressed from 2-wk-old mice. Because 274

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Results Undernutrition model and global biochemical profiling analysis. After 11 d of calorie restriction, undernourished pups averaged 28% reduced body weight (P < 0.001) and 9% reduced body length (P < 0.001) compared with control pups (Fig. 1). Nontargeted analysis of samples extracted from 6 body compartments and analyzed on 3 parallel MS platforms

FIGURE 1 Body weight (A) and nose-to-rump length (B) of control and undernourished mice at 16 d of age. The tops and bottoms of the boxes represent the interquartile range with the solid line representing the median, and the bars (whiskers) representing the range of the data points, n = 8 (pools of 4 littermate pups). *Different from control, P , 0.001. C, control pups; U, pups separated from their dams for 4 h at 5-d-old, 8 h at 6-d-old, and 12 h at 7- to 15-d-old.

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accessible body fluids, they could help elucidate mechanisms of comorbidities, identify children at risk before they become overtly underweight and stunted, and assess efficacy of therapeutic interventions designed to prevent noncommunicable diseases. Metabolomics is the nontargeted, systematic analysis of changes in the complete set of low-molecular-weight metabolites produced by cells (both host and microbial) in response to environmental or cellular stimuli (3). Metabolites derived from the diet, from intestinal bacteria, and from the host are absorbed from the intestine into the circulatory system and can be measured in multiple body compartments (4). The ideal biomarker for host nutritional status would be abundant, easily collected, easily measured, and accurately reflective of host health or disease (5). Multiple laboratory models of undernutrition exist. Among them, the neonatal mouse model of protein-energy undernutrition produced by timed separation of neonates from lactating dams is highly reproducible and has shown that calorie deprivation worsens Cryptosporidium parvum and enteroaggregative Escherichia coli infections, which in turn perpetuate further weight loss (6–8). This model has also shown that undernutrition blunts antipathogenic effects of beneficial microbes or probiotics, including enhancement of antibodies, enterocyte proliferation, and villus repopulation, in rotavirusinfected mice (9). We sought to define the complete set of metabolic changes induced by protein-energy undernutrition in neonatal mice. We hypothesized that striking biochemical abnormalities would be detectable in multiple body compartments after less than 2 wk of calorie deprivation. Our goals were to better understand the global effects of early undernutrition, to identify easily accessible biomarkers of the undernourished host, and to propose new mechanisms linking early undernutrition to long-term comorbidities.

identified an average of 328 distinct biochemicals per compartment, ranging from 235 metabolites in urine to 414 in stool. In all, 695 metabolite pairs were significantly different between the experimental groups (P < 0.05), with an additional 196 pairs approaching significance (0.05 < P # 0.10). Complete lists of significantly altered metabolites are presented in Supplemental Tables 1–6.

Global heat map of metabolites from 6 body compartments. To gain insight into which biological processes are most

The undernourished neonatal mouse metabolome

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Random forest analysis of biomarkers in urine, plasma, and stool. We sought to identify potential biomarkers of early undernutrition derived from easily accessible biofluids. Thus, random forest models were built from all metabolites derived from urine, plasma, or stool, and these models were assessed for their ability to classify individual samples as belonging to either undernourished or control mice. The resulting models correctly classified the 16 urine and 16 plasma samples with 100% accuracy, and the 16 stool samples with 87.5% accuracy. Mean decrease accuracy metrics were computed for each biochemical, and the 30 most important metabolites for distinguishing urine, plasma, and stool as derived from either undernourished or control mice were identified (Table 1). Intermediates of amino acid or lipid catabolism represented 22, 21, and 16 of the 30 most important urine, plasma, and stool metabolites, respectively, consistent with an increased catabolic state in undernourished mice. Specifically, carnitine and its conjugates, which facilitate entry of lipid breakdown products into mitochondria for energy production, accounted for 6 of the top 9 (and 10 of the top 30) urine-derived metabolites, all of which were significantly elevated in undernourished mice compared with control. The lists of most predictive metabolites also contained 9 g-glutamyl–conjugated peptides [products of g-glutamyltransferase (GGT)]; these peptides were increased in the urine and plasma and decreased in the stool of undernourished mice compared with control. These lists also contained 3 biochemicals involved in one-carbon metabolism, the primary mechanism underlying DNA methylation; namely, betaine, dimethylglycine, and sarcosine were increased in the plasma of undernourished mice, whereas the latter was also decreased in the stool of undernourished mice compared with control. Quantitative HPLC analysis independently confirmed that each amino acid appearing on the top-30 list of most distinguishing plasma metabolites, namely, alanine, histidine, proline, methionine, and tyrosine, was significantly elevated in undernourished mice (Supplemental Fig. 1). The metabolite with the greatest fold-change was deoxycholate; stool from undernourished mice contained 94% less of this secondary bile acid compared with stool from control mice. The plasma biochemical that most strongly distinguished undernourished from control mice was 2-hydroxyisobutyrate, which was increased 3-fold in undernourished mice and also appeared on the top-30 list of most distinguishing urine-derived metabolites. Finally, the heme breakdown product urobilinogen was increased 11-fold in urine of undernourished mice, and was also among the most distinguishing plasma metabolites (increased 4.5-fold in undernourished mice). These results indicate that urine-, plasma-, and feces-derived metabolomic profiles readily distinguish undernourished mice from control mice in terms of protein and fat breakdown products, but that multiple individual metabolites, particularly those related to liver or biliary function, bilirubin breakdown, and one-carbon metabolism, also strongly separate the 2 groups.

