Short-term measurements and proxies for ruminant methane emissions
Livestock production is the largest anthropogenic contributor to the global methane (CH4) budget, with enteric emissions estimated to account for 87–97 Teragrams of CH4 per year. Methane is a potent greenhouse gas with a global warming potential 28 times that of carbon dioxide and a 12-year atmospheric lifespan. Therefore the need to first accurately measure these emissions from ruminants and subsequently reduce them is becoming of critical importance in recent years. The current methods used to measure these emissions such as the sulfur hexafluoride (SF6) tracer technique and respiration chambers, the latter seen as the ‘gold standard’ in the field, are expensive, low throughput and small scale. Therefore, the use of proxies (indirect indicators/traits) as alternative tools to quantify these emissions in a quick, non-invasive, high throughput, inexpensive and large-scale manner is increasing. With the majority of methanogenesis occurring in the rumen, with a lesser extent in the cecum and colon, and rumen methane production almost exclusively coming from archaea, it is only logical to assume that their concentrations and presence would be directly linked with methane emissions, and that they could therefore be used as proxies. This methane can either be produced via the (i) hydrogenotrophic pathway, being the most widespread pathway and utilising hydrogen (H2) and carbon dioxide (CO2) as precursors, (ii) the acetoclastic pathway, utilising acetate as a precursor and (iii) the methylotrophic pathway, the lesser known pathway, utilising methyl compounds as precursors. Several studies in the field have explored the importance of diets, additives and direct methane inhibitors on both methane production and methanogen population in the rumen in ruminants. Despite these advances, the rumen remains a relatively unknown environment and the utility of methanogens as alternatives to the methods mentioned above remains to be fully assessed. The way in which microbial metabolites are explored has also been debated, with the three principal techniques being nuclear magnetic resonance (NMR), gas chromatography mass spectrometry (GC-MS) and liquid chromatography single-stage mass spectrometry (LC-MS). Of these, NMR possesses several advantages over both GC-MS and LC-MS such as high reproducibility, cost, and its ease of quantification making it a more feasible method to be used for high throughput and large-scale metabolomics studies. Therefore, this project aimed to utilise NMR to explore: 1) the broad general effect of diet on methane and metabolites; 2) the effect of direct methane inhibition using 3-nitrooxypropanol (3-NOP) on methane and metabolites; 3) the stimulation of the methylotrophic methanogenic pathway; and 4) assess the use of NMR in metabolomics-based studies. The first study investigated the effect of three broad diet types: high concentrate, mixed, and high forage, on methane emissions and rumen metabolites. This was done using historical data sets and preserved samples of rumen fluid from studies performed at SRUC for a total of 211 individual animal measurements with varying diets and breed types, coming from 4 previous studies. Methane emissions were significantly lower from the concentrate-based diet when compared to both other diets, due to the higher proportion of starch in the diet. Variation in the major VFA (acetate, butyrate and propionate) was as expected, with acetate and butyrate being more linked to the high forage/mixed diet, and propionate being more linked to the high concentrate diet. Less abundant metabolites present in the carbohydrate, aliphatic and aromatic regions such as amino acids and sugars were more correlated to the concentrate diet, with 3-phenylpropionate (3-PP) being one of the few metabolites correlated to acetate concentrations. Even though methyl compounds (methanol, methylamine) are known to be stimulated by high concentrate diets, our results indicated no significant change in their concentrations between the diets. Predictive ability of methane emissions was in line with previous studies when metabolites were used as predictors with an R2 of 0.57. However, when increasing the variables used in the prediction, the R2 increased to 0.70, indicating how external factors, especially DMI, remain of key importance when predicting methane emissions. The second study investigated the effect of a legume-based diet (red clover; Trifolium pratense) in comparison with a non-legume (grass) silage to assess its effects on methane, metabolites and metabolites related to the methylotrophic methanogenic pathway. A crossover-design was implemented, with 18 animals receiving the experimental diets. Methane emissions (g/kg DMI) were significantly lower from the animals fed the red clover diet. In total, 42 metabolites were identified through NMR analysis. A PLS-DA was used to determine which metabolites were important to distinguish between diets, and a PLS regression analysis was used to see which metabolites were more strongly associated with the variation in methane emissions. The red clover diet did not appear to have a significant effect on the metabolites related to the methylotrophic methanogenic pathway, however dimethylamine proved to be an important metabolite in the PLS-DA analysis to differentiate between diets. The PLS indicated that diet and DMI are key factors, in addition to metabolite abundance, to help explain the variation in methane emissions. Finally, in the third study, the effect of direct methane inhibition using the methane inhibitor 3-NOP was investigated using an automated continuous rumen in vitro incubation system (ACRIS). Two different experimental runs were completed, each consisting of two control vessels and two treatment vessels, with results being very similar between runs. The control vessels were pulse dosed daily with 1,2-propanediol and the treated vessels were pulse dosed daily with 3-NOP. A pulse dose of 7.5 µmol/day 3-NOP elicited a ~25% constant inhibition profile. The inhibition of methane was accompanied by peaks of hydrogen emissions. Sampling was undertaken “before” (before feeding and dosing) and “after” (after feeding and dosing), and metabolite concentrations appeared to be more closely related in the treated and control vessels with the “before” samples, whereas a greater variation was noted in the “after” samples. In conclusion, it appears that methane inhibition, whether it be from dietary modifications or direct inhibition via an inhibitor, alters the rumen metabolite community. Despite the fact that animal and dietary co-factors, especially DMI, influenced and improved model prediction, the current studies yielded promising results relating to the use of rumen metabolites, and possible future identification of metabolite biomarkers as proxies for methane emissions, and NMR proved to be a good tool for the detection and identification of these rumen metabolites.