potential biomarkers for milk production and quality ... study aimed to elucidate the metabolic...

Subscriber access provided by CHINESE ACADEMY MED SCI LIB

Journal of Proteome Research is published by the American Chemical Society. 1155Sixteenth Street N.W., Washington, DC 20036Published by American Chemical Society. Copyright © American Chemical Society.However, no copyright claim is made to original U.S. Government works, or worksproduced by employees of any Commonwealth realm Crown government in the courseof their duties.

Article

Metabolomics of Four Biofluids from Dairy Cows:Potential Biomarkers for Milk Production and Quality

Huizeng Sun, Diming Wang, Bing Wang, Jia-kun Wang, Hongyun Liu, Le Luo Guan, and Jianxin LiuJ. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/pr501305g • Publication Date (Web): 19 Jan 2015

Downloaded from http://pubs.acs.org on January 25, 2015

Just Accepted

“Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are postedonline prior to technical editing, formatting for publication and author proofing. The American ChemicalSociety provides “Just Accepted” as a free service to the research community to expedite thedissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscriptsappear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have beenfully peer reviewed, but should not be considered the official version of record. They are accessible to allreaders and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offeredto authors. Therefore, the “Just Accepted” Web site may not include all articles that will be publishedin the journal. After a manuscript is technically edited and formatted, it will be removed from the “JustAccepted” Web site and published as an ASAP article. Note that technical editing may introduce minorchanges to the manuscript text and/or graphics which could affect content, and all legal disclaimersand ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errorsor consequences arising from the use of information contained in these “Just Accepted” manuscripts.

1

Metabolomics of Four Biofluids from Dairy

Cows: Potential Biomarkers for Milk

Production and Quality

Hui-Zeng SunЖ, Di-Ming WangЖ, Bing WangЖ, Jia-Kun WangЖ*, Hong-Yun LiuЖ,

Le Luo GuanЗ, and Jian-Xin LiuЖ*

ЖInstitute of Dairy Science, MoE Key Laboratory of Molecular Animal Nutrition,

College of Animal Sciences, Zhejiang University, Hangzhou 310058, P.R. China

ЗDepartment of Agricultural, Food & Nutritional Science, University of Alberta,

Edmonton, AB, Canada, T6G 2P5

Page 1 of 42

ACS Paragon Plus Environment

Journal of Proteome Research

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

2

ABSTRACT

The fundamental understanding of the mechanisms regulating milk protein synthesis is

limited. This study aimed to elucidate the metabolic mechanisms of milk production

affected by forage quality through studying metabolites from four biofluids (rumen

fluid, milk, serum and urine) collected from 16 lactating cows fed alfalfa hay (AH,

high-quality, n = 8) and corn stover (CS, low-quality, n = 8), respectively, using gas

chromatography-time of flight/mass spectrometry. The cows fed AH exhibited higher

milk yield (P < 0.01), milk protein yield (P = 0.04) and milk efficiency (P < 0.01) than

those fed CS. A total of 165, 195, 218 and 156 metabolites were identified in the

rumen fluid, milk, serum and urine, respectively, while 29 metabolites were found in

all four biofluids. In addition 55, 8, 28 and 31 metabolites in each biofluid,

respectively, were significantly different (VIP > 1 and P < 0.05) between the AH- and

CS-fed animals. These metabolites were involved in glycine, serine and threonine

metabolism; tyrosine metabolism; and phenylalanine metabolism. Further integrated

key metabolic pathway analysis showed that the AH-fed cows may have more

comprehensive amino acid metabolisms, suggesting that these metabolite-associated

pathways may serve as biomarkers for higher milk yield and better milk protein

quality.

KEY WORDS: biofluid, dairy cow, forage, gas chromatography-time flight/mass

spectrometry, metabolic pathway, metabolomics

Page 2 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

3

INTRODUCTION

Bovine milk is an important food source for human consumption, and the high

demand for milk due to the human population growth represents a key component of

food security issues. Forage is the most important and variable input in dairy

production,1 and forage quality exerts large effects on the health and productive

performance of dairy cows and the quality of milk.2 To date, most studies have focused

on the improvement of dairy nutritional management strategies to enhance milk

production and quality, whereas the physiological and metabolic mechanisms involved

have not been well examined.

Since the beginning of 21st century, China has become one of the largest dairy

producers in the world due to changes of lifestyle and the shift in dietary structure.

However, a shortage of high-quality forage has led to a bottleneck for the development

of the dairy industry in China. As the corn grain’s by-products, corn stover is one of

the most abundant feed resources in China,3 and it is widely used in many small dairy

operations with low cost. However, the nutritional constraints of corn stover (CS), such

as the lower content of crude protein and easy-to-use carbohydrates when compared to

alfalfa,4 limit its use in modern dairy farms with high productivity. Due to such

limitation, the Chinese dairy industry has largely relied on importing alfalfa, while 80

million tons of CS have been disposed every year.5 Therefore, understanding the

physiological and metabolic mechanisms involved in milk production under

low-quality forage is helpful to developing strategies for reasonable and efficient

utilization of the abundant cereal straw and stover sources in milk production.

Metabolomics is an emerging research area that can quantitatively measure small

molecular metabolites in biological samples (biofluids or tissues) using

high-throughput approaches, such as 1H-nuclear magnetic resonance (NMR) and gas

Page 3 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

4

chromatography–mass spectrometry (GC-MS).6,7 The identification and integrative

analysis of these metabolites may enable a comprehensive characterization of

metabolism mechanisms at the molecular and cellular level under internal or external

stimulating conditions. Among the technologies in metabolomics, gas

chromatography-time of flight/mass spectrometry (GC-TOF/MS) has been widely used

due to its high resolution and detection sensitivity.8, 9 However, the information on

metabolomics of dairy cows is limited, and most of metabolomic studies to date in

dairy cows have mainly focused on metabolites in a single biofluid, especially in the

rumen,10-12 milk,13,14 and blood15,16 using NMR and GC-MS. Recent studies have also

shown that proteomics can be a fundamental research tool for better characterization of

milk proteins.17 Future integration of metabolomics and proteomics can better

understand physiological and metabolic mechanisms involved in milk production.

In this study, we aimed to elucidate the metabolic mechanisms regarding how the

quality of forage impacts milk production, especially milk protein yield by studying

the metabolite profiles of various biofluids in dairy cows. GC-TOF/MS-based

metabolomics was performed to investigate the simultaneous responses of four

biofluids (rumen fluid, milk, serum, and urine) to different forage diets to establish the

correlations among the four biofluid metabolisms and to gain insights into the

mechanisms underlying forage quality-related milk production and quality in dairy

cows.

MATERIALS AND METHODS

Animals, Diets and Experimental Design

All experimental procedures involving the use of animals were approved by the

Animal Care Committee, Zhejiang University, Hangzhou, P. R. China. Sixteen

Page 4 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

5

multiparous Holstein cows (milk yield = 29.4 ± 2.16 kg/d; day in milk=164 ± 27.5 d;

body weight = 604 ± 59.7 kg; parity = 3.6 ± 1.8; mean ± SD) were divided into 8

blocks based on milk yield and body weight, and were randomly allocated to one of

two dietary treatments (Table 1). The diets were formulated to be isonitrogenous with a

forage-to-concentrate ratio of 45:55 (dry matter (DM) basis). The diets contained

similar concentrate mixtures and 15% corn silage, with the following different forage

sources (on a DM basis): (1) alfalfa-hay (AH), a diet containing 23% alfalfa hay and

7% Chinese wild rye hay; and (2) CS, inclusion of 30% corn stover replacing alfalfa

hay and Chinese wild rye hay. Diets were fed as total mixed rations, which were mixed

using a horizontal feed mixer (9SJW-300, National Science Makoto Farming

Equipment Co., Ltd., Beijing, China). The experiment was conducted over 65 days,

with the first 15 days serving as an adaptation period.

Cows were housed in individually tethered stalls in a barn with good ventilation and

were fed and milked 3 times daily at 0630, 1400, and 2000 h using a pipeline milking

system. The cows had free access to drinking water. During the 50 sampling days, milk

production was recorded weekly for the first 3 consecutive days. The amount of feed

offered was recorded daily and was adjusted to allow for 5 to 10% of orts every week;

DM intake was calculated based on the feed offered and orts.

