Metabolomics Profiling of Serum and Urine in Three Beef Cattle

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Metabolomics Profiling of Serum and Urine in Three Beef Cattle Breeds Revealed Different Levels of Tolerance to Heat Stress Yupeng Liao, Rui Hu, Zhisheng Wang, Quanhui Peng, Xianwen Dong, Xiangfei Zhang, Huawei Zou, Qijian Pu, Bai Xue, and Lizhi Wang J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.8b01794 • Publication Date (Web): 15 Jun 2018 Downloaded from http://pubs.acs.org on June 16, 2018

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Journal of Agricultural and Food Chemistry

Metabolomics Profiling of Serum and Urine in Three Beef Cattle Breeds Revealed Different Levels of Tolerance to Heat Stress Yupeng Liao, Rui Hu, Zhisheng Wang*, Quanhui Peng, Xianwen Dong, Xiangfei Zhang, Huawei Zou, Qijian Pu, Bai Xue, and Lizhi Wang Institute of Animal Nutrition, Sichuan Agricultural University, Key Laboratory of Bovine Low-Carbon Farming and Safe Production, Chengdu, Sichuan, 611130, China. *Corresponding author, Tel: +86-0835-2885730; Fax: +86-0835-2885730; E-mail: [email protected].

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ACS Paragon Plus Environment


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ABSTRACT: This study was to determine differences in the global metabolic profiles

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of serum and urine of Xuanhan yellow cattle, Simmental crossbred cattle (Simmental

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× Xuanhan yellow cattle) and cattle-yaks (Jersey × Maiwa yak) under heat stress

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(temperature-humidity index remained above 80 for 1 week). A total of 55 different

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metabolites associated with the three breeds were identified in the serum and urine

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samples by gas chromatography-mass spectrometry. The metabolic adaptations to heat

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stress are heterogeneous. Cattle-yaks mobilize a greater amount of body protein to

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release glucogenic amino acids to supply energy, whereas the tricarboxylic acid cycle

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is inhibited. Simmental crossbred cattle mobilize a greater amount of body fat to use

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free fatty acids as an energy source. In comparison with Simmental crossbred cattle and

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cattle-yaks, Xuanhan yellow cattle have higher glycolytic activity, and possess a

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stronger antioxidant defense system and are, in conclusion, more adapted to hot and

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humid environments.

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KEYWORDS: metabolomics, gas chromatography-mass spectrometry, heat stress,

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beef cattle, metabolic pathway

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INTRODUCTION

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Heat stress is a worldwide problem for the livestock industry. In particular, cattle are

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extremely sensitive to hot and humid environments.1 In the US, heat stress leads to

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annual economic losses in the beef cattle industry of 369 million dollars.2 In southern

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China, summer heat stress causes beef cattle farms to be unprofitable.3 Furthermore, as

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global temperatures rise,4 the beef cattle industry will face more serious challenges from

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heat stress in the future. Earlier studies suggested that a reduction in dry matter intake

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(DMI) by cattle exposed to heat stress is responsible for decreases in weight gain and

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milk yield.5, 6 However, a series of recent studies have proven that heat stress reduces

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DMI only partially and accounts for about 50% of the decrease in productivity, of which

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the remainder is due to health problems and metabolic disorders in cattle.7, 8

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Breed is a natural factor that can affect many aspects of livestock production, such

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as productivity, temperature adaptability, reproductive function, temperament, and

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immune function.9,

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associated with breed.1,

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account for approximately 80% of the national herd.12 Yellow cattle have a long history

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of providing draft power, leather, milk, and meat for people in China. Xuanhan yellow

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cattle, which are a Chinese southern local yellow cattle breed lived in the hilly areas at

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low altitude, are well adapted to coarse feedstuffs and adverse conditions. However,

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since the 1980s, market changes have caused an increase in interest in beef breeds with

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other characteristics, in particular, meat yield performance. In this context, China has

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It has been reported that heat tolerance in beef cattle is closely 11

In China, yellow cattle are the predominant breeds and

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introduced a large number of foreign cattle for crossbreeding, such as Simmentals, to

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improve the productivity of local yellow cattle. The cattle-yak was bred to combine the

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tolerance of high altitudes and cold of yaks with the excellent productivity of other

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cattle. Each beef cattle breed has its unique nutrition metabolism characteristics to be

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adapted to distinct environments in the long-term evolution. In comparison with yellow

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cattle, Yaks have more efficient energy and nitrogen utilization.13 At present, the above

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three beef breeds are widely farmed in southern China, where is hot and humid

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environments in summer. Nutrition metabolism alteration is an important way to

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response to heat stress in mammals.14 However, under the same environment and diet,

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the different levels of tolerance and nutrition metabolism to response to heat stress of

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three beef breeds (Xuanhan yellow cattle, Simmental crossbreeds, and cattle-yaks) are

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still unclear.

