Metabolomics for the effect of biotin and nicotinamide on transition

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Metabolomics for the effect of biotin and nicotinamide on transition dairy cows Xiaoshi Wei, Qingyan Yin, Huihui Zhao, Yangchun Cao, Chuanjiang Cai, and Junhu Yao J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.8b00421 • Publication Date (Web): 14 May 2018 Downloaded from http://pubs.acs.org on May 15, 2018

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Journal of Agricultural and Food Chemistry

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Metabolomics for the effect of biotin and nicotinamide on transition dairy cows

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Xiaoshi Wei, Qingyan Yin, Huihui Zhao, Yangchun Cao, Chuanjiang Cai and Junhu

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Yao*

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College of Animal Science and Technology, Northwest A&F University, Yangling,

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Shaanxi 712100, PR China

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*Corresponding author: Junhu Yao

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Full Address: College of Animal Science and Technology, Northwest A&F University,

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Yangling, Shaanxi, PR China

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Tel: +86-29-87092102

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Fax: +86-29-87092164

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E-mail: [email protected]

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ACS Paragon Plus Environment


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ABSTRACT The objective of this study was to evaluate alterations in serum metabolites of

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transition dairy cows affected by biotin (BIO) and nicotinamide (NAM)

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supplementation. Forty multiparous Holsteins were paired and assigned randomly

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within block to 1 of the following 4 treatments: control (T0), 30 mg/d BIO (TB), 45

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g/d NAM (TN), and 30 mg/d BIO + 45 g/d NAM (TB+N). Supplemental BIO and NAM

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were drenched to cows from 14 days before the expected calving date. GC-TOF/MS

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was used to analyze serum samples collected from 8 cows in every groups at 14 days

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after calving. As compared with T0, all TB, TN and TB+N had higher serum glucose

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concentrations, while nonesterified fatty acid in TN and TB+N and triglyceride in TB+N

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were lower. ATP was significantly increased in TB+N. Both TN and TB+N had higher

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glutathione and lower reactive oxygen species. Moreover, TB significantly increased

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inosine and guanosine concentrations, and decreased beta-alanine, etc. Certain fatty

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acids concentrations (included linoleic acid, oleic acid, etc.) were significantly

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decreased in both TN and TB+N. Some amino acids derivatives (spermidine in TN,

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putrescine and 4-hydroxyphenylethanol in TB+N, and guanidinosuccinic acid in both TN

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and TB+N) were affected. Correlation network analysis revealed that the metabolites

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altered by NAM supplementation were more complicated than by BIO

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supplementation. These findings showed that both BIO and NAM supplementation

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enhanced AA metabolism, and NAM supplementation altered biosynthesis of

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unsaturated fatty acids metabolism. The improved oxidative status and glutathione

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metabolism further indicated the effect of NAM on oxidative stress alleviation.

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KEY WORDS: biotin; nicotinamide; transition dairy cow; metabolomics; serum

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metabolite

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INTRODUCTION

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During transition period, dairy cows are often in the negative energy balance (NEB)

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status. The feed intake of cows decreases dramatically around parturition and the

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immune function is impaired, causing cows become susceptible to many metabolic

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disorders, including ketosis, fatty liver, and mastitis.1,2

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To date, some studies have focused on alleviating NEB status by additives

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supplementation so that to improve milk performance and body health.3 Biotin (BIO)

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is a cofactor of pyruvate carboxylase and propionyl-coenzyme A carboxylase, and

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nicotinamide (NAM) is a precursor of coenzyme nicotinamide adenine dinucleotide

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(NAD). They are both actively participating in body energetic metabolism, such as

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carbon dioxide fixation in gluconeogenesis4 and oxidation-reduction reactions,

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respectively.5 It has been reported that the use of BIO supplements increases dry

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matter intake (DMI) and milk yield in lactating dairy cow using meta-analytic

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methods.6 NAM is the saturable metabolite and reactive component of nicotinic acid,7

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which possess ability to modify lipid metabolism of transition dairy cows.8,9 Besides,

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our previous study has shown that BIO and NAM supplementation could increase

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blood glucose and decrease triglycerides (TG) and nonesterified fatty acid (NEFA),10

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which are closely associated with the nutrient value and energy balance status.

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However, the comprehensive metabolic changes that may occur due to BIO and NAM

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supplementation, and the physiological and metabolic mechanisms regarding how the

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supplementation affect glucose and lipid metabolism remain to be determined.

