Milk production and composition and metabolic alterations in the mammary gland of heat-stressed lactating dairy cows

Journal of Integrative Agriculture 2019, 18(12): 2844–2853 Available online at www.sciencedirect.com

ScienceDirect

RESEARCH ARTICLE

Milk production and composition and metabolic alterations in the mammary gland of heat-stressed lactating dairy cows FAN Cai-yun1*, SU Di1*, TIAN He2, HU Rui-ting1, RAN Lei1, YANG Ying1, SU Yan-jing3, CHENG Jian-bo1 1 2 3

College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, P.R.China Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, P.R.China Bright Farming Co., Ltd., Shanghai 200436, P.R.China

Abstract This experiment was conducted to investigate the effects of heat stress (HS) on the feed intake, milk production and composition and metabolic alterations in the mammary gland of dairy cows. Twenty Holstein cows were randomly assigned to one of two treatments according to a completely randomized design. Half of the cows were allocated to the HS group in August (summer season), and the other half were assigned to the HS-free group in November (autumn season). HS reduced (P<0.01) dry matter intake (DMI), milk yield, milk protein and milk urea nitrogen (MUN) of cows compared with HSfree control, but increased (P<0.01) milk somatic cell counts (SCC). We determined the HS-induced metabolic alterations and the relevant mechanisms in dairy cows using liquid chromatography mass spectrometry combined with multivariate analyses. Thirty-four metabolites were identified as potential biomarkers for the diagnosis of HS in dairy cows. Ten of these metabolites, glucose, lactate, pyruvate, lactose, β-hydroxybutyrate, citric acid, α-ketoglutarate, urea, creatine, and orotic acid, had high sensitivity and specificity for HS diagnoses, and seven metabolites were also identified as potential biomarkers of HS in plasma, milk, and liver. These substances are involved in glycolysis, lactose, ketone, tricarboxylic acid (TCA), amino acid and nucleotide metabolism, indicating that HS mainly affects lactose, energy and nucleotide metabolism in the mammary gland of lactating dairy cows. This study suggested that HS might affect milk production and composition by affecting the feed intake and substance metabolisms in the mammary gland tissue of lactating dairy cows. Keywords: milk production, metabolomics, mammary gland, heat stress, dairy cows

1. Introduction Received 15 May, 2019 Accepted 3 October, 2019 FAN Cai-yun, E-mail: [email protected]; SU Di, E-mail: [email protected]; Correspondence SU Yan-jing, E-mail: [email protected]; CHENG Jian-bo, Tel/Fax: +86-55165786328, E-mail: [email protected] * These authors contributed equally to this study. © 2019 CAAS. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/). doi: 10.1016/S2095-3119(19)62834-0

Heat stress (HS) has a documented and significant deleterious impact on the economics of the dairy industry, including southern China, Germany and the US (Wang et al. 2010). HS not only reduces growth, reproductive efficiency, and milk yield and quality (Wheelock et al. 2010), but also harms the metabolism of dairy cows by enhancing insulin parameters (Wheelock et al. 2010) and reducing the nonesterified fatty acids (NEFA) (Shwartz et al. 2009), growth hormones (GH), and insulin-like growth factor-I