affected by undernutrition, a heat map of the global biochemical profiling data set was sorted by metabolite classification and biological pathway. This map illustrates both the direction and significance of change of metabolites among the various pairwise comparisons across the 6 body compartments (Supplemental Fig. 2A). In general, amino acids and carbohydrates were increased in the urine, plasma, and liver of undernourished mice, but decreased in luminal contents of the ileum, cecum, and colon. Undernourished mice had elevated concentrations of multiple monoacylglyercols and free FAs in the liver, where products of lipolysis are oxidized into ketone bodies to fuel the brain, and in the ileum, where the majority of intestinal lipid absorption occurs. Conversely, undernourished mice had fewer lipids in the cecum and colon compared with control. Five individual pathways that especially distinguished the undernourished group from the control are highlighted below. Carnitine-conjugated products of branched-chain ketoacid dehydrogenase, namely isobutyrylcarnitine, 2-methylbutyrylcarnitine, and isovalerylcarnitine, were significantly increased in the urine, plasma, and/or liver of undernourished mice, indicating increased BCAA catabolism (Supplemental Fig. 2B). Similarly, metabolites of the urea cycle, which helps eliminate excess nitrogenous waste from protein breakdown, were increased in the urine and liver from undernourished mice compared with control (Supplemental Fig. 2C). Elevated urinary concentrations of other protein-derived metabolites, including c-glycosyltryptophan, N6trimethyllysine, and trans-4-hydroxyproline, as well as numerous dipeptides (Supplemental Table 1), provide further evidence of increased protein breakdown in undernourished mice. Products of GGT activity were the only peptides consistently detected outside of the intestine and were increased in plasma, liver, and cecum, but decreased in the stool of undernourished mice (Supplemental Fig. 2D). Metabolites related to the carnitine biosynthetic pathway were elevated in all compartments except stool in undernourished mice (Supplemental Fig. 2E). Finally, concentrations of multiple bile acids were profoundly altered by undernutrition. In the liver, for example, undernourished mice had 83% and 82% decreased concentrations of the primary bile acids cholate and b-muricholate, respectively, and 90% decreased concentrations of the secondary bile acid taurodeoxycholate compared with control. All bile acids were found in decreased concentrations throughout the body in undernourished mice, with 2 exceptions. Both tauromuricholate (plasma) and taurolithocholate 3-sulfate (ileum, cecum, and colon) were found in increased concentrations in undernourished mice versus control (Supplemental Fig. 2F). No single metabolite was significantly altered by undernutrition in all 6 body compartments, although several metabolite changes were detectable in 5 out of 6 compartments. Amino acids histidine and proline were increased in the urine, plasma, and liver, and decreased in the ileal fluid and stool of undernourished mice, whereas urobilinogen was significantly increased in all compartments of undernourished mice except stool, where it was undetectable. Similarly, 2-hydroxyisobutyrate was increased in all compartments except stool, and the structurally related metabolite a-hydroxybutyrate was also increased in the plasma, liver, ileum, cecum, and stool, falling just short of significance in the latter compartment. Other metabolites significantly altered in 4 out of 6 compartments included arginine, b-muricholate, carnitine, citrate, deoxycarnitine, deoxycholate, galactose, g-glutamylphenylalanine, g-glutamyltyrosine, glycylproline, N-acetylneuraminate, pantothenate, phenol sulfate, phenyllactate, sarcosine, tyrosinevaline, xanthine, and xylose (Supplemental Tables 1–6).

TABLE 1 Top 30 metabolites derived from urine, plasma, and stool that distinguish undernourished from control mice at 16 d of age, relative to their predictive models1 Urine Rank

Hydroxybutyrylcarnitine Phenyllactate Imidazole lactate 3-dehydrocarnitine Hexanoylcarnitine Butyrylcarnitine 4-guanidinobutanoate Propionylcarnitine Acetylcarnitine Tryptophan Indolelactate Carnitine Hydroxyisovaleroylcarnitine 3-(4-hydroxyphenyl)lactate Tyrosine Glutarylcarnitine Glutamine Urobininogen Homovanillate sulfate Imidazole propionate 1-methylimidazoleacetate Isovalerylcarnitine 4-acetamidobutanoate Pantothenate g-glutamyllysine Nicotinamide 1-(3-aminopropyl)-2-pyrrolidone N-acetylneuraminate Cinnamoylglycine 2-hydroxyisobutyrate

FC 3.18 5.39 2.09 2.26 2.41 2.50 2.09 2.62 2.42 1.36 1.81 3.79 1.57 1.80 1.55 1.48 2.13 11.0 1.57 1.76 2.02 2.76 1.51 1.91 1.39 0.41 0.59 1.78 0.23 1.90

P value 25

8.29 3 10 1.68 3 1025 1.66 3 1027 8.00 3 1025 3.02 3 1026 4.21 3 1025 1.72 3 1026 2.56 3 1025 0.0001 1.17 3 1025 3.33 3 1025 1.56 3 1025 0.0026 1.79 3 1027 2.26 3 1025 2.81 3 1025 0.0001 4.22 3 1027 1.34 3 1025 3.36 3 1025 0.0007 0.0002 3.11 3 1025 9.35 3 1026 0.0002 0.0006 0.0018 0.0005 5.46 3 1025 0.0004

Metabolite

FC

2-hydroxyisobutyrate Phenyllactate Dimethylglycine a-hydroxybutyrate g-glutamylphenylalanine Corticosterone g-glutamyltyrosine g-glutamylalanine a-hydroxyisovalerate 3-methylglutarylcarnitine 4-guanidinobutanoate a-hydroxyisocaproate Betaine Proline g-glutamylvaline Urobilinogen Indolelactate Tyrosine Histidine Alanine Sarcosine Glutarylcarnitine N4-acetylcytidine Pyroglutamine N-acetylserine g-glutamyltryptophan Methionine Deoxycholate Homoserine Galactose