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Metabolomics approach has proven to be a powerful tool for biomarker screening,

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disease diagnosis, and metabolism pathway characterization in cattle.11,12 The

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application of metabolomics would greatly extend our understanding of how

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supplementation works. In view of the high efficiency of chromatographic separation

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and the sensitive detection of separated components, gas chromatography

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time-of-flight/mass spectrometry (GC-TOF/MS) based metabolomics was used in

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serum profiling.13,14 The objectives of this study were to profile the changes in serum

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metabolites associated with BIO and NAM supplementation of transition dairy cow,

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and then attempt to explore the relationship among metabolites and metabolisms.

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MATERIALS AND METHODS

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The experimental procedures were approved by the Institutional Animal Care and Use

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Committee of Northwest A&F University (Shaanxi, China) in accordance with the

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university’s guidelines for animal research. Data for DMI, milk production

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performance and part blood parameters of this experiment have been reported

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previously.10

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Animal and Experimental Design

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Briefly, forty multiparous (entering second lactation) Holstein cows were paired

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and assigned randomly within block to 4 groups according to expected calving date,

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body weight, and previous 305-d milk. The 4 treatments include control (T0, no

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supplementation), 30 mg/d BIO (TB), 45 g/d NAM (TN), and 30 mg/d BIO + 45 g/d

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NAM (TB+N). The two doses, BIO at 30 mg/d and NAM at 45 g/d, were determined to

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be appropriate for transition cows based on previous studies as explained in our

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previous report.10 This experiment was conducted from 14 days before the expected

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calving date to 35 days postpartum. All cows were offered same basal diets (Table S1)

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3 times daily (0700, 1200, and 1800 h) as total mixed ration for ad libitum

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consumption (5 to 10 % refusals). Moreover, the supplemental BIO and NAM were

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drenched to cows at 0700 daily. Cows were housed in a tie-stall barn with free access

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to water, and were milked 3 times daily.

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Collection of Serum

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Blood samples (10 mL) were collected 3 h after morning feeding via puncture of

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the coccygeal vein on 14 days after calving. Samples were allowed to clot at the room

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temperature for 30 min and then centrifuged at 3,000 g for 15 min. Serum (the

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supernatant) was stored in 1.5 mL centrifuge tubes at -80°C. Eight serum samples

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were randomly selected from every groups and prepared for further analysis.

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Analysis of Serum

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Serum was analyzed for glucose, NEFA, TG, and adenosine 5’-triphosphate (ATP)

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by methods described in Wei et al.10 In addition, serum urea, pyruvate, glutathione,

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reactive oxygen species (ROS), hydroxyl radical (OH-) concentrations were measured

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using commercial kits from Beijing Sino-uk institute of Biological Technology

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(Beijing, China).

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The ELISA methods were used to determine serum insulin, leptin (Bovine insulin

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ELISA kit, catalog no. CEA448Cp; and Bovine leptin ELISA kit, catalog no.

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SEA084Bo; Cloud-Clone Corporation, Houston, USA) and glucagon (Bovine

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glucagon ELISA kit, catalog no. H183; Jiancheng Bioengineering Institute, Nanjing,

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China) concentrations.

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Preparation of Samples for GC-TOF/MS

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100 µL of serum was added with 0.35 mL methanol and 20 µL

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L-2-chlorophenylalanine (1 mg/mL, serving as an internal standard, Shanghai Biotech

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Co Ltd, Shanghai, China), vortexed, and centrifuged at 4℃, 17,000 g for 15 min. The

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extracts (supernatant, 0.4 mL) were transferred into a 2 mL GC/MS glass vial, and

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dried using a vacuum concentrator without heating. The dried residue was mixed with

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60 µL methoxyamine hydrochloride (20 mg/mL in pyridine), then the liquid was

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collected. Collected liquid was incubated at 80℃ for 30 min, added with 80 µL

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bistrifluoroacetamide regent (1% trimethylsilyl chloride, v/v, REGIS Technologies,

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Inc. USA), and incubated again at 70℃ for 2 h. After cooled to the room temperature,

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the incubate was added with 8 µL FAMEs (standard mixture of fatty acid methyl

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esters, containing 1 mg/mL C8-C16 and 0.5 mg/mL C18-C24 in chloroform) and then

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subjected to GC-TOF/MS analysis. The quality control (QC) was prepared using 10

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µL of serum from every 8 samples.