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(IGF-I) levels (Baumgard and Rhoads 2013). Furthermore, it increases health care costs for dairy cows, especially for those that cannot be cooled down effectively. The problem is likely to worsen in the future, as global temperatures are predicted to rise (Key and Sneeringer 2014). The milk production reduction mechanisms induced by HS have not been elucidated; they are more likely to be associated with endogenous metabolite alterations (Allen et al. 2015) or mammary gland metabolites. Environmental stimuli are reflected by altered cellular metabolism, which leads to metabolite concentration in an organism (Bernabucci et al. 2014). Mammals can inherently adapt their homeostasis in response to HS. Further, HS reportedly increases systemic amino acid utilization, which limits the amino acid supply of milk protein synthesis in the mammary glands of lactating Holstein cows (Gao et al. 2017). Moreover, HS results in blunted mammary gland autophagy in late-gestation dairy cows, and it retards the autophagic activity by reducing the estrogen level in the mammary gland during the early dry period (Wohlgemuth et al. 2016; Sobolewska et al. 2009). Untargeted metabolomics using liquid chromatography mass spectrometry (LC-MS) is an ideal tool for the acquisition of several thousand metabolite alterations in biological samples, such as blood, urine, cells, and tissues (Naz et al. 2017). Using LC-MS, Tian et al. (2015) identified 41 plasma metabolites as candidates for HS-exposed lactating dairy cows. All of these potential diagnostic biomarkers were involved in amino acid, lipid, carbohydrate, or gut microbiome-derived metabolism, and this helped elucidate the physiological mechanisms of HS-induced metabolic disorders and evaluate these biomarkers in practical applications. Our previous research also found 33 potential metabolite candidate biomarkers for the detection of livers in HS dairy cows using LC-MS. Fifteen of these metabolites (glucose, pyruvate, lactate, β-hydroxybutyrate, acetoacetate, citric acid, fumaric acid, glycine, choline, isoleucine, proline, creatinine, leucine, urea and orotic acid) were involved in amino acid, glycolysis, tricarboxylic acid (TCA), ketone or nucleotide metabolism, indicating that HS affected energy and nucleotide metabolism in lactating dairy cows (Fan et al. 2018). However, studies of mammary tissue are more pertinent than those of blood or milk for dairy cows, as this tissue determines milk quality and yield, and studies have already shown that many environmental and management factors can affect mammary gland function at molecular and cellular levels (Tian et al. 2015; Tao et al. 2018). For example, HS cows in dry lactating period had higher gene expression of acetyl-CoA carboxylase and fatty acid synthetase in the mammary gland compared to HSfree cows (Adin et al. 2009). A genomic study on mammary glands showed that the genes have greater expression in

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Brazilian Holstein cattle, which correlated with mammary gland development and HS responses (Wetzel-Gastal et al. 2018). Therefore, mammary gland metabolomics during lactation and non-lactation periods provided integrated insight and better understanding of the key metabolites and pathways and their potential regulatory roles in lactation (Sun et al. 2017). However, how HS influences the function of mammary gland tissue is still undetermined. Therefore, identifying the metabolites in the mammary gland during lactation in the HS and HS-free treatment groups and comparing their key pathways could enhance our understanding of the HS mechanism that affects lactation. In the present study, our hypothesis was that HS might affect milk production and composition by affecting the feed intake and substance metabolisms in the mammary gland tissue of lactating dairy cows. In order to test the above hypothesis, the present study was carried out to investigate the feed intake, milk production and composition and metabolic alterations in the mammary gland of heatstressed dairy cows.

2. Materials and methods 2.1. Experimental design and treatments The experimental groups were already described in our recently published paper (Fan et al. 2018). Briefly, twenty Holstein cows were reared in a closed-type cowshed to minimize the effects of photoperiods on their metabolism between different seasons. Half of the cows were allocated to the HS group, and the other half were assigned to the HSfree group. Each treatment involved ten replicate cows, and each cow as a replication. The HS samples were collected in August (summer season), after natural temperature-humidity index (THI) was increased from 72.3 to 86.7 over 1 mon and remained stable at 81 for 1 wk. The HS-free samples were obtained in November (autumn season), after natural THI gradually decreased from 60 to 47 over 1 mon.

2.2. Animals, diets, and feeding All experiments involving animals were performed according to the principles of the Animal Care and Use Committee of Anhui Agricultural University (Hefei, China). Information about the numbers, lactation days, 305-day milk yield, and body mass of the cows is shown in Table 1. Temperature, humidity, rectal temperature, respiration rate, and serum hormones of the two groups of dairy cows are shown in Appendices A–C. The THI criteria were derived from the NRC (1971). The THI was calculated using the formula THI=(1.8×Tdb+32)–[(0.55–0.0055×RH)×(1.8×Tdb–26.8)],

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where Tdb is the dry-bulb temperature (°C) and RH is the relative humidity (%). The cows were housed in a freestall barn, and each cow was assigned to individual pens and had free access to fresh water. The total mixed ration (TMR) was fed to the cows each day at 0700, 1400, and 2000 h. Experimental diet was designed according to the NRC (2001) requirements. The ingredient and chemical compositions of the diet are shown in Table 2.

