Milk Metabolite Profiles Research Articles

Metabolite Profiling in the Liver, Plasma and Milk of Dairy Cows Exposed to Tansy Ragwort (Senecio jacobae) Pyrrolizidine Alkaloids.

Plant-derived pyrrolizidine alkaloids (PAs) in feed cause metabolic disturbances in farm animals resulting in high economic losses worldwide. The molecular pathways affected by these PAs in cells and tissues are not yet fully understood. The objective of the study was to examine the dose-dependent effects of orally applied PAs derived from tansy ragwort in midlactation dairy cows. Twenty Holstein dairy cows were treated with target exposures of 0, 0.47, 0.95 and 1.91 mg of total PA/kg of body weight/d in control, PA1, PA2 and PA3, respectively, for 28 days. Liver tissue biopsy and plasma and milk samples were taken at day 28 of treatment to assess changes in metabolic pathways. A targeted metabolomics approach was performed to detect the metabolite profiles in all compartments. The PA-affected metabolite profiling in liver tissue, plasma and milk revealed changes in three substrate classes: acylcarnitines (ACs), phosphatidylcholines (PCs) and sphingomyelins (SMs). In addition, in the plasma, amino acid concentrations were affected by PA exposure. PA exposure disturbed liver metabolism at many sites, especially devastating pathways related to energy metabolism and to amino acid utilization, most likely based on mitochondrial oxidative stress. The effects on the milk metabolite profile may have consequences for milk quality.

Milk metabolomics analyses of lactating dairy cows with divergent residual feed intake reveals physiological underpinnings and novel biomarkers.

The opportunity to select for feed efficient cows has been limited by inability to cost-effectively record individual feed efficiency on an appropriate scale. This study investigated the differences in milk metabolite profiles between high- and low residual feed intake (RFI) categories and identified biomarkers of residual feed intake and models that can be used to predict residual feed intake in lactating Holsteins. Milk metabolomics analyses were undertaken at early, mid and late lactation stages and residual feed intake was calculated in 72 lactating dairy cows. Cows were ranked and grouped into high residual feed intake (RFI >0.5 SD above the mean, n = 20) and low residual feed intake (RFI <0.5 SD below the mean, n = 20). Milk metabolite profiles were compared between high residual feed intake (least efficient) and low residual feed intake (most efficient) groups. Results indicated that early lactation was predominantly characterized by significantly elevated levels of medium chain acyl carnitines and glycerophospholipids in high residual feed intake cows. Citrate cycle and glycerophospholipid metabolism were the associated pathways enriched with the significantly different metabolites in early lactation. At mid lactation short and medium chain acyl carnitines, glycerophospholipids and amino acids were the main metabolite groups differing according to residual feed intake category. Late lactation was mainly characterized by increased levels of amino acids in high residual feed intake cows. Amino acid metabolism and biosynthesis pathways were enriched for metabolites that differed between residual feed intake groups at the mid and late lactation stages. Receiver operating characteristic curve analysis identified candidate biomarkers: decanoylcarnitine (area under the curve: AUC = 0.81), dodecenoylcarnitine (AUC = 0.81) and phenylalanine (AUC = 0.85) at early, mid and late stages of lactation, respectively. Furthermore, panels of metabolites predicted residual feed intake with validation coefficient of determination (R 2) of 0.65, 0.37 and 0.60 at early, mid and late lactation stages, respectively. The study sheds light on lactation stage specific metabolic differences between high-residual feed intake and low-residual feed intake lactating dairy cows. Candidate biomarkers that distinguished divergent residual feed intake groups and panels of metabolites that predict individual residual feed intake phenotypes were identified. This result supports the potential of milk metabolites to select for more efficient cows given that traditional residual feed intake phenotyping is costly and difficult to conduct in commercial farms.