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GC-TOF/MS Analysis

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GC-TOF/MS analysis was performed using an Agilent 7890 gas chromatograph

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system coupled with a Pegasus HT time-of-flight mass spectrometer.15 The system

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utilized a DB-5MS capillary column coated with 5% diphenyl cross-linked with 95%

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dimethylpolysiloxane (30 m × 250 µm inner diameter, 0.25 µm film thickness; J&W

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Scientific, Folsom, CA, USA), with helium as the carrier gas. The analyte was

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injected at 1 µL in splitless mode. The front inlet purge flow was 3 mL/min, and the

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gas flow rate through the column was 1 mL/min. The temperature was set to 50°C for

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1 min, raised to 310°C at a rate of 20°C/min, and kept at 310°C for 6 min. Moreover,

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the temperatures for injection, transfer line, and ion source were 280, 270, and 220°C,

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respectively. The energy was -70 eV in electron impact mode. MS data were acquired

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in the full-scan mode with m/z range of 50-500 at a rate of 20 spectra/s after a solvent

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delay of 366 s. The retention time (RT) of the L-2-chlorobenzenealanine was used to

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test the stability of sample injection.

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Statistical Analysis

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Data of serum parameters were analyzed using MIXED procedure of SAS (SAS

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Institute, 2000). Comparisons were made between the treatments (TB, TN, and TB+N)

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and the control (T0), and these comparisons were denoted as CB:0, CN:0, C(B+N):0,

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respectively. Means were considered different at P ≤ 0.05 or as a tendency of

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difference at 0.05 < P ≤ 0.1. Values reported were least squares means with standard

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errors.

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The Chroma TOF 4.3X software of LECO Corporation and LECO-Fiehn Rtx5

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database were used for raw peaks exacting, baseline filtering and calibration, peak

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alignment, deconvolution and identification, and peak area integration.16 The RT

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index was used for peak identification, and the tolerance was 5000. Peaks lower than

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50% of QC were removed.17 Moreover, the peaks were performed through

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interquartile range de-noising method, and the missing values of raw data were filled

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up by half of the minimum value. The internal standard normalization method was

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employed in this data analysis.17 The accuracy of compound identification was

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confirmed with similarity value from the LECO/Fiehn Metabolomics Library.

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The resulted three-dimensional data involving the peak number, sample name,

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and normalized peak area were fed to SIMCA14.1 (V14.1, MKS Data Analytics

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Solutions, Umea, Sweden) for principal component analysis (PCA) and orthogonal

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projections to latent structures-discriminate analysis (OPLS-DA). The PCA showed

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the distribution of the origin data. Supervised OPLS-DA was applied to improve

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group separation and classification. As the experimental cows were paired based on

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similarities in expected calving date, body weight, and previous 305-d milk yield, and

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the sample size was moderate in the present study, the metabolites with P < 0.1 were

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collected.18 Thus, differences between two groups were identified combing variable

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importance in projection (VIP) obtained from OPLS-DA analysis and Student's t-test

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(VIP > 1 and P < 0.1). The fold change (FC) value of each metabolite was calculated

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by dividing the mean value of the peak area obtained for TB, TN, and TB+N by the

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value for T0.

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In addition, databases including Kyoto Encyclopedia of Genes and Genomes

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(KEGG, http://www.genome.jp/kegg/),19 and those from National Institute of

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Standards and Technology (NIST, http://www.nist.gov/index.html) were utilized to

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analyze metabolic pathways. MetaboAnalyst,20 having high-quality KEGG metabolic

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pathways as the backend knowledgebase, was used for pathway analysis

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(http://www.metaboanalyst.ca). Differential metabolites were cross listed with the

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pathways in the KEGG, and the top altered pathways were identified.20 Spearman

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correlation coefficients were analyzed in R software and significance threshold of P
1 and P < 0.05) different

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in CB:0 (Table 2). Specifically, the concentrations of inosine, 5-methoxytryptamine,

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guanosine, and glucuronic acid were greater for TB than T0. The most up-regulated

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metabolites were inosine (FC = 4.55) and guanosine (FC = 4.78). The concentrations

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of flavin adenine degraded product, D-erythronolactone, galactonic acid, and

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D-arabitol were tended (P = 0.056 to 0.091) to increase for TB compared to T0.

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The concentrations of 12 metabolites were significantly altered (VIP > 1, P

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1, P < 0.05). In particular, the concentration of trehalose was increased 486 fold.

