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

Multiomics analysis revealed that the metabolite profile of raw milk is associated with lactation stage of dairy cows and could be affected by variations in the ruminal microbiota

The nutritional components and quality of milk are influenced by the rumen microbiota and its metabolites at different lactation stages. Hence, rumen fluid and milk samples from 6 dairy cows fed the same diet were collected during peak, early mid- and later mid-lactation. Untargeted metabolomics and 16S rRNA sequencing were applied for analyzing milk and rumen metabolites, as well as rumen microbial composition, respectively. The levels of lipid-related metabolites, L-glutamate, glucose-1-phosphate and acetylphosphate in milk exhibited lactation-dependent attenuation. Maltol, N-acetyl-D-glucosamine, and choline, which are associated with milk flavor or coagulation properties, as well as L-valine, lansioside-A, clitocine and ginsenoside-La increased significantly in early mid- and later mid-lactation, especially in later mid-lactation. The obvious increase in rumen microbial diversities (Ace and Shannon indices) were observed in early mid-lactation compared with peak lactation. Twenty-one differential bacterial genera of the rumen were identified, with Succinivibrionaceae_UCG-001, Candidatus Saccharimonas, Fibrobacter, and SP3-e08 being significantly enriched in peak lactation. Rikenellaceae_RC9_gut_group, Eubacterium_ruminantium_group, Lachnospira, Butyrivibrio, Eubacterium_hallii_group, and Schwartzia were most significantly enriched in early mid-lactation. In comparison, only 2 bacteria (unclassified_f__Prevotellaceae and Prevotellaceae_UCG-001) were enriched in later mid-lactation. For rumen metabolites, LPE(16:0), L-glutamate and L-tyrosine had higher levels in peak lactation, whereas PE(17:0/0:0), PE(16:0/0:0), PS(18:1(9Z)/0:0), L-phenylalanine, dulcitol, 2-(methoxymethyl)furan and 3-phenylpropyl acetate showed higher levels in early mid- and later mid-lactation. Multiomics integrated analysis revealed that a greater abundance of Fibrobacter contributed to phospholipid content in milk by increasing ruminal acetate, L-glutamate and LysoPE(16:0). Prevotellaceae_UCG-001 and unclassified_f_Prevotellaceae provide substrates for milk metabolites of the same category by increasing ruminal L-phenylalanine and dulcitol contents. These results demonstrated that milk metabolomic fingerprints and critical functional metabolites during lactation, and the key bacteria in rumen related to them. These findings provide new insights into the development of functional dairy products.

Milk Odd- and Branched-Chain Fatty Acids as Biomarkers of Rumen Fermentation

Cow's milk and dairy products are the primary sources of OBCFAs, which have beneficial health properties. The goal of this study was to identify the factors that influence the content of OBCFAs in cow's milk and to indicate which OBCFAs can serve as biomarkers for fermentation processes. The content of OBCFAs in milk depends on the species of ruminants, with studies showing that this varies between 3.33% (in goat's milk) and 5.02% (in buffalo's milk). These differences also stem from the animals' energy balance, lactation phases, forage-to-concentrate ratio, and the presence of bioactive compounds in feeds, as well as management practices and environmental conditions. The OBCFAs in milk fat mainly come from rumen bacteria, but can also be synthesized de novo in the mammary gland, making them potentially useful noninvasive indicators of rumen fermentation. The concentration of BCFA is lower in colostrum and transitional milk than in full lactation milk. The proportions of total OBCFAs are higher in first- and second-parity cows. The most effective predictors of the biohydrogenation of fatty acids in the rumen are likely C18:2 cis-9, trans-11, iso-C16:0, and iso-C13:0. OBCFAs have been identified as potential biomarkers for rumen function, because their synthesis depends on specific bacteria. Strong predictors of subclinical ruminal acidosis include iso-C14:0, iso-C13:0, and C15:0. The concentration of ∑ OBCFA >C16 in milk is associated with fat mobilization and serves as a significant marker of the energy balance in cows.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Multi-omics analysis reveals that the metabolite profile of raw milk is associated with dairy cows' health status

The metabolic status of dairy cows directly influences the nutritional quality and flavor of raw milk. A comprehensive comparison of non-volatile metabolites and volatile compounds in raw milk from healthy and subclinical ketosis (SCK) cows was performed using LC-MS, GC-FID, and HS-SPME/GC-MS. SCK can significantly alter the profiles of water-soluble non-volatile metabolites, lipids, and volatile compounds of raw milk. Compared with healthy cows, milk from SCK cows had higher contents of tyrosine, leucine, isoleucine, galactose-1-phosphate, carnitine, citrate, phosphatidylethanolamine species, acetone, 2-butanone, hexanal, dimethyl disulfide and lower content of creatinine, taurine, choline, α-ketoglutaric acid, fumarate, triglyceride species, ethyl butanoate, ethyl acetate, and heptanal. The percentage of polyunsaturated fatty acids in milk was lowered in SCK cows. Our results suggest that SCK can change milk metabolite profiles, disrupt the lipid composition of milk fat globule membrane, decrease the nutritional value, and increase the volatile compounds associated with off-flavors in milk.

Review: Reducing enteric methane emissions improves energy metabolism in livestock: is the tenet right?