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Metabolomics is an emerging technological and analytical field in systems biology,

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following genomics, transcriptomics, and proteomics. The global metabolic profile of

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an individual can directly reflect the final result of the interaction of a variety of factors,

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including genetic, physiological, and environmental factors.15 Metabolomics employs

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high-throughput

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chromatography-mass spectrometry, and gas chromatography-mass spectrometry (GC-

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MS), to study metabolites in biological samples (biofluids or tissues) to further explain

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the physiological status of an individual.16 Among metabolomics methods, GC-MS has

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been appreciated by researchers owing to its advantages of high resolution and

approaches,

such

as

nuclear

magnetic

4


resonance,

liquid

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detection sensitivity.17 Previous studies have demonstrated the practicability and

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advantages of using metabolomics methods to study tolerance of seasonal weight loss

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in sheep breeds.18 However, to date global metabolic profiling of cattle has focused on

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dairy cows,19, 20 and information on metabolomics research into beef cattle and, more

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importantly, the metabolic adaptation of the above three beef breeds under heat stress

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has not been reported.

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In this study, we aimed to determine differences in the global metabolic profiles of

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serum and urine in the local breed (Xuanhan yellow cattle), Simmental crossbred cattle

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(Simmental ×Xuanhan yellow cattle), and cattle-yaks (Jersey ×Maiwa yak) under heat

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stress. We utilized a GC-MS-based metabolomics platform in combination with

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multivariate statistical analysis to study different metabolic adaptations of the above

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three beef cattle breeds under heat stress. The results will be of great importance for

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understanding the heat stress tolerance of the above three beef breeds and may be

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beneficial in the selection of beef cattle breeds for hot and humid areas.

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MATERIALS AND METHODS

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Animal Experiment and Sample Collection

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The animal experiment in this study was approved by the Animal Care and Use

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Committee of the Animal Nutrition Institute at Sichuan Agricultural University and was

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carried out according to the Guide for the Care and Use of Laboratory Animals of the

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National Research Council. In brief, 24 twenty-six-month-old steers were used,

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including eight Xuanhan yellow cattle (XHC), eight Simmental × Xuanhan yellow 5



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cattle crossbred cattle (SXC), and eight Jersey ×Maiwa yak crossbred cattle (JMY). All

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animals were fed with the same diet, which was designed according to the 2004 Chinese

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feeding standard for beef cattle (NY/T 815–2004), and the concentrate-to-roughage

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ratio of the diet was 6:4. The ingredients and chemical composition of the diet are

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shown in Table S-1. The cattle were fed twice daily at 9:00 AM and 5:00 PM, and water

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was provided at all times. According to the US National Research Council (1971), the

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THI in the beef cattle house was calculated using the formula: THI = 0.72 ×(Td + Tw)

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+ 40.6, where Td is the dry-bulb temperature (°C) and Tw is the wet-bulb temperature

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(°C). Traditionally, the temperature-humidity index (THI) has been employed to

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estimate the degree of heat stress in dairy cows, which are in heat stress when the THI

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exceeds 72, and severe heat stress occurs if the THI exceeds 80.21 Serum and urine

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samples were obtained in August (summer season), and the THI in the beef cattle house

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was approximately 76–82 during this month (Fig. S-1). As for the sampling time

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according to the previous literature,22 after the THI in the beef cattle house remained

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above 80 for 1 week, fasting blood samples were obtained before morning feeding from

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the jugular vein of the beef cattle. After the blood was left to stand for 30 min, the serum

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was extracted from the blood samples by centrifugation at 3000 rpm for 10 min at 4 °C

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and immediately stored in the dark at −80 °C until analysis was carried out. Urine

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samples were collected in 15 mL Falcon tubes with a homemade urine bag at the same

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time. Then, the urine samples were centrifuged at 4000 rpm for 10 min at 4 °C and

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immediately stored in the dark at −80 °C until analysis was carried out. The rectal 6


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temperature, respiration rate, DMI, and average daily gain (ADG) of the three beef

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cattle breeds are shown in Table S-2.

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Sample Pretreatment and GC-MS Analysis

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Procedures for the extraction and derivatization of the serum and urine samples were

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employed as previously described,23, 24 and were described in detail in the Supporting

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Information (Supplemental Methods).

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For the quality control samples, 20 μL of each prepared sample extract was taken

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and mixed as previously described25 to monitor the reproducibility and stability of the

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instrument.

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The derivatized samples were analyzed by GC/MS (Agilent 7890A/5975C, Agilent

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Technologies, Santa Clara, CA, USA). The detailed parameters were described under

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Supplemental Methods.

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Data Processing

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The original GC/MS data were converted into files in netCDF format26 and

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subsequently preprocessed, including the identification, filtration, and alignment of

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peaks by XCMS software (www.bioconductor.org). Finally, a data matrix of

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information, including mass-to-charge ratios (m/z), retention times, and peak intensities,

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was obtained. Identification of metabolites was performed using the AMIDS system

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(Automated Mass Spectral Deconvolution and Identification System) by searching the

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National Institute of Standards and Technology library (http://srdata.nist.gov/gateway/)

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and the Wiley Chemical Structure Library.27 7



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The normalized GC-MS datasets were imported into the SIMCA-P 13.0 software

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package (Umetrics, Umea, Sweden) for multivariate analysis, including principal

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component analysis (PCA) and partial least squares discriminant analysis (PLS-DA).

