The relationship between milk metabolome and methane emission of Holstein Friesian dairy cows: Metabolic interpretation and prediction potential

Review of equations to predict methane emissions in dairy cows from milk fatty acid profiles and their application to commercial dairy farms

Greenhouse gas emission from the activities of all productive sectors is currently a topic of foremost importance. The major contributors in the livestock sector are ruminants, especially dairy cows. This study aimed to evaluate and compare 21 equations for predicting enteric methane emissions (EME) developed on the basis of milk traits and fatty acid profiles, which were selected from 46 retrieved through a literature review. We compiled a reference database of the detailed fatty acid profiles, determined by GC, of 992 lactating cows from 85 herds under 4 different dairy management systems. The cows were classified according to DIM, parity order, and dairy system. This database was the basis on which we estimated EME using the selected equations. The EME traits estimated were methane yield (20.63 ± 2.26 g/kg DMI, 7 equations), methane intensity (16.05 ± 2.76 g/kg of corrected milk, 4 equations), and daily methane production (385.4 ± 68.2 g/d, 10 equations). Methane production was also indirectly calculated by multiplying the daily corrected milk yield by the methane intensity (416.6 ± 134.7 g/d, 4 equations). We also tested for the effects of DIM, parity, and dairy system (as a correction factor) on the estimates. In general, we observed little consistency among the EME estimates obtained from the different equations, with exception of those obtained from meta-analyses of a range of data from different research centers. We found all the EME predictions to be highly affected by the sources of variation included in the statistical model: DIM significantly affected the results of 19 of the 21 equations, and parity order influenced the results of 13. Different patterns were observed for different equations with only some of them in accordance with expectations based on the cow's physiology. Finally, the best predictions of daily methane production were obtained when a measure of milk yield was included in the equation or when the estimate was indirectly calculated from daily milk yield and methane intensity.

Approaches for predicting dairy cattle methane emissions: from traditional methods to machine learning

Measuring dairy cattle methane (CH4) emissions using traditional recording technologies is complicated and expensive. Prediction models, which estimate CH4 emissions based on proxy information, provide an accessible alternative. This review covers the different modeling approaches taken in the prediction of dairy cattle CH4 emissions and highlights their individual strengths and limitations. Following the guidelines set out by the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA); Scopus, EBSCO, Web of Science, PubMed and PubAg were each queried for papers with titles that contained search terms related to a population of "Bovine," exposure of "Statistical Analysis or Machine Learning," and outcome of "Methane Emissions". The search was executed in December 2022 with no publication date range set. Eligible papers were those that investigated the prediction of CH4 emissions in dairy cattle via statistical or machine learning (ML) methods and were available in English. 299 papers were returned from the initial search, 55 of which, were eligible for inclusion in the discussion. Data from the 55 papers was synthesized by the CH4 emission prediction approach explored, including mechanistic modeling, empirical modeling, and machine learning. Mechanistic models were found to be highly accurate, yet they require difficult-to-obtain input data, which, if imprecise, can produce misleading results. Empirical models remain more versatile by comparison, yet suffer greatly when applied outside of their original developmental range. The prediction of CH4 emissions on commercial dairy farms can utilize any approach, however, the traits they use must be procurable in a commercial farm setting. Milk fatty acids (MFA) appear to be the most popular commercially accessible trait under investigation, however, MFA-based models have produced ambivalent results and should be consolidated before robust accuracies can be achieved. ML models provide a novel methodology for the prediction of dairy cattle CH4 emissions through a diverse range of advanced algorithms, and can facilitate the combination of heterogenous data types via hybridization or stacking techniques. In addition to this, they also offer the ability to improve dataset complexity through imputation strategies. These opportunities allow ML models to address the limitations faced by traditional prediction approaches, as well as enhance prediction on commercial farms.

