376  |  wileyonlinelibrary.com/journal/rda Reprod Dom Anim 2017;52:376–388© 2017 Blackwell Verlag GmbH

Received: 4 August 2016  |  Accepted: 30 November 2016

DOI: 10.1111/rda.12919

O R I G I N A L A R T I C L E

Metabolomic profiling in umbilical venous plasma reveals effects of dietary rumen- protected arginine or N- carbamylglutamate supplementation in nutrient- restricted Hu sheep during pregnancy

L Sun | H Zhang | Y Fan | Y Guo | G Zhang | H Nie | F Wang

Jiangsu Engineering Technology Research Center of Meat Sheep & Goat Industry, Nanjing Agricultural University, Nanjing, China

CorrespondenceFeng Wang, Jiangsu Engineering Technology Research Center of Meat Sheep & Goat Industry, Nanjing Agricultural University, Nanjing, China.Email: caeet@njau.edu.cn

Funding informationChina Agriculture Research System, Grant/Award Number: CARS-39; Key Research Program of Jiangsu Province, Grant/Award Number: BE2015362; National Science and Technology Support Program, Grant/Award Number: 2015BAD03B05-06

ContentsMaternal nutrient restriction during pregnancy is a major problem worldwide for human and animal production. Arginine (Arg) is critical to health, growth and reproduc-tion. N- carbamylglutamate (NCG), a key enzyme in arginine synthesis, is not exten-sively degraded in rumen. The aim of this study was to investigate ameliorating effects of rumen- protected arginine (RP- Arg) and NCG supplementation on dietary in under-nourished Hu sheep during gestation. From day 35 to 110 of gestation, 32 Hu ewes carrying twin foetuses were randomly divided into four groups: a control (CG) group (n = 8; fed 100% National Research Council (NRC) requirements for pregnant sheep), a nutrient- restricted (RG) group (n = 8; fed 50% NRC requirements, which included 50% mineral–vitamin mixture) and two treatment (Arg and NCG) groups (n = 8; fed 50% NRC requirements supplemented with 20 g/day RP- Arg or 5 g/day NCG, which included 50% mineral–vitamin mixture). The umbilical venous plasma samples of foe-tus were tested by 1H- nuclear magnetic resonance. Thirty- two differential metabo-lites were identified, indicating altered metabolic pathways of amino acid, carbohydrate and energy, lipids and oxidative stress metabolism among the four groups. Our results demonstrate that the beneficial effect of dietary RP- Arg and NCG supplementation on mammalian reproduction is associated with complex metabolic networks.

1  | INTRODUCTION

Maternal nutrient restriction during pregnancy is a challenge to foetal growth and development (McDonald et al., 2013). It is also a major health problem worldwide, which leads to intrauterine growth restric-tion (IUGR), and foetal undernutrition accompanies placental insuffi-ciency. Sheep has been used as an appropriate animal model of human pregnancy over the past 40 years which include the study of maternal nutrient restriction on maternal, placental and foetal development (Barry & Anthony, 2008; Bird, Zhang, & Magness, 2003; Moores, Carter, Meschia, Fennessey, & Battaglia, 1994; Sladek, Magness, & Conrad, 1997; Wallace, 2000). A previous study has reported that >50% of the National Research Council (NRC) requirements in sheep during pregnancy had not been met (Vonnahme et al., 2003). The

underfed sheep from early to mid- gestation would lose an amount of body fat and protein, and the health of maternal and foetal growth would be compromised even after supplementation in late gestation.

It is important to provide adequate amounts of essential and non- essential amino acids to animals for maintenance and produc-tion (Chacher, Liu, Wang, & Liu, 2013). At all stages of pregnancy, an adequate supply of amino acids is also very important for normal development of the placenta and foetus. Arginine (Arg), one of the nutritionally essential amino acids, regulates key metabolic pathways of the animals, which relate to health, growth, reproduction and ho-moeostasis (Lassala et al., 2010). Arg also serves as a precursor of synthesis of many biologically active molecules (such as nitric oxide (NO), ornithine, putrescine, spermidine, spermine, urea, creatine and agmatine) in cells (Wu & Morris, 1998). It is one of the functional

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amino acids for animal pregnancy. A previous study reported that Arg supplementation in pregnant ewes ameliorated foetal mortality and growth retardation (Lassala et al., 2010). Arg can be de novo synthe-sized in ruminants, but it is not sufficient to meet the requirement particularly for high level of production (Wu et al., 2013). In addition, dietary unprotected Arg supplementation will be degraded in the rumen. The rumen- protected arginine (RP- Arg) provides an effective approach to solve this problem, but its price is high. As a metabolically stable analogue of N- acetylglutamate (NAG), N- carbamylglutamate (NCG) activates a key enzyme in the process of Arg synthesis in en-terocytes. Dietary NCG supplementation in ruminants increases en-dogenous synthesis of arginine, which lowers degradation in rumen compared to Arg supplementation. In a previous experiment, the deg-radation rate of unprotected Arg and NCG in rumen after 24 hrs was 17.8% and 100.0%, respectively (Chacher, Wang, Liu, & Liu, 2012). Furthermore, the cost of NCG is lower than that of Arg by chemical synthesis.

Maternal nutrition is essential for foetal survival, growth and de-velopment. Thus, supplementation with proper nutrition in pregnant dam is vital for the foetus. Increasing evidence demonstrated that RP- Arg or NCG supplementation had beneficial effects on pregnant dam fed with proper nutrition, improved placental growth and prevented maternal nutrient restriction and IUGR in animals (Chacher et al., 2013). In our recent study, supplementation of 20 g/day RP- Arg or 5 g/day NCG to nutrient- restricted Hu ewes from day 35 to 110 of gestation significantly increased weights of foetuses and most foetal organs and markedly improved concentrations of amino acids (particu-larly arginine- family amino acids) and polyamines in maternal and foe-tal plasma, indicating that feeding RP- Arg or NCG to underfed ewes is an effective strategy to improved foetal growth restriction (Zhang, Sun, Wang, Deng, Zhang, Guo, et al., 2016). However, little is known about the effects of RP- Arg or NCG supplementation on plasma me-tabolomics in pregnant ewes.

