Integrating milk metabolite profile information for the prediction of traditional milk traits based on SNP information for Holstein cows

Buffalo milk and rumen fluid metabolome are significantly affected by green feed

The use of green feed for livestock breeding is an important strategy to encounter both the increasing demand for animal derived products and the perceptions of the consumers regarding animal welfare and sustainability. The aim of this study was to compare different feeding strategies in lactating water buffaloes by using a metabolomic approach. The study was carried out on 32 milking buffaloes that were randomly divided into two groups for a total period of 90 days (3 sampling times). DD Group (dry diet) received a standard total mixed ratio (TMR) characterized by dry forages and concentrates; ZG Group (zero grazing) fed an isoenergetic and isoproteic diet obtained using 30% of sorghum as green forage. Samples of milk and rumen fluid were analyzed by liquid chromatography-mass spectrometry (LC-MS) techniques. Data analyses revealed the presence of several differentially accumulated metabolites and among these, ten compounds were putatively identified in milk samples (i.e. L-carnitine, acetylcarnitine, propionylcarnitine, butyrylcarnitine, 2-methylbutyroylcarnitine, 2-hexenoylcarnitine, hexanoylcarnitine, glycerophosphocholine, δ-valerobetaine and γ-butyrobetaine) and four in rumen fluid (3-(2-hydroxyphenyl) propanoate, Indole-3-acrylic acid, oleamide (cis-9,10-octadecenoamide) and 20-carboxy-leukotriene B4). The modulation of these molecules in buffalo milk is significantly related to the green/dry based feeding and some the natural compound detected could be considered as health-promoting nutrients.

Practical Aspects of NMR-Based Metabolomics

While NMR-based metabolomics is only about 20 years old, NMR has been a key part of metabolic and metabolism studies for >40 years. Historically, metabolic researchers used NMR because of its high level of reproducibility, superb instrument stability, facile sample preparation protocols, inherently quantitative character, non-destructive nature, and amenability to automation. In this chapter, we provide a short history of NMR-based metabolomics. We then provide a detailed description of some of the practical aspects of performing NMR-based metabolomics studies including sample preparation, pulse sequence selection, and spectral acquisition and processing. The two different approaches to metabolomics data analysis, targeted vs. untargeted, are briefly outlined. We also describe several software packages to help users process NMR spectra obtained via these two different approaches. We then give several examples of useful or interesting applications of NMR-based metabolomics, ranging from applications to drug toxicology, to identifying inborn errors of metabolism to analyzing the contents of biofluids from dairy cattle. Throughout this chapter, we will highlight the strengths and limitations of NMR-based metabolomics. Additionally, we will conclude with descriptions of recent advances in NMR hardware, methodology, and software and speculate about where NMR-based metabolomics is going in the next 5-10 years.

Integrative analyses of genomic and metabolomic data reveal genetic mechanisms associated with carcass merit traits in beef cattle