Untargeted Metabolomic Analysis of Lactation-Stage-Matched Human and Bovine Milk Samples at 2 Weeks Postnatal

Epidemiological data demonstrate that bovine whole milk is often substituted for human milk during the first 12 months of life and may be associated with adverse infant outcomes. The objective of this study is to interrogate the human and bovine milk metabolome at 2 weeks of life to identify unique metabolites that may impact infant health outcomes. Human milk (n = 10) was collected at 2 weeks postpartum from normal-weight mothers (pre-pregnant BMI < 25 kg/m2) that vaginally delivered term infants and were exclusively breastfeeding their infant for at least 2 months. Similarly, bovine milk (n = 10) was collected 2 weeks postpartum from normal-weight primiparous Holstein dairy cows. Untargeted data were acquired on all milk samples using high-resolution liquid chromatography-high-resolution tandem mass spectrometry (HR LC-MS/MS). MS data pre-processing from feature calling to metabolite annotation was performed using MS-DIAL and MS-FLO. Our results revealed that more than 80% of the milk metabolome is shared between human and bovine milk samples during early lactation. Unbiased analysis of identified metabolites revealed that nearly 80% of milk metabolites may contribute to microbial metabolism and microbe-host interactions. Collectively, these results highlight untargeted metabolomics as a potential strategy to identify unique and shared metabolites in bovine and human milk that may relate to and impact infant health outcomes.

Goat Milk Foodomics. Dietary Supplementation of Sunflower Oil and Rapeseed Oil Modify Milk Amino Acid and Organic Acid Profiles in Dairy Goats

The dietary supplementation of vegetable oils is known to improve the dietary energy density as well as milk fatty acid profile; however, the impacts on the milk foodome is largely unknown. This study investigated the effect of two different sources of unsaturated fatty acids, rapeseed oil and sunflower oil, as a feeding supplement on the milk foodome from dairy goats. Nine Danish Landrace goats at 42 ± 5 days in milk were allocated to three treatment groups for 42 days with three animals per group. A control group received a basal diet made of forage and concentrate at an 85:15 ratio. On top of the basal diet, the second and third groups received rapeseed oil or sunflower oil supplements at 4% of dry matter, respectively. Goat milk was sampled on days 14, 21, and 42. The milk foodome was measured using gas chromatography–mass spectrometry and proton nuclear magnetic resonance spectroscopy. The milk levels of 2-hydroxyisovaleric acid, oxaloacetic acid, and taurine were higher in the milk from goats fed with sunflower oil compared to the control group. More glucose-1-phosphate was found in the milk from goats fed with rapeseed oil compared to the control group. Amino acids, valine and tyrosine, and 2-hydroxyisovaleric acid and oxaloacetic acid were higher in the sunflower group compared to the rapeseed group, while the milk from the rapeseed-fed goats had greater levels of ethanol and 2-oxoglutaric acid compared to the sunflower group. Thus, results show that foodomics is suitable for studying how milk chemistry changes as a function of feeding regime.

The potential for mitigation of methane emissions in ruminants through the application of metagenomics, metabolomics, and other -OMICS technologies

Ruminant supply chains contribute 5.7 gigatons of CO_2-eq per annum, which represents approximately 80% of the livestock sector emissions. One of the largest sources of emission in the ruminant sector is methane (CH₄), accounting for approximately 40% of the sectors total emissions. With climate change being a growing concern, emphasis is being put on reducing greenhouse gas emissions, including those from ruminant production. Various genetic and environmental factors influence cattle CH₄ production, such as breed, genetic makeup, diet, management practices, and physiological status of the host. The influence of genetic variability on CH₄ yield in ruminants indicates that genomic selection for reduced CH₄ emissions is possible. Although the microbiology of CH₄ production has been studied, further research is needed to identify key differences in the host and microbiome genomes and how they interact with one another. The advancement of “-omics” technologies, such as metabolomics and metagenomics, may provide valuable information in this regard. Improved understanding of genetic mechanisms associated with CH₄ production and the interaction between the microbiome profile and host genetics will increase the rate of genetic progress for reduced CH₄ emissions. Through a systems biology approach, various “-omics” technologies can be combined to unravel genomic regions and genetic markers associated with CH₄ production, which can then be used in selective breeding programs. This comprehensive review discusses current challenges in applying genomic selection for reduced CH₄ emissions, and the potential for “-omics” technologies, especially metabolomics and metagenomics, to minimize such challenges. The integration and evaluation of different levels of biological information using a systems biology approach is also discussed, which can assist in understanding the underlying genetic mechanisms and biology of CH₄ production traits in ruminants and aid in reducing agriculture’s overall environmental footprint.