3.16 3.19 1.70 1.64 1.43 0.61 1.61 2.13 2.39 2.12 1.88 2.01 1.65 1.50 1.28 4.51 1.45 1.42 1.21 1.56 1.82 1.57 1.22 1.84 1.39 1.44 1.28 0.36 2.01 3.90

Stool P value 211

2.19 3 10 0.0005 5.56 3 1026 2.76 3 1025 0.0007 0.0008 7.00 3 1025 0.0005 1.75 3 1027 4.41 3 1025 2.23 3 1025 6.89 3 1025 4.08 3 1025 0.0001 0.0032 0.0004 0.0004 0.0002 0.0011 0.0007 0.0053 0.0021 0.0034 0.0033 0.0009 0.0031 0.0008 0.0023 0.0009 0.0003

Metabolite

FC

P value

Tyrosine Val-val-val Deoxycholate Sorbitol Leu-leu-leu g-glutamylmethionine Campestanol Deoxycarnitine 2-hydroxy-3-methylvalerate a-hydroxyisocaproate Undecanedioate Chenodeoxycholate Leucine g-glutamylisoleucine a-muricholate Methionine Dihydroferulic acid 3-(4-hydroxyphenyl)propionate Phenylalanine g-glutamylleucine Phenylpyruvate Glucose Proline Glycylproline Pyridoxal Iduronic acid Sarcosine 4-methyl-2-oxopentanoate 3-hydroxypyridine Glycylglycine

0.62 0.52 0.060 0.59 0.24 0.63 0.45 0.72 0.41 0.36 2.51 0.20 0.60 0.46 0.22 0.61 0.26 0.34 0.68 0.58 0.48 0.56 0.76 0.61 0.66 0.39 0.54 0.47 0.56 0.63

2.40 3 1023 0.0011 0.0003 1.02 3 1025 2.70 3 1026 0.0045 0.0004 0.0002 0.0006 0.0014 6.05 3 1025 3.71 3 1025 0.0074 0.0004 0.0027 0.0098 0.0007 0.0094 0.0123 0.0019 0.0051 0.0074 0.0258 0.0009 0.0018 0.0030 0.0146 0.0141 0.0003 0.0170

1 False-discovery rates for lists of urine, plasma, and stool metabolites, based on Q-value calculations, are 0.06%, 0.045%, and 4.1%, respectively. FC, fold-change for concentration of metabolites derived from undernourished mice compared with control.

In no single compartment were the metabolic changes more striking than in the liver. In addition to the differences in GGT products, bile acids, and metabolites mediating one-carbon metabolism, liver of undernourished mice contained increased concentrations of the majority of biochemicals that feed into or serve as intermediates in the tricarboxylic acid (TCA) cycle (Fig. 2). In all, the global heat map data indicate that undernutrition induces widespread metabolic changes affecting multiple organs and multiple metabolic pathways. Markers of glutathione metabolism, oxidative stress, and inflammation. In addition to the g-glutamyl–conjugated peptides, which are involved in scavenging extracellular glutathione, transporting amino acids, and synthesizing leukotriene, undernourished mice had other markers of increased metabolism of glutathione (Table 2), a key component of redox homeostasis whose metabolism responds to increased oxidative stress. For example, relative increases in glutathione and ophthalmate, which are both products of glutathione synthase, were identified in undernourished mice. Likewise, increased concentrations of 2-aminobutyrate, cystathionine, and a-hydroxybutyrate in undernourished mice suggested increased synthesis of cysteine, a rate-limiting component for glutathione synthesis. Finally, elevations of glutathione turnover products cysteinyl-glycine and 5-oxoproline provided further evidence that undernutrition alters 276

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glutathione metabolism. Together with the striking changes in GGT activity, these data indicate that calorie deprivation induces a robust response to oxidative stress in undernourished mice. Finally, elevations in metabolites reflecting inflammation or that impact the inflammatory process were observed in undernourished mice (Table 2). Among the top 30 most distinguishing urinary metabolites was the histamine catabolite 1-methylimidazoleacetate, which was also significantly elevated in the plasma of undernourished mice. Likewise, kynurenate, the breakdown product of tryptophan metabolite kynurenine, whose production is increased in response to inflammatory cytokines, was significantly increased in the urine, cecum, and stool of undernourished mice. Similarly, the nucleotide metabolite adenosine, another anti-inflammatory signaling molecule released during tissue injury, was significantly increased in the plasma of undernourished mice, whereas corticosterone, the dominant glucocorticoid in rodents, was significantly reduced in the plasma of undernourished mice. Corticosterone represents one of the most strongly differentiating plasma biochemicals. Consistent with production of reactive oxygen species during an inflammatory response, increased concentrations of several metabolites reflecting oxidation were also significantly increased in undernourished mice. These metabolites included the lipid peroxidation products 13-hydroxyoctadecadienoic acid/9-hydroxyoctadecadienoic acid in the liver, the N-acetylglucosamine oxidation product erythronate in plasma, and the

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Metabolite

Plasma

cysteine oxidation product cystine in urine. Taken together, these metabolomic data suggest a strong link between undernutrition, oxidative stress, and inflammation.

Discussion This comprehensive metabolomic study in a neonatal mouse model of undernutrition supports our hypothesis that proteincalorie deprivation produces striking changes in the hostÕs metabolic fingerprint, detectable in multiple body compartments. Metabolites derived from easily accessible biofluids accurately predicted an undernourished versus normal-weight host, and multiple distinct metabolic pathways were affected by undernutrition. Many of the metabolic changes were consistent with the known mammalian metabolic response to starvation, but we also found evidence of liver and biliary injury, oxidative stress and inflammation, heme breakdown, and altered regulation of DNA methylation after just a brief, early period of starvation.