As a core area of systems biology, metabolomics is a widely bioanalytical method in investigation of disease processes, drug toxicity, altered gene function and reaction to physiological stim-uli (Nicholson & Lindon, 2008). Metabolomics, in combination with multivariate statistical analysis, can be used to perform investigation in biological samples for qualitative and quantitative alterations of small molecules (Won et al., 2013). To date, common metabolomic technologies include gas chromatography–mass spectrometry (GC- MS), liquid chromatography–mass spectrometry (LC- MS), nuclear magnetic resonance (NMR) and metabolite chip. Among techniques, 1H- NMR has been a popular metabolomic technique, which was fre-quently used to assess the role of metabolites in body and their as-sociation with various physiological or pathological conditions (Graaf, Prinsen, Giannini, Caprio, & Herzog, 2015). 1H- NMR- based metabo-lomics has several important advantages for clinical implementation (Sun et al., 2014). Firstly, it can provide unbiased and rapid results of a wide range of small metabolites. And secondly, sample preparation and processing in 1H- NMR platform is limited. In addition, 1H- NMR is being increasingly recognized for prenatal biomarker identification of the potential of biofluid metabolic profiling. Therefore, the aim of

this study was to explore the effects of dietary rumen- protected Arg (RP- Arg) and NCG supplementation in nutrient- restricted Hu sheep during late gestation using the approach of blood metabolomic anal-ysis by 1H- NMR.

2  | MATERIALS AND METHODS

2.1 | Animals

Forty- eight multiparous Hu ewes (BW = 40.1 ± 1.2 kg) aged 2–5 years were obtained at the Jiangyan Experimental Station of Taizhou, Jiangsu Province, China. After anthelmintic treatments, ewes were synchronized using intravaginal progesterone- impregnated vaginal implants for 12 days. Following implant removal, prostaglan-din F 2α (20 mg; LUTALYSE®, Zoetis, Kalamazoo, MI, USA) was intra-muscularly administered for initiating follicular development. After sponge withdrawal, ewes were artificially inseminated using fresh semen. The ewes were provided with the same diet before group-ing. The animal protocols of this study were performed in accord-ance with procedures approved by the Guide for the Care and Use of Laboratory Animals prepared by the Ethics Committee of Nanjing Agricultural University (SYXK2011- 0036). Human handling methods and animal care of laboratory animals were followed throughout this experiment.

The transabdominal ultrasonography (Ausonics Microimager 1000 sector scanning instrument; Ausonics Pty Ltd, Sydney, NSW, Australia) was used for pregnancy diagnosis and initial foetal counts at gestation day (GD) 28 and GD 35, respectively (11). In this study, 32 ewes carrying two foetuses were selected at GD 35 and ran-domly assigned to four groups: a control (CG) group (n = 8; fed 100% NRC requirements for pregnant sheep, which included 100% min-eral–vitamin mixture), a nutrient- restricted (RG) group (n = 8; fed 50% NRC requirements, which included 50% mineral–vitamin mix-ture) and two treatment (Arg and NCG) groups (n = 8; fed 50% NRC requirements supplemented with 20 g/day RP- Arg or 5 g/day NCG, which included 50% mineral–vitamin mixture). Ewes were housed in individual pens (1.05 by 1.60 m) after GD 35. Throughout pregnancy, 32 ewes had free access to drinking water and were fed a corn, soya bean meal and Chinese wildrye (Producers Cooperative Association) to meet 100% or 50% of the NRC- recommended maintenance re-quirements during pregnancy. The 50% NRC was achieved by feed-ing one- half of the total complete diet calculated to meet 100% of NRC requirements. The doses of RP- Arg and NCG were based on previous studies (Chacher, Zhu, Ye, Wang, & Liu, 2014; Saevre et al., 2010; Wu et al., 2012; Zeng et al., 2012). The dietary composition is described by a previous publication of our group (Zhang, Sun, Wang, Deng, Nie, Zhang, et al., 2016). Beginning on GD 50 and continu-ing at 20- day intervals, ewes were weighed and rations adjusted for weight gain. RP- Arg (Beijing Feeding Feed Science Technology Co., Beijing, China) was a 50% Arg product, calculated to have a min-imum intestinal availability of 50%. NCG (Institute of Subtropical Agriculture, the Chinese Academy of Sciences, Hunan, China) was a 50% NCG product.

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2.2 | Sample collection and handling following necropsy

At GD 110, all ewes in this experiment were stunned with a captive- bolt gun (Supercash Mark 2, Accles and Shelvoke Ltd, England) and slaughtered. The gravid uterus was quickly removed. The tip of the gravid uterine horn was opened to expose the foetus, and then, the umbilical venous blood samples (5 ml) were collected into heparinized vacutainer tubes. All blood samples were centrifuged at 3,000 g for 15 min at 4°C. The resulting supernatants were immediately trans-ferred to Eppendorf tubes and stored in at −80°C until analysed. The maternal and foetal major organs were weighed, and the results are given in a previous publication of our group.

2.3 | Preparation of plasma samples and acquisition of 1H- NMR spectral data

Prior to NMR analysis, deep- frozen umbilical venous plasma sam-ples were thawed at 4°C overnight and then were vortexed. Each sample (300 μl) was mixed with 150 μl buffer solution (pH 7.4, 0.2 mol/L Na2HPO4 and 0.2 mol/L NaH2PO4) and 150 μl sodium 3- trimethylsilyl- (2, 2, 3, 3- D4) propionate (TSP, 1 mg/ml; Sigma- Aldrich) and then vortexed. The samples were centrifuged at 12, 000 g for 10 min at 4°C to separate any precipitate. Aliquots of 550 μl of the resulting supernatants were placed into 5- mm NMR tubes, which con-tained 100 μl deuterium oxide for a field frequency lock. All 1H- NMR spectra were measured at 25°C on a Bruker AV- 500 MHz spectrom-eter. A single 90° pulse sequence was used to acquire standard one- dimensional (1D) spectra. All 1D spin- echo spectra were recorded using the Carr–Purcell–Meiboom–Gill (CPMG) sequence of D–[–90°–(τ–180°–τ)n–ACQ], where a fixed total spin- echo delay (2nτ) of 40 ms was used to attenuate the broad NMR signals from macromolecules (i.e., proteins or lipoproteins; Song et al., 2013). 1H- NMR spectra were measured with 128 scans into 32 K data points over a spectral width of 7,500 Hz (Nicholson, Foxall, Spraul, Farrant, & Lindon, 1995). The free induction decay (FID) was zero- filled to 64 K, and an exponen-tial line- broadening function of 0.3 Hz was applied to the FID prior to Fourier transformation (Beckonert et al., 2007). With TopSpin software (version 3.0; Bruker Biospin, Germany), all the spectra were manually phased and baseline- corrected and referenced to the peak of TSP (δ 0.00; Gao et al., 2008).

