Effects of Histidine Supplementation on Global Serum and Urine 1H

The aim of current study was to investigate the metabolic changes associated with histidine supplementation in serum and urine metabolic signatures an...
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Effects of Histidine Supplementation on Global Serum and Urine 1H NMR-based Metabolomics and Serum Amino Acid Profiles in Obese Women from a Randomized Controlled Study Shanshan Du, Shuhong Sun, Liyan Liu, Qiao Zhang, Fuchuan Guo, Chunlong Li, Rennan Feng, and Changhao Sun J. Proteome Res., Just Accepted Manuscript • Publication Date (Web): 27 Apr 2017 Downloaded from http://pubs.acs.org on April 28, 2017

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Effects of Histidine Supplementation on Global Serum and Urine 1H NMR-based Metabolomics and Serum Amino Acid Profiles in Obese Women from a Randomized Controlled Study

Shanshan Du1,∇, Shuhong Sun2,∇, Liyan Liu1,∇, Qiao Zhang1, Fuchuan Guo3, Chunlong Li4, Rennan Feng1,*, and Changhao Sun1,* Running title: metabolomics on histidine supplementation.

1

Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical

University, Harbin, China 2

Department of Psychiatry, the First Affiliated Hospital of Harbin Medical University,

Harbin, China 3

Department of Nutrition and Food Safety, School of Public Health, Fujian Medical

University, FuZhou, China 4

Department of General Surgery, the Second Affiliated Hospital of Harbin Medical

University, Harbin, China



Shanshan Du, Shuhong Sun and Liyan Liu contributed equally to this study.

*

Corresponding Authors: R.-N.F.: [email protected]; Fax: +86-451-87502885.

Corresponding Authors: C.-H.S.: [email protected].

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Abstract The aim of current study was to investigate the metabolic changes associated with histidine supplementation in serum and urine metabolic signatures and serum amino acid (AA) profiles. Serum and urine 1H NMR-based metabolomics and serum AAs profiles were employed in thirty two and thirty seven obese women with metabolic syndrome (MetS) intervened with placebo or histidine for 12 weeks. Multivariable statistical analysis were conducted to define characteristic metabolites. In serum 1H NMR metabolic profiles, increases in histidine, glutamine, aspartate, glycine, choline, and trimethylamine-N-oxide (TMAO) were observed, meanwhile, decreases in cholesterol, triglycerides, fatty acids and unsaturated lipids, acetone, and α/β-Glucose were exhibited after histidine supplement. In urine 1H NMR metabolic profiles, citrate, creatinine/creatine, methylguanidine and betaine + TMAO were higher, while, hippurate were lower in histidine supplement group. In serum AA profiles, ten AAs changed after histidine supplementation, including increased histidine, glycine, alanine, lysine, asparagine and tyrosine, and decreased leucine, isoleucine, ornithine and citrulline. The study showed a systemic metabolic response in serum and urine metabolomics and AA profiles to histidine supplementation, showing significantly changed metabolism in AAs, lipid and glucose in obese women with MetS. Keywords Histidine; 1H nuclear magnetic resonance-based metabolomics; amino acid profile; obesity; metabolic syndrome

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INTRODUCTION Histidine is a semi-essential amino acid in human, and has antioxidant and anti-inflammatory functions1. Its low concentration has been observed in patients with type 2 diabetes (T2DM)2, liver injury3, cardiovascular disease4, and chronic kidney disease5. In Communities (ARIC) study with 21.5 years follow-up, high serum histidine was negatively related to cardiovascular disease in African and European Americans1. Recently, higher circulating level was reported after bariatric surgery in severe obese subjects6. In our previous studies, we firstly observed decreased serum histidine of obese population in a cross-sectional study7, proved its beneficial effects on insulin resistance, inflammation and oxidative stress in obese women with metabolic syndrome (MetS) through a randomized controlled trial (RCT)8, and further verified the possible mechanism in vivo and in vitro studies8, 9. It can be seen that histidine supplementation could improve metabolic disorders.

However, how does histidine affect body metabolism, what are the systemic alterations in metabolic profiles derived from histidine, or whether serum amino acid (AA) profile is changed after histidine supplement, all these could not be explained by current limited studies on histidine supplementation in human or animal studies. Considering that metabolomics has been described as an efficient tool in identifying metabolic variations by detecting the metabolic response of living systems to pathophysiological stimuli, and can be applied in exploration of metabolic changes associated with diseases10 or nutrition11. Thus, we employed serum and urine 1H

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nuclear magnetic resonance (NMR) –based metabolomics analyses and qualified serum AA profiles to integrally analyze the metabolic events that associated with histidine supplementation in obese women with MetS. The study was an extension of our previous RCT to explore potential mechanisms for biological effects of histidine on metabolic disorders.

MATERIAL AND METHODS Histidine Intervention Experiment The study was based on a previously conducted RCT in northern China, which was to investigate the biological effect of histidine on metabolic disorders in obese women8. The trial registration was http://www.chictr.org.cn/showprojen.aspx?proj=7988. Briefly, 45 and 47 obese women with diagnosed MetS were enrolled and received either placebo or histidine (4 g/day) for 12 weeks. 32 and 37 participants from placebo and supplementation groups were included in current NMR metabolomics analyses, and quantitative determination of serum AA profiles. The study protocol abided by the standards in the Declaration of Helsinkias, and approved by the Human Research Ethics Committee of the Harbin Medical University. Written informed consents were obtained from all participants.