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Co-expression analysis identified that 3 metabolites (i.e. flavin adenine degraded

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product, lactamide, and 2,3-dihydroxypyridine) were co-altered in CB:0 and CN:0, 1

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metabolite (i.e. D-fructose 2,6-bisphosphate) in CB:0 and C(B+N):0, and 6 metabolites

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(i.e. stearic acid, oleic acid, heptadecanoic acid, palmitoleic acid, guanidinosuccinic

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acid, and isoxanthopterin) in CN:0 and C(B+N):0. In addition, glucose-1-phosphate and

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D-arabitol were co-altered in all CB:0, CN:0 and C(B+N):0. In CB:0, CN:0 and C(B+N):0, 7, 7,

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and 9 metabolites were unique altered, respectively (Figure 2b).

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Metabolic Pathways of Differential Metabolites

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The metabolome view map revealed the enriched pathways (P < 0.05) for

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metabolites that were identified in serum (Figure 3), alone with pathway impact

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values in some cases. The purine metabolism, ascorbate and aldarate metabolism, and

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beta-alanine metabolism were enriched in CB:0; and biosynthesis of unsaturated fatty

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acids, glutathione metabolism, and arginine and proline metabolism were enriched in

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CN:0. Moreover, alterations in biosynthesis of unsaturated fatty acids, arginine and

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proline metabolism, alanine, aspartate and glutamate metabolism, glutathione

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metabolism, galactose metabolism, and linoleic acid metabolism were found in

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C(B+N):0. Overall, metabolic pathways that altered by supplementation were involved

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in amino acid (AA), carbohydrate, and fatty acid metabolism.

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Metabolic Correlation Network Descriptions

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Correlation network analysis allowed to exhibit the comprehensive relationships

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between metabolites, and help to compare the metabolic profiles in serum (Figure 4).

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The degree of the node (metabolite) denotes the number of edges (correlation)

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incident upon the node, and the color denotes the class of the metabolite. Green lines

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between nodes correspond to positive correlations, whereas gray lines correspond to

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negative correlations.

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There were 16 nodes and 76 edges of the correlation network in CB:0, 20 nodes

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and 146 edges in CN:0, and 21 nodes and 172 edges in C(B+N):0. The average degree of

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the network was most in C(B+N):0. In CB:0, glucose had the highest degree (8) as same

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as guanosine, glucuronic acid, flavin adenine degraded product. In CN:0, lactamide and

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stearic acid had the highest degree (12), followed by heptadecanoic acid (11),

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glucose-1-phosphate (11), glucose (10), and oleic acid (10). The degree of

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glucose-1-phosphate was highest (15) in C(B+N):0, followed by TG (13), linoleic acid

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(13), stearic acid (11), heptadecanoic acid (11), and oleic acid (11). Moreover, the

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metabolic correlations in CN:0 and C(B+N):0 were mainly centralized in connections

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between TG and fatty acids.

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DISCUSSION

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Previously, we reported that supplementing BIO and NAM to transition dairy cows

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were associated with major changes in glucose and lipid metabolism.10 In this study,

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GC-TOF/MS based metabolomics was used to profile the serum metabolites, and the

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discussion that follows will be focused on main altered metabolites and the potential

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association between metabolites and metabolisms in transition dairy cows due to the

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supplementation.

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In our study, increased glucose concentrations were observed by supplementation

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of BIO, NAM, and both. Gluconeogenesis serves as an important source of glucose in

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dairy cow and contributes up to 70% of the energy requirement around parturition.21

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Supplementing BIO and NAM might improve gluconeogenesis as they are involved in

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gluconeogenesis as cofactors of enzymes.22 Consistent result was also obtained by

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blood metabolomics analyses, which showed that serum beta-alanine was

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significantly decreased by BIO. Supplementing BIO to dairy cow could increase the

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mRNA abundance and activity of liver pyruvate carboxylase, which is a key enzyme

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involved in gluconeogenesis.23 More beta-alanine were used for gluconeogenesis on

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11 days postpartum than prepartum or later lactation,24 and this was considered an

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increased inter-organ transfer of nitrogen from AA catabolism.25

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In addition, serum lactamide were decreased with BIO and NAM supplementation, suggesting that hepatic uptake of glucogenic precursors, and consequently

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gluconeogenesis, might be increased. The mechanism of increased gluconeogenesis

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by NAM might be that NAD are involved in the conversion of lactate to pyruvate, and

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malate to oxaloacetate between mitochondria and cytoplasm, which are key steps in

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hepatic gluconeogenesis5, 22. Moreover, it might be also due to the conversion of

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NAM to nicotinic acid as discussed previously.10 The glucose-1-phosphate,

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interconverted with glucose-6-phosphate through enzyme phosphoglucomutase,26

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were also decreased in all CB:0, CN:0 and C(B+N):0. This decrease was further supporting

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the improved gluconeogenesis and increased glucose concentrations by BIO and

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NAM supplementation, as suggested by Mitchell et al.27 The ATP was significantly

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increased in C(B+N):0, presumably due to accelerated glucose metabolism by BIO and

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NAM supplementation would provide more energy for dairy cow.

