A metabolomics comparison between sheep's and goat's milk

Accepted Manuscript A metabolomics comparison between sheep's and goat's milk

P. Caboni, A. Murgia, A. Porcu, C. Manis, I. Ibba, M. Contu, P. Scano PII: DOI: Reference:

S0963-9969(18)30863-9 doi:10.1016/j.foodres.2018.10.071 FRIN 8041

To appear in:

Food Research International

Received date: Revised date: Accepted date:

24 July 2018 11 October 2018 25 October 2018

Please cite this article as: P. Caboni, A. Murgia, A. Porcu, C. Manis, I. Ibba, M. Contu, P. Scano , A metabolomics comparison between sheep's and goat's milk. Frin (2018), doi:10.1016/j.foodres.2018.10.071

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A metabolomics comparison between sheep’s and goat’s milk

P. Cabonia,* [email protected], A. Murgiaa , A. Porcua , C. Manis a , I. Ibbab, M. Contub and P. Scanoc, d a

Department of Life and Environmental Science, University of Cagliari, via Ospedale 72,

09124 Cagliari, Italy Regional Association of Sardinian farmers, Milk Analysis Laboratory, Loc. Palloni,

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b

Nuraxinieddu, 09170 Oristano, Italy

Department of Chemical and Geological Sciences, University of Cagliari, Cittadella

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c

Universitaria, SS 554 km 4.5, 09042 Monserrato, Cagliari, Italy

Institute for Macromolecular Studies, National Research Council, Via Corti 12, 20133

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d

Milan, Italy

Corresponding author: Department of Life and Environmental Science, University of

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*

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Cagliari, via Ospedale 72, 09124 Cagliari, Italy

Abstract

Despite the worldwide consumption of bovine milk, dairy products from small

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ruminants, such as goat’s and sheep’s milk, are gaining a large interest especially in the

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Mediterranean area. The aim of this work was to study the metabolite profiles of 30 sheep’s and 28 goat’s milk using an untargeted metabolomics approach by a gas chromatography coupled with mass spectrometry (GC-MS) analysis. Results showed

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several differences in the metabolite profiles: arabitol, citric acid, α˗ketoglutaric acid, glyceric acid, myo˗inositol, and glycine were more abundant in sheep’s milk, while goat’s milk had higher levels of mannose˗6˗phosphate, isomaltulose, valine, pyroglutamic acid, leucine, and fucose. Associations between metabolite profile and milk compositional traits were also found. Predictive capabilities of statistical models indicated a good correlation between the metabolite profile and the protein content in sheep’s milk, and with the fat content in goat’s milk. This work leads to a better understanding of milk metabolites in

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small ruminants and their role in the evaluation of milk properties. Keywords Mannose-6-phosphate Citrate

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Arabitol GC-MS

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Metabolomics

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Multivariate analysis. 1. Introduction

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Nowadays, the increased request for healthier products is generating new interest for milk

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from different species, such as sheep and goats. Compared to the rest of the world, where bovine milk represents the most suitable dairy source, the Mediterranean area is

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characterized by a significant number of goat and sheep herds designated to milk rather than meat production. For this reason, the European Community decided to protect the

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uniqueness of goat and sheep dairy (Scintu & Piredda, 2007). Sardinia (Italy) is the leading Mediterranean region in the production of goat’s and sheep’s milk. In this region,

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the yield and the composition of sheep’s and goat’s milk is strongly affected by their grazing on natural pastures of native flora (Scintu & Piredda, 2007). It has been amply demonstrated that the overall composition of sheep’s and goat’s milk is very different showing the former with a higher content of proteins, fats and lactose (Park, Juárez, Ramos, & Haenlein, 2007; Raynal-Ljutovac, Lagriffoul, Paccard, Guillet, & Chilliard, 2008) compared to the latter. Although several researches have widely studied the different aspects of small ruminant dairy product (Scintu & Piredda, 2007; Park et al., 2007; Raynal-Ljutovac, et al., 2008), in literature there is still a little information regarding

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the milk metabolite profiles (Scano, Murgia, Pirisi, & Caboni, 2014; Caboni, Murgia, Porcu, Demuru, Pulina, & Nudda, 2016; Caboni, Manis, Ibba, Contu, Coroneo, & Scano, 2017). The pool of low molecular weight metabolites in milk and dairy products represents the final point of the gene expression during lactation and throughout several metabolic