Milk metabolome reveals variations on enteric methane emissions from dairy cows fed a specific inhibitor of the methanogenesis pathway

Metabolome profiling in biological fluids is an interesting approach for exploring markers of methane emissions in ruminants. In this study, a multiplatform metabolomics approach was used for investigating changes in milk metabolic profiles related to methanogenesis in dairy cows. For this purpose, 25 primiparous Holstein cows at similar lactation stage were fed the same diet supplemented with (treated, n = 12) or without (control, n = 13) a specific antimethanogenic additive that reduced enteric methane production by 23% with no changes in intake, milk production, and health status. The study lasted 6 wk, with sampling and measures performed in wk 5 and 6. Milk samples were analyzed using 4 complementary analytical methods, including 2 untargeted (nuclear magnetic resonance and liquid chromatography coupled to a quadrupole-time-of-flight mass spectrometer) and 2 targeted (liquid chromatography-tandem mass spectrometry and gas chromatography coupled to a flame ionization detector) approaches. After filtration, variable selection and normalization data from each analytical platform were then analyzed using multivariate orthogonal partial least square discriminant analysis. All 4 analytical methods were able to differentiate cows from treated and control groups. Overall, 38 discriminant metabolites were identified, which affected 10 metabolic pathways including methane metabolism. Some of these metabolites such as dimethylsulfoxide, dimethylsulfone, and citramalic acid, detected by nuclear magnetic resonance or liquid chromatography-mass spectrometry methods, originated from the rumen microbiota or had a microbial-host animal co-metabolism that could be associated with methanogenesis. Also, discriminant milk fatty acids detected by targeted gas chromatography were mostly of ruminal microbial origin. Other metabolites and metabolic pathways significantly affected were associated with AA metabolism. These findings provide new insight on the potential role of milk metabolites as indicators of enteric methane modifications in dairy cows.

Metabolic Profiling of Rumen Fluid and Milk in Lactating Dairy Cattle Influenced by Subclinical Ketosis Using Proton Nuclear Magnetic Resonance Spectroscopy

Simple Summary

Ketosis metabolic research is extremely rare in Korea. This study aimed to compare the rumen fluid and milk metabolites between healthy and subclinical ketosis-diagnosed lactating dairy cattle. Six Holstein cows were allocated into two groups based on whether they fit the criteria for subclinical ketosis, and their rumen fluid and milk samples were collected from the stomach tube and pipeline milking system. Rumen fluid and milk samples metabolites were analyzed using proton nuclear magnetic resonance spectroscopy. They were identified and quantified using the Chenomx NMR Suite 8.4 software and statistical analysis was performed using Metaboanalyst 5.0. In rumen fluid, ruminant energy source metabolites (acetate, glucose, and propionate) were significantly higher in the healthy group, whereas in milk, ketone body metabolites (3-hydroxybutyrate and acetoacetate) were significantly higher in the subclinical ketosis-diagnosed group. This report will serve as a reference guide for future studies on ketosis metabolomics in Korea.