The production of enteric methane in the gastrointestinal tract of livestock is considered as an energy loss in the equations for estimating energy metabolism in feeding systems. Therefore, the spared energy resulting from specific inhibition of methane emissions should be re-equilibrated with other factors of the equation. And, it is commonly assumed that net energy from feeds increases, thus benefitting production functions, particularly in ruminants due to the important production of methane in the rumen. Notwithstanding, we confirm in this work that inhibition of emissions in ruminants does not transpose into consistent improvements in production. Theoretical calculations of energy flows using experimental data show that the expected improvement in net energy for production is small and difficult to detect under the prevailing, moderate inhibition of methane production (≈25%) obtained using feed additives inhibiting methanogenesis. Importantly, the calculation of energy partitioning using canonical models might not be adequate when methanogenesis is inhibited. There is a lack of information on various parameters that play a role in energy partitioning and that may be affected under provoked abatement of methane. The formula used to calculate heat production based on respiratory exchanges should be validated when methanogenesis is inhibited. Also, a better understanding is needed of the effects of inhibition on fermentation products, fermentation heat, and microbial biomass. Inhibition induces the accumulation of H2, the main substrate used to produce methane, that has no energetic value for the host, and it is not extensively used by the majority of rumen microbes. Currently, the fate of this excess of H2 and its consequences on the microbiota and the host are not well known. All this additional information will provide a better account of energy transactions in ruminants when enteric methanogenesis is inhibited. Based on the available information, it is concluded that the claim that enteric methane inhibition will translate into more feed-efficient animals is not warranted.

Plasma and milk metabolomics in lactating sheep divergent for feed efficiency

Enhancing the ability of animals to convert feed into meat or milk by optimizing feed efficiency (FE) has become a priority in livestock research. Although untargeted metabolomics is increasingly used in this field and may improve our understanding of FE, no information in this regard is available in dairy ewes. This study was conducted to (1) discriminate sheep divergent for FE and (2) provide insights into the physiological mechanisms contributing to FE through high-throughput metabolomics. The ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q/TOF-MS) technique was applied to easily accessible animal fluids (plasma and milk) to assess whether their metabolome differs between high- and low-feed efficient lactating ewes (H-FE and L-FE groups, respectively; 8 animals/group). Blood and milk samples were collected on the last day of the 3-wk period used for FE estimation. A total of 793 features were detected in plasma and 334 in milk, with 100 and 38 of them, respectively, showing differences between H-FE and L-FE. The partial least-squares discriminant analysis separated both groups of animals regardless of the type of sample. Plasma allowed the detection of a greater number of differential features; however, results also supported the usefulness of milk, more easily accessible, to discriminate dairy sheep divergent for FE. Regarding pathway analysis, nitrogen metabolism (either anabolism or catabolism) seemed to play a central role in FE, with plasma and milk consistently indicating a great impact of AA metabolism. A potential influence of pathways related to energy/lipid metabolism on FE was also observed. The variable importance in the projection plot revealed 15 differential features in each matrix that contributed the most for the separation in H-FE and L-FE, such as l-proline and phosphatidylcholine 20:4e in plasma or l-pipecolic acid and phosphatidylethanolamine (18:2) in milk. Overall, untargeted metabolomics provided valuable information into metabolic pathways that may underlie FE in dairy ewes, with a special relevance of AA metabolism in determining this complex phenotype in the ovine. Further research is warranted to validate these findings.

Milk metabolome reveals pyrimidine and its degradation products as the discriminant markers of different corn silage-based nutritional strategies

The purpose of this study was to evaluate the effect of 6 different feeding systems (based on corn silage as the main ingredient) on the chemical composition of milk and to highlight the potential of untargeted metabolomics to find discriminant marker compounds of different nutritional strategies. Interestingly, the multivariate statistical analysis discriminated milk samples mainly according to the high-moisture ear corn (HMC) included in the diet formulation. Overall, the most discriminant compounds, identified as a function of the HMC, belonged to AA (10 compounds), peptides (71 compounds), pyrimidines (38 compounds), purines (15 compounds), and pyridines (14 compounds). The discriminant milk metabolites were found to significantly explain the metabolic pathways of pyrimidines and vitamin B₆. Interestingly, pathway analyses revealed that the inclusion of HMC in the diet formulation strongly affected the pyrimidine metabolism in milk, determining a significant up-accumulation of pyrimidine degradation products, such as 3-ureidopropionic acid, 3-ureidoisobutyric acid, and 3-aminoisobutyric acid. Also, some pyrimidine intermediates (such as l-aspartic acid, N-carbamoyl-l-aspartic acid, and orotic acid) were found to possess a high discrimination degree. Additionally, our findings suggested that the inclusion of alfalfa silage in the diet formulation was potentially correlated with the vitamin B₆ metabolism in milk, being 4-pyridoxic acid (a pyridoxal phosphate degradation product) the most significant and up-accumulated compound. Taken together, the accumulation trends of different marker compounds revealed that both pyrimidine intermediates and degradation products are potential marker compounds of HMC-based diets, likely involving a complex metabolism of microbial nitrogen based on total splanchnic fluxes from the rumen to mammary gland in dairy cows. Also, our findings highlight the potential of untargeted metabolomics in both foodomics and foodomics-based studies involving dairy products.