2.3. Sample collections and preparations The daily dry matter intake (DMI) for individual cows was calculated by subtracting the orts from the feed offered. Samples of offered TMR were collected daily. Cows were milked three times daily (at 0600, 1400 and 2000 h) with yields recorded at each milking. Milk samples from each cow were collected on d 1, 4 and 7. The milk samples were stored at 4°C until analysis. Protein, fat, lactose and milk urea nitrogen (MUN) concentrations were determined by mid-infrared spectrophotometry using a MilkoTMScan (MilkoScan Type FT120, Foss Electric, Hillerød, Denmark). Somatic cell counts (SCC) were determined using a Fossomatics 5000 (Foss Analytical A/S; Foss Electric, Hillerød, Denmark). A mammary gland sample was collected immediately after the cows were slaughtered. The sampling site was on the right side of the back of the mammary gland. Approximately 1 g of mammary tissue was dissected and quickly washed 10 times in ice-cold phosphate buffer saline (PBS) (pH 7.2–7.4, NaCl 137 mol L–1, KCl 2.7 mol L–1, Na2HPO4 10 mol L –1, KH 2PO 4 2 mol L –1). A portion of the tissue was immediately frozen in liquid nitrogen and stored at –80°C until use. Sample preparation was conducted as previously described, with modifications (de Castro et al. 2013). Beads were added to 30 mg of mammary gland tissue using a metal spatula pre-cleaned with high performance liquid chromatography (HPLC)-grade methanol. The tissues were homogenized in a bead disruptor using the following program: 2 quick spin cycles at 4°C at a speed 4 of m/s for 5 s. The samples were centrifuged at 12 000 r min–1 at 4°C for 5 min. The supernatants were collected and dried with Table 1 Characteristics of the Holstein dairy cows used in the study Parameter Number of cows Parity Lactation days 305-day milk yield (kg) Average mass (kg) HS, heat stress.

HS-free HS 10 10 4.2±1.3 4.4±1.2 137.6±23.6 142.1±27.2 8 973.7±797.5 8 911.2±751.4 700.6±48.5

709.2±54.7

P-value 0.89 0.91 0.98 0.96

a micVac vacuum centrifugal concentrator, then the dried samples were reconstituted in 1 mL of the initial mobile phase for LC-MS analysis. Chromatography analysis and identification were performed as previously described by Tian et al. (2015).

2.4. Data handling and statistical analyses Data for DMI, milk yield and composition were analyzed by the t-test (SAS 2003, ver. 9.2, Inst. Inc., Cary, NC). The experimental unit was an individual cow. The results were expressed as mean±SE. Statistical significance was set at P<0.05. Data handling for metabolites in mammary gland was referenced in our previous paper (Tian et al. 2015). Briefly, MM file conversion 3.9 (http://mm-fileconversion.software.informer.com/3.9/) was adopted for the conversion of raw files into mzXML. An open-source XCMS package (version 1.20.1) in R Statistical Software (version 2.10.0) was used for peak discrimination, filtering, and alignment. The SIMCA-P 13.0 Software package (Umetrics AB, Umeå, Sweden) was used for multivariate analysis. MATLAB R2012a Software was applied for color map visualization. Furthermore, an independent t-test (P<0.05) (SPSS version 13.0) was used to determine if the Table 2 Ingredients and chemical composition of diets Item Ingredient Alfalfa hay Chinese wildrye Corn silage Oat grass Whole cottonseed Beet pulp Corn Soybean meal Cottonseed meal Dry distillers grains Limestone Dicalcium phosphate Salt Sodium bicarbonate Vitamin-mineral premix1) Chemical analysis (% DM) CP NEL (Mcal kg–1 of DM)2) NDF ADF Ca P 1)

% of DM 13.6 2.0 17.0 5.6 6.3 5.0 27.0 11.5 3.9 5.0 0.8 0.71 0.41 0.73 0.45 16.9 1.7 36.7 22.1 1.05 0.42

Provided per kg of TMR: a minimum of 9 900 IU of vitamin A; 1 890 IU of vitamin D; 54 IU of vitamin E; 15.75 mg of niacin; 12.6 mg of Cu; 21.6 mg of Mn; 54 mg of Zn; 0.45 mg of Se; 0.945 mg of I; 0.54 mg of Co. 2) Calculated according to NRC (2001) based on actual dry matter intake.