2.4 | Pre- processing of NMR spectra data and multivariate pattern recognition

To minimize the changes in chemical shifts and to reduce the num-ber of variables for pattern recognition techniques, the processing method of the raw NMR data is based on the protocols described in a previous study (Wei et al., 2015). Briefly, all 1H- NMR spectra were au-tomatically exported to ASCII files using MestReNova (version 8.0.1, Mestrelab Research SL), which were then imported into “R” (http://cran.r-project.org/) and aligned further with an in- house- developed R script. The NMR spectra were binned into 0.003- ppm integrated

spectral regions (buckets) between δ 0.40 and 8.50 with the removal of regions from δ 4.15 to 5.70 (containing the residual peak from the suppressed water resonance). The remaining binned data for each NMR spectrum were autoscaled and mean- centred, and the integral values of each spectrum were probability- quotient- normalized to ac-count for different dilutions of samples, reduce the influence of any significant concentration variability among the samples and facilitate comparison between the spectra (Zhang et al., 2009).

Multivariate statistical data analyses including unsupervised principal component analysis (PCA), projections to latent structures discriminant analysis (PLS- DA) and orthogonal projections to latent structures analysis (OPLS- DA) were performed using SIMCA- P 15.0 software (Umetrics, Umeå, Sweden).

An initial overview of the PCA was carried out on the mean- centred data to decrease the dimensionality of the data, identify possible outliers and display general trends of the data sets in an un-biased way (Dieterle, Ross, Schlotterbeck, & Senn, 2006). In score plot of PCA model, data were performed with the first two principal com-ponents (PC1 and PC2) to provide the most efficient two- dimensional representation of the information contained. After that, PLS- DA and OPLS- DA were cross- validated using a 10- fold cross- validation. PLS- DA model was performed to discriminate samples according to their class membership, that is control, RG, ARG and NCG groups. The score plot of PLS- DA was generated using t[1]P and t[2]P, which represent the first component and the second component, respec-tively. On the score plot, each point represents an individual sample. Three parameters (R2X, R2Y and Q2) were used to evaluate the quality of the PLS- DA model. R2X parameter represents the total variation in X, which describes the optimization of the analytical model. R2Y parameter represents the variation in the response variable Y. Q2 parameter represents the predictive ability of the model. To further validate the quality of the PLS- DA model, permutation test (n = 200) was performed to test the degree of overfitting, and it is shown in the verification plot (Verwaest et al., 2011). The Y observation was randomly permutated, while the X matrix remained constant. The cor-relation coefficient between the original Y and the permutated Y was plotted against the cumulative R2 and Q2 for the verification models. A regression line was calculated with the R2- and Q2- intercept limits.

As an extension of the PLS- DA model and featuring integrated orthogonal signal correction, OPLS- DA was used to further ex-tract useful information relevant to identify significant metabolites that contributed to group separation. The significance of the score plot in OPLS- DA was similar to that in PLS- DA. The score plot of OPLS- DA was generated using t[1]P (the first predictive component) and t[2]O (the first orthogonal component). The Q2 and R2 values in the OPLS- DA represent the predictive ability of the model and the explained variance, respectively. Q2 > .40 and R2 > .50 in OPLS- DA models were considered valid. The colour- coded coefficient plot of the OPLS- DA model was performed to identify the differences in metab-olites between two groups (CG vs RG, CG vs Arg and CG vs NCG). In colour- coded coefficient plot, signals in the spectra correspond to me-tabolites in a given group. The signals with a positive or negative direc-tion represent metabolites at high concentration or low concentration

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between groups, respectively. Moreover, the red signals indicate a more significant contribution to the separation between the groups, and the opposite is true of blue signals.

The correlation coefficient “r” (Corr (t, X)) in OPLS- DA indicates the significance of the metabolite contribution in predicting the response, which was calculated by Pearson linear correlation coefficients using MATLAB software (MathWorks, Inc., Natick, MA, USA, version 7.1; Cloarec et al., 2005). The higher values of “r” (|r|) for metabolites repre-sent the bigger difference, and the opposite is the smaller difference of the lower the |r| values. The correlation coefficient cut- off values were dependent on the number of samples in each group (n = 8). In this study, the value of correlation coefficient |r| was used as the cut- off value to select the variables correlated with the OPLS- DA discriminant scores.

2.5 | Identification of metabolites

In this study, 1D statistical correlation spectroscopy was performed to identify the overlapped peaks, attenuate the intermolecule cor-relations to the largest possible and enhance information recovery from complex metabolomic data sets (Vandervoet, 1994). Metabolites were assigned based on chemical shift using the Chenomx NMR Suite 7.5 (Chenomx Inc., Edmonton, AB, Canada) library and identified from publicly accessible databases such as Human Metabolome Database (http://www.hmdb.ca), Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.kegg.jp) and the Biological Magnetic Resonance Data Bank (http://www.bmrb.wisc.edu) and from the literature (Wei et al., 2015). Based on statistical total correlation spectroscopy (STOCSY) technique, the pseudo- two- dimensional NMR spectrum was used to display the correlation among the intensities of the vari-ous peaks across the whole samples, which was calculated by the mul-ticollinearity of the intensity of variables in a set of NMR spectra to generate (Xu et al., 2015). The STOCSY of this study was performed by a suite of in-house developed scripts running in “R” language.

2.6 | Metabolic pathway analysis

A fit coefficient (p) from enrichment analysis and an impact factor from topology analysis for metabolic pathways were conducted using

the MetaboAnalyst 2.0 software (a web service for metabolomic data analysis; http://www.metaboanalyst.ca/). Pathways were considered significant when the p values calculated from pathway enrichment analysis and pathway topology analysis were less than .05. In the to-pology analysis, darker colour and bigger areas of circles in bubble plot indicate more significant changes in metabolites in the corre-sponding pathway and higher correlation with the centrality of the involved metabolites (Xia, Mandal, Sinelnikov, Broadhurst, & Wishart, 2012).