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Previous study has shown that galactose and glucose share a common transport

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carrier and this transport carrier has a greater affinity for glucose than galactose.28 The

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DMI is insufficient that can not meet the energy demand in early lactation, thus we

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assumed that the galactose absorbed from intestinal were same as the DMI of the

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experimental dairy cows in our study were similar among 4 groups.10 Animals can

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synthesize substantial quantities of galactose de novo from glucose. Based on what we

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discussed above, the higher serum galactose and glucose concentrations favored the

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hypothesis that BIO and NAM supplementation could improve hepatic

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gluconeogenesis. Furthermore, when the glucose was not sufficient for milk lactose,

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galactose could be used for lactose synthesis.29 This may verify the tendency for BIO

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× NAM interaction on milk lactose percentage.

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Enriched purine metabolism accompanied with multiply increased inosine and

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guanosine were found in this study. The purine nucleosides come from microbial

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nucleic acids which leave the rumen and flow to the small intestine to hydrolyzed.30

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Because rumen cellulolytic and saccharolytic microbes require BIO for growth,31 BIO

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supplementation might increase cellulose digestion and microbial protein synthesis in

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the rumen. Besides, BIO was involved in degradation of the branched-chain AA

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leucine, decarboxylation of AAs, and purine synthesis as cofactor of 4 BIO-dependent

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enzymes.32 Both inosine and guanosine have beneficial effects relative to their

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antioxidant, immunomodulatory and neuroprotective properties,33 whereas the

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oxidative status was not affect by BIO in present study.

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Serum fatty acids, including stearic acid, oleic acid, heptadecanoic acid, and

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palmitoleic acid, were decreased in CN:0 by NAM supplementation, and

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aforementioned parameters and linoleic acid decreased in C(B+N):0. These results were

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in accordance with the decreased TG and NEFA in both CN:0 and C(B+N):0. In our

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previous study,10 we considered that it was because the NAM was anti-lipolytic as

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these metabolites might be released from the adipose mobilization, which occurred as

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a response to NEB status of transition dairy cow. In general, inhibition of lipolysis

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would somehow lead to reduced energy-corrected milk9 and milk fat34,35 yield in

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transition dairy cow. However, this was not found in our study previously reported.

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Some literatures found that NAM could enhance lipogenesis of adipocytes or adipose

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tissue through inhibiting the activity of SIRT1 deacetylase,36,37 which is inhibitory to

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adipogenesis, and stimulatory to fat catabolism in the skeletal muscle. Considering

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that biosynthesis pathway of unsaturated fatty acids was altered in both CN:0 and

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C(B+N):0, NAM might play a role in fatty liver prevention by promoting lipogenesis,

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not inhibiting lipolysis in adipose tissue.

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Supplementation of NAM appeared to result in some changes in serum

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polyamines (spermidine and putrescine). Spermidine and putrescine are ubiquitous

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constituents of prokaryotic and eukaryotic cells, essentially involved in various

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processes of cell growth and differentiation.38 Putrescine serves as a precursor of

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spermidine and spermine, and is produced from decarboxylation of arginine/ornithine

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in ruminants. Additionally, arginine is a precursor of guanidinosuccinic acid (GSA),

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which was increased in C(B+N):0. Thus the arginine and proline metabolism were

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highly enriched in CN:0 and C(B+N):0. As NAD were found having positive effect on

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deamination of AAs,39 NAM supplementation might favor this process. The activity

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of AA decarboxylases would increase under acidotic conditions,40 and the ruminal

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putrescine was increased when ruminal pH was reduced by a high-barley diet.41

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However, there was no apparent changes in ruminal pH by niacin supplementation,7,42

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nor in our study. Besides, the 4-hydroxyphenylethanol, a deaminized metabolite of

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tyrosine,43 was observed fold increased in C(B+N):0. The aromatic compounds were

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derivative or byproduct of aromatic AAs (tyrosine, phenylalanine and tryptophan),

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and the metabolism of aromatic compounds would be associated with metabolic

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disease.44 Based on these observations, the increase in serum polyamines observed in

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the present study suggested that supplementation of NAM, a precursor of coenzymes

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NAD, may have enhanced the deamination of AAs. As Aschemann et al.42 reported,

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niacin supplementation could improve microbial protein synthesis and efficient use of

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dietary nitrogen of dairy cow. The enhanced AAs deamination process and AAs

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metabolism were in agreement with increased glucose concentration, mainly due to

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the inter-organ transfer of nitrogen from AA to glucose release and glucogenic

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precursor for improved hepatic glucose circulation.