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pathways (Yang et al., 2016). The study of the levels of these compounds has been very useful to understand the metabolic pathways and characteristics of milk (Klein et al., 2010;

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Melzer et al., 2013a; Melzer, Wittenburg, & Repsilber, 2013b; Scano et al., 2014;

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Sundekilde et al., 2014; Murgia et al., 2016). Moreover, correlation analyses between metabolites and milk compositional traits helped to understand the biochemical and

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technological characteristics of milk, highlighting those molecular traits that can be eligible

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as biomarkers (Klein et al., 2010; Melzer et al., 2013a; Melzer et al., 2013b; Caboni et al., 2016; Scano et al., 2016; Caboni et al., 2017).

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One of the most valuable approaches for the investigation of metabolite profiles in a biological system is metabolomics (Wang et al., 2005). This technique allows the

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determination of metabolites that are present at a low concentration in biological matrices (Zhang, Sun, Wang, Han, & Wang, 2012). The ability of GC-MS metabolomics

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in the study of the metabolite profiles of milk from different species has been thoroughly demonstrated (Klein et al., 2010; Melzer et al., 2013a; Melzer et al., 2013b; Scano et al., 2014; Murgia et al., 2016; Caboni et al., 2016). In this study, by a metabolomics approach, the milk metabolite profiles of sheep and goats bred in Sardinia were investigated also in association with measured main milk traits such as the contents of fats, proteins, caseins, lactose, and urea, together with freezing point, pH values, and the somatic cells count (SCC).

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2. Materials and methods

2.1. Chemicals and reagents Solvents, such as methanol, chloroform and hexane were all GC/MS or LC/MS grade

chloride,

N-methyl-N-(trimethylsilyl)

trifluoroacetamide

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(Sigma Aldrich, Darmstadt, Germany). Pyridine, methoxamine hydrochloride, potassium (MSTFA)

and

analytical

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standards like pyruvic acid, lactic acid, alanine, glycine, urea, valine, leucine, proline,

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succinic acid, glyceric acid, uracil, threonine, pyroglutamic acid, creatinine, α-ketoglutaric acid, glutamic acid, ribose, arabitol, fucose, orotic acid, glycerol-3-phosphate, citric acid,

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hippuric acid, galactose, lysine, tyrosine, sorbitol, palmitic acid, scyllo-inositol, myo-

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inositol, n-acetyl-glucosamine, mannose-6-phosphate, uridine and isomaltulose presented a purity greater than 95% (Sigma Aldrich, Darmstadt, Germany). Water was produced using

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2.2. Collection of samples

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a MilliQ purification system (Millipore, Milan, Italy).

We analysed 30 individual milk samples from Sarda breed sheep and 28 from Saanen

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goats; the samples came from 14 different farms. The morning milk samples were obtained from grazing sheep and goats in the winter season, after their lambs and kids were weaned. Samples were collected in sterilised tubes through manual milking and stored at -20 °C until analysis which was performed within 2 days from collection. The milk samples were provided by the Sardinian Regional Animal Farmers Association (Associazione Regionale Allevatori, ARA, Sardegna) thanks to the International Committee for Animal Recording (ICAR) program.

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2.3. GC-MS analysis Thawed milk (1 mL) was subjected to ultrasounds for 15 min, and 0.1 mL were extracted following the Folch procedure (Folch, Lees, & Sloane-Stanley, 1957) using 0.375 mL of a methanol and chloroform mixture (2/1, v/v). Samples were then vortexed every 15 min up

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to 1h, when 0.38 mL of chloroform and 0.09 mL of aqueous KCl 0.2 M solution were subsequently added to them. Samples where then vortexed again and centrifuged for 15

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min at 5139 xG at 4°C. Two hundred µL of the hydrophilic supernatant were dried in glass

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vials using a nitrogen stream and then derivatized using 0.05 mL of methoxamine chloride dissolved in pyridine at 10 mg/mL and left at room temperature overnight. The day after,

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70 µL of MSTFA were added to the samples and samples were left at room temperature up

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to 1 h, when 500 µL of a 5 mg/mL of 2,2,3,3-d4-succinic acid hexane solution were added as an internal standard. A Hewlett Packard 6850 gas chromatograph, a 5973 mass selective detector, and a 7683B series injector (Agilent Technologies, Palo Alto, CA) were used to