2.7 | Statistical analysis

The data were analysed for conformity to the normality of the distribu-tion using SPSS statistical software (Version 19.0, SPSS, Inc., Chicago, IL, USA). Data that were not normally distributed were analysed using an equivalent nonparametric test (Mann- Whitney test; Wang, Wang, Yang, & Kong, 2014). Multiple comparisons among different groups were explored using one- way analysis of variance (ANOVA), and the adjustment method was the Bonferroni correction (Ying et al., 2011). Data with repeated measurements included maternal and foetal body weight, placentome weight, metabolite concentrations in plasma and altered metabolic pathways of metabolites. Finally, values were pre-sented as mean ± SEM, and an adjusted p value < .05 was considered statistically significant.

3  | RESULTS

3.1 | Maternal body weight

On GD 35, prior to the onset of nutrient restriction, the weight of 32 ewes did not differ among the CG, RG, Arg and NCG groups (p > .05; Table 1). During the study (GD 35- 110), four groups maintained their body weight. From GD 35 to GD 110, the maternal weight increased by 22.86%, 6.14%, 6.07% and 9.76% in the CG, RG, Arg and NCG groups, respectively.

The difference in foetal weight was significant among the four groups (p < .05; Table 1). The foetuses from the CG group were significantly heavier than foetuses from the RG group (p < .05).

TABLE  1 Maternal body weight and foetal/placental characteristics from animals from gestation day (GD) 35 to GD 110a

Itema CG (n = 8) RG (n = 8) Arg (n = 8) NCG (n = 8) p

Maternal weight

GD 35 (kg) 41.85 ± 0.17 41.50 ± 0.24 42.01 ± 0.32 41.13 ± 0.28 .103

GD 50 (kg) 45.45 ± 0.27a 41.19 ± 0.23b 42.30 ± 0.21c 41.21 ± 0.23b <.001

GD 70 (kg) 48.23 ± 0.25a 42.08 ± 0.27c 42.74 ± 0.28bc 43.15 ± 0.24b <.001

GD 90 (kg) 51.63 ± 0.33a 43.55 ± 0.19d 43.95 ± 0.20c 44.45 ± 0.38b <.001

GD 110 (kg) 53.20 ± 0.25a 44.05 ± 0.28c 44.55 ± 0.37cb 45.50 ± 0.33b <.001

Foetal/placental

Placentome weight (kg) 0.56 ± 0.07 0.55 ± 0.07 0.57 ± 0.07 0.57 ± 0.11 .080

Foetal weight (kg) 1.88 ± 0.15a 1.40 ± 0.14c 1.66 ± 0.31b 1.68 ± 0.42b .030

aData are expressed in mean ± SEM. Means in a column without a common letter differ, p < .05.

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However, there was no difference between the Arg and NCG groups (p > .05). There were no differences among treatments (p ≥ .05) in the weights of the placentome. Foetal sex of ewes in this study was found to be non- significant (p > .05) and was there-fore removed.

3.2 | Metabolites identified in 1H- NMR spectra of plasma

Examples of typical 1H CPMG spectra (δ 0.40–4.15 and δ 5.70–8.50) of the plasma samples obtained from the CG, RG, Arg and NCG groups are displayed in Figure 1. The black, red, green and blue colours in Figure 1 represent the CG, RG, ARG and NCG groups, respectively. These spectra were calculated from the mean of all spectra in each group. A total of 36 metabolites were identified with good confidence based on chemical shift. A range of endogenous metabolites were ob-served in the spectra, such as several amino acids, lipids, glucose and lactate. Subsequent analyses were conducted to identify significant metabolite changes among the four groups.

3.3 | 1H- NMR spectra analysis

The multivariate data analyses of 1H- NMR spectra were performed to display the patterns in NMR data and the differential metabolites among the four groups. Initially, a PCA, an unsupervised approach, was conducted to establish a global overview for discrimination among the CG, RG, Arg and NCG groups. The score plot of PCA (Figure 2; R2X = 85.0%, Q2 = .641) was obtained with two PCs (PC1 and PC2), which represented 34.53% and 19.73% variance, respectively. But the four groups in score plots of PCA were not distinctly separated as the maximum variation.

Based on supervised analysis techniques, PLS- DA and OPLS- DA were used to remove the systematic variations unrelated to physiolog-ical or pathological conditions and to maximize the difference among the four groups. In contrast to PCA, PLS- DA focuses on class discrim-ination, and the PLS- DA score plots showed clusters corresponding to metabolic patterns in different groups. Based on the score plots of PLS- DA models, the CG and RG groups were discriminated with an R2X of .892, an R2Y of .860 and a Q2 of .805 (Figure 3a). The CG and Arg groups were discriminated with an R2X of .856, an R2Y of .460 and a Q2 of .212 (Figure 3b). The CG and NCG groups were discriminated with an R2X of .880, an R2Y of .915 and a Q2 of .702 (Figure 3c). The goodness- of- fit (R2 and Q2) of the original PLS- DA models and cluster of 200 Y- permutated models are given in validation plots in Figure 3a–c. In the validation plots, all permuted R2 values on the left are lower than the original points on the right. The results showed the PLS- DA models were valid.

The OPLS- DA models were subsequently built to remove system-atic variations unrelated to interested status and identify the most significant variations between groups. In the OPLS- DA score plots, significant biochemical differences were observed between the CG and RG groups (Figure 4a; R2X = 81.6%, Q2 = 74.6%), between the CG and Arg groups (Figure 4b; R2X = 88.2%, Q2 = 51.9%) and between the CG and NCG groups (Figure 4c; R2X = 76.5%, Q2 = 86.4%). Based on the first principal component of OPLS- DA score plots, the colour- coded coefficient plots identified discriminatory metabolites for the models (Fig. S1). From the comprehensive results of the OPLS- DA colour- coded coefficient plots, the |r| values of metabolites showed the main differential metabolites contributing to the separation of four groups (Table 2). The significance of confirmed metabolites (p < .05) in different groups was calculated using independent Student’s t test or Mann- Whitney test and is given in Table 2.

