Nuclear Magnetic Resonance to Detect Rumen Metabolites Associated with Enteric Methane Emissions from Beef Cattle

An assessment on the effects of buffers on the productive, behavioral and metabolic parameters of Holstein dairy cows

This study aimed to evaluate the impact of supplementing sodium bicarbonate or a commercial blend of buffering agents (BBA) comprising calcareous calcitic, magnesium oxide, calcareous algae, and sodium bicarbonate on the productive, behavioral and metabolic parameters of Holstein cows fed starchy diets. Over a 60-day trial period, thirty-six multiparous cows with an average milk yield of 38.84 ± 9.24 kg/day and 63.74 ± 18.63 days in milk (DIM), were randomly divided into two groups. The control group (n = 18) received a supplementation of 1.1% dry matter (DM) of sodium bicarbonate (Raudi®, Totalmix, Brazil), while the BBA group (n = 18) was administered with 0.5% DM of a blend of buffering agents (Equalizer®, Nutron/Cargill, Brazil). The mean values of ruminal pH (control 6.80 ± 0.06 and BBA 6.77 ± 0.06; P > 0.05) and volatile fatty acid (VFA) production (control: acetate 62.63 ± 1.29%, propionate 22.99 ± 1.07%, butyrate 14.30 ± 0.52%; BBA: acetate 63.07 ± 1.32%, propionate 23.47 ± 1.10%, butyrate 13.70 ± 0.57%), were similar (P > 0,05) between the two groups. The value of faecal pH was higher (P < 0.05) in the BBA group (6.25 ± 0.02) than the control group (6.12 ± 0.02). Animals treated with BBA exhibited lower (P < 0,05) dry matter intake (DMI) (24.75 ± 0.64 kg/day), higher feed efficiency (FE) (1.64 ± 0.03), and reduced feeding frequency (52.89 ± 3.73 n°/day) than the control group (DMI, 26.75 ± 0.62 kg/day; FE, 1.50 ± 0.03; feeding frequency, 66.07 ± 3.64 n°/day). Milk production remained similar across both groups (control, 39.11 ± 0.92 kg/day and BBA, 39.87 ± 0.92 kg/day; P > 0.05). Notably, the control group displayed a higher (P < 0,05) concentration of milk protein (1.21 ± 0.05 kg/day) than the BBA (1.18 ± 0.05 kg/day) group. The study concluded that both treatments effectively buffered the rumen and mitigated the risk of ruminal acidosis. Moreover, the higher faecal pH in the BBA-treated group suggests potential intestinal action attributable to the synergistic effects of diverse additives with buffering properties. Despite a reduced DMI, BBA-treated animals exhibited improved FE.

The Impact of Varying Pasture Levels on the Metabolomic Profile of Bovine Ruminal Fluid

A pasture or concentrate-based dietary regime impacts a variety of factors including both ruminal health and function, and consequently milk production and quality. The objective of this study was to examine the effect of feeding differing pasture levels on the metabolite composition of bovine ruminal fluid. Ruminal fluid was obtained from rumen-cannulated spring-calving cows (N = 9, Holstein-Friesian breed, average lactation number = 5) fed one of three diets across a full lactation season. Group 1 (pasture) consumed perennial ryegrass supplemented with 5% concentrates; group 2 received a total mixed ration (TMR) diet; and group 3 received a partial mixed ration (PMR) diet which included pasture and a TMR. Samples were taken at two timepoints: morning and evening. Metabolomic analysis was performed using nuclear magnetic resonance (1H-NMR) spectroscopy. Statistical analysis revealed significant changes across the dietary regimes in both morning and evening samples, with distinct alterations in the metabolite composition of ruminal fluid from pasture-fed cows (FDR-adjusted p-value < 0.05). Acetate and butyrate were significantly higher in samples derived from a pasture-based diet whereas sugar-related metabolites were higher in concentrate-based samples. Furthermore, a distinct diurnal impact on the metabolite profile was evident. This work lays the foundation for understanding the complex interaction between dietary regime and ruminal health.