One crucial difference between this laboratory model and most underweight and stunted children relates to availability of nutrients from the diet. In most undernourished children, absorptive capacity is compromised by enteric infections, inflammation, blunted intestinal villi, and loss of absorptive surface area—a constellation of pathologies known as environmental enteropathy, or tropical sprue (14). In contrast, our model reveals increased concentrations of macronutrients in the plasma and liver and decreased concentrations in the stool of undernourished mice, implying increased efficiency of nutrient extraction from the diet. Thus, our model system is a simplified system in which to explore the effects of undernutrition in isolation, separated from the intestinal infection and mucosal impairment that often precedes and/or coincides with growth failure. This distinction is important given the current collection of biomarkers proposed to detect an impoverished gut in developing settings. These biomarkers, which include urinary lactulose and mannitol (15–17), fecal lactoferrin (18,19), and serum antiendotoxin core antibody (20), are limited in the range of The undernourished neonatal mouse metabolome

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FIGURE 2 Liver concentrations of metabolites feeding into the TCA cycle of control and undernourished mice at 16 d of age. The top and bottom of the boxes represent the interquartile range with the solid line representing the median, the plus sign representing the mean, and the bars (whiskers) representing the range of the data points, excluding extreme points, which are labeled with open circles (these points were included in all statistical analyses). The y axes depict the relative scaled intensity; n = 8 (pools of 4 littermate pups). *Different from control, P , 0.05; a carnitine-conjugated forms of acetyl-coA, succinyl-coA, and propionyl-coA were detected. C, control pups; TCA, tricarboxylic acid; U, pups separated from their dams for 4 h at 5-d-old, 8 h at 6-d-old, and 12 h at 7- to 15-dold.

TABLE 2 Significantly altered metabolites related to glutathione metabolism, inflammation, and oxidative stress in undernourished compared with control mice at 16 d of age1 Super pathway Amino acids

Sub pathway

Compartment

FC

P value

Q value

Histidine metabolism

1-methylimidazoleacetate

Tryptophan metabolism

Kynurenate

Cysteine, methionine, SAM, taurine metabolism

Cystathionine Cystine 2-hydroxybutyrate (AHB)

Butanoate metabolism

2-aminobutyrate

Glutathione metabolism

Glutathione Ophthalmate

Urine Plasma Urine Cecum Stool Liver Urine Plasma Liver Ileum Cecum Stool Plasma Liver Liver Plasma Liver Urine Cecum Liver Liver Urine Plasma Liver Cecum Plasma Plasma

2.02 1.32 1.33 1.80 3.22 1.52 1.43 1.64 1.92 1.29 1.72 1.26 1.72 1.53 1.66 1.55 1.94 1.51 1.33 1.58 1.57 1.14 1.28 1.72 0.48 0.61 1.34

0.0007 0.0151 0.0156 0.0223 0.0016 0.004 0.0275 2.76 3 1025 4.05 3 1026 0.0069 0.0014 0.0842 0.0085 0.0027 0.0116 0.0188 0.0034 0.0013 0.0662 0.0023 0.026 0.0795 0.0036 0.017 0.0155 0.0008 0.0137

0.0009 0.0457 0.0084 0.0182 0.0146 0.0106 0.0128 0.001 0.0002 0.0626 0.0049 0.0897 0.0322 0.0477 0.0203 0.0541 0.0098 0.0013 0.0359 0.0477 0.0358 0.0294 0.0812 0.026 0.0151 0.0075 0.0436

5-oxoproline

Peptides Carbohydrates

Dipeptide Aminosugar metabolism

Oxidized CYS-GLY CYS-GLY Erythronate

Lipids

Fatty acid, monohydroxy

13-HODE/9-HODE

Nucleotides

Sterol/steroid Purine metabolism, adenine containing

Corticosterone Adenosine

This list does not include the 12 g-glutamyl–conjugated peptides, which are found in significantly different concentrations in all 6 body compartments (see Supplemental Fig.1). CYS-GLY, cysteinyl-glycine; FC, fold-change for concentration of metabolites derived from undernourished mice compared with control; HODE, hydroxyoctadecadienoic acid; SAM, S-adenosylmethionine.

1

conditions they can detect, do not always correlate with longterm outcomes, and are of limited predictive value in both acute, transient intestinal infections and in the one-third of cases of childhood undernutrition that are thought to result primarily from food insecurity instead of enteropathy. Furthermore, these biomarkers have yet to shed light on potential mechanisms linking early undernutrition to noncommunicable diseases. Our data reveal multiple potential biomarkers derived from urine, plasma, and stool that not only accurately reflect the nutritional status of the host but also suggest multiple metabolic derangements in undernourished mice. Thus, strategies to avert long-term morbidities in undernourished hosts should not only target the intestine in isolation but also the resulting metabolic disturbances. Autophagy as a response to starvation. The majority of the metabolic pathways and individual metabolites most significantly increased in the plasma, liver, and urine of undernourished mice reflect increased catabolism of proteins and fats from body stores for energy production and/or gluconeogenesis. For example, catabolism of the BCAAs valine, isoleucine, and leucine involves a number of CoA intermediates, nearly all of which were elevated in undernourished mice, and ultimately produces acetyl-CoA and succinyl-CoA that can enter the TCA cycle. The carnitine-conjugated forms of these intermediates were markedly increased in our model. Likewise, elevated concentrations of monoacylglyercols and free FAs in undernourished mice are consistent with adipose tissue wasting, whereas profound elevations in multiple TCA cycle intermedi278