F IGURE  1 Typical 500- MHz 1H- nuclear magnetic resonance (NMR) spectrum (δ 0.40–4.15 and δ 5.70–8.50) of plasma samples from the CG (black spectrum), RG (red spectrum), Arg (green spectrum) and NCG (blue spectrum) groups. A total of 36 metabolites were assigned, and their chemical shifts and peak multiplicities are listed in Table 2. Keys: 1, low- density lipoprotein/very low- density lipoprotein (LDL/VLDL); 2, isovalerate; 3, leucine; 4, valine; 5, 3- hydroxybutyrate; 6, lactate; 7, alanine; 8, lysine; 9, acetate; 10, 2- hydroxyisovalerate; 11, N- acetylmannosamine; 12, N- acetylcysteine; 13, glutamine; 14, acetone; 15, ureidopropionic acid; 16, citrate; 17, carnosine; 18, dimethylamine; 19, isocitrate; 20, creatine; 21, malonate; 22, glycerophosphocholine; 23, choline; 24, phosphocholine; 25, β- glucose; 26, betaine; 27, α- glucose; 28, glycine; 29, myo- inositol; 30, urea; 31, tyrosine; 32, 1- methylhistidine; 33, 3- methylhistidine; 34, phenylalanine; 35, urocanate; 36, inosine

NCG

Arg

RG

CG

12437

9

10

1112

131415

161617

19

202526

21

6

2224

25/276

125 437

9

10

111213

14151616

17

19

2026

21

6

236 1028

125 4379

11

101213

14151616

17

19

20

2526

21

6

222425/276

125 437

9

10

1112

131415

161617

1920

2426

21

236 10 28

6

5

25

313132

343533

32

313132

34353332

313132

34353332

313132

3433 30

30

30

30

40

36

36

29

29

29

29

36

3635 32

18

18

18

18

8

8

8

8

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3.4 | Identification of impacted metabolic pathway

The significantly altered metabolic pathways of metabolites obtained from the CG, RG, Arg and NCG groups are shown in the bubble plot (Fig. S2). Thirty- two different metabolic pathways of plasma metabo-lites were mapped. The top 12 pathways are summarized in Table 3.

4  | DISCUSSION

Maternal nutrition is an important associated factor for regulat-ing foetal survival, growth and development (Lin et al., 2012). In the past three decades, researchers have been defining nutrient requirements of animals. However, many animal species during gestation (e.g., cattle, pigs and sheep) still have a significant and worldwide problem of suboptimal nutrition (Hay, Brown, Rozance, Wesolowski, & Limesand, 2016). Maternal nutrient restriction dur-ing pregnancy can cause IUGR. There is also evidence that IUGR is one of the most common concerns to cause stillbirth in human obstetrics and domestic animal production (Wu, Bazer, Wallace, & Spencer, 2006). Compared to the healthy foetus, the IUGR

F IGURE  2 Principal component analysis (PCA) score plot based on 1H- nuclear magnetic resonance (NMR) spectra of plasma samples from the CG group (black dot, ●), RG group (blue triangle, ), Arg group (red box, ) and NCG group (green diamond, ). Each point represents an individual sample from four groups

F IGURE  3 Score plots (top panel) and validation plots (bottom panel) of projections to latent structures discriminant analysis (PLS- DA) models obtained from different groups. (a) CG group (black dot, ●) compared with RG group (red box, ); (b) CG group (black dot, ●) compared with Arg group (red box, ); (c) CG group (black dot, ●) compared with NCG group (red box, )

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foetus has 10- fold higher rates of neonatal morbidity and mortal-ity (Mcintire, Bloom, Casey, & Leveno, 1999). The current literature suggests that the IUGR might be related to 52% of stillbirths, 10% of neonatal mortality and 72% of unexplained foetal deaths (Lin et al., 2014). Moreover, epidemiological studies provide evidence that foetal intrauterine environment may be of greater importance in the aetiology of chronic diseases (such as heart disease, diabe-tes, hypertension and some metabolic diseases) in adults than of the foetus; this phenomenon is called “foetal programming” (Ross & Beall, 2008). This means that malnourished foetus or IUGR foe-tus should be programmed to exhibit a “thrifty phenotype” with increased food intake and decreased energy output and fat deposi-tion. In fact, in case of growth restriction, the foetus will suffer some alterations in the numerous organs and tissue. These changes in the foetus mainly involve central disregulation of appetite, decrease in nephron numbers, abnormal activation of adipocytes, abnormal in-sulin secretion and glucose intolerance (Ross & Beall, 2008). Despite many previous attempts at enhancing growth and development of the growth restricted foetus in both humans and animals, we still need a more satisfactory strategy to promote foetal growth and development in IUGR pregnancies. Supplement of proper nutrition could be given to the mother to improve foetal growth and reduce metabolic complications. Paradoxically, however, maternal dietary protein supplementation in nutrient restriction during pregnancy may lead to an increased risk of small- for- gestational- age (SGA) de-livery and perinatal mortality and a decrease in foetal growth (Say, Gulmezoglu, & Hofmeyr, 2003). Some therapeutic options result in beneficial perinatal outcomes (such as preventing and/or treating IUGR); for example, dietary supplementation with some functional amino acids (such as arginine, citrulline, cysteine, glutamine, isoleu-cine, leucine, proline, taurine and valine; Lin et al., 2014) could be an attractive therapeutic option. The body can produce these amino acids, but the maternal diets are often unable to meet the demand of foetal growth and development under some circumstances. From this point of view, supplement- deficient amino acids are extremely necessary for pregnant mothers and their foetus.

As a nutritionally essential amino acid for the foetus, Arg plays a key role in foetal development. Arg is an important substrate of NO and polyamine syntheses via NO synthase (NOS). NO can regulate placental–foetal blood flows and plays a major role in transferring nutrients from mother to foetus. Polyamines can regulate DNA and protein synthesis, cell proliferation and differentiation. These cru-cial roles of NO and polyamines depend on the regulating effect of Arg. A survey of the literature indicates that maternal arginine deficiency causes IUGR in the foetus and increases foetal resorp-tion and death in rats; however, dietary arginine supplementation rat models of IUGR reverse foetal growth restriction (Wu, Bazer, Cudd, Meininger, & Spencer, 2004). Yet, Arg is rapidly degraded in rumen, and RP- Arg seems to be uneconomical. NCG is a potential feed additive to rise plasma ARG concentration. NCG is also called the ARG raiser, which could be fed to ruminants without a need for coating. Furthermore, the price of NCG is much lower than that of ARG. Many studies also have revealed that supplementation with Arg or NCG can enhance reproduction, lactation, immunity and growth (Chacher et al., 2013).

