Progressive Changes in the Plasma Metabolome during Malnutrition

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Progressive Changes in the Plasma Metabolome during Malnutrition in Juvenile Pigs Pingping Jiang, Jan Stanstrup, Thomas Thymann, Per Torp Sangild, and Lars O. Dragsted J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.5b00782 • Publication Date (Web): 02 Dec 2015 Downloaded from http://pubs.acs.org on December 8, 2015

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Journal of Proteome Research

Progressive Changes in the Plasma Metabolome during Malnutrition in Juvenile Pigs

Pingping Jiang†, Jan Stanstrup‡, Thomas Thymann†, Per Torp Sangild†, Lars Ove Dragsted‡,* †

Department of Veterinary Clinical and Animal Sciences, University of Copenhagen, 68

Dyrlægevej, DK-1870 Frederiksberg C, Denmark; ‡Department of Nutrition, Exercise and Sports, University of Copenhagen, 30 Rolighedsvej, DK-1958 Frederiksberg C, Denmark This work is financially supported by the Danish Council for Strategic Research (the NEOMUNE project, 12-132401), and the animal experimentation was co-financed by the Nutriset (KU071900-50-30576). Competing Interests: The authors declare no conflict of interest. Current address of Jan Stanstrup: Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010 San Michele all’Adige, Trento, Italy. A part of this work has been presented as a poster at the 48th ESPGHAN annual meeting. The pictures used in the TOC graphic are taken by Dr. Thomas Thymann. * Corresponding author: Lars Ove Dragsted, Professor, Ph.D., Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, 30 Rolighedsvej, DK-1958 Frederiksberg C, Denmark, Tel: +45 35332698, Fax: +45 35332469, Email: [email protected]

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ABSTRACT (200 words) Severe acute malnutrition (SAM) is one of the leading nutrition-related causes of death in children under five years of age. The clinical features of SAM are well documented, but a comprehensive understanding of the development from a normal physiological state to SAM is lacking. Characterising the temporal metabolomic change may help to understand the disease progression and to define nutritional rehabilitation strategies. Using a piglet model we hypothesised that a progressing degree of malnutrition induces marked plasma metabolite changes. Four week-old weaned pigs were fed a nutrient-deficient maize diet (MAL) or nutritionally optimised reference diet (REF) for seven weeks. Plasma collected weekly was subjected to LC-MS for a non-targeted profiling of metabolites with abundance differentiation. The MAL pigs showed markedly reduced body-weight gain and leanmass proportion relative to the REF pigs. Levels of eight essential and four nonessential amino acids showed a time-dependent deviation in the MAL pigs from that in the REF. Choline metabolites and gut microbiomic metabolites generally showed higher abundance in the MAL pigs. The results demonstrated that young malnourished pigs had a profoundly perturbed metabolism and this provides basic knowledge about metabolic changes during malnourishment, which may be of help in designing targeted therapeutic foods for re-feeding malnourished children. Key words (10): severe acute malnutrition; pig; temporal metabolomics; LCMS; amino acids; microbiomic metabolite

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INTRODUCTION Severe acute malnutrition in childhood (SAM) results from a short period of nutritional deficit. SAM is one of the top three nutrition-related causes of death in children below five years of age and affects nearly 20 million children worldwide with a mortality of 0.5 to 2 million annually.1 Children with SAM exhibit stunted growth, muscle wasting,2 vulnerability to infection and impaired organ function.1, 3 Evidence from research in human patients and animal models, including mice, rats and pigs,3-5 show that non-oedematous SAM (marasmus, the most common type of SAM) includes pathological changes beyond simple muscle wasting. Clinical problems in marasmic children include hypoglycaemia, hypothermia and impaired insulin response.6 These children also have a higher risk of bacteraemia and sepsis following infections of the respiratory, urinary and gastrointestinal tracts,7-9 and they are negatively affected in the central and peripheral nervous systems.10 Although the clinical features of marasmus are well documented via cross-sectional studies of hospitalised patients, a comprehensive understanding of the temporal metabolic changes from a normal physiological state to a state of marasmus is lacking. Works on malnourished animal models including rats, dogs and pigs have revealed important information regarding the pathological changes,4, 5, 9 but indepth analysis of temporal changes in the metabolome has not been conducted. Systems biological approaches, such as proteomics and metabolomics, have been used to investigate the pathological changes in animals subjected to SAM,4, 11 but these previous studies have not studied the metabolic changes that preceded SAM pathology. We hypothesised that the plasma metabolome would be affected early after the induction of malnutrition in juvenile pigs, used as a model for children. The temporal change in the plasma metabolome of malnourished pigs was investigated aiming to document the gradual disturbance in metabolism over the time of SAM development. This temporal approach can



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also increase the credibility and liability of metabolite identification. Malnutrition was induced using a pure maize diet as it is a common household diet in low-income countries, and maize is known to be insufficient in protein, minerals and vitamins for normal growth.12 The results from this study may provide information about specific nutritional needs and the metabolic pathways being perturbed during different stages of malnutrition.


