Article Cite This: J. Agric. Food Chem. XXXX, XXX, XXX−XXX
pubs.acs.org/JAFC
Chemical Composition of Commercial Cow’s Milk Aidin Foroutan,†,‡ An Chi Guo,† Rosa Vazquez-Fresno,† Matthias Lipfert,† Lun Zhang,† Jiamin Zheng,† Hasan Badran,# Zachary Budinski,# Rupasri Mandal,† Burim N. Ametaj,‡ and David S. Wishart*,†,# †
Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada T6G 2E9 Department of Agricultural, Food and Nutritional Science, Edmonton, Alberta, Canada T6G 2P5 # Department of Computing Sciences, University of Alberta, Edmonton, Alberta, Canada T6G 2E8 J. Agric. Food Chem. Downloaded from pubs.acs.org by UNIV OF LOUISIANA AT LAFAYETTE on 04/17/19. For personal use only.
‡
ABSTRACT: Bovine milk is a nutritionally rich, chemically complex biofluid consisting of hundreds of different components. While the chemical composition of cow’s milk has been studied for decades, much of this information is fragmentary and very dated. In an effort to consolidate and update this information, we have applied modern, quantitative metabolomics techniques along with computer-aided literature mining to obtain the most comprehensive and up-to-date characterization of the chemical constituents in commercial cow’s milk. Using nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography−mass spectrometry (LC−MS), and inductively coupled plasma−mass spectrometry (ICP−MS), we were able to identify and quantify 296 bovine milk metabolites or metabolite species (corresponding to 1447 unique structures) from a variety of commercial milk samples. Through our literature analysis, we also found another 676 metabolites or metabolite species (corresponding to 908 unique structures). Detailed information regarding all 2355 of the identified chemicals in bovine milk have been made freely available through a Web-accessible database called the Milk Composition Database or MCDB (http://www.mcdb.ca/). KEYWORDS: milk, metabolomics, NMR, LC−MS, ICP−MS, literature review, chemical composition
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carbohydrates.11 At the macronutrient level, bovine milk is typically composed of water (85−87%), fats (3.8−5.5%), proteins (2.9−3.5%) and carbohydrates (5%). At a micronutrient level, bovine milk contains many bioactive compounds including vitamins, minerals, biogenic amines, organic acids, nucleotides, oligosaccharides, and immunoglobulins.12 The precise nature and relative abundance of these compounds is a function of many internal and external factors.13 These factors include the metabolic activity within the cow’s mammary tissues, general udder health conditions, the type of feed given to the cow, the activity and abundance of certain microbes in the cow’s ruminal fluid, as well as the microbial activity and enzymatic reactions occurring within the raw milk.14 Milk composition also varies with the cattle breed (i.e., Holstein, Jersey, Brown Swiss, etc.), stage of lactation, level of parity, number of viable pregnancies, as milk quality control and processing procedures after milk collection.15,16 Historically, most milk composition studies have been performed using targeted chemical analyses aimed at characterizing specific classes of compounds (i.e., sugars only, fats only). While targeted analytic approaches are very accurate, they require considerable skill, are rather limited in their chemical scope, and often require a great deal of time and manual effort. With the development of quantitative, targeted metabolomics approaches, it has been possible to achieve far more comprehensive chemical coverage of foods, biofluids, and tissues.17 Metabolomics is a branch of “omics” sciences concerned with the high-throughput, comprehensive character-
INTRODUCTION Milk is often called the “perfect food”. Produced from the mammary glands of all periparturient female mammals, milk is rich in key nutrients such as carbohydrates, proteins, fats, minerals, and vitamins that are dynamically adjusted to meet the specific developmental needs of growing newborns.1,2 Milk not only plays a key role in nourishment and hydration, it also has an essential role in establishing essential gut microflora and priming the immune system in all newborn mammals.1 While milk is normally a species-specific biofluid consumed by young mammals belonging to that species, humans uniquely consume milk produced by other species and continue to consume milk into adulthood.2 Global milk production is dominated by five animal species with 83% of total milk production coming from cows, followed by buffaloes with 