Collection of Biofluid Samples

Rumen Fluid. At the end of the experimental period (d65), rumen fluid (50 ml) was

collected using an oral stomach tube before morning feeding according to a previously

reported procedure18. The pH of the rumen fluid was measured immediately after

collection using a portable pH meter (Starter 300; Ohaus Instruments Co. Ltd.,

Shanghai, China). Each rumen fluid sample was infused into a 15-ml spiral centrifuge

tube after being filtered through 4 layers of cheesecloth and immediately placed into a

Page 5 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

6

liquid nitrogen container to minimize any possible metabolite degradation. After

thawing, the rumen fluid samples were centrifuged at 4°C, 6,000 × g for 15 min, and

the supernatant was subsequently stored in a 1.5-ml centrifuge tube at -80°C until

further analysis.

Milk. Milk samples were collected using milk sampling devices (Waikato Milking

Systems NZ Ltd., Waikato, Hamilton, New Zealand). One 50-ml aliquot of each milk

sample was collected at the first day of every week in 50 sampling days and was

collected proportional to the yield (4:3:3, composite) in the morning, noon and evening

respectively to be more representative. The composite milk sample, with added

bronopol tablets (milk preservative, D & F Control Systems, San Ramon, CA, USA),

was stored at 4°C for the subsequent infrared analysis of the protein, fat and lactose

(Laporte and Paquin, 1999) using a spectrophotometer (Foss-4000, Foss, Hillerød,

Denmark).

Milk samples used for the metabolomics study were collected in the morning of day

65 with in-line milk meters. The samples were infused into 15-ml spiral centrifuge

tubes and immediately stored in a liquid nitrogen container to minimize any possible

metabolite degradation. After thawing, the milk samples were defatted by

centrifugation at 4°C, 3,000 × g for 15 min, and the skim milk was subsequently stored

in a 1.5-ml centrifuge tube at -80°C until further analysis of metabolites14,15.

Serum. Blood samples (10 ml) were obtained from the jugular vein before rumen

fluid collection using pro-coagulation 10-ml tubes. After collection, serum was

separated by centrifugation at 4°C, 3,000 × g for 15 min within 20 min of sample

collection, and the supernatant (serum) was stored in a 1.5-ml centrifuge tube at -80°C

until further analysis.

Page 6 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

7

Urine. Urine samples were collected before feeding in the morning of day 65. Vulval

stimulation was used to induce cows to urinate. Urine samples (10ml) were infused

into 15-ml spiral centrifuge tubes and immediately stored in a liquid nitrogen container

to minimize any possible metabolite degradation. Urine samples were thawed and

subsequently stored in 1.5-ml centrifuge tubes at -80°C until further analysis.

Preparation of Samples for GC-TOF/MS

The samples of rumen fluid, milk and serum were prepared using the following

procedure. First, 350 µl of methanol and 50 µl of L-2-chlorophenylalanine were added

to a 100-µl biofluid sample, mixed by vortexing, and centrifuged at 4°C, 12,000 rpm

for 10 min; 0.35 ml of supernatant was then transferred into a 2-ml silylated vial. After

the extracts were dried using a vacuum concentrator, 80 µl of o-methyl hydroxylamine

hydrochloride was added and mixed gently; the solution was then incubated at 37°C

for 2 h. Subsequently, 100 µl of bis trifluoroacetamide (containing 1% TCMS, v/v)

was added to each sample, followed by incubation at 70°C for 1 h; the samples were

then subjected to detection by GC-TOF/MS.

For the urine sample, 10 µl of urease (160 mg/ml) was firstly added to 100 µl of

urine, and the mixture was then incubated in an oven for 1 h before adding any

chemicals. The later preparation steps were the same as the preparation of the other

three biofluids.

Identification and Quantification of Compounds Discovered by GC-TOF/MS

The retention time (RT) of an internal standard (L-2-chlorobenzene alanine) was

used to test the stability of the sample injection. The GC-TOF/MS was performed

using an Agilent 7890 GC system equipped with a Pegasus 4D TOFMS (LECO, St.

Joseph, MI, USA). The system was installed with a DB-5MS capillary column (30 m ×

250-µm inner diameter, 0.25-µm film thickness) coated with 95%

Page 7 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

8

dimethylpolysiloxane cross-linked with 5% diphenyl. The injection of a 1-µl aliquot

was run in splitless mode, with helium as the carrier gas and a front inlet purge flow of

3 ml/min; the gas flow rate was 1 ml/min. The initial temperature of the column was

kept at 80°C for 0.2 min, increased to 180°C at a rate of 10°C/min, to 240°C at a rate

of 5°C/min, and further to 290°C at a rate of 20°C/min; the column was then

maintained for 11 min. The energy was -70 eV in electron impact mode. The

temperatures of injection, transfer line, and ion source were 280°C, 245°C, and 220°C,

respectively. The MS data were acquired at a rate of 100 spectra per second after a

solvent delay of 492 s with a mass-to-charge ratio (m/z) range of 20 to 600 in full-scan

mode.

Data analysis

The Chroma TOF4.3X software (LECO) and LECO-Fiehn Rtx5 database were used

for raw peaks extraction, data baselines filtering and calibration, peak alignment,

deconvolution analysis, peak identification, and integration of the peak area. For all 4

biofluids, a numerical simulation method that fills half of the minimum value was used

to simulate the missing value of the original data. Then, noise removal was conducted

using an interquartile range to filter data, and data were standardized by internal

standard normalization methods. The similarity value obtained from the LECO/Fiehn

Metabolomics Library was used for the evaluation of the accuracy of the

discriminating compound identification. A similarity greater than 700 indicates that the

metabolite identification is reliable. If the similarity is less than 200, the compound is

defined as an “analyte”. The compound with a similarity between 200 and 700 is

considered as a putative annotation.

The matrices which contain the relative concentration of 29 common metabolites

identified in four biofluids were imported into the online analyze platform

Page 8 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

9

Metaboanalyst (http://www.metaboanalyst.ca/) to gain mutual results of pathway. The

analysis used bos taurus (cow) pathway library and integrated global test pathway

enrichment analysis and relative-betweeness centrality pathway topology analysis. All

matched pathways according to p values from pathway enrichment analysis and

pathway impact values from pathway topology analysis can be shown in the

metabolome view.

The SIMCA-P+ 13.0 software package (Umetrics, Umea, Sweden) was used for

pattern recognition multivariate analysis, including principal component analysis

(PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial

least squares discriminant analysis (OPLS-DA). PCA was used to visualize the dataset

and display the similarity and difference. The PLS-DA model was validated by 7- fold

permutation tests to check its validity. To obtain maximal covariance between the

measured data and the response variable, OPLS-DA was performed. The data scale

conversion mode used in milk and serum samples was Ctr-formatted (Mean-Centered

Scaling) processing, whereas UV-formatted (Unit Variance Scaling) processing was

used in rumen fluid and urine samples.

Data on milk yield, DMI, milk composition, milk efficiency were analyzed using

PROC MIXED of SAS (SAS Institute, 2000). Statistical significance was defined at

P< 0.05, with highly significant values at P < 0.01.

Identification of Significantly Different Metabolites and Pathways between AH and

CS fed animals

As described above, OPLS-DA was used to identify the significantly different

metabolites between the AH and CS groups. To refine this analysis, the variable

importance for the projection (VIP) values along the predictive component were

obtained. The VIP values exceeding 1.0 were firstly selected as changed metabolites.

Page 9 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

10

The remaining variables were then assessed using Student's t-test (t-test). If P > 0.05,

variables were discarded between two comparison groups. The fold change (FC) value

of each metabolite was calculated by comparing mean value of peak area obtained

from CS group to that from AH group. Differential metabolites were further identified

and validated by searching the online databases including the Kyoto Encyclopedia of

Genes and Genomes (KEGG), Bovine Metabolome Database (BMDB), PubChem

Compound, Chemical Entities of Biological Interest (ChEBI), Japan Chemical

Substance Dictionary Web (NIKKAJI), and Chemical Abstracts Service (CAS). Each

differential metabolite was then cross listed with the pathways in the KEGG, and the

top altered pathways were identified and finally constructed according to the potential

functional analysis.