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EXPERIMENTAL PROCEDURES Animal Experiment Rearing and feeding of pigs were carried out as previously described.3 Briefly, 24 female crossbred pigs (Durox × Danish Landrace × Yorkshire) were weaned at four weeks of age (mean body weight 7.0 ± 0.3 kg) and given ad libitum access to a nutritionally optimised diet for a 5-day acclimatisation period. After that, 12 of the pigs were switched to a nutritionally deficient diet consisting of pure maize to induce malnutrition (MAL, n = 12), while the other 12 pigs remained on the optimised diet as a reference group (REF, n = 12). Detailed nutritional composition of the two diets is shown in Table S1. The energy level in MAL diet was comparable to that in the REF diet, but with 61% less digestible protein. Both groups were given ad libitum access to their diets and water for seven weeks. Blood samples were collected into heparinised vacutainers by venipuncture of the jugular vein before the feeding of the MAL diet started, and once weekly on the same week day for five weeks thereafter. The heparinised plasma samples were collected and stored at -80°C for later metabolomic analysis. The blood taken at euthanasia was not included in the metabolomic analysis because the blood originated from cardiac puncture, not from the jugular vein. After seven weeks, body weight of each pig was recorded and a dual-energy X-ray absorptiometry was carried out to determine the body composition, after which the pigs were euthanised. All animal experimental procedures were approved by the Danish National Council for Animal Experimentation. UPLC-qTOF MS Prior to the metabolomic analysis, protein in the plasma samples was removed using the Sirocco Protein Precipitation Plate (Waters, Milford, MA, USA) as previously described.13 Treated



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plasma samples as well as a blank solution (0.1 % formic acid) and a house-made metabolic standard mixture were distributed randomly into two auto-injector sample plates. The in-house metabolic standard mixture consisted of 44 biologically relevant metabolites and was used, together with the blank, to check the performance of the analytical platform, such as mass accuracy, retention time (RT) shift and instrumental sensitivity drift.13 The deproteinised plasma samples and the metabolic standard mixture were re-dissolved in 500 µL 0.1 % formic acid after being dried under vacuum.13 Five µL of solution from each well in the sample plate was injected into an ultra-performance liquid chromatography system (UPLC, Waters) with an ACQUITY UPLC HSS T3 C18 column (100 mm × 2.1 mm, 1.7 µm particle size, Waters). The elution followed a gradient from 0.1 % formic acid to 0.1 % formic acid in acetonitrile: methanol (70:30, v/v) within a running time of 7.0 min. The eluates were analysed by a Premier quadrupole timeof-flight mass spectrometer (qTOF MS, Waters) with electrospray ionisation (ESI) in both negative and positive modes. The selected mass range was from 50 to 1000 m/z in full scan mode with a scan time of 0.08 s and an inter-scan delay of 0.02 s. Ion source and desolvation gas (nitrogen) temperatures were 120 and 400°C, respectively. The sampling cone and desolvation gas flow rates were 50 and 1000 L/h, respectively. A voltage of 2.8 kV (negative mode) and 3.2 kV (positive mode) was applied to the tip of capillary for ionisation of molecules. Sampling cone voltage was set at 30 kV. Data were collected in centroid mode using leucine encephalin as the lock-spray mass to calibrate mass accuracy. The blank and the metabolomic standard mixture were run three times during each batch and acceptable mass error (< 20 ppm) and retention time (RT) shift (< 0.05 min) for verification of sample running conditions were checked. The raw mass spectra were extracted and aligned by the MarkerLynx software (Waters) with the following set of parameters: RT window, 0.05 min; mass window, 0.05 Da;