13%, goats 2%, sheep 1%, camels 0.4%.3 The abilities of humans to both extract milk from domestic animals and to consume it into adulthood have played a key role in the evolution of agriculture, the establishment of civil societies, and the development of many widely used food products.4 Indeed, the ability to collect and use bovine milk has been fundamental to the health, growth, migration, and success of the human species over the past 10 000 years. Even today, milk is one of the most widely consumed beverages in the world (811 million tonnes of milk produced in 2017). It serves as the feedstock for not only liquid milk but also flavored milk, ice cream, cheese, butter, yogurt, casein powder, and many other dairy products.5 Given the significant economic and nutritional importance of milk, and especially cow’s milk, it is not surprising to learn that bovine milk has been the subject of detailed chemical or nutrient analyses for many years.6 These include comprehensive studies on milk vitamins,7 minerals,8 fats,9 proteins,10 and © XXXX American Chemical Society
Received: January 9, 2019 Revised: March 16, 2019 Accepted: March 17, 2019
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DOI: 10.1021/acs.jafc.9b00204 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
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Journal of Agricultural and Food Chemistry ization of large numbers of small molecule metabolites.18 Thanks to significant advances in analytical techniques such as mass spectrometry (MS) and NMR, metabolomics methods are able to routinely identify and quantify hundreds of compounds from a single sample. Indeed, metabolomics has already enabled the determination of extensive inventories of small molecule metabolites for a range of organisms, cell types, and biofluids.16−20 Over the past 10 years, several comprehensive metabolomic studies of bovine (and other livestock) milk samples have been performed.15,16,21−23 The study by Boudonck et al.22 identified (but unfortunately did not quantify) 93 bovine milk metabolites including amino acids, short peptides, carbohydrates, lipids, vitamins, nucleotides, and enzyme cofactors using a combination of both liquid chromatography−tandem mass spectrometry (LC−MS/MS) and gas chromatography− mass spectrometry (GC−MS). In a later study by Klein et al.,23 NMR and LC−MS were combined to look at how the chemical composition of bovine milk varied between early and late lactation in two dairy cattle breeds, Brown Swiss and Simmental. These authors identified and quantified 44 milk constituents including many amino acids, sugars, fatty acids, and organic acids. A comparative metabolomic study of milk from different dairy animals (Chinese Holstein, Jersey, yak, buffalo, goat, camel, and horse) was completed by Yang et al.15 This study compared the milk metabolite profile of Chinese Holstein and Jersey cows with other dairy animals using NMR and LC−MS. The results showed that a subset of 68, 74, 54, 58, 77, and 91 metabolites were significantly different between the milks produced by Holstein and Jersey, buffalo, yak, goat, camel, and horse, respectively. Unfortunately, this study did not provide a list of the identified metabolites or their measured concentrations. In a recent metabolomic study reported by Mung and Li, more than 2500 metabolites were putatively identified in cow’s milk using chemical isotope labeling LC−MS techniques.24,25 However, fewer than 80 compounds were positively identified, and none of the compounds were actually quantified.23,24 The most recent quantitative metabolomic study done on bovine milk was described by O’Callaghan et al.16 These authors used NMR spectroscopy to determine how the chemical composition of bovine milk varied among different feeding systems. This study resulted in the identification and quantification of 49 bovine milk metabolites. As described above, many studies looking into bovine milk composition have been conducted, but none have attempted to comprehensively identify and quantify bovine milk using more than two analytical techniques. Furthermore, many of these studies have not made their reported experimental findings publicly available. In addition, none of these metabolomic studies attempted to integrate previously published information regarding bovine milk composition to extend or validate their results. To facilitate further research into milk chemistry and milk micronutrients, we believe it is critical to comprehensively characterize the chemical composition of bovine milk using multiple, fully quantitative metabolomic techniques. Such an undertaking would benefit livestock researchers, food scientists, nutritionists, and consumers as it would create a centralized, comprehensive, and electronically accessible database of all detected or detectable metabolites/chemicals found in bovine milk. To create such a resource, we combined both experimental metabolomic techniques with computer-aided