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Prior to the multivariate analysis, mean centering and unit variance scaling were

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performed on the GC-MS data to make the metabolites equal in importance. PCA was

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utilized to visualize global clustering and display differences in metabolic profiles

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between samples. To achieve the maximum separation between samples, PLS-DA was

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used to identify differential metabolites that explained the separation between groups.

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The model quality was evaluated using the R2X, R2Y, and Q2 parameters. The R2X and

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R2Y parameters, which represent the fractions of explained X-variation and Y-variation,

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respectively, can be used to evaluate the quality of the model. The Q2 parameter

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represents the predictive ability of the model. In general, the model is acceptable when

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the values of R2X, R2Y, and Q2 are greater than 0.5.24 In addition to cross-validation,

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the PLS-DA models were verified via 100 repeated permutation tests.

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Identification of Differential Metabolites and Metabolic Pathway

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Analysis

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As described above, PLS-DA was employed to identify differential metabolites

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between groups. Furthermore, variable importance in the projection (VIP) values in the

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PLS-DA model were used to rank the metabolites based on their importance in

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discriminating between groups. Metabolites with the highest VIP values are the most

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powerful group discriminators. Traditionally, VIP values >1 are significant. In this 8


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study, metabolites with VIP values（VIP > 1.0）in the PLS-DA model and significance

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(p < 0.05) in Student’s t-test (SPSS 16.0) were selected as differential metabolites. Then,

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receiver operating characteristic (ROC) curves analysis was performed (SPSS 16.0),

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and the areas under the curves (AUCs) were calculated to determine the diagnostic

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value of these differential metabolites.28 The discriminatory power of the differential

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metabolites was ranked and visualized using heat maps.28 Metabolic pathways were

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obtained for these identified differential metabolites from the KEGG (Kyoto

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Encyclopedia of Genes and Genomes) database (http://www.genome.jp/kegg).

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Determination of Metabolism and Activity of Antioxidant Enzymes

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The levels of lactate dehydrogenase (LDH), malondialdehyde (MDA), superoxide

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dismutase (SOD), and glutathione peroxidase (GSH-Px) and the total antioxidant

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capacity (T-AOC) in serum samples were measured using enzyme-linked

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immunosorbent assay kits (Chengdu Anya Biotechnology Co., Ltd). Absorbance values

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were determined at 450 nm with a microplate reader. The calibration standards are

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assayed at the same time as the samples and allow the operator to produce a standard

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curve of Optical Density. The levels of these enzymes in the samples is then determined

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by comparing the O.D. of the samples to the standard curve.

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Enzyme activity was analyzed by univariate analysis of variance with a post hoc

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Tukey’s test using SPSS 16.0 software (Chicago, IL, USA). Statistical significance was

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set at p < 0.05, and a high level of significance at p < 0.01.

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RESULTS 9



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Metabolomics Profiling of Serum and Urine Samples

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Typical GC/MS chromatograms of the serum and urine samples from the three beef

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cattle breeds are shown in Figures 1A and B, respectively. In total, 167 and 220 valid

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peaks were identified in the chromatograms of serum and urine, respectively. A total of

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78 and 81 metabolites were identified and quantified in the serum and urine samples,

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respectively. These metabolites in serum and/or urine can be divided into eight major

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groups, namely, carbohydrates, amines, amino acids, fatty acids, organic acids,

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phosphoric acid, polyol, and nucleotides, on the basis of the characteristics of each

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chemical. Furthermore, the five metabolites with the highest concentrations in serum

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were glucose, urea, lactic acid, phosphoric acid, and cholesterol in all breeds. In contrast,

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the five metabolites with the highest concentrations in urine were slightly different in

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the three breeds: in the XHC group these were phosphoric acid, creatinine,

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pseudouridine, uric acid, and benzoic acid, whereas in the SXC and JMY groups these

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were phosphoric acid, creatinine, pseudouridine, allantoin, and benzoic acid.

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Data for the serum and urine samples acquired by GC-MS were integrated and

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analyzed using PCA (Fig. S-2). The PCA score plots for the serum samples showed a

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distinct separation between the three breeds. For the urine samples, the PCA model was

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not able to completely separate the three breeds, and we therefore focused mainly on

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the results of supervised analysis. The PLS-DA score plots for the two sample types

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(serum and urine) showed a clear separation between the XHC, SXC, and JMY groups,

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which suggested that the serum and urine metabolic profiles of the three breeds were 10


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significantly different under heat stress (Fig. 2A and E). Furthermore, to further

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investigate the metabolic status and discover potential biomarkers of each breed, PCA

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and PLS-DA analysis were also applied to each combination of two breeds (XHC

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versus SXC, XHC versus JMY, and SXC versus JMY), all of which showed distinct

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separations (Fig. S-2, Fig. 2B–D and F–H).