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During transition period, the increase in oxygen requirements with increased

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metabolic demands results in augmented production of ROS, leading to oxidative

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stress.45,46 Oxidative stress is a significant underlying factor to dysfunctional host

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immune and inflammatory responses that can increase the susceptibility of dairy cattle

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to a variety of health disorders, particularly during transition period.47,48 In present

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study, some indicators relative to oxidative status (i.e. glutathione, ROS) were found

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to be affected by NAM supplementation, so were glutathione metabolism in CN:0 and

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C(B+N):0. NAM could alleviate oxidative stress by acting as a robust cytoprotectant that

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addresses both early membrane phosphatidylserine externalization and later genomic

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DNA degradation.49 Furthermore, alleviated oxidative stress were associated with

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decrease blood NEFA and BHBA,50 which were found to be decreased by NAM in

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our study.10 NAM was reviewed having therapeutical effect in a variety of diseases

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and conditions,51 and our results were consistent with some previous studies that

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NAM supplementation could reduce oxidative stress,27,52,53 while no effect on

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oxidative stress was found in report of Yuan et al.9

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By using GC-TOF/MS, serum metabolomics profiling suggested significant changes and potential correlation of metabolites and metabolic pathways. Combined

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with the results of our previous study,10 our data revealed that BIO and NAM

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supplementation improved the gluconeogenesis and glucose circulation, and NAM

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supplementation decreased blood fatty acids through altering biosynthesis of

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unsaturated fatty acids of transition dairy cow. In addition, the changes in oxidative

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status and glutathione metabolism further supported the important effect of NAM on

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oxidative stress alleviation. More research is warranted to look at the effect and

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mechanism of NAM on rumen metabolism and nitrogen utilization.

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ASSOCIATED CONTENT

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Supporting Information

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Table S-1. Ingredient and nutrient analysis of diets fed to the experimental dairy

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cows.

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Figure S-1. Blood glucose, nonesterified fatty acids (NEFA), triglycerides (TG),

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insulin, glucagon and leptin concentrations throughout the entire experiment period of

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transition dairy cows in T0, TB, TN and TB+N. a, b Values with different superscripts at

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the same time point differed (P < 0.05). T0 = control group, TB = supplemented with

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30 mg/d biotin group, TN = supplemented with 45 g/d nicotinamide group, TB+N =

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supplemented with 30 mg/d biotin and 45 g/d nicotinamide group.

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Figure S-2. GC-TOF/MS TIC chromatograms of serum for cows in T0, TB, TN and

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TB+N. T0 = control group, TB = supplemented with 30 mg/d biotin group, TN =

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supplemented with 45 g/d nicotinamide group, TB+N = supplemented with 30 mg/d

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biotin and 45 g/d nicotinamide group. n = 8.

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AUTHOR INFORMATION

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Corresponding Author

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*

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Funding

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This work was funded by the National Natural Science Foundation of China

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(31472122, 31672451).

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Notes

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The authors declare that they have no conflict of interest.

E-mail addresses: [email protected], Tel: +86-29-87092102

396 397

ACKNOWLEDGMENTS

398

We acknowledge the members of the Innovative Research Team of Animal Nutrition

399

& Healthy Feeding of Northwest A&F University for providing valuable assistance in

400

help and care to the cows. We are also grateful to Mr. Xuemin Zhou (Biotree Biotech

401

Co., Ltd., Shanghai, China) for providing helps in data analysis.

402

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403

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group B3 vitamin nicotinamide in mice with lethal endotoxemia and

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polymicrobial sepsis. Pharmacol. Res. 2012, 65, 328-337.

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Table 1 Serum parameters of cows supplemented with biotin, nicotinamide, and both biotin and nicotinamide compared with cows in control group (n = 8).