text mining to compile essentially all of the known chemicals (endogenous and exogenous) that can be detected in bovine milk along with their respective concentrations. Experimentally, we used multiple quantitative metabolomics techniques including high-resolution NMR spectroscopy, liquid chromatography coupled with high-resolution mass spectrometry (LC−HRMS), LC−MS/MS, and ICP−MS methods to identify, quantify, and validate 296 bovine milk metabolites or metabolite species (which corresponds to 1316 lipids and 131 nonlipids). To further enhance our metabolic profiling studies, we conducted an extensive literature survey and extracted metabolite data from nearly 150 journal articles that we identified through computer-aided literature searches. This “bibliomic” effort yielded data for another 676 metabolites or metabolite species (which corresponds to 292 lipids and 616 nonlipids). The resulting data is now housed in the Milk Composition Database (MCDB), a comprehensive Webaccessible source containing concentration data, physicochemical data, and reference data for 972 metabolites or metabolite species, which corresponds to 2355 unique compound structures, found in bovine milk. Here we defined “metabolite species” as those molecules with nonunique chemical formulas or masses (such as lipids) while “unique compound structures” correspond to compounds with a unique and clearly defined chemical structure and a unique chemical name. Overall, the intent of this study was to help consumers, milk producers, nutritional chemists, and the dairy research communities address four key questions: (1) What kinds of compounds and nutrients are present in bovine milk? (2) What is the approximate variation in the concentration of metabolites in different kinds of commercial bovine milk? (3) What fraction of the milk metabolome can be identified and/or quantified using targeted, quantitative metabolomic techniques? and (4) What analytical methods (NMR, LC− HRMS, LC−MS/MS, ICP−MS) are best suited for comprehensively profiling milk? Answering these questions will provide a common foundation and a more appropriate baseline for both ongoing and future milk composition studies.
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MATERIALS AND METHODS
Milk Sample Collection. Four different types of commercially available bovine milk of varying fat content were analyzed in this study. These included commercial skim or skimmed milk (95%) of dairy cows in Western Canada are Holsteins. Following sample acquisition, 1.0 mL samples were pipetted into 1.5 mL Eppendorf tubes and stored in a freezer at −20 °C until the time of analysis. A total of 16 milk samples (four replicates from each type of milk) were analyzed by four different metabolomic techniques. NMR Spectroscopy. Milk proteins and lipoproteins can seriously compromise the quality of 1H NMR spectra though the generation of intense, broad lines that interfere with the identification and quantification of lower abundance metabolites. Deproteinization can eliminate these peaks. Deproteinization of the milk samples was done by centrifugation and ultrafiltration using 3-kDa cutoff centrifuge filter units (Amicon Micoron YM-3; Sigma-Aldrich, St. Louis, MO), following a previously reported deproteinization procedure.20 It is important to note that the 3-kDa filter eliminates not only proteins but also lipids and reduces the concentrations of certain (known) hydrophobic compounds. The deproteinized milk samples (280 μL) were then transferred to a 1.5 mL Eppendorf tube, to which an B
DOI: 10.1021/acs.jafc.9b00204 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