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Identification of Different Metabolites

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In the present study, different metabolites were selected according to their VIP values

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in the PLS-DA model (VIP > 1) and p-values from Student’s t-test (p < 0.05). A total

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of 42 and 23 different metabolites associated with serum and urine from the breeds are

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listed in Tables 1 and 2, respectively. Specifically, 25, 30, and 23 metabolites from

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serum were identified in the XHC group in comparison with the SXC group, in the XHC

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group in comparison with the JMY group, and in the SXC group in comparison with

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the JMY group, respectively. Moreover, 11, 16, and 12 metabolites from urine were

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identified in the XHC group in comparison with the SXC group, in the XHC group in

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comparison with the JMY group, and in the SXC group in comparison with the JMY

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group, respectively.

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In order to determine the diagnostic value of these different metabolites for

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differentiating any two of the XHC, SXC, and JMY groups, ROC analysis was carried

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out. A heat map shows any two groups of different metabolites ranked in order of their

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AUC values (Fig. S-3). All the different metabolites displayed good diagnostic abilities,

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with AUC values of 0.73–1.00. 11



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Integration of Key Different Metabolic Pathways

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The KEGG pathway database was utilized for analyzing related metabolic pathways

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of 55 metabolites found in serum and urine. As summarized in Tables 1 and 2, we found

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that these metabolites were involved in multiple biochemical pathways, such as

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glycolysis, amino acid metabolism, fatty acid metabolism, inositol phosphate

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metabolism, vitamin E metabolism, purine metabolism, and the tricarboxylic acid

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(TCA) cycle. Finally, we combined these results to draw a metabolic network map (Fig.

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3) to show a more intuitive correlation between these metabolites.

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Different Activities of Metabolic and Antioxidant Enzymes

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Figure 4 shows the activities of key enzymes related to glycolysis and the antioxidant

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defense system. LDH activity was very significantly higher in the XHC group in

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comparison with the SXC and JMY groups (Fig. 4A). Furthermore, MDA levels were

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significantly lower in the XHC group in comparison with the SXC and JMY groups

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(Fig. 4B), and SOD activity and T-AOC were significantly higher in the XHC group in

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comparison with the SXC and JMY groups (Fig. 4B).

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DISCUSSION

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We found heat stress induced significantly reduction of dry matter intake per kg

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BW (-18.27%, -24.75%, and -28.77%, respectively) and average daily gain (-17.33%,

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-21.43%, and -24.68%, respectively) of Xuanhan yellow cattle, Simmental crossbred

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cattle and cattle-yaks when compared with our previous data during non-heat stress

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period. Accordingly, heat stress may had lowest impact on production performance of 12


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Xuanhan yellow cattle among three breeds and cattle-yak suffered the greatest

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negative interference. So we wanted to understand differences in the global metabolic

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profiles of the three breeds under heat stress. In this study, we utilized GC-MS-based

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metabolomics to determine differences in the metabolite profiles of serum and urine

236

in Xuanhan yellow cattle, Simmental crossbred cattle, and cattle-yaks under heat

237

stress. A total of 78 and 81 metabolites were identified and quantified in serum and

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urine, respectively. In total, 55 different metabolites associated with the three breeds

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were obtained under heat stress. These metabolites were involved in glycolysis, amino

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acid metabolism, fatty acid metabolism, the TCA cycle, and purine metabolism. Our

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results revealed the bases of different metabolic adaptations in the three breeds under

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heat stress and could benefit breeding programs.

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Glycolysis

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A previous study showed that heat stress enhanced glycolysis, which led to

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increases in the levels of pyruvic acid and lactic acid in dairy cows.22 In the present

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study, we found that serum levels of glucose, pyruvic acid, and lactic acid were higher

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in the XHC group in comparison with the SXC and JMY groups. Moreover, LDH

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activity was significantly higher in the XHC group in comparison with the SXC and

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JMY groups. All the above results indicate that glycolytic activity and anaerobic cell

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respiration rates were higher in the XHC group in comparison with the SXC and JMY

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groups. Pyruvic acid, which is formed by the breakdown of glucose, is an important

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pathway junction in carbohydrate catabolism and can be further converted into acetyl13



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CoA to supply energy via the TCA cycle under aerobic conditions.29 However, under

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anaerobic conditions pyruvic acid is reduced to lactic acid by LDH. Lactic acid is thus

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the end-product of glucose metabolism under anaerobic conditions,30 which prevent

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the inhibition of glycolysis, but lactic acid can be oxidized to pyruvic acid in the

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presence of oxygen. Therefore, the higher glycolytic activity in the XHC group could

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be a metabolic adaptation to supply more energy under heat stress.