1

H NMR Metabolomics

Blood samples were collected from all participants after an overnight fasting, at baseline and 12th week, centrifuged to obtain serum and remove precipitate, then

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stored at -80 °C until metabolomics measurements. 300 µL serum and 300 µL 0.9% NaCl (w/v) solution containing 20% D2O were mixed by vortex for 2 min, and centrifuged at 10,000 rpm, 4 °C, for 15 min. 550 µL supernatant was neutralized to pH 7.0 (±0.1), and transferred to standard 5mm NMR tubes. After incubation at 4 °C for 12 h and equilibration at room temperature for 30 min, all samples were queued in auto-sampler for spectra acquisition. 1H NMR spectra were recorded at 300 K on Bruker Avance III spectrometer at 500 MHz (Bruker BioSpin, Karlsruhe, Germany), operating with an broad-band inverse probe. A standard one-dimensional (1D) NMR spectrum is the combination of a 1D nuclear overhauser enhancement spectroscopy (NOESY) and T2-edited spectrum. 1D NOESY, representing the total metabolite composition, was acquired by the standard 1D pulse sequence to achieve water presaturation [RD-90°-t1-90°-tm-90°-ACQ] with 3 µs inter-pulse delay t1, 100 ms mixing time (tm) and 1s relaxation delay (RD). T2-edited spectrum was to decrease macromolecule signals with short spin–spin relaxation times. It was acquired using Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence [90°–(τ–180°–τ)n–acq] with presaturation, a total 100 ms spin-spin RD (2nτ). The τ value was 350 µs and 2nτd was 35 ms. A total of 256 increments were measured in 10 ppm spectra, and acquisition time was 0.28 s. 2D 1H NMR spectra for selected serum samples were measured for metabolite identifications. It was derived from 2D 1H-1H J-resolved (JRES) NMR spectra through standard Bruker pulse programs.

Urine samples were also collected from all participants after an overnight fasting, at

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baseline and 12 wk, centrifuged to acquire liquid supernatant, and stored at -80 °C until metabolomics measurements. 500 µL urine samples were thawed at room temperature and mixed with 50 µL phosphate buffer (1.5 M K2HPO4/NaH2PO4, 2 mmol/L 3-trimethylsilyl-propionic-2,2,3,3-d4 acid (TSP), D2O solution, pH=7.4). After mixed by vortex and centrifuged, the mixtures were removed to NMR tubes, incubated, and equilibrated to room temperature for measurement. The water resonance was suppressed by a 1D NOESY pulse sequence with a ‫ ݉ݐ‬of 100 ms, RD of 4s, acquisition time of 2.66 s, pulse recycle time of 7.68 s. 256 free induction decays were zero-filled to 64K prior to Fourier transformation. 2D 1H-1H J-resolved (JRES) NMR spectra of selected samples were also measured.

Serum AA Measurements Serum preparation for AA quantitation was carried out as previously described12, 13. Targeted analysis of serum AA profiles was performed by a waters ACQUITY Ultra performance liquid chromatography (UPLC) system (Waters Corporation, Milford, MA, USA) coupled to a Waters Xevo TQD Mass Spectrometer (MS) (Waters Corporation, Manchester, UK). The methods of UPLC and MS were described and validated in previous study13. 20 amino acids, threonine, glutamine, arginine, valine, leucine, isoleucine, phenylalanine, tryptophan, serine, methionine, glycine, proline, histidine, alanine, lysine, glutamic acid, asparagine, tyrosine, ornithine and citrulline, and 5 amino metabolites, creatinine, dimethylglycine, taurine, creatine and aminbutyric acid, were determined and included in current study.

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Data Processing 1

H NMR Metabolomics

NMR spectra was phase-corrected manually and baseline corrected using Topspin (V3.2, Bruker Biospin, Germany). Blood samples were referred with the anomeric resonance of α-D-glucose (δ = 5.23 ppm), while, urine samples were referenced to the TSP resonance (δ = 0.00 ppm). Regions that affected by solvent suppression were excluded (serum: water signals (δ 4.7-5.0); urine: water and urea signals (δ 4.5-6.2)). The spectra region δ 0.6-9.0 ppm were binned into segments with the width of 0.002 ppm and automatically integrated with AMIX package (V3.9.15, Bruker Biospin, Germany). No normalization was used in serum spectra. In urine spectra, Probabilistic Quotient Normalization (PQN) was performed to eliminate the size effect of sample dilutions with R-library e1071 (http://cran.r-project.org/web/packages/e1071). Firstly, the median spectrum of all controls were selected as reference. Then, the quotient of a given test spectrum and reference spectrum were calculated, and the median of all quotients were estimated. Finally, all variables of the given test spectrum were divided by the median quotient14. All data was imported into Simca-p 11.5 for multivariable statistical analysis.

Multivariable statistical analysis was applied in pattern recognition analyses by Simca-p 11.5 (version 11.5; Umetrics, Umea, Sweden), including principal components analysis (PCA), partial least squared discriminant analysis (PLS-DA) and

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orthogonal partial least squared discriminant analysis (OPLS-DA). We used PCA to explore any clustering behavior of samples, identify outliers, and show a condensed summary of the data. PLS-DA and OPLS-DA was applied to visualize the maximal different metabolites associated with histidine supplementation. The fitness of PLS-DA model was validated by a default seven fold cross-validation with R2Y and Q2Y through random permutations using X-matrix (unit-variance scaled data) and Y-matrix (sample group)11. The model was valid, when R2Y and Q2Y were highest in permutation tests (n = 500). All these pattern recognitions and clustering behaviors were visualized in the form of cross-validated score plots.

Metabolites in NMR spectra were identified according to their chemical shift assignments for peaks and clusters in previously published studies15-17 and free online databases,

Human

Metabolome

Database

(HMDB)

(http://www.hmdb.ca).

Metabolites, that contributed to sample classification, were identified by their values of variable importance in the projection (VIP, > 1.5) and false discovery rate (FDR) in OPLS-DA model. FDR was implemented to correct for multiple testing by R-package fdrtool. Cross-validated score value and model score value were used to build score plot to make cross-variation more transparent, and S-plot was used to visualize the variable effects in OPLS-DA models18. HMDB and Kyoto Encyclopedia of Genes and Genomes (KEGG) database were used in metabolic pathway analyses to interpret implicated pathways of changed metabolites.