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noise elimination level, three standard deviations above background; and intensity threshold, 20 counts per second. In the following, a detected ion is termed as a “feature” after the alignment procedure and as a “marker” after selection by statistical analysis. Data Processing and Statistics After detection, the data set of all features containing information of m/z and RT as well as the relative abundance in each sample was exported into Excel (Microsoft) and the zero values in abundance generated in detection were marked as "not available". Only features present in at least 80 % of the pigs in either dietary group at no less than two time-points were aligned with the information of experimental round, sow number, pig number, feeding week and diets, and imported into R14 integrated with R Studio15 for statistical analysis. Similar to the method suggested by Everitt and Hothorn,16 each feature was fitted to a linear mixed-effects model for repeated measurements with a random intercept of pigs using the lme function.17 The interaction of feeding week and diet type was set as fixed factor (time × diet), while the experimental round, sow number and pig number was set as nested random factors with experimental round being the outermost and pig number the innermost. P values for the significance of the interaction of time and diet was generated by the anova function. Significant P values were further adjusted by the two-stage Benjamini & Hochberg (TSBH) step-up False Discovery Rate (FDR)controlling procedure with type I error rate (α) set to 0.2 using the mt.rawp2adjp function.18, 19 Only the features with FDR-adjusted P value less than 0.05 were selected for metabolite annotation as ‘treatment markers’ to avoid excessive type I errors in this explorative investigation.



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The difference in the means of abundance between the MAL and REF pigs in each feeding week was tested within each feature with an F-test using the glht function20 and further adjusted with the aforementioned FDR process across the whole dataset. Metabolite Annotation The annotation process was described previously,21 and complies with the reporting guidelines of metabolomic studies.22 Briefly, m/z of markers was used to search in metabolite databases including the Human Metabolome Database23 and the METLIN24 for potential quasi-molecular ions or adducts. Besides, the mass spectral information was matched to the theoretical fragmentation using the MassFragment applet (Waters). RT and fragmentation pattern of proposed metabolites were further matched with chemical standards, if commercially available. Identified metabolites were categorised at four levels, level 1, identified compound; level 2, putatively annotated compound; level 3, putatively characterised compound class; level 4, unknown compound.22 Metabolites at level 1 show RT and mass spectra that match with the chemical standard or in-house database of metabolites run previously on the same system in the same running condition (accepting historical RT-shift within 0.02 min), whereas metabolite at level 2 matches a specific compound in the aforementioned databases in mass spectrum but could not be verified with standards. Markers at level 3 were only assigned with a chemical class without an exact identity. A graph showing the relative abundance changes of annotated metabolites (quasi-molecular ions or adducts) over the time of feeding was produced in R with the Lattice package.25 Graphs of representative quasi-molecules or adducts are shown in Figures 1 and 2, while others can be found in the Figure S-2 (Supporting Information).


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Regarding the identification of lysophosphatidylcholines (LPCs) where some standards are not available, an in-house algorithm previously reported21 was adopted to predict the LPC species. RT of proposed LPC species containing specific number of carbons were extracted from the chromatograms and plotted against the number of double bonds with five LPC standards (LPC (16:0), LPC (18:0), LPC (18:1), LPC (20:0), LPC (22:0)) being run to verify the RTs (Figure S-1). An amino acid mixture containing acidic, neutral and basic amino acids, N,N-dimethylglycine, betaine, pantothenic acid, phenylactetylglycine, pyroglutamic acid and allantoin were purchased from Sigma-Aldrich (Copenhagen, Denmark). Pseudouridine was from Santa Cruz (Dallas, TX, USA). LPC (22:0), LPC (20:0), LPC (18:1), LPC (18:0) and LPC (16:0) were purchased from Avanti Polar Lipids (Alabaster, AL, USA).



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RESULTS SAM in Pigs After the intervention, the MAL pigs showed markedly lower body weight (8.0 ± 0.6 kg vs 32.4 ± 1.2 kg, P < 0.001, two-tailed t-test) and lower lean mass (83.4 ± 0.7 % vs. 87.0 ± 0.2 %, P < 0.001) indicatory of markedly altered metabolism. No oedema was observed in any MAL pigs. As presented in a separate paper3 all MAL pigs developed clear symptoms of malnutrition, including low haematocrit, haemoglobin and albumin, gut mucosal atrophy and hepatic fat infiltration. Metabolite Annotation After the pre-processing of the raw MS data, 977 out of 4572 features in the negative (ESI-) and 1371 out of 8050 features in the positive (ESI+) MS mode were applied to statistical analysis, respectively. As a result, 135 and 212 markers showed statistical significance in the interaction of feeding time and diets (adjusted P < 0.05). The basic information of identified metabolites at level 1, including the identity, theoretical monoisotopic mass, RT (min), detected m/z and ion assignment, is listed with major quasi-molecular ion, adduct and/or fragment in Table 1. Putative annotations at level 2 and 3 are listed in Table S-2 (Supporting Information). The metabolites identified cover a wide polarity range from highly hydrophilic compounds such as lysine to polar lipids like LPCs. Based on the biochemical traits, the identified metabolites were categorised into seven groups, amino acids, amino acid metabolism-related, nucleoside metabolismrelated, choline metabolism-related, gut microbiome-related, LPCs and others. Among the identified metabolites, there are eight essential amino acids including histidine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan and valine, and four nonessential ones, glutamic acid, glutamine,