Item

urea (mmol/L) pyruvate (mmol/L) ATPc (µmol/L) glutathionec (µmol/L) ROSc (FIc/mL) OH-c (U/mL) a

P-value CN:0b C(B+N):0b

T0a

TBa

TNa

TB+Na

5.57

5.87

5.60

6.58

0.691

0.971

0.215

0.061

0.059

0.071

0.055

0.815

0.323

0.591

0.458

0.516

0.502

0.537

0.128

0.244

0.042

0.150

0.178

0.199

0.207

0.132

0.010

0.003

992.6

828.2

646.3

570.8

0.108

0.002

< 0.001

514.5

537.6

482.1

562.0

0.470

0.329

0.157

CB:0b

T0: control group, TB: supplemented with 30 mg/d biotin group, TN: supplemented

with 45 g/d nicotinamide group, TB+N: supplemented with 30 mg/d biotin and 45 g/d nicotinamide group. b

CB:0 represents the comparison of serum metabolite in biotin supplementation group

versus that in control group, CN:0 represents the comparison of serum metabolite in nicotinamide supplementation group versus that in control group, and C(B+N):0 represents the comparison of serum metabolite in both biotin and nicotinamide supplementation group versus that in control group. c

ATP: adenosine 5’-triphosphate. ROS: reactive oxygen species. FI: fluorescence

intensity. OH-: hydroxyl radical.

28


Page 29 of 41


Table 2 Identification of different serum metabolites of cows supplemented with biotin, nicotinamide, and both biotin and nicotinamide compared with cows in control group (n = 8).