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Serum AA Profiles Paired t test was used in the comparisons of serum AA concentrations before and after intervention, and ANCOVA was used in the comparisons between the two groups, with covariates, including age, body mass index, physical activities, tobacco use, drink, dietary protein intake and baseline values. Multivariable statistical analysis including PCA, PLS-DA and OPLS-DA, was also performed to explore the significantly changed AAs or amino metabolites after histidine supplement. VIP > 1.0 was set as the cut-off of variable importance.

RESULTS The Demographic and Clinical Characteristics of All Participants The demographic and biochemical characteristic of all participants were depicted in Table S1. There were no differences between two groups at baseline. Glutathione peroxidase (GSH-Px) and superoxide dismutase (SOD) were increased, while, insulin resistance, non-essential fatty acid (NEFA), tumor necrosis factor-α (TNF-α), and C-reactive protein (CRP) were decreased after histidine supplementation for 12 weeks.

Metabolic Fingerprinting of Serum 1H NMR Metabolomics The representative serum 1H NMR spectrum and metabolites was depicted in Figure 1A. 34 metabolites were detectable and assigned in 1D and 2D 1H NMR spectra (Table S2). Multivariable statistical analysis were then conducted. In the PCA score

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plot (placebo group at baseline, intervention group at baseline, placebo group at 12th week, intervention group at 12th week), participants from intervention group at 12th week clustered away from those of other groups, and the other three groups were overlapped with each other (Figure S1A). In the comparisons between two groups, all participants at baseline were irregularly distributed in PCA score plot, showing no difference in 1H NMR spectra (Figure S1B). After 12 week intervention, the PCA score plot for the first two components showed clustering trends between two groups (Figure 1A). The score plot of PLS-DA model was obtained by two components (Figure 1B), which was reliable in explaining and predicting the variations of variables and sample groups for the highest R2Y and Q2Y (0.754 and 0.715) in 500 random permutation tests (Figure S1C). All participants were separated into two distinct

clusters,

indicating

specific

metabolic

alterations

after

histidine

supplementation.

To identify the metabolites that contributed to the distinction between two groups, OPLS-DA was discriminated, and its score plot and S-plot was displayed in Figure 3A and B. Variables responsible for clustering, were summarized in Table 1. Histidine, glycine, aspartate, glutamine, choline and trimethylamine-N-oxide (TMAO) were higher in intervention than placebo group. Meanwhile, cholesterol/low and very low density lipoprotein (LDL and VLDL), triglycerides/fatty acids (FAs), unsaturated lipids, acetone and α/β-Glucose were decreased after histidine supplementation.

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Metabolic Fingerprinting of Urine 1H NMR Metabolomics The representative whole urine 1H NMR spectra were depicted in Figure 1B, and it was dominated by 41 metabolites (Table S3). In the PCA score plot of all urine 1H NMR spectra, samples from intervention group at 12th week were clearly separated from those from intervention group at baseline and placebo group at baseline and 12th week (Figure S1D). In comparison of the two groups, the urine metabolic profiles showed no cluttering in PCA model at baseline (Figure S1E), and separated in PCA model after 12 week intervention (Figure 2C). The PLS-DA plot (Figure 2D) also showed clear group discriminations, and was reliable in representing statistical significance for highest points of R2Y and Q2Y in 500 random permutation tests (R2Y: 0.800; Q2Y: 0.694) (Figure S1F). Associated with score plot for OPLS-DA model (Figure 3C) and S-plot for urinary metabolic signature (Figure 3D), participants treated with histidine were exhibited higher urine histidine, citrate, betaine + TMAO, creatinine/creatine and methylguanidine, and lower hippurate than placebo group (Table 1).

Serum AA Profiles The serum concentrations of 20 AAs and 5 amino metabolites were shown in Table 2. No difference was observed at baseline between two groups. Histidine, Glycine, alanine, lysine, asparagine and tyrosine in histidine supplement group at 12 week were higher than baseline values and those in placebo group, while leucine, isoleucine, tryptophan and taurine were all lower. In histidine supplement group, glutamine

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increased, and phenylalanine decreased at 12 week, compared with their baseline levels. At 12th week, proline, ornithine and citrulline in histidine supplement group were lower than control group.

AA profiles of participants after histidine supplement clustered together, and separated with their baseline and placebo group at baseline or 12th week in PCA score plot (Figure S1G). Clear discriminations between two groups at 12th week was shown in both PCA and PLS-DA score plots (Figure 2E and 2F). The random permutation test was shown in Supplemental Figure 1F (R2Y: 0.973; Q2Y: 0.905). The OPLS-DA score plots and S-plots were shown in Figure 2E and 2F. Six AAs, histidine, glycine, alanine, lysine, asparagine and tyrosine were higher, and four AAs, leucine, isoleucine, ornithine and citrulline were lower in histidine supplementation than placebo group (Table 1).

DISCUSSION The present study was an extension of previous RCT, which provided the first clinical evidence on the beneficial effects of histidine supplementation on metabolic disorders in obese women. The results from serum and urine 1H NMR-based metabolomics and serum AA profiles all showed significant metabolic changes after histidine supplementation, confirming the significant effects of histidine on metabolic profiles in obese women with MetS.