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proline and tyrosine. Leucine, lysine, tryptophan and tyrosine showed quasi-molecular ions in both ESI- and ESI+ mode. Betaine (N,N,N-trimethylglycine) and N,N-dimethylglycine identified were from the choline metabolism. Identified metabolites involved in nucleoside metabolism include pseudouridine, uric acid and allantoin. Five LPCs were identified, LPC (16:0), LPC (18:1), LPC (18:2), LPC (20:2) and LPC (22:6). LPC (22:6) showed in two different adducts, [M+Na]+ and [M+HCOOHH]-. Profile of Change in Metabolite Abundance In general the quasi-molecular ions of identified metabolites showed uniformly the same direction of change between the MAL and REF groups over the time of feeding, although some metabolites changed around week 2 and a few were not different any more by the end of the experiment. Among the eight identified essential amino acids, only histidine showed a generally higher abundance in the MAL pigs over the feeding period whereas others, such as valine, methionine, phenylalanine, leucine, lysine, threonine and tryptophan, were higher in the REF pigs at most timepoints (Figure 1A). Nonessential amino acids also showed higher abundance in the REF pigs (Figure 1B). However, different amino acids demonstrated different patterns over the time of feeding. The difference between the REF and the MAL pigs of histidine and proline was most prominent in week 1, i.e. one week after the start of malnutrition, while the significant change of lysine did not appear until week 2. Tryptophan responded early, showing a significant difference already from week 1, and the difference persisted until week 5. Besides, methionine, glutamic acid, glutamine and phenylalanine demonstrated significant differences from week 4. The different quasi-molecular ions or adducts of any metabolite showed similar trends of change (Figure S-2), however only one quasi-molecular ion or adduct is presented in Figures 1 and 2.



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Nucleoside-related metabolites, pseudouridine and uric acid, showed an overall higher abundance in the MAL pigs, but allantoin was lower (Figure 2A). The gut microbial metabolites, hippurate, phenylacetylglycine and p-cresol glucuronide, were generally higher in the MAL pigs, starting from week 2 or later (Figure 2B). Betaine and N,N-dimethylglycine were found with a generally higher abundance in the MAL pigs. However, they showed different patterns over the time of feeding (Figure 2C). The MAL pigs tended to have higher levels of betaine and N,N-dimethylglycine, increasing from weeks 1-5 while the REF pigs had lower levels and only the level of betaine increased. Among the identified LPCs, LPC (16:0), LPC (18:2) and LPC (22:6) demonstrated higher abundance in the REF pigs, whereas LPC (18:1) and LPC (20:2) had higher abundance in the MAL pigs (Figure 2D). The change of other identified metabolites can be found in Figure 2E. The change over the time of feeding for the markers identified at level 2 and 3 is shown in Figure S-3.


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DISCUSSION In this study, plasma metabolites were profiled with increasing time after feeding a nutrientdeficient diet based on pure maize to weaned pigs. In human nutrition, maize is known to be deficient in lysine, tryptophan, vitamin A and B, causing malnutrition symptoms during chronic intake.12 In the present study, the MAL pigs fed the maize-diet showed symptoms of non-oedematous SAM (marasmus) including a marked reduction in body weight and lean mass, as well as lower levels of albumin and haemoglobin, as described previously.3 Relative to the REF diet, the MAL diet induced changes in many metabolites, including 12 amino acids, amino acid metabolites, choline and nucleosides metabolites, and metabolites that were judged to originate from the gut microbiome. Amino Acids and Related Metabolites Most of the amino acids identified showed an overall lower level in the MAL pigs compared with REF. This was particularly obvious for the essential amino acids which were adequately included in the REF diet, but inadequate in the pure maize diet (Figure 1A). A similar phenomenon has also been found in marasmic children.26, 27 The MAL diet contained 61% less digestible protein relative to the REF diet, and had an imbalanced amino acids profile (Table S-1). Not surprisingly, level of multiple essential amino acids (lysine, methionine and phenylalanine) in the MAL pigs only slight changed over the feeding time while that in the REF pigs increased drastically (Figure 1A). Similarly, children with non-oedematous SAM, unlike their oedematous counterparts, can keep a protein breakdown rate similar to normal children but this is merely enough to maintain homeostasis.28, 29 The difference between the MAL and REF pigs appeared as early as one or two weeks after the commencement of the feeding and became increasingly pronounced, more due to the increase in the