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ates, most prominently in liver, are consistent with increased FA oxidation and amino acid catabolism. These metabolic changes reflect a homeostatic process known as autophagy, which takes place at the organelle, cellular, and organismal level in a highly selective manner during embryogenesis and helps maintain homeostasis throughout the life of the organism by selectively replacing older, damaged cells with new cells (21). During periods of starvation, autophagy induces widespread, bulk degradation of cytoplasm and organelles, fueling the brain with ATP until the diet becomes sufficient and the host can derive energy from food (22). Thus, our data set essentially represents the metabolome of starvation-induced autophagy during early development. Although autophagy may enable the host to survive periods of famine, our data reveal that it also brings unwelcome consequences. Liver dysfunction. Multiple metabolites that correspond with liver injury were detected after just 11 d of protein-calorie restriction. Elevated concentrations of g-glutamyl–conjugated peptides, markers of increased GGT activity, were among the top plasma-derived metabolites that most strongly distinguished undernourished from control metabolic profiles. Although little is known of their specific functions, these peptides are synthesized predominantly in the liver by GGT, which catalyzes the degradation of the antioxidant glutathione by transferring the g-glutamyl group to recipient amino acids or peptides. These metabolites are synthesized in settings of antioxidant defense, detoxification, and response to inflammation (23).

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Metabolite

Mechanisms underlying the ‘‘thrifty phenotype.’’ Mounting evidence that poor nutrition in intrauterine or early postnatal life confers increased risk of metabolic diseases later in life led Hales and Barker (33) in 1992 to propose the ‘‘thrifty phenotype hypothesis.’’ This hypothesis concludes that nutrient deprivation during a critical period of early development programs the host for an environment of metabolic ‘‘thrift,’’ and that this reprogramming is permanent, causing problems when the environment changes and macronutrients are no longer scarce.

Identifying mechanisms of and therapeutic targets for this metabolic reprogramming is among todayÕs greatest global health challenges. One of the most important metabolites distinguishing plasma in undernourished from control mice was a-hydroxybutyrate, a product of amino acid catabolism and glutathione synthesis. A recent metabolomic study found that a-hydroxybutyrate was the most important biochemical distinguishing insulin-resistant from insulin-sensitive nondiabetic adults (34). Furthermore, our data show the structurally related biochemical 2-hydroxyisobutyrate to be the single most important metabolite in plasma and among the top 30 most important metabolites in urine that distinguished undernourished from control mice. Little is known of the function of 2-hydroxyisobutyrate, although increased concentrations have been found in the urine of patients with lactic acidosis (35), and evidence suggests that certain bacteria have the enzymatic machinery for its production (36). Furthermore, 2-hydroxyisobutyrate was elevated in the urine of morbidly obese patients qualifying for bariatric surgery (37,38), and in pregnant women who subsequently developed gestational diabetes mellitus (39). Thus, emerging evidence points to a-hydroxybutyrate and 2-hydroxyisobutyrate as early biomarkers of dysregulated glucose metabolism. Mechanisms underlying these metabolic abnormalities are unknown, although recent studies suggest a potential role for bile acids. Bile acid receptors are located in multiple target organs including liver, pancreas, adipose tissue, intestine, and vascular epithelium, and certain bile acid profiles might confer increased risk of metabolic syndrome (40). Given the drastically altered bile acid profiles in undernourished mice, further studies are warranted to determine whether bile acid profiles specific to undernutrition might predispose the host to dysregulated glucose metabolism or other metabolic abnormalities. Why the thrifty phenotype persists long after an acute period of caloric restriction has been corrected is also unknown. Some hypothesize that epigenetics, mitotically heritable changes in gene function that are not due to changes in DNA sequence (41,42), are responsible for the persistence of the thrifty phenotype (43). DNA methylation is the best-characterized form of epigenetic regulation, and is driven by one-carbon metabolism (44), a pathway prominently activated in our undernourished mice. Future studies could seek to determine whether these observed metabolic abnormalities persist after refeeding and whether one-carbon metabolism should be pursued as a potential target for therapeutic intervention. Another potential mechanism of an acquired, persistent thrifty phenotype is changes in the intestinal microbiome. It is clear that gut microbes affect host metabolism. Certain groups of commensal microbes, with varying capacities to harvest energy from the diet, have been linked to obesity in adults (45), whereas others are thought to contribute to weight loss in severely undernourished children (46,47). The field of functional metagenomics, the study of members of the microbiome and their functional contributions to host metabolism, health, and disease (48), suggests that specific host metabolic profiles may be conferred by resident bacteria and could be passed from one host to another or taken up from the environment. Thus, the intestinal microbiome could be another potential therapeutic target in undernutrition. Initial studies in functional metagenomics have relied upon metabolomics to aid in the discovery of biomarkers derived from easily accessible body fluids that provide snapshots of hostmicrobial interactions occurring within the intestinal tract. For example, urinary 2-hydroxyisobutyrate is a biomarker representing The undernourished neonatal mouse metabolome