Metataxonomic and metabolomic profiling revealed Pinus koraiensis cone essential oil reduced methane emission through affecting ruminal microbial interactions and host-microbial metabolism

BACKGROUND

Pinus koraiensis cone essential oil (PEO) contains functional compounds such as monoterpene hydrocarbons, and the administration of PEO reduced methane (CH4) emissions during growing phase of goats. However, the mode of action of PEO driven CH4 reduction is not known, especially how the administration of PEO can affect rumen microbiota and host metabolism in goats during the fattening phase. This study aimed to elucidate the potential microbial and host responses PEO supplementation in goats using metataxonomics (prokaryotes and protozoa) and metabolomics (rumen fluid and serum).

RESULTS

Ten fattening Korean native goats were divided into two dietary groups: control (CON; basal diet without additives) and PEO (basal diet + 1.5 g/d of PEO) with a 2 × 2 crossover design and the treatment lasted for 11 weeks. Administration of PEO reduced CH4 concentrations in the exhaled gas from eructation by 12.0-13.6% (P < 0.05). Although the microbial composition of prokaryotes (bacteria and archaea) and protozoa in the rumen was not altered after PEO administration. MaAsLin2 analysis revealed that the abundance of Selenomonas, Christensenellaceae R-7 group, and Anaerovibrio were enriched in the rumen of PEO supplemented goats (Q < 0.1). Co-occurrence network analysis revealed that Lachnospiraceae AC2044 group and Anaerovibrio were the keystone taxa in the CON and PEO groups, respectively. Methane metabolism (P < 0.05) was enriched in the CON group, whereas metabolism of sulfur (P < 0.001) and propionate (P < 0.1) were enriched in the PEO group based on microbial predicted functions. After PEO administration, the abundance of 11 rumen and 4 serum metabolites increased, whereas that of 25 rumen and 14 serum metabolites decreased (P < 0.1). Random forest analysis identified eight ruminal metabolites that were altered after PEO administration, among which four were associated with propionate production, with predictive accuracy ranging from 0.75 to 0.88. Additionally, we found that serum sarcosine (serum metabolite) was positively correlated with CH4 emission parameters and abundance of Methanobrevibacter in the rumen (|r|≥ 0.5, P < 0.05).

CONCLUSIONS

This study revealed that PEO administration reduced CH4 emission from of fattening goats with altered microbial interactions and metabolites in the rumen and host. Importantly, PEO administration affected utilizes various mechanisms such as formate, sulfur, methylated amines metabolism, and propionate production, collectively leading to CH4 reduction. The knowledge is important for future management strategies to maintain animal production and health while mitigate CH4 emission.

Ruminal and feces metabolites associated with feed efficiency, water intake and methane emission in Nelore bulls

The objectives of this study were twofold: (1) to identify potential differences in the ruminal and fecal metabolite profiles of Nelore bulls under different nutritional interventions; and (2) to identify metabolites associated with cattle sustainability related-traits. We used different nutritional interventions in the feedlot: conventional (Conv; n = 26), and by-product (ByPr, n = 26). Thirty-eight ruminal fluid and 27 fecal metabolites were significantly different (P < 0.05) between the ByPr and Conv groups. Individual dry matter intake (DMI), residual feed intake (RFI), observed water intake (OWI), predicted water intake (WI), and residual water intake (RWI) phenotypes were lower (P < 0.05) in the Conv group, while the ByPr group exhibited lower methane emission (ME) (P < 0.05). Ruminal fluid dimethylamine was significantly associated (P < 0.05) with DMI, RFI, FE (feed efficiency), OWI and WI. Aspartate was associated (P < 0.05) with DMI, RFI, FE and WI. Fecal C22:1n9 was significantly associated with OWI and RWI (P < 0.05). Fatty acid C14:0 and hypoxanthine were significantly associated with DMI and RFI (P < 0.05). The results demonstrated that different nutritional interventions alter ruminal and fecal metabolites and provided new insights into the relationship of these metabolites with feed efficiency and water intake traits in Nelore bulls.