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Journal of Agricultural and Food Chemistry additional 70 μL of the standard NMR buffer solution (1 mM DSS (disodium-2,2-dimethyl-2-silapentane-5-sulfonate), in 10% D2O, 150 mM sodium phosphate buffer, pH 7.0) was added. These samples (a total volume of 350 μL) were then transferred to 3 mm NMR tubes for spectral analysis. All 1H NMR spectra were collected on a Bruker Avance III Ascend 700 MHz spectrometer equipped with a 5 mm cryo-probe (Bruker Biospin, Rheinstetten, Germany). 1H NMR spectra were collected at 25 °C using the first transient of a nuclear Overhauser effect spectroscopy (NOESY)-presaturation pulse sequence. This pulse sequence was selected based on its excellent quantitative accuracy.25 NMR spectra were acquired with 128 scans employing a 4 s acquisition time and a 1 s recycle delay. NMR Compound Identification and Quantification. Prior to spectral deconvolution, all free induction decays (FIDs) were zerofilled to 240 k data points and a 0.5 Hz line broadening function was applied. The methyl singlet of the added DSS (set to 0.00 ppm) served both as an internal chemical shift referencing standard and as an internal standard for quantification. All 1H NMR spectra were processed using the Chenomx NMR Suite 8.1 software package (Edmonton, Canada) for quantification as previously described.26 A minimum of two experienced NMR spectroscopists processed and analyzed the spectra to eliminate compound identification and quantification errors. Sample spike-in experiments were also used to confirm the identity of a number of compounds suspected to be present in our NMR spectra. A spike-in experiment involves adding 50−500 μM of the presumptive compound to selected milk samples to test if the corresponding 1H NMR signals changed as expected. All milk samples were also assessed over multiple time periods (up to 24 h after the first acquisition) to ensure that there were no significant changes in metabolite concentrations. LC−HRMS Compound Identification and Quantification. A targeted, fully quantitative metabolite profiling approach was employed that combined direct flow injection (DFI)−mass spectrometry with reverse-phase LC−HRMS to determine concentrations of amino acids, biogenic amines, monosaccharides, acylcarnitines (ACs), diglycerides (DGs), triglycerides (TGs), phosphatidylcholines (PCs), lysophosphatidylcholines (LysoPCs), sphingomyelins (SMs), ceramides (Cers), and cholesteryl esters (CEs) in our milk samples. These analyses were enabled by a newly released quantitative metabolomics kit (AbsoluteIDQ p400 HR), available from Biocrates Life Sciences AG (Innsbruk, Austria). This kit, when used with a QExactive HF OrbiTrap mass spectrometer, can identify and quantify up to 408 compounds (365 lipids and 43 small molecules) covering 11 metabolite classes. The absolute quantification of amino acids and biogenic amines is ensured by using two separate UPLC injections, while sugars and lipids are measured by two column-free, DFI methods. While primarily designed for human metabolomic studies, the measurable ranges of metabolite concentrations available through the p400 kit match very closely with the known or expected metabolite concentrations in bovine milk. The detection of each metabolite relies almost solely on measuring the exact mass of molecular ions or adducts without further fragmentation. The kit incorporates both isotope-labeled internal standards and other quality control (QC) standards into its 96-well plate filter to ensure accurate compound quantification. The kit was originally developed to assess plasma or serum samples but it also performs very well for related biofluids (such as milk). The first 14 wells in the kit’s 96-well plate are used for QC and internal standardization, while the other 82 wells are used for sample analysis. Altogether, 16 commercial milk samples were metabolically profiled using the AbsoluteIDQ p400 HR protocol described in the user manual. In brief, milk samples were first thawed on ice, vortexed, and then spun in a centrifuge at 13 000 rpm for 5 min. A volume of 10 μL of each commercial milk sample was carefully pipetted into an appropriate sample well of the upper 96-well filter plate and dried using a stream of nitrogen gas. Amino acid derivatization was done by adding 50 μL of a 5% solution of phenyl-isothiocyanate (PITC) and incubating the solution for 20 min. After incubation and PITC derivatization, the samples were dried down using an evaporator. The