To our knowledge, this study is the first to systematically identify expressed metabolites in umbilical venous plasma from supplementa-tion of Arg or NCG to Hu sheep foetuses with nutrient restriction. In this study, 32 ewes carrying two foetuses were assigned to the CG, RG, Arg and NCG groups without the effect of foetal quantity on status of maternal nutrition. This experimental design aimed to disclose the pos-sible metabolic differences in umbilical venous plasma from four groups exposed to prenatal under nutrient restriction and supplementation of RP- Arg or NCG. The experimental results clearly showed that there were significant differences in foetus plasma 1H- NMR spectra of ewes with nutrient restriction or supplementation compared to the controls. 1H- NMR- based metabolomic approach and conventional biochemical methods revealed metabolic changes induced by oxidative stress and disorders of amino acid, carbohydrate, energy and lipid metabolism. To investigate the variations in endogenous metabolites in the case of undernourished Hu ewes and the treatment effect of RP- Arg and NCG supplementation on dietary, 1H- NMR- based metabolomic approach

F IGURE  4 Score plots derived from 1H- nuclear magnetic resonance (NMR) spectra of plasma of orthogonal projections to latent structures analysis (OPLS- DA) models obtained from different groups. (a) CG group (black dot, ●) compared with RG group (red box, ); (b) CG group (black dot, ●) compared with Arg group (red box, ); (c) CG group (black dot, ●) compared with NCG group (red box, )

     |  383SUN et al.

was performed to explore the metabolite changes, based on which corresponding metabolic pathways were proposed (Figure 5).

4.1 | Alteration in amino acid and protein metabolism

It is well known that amino acids are necessary for the synthesis of proteins for maternal and foetal nutrition at all stages of gestation.

Metabolomic analyses indicate lower concentrations of tyrosine, phe-nylalanine and urocanate in the RG group compared with the CG group. As the aromatic amino acids, the tyrosine and phenylalanine levels were observed to be decreased in the RG and Arg groups. Aromatic amino acids are essential precursors of protein synthesis and energy produc-tion (Xu et al., 2015). They also are the precursors of catecholamines in the body (tyramine, dopamine, epinephrine and norepinephrine). Additionally, a previous study has reported the increased amount of

No. Metabolitesa

1H chemical shifts (δ) and multiplicityb

Expression changec

CG- RG CG- ARG CG- NCG

1 1- Methylhistidine 7.06 (s), 7.68 (s) ↓* ↑ ↑

2 2- Hydroxyisovalerate 2.01 (m), 3.84 (d) ↑ ↑ ↑

3 3- Hydroxybutyrate 1.20 (d) – ↓ ↓

4 3- Methylhistidine 7.07 (s) ↓* – –

5 Acetate 1.92 (s) ↑ – ↑*

6 Acetone 2.23 (s) – ↓ –

7 Alanine 1.48 (d) ↓ – –

8 Betaine 3.25 (s) ↓ ↓ ↓

9 Carnosine 2.68 (dt) ↓ ↓ –

10 Choline 3.20 (s) – ↑ ↑*

11 Citrate 2.54 (d), 2.68 (d) ↓ ↓ ↓

12 Creatine 3.03 (s) ↓* ↓ ↓

13 Glutamine 2.14 (m) ↓ ↓ ↓

14 Glycerophosphocholine 3.19 (s) – – ↑

15 Glycine 3.55 (s) ↓* ↓* ↓

16 Isocitrate 2.97 (m) – ↓ ↑

17 Isovalerate 0.90 (d) ↓ ↓ –

18 Lactate 1.33 (d), 4.12 (q) ↓ ↓ ↓

19 Leucine 0.96 (d), 1.70 (m) ↓ ↑ ↑

21 Malonate 3.13 (s) – ↓ –

22 N- Acetylcysteine 2.05 (s) ↓ – ↓

23 N- Acetylmannosamine 2.04 (s) ↓ ↓ ↓

24 Phenylalanine 7.32 (m), 7.37 (m), 7.42 (m) ↓ ↓ –

25 Phosphocholine 3.21 (s) – ↑ ↑

26 Tyrosine 6.87 (m), 7.17 (m) ↓ ↓ ↓

27 Urea 5.78 (br) ↓ ↓ ↓

28 Ureidopropionic acid 2.42 (t) ↓* ↓* ↓

29 Urocanate 7.86 (s) ↓ ↓ ↓

30 Valine 1.04 (d) ↓ ↑ ↑

31 α- Glucose 3.42 (t), 3.54 (dd), 3.71 (t), 3.73 (m), 3.84 (m),5.23 (d)

↓ ↓ ↑

32 β- Glucose 3.25 (dd), 3.41 (t), 3.46 (m), 3.90 (dd)

↓ ↓ ↑

aLDL/VLDL: CH3- (CH2)n- (low- density lipoprotein and very low- density lipoprotein).bMultiplicity: s, singlet; d, doublet; t, triplet; q, quartet; dd, doublet of doublets; m, multiplet; br, broad resonance.c“↑” and “↓” represent increased and decreased levels in the RG, Arg or NCG group compared with the CG group. “–” means no change in the RG, Arg or NCG group compared with the CG group.*Significant difference (p < .05).

TABLE  2 Orthogonal projections to latent structures analysis (OPLS- DA) coefficients derived from 1H- nuclear magnetic resonance (NMR) data of plasma metabolites obtained from the CG, RG, Arg and NCG groups

384  |     SUN et al.

tyrosine in the blood of premature infants, which is marked by a de-creased motor activity, lethargy and poor feeding (Elfenbein, Barness, Pomerance, & Barness, 2000). Phenylalanine also is the precursor of tyrosine. Notably, the concentrations of phenylalanine and tyrosine were used as blood biomarkers for phenylketonuria in newborns (Deng, Shang, Hu, & Zhang, 2002). The observation of a decrease in phenyla-lanine in this study is similar to previous report on late- IUGR foetuses (Cruz- Martinez, Figueras, Hernandez- Andrade, Oros, & Gratacos, 2011).