RESULTS

Animal Performance Measurement

The animal performance-related features of feed intake, milk yield and composition

are presented in Table 2. The DM intake of diet did not differ (P = 0.43) between the

CS and AH diets, with an average of 18.7 kg/d. The milk yield in AH-fed cows was

higher than in CS-fed (P < 0.01) animals, resulting in higher milk efficiency (milk

yield/DM intake) for the AH group (P < 0.01). The milk protein content was also

higher (P = 0.04) for cows fed AH compared with those fed CS. The dietary treatments

did not have a significant effect on the milk fat content (P = 0.72), but the lactose

content was different between the two diets (P = 0.02).

Identification and Quantification of GC-MS Compounds

The GC-TOF/MS TIC chromatograms of four fluids from cows fed CS and AH diets

are shown in Figure 1. There were various differences in the shape and quantity of

Page 10 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

11

peaks among the four biofluids, with unique peaks in each biofluid. In total, 268, 325,

319 and 251 valid peaks were identified in the rumen fluid, milk, serum and urine,

respectively. However, a clear discrimination in the abundance of typical peaks

(highlighted by arrows) was also observed between the CS and AH diets for each

biofluid (Figure 1). Thus, these TIC chromatograms identified by GC-TOF/MS could

directly reflect the difference in metabolite profiles among the four biofluids and

between the two diets within each biofluid.

Based on LECO/Fiehn Metabolomics Library, the majority of the peaks were

endogenous metabolites, and some of these peaks may be attributed to the derivatives

of byproducts. In total, 165, 195, 218, and 156 metabolites were quantified in the

rumen fluid, milk, serum, urine, of which 46, 56, 61, and 41 metabolites were unique

to each corresponding biofluid, respectively (Figure 2). The metabolites such as

2,4-diamino butyric acid, 4-aminobutyric acid, cellobiose and 43 others that were

produced by rumen fermentation were only detected in the rumen fluid, whereas

metabolites produced by the synthesis or degradation of milk composition, including

orotic acid, galactose, Thr, linoleic acid and 52 other metabolites, were only identified

in the milk. Metabolites such as cholesterol, 21-hydroxypregnenolone, cortexolone and

synephrine and 57 other metabolites were only detected in the serum, and hippuric

acid, allantoic acid and 38 other compounds were only identified in the urine. In

addition, 29 common metabolites were identified in all four biofluids, while 55 were

common in the rumen, milk and serum; 38 in the rumen fluid, milk, and urine; 41 in

the rumen fluid, serum, and urine; and 52 in the milk, serum, and urine. The numbers

of metabolites that were identified in any two biofluids were also illustrated in Figure

2. The detailed information of all the identified metabolites in four biofluids are

provided in the Supporting Information (Table S1).

Page 11 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

12

Metabolic Pathway of Common Metabolites

As shown in Figure 3, the metabolome view map revealed that the enriched

pathways (p < 0.05) for 29 metabolites identified in all four biofluids were galactose

metabolism, Phe metabolism, Gly, Ser and Thr metabolism, starch and sucrose

metabolism, glutathione metabolism, arginine and proline metabolism, citrate cycle,

glycerolipid metabolism, Tyr metabolism, primary bile acid biosynthesis, valine,

leucine and isoleucine biosynthesis. However, only 5 of them had pathway’s impact

value higher than 0.1, which is the cut-off value for the relevance. The impact value of

valine, leucine and isoleucine biosynthesis, Gly, Ser and Thr metabolism, glycerolipid

metabolism, Phe metabolism, and Tyr metabolism were 0.33, 0.29, 0.28, 0.22 and 0.11

respectively. Based on both p value and impact value, Gly, Ser and Thr metabolism,

glycerolipid metabolism, Phe metabolism and Tyr metabolism were characterized as

the significantly relevant pathways. The metabolites involved in three of them were

involved in amino acid metabolism.

Statistical Comparison of Metabolites in Biofluids between Two Diets

The PCA analysis of GC-TOF/MS metabolic profiles of four biofluids showed

significantly separated clusters between the AH and CS groups in each 3D-PCA score

plot (Figure 4 A, D, G and J). The R2X values of PCA model that represent explained

variance in rumen fluid, milk, serum, and urine were 0.707, 0.785, 0.530, and 0.825,

respectively. All the samples in the score plots of the four biofluids were within the

95% Hotelling T2 ellipse. The parameters for the assessment of the PLS-DA model

quality in discriminating CS and AS diets in the different biofluids could be

represented by the validation plots, as shown in Figure 4 B, E, H and K. The

corresponding Q2Y value of PLS-DA models in rumen fluid, milk, serum, and urine

were 0.899, 0.967, 0.982, and 0.899, respectively. The permutation tests of the four

Page 12 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

13

biofluids were all in a better range with the R2Y values of the four biofluids all greater

than 0.899, indicating a satisfactory effectiveness of the model.

Figures 4 C, F, I and L display the OPLS-DA results of the rumen fluid, milk, serum

and urine from the two dietary treatments, respectively. All the samples in the score

plots of the rumen fluid, milk, and serum were inside the 95% Hotelling T2 ellipse,

whereas only one sample of urine was not inside the ellipse. Clear separation and

discrimination were found between the CS and AH diets, indicating that the OPLS-DA

model can be used to identify the difference between the two diets.

Significantly Different Metabolites and Key Different Metabolic Pathways between

the Two Diets

In total, 55, 28, 31 and 8 significantly different metabolites (VIP > 1 and P < 0.05)

were identified in the rumen fluid, serum, urine and milk, respectively, between the CS

and AH diets (Table 3). The fold-change (FC) value was used to indicate the specific

variable quantity in the CS diet compared with the AH diet. As shown in Table 3, out

of the 55 significantly different metabolites in rumen fluid, 49 had higher

concentrations in the AH-fed animals than in the CS-fed animals, with 15 metabolites

belonging to amino acids, peptides and analogues including methionine (FC=0.546),

lysine (FC=0.508), alanine (FC=0.417), norleucine (FC=0.127), phenylalanine

(FC=0.540), glutamic acid (FC=0.403), citrulline (FC=0.213), L-allothreonine

(FC=0.581), aspartic acid (FC=0.296), 5-aminovaleric acid (FC=0.458), aminomalonic

acid (FC=0.130); 6 carbohydrates metabolites and other 12 organic acids and

derivatives like malonic acid, azelaic acid, adipic acid, pimelic acid, etc. For 31

significantly different metabolites in urine including aromatic compounds, amino

acids, peptides and analogues, carbohydrates, and lipids, 22 metabolites were found at

higher concentrations in the CS-fed animals than in the AH-fed animals. In milk, 5

Page 13 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

14

metabolites were found at higher concentrations in the AH-fed animals than in the

CS-fed animals among the 8 significantly different metabolites. Among the 28

significantly different metabolites in serum, 19 metabolites including aromatic

compounds, lipids, amino acids and carbohydrates were found at higher concentrations

in the CS-fed animals than in the AH-fed animals. Furthermore, the KEGG pathway

analysis of identified significantly different metabolites from four biofluids identified

that glycine (Gly), serine (Ser) and threonine (Thr) metabolism was the only one key

different metabolic pathway in all four biofluids between two diets, while tyrosine

(Tyr) and phenylalanine (Phe) metabolism was the key different metabolic pathways in

the rumen fluid, serum and urine (Table 4) between AH and CS.

Integrated Key Different Metabolic Pathways

The integrated key metabolic pathways were manually linked together based on the

results of common key different metabolic pathways from four biofluids and the

significantly changed pathways from common metabolites in four biofluids. Three key

metabolic pathways were displayed in Figure 5 including Gly, Ser and Thr

metabolism; Tyr metabolism; and Phe metabolism with 19 significantly different or

mutual metabolites in the four biofluids and the related key metabolic enzymes in each

pathway.