metabolites were extracted by adding an ammonium acetate/ methanol mixture (5 mM ammonium acetate dissolved in 300 μL methanol) to the upper 96-well filter plate and then centrifuging the plates so that the extract bled into the lower 96-deep well plate. The resulting extract was split for LC−HRMS (50 μL) and DFI−MS (10 μL) analyses followed by a dilution step with 450 μL of 40% methanol for LC−MS analysis and with 490 μL of the kit’s MS running solvent for DFI−MS analysis. LC−HRMS analyses were conducted on a Thermo Scientific QExactive HF OrbiTrap mass spectrometer from Thermo Scientific (Mississauga, Canada) equipped with a Thermo Scientific Vanquish ultra-high-performance liquid chromatography (UHPLC) system. The Biocrates MetIQ software (Innsbruk, Austria) controls the entire assay’s workflow. This includes sample registration, automated metabolite concentration calculation, and data export to other data analysis programs. Characterization of Milk Free Fatty Acids Using LC−MS/MS. Free fatty acids in the commercial milk samples were analyzed using a previously described LC−MS/MS method,27 with some modifications, including a 3-nitrophenylhydrazine (NPH) derivatization step. The isotopically labeled 13C6-3-nitrophenylhydrazine (which was used for derivatization and quantification) was purchased from Cayman Chemical (Ann Arbor, MI). All other reagents and solvents, including all the fatty acid standards, were acquired from Sigma-Aldrich (Oakville, Canada). In conducting the fatty acid assay, 50 μL of the samples (PBS for the “blank” control samples, seven calibrants numbered from Cal 1 to Cal 7, and the milk samples) were pipetted into individual 1.5 mL Eppendorf tubes, then 150 μL of ice-cold methanol was added to each Eppendorf tube, mixed thoroughly, and left in a −20 °C freezer overnight for protein precipitation. After the precipitation step, all the tubes were centrifuged at 13 000 rpm for 20 min, whereupon 50 μL of the supernatant was pipetted to each well of the Nunc 96 DeepWell plate. To each well 25 μL of each of the following three solutions was added: (1) EDC (150 mM in methanol), 3-NPH (250 mM in 50% methanol), and pyridine (7.5% in 75% methanol). A volume of 50 μL of the calibration curve standard mixture solution with the highest concentration (i.e., Cal 7) was again pipetted into the center of an empty well, followed by the addition of 25 μL each of 13C6-labeled 3NPH, EDC, and pyridine solution, which was used as isotope-labeled internal standard (ISTD). The whole 96-well plate was placed on a shaker (400 rpm) at 20 °C for 2 h to complete the derivatization reaction. After the derivatization step, 25 μL of the BHT solution (2 mg/mL in methanol) and 350 μL of water were added to each well in the plate. This was done to dilute and stabilize the final solution. Finally, 90 μL of each sample solution was transferred to another Nunc 96 DeepWell plate and mixed well with 10 μL of the ISTD solution. In total, 20 μL of the final mixture solution was injected into a HPLC-equipped QTRAP 4000 mass spectrometer for LC−MS/MS analysis. Characterization of Milk Vitamins Using LC−MS/MS. Both water-soluble and fat-soluble vitamins found in milk were analyzed via a targeted LC−MS/MS method. Water-soluble vitamin standards including vitamin B1, vitamin B2, vitamin B3-amide, vitamin B5, vitamin B6, vitamin B7, vitamin B9, vitamin B12, and vitamin C were purchased from AccuStandard (Chromatographic Specialties, Brockville, Canada). Fat-soluble vitamin standards including retinol (vitamin A), cholecalciferol (vitamin D3), calcifediol (25-hydroxyvitamin D3 or vitamin OH D3), and α-tocopherol (vitamin E) were purchased from Sigma-Aldrich (Oakville, Canada). Isotopically labeled water-soluble vitamin standards 4,5,4-methyl-13C3-thiamine chloride, d4-nicotinamide, 13C3,15N-pantothenic acid, ring-6,6-d2biotin, and 13C-ascorbic acid were bought from Cambridge Isotope Laboratories, Inc. (Tewksbury, MA); 13C4,15N2-riboflavin was purchased from IsoSciences (Ambler, PA); d2-pyridoxine hydrochloride was acquired from C/D/N Isotopes Inc. (Quebec, Canada); and d4-folic acid was bought from Santa Cruz Biotechnology, Inc. (Dallas, Texas). The fat-soluble vitamin standards retinyl acetate, cholecalciferol-d3, and tocopheryl acetate were bought from SigmaAldrich (Oakville, Canada). HPLC-grade methanol, water, trichloroacetic acid (TCA), hexane, zinc sulfate, and ammonium formate were C
DOI: 10.1021/acs.jafc.9b00204 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