Improvement of carcass merit traits is a priority for the beef industry. Discovering DNA variants and genes associated with variation in these traits and understanding biological functions/processes underlying their associations are of paramount importance for more effective genetic improvement of carcass merit traits in beef cattle. This study integrates 10,488,742 imputed whole genome DNA variants, 31 plasma metabolites, and animal phenotypes to identify genes and biological functions/processes that are associated with carcass merit traits including hot carcass weight (HCW), rib eye area (REA), average backfat thickness (AFAT), lean meat yield (LMY), and carcass marbling score (CMAR) in a population of 493 crossbred beef cattle. Regression analyses were performed to identify plasma metabolites associated with the carcass merit traits, and the results showed that 4 (3-hydroxybutyric acid, acetic acid, citric acid, and choline), 6 (creatinine, L-glutamine, succinic acid, pyruvic acid, L-lactic acid, and 3-hydroxybutyric acid), 4 (fumaric acid, methanol, D-glucose, and glycerol), 2 (L-lactic acid and creatinine), and 5 (succinic acid, fumaric acid, lysine, glycine, and choline) plasma metabolites were significantly associated with HCW, REA, AFAT, LMY, and CMAR (P-value < 0.1), respectively. Combining the results of metabolome-genome wide association studies using the 10,488,742 imputed SNPs, 103, 160, 83, 43, and 109 candidate genes were identified as significantly associated with HCW, REA, AFAT, LMY, and CMAR (P-value < 1 × 10^-5), respectively. By applying functional enrichment analyses for candidate genes of each trait, 26, 24, 26, 24, and 28 significant cellular and molecular functions were predicted for HCW, REA, AFAT, LMY, and CMAR, respectively. Among the five topmost significantly enriched biological functions for carcass merit traits, molecular transport and small molecule biochemistry were two top biological functions associated with all carcass merit traits. Lipid metabolism was the most significant biological function for LMY and CMAR and it was also the second and fourth highest biological function for REA and HCW, respectively. Candidate genes and enriched biological functions identified by the integrative analyses of metabolites with phenotypic traits and DNA variants could help interpret the results of previous genome-wide association studies for carcass merit traits. Our integrative study also revealed additional potential novel genes associated with these economically important traits. Therefore, our study improves understanding of the molecular and biological functions/processes that influence carcass merit traits, which could help develop strategies to enhance genomic prediction of carcass merit traits with incorporation of metabolomic data. Similarly, this information could guide management practices, such as nutritional interventions, with the purpose of boosting specific carcass merit traits.

Comparative metabolomics analysis of milk components between Italian Mediterranean buffaloes and Chinese Holstein cows based on LC-MS/MS technology

Buffalo and cow milk have a very different composition in terms of fat, protein, and total solids. For a better knowledge of such a difference, the milk metabolic profiles and characteristics of metabolites was investigated in Italian Mediterranean buffaloes and Chinese Holstein cows were investigated by liquid chromatography tandem-mass spectrometry (LC-MS/MS) in this study. Totally, 23 differential metabolites were identified to be significantly different in the milk from the two species of which 15 were up-regulated and 8 down-regulated in Italian Mediterranean buffaloes. Metabolic pathway analysis revealed that 4 metabolites (choline, acetylcholine, nicotinamide and uric acid) were significantly enriched in glycerophospholipid metabolism, nicotinate and nicotinamide metabolism, glycine, serine and threonine metabolism, as well as purine metabolism. The results provided further insights for a deep understanding of the potential metabolic mechanisms responsible for the different performance of Italian Mediterranean buffaloes' and Chinese Holstein cows' milk. The findings will offer new tools for the improvement and novel directions for the development of dairy industry.

Milk Consumption and Respiratory Function in Asthma Patients: NHANES Analysis 2007-2012

Per the Centers for Disease Control and Prevention, asthma prevalence has steadily risen since the 1980s. Using data from the National Health and Nutrition Examination Survey (NHANES), we investigated associations between milk consumption and pulmonary function (PF). Multivariable analyses were performed, adjusted for a priori potential confounders for lung function, within the eligible total adult population (n = 11,131) and those self-reporting asthma (n = 1,542), included the following variables: milk-consumption, asthma diagnosis, forced vital capacity (FVC), FVC%-predicted (%), forced expiratory volume in one-second (FEV1), FEV1% and FEV1/FVC. Within the total population, FEV1% and FVC% were significantly associated with regular (5+ days weekly) consumption of exclusively 1% milk in the prior 30-days (β:1.81; 95% CI: [0.297, 3.325]; p = 0.020 and β:1.27; [0.16, 3.22]; p = 0.046). Among participants with asthma, varied-regular milk consumption in a lifetime was significantly associated with FVC (β:127.3; 95% CI: [13.1, 241.4]; p = 0.002) and FVC% (β:2.62; 95% CI: [0.44, 4.80]; p = 0.006). No association between milk consumption and FEV1/FVC was found, while milk-type had variable influence and significance. Taken together, we found certain milk consumption tendencies were associated with pulmonary function values among normal and asthmatic populations. These findings propound future investigations into the potential role of dairy consumption in altering lung function and asthma outcomes, with potential impact on the protection and maintenance of pulmonary health.