DISCUSSION

As an emerging technological and analytical approach, metabolomics has been used

to study metabolites in fluids or tissues19 and to understand the biosystem’s

physiological and biochemical status with further interpretation of the biological

principles.20 Although NMR-based metabolomics has been widely used because of its

easy preparation of samples and low cost, MS-based metabolomics is more sensitive in

Page 14 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

15

detecting low-abundance metabolites.21 In this study, GC-TOF/MS based

metabolomics was used to compare the metabolites and to detect their changes in the

rumen, serum, urine and milks from dairy cows under two types of forage with

different quality, aiming to discover potential biomarkers for milk quality. This is the

first study to profile the metabolites of four different biofluids simultaneously. The

unique compounds identified in each of the four biofluids are in line with traditional

biochemical results and represent the typical characteristics of the four different

biofluids in dairy cows.22-24

The GC-TOF/MS based metabolomics approach enhanced the detection of

metabolites in dairy cows comparing to previous studies. In ruminants, the rumen is an

important organ where microbes ferment the plant materials to nutrients that can be

absorbed by the host. The metabolites in rumen fluid mainly contain the degraded feed

compositions such as amino acids, sugars and organic acids.25 Traditional method used

in animal nutrition studies can identify rumen metabolites including common amino

acids, short-chain fatty acids, organic acids, purine, and pyrimidine.22 Previous studies

identified 46 rumen metabolites by 1H-NMR11; and 256 rumen metabolites by used

multiple metabolomics platforms and technologies (combined NMR, inductively

coupled plasma mass spectroscopy (ICP-MS), gas chromatography-mass spectrometry

(GC-MS), direct flow injection (DFI) mass spectrometry and lipidomics with

computer-aided literature)13. Comparing to these studies, we identified 165 compounds

in the rumen fluid by GC-TOF/MS, the only one platform which identified secondary

metabolites and metabolic intermediates such as dihydroxyacetone,

3-hydroxyphenylacetic acid, and 5-aminovaleric acid that have not been reported from

previous studies. This suggests that GC-TOF/MS based metabolomics can detect

low-molecular-weight metabolites and their intermediates and provide more

Page 15 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

16

information on rumen metabolisms. Moreover, we identified 195 compounds in the

milk by GC-TOF/MS. Comparing to 23 and 44 milk metabolites detected by NMR15

and GC-MS14, respectively; and to 120 milk metabolites reported by Boudonck et al.26

who used LC/MS/MS, higher number of metabolites identified in this study provides

evidence that the combination of TOF with GC-MS increases the extent of detection

and identification, which enhances the qualitative identification of metabolites. Our

results also clearly showed that the metabolites in each of the four biofluids were

different when cows were fed with different types of forage. In the rumen, 40 out of 55

significantly different metabolites had higher concentration including amino acids,

peptides and analogues as well as carbohydrates in AH-fed cows. These amino acids

and peptides can be used as substrates for synthesizing MCP, while carbohydrates can

provide energy for synthesizing MCP27. These suggest that cows may obtain more

nutrients from AH than from CS to produce more MCP.

It has been shown in many studies that metabolomics is a useful tool for elucidating

how diets under dietary intervention affect the urine metabolite profile.28-30 Based on

the current study, 71% of the significantly different metabolites in the urine were

higher for the CS group than the AH group. Most metabolites in the urine are end

metabolic products or metabolic wastes that are useless for the animal body. For

example, urea (6.84 fold higher in CS group) is an end product of protein catabolism

and constitutes about half of the total urinary solids, which has no physiological

function and is essentially a waste product. Among the 31 significantly different

metabolites, 16 metabolites represented the ineffectively-used nutrients including

amino acids, carbohydrates and lipids. In addition, aromatic compounds were

derivative or byproduct of aromatic amino acid such as 4-hydroxyphenylacetic acid

(2.85 fold higher in CS group) is a metabolite of tyrosine.31 Benzoic acid is the

Page 16 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

17

simplest aromatic carboxylic acid and a byproduct of phenylalanine metabolism, most

often conjugated to glycine in the liver and excreted as hippuric acid. Higher

concentration of this product in CS-group (3.07 fold) suggests that less utilization of

glycine and more utilization of phenylalanine Overall, the higher concentration of

metabolites in the urine indicated that more metabolic wastes or ineffectively-used

nutrients were excreted when cows were fed the CS diet. These results are in

agreement with phenotype measures such as lower milk performance (Table 2) and

nutrient digestibility and lower feed efficiency in CS-fed animals.

The milk protein secretion in dairy cows is closely associated with the supply of

metabolizable protein that is derived from microbial protein and undegraded dietary

protein in the rumen.27 Urine metabolites such as purine derivatives have been used to

estimate microbial protein synthesis in the rumen.32 Microbial nucleic acids leave the

rumen and flow to the small intestine with extensive digestion so that purine

nucleotides are hydrolyzed into purine nucleoside.33 Purine nucleoside can be absorbed

from the intestinal lumen, degraded in the liver and finally excreted in the urine as their

derivatives. A strong correlation has been observed between allantoin and purine

metabolites in urine34. Allantoic acid is also a byproduct of purine metabolism and is a

direct intermediate compound that links allantoin to urea (KEGG map 00230). In this

study, allantoic acid was identified at the retention time of 31.85 min with a similarity

of 865 according to the LECO/Fiehn metabolomics library. Allantoic acid was

characterized as a significantly different urinary metabolite between the CS and AH

groups (VIP = 1.12, P = 0.048) with 402% higher in CS, further indicating the

mechanisms led to high urea, possibly low microbial protein yield in CS group. This

may partially explain the variation in terms of milk protein yield between CS and AH

groups. In addition, a negative relationship existed between allantoin and nitrogen

Page 17 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

18

efficiency in the present study. These further reveals the potential mechanisms led to

low nitrogen efficiency when cows fed with CS.

In other studies, several common metabolites such as non-esterified fatty acid,

β-hydroxybutyric acid, triglyceride, glucose, and insulin were identified from serum

and characterized by indicators of lipomobilization and hepatic function in

high-yielding dairy cows.35 More small-molecule metabolites were detected in the

serum from this study, 19 out of 28 significantly different metabolites, such as

synephrine, cortexolone, and pyridoxine, were higher for the AH group than for CS,

indicating that cows in the AH group had higher levels of hormones, which may lead

to better fertility. 36

Current study not only detected the different metabolites between AH- and CS- diet,

but also pinpointed the pathways that these metabolites are involved in. Based on

metabolic pathway analysis, Gly, Ser and Thr metabolism; Tyr metabolism; and Phe

metabolism were the key different metabolic pathways identified from the four

biofluids which may also be the most important pathways associated with metabolic

changes in these biofluids of cows fed the CS diet. Key differences in the

metabolisms in the four biofluids revealed the comprehensive and essential metabolic

changes, as shown by the integrated pathways when cows were fed low-quality forage.

In the cows fed the CS diet, Gly, Ser and Thr metabolism was altered, as indicated by

increased 1,3-diaminopropane and decreased L-aspartic acid, glycocyamine and

L-allothreonine in rumen fluid; decreased serum glycocyamine; and increased milk

creatine and urine glycerate. Gly is the simplest amino acid and a basic nutrient. As

part of endogenous antioxidant glutathione, Gly is semi-essential amino acid. It has

been reported that Gly plays an important role in ischemia reperfusion injury, oxygen

stress, cell membrane injury, arthritis, liver fibrosis, tumor metastasis, and other

Page 18 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

19

processes.37 The consumption of Gly had a close relationship with the proliferation rate

of cancer cells.38 The decreased Gly in milk under CS diet suggests that maybe fewer

mammary gland cells are needed to repair the cell damage for alfalfa-fed cows.39

Although glycine was not significantly different between two diets, the concentration

of glycine tended to be lower in rumen fluid (P =0.064), milk (P=0.054), serum

(P=0.072) and urine (P=0.092), respectively in CS animals than that in AH group.