Abstract

Ketosis metabolic research on lactating dairy cattle has been conducted worldwide; however, there have been very few Korean studies. Biofluids from lactating dairy cattle are necessary to study ketosis metabolic diseases. Six Holstein cows were divided into two groups (healthy (CON) and subclinical ketosis diagnosed (SCK)). Rumen fluid and milk samples were collected using a stomach tube and a pipeline milking system, respectively. Metabolites were determined using proton nuclear magnetic resonance (NMR) spectroscopy and they were identified and quantified using the Chenomx NMR Suite 8.4 software and Metaboanalyst 5.0. In the rumen fluid of the SCK group, butyrate, sucrose, 3-hydroxybutyrate, maltose, and valerate levels were significantly higher than in the CON group, which showed higher levels of N,N-dimethylformamide, acetate, glucose, and propionate were significantly higher. Milk from the SCK group showed higher levels of maleate, 3-hydroxybutyrate, acetoacetate, galactonate, and 3-hydroxykynurenine than that from the CON group, which showed higher levels of galactitol, 1,3-dihydroxyacetone, γ-glutamylphenylalanine, 5-aminolevulinate, acetate, and methylamine. Some metabolites are associated with ketosis diseases and the quality of rumen fluid and milk. This report will serve as a future reference guide for ketosis metabolomics studies in Korea.

Comparative untargeted metabolome analysis of ruminal fluid and feces of Nelore steers (Bos indicus)

We conducted a study to identify the fecal metabolite profile and its proximity to the ruminal metabolism of Nelore steers based on an untargeted metabolomic approach. Twenty-six Nelore were feedlot with same diet during 105 d. Feces and rumen fluid were collected before and at slaughter, respectively. The metabolomics analysis indicated 49 common polar metabolites in the rumen and feces. Acetate, propionate, and butyrate were the most abundant polar metabolites in both bio-samples. The rumen presented significantly higher concentrations of the polar compounds when compared to feces (P < 0.05); even though, fecal metabolites presented an accentuated representability of the ruminal fluid metabolites. All fatty acids present in the ruminal fluid were also observed in the feces, except for C20:2n6 and C20:4n6. The identified metabolites offer information on the main metabolic pathways (higher impact factor and P < 0.05), as synthesis and degradation of ketone bodies; the alanine, aspartate and glutamate metabolisms, the glycine, serine; and threonine metabolism and the pyruvate metabolism. The findings reported herein on the close relationship between the ruminal fluid and feces metabolic profiles may offer new metabolic information, in addition to facilitating the sampling for metabolism investigation in animal production and health routines.

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.

Short communication: The effect of linseed oil and DGAT1 K232A polymorphism on the methane emission prediction potential of milk fatty acids

Several in vivo CH₄ measurement techniques have been developed but are not suitable for precise and accurate large-scale measurements; hence, proxies for CH₄ emissions in dairy cattle have been proposed, including the milk fatty acid (MFA) profile. The aim of the present study was to determine whether recently developed MFA-based prediction equations for CH₄ emission are applicable to dairy cows with different diacylglycerol o-acyltransferase 1 (DGAT1) K232A polymorphism and fed diets with and without linseed oil. Data from a crossover design experiment were used, encompassing 2 dietary treatments (i.e., a control diet and a linseed oil diet, with a difference in dietary fat content of 22 g/kg of dry matter) and 24 lactating Holstein-Friesian cows (i.e., 12 cows with DGAT1 KK genotype and 12 cows with DGAT1 AA genotype). Enteric CH₄ production was measured in climate respiration chambers and the MFA profile was analyzed using gas chromatography. Observed CH₄ emissions were compared with CH₄ emissions predicted by previously developed MFA-based CH₄ prediction equations. The results indicate that different types of diets (i.e., with or without linseed oil), but not the DGAT1 K232A polymorphism, affect the ability of previously derived prediction equations to predict CH₄ emission. However, the concordance correlation coefficient was smaller than or equal to 0.30 for both dietary treatments separately, both DGAT1 genotypes separately, and the complete data set. We therefore concluded that previously derived MFA-based CH₄ prediction equations can neither accurately nor precisely predict CH₄ emissions of dairy cows managed under strategies differing from those under which the original prediction equations were developed.