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In current study, we observed higher serum histidine, glycine, alanine, lysine, glutamine, aspartate, asparagine and tyrosine after histidine supplementation in serum AA profiles and 1H NMR metabolomics. Many AAs are associated with a variety of MetS components. Histidine, glycine, alanine and lysine supplementations could decrease oxidative stress, plasma cholesterol, blood pressure and insulin resistance8, 19-21

, and increased serum glutamine, aspartate, asparagine and tyrosine are associated

with improve metabolic disorders22-26. To illustrate the possible effects of histidine supplementation on other AAs, we searched metabolic pathways in HMDB and KEGG, and found aspartate and glutamate may be crucial intermediates (Figure 4). Histidine could increase the biosynthesis of aspartate, which further participates in the biosyntheses of glycine, alanine, lysine and asparagine27. Glutamate, could also be generated from histidine metabolism, then convert into glutamine, aspartate, alanine, ornithine and citrulline24. However, ornithine and citrulline were decreased after histidine supplementation. Two branched-chain amino acids (BCAA), leucine and isoleucine, were also decreased. In previous studies on the associations between ornithine, citrulline, leucine and isoleucine and metabolic disorders, no consistent opinions have been reached. Ornithine was higher in insulin resistance28 and MetS29, while, not changed in diabetes30. High or unchanged circulating citrulline levels were also reported in metabolic disorders29-32. Increased circulating BCAAs were associated insulin resistance33 and have been reported in MetS patients29. But their dietary consumption was inversely associated with cardiometabolic risk factors34. On the whole, histidine supplementation changed serum AA profiles, but how these

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changes were connected with improved metabolic disorders should be explored in further RCT or cohort studies.

Previous studies revealed that histidine supplementation decreased body fat mass and serum fatty acids, meanwhile, improved insulin resistance among obese women with MetS8. And increased serum histidine was negatively associated with fasting and postprandial blood glucose in glucose tolerance tests among men without T2DM or with newly diagnosed T2DM2. In animal models, histidine supplementation decreased liver triglyceride and cholesterol contents3, and suppressed low density lipoprotein oxidation35, and reduced hepatic glucose production36. Similarity, current 1H NMR metabolomics showed lower serum cholesterol/LDL and VLDL, triglycerides/fatty acids, unsaturated lipids, and α/β-Glucose after histidine supplementation. These may be partly explained by increased aspartate, glutamine, glycine and alanine, all of which could enhance tri-carboxylic acid (TCA) cycle in metabolism of lipid and glucose24 (Figure. 4). Citrate, an important substrates in TCA cycle38, 39, is often used in oxidative energy production assessment, and its urine level could help estimate an individual’s metabolic status37. Patients with higher insulin resistance often associated with lower urine citrate excretion38. In current study, its elevated level was observed after histidine supplementation, indicating increased oxidative energy production. Moreover, histidine supplementation also decreased serum acetone. Acetone, a non-normal intermediate in the decomposition of fatty acids, was increased when glucose oxidation is restricted39 or hyperlipidemia occurs, and decreased after

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anti-hyperlipidemia treatment40. In previous studies, histidine can also inhibit the activities or mRNA expressions of HMG-CoA reductase, malic enzyme, fatty acid synthase, SREBP-1c and SREBP-2, which were all essential in the biosynthesis of fatty acids, cholesterol and triglycerides, thus, resulting in decreased de novo lipogenesis3. Circulating histidine can act as activator of signal transducer and activator of transcription-3, suppressing gluconeogenesis in the liver, resulting in low expression of glucose-6-phosphatase gene and enzyme, and suppression of hepatic glucose production36. Considering all above clues, histidine supplementation may improve metabolic disorders via enhancing oxygenolysis and reducing production of liid and glucose.

Increased choline, betaine, and TMAO (metabolites in choline metabolism), and decreased hippurate were also showed in 1H NMR metabolomics after histidine supplementation. Choline, as a methyl donor, regulates intracellular methyl balance, and prevents endothelial damage41. It was decreased in hyperlipidemia mice40 and high-fat fed rats42, and increased when lipoproteins concentration decreased in mice colonized by human baby flora43. Betaine, oxidized from choline, could decrease liver triglyceride deposition and liver injury induced by high-fat diet in rats44, and was a promising agent in treatment of nonalcoholic steatohepatitis45. TMAO, metabolized from choline by intestinal microflora46, was reported increased after bariatric surgery in severe obese subjects6. However, previous study has reported its adverse effect on atherosclerosis and cardiovascular disease47. Hippurate is also a metabolite derived

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from intestinal microflora, its high urinary extraction was associated with high glycosuria, indicating deterioratived metabolic control in T2DM patients, however, low urinary level was also identified in individuals with obesity or hypertension48. All above metabolic alterations were all associated with intestinal tract, indicating histidine supplementation may affect intestinal tract microbiota metabolism. It has been studied that decreased serum histidine concentration can be used as a predictor of relapse in patients with ulcerative colitis49, and dietary histidine can alleviate intestinal inflammation50. One thing to note is that many other conditions like renal function, diet and potential differences in microbiota may effect urinary metabolite extractions47, above results should be interpreted with caution. More studies are still needed to investigate the association between histidine and intestinal tract.

Furthermore, high creatine and related metabolites (creatinine and methylguanidine) were also observed in metabolomics after histidine supplementation. Creatine can protect DNA and RNA from oxidative damage, and improve health and survival of mice51. Creatinine is the degradation product of creatine52. Methylguanidine is a product in creatinine metabolism, and can scavenge free radical. Its high level has been observed in treatment of fatty liver53. However, no changes in creatine or creatinine were observed in serum 1H NMR metabolomics or AA profiles, it was hard to conclude whether creatine and related metabolites changed in current study.

The limitations of current study included the small sample size and only obese women

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in the RCT of histidine supplementation, which may lack generality to the whole population. Therefore, all above findings should be explained with caution, and future studies with enlarged sample size in general population should be conducted.