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analyse samples using Helium as the carrier gas at 1.0 mL/min flow. One μL samples were

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injected in split-less mode and separated using a 30 m × 0.25 mm × 0.25 μm DB-5MS column (Agilent Technologies, Palo Alto, CA). The temperatures for the inlet, interface,

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and ion source were 250, 250 and 230°C, respectively. The oven temperature was programmed to increase from 50 to 230°C by 5°C/min over 36 min and kept at this temperature for 2 min. The mass range was set between 50 and 550 m/z using and electron voltage of 70. MSD ChemStation software (Agilent Technologies, Santa Clara, CA) was used to elaborate the data. The GC-MS spectra deconvolution was performed by the AMDIS tool in the NIST08 library. For each metabolite, the GC-MS peak area related to the most abundant mass fragment, calculated using the MSD Chemstation, was choosed for the data tabulation. Retention times and relative mass spectra of each compound were

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compared with the related analytical standards with the aim to identify those compounds previously recognised by consulting the NIST08 library of the National Institute of Standards and Technology (Gaithersburg, MD), and the library developed at the Max Planck

Institute

of Golm (http://gmd.mpimp-golm.mpg.de/).

Retention indexes were

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calculated according to Kovats, using a C9-C24 series of alkanes. The relative levels of

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metabolites within each sample were normalized for the fat free dry matter.

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2.4. Milk composition analysis

Fat, proteins, caseins, lactose, and urea contents, freezing points (expressed in Horvet

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degree), and pH values were obtained with a Milkoscan FT6000 (Foss, Denmark)

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following the procedure ISO 9622:2013 (ISO 9622:2013 IDF 141:2013). The fatty acid (FA) content was determined by MIR spectroscopy (Caredda et al., 2016). Somatic cells

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were counted following the procedure ISO 13366-2-2006 (ISO 13366-2 IDF 148-2 2006)

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by a Fossomatic 5000 (Foss, Denmark).

2.5. Statistical data analysis

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To obtain descriptive information on the data, and to calculate the means and their standard error (SEM),

we performed

univariate analysis with the software OriginPro2016

(OriginLab, Northampton, MA). When the two groups of samples were compared, the null hypothesis (“the means are not significantly different among the two sets of samples”) was tested by using the unpaired samples two-tailed Student t-test. Linear correlations between two variables were calculated using the Pearson correlation coefficient r, and those having -0.75 > r > +0.75 and with a p-value < 0.05 were taken as significant. MVA was performed as implemented in SIMCA-P+ software (version 14.1, Umetrics, Umeå, Sweden).

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Variables were mean centered and scaled to unit variance. At first, for sample distribution overview, to detect outliers, deviating features and common trends, we performed a Principal Component Analysis (PCA). Subsequently, to identify discriminant metabolites between the two milks’ typologies, we performed an Orthogonal Partial Least-Squares -

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Discriminant Analysis (OPLS-DA) The obtained variable importance in projection (VIP) scores summarize the contribution of each variable to the model. In this work, we analyzed

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VIP scores in the predictive component of OPLS-DA and only those metabolites having

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VIP values > 1 were deemed as discriminant between the classes. Milk metabolite profiles were correlated to the measured main traits by a single-Y Orthogonal extension of Partial

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Least-Squares (OPLS) regression, in order to highlight the metabolites mostly involved in

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each milk trait. The quality of the models was evaluated based on the cumulative parameters R2 X, R2 Y, and Q 2 Y estimated by the default leave-1/7th-out cross validation

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and also tested for overfitting using y-table permutation test (permutations n = 400) as implemented in SIMCA-P+ program (Eriksson, Byrne, Johansson, Trygg, & Vikström,

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Q 2 Y was < 0.50.