Therefore, we speculate that the glycine pathway was affect by the CS, and the

variation of other significantly different metabolites in the pathway may affected by

glycine. In addition, the higher concentration of Gly in rumen fluid (FC=1.64), milk

(FC=17.4), serum (FC=1.09) and serum (FC=1.23), respectively, in AH may lead to

more glycocyamine (286 fold higher in AH group in rumen fluid and 2.38 fold higher

in AH group in serum), which is a metabolite in the urea cycle and metabolism of

amino groups, and a precursor of creatine as an essential substrate for muscle energy

metabolism.40 L-allothreonine (1.72 fold higher in AH group in rumen) can be directly

generated through Gly and is also the substrate of Ser hydroxymethyl transferase, a

coenzyme that catalyzes the invertible interaction of Gly and Ser.41 Glyceric acid (2.20

fold higher in CS group in urine) is usually obtained from the oxidation of glycerol and

is mediated in the transfer of glucose carbon to Ser in cows.42 When glyceric acid is

secreted excessively in the urine, the animal body may suffer from D-glyceric aciduria

and D-glycerate anemia, resulting in metabolic acidosis, progressive neurological

impairment, seizures, hypotonia, and other adverse effects.43 These findings suggest

that feeding low-quality forage to cows not only leads to low yield and quality milk

production but may also lead to poor health.

Based on the integrated key metabolic pathway analysis of the 29 mutual

metabolites of the four biofluids (Figure 5), Tyr metabolism and Phe metabolism can

Page 19 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

20

be associated with each other through several metabolites (Figure 5). Phe is an

essential amino acid and the precursor of catecholamines, which are neurotransmitters

and adrenalin-like substances.44 Hyperactivity and developmental problems in

children45 have been reported to be associated with a genetic error in Phe metabolism

that is associated with phenylketonurics. Phe is the precursor of Tyr and usually

converts to Tyr through Phe hydroxylase and biopterin cofactor.46 Tyr is an important

amino acid in many proteins, peptides and even enkephalins and is also the precursor

for hormones such as thyroid and catecholestrogens.47 As is well known, liver is the

important amino acid metabolic pool and the vital place for most amino acid except

branched chain amino acid decomposition and utilization for dairy cow.48 The

concentrations of Phe and Tyr in rumen fluid exhibited lower FC values (0.54 and 1.19,

respectively) than those in the serum (1.01 and 1.34, respectively). Therefore, Phe and

Tyr may be utilized more in the liver in the AF-fed cows than in the animals fed CS.

Compared with the rumen fluid and serum, the higher FC value (FC = 2.84) of Tyr in

the urine of the CS-fed cows indicates that Tyr was not used efficiently. It is likely that

this finding represents the main difference in amino acid metabolism in the liver

between the two dietary treatments.

CONCLUSIONS

In summary, by using the GC-TOF/MS, metabolomics profiling of the rumen fluid,

milk, serum and urine from lactating dairy cows fed AH or CS diets indicated

significant changes in the metabolites and metabolic pathways between two nutritional

conditions. Most metabolites decreased in the rumen fluid and increased in the urine

when the cows were fed with the low-quality CS forage, indicating higher N loss and

low N utilization efficiency when low quality of forage was fed. The variable

Page 20 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

21

metabolic pathways were closely related to the improvement of milk performance,

especially Gly, Ser and Thr metabolism; Tyr metabolism; and Phe metabolism. The

results from the present study provide further insights into the mechanism at a

metabolomics level that can partially explain the variation of milk production in dairy

cows under different quality of forages, providing a better understanding of the use of

CS. The identified differential metabolites and related pathways may serve as potential

biomarkers for milk yield and quality, which are essential to develop strategies for

better utilization of CS and other low quality forages to improve milk production and

quality in China.

ASSOCIATED CONTENT

Supporting Information Available:

Table S1. Information of identified metabolites. This material is available free of

charge via the Internet at http://pubs.acs.org.

AUTHOR INFORMATION

* Co-Corresponding Authors

Phone & fax: +86-571-88982389. E-mail: [email protected];

Phone & fax: +86 571 88982097. E-mail: [email protected]

Notes

The authors declare no competing financial interests.

ACKNOWLEDGMENTS

This research was supported by grants from the National Basic Research Program of

the China Ministry of Science and Technology (No. 2011CB100801) and from the

National Natural Science Foundation of China (No. 31472121). The authors gratefully

thank the personnel of Hangjiang Dairy Farm (Hangzhou, China) for their assistance in

Page 21 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

22

the feeding and care of the animals. We are grateful to Mr. Junliang Deng (Biotree

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

acknowledge the members of the Institute of Dairy Science at Zhejiang University

(Hangzhou, China) for their assistance in the sampling and analysis of the samples.

REFERENCES

(1) Kendall, C.; Leonardi, C.; Hoffman, P.; Combs, D. Intake and milk production of

cows fed diets that differed in dietary neutral detergent fiber and neutral detergent fiber

digestibility. J. Dairy Sci. 2009, 92(1), 313-323.

(2) Weiss, W. P.; St-Pierre, N. R.; Willett, L. B. Varying type of forage,

concentration of metabolizable protein, and source of carbohydrate affects nutrient

digestibility and production by dairy cows. J. Dairy Sci. 2009, 92(11), 5595-5606.

(3) Jin, Q.; Zhang, H.; Yan, L.; Qu, L.; Huang, H. Kinetic characterization for

hemicellulose hydrolysis of corn stover in a dilute acid cycle spray flow-through

reactor at moderate conditions. Biomass Bioenergy 2011, 35(10), 4158-4164.

(4) Wang, L.; Shahbazi, A.; Hanna, M. A. Characterization of corn stover, distiller

grains and cattle manure for thermochemical conversion. Biomass Bioenergy 2011,

35(1), 171-178.

(5) Lv K. Y.; Qiu H. G.; Bai J. F.; Xu Z. G. Development of direct return of corn

stalk to soil: current status, driving forces and constraints. China Popul. Res. Envir.

2013, 23(3), 171-176.

(6) Nicholson, J. K.; Lindon, J. C. Systems biology: metabonomics. Nature 2008,

455(7216), 1054-1056.

Page 22 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

23

(7) He, Q.; Kong, X.; Wu, G.; Ren, P.; Tang, H.; Hao, F.; Huang, R.; Li, T.; Tan, B.;

Li, P. Metabolomic analysis of the response of growing pigs to dietary L-arginine

supplementation. Amino Acids 2009, 37(1), 199-208.

(8) Lindon, J. C.; Holmes, E.; Nicholson, J. K. Metabonomics techniques and

applications to pharmaceutical research & development. Pharmac. Res. 2006, 23(6),

1075-1088.

(9) Oliver, M. J.; Guo, L.; Alexander, D. C.; Ryals, J. A.; Wone, B. W.; Cushman, J.

C. A sister group contrast using untargeted global metabolomic analysis delineates the

biochemical regulation underlying desiccation tolerance in Sporobolus stapfianus.

Plant Cell 2011, 23(4), 1231-1248.

(10) Ametaj, B. N.; Zebeli, Q.; Saleem, F.; Psychogios, N.; Lewis, M. J.; Dunn, S.

M.; Xia, J.; Wishart, D. S. Metabolomics reveals unhealthy alterations in rumen

metabolism with increased proportion of cereal grain in the diet of dairy cows.

Metabolomics 2010, 6(4), 583-594.

(11) Saleem, F.; Ametaj, B. N.; Bouatra, S.; Mandal, R.; Zebeli, Q.; Dunn, S. M.;

Wishart, D. S. A metabolomics approach to uncover the effects of grain diets on rumen

health in dairy cows. J. Dairy Sci. 2012, 95(11), 6606-6623.

(12) Saleem, F.; Bouatra, S.; Guo, A. C.; Psychogios, N.; Mandal, R.; Dunn, S. M.;

Ametaj, B. N.; Wishart, D. S. The bovine ruminal fluid metabolome. Metabolomics

2013, 9(2), 360-378.

(13) Klein, M. S.; Buttchereit, N.; Miemczyk, S. P.; Immervoll, A.-K.; Louis, C.;

Wiedemann, S.; Junge, W.; Thaller, G.; Oefner, P. J.; Gronwald, W. NMR

metabolomic analysis of dairy cows reveals milk glycerophosphocholine to

phosphocholine ratio as prognostic biomarker for risk of ketosis. J. Proteome Res.