CONCLUSION In conclusion, the 1H NMR-based metabolomics and serum AA profiles analyses of obese women showed a systemic response of human bio-samples to histidine supplementation. Histidine supplementation significantly changed metabolism of AAs, lipid and glucose. This work provides supporting experiment evidence for the practical application of histidine in prevention and treatment of chronic metabolic disorders.

ASSOCIATED CONTENT AVAILABLE Table S1 The demographic, dietary and clinical data at baseline and 12 weeks in histidine supplementation and placebo groups. Table S2 Metabolites and chemical shifts in serum 1H NMR spectra. Table S3 Metabolites and chemical shifts in urine 1H NMR spectra. Figure S1 Score plots derived from serum and urine 1H NMR metabolomics and serum AA profiles of placebo and histidine supplementation groups.

AUTHOR INFORMATION Author Contributions

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R.N.F. and C.H.S.: conceived and designed the study; S.S.D., S.H.S., L.Y.L. and Q.Z. performed the human study; F.C.G. and C.L.L.: carried out the 1H NMR data collection; L.Y.L. and Q.Z. carried out UPLC-MS/MS data collection; S.S.D., L.Y.L. and F.C.G. carried out data analysis and interpretation; R.N.F., S.S.D., S.H.S. and L.Y.L. drafted the manuscript; and all authors read and approval the final version of the manuscript. The authors declared no competing financial interest.

ACKNOWLEDGMENTS Rennan Feng received fundings from the National Natural Science Fund of China No. 81573133 and 8202184, and Natural Science Fund of Heilongjiang Province No. H2016018. Fuchuan Guo received funding from the National Natural Science Fund of China No. 81202185.

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with the levels of eight amino acids in 9,369 Finnish men. Diabetes 2012, 61, (7), 1895-1902. (3) Mong, M. C.; Chao, C. Y.; Yin, M. C., Histidine and carnosine alleviated hepatic steatosis in mice consumed high saturated fat diet. Eur J Pharmacol 2011, 653, (1-3), 82-88. (4) Hasegawa, S.; Ichiyama, T.; Sonaka, I.; Ohsaki, A.; Okada, S.; Wakiguchi, H.; Kudo, K.; Kittaka, S.; Hara, M.; Furukawa, S., Cysteine, histidine and glycine exhibit anti-inflammatory effects in human coronary arterial endothelial cells. Clin Exp Immunol 2012, 167, (2), 269-274. (5) Watanabe, M.; Suliman, M. E.; Qureshi, A. R.; Garcia-Lopez, E.; Barany, P.; Heimburger, O.; Stenvinkel, P.; Lindholm, B., Consequences of low plasma histidine in chronic kidney disease patients: associations with inflammation, oxidative stress, and mortality. Am J Clin Nutr 2008, 87, (6), 1860-1866. (6) Gralka, E.; Luchinat, C.; Tenori, L.; Ernst, B.; Thurnheer, M.; Schultes, B., Metabolomic fingerprint of severe obesity is dynamically affected by bariatric surgery in a procedure-dependent manner. Am J Clin Nutr 2015, 102, (6), 1313-1322. (7) Niu, Y. C.; Feng, R. N.; Hou, Y.; Li, K.; Kang, Z.; Wang, J.; Sun, C. H.; Li, Y., Histidine and arginine are associated with inflammation and oxidative stress in obese women. Br J Nutr 2012, 108, (1), 57-61. (8) Feng, R. N.; Niu, Y. C.; Sun, X. W.; Li, Q.; Zhao, C.; Wang, C.; Guo, F. C.; Sun, C. H.; Li, Y., Histidine supplementation improves insulin resistance through suppressed inflammation in obese women with the metabolic syndrome: a randomised controlled trial. Diabetologia 2013, 56, (5), 985-994. (9) Sun, X.; Feng, R.; Li, Y.; Lin, S.; Zhang, W.; Li, Y.; Sun, C.; Li, S., Histidine supplementation alleviates inflammation in the adipose tissue of high-fat diet-induced obese rats via the NF-kappaB- and PPARgamma-involved pathways. Br J Nutr 2014, 112, (4), 477-485. (10) Liu, L.; Wang, M.; Yang, X.; Bi, M.; Na, L.; Niu, Y.; Li, Y.; Sun, C., Fasting serum lipid and dehydroepiandrosterone sulfate as important metabolites for ACS Paragon Plus Environment

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isolated

postchallenge

diabetes:

serum

metabolomics

via

ultra-high-performance LC-MS. Clin Chem 2013, 59, (9), 1338-1348. (11) Wang, M.; Yang, X.; Wang, F.; Li, R.; Ning, H.; Na, L.; Huang, Y.; Song, Y.; Liu, L.; Pan, H.; Zhang, Q.; Fan, L.; Li, Y.; Sun, C., Calcium-deficiency assessment and biomarker identification by an integrated urinary metabonomics analysis. BMC Med 2013, 11, 86. (12) Liu, L.; Wang, X.; Li, Y.; Sun, C., Postprandial Differences in the Amino Acid and Biogenic Amines Profiles of Impaired Fasting Glucose Individuals after Intake of Highland Barley. Nutrients 2015, 7, (7), 5556-5571. (13) Liu, L.; Feng, R.; Guo, F.; Li, Y.; Jiao, J.; Sun, C., Targeted metabolomic analysis reveals the association between the postprandial change in palmitic acid, branched-chain amino acids and insulin resistance in young obese subjects. Diabetes Res Clin Pract 2015, 108, (1), 84-93. (14) Kohl, S. M.; Klein, M. S.; Hochrein, J.; Oefner, P. J.; Spang, R.; Gronwald, W., State-of-the art data normalization methods improve NMR-based metabolomic analysis. Metabolomics 2012, 8, (Suppl 1), 146-160. (15) Nicholson, J. K.; Foxall, P. J.; Spraul, M.; Farrant, R. D.; Lindon, J. C., 750 MHz 1H and 1H-13C NMR spectroscopy of human blood plasma. Anal Chem 1995, 67, (5), 793-811. (16) Liu, M.; Nicholson, J. K.; Parkinson, J. A.; Lindon, J. C., Measurement of biomolecular diffusion coefficients in blood plasma using two-dimensional 1H-1H diffusion-edited total-correlation NMR spectroscopy. Anal Chem 1997, 69, (8), 1504-1509. (17) Bouatra, S.; Aziat, F.; Mandal, R.; Guo, A. C.; Wilson, M. R.; Knox, C.; Bjorndahl, T. C.; Krishnamurthy, R.; Saleem, F.; Liu, P.; Dame, Z. T.; Poelzer, J.; Huynh, J.; Yallou, F. S.; Psychogios, N.; Dong, E.; Bogumil, R.; Roehring, C.; Wishart, D. S., The human urine metabolome. PLoS One 2013, 8, (9), e73076. (18) Wiklund, S.; Johansson, E.; Sjostrom, L.; Mellerowicz, E. J.; Edlund, U.; Shockcor, J. P.; Gottfries, J.; Moritz, T.; Trygg, J., Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically ACS Paragon Plus Environment