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Amino Acid Metabolism

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Heat stress increases the disintegration of proteins and mobilization of amino acids

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to enable the production of additional energy. These processes produce higher levels

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of glucogenic amino acids, creatinine, and urea.31, 32 In the present study, we found

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that serum levels of glucogenic amino acids, such as methionine, glutamine,

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phenylalanine, tyrosine, and asparagine, which could generate pyruvic acid, α-

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ketoglutaric acid, fumaric acid, and oxaloacetic acid,33 were significantly higher in the

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JMY group in comparison with the SXC and XHC groups. Moreover, levels of

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ornithine and urea, which participate in the urea cycle,34 were also higher in the JMY

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group. On the other hand, levels of glutamic acid, glutamine, asparagine, ornithine,

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and urea were significantly higher in the SXC group in comparison with the XHC

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group. In addition, serum creatinine levels were significantly higher in the JMY and

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SXC groups in comparison with the XHC group. Creatinine, which is the degradation

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product of creatine phosphate, plays an important role in energy balance when

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animals have high energy requirements. The higher levels of creatinine in the JMY 14


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and SXC groups could indicate an increase in the mobilization of creatine phosphate

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in muscle tissue for energy supply.35 All the above results indicate that the catabolism

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of proteins and mobilization of amino acids were highest in the JMY group, followed

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by the SXC group, and lowest in the XHC group. Therefore, the mobilization of

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muscle protein in the JMY group could be a metabolic adaptation to release

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glucogenic amino acids that the body can subsequently convert into energy-rich

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metabolites under heat stress.

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Interestingly, serum levels of putrescine, which is a polyamine derived from

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arginine, were significantly lower in the JMY group in comparison with the XHC and

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SXC groups. Putrescine can improve the response of heat shock proteins36 and change

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the topology of DNA to an extent that promotes survival.37 In combination with the

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higher level of ornithine, we think that the JMY breed may require more putrescine to

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resist heat stress. However, lower levels of putrescine may interfere with heat

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adaptability in the JMY breed.

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Lipid Metabolism

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Heat stress leads to an increase in circulating free fatty acids, including linoleic

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acid, oleic acid, and arachidonic acid, because of lipid catabolism.38 In the present

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study, we found that serum and urine levels of glycerol and free fatty acids, including

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palmitic acid, tetradecanoic acid, oleic acid, linoleic acid, and arachidonic acid, which

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belong to the family of long-chain fatty acids, were significantly higher in the SXC

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group in comparison with the XHC and JMY groups. Moreover, levels of glycerol and 15



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free fatty acids were significantly higher in the JMY group in comparison with the

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XHC group. In addition, urine levels of myo-inositol and nicotinic acid, which can

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both play an important role in fat metabolism,39 were higher in the SXC and JMY

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groups in comparison with the XHC group. All the above results suggest that fat

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catabolism was highest in the SXC group, followed by the JMY group, and lowest in

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the XHC group. In the liver, free fatty acids can be converted into acetyl-CoA by β-

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oxidation to supply energy via the TCA cycle. Glycerol can be converted into

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glycerol-3-phosphate and glyceric acid via glycolysis to supply energy.40 Therefore,

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the mobilization of body fat in the SXC group could be a metabolic adaptation to use

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free fatty acids and glycerol as energy sources under heat stress.

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However, it is noteworthy that previous research has confirmed that palmitic acid is

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associated with the activation of inflammatory responses in animal models,41 and

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omega-6 linoleic acid and arachidonic acid also promote inflammation.42 Therefore,

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the SXC and JMY breeds may have a greater risk of inflammation in comparison with

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the XHC breed.

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TCA Cycle

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In previous studies, heat stress led to upregulation of the TCA cycle, which

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increased levels of TCA cycle intermediates, such as citric acid, cis-aconitic acid, α-

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ketoglutaric acid, succinic acid, fumaric acid, and malic acid.38, 43 In the present study,

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we found that serum and urine levels of citric acid, aconitic acid, and fumaric acid

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were significantly higher in the SXC group in comparison with the JMY and XHC 16


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groups. Moreover, levels of citric acid, aconitic acid, and succinic acid were

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significantly higher in the XHC group in comparison with the JMY group. These

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results suggest that TCA cycle activity was highest in the SXC group, followed by the

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XHC group, and lowest in the JMY group. This may result from the higher level of

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acetyl-CoA due to the β-oxidation of fatty acids in the SXC group.

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Interestingly, the JMY group had higher urine levels of methylmalonic acid and

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methylcitric acid in comparison with the SXC and XHC groups. Medically, the

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methylmalonic acid concentration is used to diagnose methylmalonic aciduria, which

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is caused by an inherited deficiency of the mitochondrial enzyme methylmalonyl-CoA

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mutase or a metabolic disorder involving its coenzyme cobalamin.44 A deficiency of

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methylmalonyl-CoA mutase, which catalyzes the isomerization of methylmalonyl-

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CoA to succinyl-CoA, results in an accumulation of methylmalonic acid and

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methylcitric acid and thus inhibits the TCA cycle.45 It was first discovered that the

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JMY breed has a greater risk of methylmalonic aciduria in comparison with the SXC

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and XHC breeds. In brief, under heat stress the TCA cycle activity in the JMY breed

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is the lowest in the three breeds, the reason for which needs further research.

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Oxidative Stress

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Heat stress increases the production of reactive oxygen species (ROS), including

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superoxide ions (O2-), nitrogen monoxide (NO), and hydroxyl radicals (HO·), which

335

destroy the structure and function of the cell membrane and damage the mitochondria,

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eventually leading to cell death.46 ROS are balanced by natural antioxidant 17



337

compounds such as glutathione, GSH-Px, SOD, vitamin C, and vitamin E.47 Previous

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studies revealed that heat stress increased oxidative stress, which increased MDA

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levels48 and decreased levels of SOD and GSH-Px in cattle exposed to heat stress.49

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We found that serum MDA levels were significantly lower in the XHC group in

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comparison with the SXC and JMY groups, and SOD activity and T-AOC were

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significantly higher in the XHC group in comparison with the SXC and JMY groups.