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Traditionally, GGT has been used as a serum marker for liver, biliary, and pancreatic disease. More recently, it was found to correlate with obesity, hypertension, dyslipidemia, and hyperglycemia in adults, and to predict subsequent cardiovascular disease, metabolic syndrome, and mortality (24). GGT is now thought to mediate low-density lipoprotein oxidation in atherosclerotic plaques, with resulting oxidative stress contributing to atherogenesis (25). In liver, GGT activity may increase in response to 1) oxidative stress because of reliance on excess FA oxidation for energy, 2) the chronic low-grade inflammation believed to be present in type 2 diabetes mellitus and metabolic syndrome, or 3) hepatocellular damage (and subsequent insulin resistance) by fatty infiltration (26). These metabolic changes may contribute to steatosis, a later complication of severe malnutrition in children (27,28). Our survey of metabolites was limited by the inability to detect GGT itself; therefore, future studies could explore whether GGT is increased in plasma of undernourished mice and could serve as an early marker of undernutrition-induced liver pathology. In addition to oxidative stress, inflammation, and fatty infiltration, liver function could also be compromised by autophagy of the liver itself. In mice, starvation induces autophagy in nearly all tissue, most prominently in skeletal muscle, but also in vital organs including the pancreas and liver; organ mass continues to decrease throughout starvation (29). Thus, as hepatocellular mass diminishes, the liver might gradually lose synthetic capacity, which could also explain the profound alterations in bile acid profiles. Primary bile acids are synthesized from cholesterol and conjugated to glycine or taurine in the liver, stored in the gallbladder, and released into the small intestine, where most are deconjugated by intestinal bacteria and structurally modified into secondary bile acids. In the ileum, 95% percent of bile acids are reabsorbed and transported via enterohepatic circulation back to the liver, where they are reconjugated and secreted as bile; the remaining 5% are excreted in stool (30). In our model, nearly all bile acids were found in decreased concentrations in the stool of undernourished mice, suggesting decreased synthetic capacity, given that none of the bile acids were found in increased concentrations in the liver of undernourished mice. This resulting reduction in the total bile acid pool likely has detrimental effects on dietary fat digestion and absorption. The significance of increased concentrations of taurolithocholate 3-sulfate in the context of global decreases in all other bile acids in the bowel of undernourished mice is yet to be determined. Taurolithocholate 3-sulfate is the most hydrophobic, naturally occurring bile acid (31) and potently induces pancreatic acinar cell injury and apoptosis by producing reactive oxygen species (32). The relation between this bile acid and development of pancreatic disease, including dysregulation of insulin secretion, is also unknown. Together with the increased GGT activity and glutathione metabolism suggesting oxidative stress in liver, these data indicate that undernutrition negatively impacts liver and biliary function.

The road to clinical impact. Identification of the metabolome in the undernourished host presents an initial step in identifying not only early biomarkers, but also potential molecular links between macronutrient deprivation in childhood and persistent metabolic disease. Additional studies are needed to determine whether our choice of separating undernourished pups during the day (instead of during rodentsÕ active period at night) impacted growth or metabolism. It is also unclear whether undernourished pups consumed calories at a greater or lesser rate compared with control pups during their 12 h per day with dams, or whether any psychological elements related to prolonged separation from dams contributed to the undernourished pupsÕ metabolic changes, given that actual energy intake could not be directly quantified. Further studies might also seek to validate our identified biomarkers with a different mouse strain. Additionally, our data highlight multiple avenues for further study. They include determining the role of the microbiome in undernutrition and its links to the metabolomic alterations described here; identifying whether markers of liver pathology, inflammation, oxidative stress, and early insulin resistance persist after re-feeding; targeting the epigenome or the microbiome as a means of reversing these abnormal metabolic profiles; and confirming our findings in a more complicated model system of environmental enteropathy, including enteric pathogens. Finally, we are interested in how the altered metabolome of the undernourished host influences other comorbidities such as anemia, increased host susceptibility to exogenous microparasitic and macroparasitic infections, and decreased responses to childhood vaccines, among other relevant clinical and epidemiologic phenomena that beset children in resource-poor settings. Ultimately, a more complete understanding of the metabolic biology unique to the undernourished host could help optimize 280

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current and future therapeutic interventions, and thus clinical impact, where they are most needed. Acknowledgments The authors thank Dr. Brendan H. Lee for his thoughtful feedback regarding the data. G.A.P. and P.J.H. designed the research; G.A.P. and B.C.B. conducted the research; G.A.P., M.A.K., P.M.C., M.E.C., and P.J.H. analyzed the data; and G.A.P., M.A.K., P.M.C., B.C.B., M.E.C., and P.J.H. wrote the paper. G.A.P. had primary responsibility for the final content. All authors read and approved the final manuscript.

Literature Cited 1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

12.

13. 14.

15.

16.

17.

United Nations Children’s Fund (UNICEF), World Health Organization (WHO), World Bank. UNICEF-WHO-World Bank Joint Child Malnutrition Estimates. New York: UNICEF-WHO-World Bank; 2012. Guerrant RL, Deboer MD, Moore SR, Scharf RJ, Lima AA. The impoverished gut—a triple burden of diarrhoea, stunting and chronic disease. Nat Rev Gastroenterol Hepatol. 2013;10:220–9. van der Werf MJ, Jellema RH, Hankemeier T. Microbial metabolomics: replacing trial-and-error by the unbiased selection and ranking of targets. J Ind Microbiol Biotechnol. 2005;32:234–52. Nicholson JK, Holmes E, Lindon JC, Wilson ID. The challenges of modeling mammalian biocomplexity. Nat Biotechnol. 2004;22: 1268–74. Preidis GA, Hill C, Guerrant RL, Ramakrishna BS, Tannock GW, Versalovic J. Probiotics, enteric and diarrheal diseases, and global health. Gastroenterology. 2011;140:8–14. Coutinho BP, Oria RB, Vieira CM, Sevilleja JE, Warren CA, Maciel JG, Thompson MR, Pinkerton RC, Lima AA, Guerrant RL. Cryptosporidium infection causes undernutrition and, conversely, weanling undernutrition intensifies infection. J Parasitol. 2008;94:1225–32. Roche JK, Cabel A, Sevilleja J, Nataro J, Guerrant RL. Enteroaggregative Escherichia coli (EAEC) impairs growth while malnutrition worsens EAEC infection: a novel murine model of the infection malnutrition cycle. J Infect Dis. 2010;202:506–14. Costa LB, Noronha FJ, Roche JK, Sevilleja JE, Warren CA, Oria R, Lima A, Guerrant RL. Novel in vitro and in vivo models and potential new therapeutics to break the vicious cycle of Cryptosporidium infection and malnutrition. J Infect Dis. 2012;205:1464–71. Preidis GA, Saulnier DM, Blutt SE, Mistretta TA, Riehle KP, Major AM, Venable SF, Barrish JP, Finegold MJ, Petrosino JF, et al. Host response to probiotics determined by nutritional status of rotavirus-infected neonatal mice. J Pediatr Gastroenterol Nutr. 2012;55:299–307. Ohta T, Masutomi N, Tsutsui N, Sakairi T, Mitchell M, Milburn MV, Ryals JA, Beebe KD, Guo L. Untargeted metabolomic profiling as an evaluative tool of fenofibrate-induced toxicology in Fischer 344 male rats. Toxicol Pathol. 2009;37:521–35. Evans AM, DeHaven CD, Barrett T, Mitchell M, Milgram E. Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems. Anal Chem. 2009;81:6656–67. Dehaven CD, Evans AM, Dai H, Lawton KA. Organization of GC/MS and LC/MS metabolomics data into chemical libraries. J Cheminform. 2010;2:9. Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci USA. 2003;100:9440–5. Tomkins AM, Wright S, James WP, Drasar BS. Proceedings: jejunal colonization with enterobacteria in symptomatic tropical residents without acute sprue. Gut. 1975;16:392. Barboza Junior MS, Silva TM, Guerrant RL, Lima AA. Measurement of intestinal permeability using mannitol and lactulose in children with diarrheal diseases. Braz J Med Biol Res. 1999;32:1499–504. Goto R, Mascie-Taylor CG, Lunn PG. Impact of intestinal permeability, inflammation status and parasitic infections on infant growth faltering in rural Bangladesh. Br J Nutr. 2009;101:1509–16. Camilleri M, Nadeau A, Lamsam J, Nord SL, Ryks M, Burton D, Sweetser S, Zinsmeister AR, Singh R. Understanding measurements of