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REF pigs (Figure 1A). However, in non-essential amino acids, the substantial change did not appear until later in the malnourishment period (Figure 1B), which, taken together with the decreased proportion of lean mass and decreased circulating level of albumin and haemoglobin,3 suggests an adaptive muscle loss to supply amino acids. Starting from week 3, most of the identified amino acids showed a lower level in the MAL pigs, indicating that the mobilisation of lean body mass was insufficient to maintain near-normal amino acid levels already after two weeks of malnutrition in our experimental condition. Histidine was the only amino acid with a higher level in the MAL versus the REF pigs (Figure 1A). This finding is in agreement with previous studies in children30,

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and marasmic rats.32 One

possible source for histidine could be muscle carnosine, a dipeptide of β-alanine and L-histidine, which is released into circulation and degraded.33 A rise in the circulating level of histidine, a precursor of the neurotransmitter, histamine, together with a reduction of other precursors of neurotransmitters such as tryptophan and phenylalanine/tyrosine, has also been related to malnutrition.34 These changes could raise the level of histidine and histamine in the brain which profoundly affects the central nervous system and may play a role in depression of food intake regulation.34 Sustained low levels of plasma amino acids, especially essential ones, will compromise many physiological processes, especially during early life. In mice, low glutamine level was associated with less efficient macrophage activation as a part of a malnutrition-associated impairment of

the

inflammatory response.9 A shortage of tyrosine (or its precursor, phenylalanine) may be involved in neurological and metabolic abnormalities and skin changes associated with SAM.35 The lack of tryptophan, the precursor of the neurotransmitter serotonin, affects the development of CNS.36 In protein catabolism, leucine is turned into isovaleryl-CoA, which is turned into acetyl-CoA to enter the


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Krebs cycle or into isovalerylglycine by glycine-N-acylase.37 The amount of isovalerylglycine in plasma is associated with the accumulation of isovaleryl-CoA enters in the mitochondrion for energy generation.37 Interestingly, the level of isovalerylglycine was higher in the MAL pigs than in the REF ones (Figure 1C), whereas the level of leucine was lower in the MAL pigs (Figure 1A). This may indicate an up-regulated leucine catabolism, and whether this accumulation of isovalerylglycine in blood would exert any detrimental effect remains to be determined. Fluxes of leucine,28 arginine,38 glycine39 and methionine40 have been studied in children suffering from both oedematous and nonoedematous SAM, but the fluxes were not studied until symptoms of SAM were evident. Our findings show that different amino acids fluctuate quite differently during the progression of malnutrition, which implies that that optimal nutritional support for children with progression of SAM may be highly timedependent. In particular lysine, methionine and the aromatic amino acids are needed already in early stages while a more general need of all amino acids is evident only a few weeks into the malnutrition period. Creatine is synthesised from arginine and glycine in the kidney and liver, phosphorylated and transported via blood to muscle and brain as an energy reserve. The overall higher level of creatine in the MAL pigs may be due to muscle break down as indicated by the lower lean tissue mass in the MAL pigs.3 This is in line with data from rats with restricted food intake

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or protein-energy malnutrition.4

However, the decreasing trend of creatine over SAM development could be due to a diminished synthesis because of less availability of precursor amino acids like glycine and arginine, as well as less availability of methyl donors as indicated by the apparent metabolism of choline into dimethylglycine as discussed below.



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Pyroglutamic acid is an intermediate in the synthesis of glutathione from amino acids.42 When protein intake is restricted as in the MAL pigs, glutathione synthesis is limited and causes a decreased plasma level of pyroglutamic acid. Pyroglutamic acid is also present in the heated milk-powder used in the REF diet (Table S-1). Thus, the difference in pyroglutamic acid found in this study may arise from the perturbed metabolism in the MAL pigs as well as the difference in the diets. Nucleoside Metabolism-Related Metabolites Pseudouridine, uric acid and allantoin involved in nucleoside metabolism were generally found in higher levels in the MAL pigs. However, difference between the MAL and the REF pigs in pseudouridine appeared as early as in week 1 after the start of malnutrition, but rather late for uric acid (not until week 4, Figure 2A). Pseudouridine, the C-glycoside isomer of uridine, is released from whole-body catabolism of tRNA and rRNA, and appears in the blood for urinary excretion.43 Higher level of pseudouridine has been associated with different kinds of cancers in the lung, the large intestine, the urinary bladder and the prostate43 where the turnover of tissues, cells and nuclear acids is accelerated. Possibly, the higher level of pseudouridine in the MAL pigs reflects a higher turn-over of muscle cells and other tissue to allow adequate metabolism in more vital organs and may explain why SAM is particularly affecting children since tissue remodelling may compete with growth. In humans, uric acid is the end product of purine metabolism by xanthine oxidase, but it is further metabolised into allantoin in pigs.44 In accordance with our results, malnourished children with low birth-weight have higher plasma uric acid,45 which is also found in the patients undergoing chronic haemodialysis treatment and patients with the malnutrition-inflammation syndrome.46 The circulating level of uric acid does not change significantly in kwashiorkor children,47 so it may possess the