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Furthermore, in the present study we found that urine levels of uric acid were

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significantly higher and those of allantoin were lower in the XHC group in

345

comparison with the SXC and JMY groups. As we know that cattle can express the

346

enzyme uricase, which converts uric acid into allantoin. Allantoin is thus the final

347

oxidation product of purine metabolism. In the presence of oxidative stress, uric acid

348

is oxidized by ROS to allantoin.50 Previous studies have shown that allantoin is a

349

potential marker for monitoring oxidative status in humans.51 The higher levels of uric

350

acid and lower levels of allantoin in the XHC group may indicate that this breed

351

suffers from less oxidative stress under heat stress. α-Tocopherol and γ-tocopherol are

352

forms of vitamin E and possess potent anti-inflammatory and antioxidant properties.52

353

In the present study, we found that serum levels of α-tocopherol and γ-tocopherol

354

were significantly higher in the XHC group in comparison with the SXC group.

355

Moreover, serum levels of γ-tocopherol were significantly higher in the XHC group in

356

comparison with the JMY group. The lower levels of tocopherols in the SXC and

357

JMY groups may result from more severe lipid inflammation and greater oxidative 18


Page 18 of 41

Page 19 of 41


358

stress under heat stress. All the above results suggest that the XHC breed suffers from

359

less oxidative stress and possesses a stronger antioxidant defense system in

360

comparison with the SXC and JMY breeds under heat stress.

361

In summary, under heat stress cattle-yaks (Jersey × Maiwa yak) mobilize a greater

362

amount of body protein to release glucogenic amino acids to supply energy, which

363

may represent their metabolic adaptation mechanism, whereas the TCA cycle is

364

inhibited; the above results indicate that the heat stress tolerance of cattle-yaks is the

365

lowest among the three breeds. In contrast, Simmental crossbred cattle (Simmental ×

366

Xuanhan yellow cattle) mobilize a greater amount of body fat in order to use free fatty

367

acids and glycerol as energy sources, and the TCA cycle activity is higher, which may

368

represent their metabolic adaptation mechanism. In comparison with Simmental

369

crossbred cattle and cattle-yaks, Xuanhan yellow cattle change their nutritional energy

370

source without significantly compromising the storage of body fat and protein and

371

have higher levels of pyruvic acid and lactic acid, which indicates that the glycolysis

372

process is enhanced. Furthermore, Xuanhan yellow cattle suffer from less oxidative

373

stress and possess a stronger antioxidant defense system under heat stress and are thus

374

more adapted to hot and humid environments.

375

ASSOCIATED CONTENT

376

Supporting Information

377

Figure S-1, THI in August; Figure S-2, PCA score plots for the serum and urine

378

samples; Figure S-3, heat maps showing the AUC values of different metabolites; 19



379

Table S-1, ingredients and chemical composition of the diet; Table S-2, physiological

380

index and production performance; supplemental methods.

381

AUTHOR INFORMATION

382

Corresponding Author

383

*Phone/fax: +86-0835-2885730. E-mail: [email protected].

384

Author Contributions

385

Y.L. and R.H. contributed equally and should be considered co-first authors.

386

Notes

387

The authors declare no competing financial interest.

388

ACKNOWLEDGMENTS

389

The authors gratefully thank the members of the Institute of Animal Nutrition,

390

Sichuan Agricultural University for their assistance in the sampling and analysis of

391

the samples. We also thank Suzhou BioNovoGene Co., Ltd for their assistance in the

392

original data processing and related bioinformatics analysis of GC-MS.

393

FUNDING SOURCES

394

This work was funded by National key research and development program of China

395

(2017YFD0502005), Sichuan Science and Technology Program (2018NZ0002) and

396

China Agriculture (Beef Cattle/Yak) Research System (CARS-37)

397

(http://www.beefsys.com/).

398 399 20


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400 401 402 403 404


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Figure captions Figure 1. Typical GC/MS chromatograms of the three beef cattle breeds from (A) serum and (B) urine. Figure 2. PLS-DA loading plots based on the serum and urine metabolic profilings. (A) The three breeds from serum (3 components, R2X= 0.387, R2Y=916, Q2 = 0.813). (B) XHC vs. SXC from serum (2 components, R2X= 0.352, R2Y=981, Q2 = 0.900). (C) XHC vs. JMY from serum (2 components, R2X= 0.344, R2Y=984, Q2 = 0.904). (D) SXC vs. JMY from serum (2 components, R2X= 0.347, R2Y=904, Q2 = 0.573). (E) The three breeds from urine (4 components, R2X= 0.579, R2Y=935, Q2 = 0.792). (F) XHC vs. SXC from urine (4 components, R2X= 0.658, R2Y=995, Q2 = 0.835). (G) XHC vs. JMY from urine (4 components, R2X= 0.658, R2Y=995, Q2 = 0.872). (H) SXC vs. JMY from urine (3 components, R2X= 0.562, R2Y=969, Q2 = 0.818). Figure 3. Different metabolic pathways from the three beef cattle breeds under heat stress. Red-colored symbols represent significant higher of metabolites in latter group compared with the former group, and black-colored symbols mean no difference, while blue-colored symbols indicate lower. Figure 4. Key enzymes related to the three beef cattle breeds in glycolysis, lipid metabolism and antioxidant defense system. (A) LDH activity. (B) MDA concentration and SOD, GSH-Px, T-AOC activity. * P < 0.05, ** P < 0.01.