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differences between the levels of candidate biomarkers obtained from orthogonal partial least squares discrimination analysis (OPLS-DA) of the HS (10 samples) and HS-free (10 samples) groups were statistically significant. Receiver operating characteristic (ROC) analysis was further used to assess the discriminatory power of each candidate. Those with area under curve (AUC) values larger than 0.85 are regarded as powerful biomarkers for the discrimination of different pathophysiological states in the organisms (Tian et al. 2015; Fan et al. 2018).

(Fig. 1-B). Overall, the results indicate that the predictive capabilities of the OPLS-DA models of the LC-MS data were reliable.

3.3. ROC evaluation of potential diagnostic biomarkers The diagnostic power of the 34 candidate metabolites in the mammary gland in describing HS status was assessed by ROC analysis (Fig. 2). The discriminatory power of each candidate was ranked using heat maps, and those with

3. Results

AUC>0.65 were selected. In addition, perturbations of metabolic pathways inside cells involve multiplex alterations

3.1. DMI, milk yield, milk composition and SCC of HS-free and HS cows

in systematic metabolism. Therefore, metabolites with AUC>0.85 were utilized to set up a potentially powerful biomarker panel, including lactate, lactose, pyruvate,

The effects of HS on DMI, milk yield, milk composition and SCC are shown in Table 3. HS reduced (P<0.01) DMI and milk yield of cows compared with the HS-free control. Milk protein and MUN were decreased (P<0.01) in HS dairy cows, but HS had no effect (P>0.05) on milk fat and milk lactose content. In addition, HS increased (P<0.01) milk SCC.

glucose, β-hydroxybutyrate, α-ketoglutarate, citric acid, urea, creatine, and orotic acid. Combined biomarkers were processed by binary logistic regression and followed by ROC curve determination. The results (Fig. 2-B) showed powerful discrimination between HS and HS-free groups with an AUC of 0.957. Table 3 Effects of heat stress on production parameters of dairy cows

3.2. Comparison of the metabolic profiles of HS-free and HS cows

Parameter1) Number of cows DMI (kg d–1) Milk yield (kg d–1) Milk component Fat (%) Protein (%) Lactose (%) MUN (mg dL–1) SCC (×103 mL–1)

The OPLS-DA plots for analyzing LC-MS data of dairy cows show a clear separation between the HS-free and HS groups (Fig. 1-A). This model produced one predictive component and two orthogonal components with satisfactory modeling and predictive abilities [R2(X)=59.7%, R2(Y)=81.5%, and Q2(cum)=64.5%]. To avoid model overfitting, a default of seven rounds of cross-validation across three components was used. Validation with 999 random permutation tests produced intercepts of R2=0.0756 and Q2=−0.175 for all data A

HS-free 10 23.17±3.24 38.6±4.7

HS 10 18.45±2.84 28.3±3.1

P-value

3.48±0.35 3.25±0.21 4.96±0.11 13.76±1.91 112.35±20.23

3.33±0.32 2.91±0.34 4.88±0.13 11.52±1.43 365.61±43.25

0.39 <0.01 0.91 0.01 <0.01

1)

MUN, milk urea nitrogen; SCC, somatic cell count. Data are mean±SE (n=10).

B

30 10

Control

–10

0 –0.2

–30 –50 –25

R2 Q2

0.2 Heat stress

<0.01 <0.01

–5 5 –15 15 R2(X): 59.7; R2(Y): 81.5%; Q2(cum): 64.5%

–0.4 –0.2

0

0.2

0.4

0.6

0.8

1

Intercepts: R2=0.0756; Q2=–0.175

Fig. 1 Differentiation of control and heat stress (HS) groups using multivariate analysis. A, OPLS-DA plots of LC-MS data for the plasma metabolomes. B, validation plots of the partial least squares discriminant analysis models acquired through 999 permutation tests for LC-MS data of mammary gland metabolome.

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Lactate Lactose Glucose BHBA α-Ketoglutarate Citric acid Urea Creatine Orotic acid Acetoacetate Malic acid Glutamate Glutamine Uric acid Creatinine

Adenosine monophosphate

B ROC curve 0.90

1.0

AUC=0.957 0.8

0.85

0.80

0.75

Sensitivity

A

0.6

0.4

0.2

Succinic acid Proline Isoleucine Uracil γ-Aminobutyrate

0.70

0

0.2

0.4

0.6

0.8

1.0

Specificity 0.65

Fig. 2 Discriminatory power of individual and a combination of diagnostic biomarkers. Heat maps showing the discriminative capacities of selected biomarkers (A). The red and blue colors represent high and low area under the curve (AUC), respectively. ROC curve for the combination of potential biomarkers (AUC>0.85) (B).