Methane emissions and rumen metabolite concentrations in cattle fed two different silages

In this study, 18 animals were fed two forage-based diets: red clover (RC) and grass silage (GS), in a crossover-design experiment in which methane (CH4) emissions were recorded in respiration chambers. Rumen samples obtained through naso-gastric sampling tubes were analysed by NMR. Methane yield (g/kg DM) was significantly lower from animals fed RC (17.8 ± 3.17) compared to GS (21.2 ± 4.61) p = 0.008. In total 42 metabolites were identified, 6 showing significant differences between diets (acetate, propionate, butyrate, valerate, 3-phenylopropionate, and 2-hydroxyvalerate). Partial least squares discriminant analysis (PLS-DA) was used to assess which metabolites were more important to distinguish between diets and partial least squares (PLS) regressions were used to assess which metabolites were more strongly associated with the variation in CH4 emissions. Acetate, butyrate and propionate along with dimethylamine were important for the distinction between diets according to the PLS-DA results. PLS regression revealed that diet and dry matter intake are key factors to explain CH4 variation when included in the model. Additionally, PLS was conducted within diet, revealing that the association between metabolites and CH4 emissions can be conditioned by diet. These results provide new insights into the methylotrophic methanogenic pathway, confirming that metabolite profiles change according to diet composition, with consequences for CH4 emissions.

Evaluation of the Links between Lamb Feed Efficiency and Rumen and Plasma Metabolomic Data

Feed efficiency is one of the keystones that could help make animal production less costly and more environmentally friendly. Residual feed intake (RFI) is a widely used criterion to measure feed efficiency by regressing intake on the main energy sinks. We investigated rumen and plasma metabolomic data on Romane male lambs that had been genetically selected for either feed efficiency (line rfi−) or inefficiency (line rfi+). These investigations were conducted both during the growth phase under a 100% concentrate diet and later on under a mixed diet to identify differential metabolite expression and to link it to biological phenomena that could explain differences in feed efficiency. Nuclear magnetic resonance (NMR) data were analyzed using partial least squares discriminant analysis (PLS-DA), and correlations between metabolites’ relative concentrations were estimated to identify relationships between them. High levels of plasma citrate and malate were associated with genetically efficient animals, while high levels of amino acids such as L-threonine, L-serine, and L-leucine as well as beta-hydroxyisovalerate were associated with genetically inefficient animals under both diets. The two divergent lines could not be discriminated using rumen metabolites. Based on phenotypic residual feed intake (RFI), efficient and inefficient animals were discriminated using plasma metabolites determined under a 100% concentrate diet, but no discrimination was observed with plasma metabolites under a mixed diet or with rumen metabolites regardless of diet. Plasma amino acids, citrate, and malate were the most discriminant metabolites, suggesting that protein turnover and the mitochondrial production of energy could be the main phenomena that differ between efficient and inefficient Romane lambs.

Comparison of HPLC and NMR for quantification of the main volatile fatty acids in rumen digesta

Accurate quantification of volatile fatty acid (VFA) concentrations in rumen fluid are essential for research on rumen metabolism. The study comprehensively investigated the pros and cons of High-performance liquid chromatography (HPLC) and 1H Nuclear magnetic resonance (1H-NMR) analysis methods for rumen VFAs quantification. We also investigated the performance of several commonly used data pre-treatments for the two sets of data using correlation analysis, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The molar proportion and reliability analysis demonstrated that the two approaches produce highly consistent VFA concentrations. In the pre-processing of NMR spectra, line broadening and shim correction may reduce estimated concentrations of metabolites. We observed differences in results using multiplet of different protons from one compound and identified "handle signals" that provided the most consistent concentrations. Different data pre-treatment strategies tested with both HPLC and NMR significantly affected the results of downstream data analysis. "Normalized by sum" pre-treatment can eliminate a large number of positive correlations between NMR-based VFA. A "Combine" strategy should be the first choice when calculating the correlation between metabolites or between samples. The PCA and PLS-DA suggest that except for "Normalize by sum", pre-treatments should be used with caution.

The rumen microbiome inhibits methane formation through dietary choline supplementation

Enteric fermentation from ruminants is a primary source of anthropogenic methane emission. This study aims to add another approach for methane mitigation by manipulation of the rumen microbiome. Effects of choline supplementation on methane formation were quantified in vitro using the Rumen Simulation Technique. Supplementing 200 mM of choline chloride or choline bicarbonate reduced methane emissions by 97–100% after 15 days. Associated with the reduction of methane formation, metabolomics analysis revealed high post-treatment concentrations of ethanol, which likely served as a major hydrogen sink. Metagenome sequencing showed that the methanogen community was almost entirely lost, and choline-utilizing bacteria that can produce either lactate, ethanol or formate as hydrogen sinks were enriched. The taxa most strongly associated with methane mitigation were Megasphaera elsdenii and Denitrobacterium detoxificans, both capable of consuming lactate, which is an intermediate product and hydrogen sink. Accordingly, choline metabolism promoted the capability of bacteria to utilize alternative hydrogen sinks leading to a decline of hydrogen as a substrate for methane formation. However, fermentation of fibre and total organic matter could not be fully maintained with choline supplementation, while amino acid deamination and ethanolamine catabolism produced excessive ammonia, which would reduce feed efficiency and adversely affect live animal performance.