2011, 11(2), 1373-1381.

Page 23 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

24

(14) Sundekilde, U. K.; Gustavsson, F.; Poulsen, N. A.; Glantz, M.; Paulsson, M.;

Larsen L. B.; Bertram H. C. Association between the bovine milk metabolome and

rennet-induced coagulation properties of milk. J. Dairy Sci. 2014, 97(10), 6076-6084.

(15) Hailemariam, D.; Mandal, R.; Saleem, F.; Dunn, S.M.; Wishart, D.S.; Ametaj,

B.N. Identification of predictive biomarkers of disease state in transition dairy cows. J.

Dairy Sci. 2014, 97(5), 2680-2693.

(16) Sun, L.W.; Zhang, H.Y.; Wu, L.; Shu, S.; Xia, C.; Xu, C.; Zheng, J.S.

1H-Nuclear magnetic resonance-based plasma metabolic profiling of dairy cows with

clinical and subclinical ketosis. J. Dairy Sci. 2014, 97(3), 1552-1562.

(17) Roncadaa, P.; Pirasb, C.; Soggiuc, A.; Turkd, R.; Urbanie, A.; Bonizzic, L.

Farm animal milk proteomics. J. Proteomics 2012, 75(14), 4259- 4274.

(18) Imhasly, S.; Naegeli, H.; Baumann, H.; Bergen, M.; Luch, A.; Jungnicke, H.;

Potratz, S.; Gerspach, C. Metabolomic biomarkers correlating with hepatic lipidosis in

dairy cows. BMC Vet. Res. 2014, 10, 122.

(19) Psychogios, N.; Hau, D. D.; Peng, J.; Guo, A. C.; Mandal, R.; Bouatra, S.;

Sinelnikov, I.; Krishnamurthy, R.; Eisner, R.; Gautam, B. The human serum

metabolome. PLoS One 2011, 6(2), e16957.

(20) Zhao, S.; Zhao, J.; Bu, D.; Sun, P.; Wang, J.; Dong, Z. Metabolomics analysis

reveals large effect of roughage types on rumen microbial metabolic profile in dairy

cows. Lett. Appl. Microbiol. 2014, 59(1), 79-85.

(21) Scano, P; Murgia, A; Pirisi, F. M.; Caboni, P. A gas chromatography-mass

spectrometry-based metabolomic approach for the characterization of goat milk

compared with cow milk. J. Dairy Sci. 2014, 97(10), 6057-6066.

Page 24 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

25

(22) Folman, Y.; Neumark, H.; Kaim, M.; Kaufmann, W. Performance, rumen and

blood metabolites in high-yielding cows fed varying protein percents and protected

soybean. J. Dairy Sci. 1981, 64(5), 759-768.

(23) MacIntosh, D. L.; Needham, L. L.; Hammerstrom, K. A.; Ryan, P. B.

Longitudinal investigation of selected pesticide metabolites in urine. J. Expos. Anal.

Environ. Epidemiol. 1999, 9(5), 494-501.

(24) Wittenburg, D.; Melzer, N.; Willmitzer, L.; Lisec, J.; Kesting, U.; Reinsch, N.;

Repsilber, D. Milk metabolites and their genetic variability. J. Dairy Sci. 2013, 96(4),

2557-2569.

(25) Donzé, G.; McMahon, C.; Guerin, P. M. Rumen metabolites serve ticks to

exploit large mammals. J. Exp. Biol. 2004, 207, 4283-4389.

(26) Boudonck, K. J.; Mitchell, M. W.; Wulff, J.; Ryals, J. A. Characterization of the

biochemical variability of bovine milk using metabolomics. Metabolomics 2009, 5(4),

375-386.

(27) NRC. 2001. Nutrient Requirements of Dairy Cattle. 7th rev. ed. Natl. Acad. Sci.,

Washington, DC.

(28) Bertram, H. C.; Yde, C. C.; Zhang X.; Kristensen N. B. Effect of dietary

nitrogen content on the urine metabolite profile of dairy cows assessed by nuclear

magnetic resonance (NMR)-based metabolomics. J. Agr. Food Chem. 2011, 59,

12499-12505.

(29) Chen, X. B.; Mejia, A. T.; Kyle, D. J.; Ørskov, E. R. Evaluation of the use of

purine derivative-creatinine ratio in spot urine and plasma samples as an index of

microbial protein supply in ruminants studies in sheep. J. Agric. Sci. 1995, 125,

137-143.

Page 25 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

26

(30) Shingfield, K. J.; Offerb N.W. Simultaneous determination of purine

metabolites, creatinine and pseudouridine in ruminant urine by reversed-phase

high-performance liquid chromatography. J. Chromatogr. B. 1999, 723(1-2), 84-91.

(31) Brier, S; Fagnocchi, L; Donnarumma, D; Scarselli, M.; Rappuoli, R; Nissum,

M.; Delany, I.; Norais, N. Structural insight into the mechanism of DNA-binding

attenuation of the neisserial adhesion repressor NadR by the small natural ligand

4-hydroxyphenylacetic acid. Biochem. 2012, 51 (34), 6738–6752.

(32) Boudra, H.; Doreau, M.; Noziere, P. Simultaneous analysis of the main markers

of nitrogen status in dairy cow's urine using hydrophilic interaction chromatography

and tandem mass spectrometry detection. J. Chromatogr. A. 2012, 1256 (21), 169-176.

(33) Boeroa, P. O.; Seradjb, A.R.; Fondevilac, M.; Noland, J.; Balcellsb, J.

Modelling urinary purine derivatives excretion as a tool to estimate microbial rumen

outflow in alpacas (Vicugna pacos). Small Ruminant Res. 2012, 107(2-3), 101-104.

(34) George, S. K.; Verma, A. K.; Mehra. U. R.; Dipu, M.T.; Singh, P. Evaluation of

purine metabolites - creatinine index to predict the rumen microbial protein synthesis

from urinary spot samples in Barbari goats. J. Anim. Feed Sci. 2011, 20, 509–525.

(35) González, F. D.; Muiño, R.; Pereira, V.; Campos, R.; Benedito, J. L.

Relationship among blood indicators of lipomobilization and hepatic function during

early lactation in high-yielding dairy cows. J. Vet. Sci. 2011, 12(3), 251-255.

(36) Higgins, H. M.; Ferguson, E.; Smith, R. F.; Green, M. J. Using hormones to

manage dairy cow fertility: The clinical and ethical beliefs of veterinary practitioners.

PLoS One 2013, 8(4), e62993.

(37) Zhang, W. C.; Shyh-Chang, N.; Yang, H.; Rai, A.; Umashankar, S.; Ma, S.;

Soh, B. S.; Sun, L. L.; Tai, B. C.; Nga, M. E. Glycine decarboxylase activity drives

Page 26 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

27

non-small cell lung cancer tumor-initiating cells and tumorigenesis. Cell 2012, 148(1),

259-272.

(38) Jain, M.; Nilsson, R.; Sharma, S.; Madhusudhan, N.; Kitami, T.; Souza, A. L.;

Kafri, R.; Kirschner, M. W.; Clish, C. B.; Mootha, V. K. Metabolite profiling identifies

a key role for glycine in rapid cancer cell proliferation. Science 2012, 336(6084),

1040-1044.

(39) Zhang, J. F. Study on protein synthesis in mammary gland in lactating cows fed

different diets. Ph. D. Dissertation, Inner Mongolian Agric. Univ., China. 2013, 46-48.

(40) Brosnan, M. E.; Edison, E. E.; da Silva, R.; Brosnan, J. T. New insights into

creatine function and synthesis. Adv. Enzyme Regul. 2007, 47(1), 252-260.

(41) Heo, J. C.; Lee, S. H. Alleviation of asthma-related symptoms by a derivative of

L-allothreonine. Intl. J. Molec. Medic. 2013, 31(4), 881-887.

(42) Black, A.; Kleiber, M.; Baxter, C. Glucose as a precursor of amino acids in the

intact dairy cow. Biochim. Biophys. Acta 1955, 17, 346-353.