Downloaded from jn.nutrition.org by guest on November 14, 2015

microbial degradation of undigested dietary proteins by Faecalibacterium prausnitzii (48), whereas metabolites of carnitine, produced by specific classes of microbes and detectable in plasma, were recently linked to atherosclerosis and cardiac events in adults (49). The utility of functional metagenomics and metabolomics continues to grow as new microbes and microbial genes are discovered using culture-independent techniques, and as a higher proportion of metabolites are being identified as host-and microbial-derived molecules that influence health and disease. Bile acids provide one theoretical link between the microbiome and the metabolome. In addition to their role in fat digestion, bile acids serve an important barrier function, preventing microbial overgrowth and translocation across the intestine (50). On the other hand, specific gut microbes dehydroxylate and deconjugate glycine and taurine groups from secondary bile acids, facilitating enterohepatic recirculation and thereby regulating the bile acid pool (50,51). Although multiple studies have reported drastic changes to the intestinal microbiome in the context of acute gastroenteritis and other secretory diarrheal diseases (5), it remains unclear whether undernutrition alters the intestinal niche in a way that changes the composition and metabolic function of the intestinal microbiome. It also remains unclear whether acquisition of a different metagenomic profile could confer persistent metabolic changes once the period of nutrient deprivation has resolved. If intestinal microbes are directly implicated in the pathogenesis of undernutrition and its associated comorbidities, the potential for intervention via targeted antimicrobial or bacterial-replacing therapies would hold great promise.

18.

19.

20.

21. 22. 23. 24.

25.

27.

28. 29.

30. 31.

32.

33. 34.

35. Kumps A, Duez P, Mardens Y. Metabolic, nutritional, iatrogenic, and artifactual sources of urinary organic acids: a comprehensive table. Clin Chem. 2002;48:708–17. 36. Yaneva N, Schuster J, Schafer F, Lede V, Przybylski D, Paproth T, Harms H, Muller RH, Rohwerder T. Bacterial acyl-CoA mutase specifically catalyzes coenzyme B12-dependent isomerization of 2hydroxyisobutyryl-CoA and (S)-3-hydroxybutyryl-CoA. J Biol Chem. 2012;287:15502–11. 37. Calvani R, Miccheli A, Capuani G, Tomassini Miccheli A, Puccetti C, Delfini M, Iaconelli A, Nanni G, Mingrone G. Gut microbiome-derived metabolites characterize a peculiar obese urinary metabotype. Int J Obes (Lond). 2010;34:1095–8. 38. Friedrich N, Budde K, Wolf T, Jungnickel A, Grotevendt A, Dressler M, Volzke H, Bluher M, Nauck M, Lohmann T, et al. Short-term changes of the urine metabolome after bariatric surgery. OMICS. 2012;16:612–20. 39. Diaz SO, Pinto J, Graca G, Duarte IF, Barros AS, Galhano E, Pita C, Almeida Mdo C, Goodfellow BJ, Carreira IM, et al. Metabolic biomarkers of prenatal disorders: an exploratory NMR metabonomics study of second trimester maternal urine and blood plasma. J Proteome Res. 2011;10:3732–42. 40. Porez G, Prawitt J, Gross B, Staels B. Bile acid receptors as targets for the treatment of dyslipidemia and cardiovascular disease. J Lipid Res. 2012;53:1723–37. 41. Reik W, Dean W, Walter J. Epigenetic reprogramming in mammalian development. Science. 2001;293:1089–93. 42. Li E. Chromatin modification and epigenetic reprogramming in mammalian development. Nat Rev Genet. 2002;3:662–73. 43. Sto¨ger R. The thrifty epigenotype: an acquired and heritable predisposition for obesity and diabetes? Bioessays. 2008;30:156–66. 44. Anderson OS, Sant KE, Dolinoy DC. Nutrition and epigenetics: an interplay of dietary methyl donors, one-carbon metabolism and DNA methylation. J Nutr Biochem. 2012;23:853–9. 45. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 2006;444:1027–31. 46. Trehan I, Goldbach HS, LaGrone LN, Meuli GJ, Wang RJ, Maleta KM, Manary MJ. Antibiotics as part of the management of severe acute malnutrition. N Engl J Med. 2013;368:425–35. 47. Smith MI, Yatsunenko T, Manary MJ, Trehan I, Mkakosya R, Cheng J, Kau AL, Rich SS, Concannon P, Mychaleckyi JC, et al. Gut microbiomes of Malawian twin pairs discordant for kwashiorkor. Science. 2013;339:548–54. 48. Li M, Wang B, Zhang M, Rantalainen M, Wang S, Zhou H, Zhang Y, Shen J, Pang X, Zhang M, et al. Symbiotic gut microbes modulate human metabolic phenotypes. Proc Natl Acad Sci USA. 2008;105: 2117–22. 49. Koeth RA, Wang Z, Levison BS, Buffa JA, Org E, Sheehy BT, Britt EB, Fu X, Wu Y, Li L, et al. Intestinal microbiota metabolism of l–carnitine, a nutrient in red meat, promotes atherosclerosis. Nat Med. 2013;19: 576–85. 50. Floch MH. Bile salts, intestinal microflora and enterohepatic circulation. Dig Liver Dis. 2002;34(Suppl 2):S54–7. 51. Martin FP, Dumas ME, Wang Y, Legido-Quigley C, Yap IK, Tang H, Zirah S, Murphy GM, Cloarec O, Lindon JC, et al. A top-down systems biology view of microbiome-mammalian metabolic interactions in a mouse model. Mol Syst Biol. 2007;3:112.