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potential to differentiate oedematous and non-oedematous SAM. Hyperuricemia can cause impaired glucose tolerance and insulin resistance in mice,48 and prolonged hyperuricemia is associated with elevated risk for diabetes, cardiovascular disease49 and renal injury,44 which may help to explain that diabetes mellitus is commonly found in adults with a history of SAM. Gut Microbiome-Related Metabolites Changes in the gut microbiome have been observed following malnutrition and muscle wasting,50 which support the treatment of malnutrition with antibiotics51 and pre/probiotics.52 The progression of SAM may disturb the symbiotic metabolism of the gut microbiome and thereby change the microbial metabolites being absorbed by the host.50, 53 In this study substantial differences in the gut microbiome were found between the MAL and the REF pigs. The MAL pigs had a gut microbiome enriched in Verrucomicrobia, with relatively lower abundance of Bacteroidetes and Firmicutes, compared with the REF pigs (to be published in a separate report). Not surprisingly, gut microbialmammalian co-metabolites including hippuric acid, phenylacetylglycine, and p-cresol glucuronide were different between groups (Figure 2B). The gut microbiome converts polyphenols and aromatic amino acids such as phenylalanine and tyrosine into benzoic acid and phenylacetic acid, which is conjugated with glycine in the liver to form hippuric acid54 and phenylacetylglycine,55 respectively. pCresol is mainly generated as an end product of tyrosine biotransformation by anaerobic intestinal bacteria, such as clostridia, bacteroids and bifidobacteria.56 In pigs, p-cresol is conjugated to form a glucuronide rather than sulphate, as seen in humans

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. The increased level of hippuric acid and

phenylacetylglycine found in the MAL pigs over the development of SAM is more likely due to the disturbed metabolism of the corresponding precursors rather than the difference in the polyphenol content between two diets as reduced appetite was found in the MAL pigs as previously reported.3 It is



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worth noting that all three metabolites identified showed a higher level in the MAL pigs, relative to the REF pigs, implying higher gut permeability or a particular microbiome generating the precursors of these co-metabolites in MAL pigs. Thus, re-feeding strategy combining gut microbiome-manipulating agent such as probiotics with a proper dietary regimen is at least biologically plausible and warrants further studies. Choline Metabolism-Related Metabolites Choline metabolism has been associated with brain development of foetuses and preschool children by involvement in neurotransmitter synthesis and myelination as well as epigenetic modification of chromatin.57 Betaine, of dietary origin or produced from choline, is the methyl donor in methionine regeneration from homocysteine and is oxidised into N,N-dimethylglycine.58 Despite the lower content of betaine and choline in the diet, and appetite loss in the later stage of feeding, MAL pigs generally showed higher levels of betaine and N,N-dimethylglycine, and both betaine and N,Ndimethylglycine increased over the time of feeding (Figure 2C). This, taken together with the generally stable level of methionine, suggests an increasingly active conversion of choline to betaine and N,Ndimethylglycine, possibly to maintain methionine regeneration from homocysteine. In the REF pigs more betaine, but not N,N-dimethylglycine, was produced over the time of feeding indicating an increased requirement of betaine in normal development. Thus, the increase of N,N-dimethylglycine and betaine in the MAL pigs implies that during protein restriction, choline may tend to be oxidised, which potentially limits the availability of choline for brain development. This, together with the effect on the neurotransmitter precursors, such as histidine, tryptophan and phenylalanine/tyrosine in the MAL pigs, underline the potential detrimental effects of protein restriction on brain function and development in SAM children. Scarcity of one-carbon precursors seem to be the underlying cause and


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providing methionine as well as vitamins involved in one-carbon metabolism may be one strategy for improving the situation in malnourished children, however providing creatine to alleviate the needs for methyl groups may be equally effective. LPCs play important physiological and pathophysiological roles including vascular development, reproduction and myelination in neuronal diseases and cancer development in both humans and animals.59 Most of the LPCs identified are also related to choline metabolism and the generally lower level in the MAL pigs (Figure 2D) suggests competitive use of choline for production of betaine or glycine, though the contribution of the lower fat content in the MAL diet may not be completely excluded. Decreased plasma level of LPCs (LPC (16:0), LPC (18:2)) has been proposed as a pre-diabetic metabolic state.60 Besides, the different changing patterns of different LPCs (Figure 2D) suggest that different LPCs are synthesised via different pathways. In childhood, an increased intake and fast turn-over of nutrients is needed for growth and organ development, which is probably reflected in the age-related increment in the level of multiple metabolites in the REF pigs. Conversely, it is known for SAM children that the level of various metabolites in blood differ in response to restricted intake of nutrients.29 A similar situation was found in the MAL pigs fed with a diet restricted in both protein and other nutrients, but not in energy. The change of multiple amino acids may serve as a better indicator for the progression of acute malnutrition than the classical amino acid ratio, the accuracy and validity of which can be questioned.61 The current study showed that the metabolism of different amino acids changes at the different stages of nutritioninduced SAM, suggesting that the need for supplementation of different amino acids depends on the developmental stage of SAM with apparent need for all amino acids at later stages. Amino acid flux studies at different stages of malnutrition in both children and animal models may help to define the