3.4. Potential diagnostic biomarkers of HS status The concentrations of candidate metabolites in the mammary gland samples are listed in Table 4 and the related pathways are shown in Fig. 3. In this study, 34 metabolites that could potentially be used to diagnose the HS status of dairy cows were identified. Ten metabolites had ROC values>0.85, indicating high sensitivity and specificity for the diagnosis of HS. Combined with classical HS parameters, such as THI, rectal temperature, respiration rate, etc., we contend that these 10 metabolites may be used to provide exact information about the severity of HS in dairy cows, and they may facilitate the selection of breeds and individual animals that best tolerate high THI.

3.5. Metabolic alterations The concentrations of the candidate metabolites are listed in Table 4. The concentrations of glucose, lactose, and galactose-1-phosphate decreased (P<0.01) 0.49to 0.62-fold in the HS group compared to the HS-free group, whereas lactate and pyruvate increased (P<0.01) 1.91- and 1.42-fold, respectively. The concentrations of acetoacetate and BHBA (ketone metabolites) increased (P<0.01) 1.65- and 1.85-fold in the HS group compared

to the HS-free group, respectively. The concentrations of TCA-related metabolites, such as malic acid, ketoglutaric acid, succinic acid, and fumaric acid, decreased (P<0.01) 0.54- to 0.72-fold, and the concentrations of citric acid and oxaloacetic acid increased (P<0.01) 1.42- and 1.84fold in the HS group compared to the HS-free group, respectively. Amino acid-related metabolites, including glycine, glutamate, glutamine, threonine, proline, valine, methionine, isoleucine, and leucine were down-regulated (P<0.01) 0.53- to 0.77-fold in the HS group compared to the HS-free group, whereas γ-aminobutyrate (GABA), α-ketoglutarate (α-keto), urea, creatinine, creatine and phosphocreatine were up-regulated (P<0.01) 1.38- to 1.66fold. The concentrations of nucleotide-related metabolites, such as uridine 5´-monophosphate, uric acid, adenosine monophosphate, uridine, and uracil, increased (P<0.01) 1.37- to 1.92-fold in the HS group compared to the HS-free group, whereas orotic acid decreased (P<0.01) 0.62-fold.

4. Discussion In this study, heat-stressed cows reduced feed intake and milk yield, which has been confirmed in many early studies (West 2003; Rhoads et al. 2009; Bernabucci et al. 2014). It has been documented that there is a significant

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Table 4 Candidate metabolites in the heat-stressed (HS) groups identified by LC-MS/MS No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34

Metabolic pathway Glycolysis Glycolysis Glycolysis Lactose Lactose Ketone Ketone TCA TCA TCA TCA TCA TCA Amino acid Amino acid Amino acid Amino acid Amino acid Amino acid Amino acid Amino acid Amino acid Amino acid Amino acid Amino acid Amino acid Amino acid Amino acid Nucleotide Nucleotide Nucleotide Nucleotide Nucleotide Nucleotide

Metabolite Glucose4) Lactate4) Pyruvate4) Lactose Galactose-1-phosphate Acetoacetate4) β-Hydroxybutyrate5) Malic acid4) Ketoglutaric acid5) Succinic acid4) Fumaric acid4) Citric acid4) Oxalacetic acid4) Glycine4) Glutamate4) Glutamine Threonine4) Proline4) Valine4) Methionine4) Isoleucine4) Leucine4) α-Ketoglutarate γ-Aminobutyrate Urea5) Creatinine5) Creatine Phosphocreatine Orotic acid5) Uridine 5´-monophosphate5) Uric acid5) Adenosine monophosphate5) Uridine5) Uracil5)

Identification m/z1) 203.05202 89.02436 87.00854 343.12341 259.02244 101.02421 103.04001 133.01427 145.01411 117.01915 115.00351 191.01925 130.99841 76.03918 148.06034 147.07641 120.06552 116.07047 118.08601 150.05811 132.10178 132.10181 142.99816 104.07050 121.07191 114.06602 132.07668 212.04302 155.00973 323.02859 167.02098 346.05587 245.07643 113.03432