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2013). The models were considered significant only when the difference between R2 Y and

3. Results and discussion By GC˗MS analysis of the methanol fraction of milk samples, we identified 38 low molecular weight metabolites (Table 1S). These compounds were mainly short chain hydroxylated carboxylic acids, organic acids, amino acids, polyols and sugars. Initially, the GC-MS dataset was submitted to a PCA. As shown in the score plot of Figure 1A, in the space of the first 2 principal components, sheep’s and goat’s milk samples clustered in two different groups, thus indicating a clear difference in their metabolite profiles (loading plot

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in Figure 1B). In particular, sheep’s milk samples showed a greater variability being, in the score plot, more spread than goat’s samples (Figure 1A). In order to find discriminating metabolites between sheep’s and goat’s milk, we performed a pair-wise OPLS-DA. The discriminant analysis correctly classified our samples (R2 Y=0.92, Q2 Y=0.88). As reported

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from the VIP’s analysis (Table 1), sheep’s milk was richer in α-ketoglutarate when compared to goat’s milk. α-ketoglutarate plays a crucial role in several metabolic pathways

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derived either from the Krebs cycle or from the rumen anaerobic bacteria activity

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(Bacteriodes) (Allison, Robinson, & Baetz, 1979). Increased levels of this compound have been associated with a number of health benefits (Andersen, Tatara, Krupski, Majcher, &

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Harrison, 2008) such as decreased cholesterol levels in rats (Radzki, Bieńko, &

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Pierzynowski, 2009), protection of gut mucosa and improved kidney function (Dąbek et al., 2005). Furthermore, α-ketoglutarate highly enhances the degradation of aromatic and branched-chain amino acids, which strongly contribute to the aroma of several cheese

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(Yvon, Berthelot, & Gripon, 1998). Furthermore, sheep’s milk contained higher level of

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myo-inositol. This polyol is an important constituent of living cells, being the precursor to phosphatidylinositol, and is recognized as a lipotropic nutrient. Milk is a significant source

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of myo-inositol and for its role in neonatal nutrition, this polyol is often added to infant formulas to prevent a potential deficiency during early neonatal growth stage (Woollard, Macfadzean, Indyk, McMahon, & Christiansen, 2014). Citrate, which was found in higher level in sheep’s milk samples, together with free amino acids, has been suggested as the nutrient source used by lactic acid bacteria (LAB) and nonstarter LAB during cheese making (Williams, Withers, and Banks, 2000). Sheep’s milk had also a higher content of arabitol, probably produced by Kluyveromyces lactis from lactose (Toyoda & Ohtaguchi, 2011), yeast strain found in Sardinian sheep’s milk and dairy products (Fadda, Mossa,

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Pisano, Deplano, & Cosentino, 2004). Compared to sheep’s samples, goat’s milk showed higher levels of mannose-6phospate

and

fucose.

Mannose-6-phosphate

is implicated

in the biosynthesis of

glycoproteins and glycophospholipids and participate to the glycolytic pathway (Kalhan,

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2009). Fucose is a monosaccharide which promotes the neonatal bifidobacterial growth, the intestinal mucosa cell protection against pathogens, and has a role in the neonatal brain

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development (Raynal-Ljutovac et al., 2008). Mannose-6-phospate and fucose are involved

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in the synthesis of oligosaccharides. Compared to other ruminant’s milk, goat’s milk is the richest in oligosaccharides (Urashima, Taufik, Fukuda, & Asakuma, 2013) which share

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similar structural elements with human milk (Urashima et al., 2013). The presence of

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fucosylated oligosaccharides was reported in milk from O/O genotype goats (α-s1-casein non-producers) (Meyrand, Dallas, Caillat, Bouvier, Martin, & Barile, 2013). In a previous study,

trying to

identify milk metabolite differences between goats with different

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expression of αS1-casein genotypes, we found that fucose and mannose-6-phosphate

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contents were statistically increased in α-s1-casein weak null allele class when compared with the strong homozygous genotype AA, indicating changes in the carbohydrate

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metabolism (Caboni et al., 2016). In the present work, we found that, in goat’s milk, fucose was correlated with galactose (r = 0.76), a milk sugar component of the oligosaccharide backbone.

Goat’s milk levels of valine and leucine resulted higher compared with sheep’s milk. These two essential branched amino acids are involved in the metabolic pathways for the production of branched-chain fatty acids (Massart-Leёn & Massart, 1981) contributing to the flavour of goat’s dairy products (Chilliard, Ferlay, Rouel & Lamberet, 2003). Pyroglutamic acid, produced by thermophilic LAB starters, also has a strong impact on

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cheese flavour. Pyroglutamic acid was correlated with glutamine (r = 0.78) and tyrosine (r = 0.77), suggesting that it is involved in the metabolism of aromatic amino acids. The level of isomaltulose, a naturally occurring disaccharide, composed of α-1,6-linked glucose and fructose, was higher in goat’s milk than in sheep’s milk, Isomaltulose is a digestible

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carbohydrate that produces glucose in a longer period (Lina, Jonker, & Kozianowski, 2002) and is completely hydrolyzed and absorbed by the small intestine (Holub et al.,

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2010). In our previous studies, comparing cow’s milk-based infant formula, human breast

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milk and donkey’s milk (Murgia et al., 2016; Scano et al., 2016), isomaltulose was

possible biomarker for ruminant’s milks.