Compositional Data Analysis of Microbiome and Any-Omics Datasets: A Validation of the Additive Logratio Transformation

Microbiome and omics datasets are, by their intrinsic biological nature, of high dimensionality, characterized by counts of large numbers of components (microbial genes, operational taxonomic units, RNA transcripts, etc.). These data are generally regarded as compositional since the total number of counts identified within a sample is irrelevant. The central concept in compositional data analysis is the logratio transformation, the simplest being the additive logratios with respect to a fixed reference component. A full set of additive logratios is not isometric, that is they do not reproduce the geometry of all pairwise logratios exactly, but their lack of isometry can be measured by the Procrustes correlation. The reference component can be chosen to maximize the Procrustes correlation between the additive logratio geometry and the exact logratio geometry, and for high-dimensional data there are many potential references. As a secondary criterion, minimizing the variance of the reference component's log-transformed relative abundance values makes the subsequent interpretation of the logratios even easier. On each of three high-dimensional omics datasets the additive logratio transformation was performed, using references that were identified according to the abovementioned criteria. For each dataset the compositional data structure was successfully reproduced, that is the additive logratios were very close to being isometric. The Procrustes correlations achieved for these datasets were 0.9991, 0.9974, and 0.9902, respectively. We thus demonstrate, for high-dimensional compositional data, that additive logratios can provide a valid choice as transformed variables, which (a) are subcompositionally coherent, (b) explain 100% of the total logratio variance and (c) come measurably very close to being isometric. The interpretation of additive logratios is much simpler than the complex isometric alternatives and, when the variance of the log-transformed reference is very low, it is even simpler since each additive logratio can be identified with a corresponding compositional component.

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.

On the effects of hard and soft equality constraints in the iterative outlier elimination procedure

Reliability analysis allows for the estimation of a system’s probability of detecting and identifying outliers. Failure to identify an outlier can jeopardize the reliability level of a system. Due to its importance, outliers must be appropriately treated to ensure the normal operation of a system. System models are usually developed from certain constraints. Constraints play a central role in model precision and validity. In this work, we present a detailed investigation of the effects of the hard and soft constraints on the reliability of a measurement system model. Hard constraints represent a case in which there exist known functional relations between the unknown model parameters, whereas the soft constraints are employed where such functional relations can be slightly violated depending on their uncertainty. The results highlighted that the success rate of identifying an outlier for the case of hard constraints is larger than soft constraints. This suggested that hard constraints be used in the stage of pre-processing data for the purpose of identifying and removing possible outlying measurements. After identifying and removing possible outliers, one should set up the soft constraints to propagate their uncertainties to the model parameters during the data processing.

Metabolomics Comparison of Hanwoo (Bos taurus coreanae) Biofluids Using Proton Nuclear Magnetic Resonance Spectroscopy

The aim of this study was to identify the metabolomic profiles of rumen fluid, serum, and urine from Hanwoo (Bos taurus coreanae), using proton nuclear magnetic resonance (1H-NMR) spectroscopy. In all, 189, 110, and 188 metabolites were identified in rumen fluid, serum, and urine, and 107, 49, and 99 were quantified, respectively. Organic acids, carbohydrates, and aliphatic acyclic compound metabolites were present at the highest concentrations in rumen fluid, serum, and urine, respectively. In addition, acetate, glucose, and urea were the most highly concentrated individual metabolites in rumen fluid, serum, and urine, respectively. In all, 77 metabolites were commonly identified, and 19 were quantified across three biofluids. Metabolic pathway analysis showed that the common quantified metabolites could provide relevant information about three main metabolic pathways, phenylalanine, tyrosine, and tryptophan biosynthesis; caffeine metabolism; and histidine metabolism. These results can be useful as reference values for future metabolomic research on Hanwoo biofluids in Korea.