30


Page 30 of 41

Page 31 of 41


Table 1. Identification of Different Metabolites from Any Two Groups of XHC, SXC and JMY in Serum XHC vs. SXC Metabolites

VIPa

Pb

XHC vs. JMY

FCc VIPa

Pb

FCc

SXC vs. JMY

VIPa

Pb

FCc

Metabolic pathway

Glucose

1.55 0.006 0.88 1.73 < 0.001 0.86

-

-

-

Glycolysis

Pyruvic acid

1.85 < 0.001 0.74 1.58 0.002

0.81

-

-

-

Glycolysis

Lactic acid

2.04 < 0.001 0.63 1.44 0.009

0.80

1.54 0.015 1.26 Glycolysis Pentose and glucuronate

Xylitol

-

-

-

-

-

-

2.00 < 0.001 0.71 interconversions Pentose and glucuronate

Glucuronic acid

-

-

-

-

-

-

1.58 0.007 0.79 interconversions Cysteine and methionine

Methionine

-

-

-

1.35 0.013

1.34

1.45 0.011 1.30 metabolism Cysteine and methionine

Cysteine

-

-

-

1.55 0.007

1.47

-

-

metabolism D-Glutamine and D-

Glutamic acid

1.38 0.025 1.50 1.50 0.011

1.64

-

-

glutamate metabolism D-Glutamine and D-

Glutamine

1.17 0.011 2.99 1.73 < 0.001 10.10 1.68 0.005 3.38 glutamate metabolism

2-amino-Butyric acid

1.33 0.028 1.95 1.49 0.009

2.25

-

-

-

Glutamate metabolism Phenylalanine and tyrosine

Phenylalanine

-

-

-

1.47 0.005

1.22

1.73 0.002 1.23 metabolism Phenylalanine and tyrosine

Tyrosine

-

-

-

1.27 0.025

2.27

1.29 0.035 2.19 metabolism Alanine, aspartate and

Asparagine

1.45 0.011 1.78 1.62 < 0.001 3.59

1.51 0.009 2.02 glutamate metabolism Arginine and proline

Ornithine

1.56 0.004 1.40 1.83 < 0.001 1.92

4-Hydroxyproline

1.40 0.006 1.41 1.24 0.030

1.24

1.71 0.003 1.37

-

-

-

metabolism Arginine and proline metabolism Arginine and proline

Creatinine

1.39 0.014 1.14 1.48 0.003

1.20

-

-

metabolism Arginine and proline

Putrescine

-

-

-

1.51 0.003

0.85

1.63 0.006 0.86 metabolism Arginine and proline

Urea

-

-

-

-

-

-

1.42 0.016 1.31 metabolism

Oleic acid

1.58 < 0.001 2.05

-

-

Linoleic acid

1.84 < 0.001 1.43 1.28 0.009

1.37

1.48 0.004 0.55 Fatty acid metabolism -

31


-

-

Fatty acid metabolism


Arachidonic acid

1.58 0.001 2.32 1.28 0.009

Palmitic acid

1.57 0.001 1.54

-

Tetradecanoic acid

1.24 0.012 1.55

-

Heptanoic acid

1.44 0.010 1.36 1.68 < 0.001 1.24

Glycerol

1.46 0.002 1.69 1.08 0.040

1.21

Glyceric acid

1.63 0.001 0.75 1.48 0.002

0.77

Cholesterol

-

-

-

myo-Inositol-1-phosphate

-

-

-

-

Page 32 of 41

1.32

1.42 0.014 0.57 Fatty acid metabolism

-

-

1.42 0.010 0.72 Fatty acid metabolism

-

-

1.77 < 0.001 0.78 Fatty acid metabolism

-

-

-

-

-


1.27 0.021 0.71 Glycerolipid metabolism -

-

-

Glycerolipid metabolism

1.33 0.034 0.86 Steroid biosynthesis Inositol phosphate

1.00 0.050

0.67

1.53 0.005 0.51 metabolism Inositol phosphate

Myo-inositol

-

-

-

1.29 0.016

1.34

-

-

metabolism

Citric acid

1.23 0.032 1.23

-

-

-

Fumaric acid

1.92 < 0.001 1.85

-

-

-

1.99 < 0.001 0.54 TCA Cycle

-

-

-

1.30 0.031 0.86 TCA Cycle

α-Ketoglutaric acid

-

-

γ-Tocopherol

1.77 0.004 0.51 1.38 0.011

α-Tocopherol

1.71 0.001 0.71

-

-

TCA Cycle

0.68

-

-

-

Vitamin E metabolism

-

-

-

-

Vitamin E metabolism

1.27 0.013

3.71

-

-

-

Uric acid metabolism

Ribose

1.30 0.024 1.37 1.27 0.031

1.48

-

-

-

Purine metabolism

Oxalic acid

1.72 < 0.001 1.69 1.11 0.028

1.51

-

-

-

Others

1-Monooctadecanoylglycerol

1.21 0.025 1.30

Allantoin

aVIP

-

-

-

-

-

-

-

-

-

-

1.66 0.002 0.64 Others

Monomethylphosphate

-

-

-

1.67 < 0.001 0.66