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interesting compounds using OPLS class models. Anal Chem 2008, 80, (1), 115-122. (19) Diaz-Flores, M.; Cruz, M.; Duran-Reyes, G.; Munguia-Miranda, C.; Loza-Rodriguez, H.; Pulido-Casas, E.; Torres-Ramirez, N.; Gaja-Rodriguez, O.; Kumate,

J.;

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Cells With KRAS Mutations and Xenograft Tumor Progression in Mice via Intracellular Synthesis of Aspartate. Gastroenterology 2016, 15, (5), 945-960. (27) Reitzer, L., Biosynthesis of Glutamate, Aspartate, Asparagine, L-Alanine, and D-Alanine. EcoSal Plus 2004, 1, (1), 1-18. (28) Tai, E. S.; Tan, M. L.; Stevens, R. D.; Low, Y. L.; Muehlbauer, M. J.; Goh, D. L.; Ilkayeva, O. R.; Wenner, B. R.; Bain, J. R.; Lee, J. J.; Lim, S. C.; Khoo, C. M.; Shah, S. H.; Newgard, C. B., Insulin resistance is associated with a metabolic profile of altered protein metabolism in Chinese and Asian-Indian men. Diabetologia 2010, 53, (4), 757-767. (29) Ho, J. E.; Larson, M. G.; Ghorbani, A.; Cheng, S.; Chen, M. H.; Keyes, M.; Rhee, E. P.; Clish, C. B.; Vasan, R. S.; Gerszten, R. E.; Wang, T. J., Metabolomic Profiles of Body Mass Index in the Framingham Heart Study Reveal Distinct Cardiometabolic Phenotypes. PLoS One 2016, 11, (2), e0148361. (30) Thalacker-Mercer, A. E.; Ingram, K. H.; Guo, F.; Ilkayeva, O.; Newgard, C. B.; Garvey, W. T., BMI, RQ, diabetes, and sex affect the relationships between amino acids and clamp measures of insulin action in humans. Diabetes 2014, 63, (2), 791-800. (31) Yamakado, M.; Nagao, K.; Imaizumi, A.; Tani, M.; Toda, A.; Tanaka, T.; Jinzu, H.; Miyano, H.; Yamamoto, H.; Daimon, T.; Horimoto, K.; Ishizaka, Y., Plasma Free Amino Acid Profiles Predict Four-Year Risk of Developing Diabetes, Metabolic Syndrome, Dyslipidemia, and Hypertension in Japanese Population. Sci Rep 2015, 5, 11918. (32) Zhou, Y.; Qiu, L.; Xiao, Q.; Wang, Y.; Meng, X.; Xu, R.; Wang, S.; Na, R., Obesity and diabetes related plasma amino acid alterations. Clin Biochem 2013, 46, (15), 1447-1452. (33) Jang, C.; Oh, S. F.; Wada, S.; Rowe, G. C.; Liu, L.; Chan, M. C.; Rhee, J.; Hoshino, A.; Kim, B.; Ibrahim, A.; Baca, L. G.; Kim, E.; Ghosh, C. C.; Parikh, S. M.; Jiang, A.; Chu, Q.; Forman, D. E.; Lecker, S. H.; Krishnaiah, S.; Rabinowitz, J. D.; Weljie, A. M.; Baur, J. A.; Kasper, D. L.; Arany, Z., A branched-chain amino acid metabolite drives vascular fatty acid transport and causes insulin ACS Paragon Plus Environment