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precise need at different stages of SAM. Further studies on the dynamics of different nutrients, such as choline and methionine, and on the competitive use of nutrients between the host and the gut microbiome in SAM are also needed. Finally the wasting in malnutrition leading to continuous tissue remodelling and loss of nutrients to the gut microbiome, may be a central feature explaining why SAM is particularly affecting the growing child. Thus, the differential need of amino acids, one-carbon donors, perturbed choline and gut microbiome metabolism may be some of the critical factors for the design of therapeutic foods for re-feeding children with SAM.


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ASSOCIATED CONTENT Supporting Information Table S-1, components and nutrient composition of the diets used; Table S-2, putatively annotated metabolites with differentiating time trends for their plasma levels in the MAL and the REF pigs; Figure S-1, LPC structure prediction plot using the adduct ([M+Na]+). The black dots represent the standards run as verification. Figure S-2, change in abundance of other quasi-molecules and adducts of metabolites identified at level 1. Figure S-3, change in abundance of metabolites annotated at level 2 (A) and 3 (B). ACKNOWLEDGMENTS The authors would like to thank Dr. Christian Reitz at Department of Nutrition, Sports and Exercise, University of Copenhagen, for statistical consultation. ABBREVIATIONS CNS, central nervous system; ESI, electrospray ionisation; FDR, false discovery rate; LPC, lysophosphatidylcholine; qTOF, quadrupole time-of-flight mass spectrometer; RT, retention time; SAM, severe acute malnutrition; UPLC, ultra-performance liquid chromatography



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Table 1. Identified Metabolites Differing Between the MAL and the REF Pigs Metabolite

Monoisotopic mass

RT (min)