An Untargeted Metabolomic Comparison of Milk Composition from Sheep Kept Under Different Grazing Systems

This study aimed to evaluate the effects of different feedings on main traits and polar and semi-polar metabolite profiles of ovine milk. The milk metabolome of two groups of Sarda sheep kept under different grazing systems were analyzed by gas chromatography coupled with mass spectrometry (GC-MS) and multivariate statistical analysis (MVA). The results of discriminant analysis indicated that the two groups showed a different metabolite profile, i.e., milk samples of sheep kept under Grazing System 1 (GS1) were richer in nucleosides, inositols, hippuric acid, and organic acids, while milk of sheep under Grazing System 2 (GS2) showed higher levels of phosphate. Statistical analysis of milk main traits indicates that fat content was significantly higher in GS1 samples while milk from GS2 sheep had more urea, trans-vaccenic acid, and rumenic acid. MVA studies of the associations between milk main traits and metabolite profile indicated that the latter reflects primarily the long chain fatty acid content, the somatic cell count (SCC), and lactose levels. All together, these results demonstrated that an integrated holistic approach could be applied to deepen knowledge about the effects of feeding on sheep’s milk composition.

Blood Plasma Metabolomics Predicts Pregnancy in Holstein Cattle Transferred with Fresh and Vitrified/Warmed Embryos Produced in Vitro

Metabolomics may identify biomarkers in blood that differentiate pregnant from open embryo recipients. Fresh and vitrified/warmed, in vitro-produced embryos were transferred to Holstein recipients (discovery group). Recipient blood plasma collected on Day-0 (estrus) and Day-7 (before embryo transfer) were analyzed by nuclear magnetic resonance (N = 36 metabolites quantified). Metabolites whose concentrations differed between open and pregnant recipients were analyzed [(P < 0.05); false discovery rate (FDR) (P < 0.05)]. Biomarkers were identified in Day-7 plasma (receiver operator characteristic-area under curve (ROC-AUC) > 0.650; t-test P < 0.05; random forests, mean decrease accuracy) and cross-validated in independent Holstein, beef, and crossbred recipients (overall classification accuracy -OCA-; P < 0.05). Recipients with fresh embryos showed N = 6 biomarkers consistently on Day-40, Day-62, and at birth. Recipients with vitrified embryos showed N = 5 biomarkers on Day-40 and Day-62 but only one biomarker at birth. The most predictive biomarkers identified at birth within fresh embryos were oxoglutaric acid (ROC-AUC = 0.709; OCA = 0.812) and ornithine (ROC-AUC = 0.731; OCA = 0.727), while l-glycine was identified in vitrified embryos (ROC-AUC = 0.796; OCA = 0.667) together with other predictive biomarkers not identified at birth (Day-62: l-glutamine ROC-AUC = 0.757; OCA = 0.767) and l-lysine (Day-62: ROC-AUC = 0.680; OCA = 0.767). Pathway enrichment analysis distinguished between pregnant recipients for fresh (enriched energy oxidative metabolism from fat) and vitrified (lower lipid metabolism) embryos. Metabolomics can select individuals that will become pregnant in a defined cycle.

Identification of a metabolomic signature associated with feed efficiency in beef cattle

BACKGROUND

Ruminants play a great role in sustainable livestock since they transform pastures, silage, and crop residues into high-quality human food (i.e. milk and beef). Animals with better ability to convert food into animal protein, measured as a trait called feed efficiency (FE), also produce less manure and greenhouse gas per kilogram of produced meat. Thus, the identification of high feed efficiency cattle is important for sustainable nutritional management. Our aim was to evaluate the potential of serum metabolites to identify FE of beef cattle before they enter the feedlot.