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resistance. Nat Med 2016, 22, (4), 421-426. (34) Cogate, P. G.; Natali, A. J.; de Oliveira, A.; Alfenas, R. C.; Hermsdorff, H. H., Consumption of Branched-Chain Amino Acids Is Inversely Associated with Central Obesity and Cardiometabolic Features in a Population of Brazilian Middle-Aged Men: Potential Role of Leucine Intake. J Nutr Health Aging 2015, 19, (7), 771-777. (35) Lee, Y. T.; Hsu, C. C.; Lin, M. H.; Liu, K. S.; Yin, M. C., Histidine and carnosine delay diabetic deterioration in mice and protect human low density lipoprotein against oxidation and glycation. Eur J Pharmacol 2005, 513, (1-2), 145-150. (36) Kimura, K.; Nakamura, Y.; Inaba, Y.; Matsumoto, M.; Kido, Y.; Asahara, S.; Matsuda, T.; Watanabe, H.; Maeda, A.; Inagaki, F.; Mukai, C.; Takeda, K.; Akira, S.; Ota, T.; Nakabayashi, H.; Kaneko, S.; Kasuga, M.; Inoue, H., Histidine augments the suppression of hepatic glucose production by central insulin action. Diabetes 2013, 62, (7), 2266-2277. (37) Nunez, P.; Diaz, I.; Perillan, C.; Arguelles, J.; Diaz, E., Circadian urinary citrate excretion in a rat model of exercise. Life Sci 2017, 169, 65-68. (38) Csete, M.; Doyle, J., Bow ties, metabolism and disease. Trends Biotechnol 2004, 22, (9), 446-450. (39) Cotter, D. G.; Schugar, R. C.; Crawford, P. A., Ketone body metabolism and cardiovascular disease. Am J Physiol Heart Circ Physiol 2013, 304, (8), H1060-1076. (40) Li, Z. Y.; Ding, L. L.; Li, J. M.; Xu, B. L.; Yang, L.; Bi, K. S.; Wang, Z. T., (1)H-NMR and MS based metabolomics study of the intervention effect of curcumin on hyperlipidemia mice induced by high-fat diet. PLoS One 2015, 10, (3), e0120950. (41) Zielinski, J.; Kusy, K.; Rychlewski, T., Effect of training load structure on purine metabolism in middle-distance runners. Med Sci Sports Exerc 2011, 43, (9), 1798-1807. (42) An, Y.; Xu, W.; Li, H.; Lei, H.; Zhang, L.; Hao, F.; Duan, Y.; Yan, X.; Zhao, Y.; Wu, J.; Wang, Y.; Tang, H., High-fat diet induces dynamic metabolic alterations ACS Paragon Plus Environment

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in multiple biological matrices of rats. J Proteome Res 2013, 12, (8), 3755-3768. (43) Martin, F. P.; Dumas, M. E.; Wang, Y.; Legido-Quigley, C.; Yap, I. K.; Tang, H.; Zirah, S.; Murphy, G. M.; Cloarec, O.; Lindon, J. C.; Sprenger, N.; Fay, L. B.; Kochhar, S.; van Bladeren, P.; Holmes, E.; Nicholson, J. K., A top-down systems biology view of microbiome-mammalian metabolic interactions in a mouse model. Mol Syst Biol 2007, 3, 112. (44) Deminice, R.; da Silva, R. P.; Lamarre, S. G.; Kelly, K. B.; Jacobs, R. L.; Brosnan, M. E.; Brosnan, J. T., Betaine supplementation prevents fatty liver induced by a high-fat diet: effects on one-carbon metabolism. Amino Acids 2015, 47, (4), 839-846. (45) Abdelmalek, M. F.; Angulo, P.; Jorgensen, R. A.; Sylvestre, P. B.; Lindor, K. D., Betaine, a promising new agent for patients with nonalcoholic steatohepatitis: results of a pilot study. Am J Gastroenterol 2001, 96, (9), 2711-2717. (46) 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, (6), 1095-1098. (47) Tang, W. H.; Hazen, S. L., The contributory role of gut microbiota in cardiovascular disease. J Clin Invest 2014, 124, (10), 4204-4211. (48) Lees, H. J.; Swann, J. R.; Wilson, I. D.; Nicholson, J. K.; Holmes, E., Hippurate: the natural history of a mammalian-microbial cometabolite. J Proteome Res 2013, 12, (4), 1527-1546. (49) Hisamatsu, T.; Ono, N.; Imaizumi, A.; Mori, M.; Suzuki, H.; Uo, M.; Hashimoto, M.; Naganuma, M.; Matsuoka, K.; Mizuno, S.; Kitazume, M. T.; Yajima, T.; Ogata, H.; Iwao, Y.; Hibi, T.; Kanai, T., Decreased Plasma Histidine Level Predicts Risk of Relapse in Patients with Ulcerative Colitis in Remission. PLoS One 2015, 10, (10), e0140716. (50) Andou, A.; Hisamatsu, T.; Okamoto, S.; Chinen, H.; Kamada, N.; Kobayashi, T.; Hashimoto, M.; Okutsu, T.; Shimbo, K.; Takeda, T.; Matsumoto, H.; Sato, A.; Ohtsu, H.; Suzuki, M.; Hibi, T., Dietary histidine ameliorates murine colitis by ACS Paragon Plus Environment

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inhibition of proinflammatory cytokine production from macrophages. Gastroenterology 2009, 136, (2), 564-574 e562. (51) Sestili, P.; Martinelli, C.; Colombo, E.; Barbieri, E.; Potenza, L.; Sartini, S.; Fimognari, C., Creatine as an antioxidant. Amino acids 2011, 40, (5), 1385-1396. (52) Brosnan, J. T.; Brosnan, M. E., Creatine metabolism and the urea cycle. Molecular genetics and metabolism 2010, 100, S49-S52. (53) Miccheli, A.; Capuani, G.; Marini, F.; Tomassini, A.; Pratico, G.; Ceccarelli, S.; Gnani, D.; Baviera, G.; Alisi, A.; Putignani, L.; Nobili, V., Urinary (1)H-NMR-based metabolic profiling of children with NAFLD undergoing VSL#3 treatment. Int J Obes (Lond) 2015, 39, (7), 1118-1125.

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Figure legends Figure 1 Representative spectra and metabolites in serum and urine 1H NMR Metabolomics. Note. A: serum 1H NMR spectra and metabolites identified in CPMG pulse sequence; B: urine 1H NMR spectra and metabolites identified in NOESY pulse sequence.

Figure 2 Pattern recognition analyses of serum and urine 1H NMR spectra and serum AA profiles from placebo and histidine supplementation groups at 12th week. Note. A: PCA score plot of serum 1H NMR spectra; B: PLS-DA score plot in serum 1

H NMR spectra; C: PCA score plot in urine 1H NMR spectra; D: PLS-DA score plot

in urine 1H NMR spectra; E: PCA score plot of serum AA profiles; F: PLS-DA score plot in serum AA profiles; red box: histidine supplementation group at 12th week; black diamond: placebo group at 12th week.