Measured m/z

Assignment

155.0695

0.40

156.0761

[M+H]+

0.40

110.0703

[M-HCOOH+H]+

Amino acids Histidine

131.0946

Leucine

Lysine

146.1055

149.0510

Methionine

Phenylalanine

165.0790

0.92

130.0492

[M-H]-

0.96 0.96

132.1011 86.0957

[M+H]+ [M-HCOOH+H]+

0.96

263.1962

[2M+H]+

0.40

145.0971

[M-H]-

0.40

147.1119

[M+H]+

0.40

133.0969

[M-CH+H]+

0.40

130.0857

[M-NH3+H]+

0.40 0.67

84.0801 150.0581

[M- CH5NO2+H]+ [M+H]+

0.67

133.0317

[M -NH3+H]+

0.67

104.0527

[M-HCOOH+H]+

0.67

87.0263

1.57

164.0696

[M-H]-

1.57

147.0447

[M-H3N-H]-

1.57

107.0492

[M- C2H4NO +H]+

[M -NH3-HCOOH+H]+

Threonine

119.0582

0.48

118.0503

[M-H]-

Tryptophan

204.0899

2.36

203.0800

[M-H]-

2.36

205.0967

[M+H]+

2.36

188.0698

[M-H3N+H]+

2.36

159.0910

[M-CH2O2+H]+

2.36

146.0596

[M-C2H4NO+H]+

2.36

144.0800

[M-NH3CO2+H]+

2.36

132.0802

[M-C2H3NO2+H]+

2.36

429.1533

[2M+Na-2H]-

0.58

140.0678

[M+Na]+

Valine

117.0790


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Glutamic acid

0.58

118.086

[M+H]+

0.58

72.0806

[M-CH2O2+H]+

0.47

147.0750

[M]+

0.47

306.9591

[2M+Na]+

0.47

124.0057

[M-H2O-H]-

0.49

132.0757

[M-O+H]+

0.48

145.0603

[M-H]-

0.48

130.0495

[M-NH3+H]+

0.48

84.0436

115.0633

0.51

116.0704

[M+H]+

181.0739

0.50 0.76

70.0652 182.0806

[M-HCOOH+H]+ [M+H]+

0.77

180.0649

[M-H]-

0.76

165.0538

[M-H3N+H]+

0.76

147.0437

[M-H5NO+H]+

0.77

136.0743

[M-HCOOH+H]+

0.76

123.0438

[M-C2H4NO+H]+

0.76

119.0488

[M-CH5NO2+H]+

0.77

363.1526

[2M+H]+

0.50

130.0604

[M-H]-

0.50

88.0399

[M-CH2N2-H]-

90.0543 128.0344

[M-CH2N2+H]+ [M-H][M-H]-

147.0532

Glutamine

146.1055

Proline Tyrosine

[M-CH5NO2+H]+

Amino acid metabolism-related Creatine

131.0695

Pyroglutamic acid

129.0426

0.49 0.75

Isovalerylglycine (fatty acid metabolism)

159.0895

2.72

158.0813

2.71

74.0256

0.59

243.0614

[M-H]-

0.58

267.0583

[M+Na]+

0.59

209.0547

[M-H2O+H]+

[M-C5H8O-H]-

Nucleoside metabolism Pseudouridine

244.0695

Uric acid

168.0283

0.63

167.0195

[M-H]¯

Allantoin Choline metabolism-related Betaine (N,N,N-trimethylglycine)

158.0440

0.51

157.0328

[M-H]-

117.0790

0.49

118.0858

[M+H]+

N.N-dimethylglycine Gut microbiome-related Hippuric acid

103.0633

0.47

104.1064

[M+H]+

179.0582

3.04

180.0650

[M+H]+

3.04

202.0472

[M+Na]+

3.04

77.0383

3.38

216.0625

[M+Na]+

3.38

194.0804

[M+H]+

Phenylacetylglycine

193.0739


[M-C3H5NO3+H]+


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 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

p-Cresol glucuronide

284.0896

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[M-H2O+H]+ [M-C3H5NO3+H]+

3.38 3.38

176.0703 91.0540

3.38

76.0392

3.39

407.1220

[2M+Na-2H]-

3.39

192.0637

[M-H]-

3.39

74.0246

3.46

283.0796

[M-H]-

3.46

307.0781

[M+Na]+

3.46

175.0238

[M-C7H8O-H]-

3.46

113.0235

[M-C8H10O4-H]-

[M-C8H6O+H]+

[M-C8H6O-H]-

Phospholipids LPC(16:0)

495.3325

4.75

540.3277

[M+HCOOH-H]-

LPC(18:1)

521.3481

4.75

594.3554

[M+2Na-H]+

LPC(18:2)

519.3325

4.69

542.3217

[M+Na]+

LPC(20:2)

547.3638

4.78

570.3525

[M+Na]+

LPC(22:6)

567.3325

4.67

590.3219

[M+Na]+

4.69

612.3290

[M+HCOOH-H]-

180.0634

0.40

225.0982

[M+HCOOH-H]-

192.027

0.71

191.0173

[M-H]-

0.71

147.0294

[M-CO2-H]-

0.71

405.0278

[2M+Na-2H]-

1.97

218.1025

[M-H]-

1.97

88.0403

1.97

220.1173

[M+H]+

0.53

144.1009

[M+H]+

Others Glucose Citric acid/isocitric acid

Pantothenic acid (Vitamin B5)

219.1107

Proline betaine

143.0946


[M-C6H10O3-H]-

Page 33 of 36

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 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


FIGURE LEGENDS Figure 1. Change in abundance of essential amino acids (A), non-essential amino acids (B) and metabolites related to amino acid metabolism (C). Data are shown as mean ± S.E.M. *, adjusted P < 0.05 vs corresponding REF group. Figure 2. Change in abundance of metabolites related to nucleoside metabolism (A), gut microbiome (B), and choline metabolism (C), LPCs (D) and other metabolites (E). Data are shown as mean ± S.E.M. *, adjusted P < 0.05 vs corresponding REF group.



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 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 1 Change in abundance of essential amino acids (A), non-essential amino acids (B) and metabolites related to amino acid metabolism (C). Data are shown as mean ± S.E.M. *, adjusted P < 0.05 vs corresponding REF group. 177x130mm (300 x 300 DPI)


Page 34 of 36

Page 35 of 36

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 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 2 Change in abundance of metabolites related to nucleoside metabolism (A), gut microbiome (B), and choline metabolism (C), LPCs (D) and other metabolites (E). Data are shown as mean ± S.E.M. *, adjusted P < 0.05 vs corresponding REF group. 177x130mm (300 x 300 DPI)



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 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

For TOC only 85x43mm (300 x 300 DPI)


Page 36 of 36

Progressive Changes in the Plasma Metabolome during Malnutrition

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