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Journal of Agricultural and Food Chemistry bought from Sigma-Aldrich (Oakville, Canada). LC−MS grade formate was acquired from Fisher Scientific (Ottawa, Canada). Individual stock solutions of each of the above-mentioned watersoluble vitamin standards (2 mM) and isotopically labeled standards (3 mM) were prepared in HPLC-grade water. Individual standard stock solutions were then mixed together to prepare 7 calibrants with concentration ranges as follows: 0.01 to 1 μM for vitamin B1, vitamin B2, vitamin B6, vitamin B7, vitamin B9, and vitamin B12; 0.05 to 5 μM for vitamin B3-amide and vitamin B5, and 0.4 to 40 μM for vitamin C. Similarly, individual internal standard stock solutions were mixed to make the internal standard mixture solution with a final concentration of each water-soluble internal standard as follows: 4,5,4-methyl-13C3thiamine chloride (3 μM), 13C4,15N2-riboflavin (3 μM), d4nicotinamide (30 μM), 13C3,15N-pantothenic acid (15 μM), d2pyridoxine hydrochloride (3 μM), ring-6,6-d2-biotin (3 μM), d4-folic acid (3 μM), and 13C-ascorbic acid (120 μM). To quantify the watersoluble vitamins in our milk samples, 7-point calibration curves were generated by adding 20 μL of the isotopically labeled internal standard mixture to 100 μL of the calibration solutions in Eppendorf tubes. Milk samples were also prepared by adding the isotopically labeled internal standard mixture to 100 μL of milk. A total of 120 μL of an aqueous TCA solution (50 mg/mL) was pipetted to each Eppendorf tube containing the calibrants or milk samples. Each tube was vortexed for 30 s for thorough mixing and then left on ice for 1 h. After cooling, each tube was centrifuged at 13 000 rpm for 20 min, and 200 μL of the supernatant was transferred to a new HPLC vial. A volume of 10 μL was injected for LC−MS/MS analysis. An Agilent 1100 series HPLC system (Agilent Technologies, Palo Alto, CA) coupled with an AB Sciex QTRAP 4000 mass spectrometer (Sciex Canada, Concord, Canada) was used to analyze water-soluble vitamins in our commercial milk samples. An Agilent reversed-phase Zorbax Eclipse XDB C18 column (3.0 mm × 100 mm, 3.5 μm particle size, 80 Å pore size) coupled to a Phenomenex (Torrance, CA) SecurityGuard C18 precolumn (4.0 mm × 3.0 mm) was used for the separation of all water-soluble vitamins in the milk samples. A solution gradient separation was performed using two solvents: (1) 5 mM ammonium formate and 0.1% (v/v) formate in water (solvent A) and (2) 5 mM ammonium formate and 0.1% (v/v) formate in methanol (solvent B). The gradient, with a flow rate of 400 μL/min, started at 0% (solvent B) for 1 min, then ramped up from 0% to 30% (solvent B) in 1.5 min, then from 30% to 60% (solvent B in 2 min, then from 60% to 75% (solvent B) in 1.5 min, and held at 75% for 0.5 min. After that, the column was equilibrated back to 0% (solvent B) for 3 min before the next injection. The column oven temperature was maintained at 40 °C. The positive electrospray ionization MRM (multiple reaction monitoring) mode was used for the MS/MS analysis. The IonSpray voltage was set to 5500 V while the ion source temperature was set to 450 °C. The curtain gas (CUR), ion source gas 1 (GAS1), and ion source gas 2 (GAS2) were set at 20, 40, and 60, respectively, and collision gas (CAD) parameter was set as medium. Individual stock solutions of each above-mentioned fat-soluble vitamin standards (5 mM) and internal standards (5 mM) were prepared using HPLC-grade methanol. Individual standard stock solutions were then mixed together to prepare 7 calibrants with concentration ranges as follows: vitamin A (0.313−20 μM), vitamin D3 (0.0156−1 μM), vitamin 25-hydroxyvitamin D3 (0.00625−0.4 μM), and vitamin E (1.25−80 μM). Individual internal standard stock solutions were mixed to make the internal standard mixture solution with a final concentration as follows: retinyl acetate (5 μM), cholecalciferol-d3 (0.2 μM), and tocopheryl acetate (5 μM). For the LC−MS/MS analysis of fat-soluble vitamins, 50 μL of calibration solutions or milk samples were pipetted into glass vials, followed by adding 50 μL of internal standard mixture solution. Subsequently, a 300 μL of methanol and 0.2 M ZnSO4 mixture solution (1:1 v/v) was added to precipitate the milk proteins and to facilitate the release of 25-hydroxyvitamin D3 from vitamin D binding protein. After this precipitation step, 1 mL of hexane was added to every sample to extract the fat-soluble vitamins. All the samples were vortexed for 10 min and centrifuged at 13 000 rpm for 20 min. Subsequently 650 μL