Retention time (min)

P-value2)

FC3)

1.43 2.57 3.59 1.50 1.32 1.41 4.75 2.03 2.41 4.53 2.03 3.40 1.74 1.30 1.42 1.76 1.41 1.64 2.35 2.87 4.76 4.84 3.11 1.48 1.45 1.58 1.65 1.47 2.20 2.26 3.42 2.94 4.78 4.78

4.15×10–7 2.08×10–8 2.51×10–4 3.54×10–6 4.12×10–7 1.99×10–5 3.40×10–3 1.32×10–4 4.18×10–4 5.18×10–5 2.31×10–4 1.26×10–4 1.83×10–4 1.17×10–3 7.42×10–4 1.82×10–6 2.64×10–3 3.02×10–4 4.81×10–5 1.58×10–3 4.99×10–4 6.25×10–4 1.38×10–5 2.37×10–5 2.71×10–6 3.19×10–3 3.12×10–4 5.03×10–3 1.41×10–4 3.48×10–6 1.70×10–4 5.47×10–3 7.24×10–3 6.12×10–4

0.49 1.91 1.42 0.53 0.62 1.65 1.85 0.63 0.71 0.54 0.72 1.84 1.42 0.72 0.77 0.59 0.71 0.63 0.58 0.75 0.67 0.53 1.66 1.64 1.66 1.39 1.47 1.38 0.62 1.92 1.74 1.58 1.37 1.61

1)

The non-italicized and italicized m/z values represent the metabolites detected separately in positive and negative ion modes, respectively. P-value, independent t-test for HS vs. HS-free cows. 3) FC, fold difference of metabolite concentration (HS/HS-free). 4) Metabolites verified using standard compounds. 5) Metabolites putatively identified by database comparison and characteristic fragmentation. 2)

positive correlation between milk yield and DMI in HS dairy cows (West 2003). Traditionally, it has been assumed that inadequate feed intake caused by HS is responsible for decreased milk production (Fuquay 1981; Beede and Collier 1986; West 2003). However, several researchers recently designed a series of pair-feeding experiments to evaluate HS while eliminating the confounding effects of dissimilar nutrient intake, and these results suggested that the reduction of nutrient intake induced by HS can account for approximately 35–50% of the reduction in milk synthesis (Rhoads et al. 2009; Wheelock et al. 2010). These results may suggest that HS reduces milk synthesis by both direct and indirect (i.e., via reduced feed intake) mechanisms. In this study, HS affected energy metabolism, including the

decrease in the concentrations of glucose, lactose, and galactose-1-phosphate, the increase in the concentrations of acetoacetate and BHBA (ketone metabolites), and the changes of TCA-related metabolites. These changes in nutrient partitioning may affect the nutrition utilization and the milk synthesis in mammary gland. In this study, HS reduced milk protein concentration. The reasons for this may be that HS altered amino acid metabolism, including glycine, glutamate, glutamine, threonine, proline, valine, methionine, isoleucine, and leucine were down-regulated in the HS group compared to the HS-free group. Previous studies reported that the effects of HS on milk protein composition can be attributed to specific down-regulation of mammary protein synthetic activity, and they are not

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Glucose

Uracil

Pyruvate Lactate

UMP

CMP

Leucine

Uridine Acetyl-CoA

Cytidine

Acetoacetyl-CoA

Acetoacetate

Citrate Oxaloacetate Cis-Aconitate

Arginosuccinate

Citrulline

Arginine

Malic acid

Ornithine

Isocitrate

TCA cycle

Urea cycle Urea

Proline

Fumarate α-Ketoglutarate

Glutamate

Succinate Succinyl-CoA

Glutamine Valine Isoleucine

Phosphocreatine

Creatinine

Creatine

* Threonine Threonine

Glycine

Methionine

Fig. 3 Metabolic pathway altered by heat stress (HS). Red circle represents up-regulation of metabolites in HS group, and blue means down-regulation of metabolite concentrations in HS group. Blank circle represents no alteration detected.