TÜRKİYE'DE ÜÇ BÖLGEDEN TOPLANAN GELENEKSEL SÜTLERİN METABOLİK İÇERİĞİNİN NMR SPEKTROSKOPİSİ İLE ANALİZİ

Milk is one of the primary animal-based foods in a healthy diet which provides vitamins, fats and particularly calcium to meet the daily requirements. Currently, there are a limited number of metabolomics studies on milk and more studies are required to establish reliable international standards and databases for milk metabolite profiles. In this study, metabolite analysis of three types of UHT milk which are whole-milk, semi-skimmed and lactose-free from Türkiye was investigated by NMR, with emphasis on the differences in metabolite concentrations related to lactose and energy metabolism. In all, 31 different metabolites were successfully identified and quantified with a single NMR experiment. Most of these metabolites are involved in energy and amino acid metabolism, and these findings show that NMR can easily detect perturbations of metabolites in these relevant pathways. In this study, the lactose levels of lactose-free labelled products also were analysed and compared with international threshold levels.

Dietary Lysozyme Alters Sow’s Gut Microbiota, Serum Immunity and Milk Metabolite Profile

The aim of current study was to determine variations in sow’s gut microbiota, serum immunity, and milk metabolite profile mediated by lysozyme supplementation. Twenty-four pregnant sows were assigned to a control group without supplementation and two treatments with 0.5 kg/t and 1.0 kg/t lysozyme provided in formula feed for 21 days (n = 8 per treatment). Microbiota analysis and metagenomic predictions were based on 16s RNA high-throughput sequencing. Milk metabolome was assessed by untargeted liquid chromatography tandem mass spectrometry. Serum biochemical indicators and immunoglobulins were also determined. Gut microbial diversity of sows receiving 1.0 kg/t lysozyme treatment was significantly reduced after the trial. Spirochaetes, Euryarchaeota, and Actinobacteria significantly increased while Firmicutes showed a remarkable reduction in 1.0 kg/t group compared with control. Lysozyme addition rebuilt sow’s gut microbiota to beneficial composition identified by reduced richness of Escherichia coli and increased abundance of Lactobacillus amylovorus. Accordingly, microbial metabolic functions including pyrimidine metabolism, purine metabolism, and amino acid related enzymes were significantly up-regulated in 1.0 kg/t group. Microbial metabolic phenotypes like the richness of Gram-positive bacteria and oxidative stress tolerance were also significantly reduced by lysozyme treatment. Serum alanine transaminase (ALT) activity and IgA levels were significantly down-regulated in the 1.0 kg/t group compared with control, but IgM levels showed a significantly increase in 1.0 kg/t group. Milk metabolites such as L-glutamine, creatine, and L-arginine showed significantly dose-dependent changes after treatment. Overall, lysozyme supplementation could effectively improve the composition, metabolic functions, and phenotypes of sow’s gut microbiota and it also benefit sows with better serum immunity and milk composition. This research could provide theoretical support for further application of lysozyme in promoting animal gut health and prevent pathogenic infections in livestock production.

Characterization of donkey milk and metabolite profile comparison with human milk and formula milk

Donkey milk is considered a potential substitute to human milk for infants affected by cows’ milk protein allergy. With the aim to widen our knowledge on this valuable food, we explored the compositional characteristics of Sardinian donkey milk. Donkey milk showed a low lipid content and high lysozyme levels compared to human milk, and a bacterial count below the recommended threshold. Hydrophilic compounds such as amino acids, organic acids and mono and disaccharides, were analyzed by GC-MS for donkey milk, formula milk and human milk. Results of the multivariate statistical analysis indicated that the metabolite profile of donkey milk is more similar to human milk than cow milk based formulae, the latter being richer in sugars and lower in amino acids. Moreover, modifications of human milk and donkey milk metabolite profiles during lactation time were studied. An increase of protein levels was observed in donkey milk, while in human milk pyroglutamic acid and myo-inositol levels increased and decreased, respectively.