(43) Sass, J. O.; Fischer, K.; Wang, R.; Christensen, E.; SchollϋBürgi, S.; Chang,

R.; Kapelari, K.; Walter, M. D-glyceric aciduria is caused by genetic deficiency of

D-glycerate kinase (GLYCTK). Human Mutation 2010, 31(12), 1280-1285.

(44) Waisbren, S. E.; Noel, K.; Fahrbach, K.; Cella, C.; Frame, D.; Dorenbaum, A.;

Levy, H. Phenylalanine blood levels and clinical outcomes in phenylketonuria: a

systematic literature review and meta-analysis. Molec. Genet. Metab. 2007, 92(1),

63-70.

(45) Alonso-Fernández J. R.; Colón C. Newborn screening in Spain, with particular

reference to Galicia: Echoes of Louis I. Woolf. Mol. Genet. Metab. 2010, 101(2-3),

95-98.

Page 27 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

28

(46) Gilbert, H. F. Basic Concepts in Biochemistry: A Students Survival Guide.

McGraw-Hill Health Professions Division: 2000.

(47) Lemmon, M. A.; Schlessinger, J. Cell signaling by receptor tyrosine kinases.

Cell 2010, 141(7), 1117-1134.

(48) Haque, M. N.; Rulquin, H.; Andrade, A.; Faverdin, P.; Peyraud, J. L.;

Lemosquet, S. Milk protein synthesis in response to the provision of an “ideal” amino

acid profile at 2 levels of metabolizable protein supply in dairy cows. J. Dairy Sci.

2012, 95(10), 5876-5887.

Page 28 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

29

Table 1. Ingredients of the experimental diets

Item, % as a DM basis Treatmenta

CS AH

Ground corn grain 27.0 27.0

Wheat bran 8.1 8.1

Soybean meal 7.5 7.5

Cottonseed meal 4.3 4.3

Beet pulp 2.2 4.1

Corn silage 15.0 15.0

Alfalfa hat 0.0 23.0

Corn stover 30.0 0

Chinese wild ryegrass hay 0.0 7.0

Urea 1.0 1.0

Premixb 4.0 4.0

Dicalcium phosphate 0.1 0

Limestone 0.8 0

aCS = TMR containing corn stover as main forage; AH = TMR containing alfalfa

hay as main forage.

bFormulated to provide (per kg of DM) 500,000-700,000 IU of vitamin A,

140,000-170,000 IU of vitamin D3, 2,000-4,000 IU of vitamin E, 7,000-9,000 mg of

Zn, 40-80 mg of Se, 180 mg of I, 1,400-2,500 mg of Fe, 15-30 mg of Co, 1,4000-2,500

mg of Mn, and 1,400-2,500 mg of Cu.

Page 29 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

30

Table 2 Effects of corn stover, alfalfa and Chinese wild rye hay as main dietary forage

sources on dry matter intake and milk yield and composition

Item Treatmenta SEM P-value

CS AH

Dry matter intake, kg/d 18.9 18.6 0.19 0.43

Milk yield, kg/d 20.3 24.9 0.32 < 0.01

Milk composition, %

Protein 3.17 3.26 0.031 0.04

Fat 4.25 4.22 0.083 0.72

Lactose 4.84 4.91 0.022 0.02

Milk efficiencyb 1.08 1.34 0.020 < 0.01

aCS = TMR containing corn stover as main forage; AH = TMR containing alfalfa

and Chinese wild rye hay as main forage.

bMilk efficiency = milk yield/dry matter intake.

Page 30 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

31

Table 3 Identification of significantly different metabolites in rumen fluid, milk, serum