artifacts of a general reduction in milk yield (Cowley et al. 2015). Similarly, HS reduces milk protein synthesis beyond the expected reduction in feed intake, suggesting that elevated temperature itself directly affects milk production (Gao et al. 2017). Since no pair-feeding group was created in this study to control animal feed intake to eliminate its effects, all results were jointly influenced by heat stress and feed intake changes. Therefore, HS might affect milk production and composition by affecting the feed intake and substance metabolisms in the mammary gland tissue of lactating dairy cows. These results help further elucidatie the HS mechanism in dairy cows. In this study, HS induced the alterations of metabolites in the mammary glands of lactating dairy cows, including glycolysis, lactose, ketone, TCA, amino acid, or nucleotide metabolism, which limits the substances’ supply of milk

synthesis in the mammary gland in lactating Holstein cows. Therefore, HS might affect milk production and composition by affecting metabolisms of substances in the mammary gland tissue of lactating dairy cows. In addition, these metabolites in the mammary gland may provide some potential regulatory biomarkers in lactation during HS. The up-regulation of lactate and pyruvate in the mammary gland in the HS group in this study was in agreement with our previous findings in the blood, milk, and livers of HS dairy cows, further confirming the whole-body enhancement of anaerobic glycolysis and the transportation of these two metabolites from the blood into the milk through the mammary glands (Tian et al. 2015). The lower concentration of glucose likely resulted from its poorer supply from the blood, which is consistent with our metabolomic study of HS blood (Tian et al. 2015), and likely due to the HS-induced

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reduction in DMI by dairy cows (Baumgard et al. 2011). Lactose, one of the main components of milk, plays a major role in mammary gland function during lactation as a nutrient, a source of energy, and a precursor of milk constituents (Macheda et al. 2003), and it was down-regulated in HS mammary glands, probably due to a reduction of glucose, the raw material required for its synthesis. Thus, our study’s finding of reduced milk lactose yield as the effect of HS on milk (Tian et al. 2016) can be explained by the present results implying its decreased synthesis in mammary glands. Galactose-1-phosphate, a downstream product of lactose, was accordingly present in a lower concentration in HS mammary glands, and this trend was also seen in our study of milk from HS cows (Tian et al. 2016). The up-regulation of acetoacetate and BHBA in the HS group in the present study was also previously recorded in blood, milk, and the liver (Tian et al. 2015; Fan et al. 2018), thus suggesting that the two metabolites were over-supplied from the blood into the mammary gland (and then secreted into the milk). These results are also consistent with the theory of greater β-oxidation of free fatty acids taking place in HS dairy cows in conditions of negative energy balance (NEB) (Dunning et al. 2014). However, the β-oxidation of NEFA may produce more metabolic heat than that of carbohydrates, thus adding to the effects of HS (Dunning et al. 2014). Considerable alterations in the concentrations of TCA intermediates were observed in the HS dairy cows, indicating mitochondrial dysfunction and energy crisis (Ippolito et al. 2014). These trends (higher concentrations of citric acid and oxaloacetic acid, along with lower concentrations of malic acid, ketoglutaric acid, succinic acid, and fumaric acid) suggest that HS has contrasting effects on the enzymes which catalyze different steps in the TCA cycle. Similar TCA alterations were also found in the liver in our published study (Fan et al. 2018), indicating cell metabolic perturbations in response to HS. The significant increases in citric acid and oxaloacetic acid imply higher energy demand by lactating dairy cows in NEB states. Citric acid has been proposed to be a milk marker of fat mobilization (Chaiyabutr et al. 1981) and ketosis in dairy cows during early lactation (Erhardt and Senft 1982). The HS-induced up-regulation of β-hydroxybutyrate is an additional sign of a disturbance in mitochondrial function (Baumgard and Rhoads 2007). Down-regulation of many amino acids was detected in HS mammary glands (glycine, glutamate, threonine, proline, valine, methionine, and isoleucine), possibly suggesting that protein synthesis is increasingly using these substrates in NEB dairy cows (Monteiro et al. 2016), along with HSinduced reduction of DMI (Tian et al. 2015). In the mammary gland, amino acids can be employed in three ways: synthesis of milk proteins, retention in the form of structural