The undernourished neonatal mouse metabolome

281

Downloaded from jn.nutrition.org by guest on November 14, 2015

26.

intestinal permeability in healthy humans with urine lactulose and mannitol excretion. Neurogastroenterol Motil. 2010;22:e15–26. Huicho L, Garaycochea V, Uchima N, Zerpa R, Guerrant RL. Fecal lactoferrin, fecal leukocytes and occult blood in the diagnostic approach to childhood invasive diarrhea. Pediatr Infect Dis J. 1997;16:644–7. Gill CJ, Lau J, Gorbach SL, Hamer DH. Diagnostic accuracy of stool assays for inflammatory bacterial gastroenteritis in developed and resource-poor countries. Clin Infect Dis. 2003;37:365–75. Campbell DI, Elia M, Lunn PG. Growth faltering in rural Gambian infants is associated with impaired small intestinal barrier function, leading to endotoxemia and systemic inflammation. J Nutr. 2003;133: 1332–8. Finn PF, Dice JF. Proteolytic and lipolytic responses to starvation. Nutrition. 2006;22:830–44. Rabinowitz JD, White E. Autophagy and metabolism. Science. 2010;330:1344–8. Zhang H, Forman HJ. Redox regulation of gamma-glutamyl transpeptidase. Am J Respir Cell Mol Biol. 2009;41:509–15. Lee DS, Evans JC, Robins SJ, Wilson PW, Albano I, Fox CS, Wang TJ, Benjamin EJ, D’Agostino RB, Vasan RS. Gamma glutamyl transferase and metabolic syndrome, cardiovascular disease, and mortality risk: the Framingham Heart Study. Arterioscler Thromb Vasc Biol. 2007;27: 127–33. Franzini M, Corti A, Martinelli B, Del Corso A, Emdin M, Parenti GF, Glauber M, Pompella A, Paolicchi A. Gamma-glutamyltransferase activity in human atherosclerotic plaques–biochemical similarities with the circulating enzyme. Atherosclerosis. 2009;202:119–27. Ortega E, Koska J, Salbe AD, Tataranni PA, Bunt JC. Serum gammaglutamyl transpeptidase is a determinant of insulin resistance independently of adiposity in Pima Indian children. J Clin Endocrinol Metab. 2006;91:1419–22. Doherty JF, Adam EJ, Griffin GE, Golden MH. Ultrasonographic assessment of the extent of hepatic steatosis in severe malnutrition. Arch Dis Child. 1992;67:1348–52. Truswell AS, Miller JC. Pathogenesis of the fatty liver in protein-energy malnutrition. Am J Clin Nutr. 1993;57:695–6. Mizushima N, Yamamoto A, Matsui M, Yoshimori T, Ohsumi Y. In vivo analysis of autophagy in response to nutrient starvation using transgenic mice expressing a fluorescent autophagosome marker. Mol Biol Cell. 2004;15:1101–11. Chiang JY. Bile acids: regulation of synthesis. J Lipid Res. 2009;50: 1955–66. Hofmann AF, Roda A. Physicochemical properties of bile acids and their relationship to biological properties: an overview of the problem. J Lipid Res. 1984;25:1477–89. Booth DM, Murphy JA, Mukherjee R, Awais M, Neoptolemos JP, Gerasimenko OV, Tepikin AV, Petersen OH, Sutton R, Criddle DN. Reactive oxygen species induced by bile acid induce apoptosis and protect against necrosis in pancreatic acinar cells. Gastroenterology. 2011;140:2116–25. Hales CN, Barker DJ. Type 2 (non-insulin-dependent) diabetes mellitus: the thrifty phenotype hypothesis. Diabetologia. 1992;35:595–601. Gall WE, Beebe K, Lawton KA, Adam KP, Mitchell MW, Nakhle PJ, Ryals JA, Milburn MV, Nannipieri M, Camastra S, et al. Alphahydroxybutyrate is an early biomarker of insulin resistance and glucose intolerance in a nondiabetic population. PLoS ONE. 2010;5:e10883.