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detected only in cow’s milk-based formula, leading us to consider this compound as a

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In this work, we studied the milk content of proteins, caseins, fat, lactose, fatty acids and other parameters, hereafter expressed as mean ± SEM (Table 2). Accordingly with previous results (Raynal-Ljutovac et al., 2008; Park et al., 2007), we found that

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sheep’s milk compared to goat’s milk had significantly higher contents of proteins (5.71 ±

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0.18 and 3.80 ± 0.23 g/100 mL, for sheep and goat’s milk, respectively), caseins (4.20 ± 0.07 and 2.87 ± 0.07 g/100 mL) and lactose (5.02 ± 0.03 and 4.75 ± 0.04 g/100 mL).

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Among fatty acids (FA), due to their health benefits (Júnior et al., 2012), a particular attention has been paid to rumenic acid (18:2 cis-9, trans-11), trans-vaccenic acid (18:1 trans-11) and α-linolenic acid (18:3 cis-9,12,15). Milk and milk-derivatives, are the only food products that naturally contain trans-vaccenic acid and rumenic acid. The ruminal biohydrogenation

and

the

mammary

lipogenic

and

Δ-9

desaturation

pathways,

considerably modifies the profile of dietary FA and the milk composition (Scintu & Piredda, 2007). The pasture plays a pivotal role in increasing levels of trans-vaccenic acid, rumenic acid and α-linolenic acid. It is well established that the concentration of rumenic

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acid is higher in the milk of free-grazing ruminants compared to animals fed on conserved forages (Scintu & Piredda, 2007). Usually, under the same diet, sheep’s milk has a higher rumenic acid content than goat’s milk (Zervas & Tsiplakou, 2011). According to these observations, we found that the contents of trans-vaccenic acid and rumenic acid, as well

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as α-linolenic acid, were statistically higher in sheep’s milk (3.36 ± 0.16, 1.52 ± 0.05, and 1.14 ± 0.04 % of FA, respectively) than in goat’s milk (1.48 ± 0.01, 0.91 ± 0.05, and 0.62 ±

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0.07 % of FA, respectively) (Table 2). Similar contents of trans-vaccenic acid and rumenic

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acid were reported for the Sardinian sheep by Nudda et al. (Nudda, McGuire, Battacone, & Pulina, 2005).

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The content and the characteristics of somatic cells in milk are related to the

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animal’s health (Pirisi et al., 2007), season, stage of lactation and parity (Raynal-Ljutovac, Pirisi, De Cremoux, & Gonzalo, 2007) and differ between cows, sheep and goats (Paape, Poutrel, Contreras, Marco, & Capuco, 2001). In our work, in agreement with the literature,

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goat’s milk showed a higher SCC mean value (789 ± 128 × 10 3 cell/mL) when compared

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with sheep’s milk (423 ± 103 × 103 cell/mL) (Paape, et al., 2001). As already observed by Raynal-Ljutovac et al. (2008), goat’s milk contained

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significantly higher concentration of chlorides compared to sheep’s milk (239.1 ± 8.2 and 109.4 ± 4.1 mg/100 mL), that, together with lower levels of lactose, confers a slightly salty taste to goat’s milk (Ribeiro & Ribeiro, 2010). As found in a previous study (Caboni et al., 2017), the content of chloride ions in goat’s milk samples was negatively correlated (r = 0.88) with the lactose content. Lactose and chloride are osmolytes and thus influence the milk’s freezing point. To keep milk isotonic to blood, the lower levels of lactose in goat’s milk compared to sheep’s milk were counterbalanced by higher levels of chloride ions (Caboni et al., 2017).