Glucogenic amino acids (glutamine, glycine, valine and alanine) are the main sources of cellular energy in foetal life (Neu, 2001). Previous studies have revealed that their concentration in umbilical cord blood depends not only on the transfer from maternal blood but also on placental synthesis (Sanz- Cortes et al., 2013). Of these amino acids, glutamine also plays an important role in foetal and placental metabolism. A previous study in the maternal–foetal–placental unit suggested that glutamine supplementation in low- birthweight infants

No. Pathways Metabolitesa Hitsb −log (p) Impactc

1 Phenylalanine, tyrosine and tryptophan biosynthesis

4 2 6.080 .980

2 Valine, leucine and isoleucine biosynthesis 11 2 3.952 .667

3 Phenylalanine metabolism 9 2 4.351 .407

4 Glyoxylate and dicarboxylate metabolism 16 2 3.234 .296

5 Glycine, serine and threonine metabolism 32 4 5.742 .292

6 β- Alanine metabolism 17 1 1.225 .222

7 Tyrosine metabolism 42 1 0.545 .145

8 Amino sugar and nucleotide sugar metabolism 37 1 0.628 .142

9 Histidine metabolism 14 3 6.077 .130

10 Alanine, aspartate and glutamate metabolism 23 1 0.978 .127

11 Glycerophospholipid metabolism 29 3 3.973 .096

12 Citrate cycle (TCA cycle) 20 2 2.824 .095

aTotal number of compounds in pathways based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.bMatched number of identified metabolites in plasma from the CG, RG, Arg and NCG groups.cThe pathway impact value calculated from pathway topology analysis.

TABLE  3 Significantly altered metabolic pathways of metabolites obtained from the CG, RG, Arg and NCG groups

F IGURE  5 Schematic diagram of the major perturbed metabolic pathways detected by 1H- nuclear magnetic resonance (NMR) plasma analysis. The interrelationship of the identified metabolic pathways was referenced to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The arrows (“↑/↓”) in different colours represent the increase or decrease in metabolites in the RG group (blue), Arg group (red) and NCG group (green) relative to the CG group, respectively. The yellow ovals represent the identified metabolic pathways

Fumarate

Succinate

Malate

Oxaloacetate

2-Ketogluarate

Succinate-CoA

Isocicrate

Citrate

TCAcycle

ValineLeucine

Valine, leucine andisoleucinebiosynthesisGlucose

Ketone bodiesCitrulline

Urocanate Histidine 1-Methylhistidine3-Methylhistidine

Histidinemetabolism

Glutamine

Carbamoyl-Phosphate

Glutamate

Ornithine Arginine

Arginosuccinate

Urea

Urea

Glyoxylate Glycine Creatine

Glyoxylate anddicarboxylatemetabolism

TyrosinePhenylalaine

Phenylalanine, tyrosine andtryptophan biosynthesis

Pyruvate Acetyl-CoA +Acetyl-CoA

Acetate

Glucose-6-phosphate

Glucose

Glucosamine-6-phosphate

N-AcetylmannosamineAmino sugar andnucleotide sugarmetabolism

Acetoacelyl-CoA Acetoacetate

Acetone3-Hydroxybutyrate

Leucine

Fatty acids

Triglycerides

Glycerol

β-Oxidation

Lipolysis

Malonyl-CoA MalonateFatty acidbiosynthesis

LDL/VLDL Choline

PhosphocholineGlycerophosphocholine

Glycerophospholipidmetabolism

Lactate

Alanine

Creatine

Ureidopropionicacid

GlycineUracil

Barbiturate

MalonateUrea

CanosineHistidine

β-Alaninemetabolism

Cysteine

N-Acetylcysteine

Glycolysis

Betaine

Glycine

Glycine, serine andthreonine metabolism

Leucine

Isovalerate

     |  385SUN et al.

had a beneficial effect on decreasing nosocomial infections (Neu, 2001). The glutamine and glycine levels were found to be decreased in the RG, Arg and NCG groups as compared with the CG group, which implies the lack of energy substrates in the foetus. Valine and leucine, also defined as branched- chain amino acids, were significantly de-creased in early or late IUGR of pig foetuses as compared with normal controls in a previous study (Fowden & Apatu, 1995). The levels of leucine, valine and alanine only decreased in the RG group as com-pared with CG group, but the levels of leucine and valine increased in the Arg and NCG groups. Combined with the previous study, this result could be explained: ewes in the supplementation groups in-creased leucine, valine or alanine supply to foetus by the inherent hypercatabolic status in association with decreased glucose level (Darmaun, Roig, Auestad, Sager, & Neu, 1997). Urocanate, a break-down (deamination) product of histidine, has high correlation with protein malnutrition of human or animals (Rao, Deodhar, & Hariharan, 1965). When rats are fed a low protein diet, the urocanate will lose activity (Hug, Hunter, & Dunkerson, 1998). If the level of urocanate is low, the histidine pathway will be inoperative and the malnourished organism will catabolize tissue protein. Moreover, histidine is an im-portant source to catabolism, when there is a lack of dietary protein. Low level of plasma histidine also may be closely related to oxidative stress (Kumar et al., 2010). In this study, urocanate decreased in the RG and NCG groups as compared with the CG group. The level of 1- methylhistidine and 3- methylhistidine is lower in the RG group than in the CG group. Compared with the CG group, the Arg group had a high level of 1- methylhistidine and 3- methylhistidine. This result im-plies that maternal nutrient restriction can lead to the development of foetuses, but supplementation of Arg or NCG to underfed ewes provides support for foetuses.

Urea is formed in the urea cycle from ammonia produced by the deamination of amino acids. It also is the principal end product of protein catabolism. Although urea cycle disorder is commonly associ-ated with humans in infancy or childhood, no research has shown this disease in the foetus (Smith et al., 2005). As a derivative of urea, the change in ureidopropionic acid level is similar to urea in this study. In this experiment, the low urea level of plasma samples in the RG, Arg and NCG groups as compared with CG group was possibly related to urea cycle disorders or low level of protein catabolism.

4.2 | Alteration in carbohydrate and energy metabolism

Glucose is a major substrate to provide energy for maternal nutrition, foetal growth and foetal development. The α- glucose and β- glucose levels were decreased in metabolomic analyses of umbilical venous plasma samples in the RG and Arg groups as compared with the CG group, but were higher than in the NCG group. These results are con-sistent with the findings showing the low circulating concentrations of glucose in umbilical venous of IUGR foetuses during the decreased availability of glucose in the conceptus of rats fed with protein- restricted diets, compared with naturally occurring IUGR piglets (Lin et al., 2012).