Metabolites

CB:0b

a

RT

CN:0b

C(B+N):0b

VIPc

P-value

FCc

VIPc

P-value

FCc

VIPc

P-value

FCc

hydroxylamine

7.285

1.093

0.268

1.188

1.152

0.137

1.227

1.424

0.093

1.196

3-hydroxybutyric acid

7.613

0.315

0.842

0.875

0.192

0.738

0.779

1.690

0.030

2.589

lactamide

7.708

1.978

0.057

0.779

2.150

0.016

0.701

0.394

0.916

0.984

beta-alanine

7.844

1.852

0.037

0.585

1.079

0.485

0.852

1.218

0.226

1.250

2,3-dihydroxypyridine

8.789

1.186

0.026

0.658

1.930

0.043

0.739

0.186

0.796

1.046

D-erythronolactone

9.571

1.777

0.056

2.223

1.231

0.145

3.015

0.358

0.437

1.831

2,4-diaminobutyric acid

9.598

1.125

0.148

1.315

0.128

0.583

1.145

1.131

0.049

1.548

aspartic acid

9.978

0.013

0.930

1.016

0.391

0.356

1.236

1.287

0.083

1.457

4-hydroxyphenylethanol

10.394

0.358

0.363

2.268

0.826

0.265

2.491

1.850

0.025

3.864

phosphoglycolic acid

10.455

0.225

0.583

1.116

1.371

0.077

1.309

0.627

0.404

1.150

guanidinosuccinic acid

10.987

1.120

0.243

1.690

1.845

0.017

2.389

1.492

0.064

2.010

2,5-dihydroxybenzaldehyde

11.101

0.165

0.623

1.139

1.839

0.024

1.555

0.464

0.641

1.116

o-aminobenzenesulfonic acid

11.177

1.196

0.224

4.182

1.644

0.024

6.552

0.947

0.310

2.784

D-arabitol

11.183

1.796

0.066

1.575

1.924

0.023

1.503

1.895

0.031

1.380

29



Page 30 of 41

flavin adenine degrad product 11.319

1.592

0.079

1.511

1.450

0.086

1.359

glucose-1-phosphate

11.380

1.801

0.002

0.392

3.241

< 0.0001

< 0.0001

putrescine

12.173

0.652

0.358

1.268

0.703

0.235

1.423

1.679

0.044

1.636

galactose

12.210

0.645

0.657

1.109

0.923

0.214

1.363

1.507

0.078

1.651

D-altrose

12.213

0.043

0.604

1.504

1.380

0.076

3.082

0.031

0.403

2.050

glucuronic acid

12.314

1.802

0.046

1.823

0.772

0.162

1.735

0.894

0.067

1.972

galactonic acid

12.593

1.694

0.091

1.423

1.410

0.110

1.588

1.017

0.191

1.350

palmitoleic acid

12.759

1.675

0.192

1.641

1.885

0.050

0.230

1.863

0.097

0.363

heptadecanoic acid

13.298

0.892

0.253

0.655

2.288

0.024

0.279

2.304

0.023

0.272

isoxanthopterin

13.307

0.383

0.228

0.652

1.615

0.018

0.299

2.429

0.004

0.035

D-fructose 2,6-biphosphate

13.528

1.579

0.096

0.766

0.668

0.638

0.922

2.264

0.013

0.595

spermidine

13.547

1.447

0.120

0.822

1.697

0.084

0.803

1.688

0.140

0.779

linoleic acid

13.607

0.382

0.673

0.881

0.698

0.062

0.537

2.436

0.001

0.076

oleic acid

13.622

0.934

0.443

1.248

2.392

0.017

0.313

2.454

0.012

0.260

stearic acid

13.721

0.248

0.784

0.950

2.361

0.007

0.494

2.369

0.006

0.460

fructose-6-phosphate

14.008

1.536

0.220

0.794

1.538

0.075

0.757

1.743

0.131

0.711

5-methoxytryptamine

14.650

2.060

0.032

1.655

0.978

0.332

0.837

1.099

0.544

0.857

inosine

15.082

1.722

0.018

4.552

2.029

0.265

2.014

1.038

0.969

1.035

trehalose

15.838

1.533

0.177

149.3

0.930

0.349

1.089

1.769

0.050

486.2

guanosine

15.974

2.125

0.012

4.776

1.306

0.341

1.915

0.048

0.918

0.903

30


1.106

0.180

1.311

3.129 < 0.0001 < 0.0001

Page 31 of 41


a

RT: retention time, minute.

b

CB:0 represents the comparison of serum metabolite in biotin supplementation group versus that in control group, CN:0 represents the comparison

of serum metabolite in nicotinamide supplementation group versus that in control group, and C(B+N):0 represents the comparison of serum metabolite in both biotin and nicotinamide supplementation group versus that in control group. c

VIP: variable importance in projection. FC: fold change, calculated as the mean value of peak area obtained from treatment group/mean value of

peak area obtained from control group. If the FC value is less than 1, it means that there is less metabolite in treatment group than in control group.

31



FIGURES CAPTIONS Figure 1. Relative concentrations of serum glucose, nonesterified fatty acid (NEFA), triglycerides (TG), insulin, glucagon and leptin. T0 = control group, TB = supplemented with 30 mg/d biotin group, TN = supplemented with 45 g/d nicotinamide group, TB+N = supplemented with 30 mg/d biotin and 45 g/d nicotinamide group. Relative amounts of every index in 4 groups were shown and expressed as the percentage of the control group. Data were presented as mean ± SEM. * 0.01 < P < 0.05, significantly different than the control group. **P < 0.01, highly significantly different than the control group. n = 8.

Figure 2. Corresponding validation plots of orthogonal projections to latent structures-discriminate analysis (OPLS-DA) (2a), and Venn diagrams of altered metabolites (2b) derived from the GC-TOF/MS metabolite profiles of serum. CB:0, CN:0, C(B+N):0 were used to represent the comparison of TB, TN and TB+N with T0, respectively. T0 = control group, TB = supplemented with 30 mg/d biotin group, TN = supplemented with 45 g/d nicotinamide group, TB+N = supplemented with 30 mg/d biotin and 45 g/d nicotinamide group. n = 8.

Figure 3. Metabolome view map of common metabolites identified in serum. CB:0, CN:0, C(B+N):0 were used to represent the comparison of TB, TN and TB+N with T0, respectively. T0 = control group, TB = supplemented with 30 mg/d biotin group, TN = supplemented with 45 g/d nicotinamide group, TB+N = supplemented with 30 mg/d

32


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biotin and 45 g/d nicotinamide group. n = 8. The x-axis represents the pathway impact, and y-axis represents the pathway enrichment. Larger size and darker color represent higher pathway enrichment and higher pathway impact value, respectively.

Figure 4. Correlation network of serum metabolites in CB:0, CN:0, C(B+N):0 based on Spearman correlation coefficients (P < 0.1). CB:0, CN:0, C(B+N):0 were used to represent the comparison of TB, TN and TB+N with T0, respectively. T0 = control group, TB = supplemented with 30 mg/d biotin group, TN = supplemented with 45 g/d nicotinamide group, TB+N = supplemented with 30 mg/d biotin and 45 g/d nicotinamide group. (n = 8). Node size and color denote the degree and classification, respectively. Green lines correspond to positive correlations, whereas gray lines correspond to negative correlations.

33



Figure 1

34


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Page 35 of 41


35



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Figure 2

CB:0

CN:0

2b

C(B+N):0

2a

36


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Figure 3

37



CB:0

CN:0

38


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Page 39 of 41


C(B+N):0

39



Figure 4

CB:0

CN:0

C(B+N):0

40


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Graphic for Table of Contents Only

41


Metabolomics for the effect of biotin and nicotinamide on transition

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