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By applying OPLS analysis, we tested the correlations between the GC-MS metabolite profile and the measured milk traits, and the results are reported in Table 2S and shown in Figure 2. Overall, the models for goat’s milk had better performances than those for sheep’s milk. The metabolite profile showed a stronger correlation to the protein

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content in sheep’s milk (Figure 2B), and to the fat content in goat’s milk (Figure 2D). The SCC is well predicted in both milks (Figure 2C and 2F). Interestingly, models for urea

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content failed to perform well (Table 2S). In Table 3, we reported the metabolites mostly

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correlated with protein, fat and SCC contents in sheep’s and goat’s milk, and we can see that they are quite different between the two typologies of milk, thus indicating different

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metabolic routes for milk production in these small ruminants. Metabolites associated with

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both protein and fat levels were glycine, glyceric acid, galactose, alanine, and fucose for sheep’s, and hippuric acid and inositols for goat’s milk. The presence of hippuric acid in

dietary

regimens

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goat’s milk has been proposed as a possible marker to discriminate between different (free-grazing

vs.

conserved

forage)

(Carpio

et

al.,

2013).

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Phosphoethanolamine correlated with fat in both milks, and in goat’s milk, also scylloinositol and myo-inositol were associated with fat (Table 3). The levels of these

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compounds can be linked with the pathway involved in the production of phospholipids, this is also supported by the correlation in goat’s milk between scyllo-inositol and the polyunsaturated fatty acid content (r = 0.75), which are components of phospholipids. The OPLS results indicated that the metabolite profile was also associated with the SCC (Table 2S). Ribose is a component of nucleic acids and its association to the SCC in sheep’s milk may reflect the presence of cell fragments in the milk. Moreover, in sheep’s milk, correlations of different amino acids (tyrosine, lysine, valine, glutamic acid) with the SCC model (Table 3) can be a consequence of the release of proteolytic enzymes from somatic

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cells (Li, Richoux, Boutinaud, Martin, & Gagnaire, 2014). In goat’s milk, metabolites associated with SCC values were pyruvic acid and lactic acid. A relationship between lactic acid levels and SCC values has been reported for cow’s milk (Hamann, 2002; Davis et al., 2004; Melzer et al., 2013a) and lactic acid was proposed as a biomarker candidate

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for mastitis even in the early onset of the disease (Farr, Prosser, Nicholas, Lee, Hart, &

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Davis, 2002).

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4. Conclusions

In summary, by this GC-MS based metabolomics approach, it was possible to highlight the

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metabolite differences between sheep’s and goat’s milk, and the possible different

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metabolic routes for milk production in these small ruminants. The associations between metabolites and milk main traits support the potential use of the metabolite profile as a

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predictive biomarker in different areas such as the dairy industry. Although not taking into account the effect of season variability in the levels of milk metabolites, the promising

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results of this work encourage further studies in this direction. Acknowledgements

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This study was supported by grants provided by Regione Autonoma della Sardegna within the frame of the project “Valorizzazione e innovazione delle produzioni lattiero-casearie caprine in Sardegna. Indagini chimico-fisiche e nutrizionali” (Legge Regionale 7 Agosto 2007, N.7). We also would like to thank Mariano Medda for proof editing. References 1. Allison, M. J., Robinson, I. M., & Baetz, A. L. (1979). Synthesis of alphaketoglutarate by reductive carboxylation of succinate in Veillonella, Selenomonas, and Bacteriodes species. Journal of Bacteriology, 140, 980-986.

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Relationships

between

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cell count

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Figure 1. PCA of GC-MS data of milk samples. (A) Score plot with samples projected in the first two principal components (PC). Explained variance=36%, the ellipse depicted in dashed line encloses the 95% Hotelling T2 confidence region. Sheep’s milk samples (n=30) are shown as grey boxes and goat’s milk samples (n=28) as empty circles. (B) Loading

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plot. Only identified metabolites are shown and abbreviated as proposed in Table 1S. Figure 2. OPLS correlation plots between experimental (y-axis) and predicted (x-axis) milk

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traits. A) fat, B) proteins, C) log somatic cell count (SCC), for sheep’s milk samples (n=30); D)

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fat, E) proteins, F) log somatic cell count (SCC), for goat’s milk (n=28). Results of OPLS

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models are reported in Table 2S.

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HIGHLIGHTS Goat’s and sheep’s milk are gaining interest for nutritional and health effects

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Metabolomics has been applied to study dairy products

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Milk metabolites can reflect the physiological mechanism of milk production

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Sheep’s milk was found richer in myo-inositol

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Metabolite profiles were associated to milk traits, such as fat, proteins and SCC

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