Metabolomic biomarkers identify differences in milk produced by Holstein cows and other minor dairy animals

Several milk metabolites are associated with breeds or species of dairy animals. A better understanding of milk metabolites from different dairy animals would advance their use in evaluating milk traits and detecting milk adulteration. The objective of this study was to characterize the milk metabolite profiles of Chinese Holstein, Jersey, yak, buffalo, goat, camel, and horse and identify any differences using non-targeted metabolomic approaches. Milk samples were tested using nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography–tandem mass spectrometry (LC-MS). Data were analyzed using a multivariate analysis of variance and differences in milk metabolites between Holstein and the other dairy animals were assessed using orthogonal partial least-squares discriminant analysis. Differential metabolites were identified and some metabolites, such as choline and succinic acid, were used to distinguish Holstein milk from that of the other studied animals. Metabolic pathway analysis of different metabolites revealed that glycerophospholipid metabolism as well as valine, leucine, and isoleucine biosynthesis were shared in the other ruminant animals (Jersey, buffalo, yak, and goat), and biosynthesis of unsaturated fatty acids was shared in the non-ruminant animals (camel and horse). These results can be useful for gaining a better understanding of the differences in milk synthesis between Holstein and the other dairy animals.

Integrating Milk Metabolite Profile Information for the Prediction of Traditional Milk Traits Based on SNP Information for Holstein Cows

In this study the benefit of metabolome level analysis for the prediction of genetic value of three traditional milk traits was investigated. Our proposed approach consists of three steps: First, milk metabolite profiles are used to predict three traditional milk traits of 1,305 Holstein cows. Two regression methods, both enabling variable selection, are applied to identify important milk metabolites in this step. Second, the prediction of these important milk metabolite from single nucleotide polymorphisms (SNPs) enables the detection of SNPs with significant genetic effects. Finally, these SNPs are used to predict milk traits. The observed precision of predicted genetic values was compared to the results observed for the classical genotype-phenotype prediction using all SNPs or a reduced SNP subset (reduced classical approach). To enable a comparison between SNP subsets, a special invariable evaluation design was implemented. SNPs close to or within known quantitative trait loci (QTL) were determined. This enabled us to determine if detected important SNP subsets were enriched in these regions. The results show that our approach can lead to genetic value prediction, but requires less than 1% of the total amount of (40,317) SNPs., significantly more important SNPs in known QTL regions were detected using our approach compared to the reduced classical approach. Concluding, our approach allows a deeper insight into the associations between the different levels of the genotype-phenotype map (genotype-metabolome, metabolome-phenotype, genotype-phenotype).

NMR-Based Milk Metabolomics.

Milk is a key component in infant nutrition worldwide and, in the Western parts of the world, also in adult nutrition. Milk of bovine origin is both consumed fresh and processed into a variety of dairy products including cheese, fermented milk products, and infant formula. The nutritional quality and processing capabilities of bovine milk is closely associated to milk composition. Metabolomics is ideal in the study of the low-molecular-weight compounds in milk, and this review focuses on the recent nuclear magnetic resonance (NMR)-based metabolomics trends in milk research, including applications linking the milk metabolite profiling with nutritional aspects, and applications which aim to link the milk metabolite profile to various technological qualities of milk. The metabolite profiling studies encompass the identification of novel metabolites, which potentially can be used as biomarkers or as bioactive compounds. Furthermore, metabolomics applications elucidating how the differential regulated genes affects milk composition are also reported. This review will highlight the recent advances in NMR-based metabolomics on milk, as well as give a brief summary of when NMR spectroscopy can be useful for gaining a better understanding of how milk composition is linked to nutritional or quality traits.

Investigating associations between milk metabolite profiles and milk traits of Holstein cows

In the field of dairy cattle research, it is of great interest to improve the detection and prevention of diseases (e.g., mastitis and ketosis) and monitor specific traits related to the state of health and management. During the standard milk performance test, traditional milk traits are monitored, and quality and quantity are screened. In addition to the standard test, it is also now possible to analyze milk metabolites in a high-throughput manner and to consider them in connection with milk traits to identify functionally important metabolites that can also serve as biomarker candidates. We present a study in which 190 milk metabolites and 14 milk traits of 1,305 Holstein cows on 18 commercial farms were investigated to characterize interrelations of milk metabolites between each other, to milk traits from the milk standard performance test, and to influencing factors such as farm and sire effect (half-sib structure). The effect of influencing factors (e.g., farm) varied among metabolites and traditional milk traits. The investigations of associations between metabolites and milk traits revealed groups of metabolites that show, for example, positive correlations to protein and casein, and negative correlations to lactose and pH. On the other hand, groups of metabolites jointly associated with the investigated milk traits can be identified and functionally discussed. To enable a multivariate investigation, 2 machine learning methods were applied to detect important metabolites that are highly correlated with the investigated traditional milk traits. For somatic cell score, uracil, lactic acid, and 9 other important metabolites were detected. Lactic acid has already been proposed as a biomarker candidate for mastitis in the recent literature. In conclusion, we found sets of metabolites eligible to predict milk traits, enabling the analysis of milk traits from a metabolic perspective and discussion of the possible functional background for some of the detected associations.