RESULTS

A total of 3598 and 4210 m/z features was detected in negative and positive ionization modes via liquid chromatography-mass spectrometry. A single feature was different between high and low FE groups. Network analysis (WGCNA) yielded the detection of 19 and 20 network modules of highly correlated features in negative and positive mode respectively, and 1 module of each acquisition mode was associated with RFI (r = 0.55, P < 0.05). Pathway enrichment analysis (Mummichog) yielded the Retinol metabolism pathway associated with feed efficiency in beef cattle in our conditions.

CONCLUSION

Altogether, these findings demonstrate the existence of a serum-based metabolomic signature associated with feed efficiency in beef cattle before they enter the feedlot. We are now working to validate the use of metabolites for identification of feed efficient animals for sustainable nutritional management.

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

Despite the worldwide consumption of bovine milk, dairy products from small ruminants, such as goat's and sheep's milk, are gaining a large interest especially in the 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 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 small ruminants and their role in the evaluation of milk properties.

Estimation of Recombination Rate and Maternal Linkage Disequilibrium in Half-Sibs

A livestock population can be characterized by different population genetic parameters, such as linkage disequilibrium and recombination rate between pairs of genetic markers. The population structure, which may be caused by family stratification, has an influence on the estimates of these parameters. An expectation maximization algorithm has been proposed for estimating these parameters in half-sibs without phasing the progeny. It, however, overlooks the fact that the underlying likelihood function may have two maxima. The magnitudes of the maxima depend on the maternal allele frequencies at the investigated marker pair. Which maximum the algorithm converges to depends on the chosen start values. We present a stepwise procedure in which the relationship between the two modes is exploited. The expectation maximization algorithm for the parameter estimation is applied twice using different start values, followed by a decision process to assess the most likely estimate. This approach was validated using simulated genotypes of half-sibs. It was also applied to a dairy cattle dataset consisting of multiple half-sib families and 39,780 marker genotypes, leading to estimates for 12,759,713 intrachromosomal marker pairs. Furthermore, the proper order of markers was verified by studying the mean of estimated recombination rates in a window adjacent to the investigated locus as well as in a window at its most distant chromosome end. Putatively misplaced markers or marker clusters were detected by comparing the results with the revised bovine genome assembly UMD 3.1.1. In total, 40 markers were identified as candidates of misplacement. This outcome may help improving the physical order of markers which is also required for refining the bovine genetic map.

Livestock metabolomics and the livestock metabolome: A systematic review

Metabolomics uses advanced analytical chemistry techniques to comprehensively measure large numbers of small molecule metabolites in cells, tissues and biofluids. The ability to rapidly detect and quantify hundreds or even thousands of metabolites within a single sample is helping scientists paint a far more complete picture of system-wide metabolism and biology. Metabolomics is also allowing researchers to focus on measuring the end-products of complex, hard-to-decipher genetic, epigenetic and environmental interactions. As a result, metabolomics has become an increasingly popular “omics” approach to assist with the robust phenotypic characterization of humans, crop plants and model organisms. Indeed, metabolomics is now routinely used in biomedical, nutritional and crop research. It is also being increasingly used in livestock research and livestock monitoring. The purpose of this systematic review is to quantitatively and objectively summarize the current status of livestock metabolomics and to identify emerging trends, preferred technologies and important gaps in the field. In conducting this review we also critically assessed the applications of livestock metabolomics in key areas such as animal health assessment, disease diagnosis, bioproduct characterization and biomarker discovery for highly desirable economic traits (i.e., feed efficiency, growth potential and milk production). A secondary goal of this critical review was to compile data on the known composition of the livestock metabolome (for 5 of the most common livestock species namely cattle, sheep, goats, horses and pigs). These data have been made available through an open access, comprehensive livestock metabolome database (LMDB, available at http://www.lmdb.ca). The LMDB should enable livestock researchers and producers to conduct more targeted metabolomic studies and to identify where further metabolome coverage is needed.