and urine between the CS and AH groups

Metabolites name RTa Mass Similarity VIP P-value FCb

Rumen fluid

Lactic acid 7.066 117 966 1.683 0.005 0.435

Glycolic acid 7.251 147 935 1.330 0.027 0.665

Citrulline 7.410 157 770 1.645 0.002 0.213

Alanine 7.766 73 671 1.517 0.021 0.417

Prostaglandin E2 8.793 117 643 1.309 0.020 0.216

Oxoproline 9.111 55 706 1.117 0.018 0.389

2-Ketoadipate 9.357 84 574 1.358 0.032 0.224

Dihydroxyacetone 9.485 42 510 1.304 0.046 0.788

Benzoic acid 9.533 105 972 1.616 0.008 0.573

Phenylacetic acid 10.14 73 956 1.937 0.001 0.489

Succinic acid 10.19 129 940 1.800 0.002 0.313

Nicotinic acid 10.44 196 563 1.607 0.006 0.504

Uracil 10.51 99 940 1.447 0.029 0.531

Itaconic acid 10.54 117 941 1.106 0.005 0.436

L-Allothreonine 11.01 112 911 1.169 0.046 0.581

Thymine 11.30 113 887 1.519 0.016 0.503

Hydrocinnamic acid 11.68 104 982 1.476 0.023 1.391

Oxalic acid 11.86 55 872 1.309 0.002 0.290

5-Dihydrocortisol 12.57 73 899 1.674 0.010 0.502

Adipic acid 12.67 111 745 2.087 <0.001 0.302

Aspartic acid 12.82 73 476 1.888 0.002 0.296

Methionine 12.90 128 734 1.253 0.034 0.546

Pimelic acid 13.08 125 753 1.379 0.024 0.696

Tropic Acid 13.34 118 489 1.241 0.021 0.330

Norleucine 13.537 158 514 1.327 0.022 0.127

Fructose 13.540 142 682 1.435 0.029 0.157

Page 31 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

32


4-Vinylphenol dimer 13.66 192 847 1.612 0.024 0.481

3-(4-hydroxyphenyl)-

propionic acid 13.67 179 830 1.512 0.013 0.365

3-Hydroxyphenylacetic acid 14.15 164 788 1.384 0.023 0.037

Glutamic acid 14.21 84 476 1.016 0.015 0.403

Cyclohexylsulfamic

acid 14.26 208 860 1.071 0.032 0.290

2-Deoxyuridine 14.28 170 786 1.919 0.003 0.007

Aminomalonic acid 14.34 56 869 1.181 0.012 0.130

5-Aminovaleric acid 14.46 174 801 1.847 0.002 0.458

Phenylalanine 14.47 218 839 1.225 0.033 0.540

1,3-Diaminopropane 14.75 174 555 1.411 0.028 1.422

Azelaic acid 15.45 55 932 1.608 0.005 0.523

Valine 15.82 152 424 1.859 0.002 1.491

Glycocyamine 15.94 208 811 1.925 0.001 0.003

Fucose 15.96 73 902 2.352 <0.001 0.034

2,3-Dihydroxybenzoic acid 16.11 355 936 2.200 <0.001 0.004

5-Methoxytryptamine 16.13 174 684 1.389 0.029 0.553

Hydroxyurea 16.82 277 872 1.514 0.003 0.269

Hypoxanthine 17.20 265 497 1.463 0.019 0.397

L-Threose 18.70 205 771 1.260 0.008 0.342

Lysine 18.95 73 812 1.378 0.032 0.508

Pentadecanoic acid 20.07 75 687 1.539 0.015 0.581

Tartaric acid 20.24 73 620 1.319 0.042 1.293

Glucuronic acid 21.59 202 688 2.024 0.001 0.149

10-Hydroxydecanoic

acid 21.60 75 674 1.726 0.007 0.463

Malonic acid 23.06 73 500 1.274 0.034 0.628

Dihydrocarveol 24.01 73 718 1.689 0.007 4.497

Norvaline 24.45 73 816 1.359 <0.001 2.852

Lactose 26.72 73 739 1.421 0.039 0.390

Page 32 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

33


Cellobiose 27.12 73 790 1.561 0.032 0.451

Milk

Alanine 7.796 116 620 1.907 0.044 28.00

Hydroxylamine 12.17 71 415 1.974 0.042 0.093

Phosphomycin 12.65 70 615 3.048 0.019 0.078

Thymol 12.67 207 574 1.473 0.026 7.930

Creatine 13.44 115 936 1.543 0.032 2.998

Putrescine 13.95 174 731 1.569 0.015 2.990

Ribulose-5-phosphate 21.79 315 519 2.710 0.044 <0.001

Citraconic acid 24.97 89 668 2.122 0.040 4.966

Serum

Mono(2-ethylhexyl)phthalate 7.093 221 642 2.439 0.002 0.123

Synephrine 12.17 71 842 4.007 <0.001 <0.001

Glutamine 7.444 155 644 2.089 0.015 2.163

Putrescine 7.717 174 717 1.144 0.035 0.624

DL-Anabasine 8.287 156 700 3.023 0.001 <0.001

Indolelactate 8.470 130 647 2.415 0.014 5.138

Malonamide 8.769 96 553 1.323 <0.001 0.296

Naringenin 9.059 128 530 1.669 0.036 0.390

Oxoproline 9.087 156 674 2.871 0.017 3.211

Succinate semialdehyde 9.091 89 969 3.676 0.001 <0.001

Cortexolone 10.19 213 846 3.235 0.001 <0.001

Halostachine 9.341 110 512 1.262 0.050 0.275

Pyridoxine 9.701 280 585 3.514 <0.001 0.051

Malonic acid 9.744 221 642 2.295 0.004 2.533

Phenylacetaldehyde 10.21 70 518 4.425 <0.001 <0.001

Glycocyamine 13.02 171 634 2.174 0.004 0.422

2-Keto-isovaleric acid 10.36 186 736 3.396 0.001 <0.001

Nicotinoylglycine 10.40 57 502 2.071 0.015 0.350

5-Methoxytryptamine 10.68 174 762 2.420 0.010 2.313

Glutaraldehyde 10.75 112 366 2.118 0.029 6.914

Page 33 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

34


2-Amino-1-phenylethanol 10.79 174 868 1.335 0.003 2.307

Synephrine 7.352 116 842 3.098 <0.001 0.200

D-Altrose 15.45 103 807 1.667 0.035 0.587

Tricetin 10.23 128 632 4.309 <0.001 <0.001

Monoolein 18.18 73 683 1.394 0.014 0.616

Phytanic acid 23.17 117 549 3.705 <0.001 0.124

Phytosphingosine 26.75 73 699 1.909 0.033 10.39

Galactinol 27.97 73 777 2.439 0.002 1.919

Urine

2-Hydroxypyridine 8.538 152 890 1.587 0.028 3.130

21-Hydroxypregnenolone 9.453 157 668 1.576 0.038 3.557

Benzoin 13.39 179 673 1.174 0.033 2.647

Benzoic acid 13.63 105 836 1.779 0.011 3.069

N-Methyl-L-glutamic acid 13.90 98 974 2.104 <0.001 0.033

D-Glyceric acid 15.46 189 666 1.084 0.044 2.196

Carbamoyl-aspartic acid 17.24 116 703 1.352 0.045 4.912

DL-Anabasine 17.34 239 912 1.853 0.014 3.703

5-Methylresorcinol 18.37 268 630 2.232 0.011 7.687

Ethanolamine 19.88 267 862 2.275 0.001 0.089

4-Vinylphenol dimer 21.18 83 766 1.650 0.020 4.044

4-Hydroxyphenylacetic acid 22.85 73 723 1.351 0.031 2.847

Lyxose 23.07 103 869 1.877 0.015 550.7

Caffeic acid 24.31 101 679 2.729 <0.001 <0.001

2,3-Dihydroxybenzoic acid 25.04 355 769 2.372 <0.001 <0.001

Conduritol b epoxide 25.07 73 799 1.207 0.047 2.492

Gentisic acid 25.49 73 580 1.215 0.043 0.469

Aminooxyacetic acid 26.13 146 589 1.618 0.018 0.363

5-Hydroxytryptophan 27.47 290 642 1.237 0.044 2.418

Hippuric acid 27.57 105 989 1.929 0.017 5.966

Pentadecanoic acid 31.47 117 806 1.554 0.039 3.125

Allantoic acid 28.29 331 793 1.124 0.048 4.021

Page 34 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

35


Tyrosine 33.12 179 554 1.380 0.020 2.836

Stearic acid 33.34 117 911 1.911 0.040 7.878

Lactobionic Acid 33.62 73 738 1.548 0.020 8.399

Glucose-1-phosphate 34.27 217 701 1.164 0.035 3.146

Malonic acid 35.15 73 195 1.660 0.003 0.290

2-Monoolein 35.98 73 817 1.700 0.036 4.227

Galactinol 36.69 204 670 1.990 0.002 1.919

Sedoheptulose 37.96 73 729 1.928 0.002 0.187

Gluconic lactone 38.87 73 628 1.325 0.032 0.261

aRT=Retention time.

bFC=fold change, mean value of peak area obtained from CS group / mean value of

peak area obtained from AH group., if the FC value less than 1, it means that

metabolites is less in CS than AH.

Page 35 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

36

Table 4 The metabolic pathways identified from the significantly different metabolites

(SDMs) from the four biofluids between AH and CS diets

SDM

Metabolic pathways

Gly, Ser and Thra Tyrb Phec

Rume

n fluid

(1.42)d1,3-Diaminopropa

ne

(0.30)L-Aspartic acid

(0.0035)Glycocyamine

(0.58)L-Allothreonine

(0.037)3-hydroxyphen

yl-

acetic acid

(0.31)Succinic acid

(0.023)3-hydroxyphenyl-

acetic acid

(0.57)Benzoic acid

(0.037)Phenylacetic acid

(0.31)Succinic acid

(0.54)Phenylalanine

Milk (3.00)Creatine

Serum (0.42)Glycocyamine (1.3*10-7)Succinate-

semialdehyde

(4.5*10-7)Phenylacetaldehy

de

Urine (2.20)D-Glycerate (2.85)4-Hydroxy-

phenylacetic acid

(0.47)Gentisic acid

(2.84)Tyrosine

(2.85)4-Hydroxy-

phenylacetic acid

(3.07)Benzoic acid

(2.84)Tyrosine

aGly, Ser and Thr = glycine, serine and threonine metabolism.

bTyr = tyrosine metabolism.

cPhe = phenylalanine metabolism.

dThe number in the bracket is the fold change (CS/AH) value.

Page 36 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

37

Figure 1. GC-TOF/MS TIC chromatograms of rumen fluid (a and b), milk (c and d), serum (e and f) and urine (g and h) for cows fed CS (a, c, e and g) and AH (b, d, f and h). CS = TMR containing corn stover as main forage; AH = TMR containing alfalfa and Chinese wild rye hay as main forage. Arrows indicate significantly different peaks when AH and CS were compared.

Page 37 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

38

Figure 2. Unique and mutual metabolites identified in the rumen fluid, milk, serum,

and urine of dairy cows.

Page 38 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

39

Figure 3. The metabolome view map of the common metabolites identified in four

biofluids from dairy cows fed CS and AH diets. The x-axis represents the pathway

impact, and y-axis represents the pathway enrichment. Larger sizes and darker colors

represent higher pathway enrichment and higher pathway impact values, respectively.

CS = TMR containing corn stover as main forage; AH = TMR containing alfalfa and

Chinese wild rye hay as main forage.

Page 39 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

40

Figure 4. PCA 3D score map (A, D, G and J), corresponding validation plots of

PLS-DA (B, E, H and K), and OPLS-DA score plots (C, F, I and L) derived from the

GC-TOF/MS metabolite profiles of rumen fluid (A−C), milk (D−F), serum (G−I) and

urine (J-L) for cows fed CS and AH. Black represents cows fed CS, and red represents

cows fed AH. CS = TMR containing corn stover as main forage; AH = TMR

containing alfalfa and Chinese wild rye hay as main forage.

Page 40 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

41

Figure 5. Kyoto Encyclopedia of Genes and Genomes pathways are manually linked

together. The map illustrates significantly different metabolites in the four biofluids

and three key metabolic pathways including glycine, serine and threonine metabolism;

tyrosine metabolism; and phenylalanine metabolism.

Page 41 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

42

Abstract Graphic

Page 42 of 42



123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

potential biomarkers for milk production and quality ... study aimed to elucidate the metabolic...

Documents