of the hexane layer was then transferred to a new HPLC vial for evaporation under nitrogen gas at 40 °C until dried. Finally, 200 μL of methanol was added to each dried sample to reconstitute the analytes, and 10 μL was injected for LC−MS/MS analysis. An Agilent 1100 series HPLC system (Agilent Technologies, Palo Alto, CA) coupled with an AB Sciex QTRAP 4000 mass spectrometer (Sciex Canada, Concord, Canada) was used to analyze the fat-soluble vitamins in all commercial milk samples. A Phenomenex Kinetex C18 column (3.0 mm × 100 mm, 2.6 μm particle size, 100 Å pore size) connected to a Phenomenex SecurityGuard C18 precolumn (4.0 mm × 3.0 mm), was used to separate the fat-soluble vitamins. A solvent gradient was run as follows: (1) 0.1% (v/v) formate in water (solvent A) and (2) 0.1% (v/v) formate in methanol (solvent B). The gradient, at a flow of 800 μL/min, started with 85% (solvent B), then moved from 85% to 100% (solvent B) in 1.8 min where it was maintained at 100% for 2.2 min. The column was then equilibrated back to 85% (solvent B) for 3 min before the next injection. The column oven temperature was kept at 40 °C. Positive electrospray ionization MRM mode was used for the MS/MS analysis. The IonSpray voltage was set to 5500 V, and ion source temperature was set to 400 °C. The CUR, GAS1, and GAS2 parameters were set at 30, 40, and 50, respectively, and the CAD parameter was set as medium. Data analysis for the water-soluble and fat-soluble vitamins was done using Sciex Analyst 1.6.2. Trace Elemental Analyses Using ICP−MS. ICP−MS is a powerful and sensitive approach for measuring metal ions and other trace elements in biological samples. For this study, all trace elemental analysis done on the commercial milk samples was performed on a PerkinElmer Sciex Elan 6000 quadrupole ICP−MS (Woodbridge, Canada), operating in a dual detector mode using previously described methods.28,29 Milk Metabolites in the Literature. We also conducted an extensive literature review of known bovine milk metabolites and their concentrations by using several in-house text-mining software packages that were originally developed for the Human Metabolome Project (HMP) and the Human Metabolome Database (HMDB).17 Two of the most useful programs were PolySearch 30 and PolySearch2.31 These programs are able to take simple keywords (i.e., “milk”, “bovine”, etc.) as input and to rapidly create hyperlinked lists of abstracts and papers from PubMed (and other data sources) containing information about milk metabolites and their corresponding concentration data. PolySearch2 was able to compile a ranked list of milk metabolites by measuring word co-occurrence frequency using terms such as “cow’s milk”, “milk”, “dairy”, “bovine” and “cattle” in conjunction with words such as “concentration”, “identification”, “quantification”, “mM”, or “micromol”. PolySearch2 also extracted key sentences from the abstracts, then labeled and hyperlinked the metabolites mentioned in the text. This led to the identification of ∼150 papers, abstracts, and textbooks with relevant chemical information on bovine milk. These PolySearch results were supplemented by additional data retrieval from various online National food composition tables such as the United States Department of Agriculture [USDA] Food Composition Databases [FCD] (https://ndb.nal.usda.gov/ndb/), The Frida Food Data database (https://frida.fooddata.dk/), FooDB (http://www.foodb. ca), as well as Phenol-Explorer (www.phenol-explorer.eu).32−34 All literature-derived compounds, along with their concentrations and references, were compiled, compared, and their names “normalized” to match HMDB,17 CAS, and PubChem identifiers. The manually derived compound data was further annotated using an in-house program called DataWrangler17 which automatically generates names, synonyms, descriptions, structures, chemical taxonomies, physical property data, and bioavailability. The information generated by DataWrangler was manually checked by three different scientists with postgraduate degrees in biochemistry, physiology, and/or animal sciences. After the manual check was complete, the data were then entered into the Milk Composition Database (MCDB). Concentrations were cross-checked manually to identify large discrepancies (>3×) between entered values. Those that exceeded this threshold were reanalyzed to see if data entry errors had D
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Journal of Agricultural and Food Chemistry been made. For highly discrepant values, a “majority wins” scheme was used to select the best or most likely value. On the other hand, if our experimental data matched best with one of the discrepant values, then that value was selected over other reported value(s). The resulting list of 1030 literature-derived milk metabolites (including 122 overlapping experimentally derived metabolites), along with their concentration data (when available), helped to confirm many of the metabolites and metabolite concentrations previously found in our experimental analyses.