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proteins or enzymes, and usage in metabolic reactions (e.g., yielding urea or carbon dioxide). A final alternative is that they pass unchanged into milk, blood or lymph (Mepham 1982). The up-regulated urea and down-regulated amino acids detected in the mammary gland samples in the present study suggest that HS preferentially drives the third pathway of amino acid use (Tian et al. 2016). These results may explain the lower milk protein and yield, since less amino acid is partitioned into milk protein synthesis (Appendix D). Further, γ-aminobutyrate (GABA) and α-ketoglutarate (α-keto) concentrations were higher in the HS group. The two metabolites are derived from glutamate (Sun and Denko 2014), and their production is accompanied by ammonia generation to provide more precursor material for citrate synthesis during NEB in lactating dairy cows. Higher creatinine, phosphocreatine, and urea in the mammary glands of HS dairy cows, also found in blood, milk, or the liver in our previous study (Tian et al. 2015), are the mobilization signals for muscle tissues, which are broken down to keep up with the demands of NEB dairy cows (Tian et al. 2015). This process exacerbates ammonia accumulation, indicating a potential danger of ammonia intoxication and representing a management target for HS dairy cows. The concentrations of nucleotide metabolites (uridine 5´-monophosphate, uric acid, adenosine monophosphate, uridine, and uracil) were up-regulated in HS mammary glands, indicating the activation of nucleotide metabolism (Xu et al. 2015). Orotic acid, also known as vitamin B-13, was lower in the HS group, and this trend was also shown in milk from HS cows (Tian et al. 2015), suggesting lower secretion of this metabolite by mammary glands into milk. Nucleotide metabolic changes in present mammary glands were in agreement with our study regarding the liver, and their levels may be adopted for selection of HS-tolerant dairy cows (Fan et al. 2018). In reviewing our previous studies on plasma (Tian et al. 2015), milk (Tian et al. 2016), the liver (Fan et al. 2018), and presently, mammary glands in HS dairy cows, we found common metabolic traits of up-regulation for pyruvate, lactate, urea, and β-hydroxybutyrate among these tissues and biofluids. This proves an increase in anaerobic glycolysis, abnormal energy metabolism, and disturbed fatty acid metabolism in HS dairy cows. Although simultaneous disturbances in proline, glycine, and isoleucine were detected in the plasma, milk, livers, and mammary glands of HS dairy cows, alteration trends of these metabolite levels presented differently. There was up-regulation in HS cows’ plasma and down-regulation in HS cows’ milk, livers, and mammary glands, thus representing different HS adaptation mechanisms in different body organs. These results imply that these candidates may best represent the whole-body

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FAN Cai-yun et al. Journal of Integrative Agriculture 2019, 18(12): 2844–2853

metabolism of dairy cows subjected to HS. Therefore, the results of metabolomic analyses of mammary glands from HS dairy cows were highly consistent with our previous findings in plasma, milk, and the liver, including HS-induced alterations of glycolysis, TCA, amino acids, and nucleotides that may partially contribute to the reduction of milk yield and milk protein percentages. The above findings can reflect changes in the whole-body physiological state of heat-stressed dairy cows and provide a reference for the selection of heat-tolerant dairy cattle. In addition, improved strategies may be designed to mitigate the deleterious effects of HS in dairy cows.

5. Conclusion Heat-stressed cows reduced DMI, milk yield, milk protein and MUN contents, but increased milk SCC. Seven candidate biomarkers were identified throughout the plasma, milk, livers, and mammary gland tissues of cows (lactate, pyruvate, BHBA, glycine, proline, isoleucine and urea), indicating that HS affects lactose, energy, amino acid and nucleotide metabolism in the mammary gland of lactating dairy cows. This study suggested that HS might affect milk production and composition by affecting the feed intake and substance metabolisms in the mammary gland tissue of lactating dairy cows. However, it is necessary to perform further analyses of other organs, such as the brain, hypothalamus and pituitary gland, to provide a more comprehensive perspective on the pathophysiological mechanisms involved in HS-induced metabolic alterations.

Acknowledgements This study was supported financially by the National Key Research and Development Program of China (2016YFD0500503) and the Shanghai Science and Technology Promotion Project for Agriculture (Shanghai Agriculture Science Promotion Project (2019) No. 1-2). We thank for the help of LipidALL Technologies Ltd. (China) in detecting all the samples. Appendices associated with this paper can be available on http://www.ChinaAgriSci.com/V2/En/appendix.htm

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