Figure 3 OPLS-DA score plots and S-plots revealing the metabolites with large intensities contributing to the clustering in pattern recognition analyses at 12th week Note. A: OPLS-DA score plot of serum 1H NMR spectra at 12th week; B: S-plot of A; C: OPLS-DA score plot of urine 1H NMR spectra at 12th week; D: S-plot of C; E: OPLS-DA score plot for serum AA profiles at 12th week; F: S-plot of E.

Figure 4 Potential metabolic pathways associated with histidine supplementation. Note. red: increased metabolites after histidine supplementation; blue: decreased metabolites after histidine supplementation.

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Table 1 The significantly changed metabolites in serum and urine 1H NMR metabolomics and serum AA profiles after histidine supplementation Relative Relative Metabolites Metabolites change change 1 Serum H NMR metabolomics Cholesterol/LDL/VLDL Glutamine ↓ ↑ Lipids (triglycerides and FAs) ↓ Glycine ↑ Unsaturated lipids Choline ↓ ↑ Acetone



TMAO



Histidine



α/β-Glucose

Aspartate



-

↓ -



Betaine + TMAO



Creatinine



Methylguanidine



Creatinine/Creatine



Hippurate





Tyrosine



Glycine



Leucine



Alanine



Isoleucine



Lysine



Ornithine



Asparagine



Citrulline



1

Urine H NMR metabolomics Citrate

Serum AA profiles Histidine

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Table 2 Serum amino acid profiles at baseline and 12 weeks in histidine supplementation and placebo groups. Amino acids (µmol/L)

Placebo (32)

Histidine (37)

Baseline

12 weeks

Baseline

12 weeks

Threonine

102.51±31.47

106.41±28.35

101.41±26.45

116.64±27.57

Glutamine Arginine

526.29±94.35 134.80±48.6

532.94±89.51 146.09±31.13

496.70±88.09 138.99±56.68

562.86±101.20* 135.58±28.27

Valine Leucine

150.14±17.73 89.86±17.64

135.46±20.91 83.11±15.48

144.39±25.97 86.10±20.55

139.28±21.64 67.22±14.72†*

Isoleucine Phenylalanine

58.65±10.91 111.17±21.54

54.08±11.70 101.60±21.78

56.56±15.78 111.75±32.12

45.06±11.08†* 97.26±38.71*

Tryptophan Serine

43.05±9.72 126.06±32.55

36.52±8.94 129.56±33.50

38.02±12.17 129.14±33.00

30.47±5.44†* 141.73±25.62

Methionone Glycine

8.66±4.11 232.44±92.73

10.77±2.87 239.33±66.47

9.18±4.05 218.6±72.72

9.78±2.43 285.99±56.88†*

Proline Histidine

177.83±40.20 52.61±7.71

184.31±47.79 50.36±10.25

177.31±51.27 52.08±9.92

163.25±40.16† 62.79±6.72†*

Alanine Lysine Creatinine Dimethylglycine

451.73±109.43 315.71±52.36 29.54±8.02 15.51±5.00

426.71±122.01 311.82±46.34 27.98±7.74 17.88±6.52

439.54±117.92 480.48±114.85†* 321.29±62.72 346.30±49.94†*

Creatine Glutamic acid

106.28±39.14 22.06±4.12

Taurine Asparagine

27.38±9.80 15.30±6.81

27.98±7.74 17.10±4.06

96.03±43.25 21.95±4.87

108.99±37.42 23.33±5.31

107.74±39.63 20.66±6.09

50.83±12.11 42.28±9.37

48.13±5.90 40.61±8.60

Tyrosine

13.41±2.23

14.53±3.34

49.23±10.28 40.91±9.34 13.76±2.37

43.99±7.98†* 46.58±6.86†* 16.80±4.30†*

Aminbutyric acid

13.71±4.50

15.46±5.99

Ornithine Citrulline

85.79±24.29 39.78±8.92

94.18±26.81 39.74±11.81

13.97±5.89 86.78±21.10 36.23±7.32

14.48±3.83 79.45±25.60† 33.10±6.88†

All values are means±SD. *P < 0.05, compared with baseline values (paired samples t test); †

P < 0.05, comparing the two groups at 12 weeks (ANCOVA; the covariates included age, body mass index, physical activities, tobacco use, drink, dietary protein intake and baseline values).

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1 2

1

Figure 1 Representative spectra and metabolites in serum and urine H NMR Metabolomics.

3

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Figure 2 Pattern recognition analyses of serum and urine 1H NMR spectra and serum AA profiles from placebo and histidine supplementation groups at 12th week. Note. A: PCA score plot of serum 1H NMR spectra; B: PLS-DA score plot in serum 1H NMR spectra; C: PCA score plot in urine 1H NMR spectra; D: PLS-DA score plot in urine 1H NMR spectra; E: PCA score plot of serum AA profiles; F: PLS-DA score plot in serum AA profiles; red box: histidine supplementation group at 12th week; black diamond: placebo group at 12th week.

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Figure 3 OPLS-DA score plots and S-plots revealing the metabolites with large intensities contributing to the clustering in pattern recognition analyses at 12th week Note. A: OPLS-DA score plot of serum 1H NMR spectra at 12th week; B: S-plot of A; C: OPLS-DA score plot of urine 1H NMR spectra at 12th week; D: S-plot of C; E: OPLS-DA score plot for serum AA profiles at 12th week; F: S-plot of E.

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Figure 4 Potential metabolic pathways associated with histidine supplementation. Note. red: increased metabolites after histidine supplementation; blue: decreased metabolites after histidine supplementation.

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