Lactate plays an important role in some biochemical processes. It can be converted into pyruvate by the enzyme lactate dehydrogenase; pyruvate is further converted to glucose via gluconeogenesis (Sun et al., 2014). A previous study has reported that lactate can be used to assess the severity of the supply/demand imbalance (Valenza et al., 2005). The most normal source of energy in the body is supplied by glucose oxidation through aerobic respiration. In respiratory process continued with the tricarboxylic acid (TCA) cycle, pyruvate is converted to citrate. Creatine functions as an essential metabolite of the cell’s energy metabolism through the production of adenosine triphosphate, and an increased level has been reported in preterm IUGR neonates (Sloboda, Newnham, & Challis, 2000). This report investigated whether an increased level of creatine in IUGR could be highly correlated with a metabolic substrate- deficient condition. A NMR- based metabolomic study reported a reduced concentration of creatine in the brain of IUGR foetuses. Inconformity in the result of a NMR- based metabolomic study proved the increased concentration of creatine in the brain of IUGR foe-tuses (Story et al., 2011). The lower lactate, citrate and creatine levels of umbilical venous plasma of foetuses in the RG, Arg and NCG groups compared with the CG group reflected a lack of maternal nutrient.

4.3 | Alteration in lipid metabolism

In this work, one of the major findings was the change in lipid metabo-lism. The reduced concentrations of isovalerate, 3- hydroxybutyrate and acetone but elevated levels of acetate, low- density lipoprotein/very low- density lipoprotein (LDL/VLDL), 2- hydroxyisovalerate and malonate in the umbilical venous plasma in the RG, Arg or NCG group compared with the CG group indicate disorders of lipid metabolism. A previous study reported enhanced lipogenesis was found in IUGR piglets (He et al., 2011). Malonate, as a competitive inhibitor, acts against succinate dehydrogenase in the respiratory electron transport chain within the TCA cycle (Klein, Gal, & Segal, 1993). A lower level of malonate was observed in the Arg group than in the CG group, resulting in a pronounced metabolism disturbance of the TCA cycle in the foetus. A previous study reported higher LDL/VLDL levels in the umbilical venous of placental insufficiency- induced IUGR infants than in that of neonatal- birthweight neonates at birth (Lin et al., 2012).Another study also reported higher levels of LDL/VLDL in the um-bilical venous of placental insufficiency- induced IUGR infants than in that of neonatal- birthweight neonates at birth (Leduc et al., 2011). Similarly, our work showed higher levels of LDL/VLDL in the supple-mentation groups than in the CG group. Thus, the changes in lipid metabolism of foetuses have significance of foetal programming of obesity and cardiovascular disease in adult offspring.

4.4 | Alteration in oxidative stress

Free radicals and reactive oxygen species (ROS) could attack mem-brane lipids, proteins and enzymes and induce apoptosis in the body (Farombi, Ajimoko, & Adelowo, 2008). Oxidative stress is an imbal-ance between the free radical generation and the ability of the body to counteract or detoxify their harmful effects (Slaninova, Smutna,

386  |     SUN et al.

Modra, & Svobodova, 2009). As antioxidants, the lower levels of N- acetylcysteine, carnosine and betaine were indicative of a degree of oxidative stress in the RG, Arg and NCG groups as compared with the CG group. In support of this view, increased oxidative stress has been found in the case of the very low- birthweight materno- foetal unit vesicles (Tea et al., 2012). It is worth noting that betaine, which is a methyl donor, is important in development from the pre- implantation embryo to infancy (Lever & Slow, 2010).

As components of phospholipids that are crucial for structural integrity of cell membranes, the increase in choline, phosphocholine and glycerophosphocholine in rats after chronic ethanol consump-tion could be ascribed to ROS- induced membrane damage (Lee et al., 2012). These choline- containing compounds are also important as methyl donors for the production of S- adenosylmethionine, a sub-strate of deoxyribonucleic acid (DNA), and histone methyltransferases (Mehedint, Niculescu, Craciunescu, & Zeisel, 2010). A previous study has revealed that maternal choline alterations during pregnancy mod-ify foetal histone, DNA methylation and the expression of multiple genes (Ivorra et al., 2012). Although the levels of choline- containing compounds were similar in the plasma in the CG and RG groups (with a trend on higher levels for the latter), the levels of choline- containing compounds were higher in the Arg or NCG groups compared with the CG group. It has been reported that Arg is the biological precursor of NO synthesis (Blachier, Davila, Benamouzig, & Tome, 2011). This result suggests the epigenetic programming of the foetus is caused by the influence of the types of nutrients available through the maternal diet or the altered metabolic milieu of pregnancy.

5  | CONCLUSIONS

This study demonstrated that the overall metabolic changes in ewes with nutrient restriction supplemented with RP- Arg or NCG can be identified using 1H- NMR- based plasma metabolomics. The dif-ferences in the concentrations of metabolites between umbilical venous plasma brought about by maternal supplementation of RP- Arg or NCG are nutritionally meaningful. The results established that 1H- NMR spectroscopic patterns of umbilical venous plasma metabo-lites could provide useful information about nutritional status of ewes, expressed as well- defined metabolic changes as a function of nutrient restriction and supplementation of RP- Arg or NCG. Furthermore, our results revealed that the beneficial effect of dietary RP- Arg or NCG supplementation on mammalian reproduction is associated with com-plex metabolic networks and signal transduction. Moreover, NCG is more beneficial than RP- Arg, because of the lower rumen degradation and because it is cheaper in ruminant industry. Future studies should focus on other metabolomic technologies and biomarker validations to further understand dietary NCG supplementation.

ACKNOWLEDGEMENTS

The project was supported by the China Agriculture Research System (grant number CARS- 39), the Key Research Program of Jiangsu

Province (grant number BE2015362) and the National Science and Technology Support Program (grant number 2015BAD03B05- 06). We express our thanks to all members in F. Wang’s laboratory who contributed to sample determination.

CONFLICT OF INTEREST

None of the authors have any conflict of interest to declare.

AUTHOR CONTRIBUTIONS

LW Sun, H Zhang, HT Nie and F Wang designed the study; LW Sun wrote the manuscript; YX Fan and LW Sun carried out the study and collected the data; LW Sun, YX Guo and GM Zhang made some modi-fications in the manuscript.

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How to cite this article: Sun L, Zhang H, Fan Y, et al. Metabolomic profiling in umbilical venous plasma reveals effects of dietary rumen- protected arginine or N- carbamylglutamate supplementation in nutrient- restricted Hu sheep during pregnancy. Reprod Dom Anim. 2017;52:376–388. https://doi.org/10.1111/rda.12919