Nuclear magnetic resonance metabonomics reveals strong association between milk metabolites and somatic cell count in bovine milk

Somatic cell count (SCC) is associated with changes in milk composition, including changes in proteins, lipids, and milk metabolites. Somatic cell count is normally used as an indicator of mastitis infection. The compositional changes in protein and fat affect milk coagulation properties, and also the metabolite composition is thought to contribute to differential milk properties. Milk somatic cells comprise different cell types, which may contribute to differential milk metabolite fingerprints. In this study, milk from a relatively large number of individual cows, representing significant differences in SCC, were analyzed by nuclear magnetic resonance (NMR)-based metabonomics, and the milk metabolite profiles were analyzed for differences related to SCC. Global principal component analysis performed on 876 samples from 2 Danish dairy breeds and orthogonal projection of latent structures discriminant analysis performed on a smaller subset (n=70) representing high (SCC >7.2×105 cells/mL) and low (SCC <1.4×104 cells/mL) milk SCC identified latent variables, which could be attributed to milk with elevated SCC. In addition, partial least squares regression between the NMR milk metabolite profiles and SCC revealed a strong correlation. The orthogonal projection of latent structures discriminant analysis and partial least squares regressions pinpointed specific NMR spectral regions and thereby identification of milk metabolites that differed according to SCC. Relative quantification of the identified metabolites revealed that lactate, butyrate, isoleucine, acetate, and β-hydroxybutyrate were increased, whereas hippurate and fumarate were decreased in milk with high levels of somatic cells.

Metabolites in human breastmilk identified as markers for sample storage quality

Detailed understanding of how storage temperature affects specific breastmilk metabolites is scarce. Current storage recommendations of expressed breastmilk for either scientific or domestic use is based exclusively on studies of bacterial growth and immuno capacity. Metabolomic profiling could reveal significant changes in metabolite levels in breast milk due to different exposure conditions that may affect the overall transfer of metabolites from the mother to the infant, ultimately affecting the health of the newborn.A pilot study was conducted using gas chromatography coupled with mass spectrometry (GC‐MS) to determine the milk metabolite profile under different storage temperatures over a 24‐hour period. Milk expressed from 5 lactating mothers, between 3 to 7 months post‐partum was collected, aliquoted, then stored immediately at room temperature, 4°C, −20°C or −80°C, before sample processing and analysis by GC‐MS.Levels of specific metabolites in stored milk samples were found to have changed at room temperature and/or 4°C but not at −20°C or −80°C in the first 24 hours. These results established the need for breastmilk storage at &lt; −20°C for both scientific and/or domestic use. Moreover, changes in metabolite levels could potentially serve as biomarkers/indicators for breast milk sample integrity.Funded by Medela AG. Switzerland.

Relationship between the Metabolite Profile and Technological Properties of Bovine Milk from Two Dairy Breeds Elucidated by NMR-Based Metabolomics

The aim of the present study was to investigate the relationship between the metabolite profile of milk and important technological properties by using nuclear magnetic resonance (NMR)-based metabolomics. The metabolomics approach was introduced for the metabolic profiling of a set of milk samples from two dairy breeds representing a wide span in coagulation properties. The milk metabolite profiles obtained by proton and carbon NMR spectroscopy could be correlated to breed and, more interestingly, also with the coagulation profile, as established by traditional methods by using principal component analysis (PCA). The metabolites responsible for the separation into breed could mainly be ascribed to carnitine and lactose, whereas the metabolites varying in the samples with respect to coagulation properties included citrate, choline, carnitine, and lactose. The results found in the present study demonstrated a promising potential of NMR-based metabolomics for a rapid analysis and classification of milk samples, both of which are useful for the dairy industry.