Literature Survey of Bovine Milk Metabolites. The MCDB provides both concentration averages and concentration ranges for 497 quantified milk metabolites or metabolite species. This corresponds to 1318 lipids with unique structures as well as 334 nonlipid compounds. In addition to the experimentally derived values obtained from the quantitative metabolomic methods described earlier, the MCDB also contains literature-derived data (including reported concentrations) of bovine milk metabolites or chemical constituents with references to the source literature (PubMed IDs or to non-PubMed journals and textbooks). In many cases, multiple concentration values are given for “normal” conditions. This is done to provide users/readers with a better estimate of the potential concentration variations that different laboratories or technologies may measure. Overall, we found very good agreement between the results reported for most methods and most laboratories. A number of questionable or profoundly different literature-derived concentration values of milk compounds were eliminated through the curation process after being deemed mistaken, disproven (by subsequent published studies), mis-typed or physiologically impossible. Much of the curation process involved having multiple curators carefully reading and double-checking the primary literature to annotate the concentration unit type, to perform unit conversions, and catch typographical inconsistencies. Other than lactose (98−153 mM) and a variety of inorganic ions or minerals such as potassium (31−43 mM), calcium (26−32 mM), phosphorus (19−23 mM), sodium (17−28 mM), and magnesium (4−6 mM), the most abundant organic metabolites reported in milk are citrate (3.0−9.8 mM), creatine (312−543 μM), D-glucose (246−478 μM), choline (152−479 μM), and myo-inositol (122−588 μM). The least abundant compounds in milk include several vitamins, such as vitamin D3 (0.1−0.3 nM), vitamin D2 (0.1−2.5 nM), vitamin OH D3 (0.5 nM) as well as several trace elements including neodymium (Nd) (0.3−4 nM), lanthanum (La) (0.2−4 nM), cerium (Ce) (2.4−7 nM), and thallium (Tl) (3−4 nM). A number of other low-abundance compounds detected in bovine milk include veterinary antimicrobial agents such as tetracycline (3.76 μM), lincomycin (0.19 μM), tylosin (0.06 μM), amoxicillin (0.052 μM), penicillin G (0.036 μM), and oxolinic acid (0.036−0.058 μM).37,38 Antibiotics are commonly given to dairy cattle to treat diseases such as mastitis. Hence, varying amounts of antibiotic residues can be found in milk. In a comprehensive screening of bovine milk for antibiotics, Freitas and colleagues36 detected more than 30 different antibiotics from 5 different antibiotic classes when analyzing bovine, ovine, and caprine milk samples. The reported concentrations ranged from 0.00001 to 0.0102 μM. Other low-abundance compounds found in milk include pesticide and herbicide residues.39 These agricultural contaminants are an obvious public safety concern. Therefore, the European Union (EU) and United States Environmental Protection Agency (USEPA) have legislated a restriction on the maximum levels of herbicide and pesticide residues in human food.40 These contaminants can be found in cow’s milk if used for weed and grass treatment in the feedstuff used for animal consumption.41 Some of the herbicide and pesticide residues reported in bovine milk include triazine herbicides such as atrazine (0.009−0.019 μM), cyanazine (