1.52 0.008 0.73 Others

2-Oxoisocaproic acid

-

-

-

1.40 0.008

1.32

1.82 0.001 1.28 Others

Erythronic acid

-

-

-

1.48 0.004

1.23

-

-

-

Others

1,3-Di-tert-butylbenzene

-

-

-

1.31 0.013

0.90

-

-

-

Others

(variable importance in the projection) values were obtained from PLS-DA models with a threshold of 1. bP-

values were calculated from student's t-test with a threshold of 0.05. cFC (fold change) values were obtained from mean peak area of latter group/mean peak area of former group obtained. If the FC value is greater than 1, it means 32


Page 33 of 41


that metabolite level is higher in latter group compared with the former group.

33



Page 34 of 41

Table 2. Identification of Different Metabolites from Any Two Groups of XHC, SXC and JMY in Urine XHC vs. SXC

XHC vs. JMY

SXC vs. JMY

Metabolites

Metabolic pathway VIPa

Pb

FCc VIPa

Pb

FCc VIPa

Lactic acid

1.49

0.037 0.70 1.56 0.014 0.63

Glycerol-3-phosphate

2.25

0.001 2.49

-

-

-

-

Pb

FCc

-

-

Glycolysis

1.76

0.012 0.57 Glycolysis 0.007 0.41 Glycolysis

Glyceric acid-3-phosphate

-

-

-

2.15 0.001 0.53 1.84

Fructose

-

-

-

1.45 0.039 0.49

-

-

-

Glycolysis

Galactose

-

-

-

1.37 0.044 0.65

-

-

-

Galactose metabolism

Xylitol

-

-

-

Pentose and glucuronate -

-

-

1.63

0.009 0.48 interconversions

2-Aminoadipic acid

-

-

-

-

-

-

1.48

0.022 0.51 Lysine degradation Arginine and proline

Urea

2.32 < 0.001 2.33 1.95 0.002 2.09

-

-

metabolism Arginine and proline

Putrescine

-

-

-

-

-

-

1.72

0.008 0.77 metabolism

Oleic acid

2.11

0.001 1.72 1.62 0.015 1.37

Linoleic acid

1.67

0.012 1.47

myo-Inositol

2.37 < 0.001 2.63 1.77 0.003 1.93 1.45

-

-

-

1.35

-

-


0.049 0.78 Fatty acid metabolism Inositol phosphate 0.032 0.73 metabolism

Citric acid

-

-

-

1.55 0.041 0.70 1.80

0.004 0.53 TCA Cycle

Aconitic acid

2.04

0.002 1.54 1.76 0.008 0.66 2.24 < 0.001 0.43 TCA Cycle

Succinic acid

-

-

-

1.39 0.024 0.35

Methylmalonic acid

-

-

-

1.97 0.002 2.13 1.93


Methylcitric acid

-

-

-

1.57 0.023 1.82 1.70


-

-

-

TCA Cycle

Nicotinate and Nicotinic acid

1.87

0.004 1.62 1.61 0.006 1.97

-

-

nicotinamide metabolism

Uric acid

1.30

0.040 0.74 1.47 0.019 0.70

-

-

-

Purine metabolism

Allantoin

1.77

0.030 2.74 1.80 0.013 2.41

-

-

-

Uric acid metabolism

trans-Ferulic acid

2.07

0.004 0.59

-

-

-

Others

-

-

-

Others

1,3-Di-tert-butylbenzene

-

-

-

2,3-Dihydroxybutanedioic acid

-

-

-

aVIP

-

-

-

1.56 0.020 0.90 -

-

-

1.46

0.039 0.75 Others

(variable importance in the projection) values were obtained from PLS-DA models with a threshold of 1. bP-

values were calculated from student's t-test with a threshold of 0.05. cFC (fold change) values were obtained from 34


Page 35 of 41


mean peak area of latter group/mean peak area of former group obtained. If the FC value is greater than 1, it means that metabolite level is higher in latter group compared with the former group.

35



Figure 1 A

B

36


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Page 37 of 41


Figure 2 A

B

C

D

E

F

37



G

H

38


Page 38 of 41

Page 39 of 41


Figure 3

39



Figure 4 XHC

SXC

JMY

A

B

40


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Table of Contents (TOC) Graphic

41


Metabolomics Profiling of Serum and Urine in Three Beef Cattle

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