Rumen methanogens are major sources of anthropogenic methane emissions, and these archaea are targets in strategies aimed at reducing methane emissions. Here we show that the poorly characterised Thermoplasmata archaea in bovine rumen are methylotrophic methanogens and that they are reduced upon dietary supplementation with rapeseed oil in lactating cows. In a metatranscriptomic survey, Thermoplasmata 16S rRNA and methyl-coenzyme M reductase (mcr) transcripts decreased concomitantly with mRNAs of enzymes involved in methanogenesis from methylamines that were among the most abundant archaeal transcripts, indicating that these Thermoplasmata degrade methylamines. Their methylotrophic methanogenic lifestyle was corroborated by in vitro incubations, showing enhanced growth of these organisms upon methylamine supplementation paralleled by elevated methane production. The Thermoplasmata have a high potential as target in future strategies to mitigate methane emissions from ruminant livestock. Our findings and the findings of others also indicate a wider distribution of methanogens than previously anticipated.

Enteric methane (CH₄) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH₄ is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH₄ production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH₄ production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH₄ production (g/day per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross‐validate their performance; and (4) assess the trade‐off between availability of on‐farm inputs and CH₄ prediction accuracy. The intercontinental database covered Europe (EU), the United States (US), and Australia (AU). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6%, 14.7%, and 19.8% for intercontinental, EU, and United States regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH₄ production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH₄ emission conversion factors for specific regions are required to improve CH₄ production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary neutral detergent fiber (NDF) concentration, improve the prediction. For enteric CH₄ yield and intensity prediction, information on milk yield and composition is required for better estimation.

Ruminant production systems are important contributors to anthropogenic methane (CH4) emissions, but there are large uncertainties in national and global livestock CH4 inventories. Sources of uncertainty in enteric CH4 emissions include animal inventories, feed dry matter intake (DMI), ingredient and chemical composition of the diets, and CH4 emission factors. There is also significant uncertainty associated with enteric CH4 measurements. The most widely used techniques are respiration chambers, the sulfur hexafluoride (SF6) tracer technique, and the automated head-chamber system (GreenFeed; C-Lock Inc., Rapid City, SD). All 3 methods have been successfully used in a large number of experiments with dairy or beef cattle in various environmental conditions, although studies that compare techniques have reported inconsistent results. Although different types of models have been developed to predict enteric CH4 emissions, relatively simple empirical (statistical) models have been commonly used for inventory purposes because of their broad applicability and ease of use compared with more detailed empirical and process-based mechanistic models. However, extant empirical models used to predict enteric CH4 emissions suffer from narrow spatial focus, limited observations, and limitations of the statistical technique used. Therefore, prediction models must be developed from robust data sets that can only be generated through collaboration of scientists across the world. To achieve high prediction accuracy, these data sets should encompass a wide range of diets and production systems within regions and globally. Overall, enteric CH4 prediction models are based on various animal or feed characteristic inputs but are dominated by DMI in one form or another. As a result, accurate prediction of DMI is essential for accurate prediction of livestock CH4 emissions. Analysis of a large data set of individual dairy cattle data showed that simplified enteric CH4 prediction models based on DMI alone or DMI and limited feed- or animal-related inputs can predict average CH4 emission with a similar accuracy to more complex empirical models. These simplified models can be reliably used for emission inventory purposes.

Muscle protein turnover following resistance exercise and amino acid availability are relatively well described. By contrast, the beneficial effects of different sources of intact proteins in relation to exercise need further investigation. Our objective was to compare muscle anabolic responses to a single bolus intake of whey or casein after performance of heavy resistance exercise. Young male individuals were randomly assigned to participate in two protein trials ( n = 9) or one control trial ( n = 8). Infusion of l-[1-¹³C]leucine was carried out, and either whey, casein (0.3 g/kg lean body mass), or a noncaloric control drink was ingested immediately after exercise. l-[1-¹³C]leucine-labeled whey and casein were used while muscle protein synthesis (MPS) was assessed. Blood and muscle tissue samples were collected to measure systemic hormone and amino acid concentrations, tracer enrichments, and myofibrillar protein synthesis. Western blots were used to investigate the Akt signaling pathway. Plasma insulin and branched-chain amino acid concentrations increased to a greater extent after ingestion of whey compared with casein. Myofibrillar protein synthesis was equally increased 1–6 h postexercise after whey and casein intake, both of which were higher compared with control ( P < 0.05). Phosphorylation of Akt and p70^S6Kwas increased after exercise and protein intake ( P < 0.05), but no differences were observed between the types of protein except for total 4E-BP1, which was higher after whey intake than after casein intake ( P < 0.05). In conclusion, whey and casein intake immediately after resistance exercise results in an overall equal MPS response despite temporal differences in insulin and amino acid concentrations and 4E-BP1.

Ruminant livestock is a major source of the potent greenhouse gas methane. The complex rumen microbiome, consisting of bacteria, archaea, and microbial eukaryotes, facilitates anaerobic plant biomass degradation in the cow rumen, leading to methane emissions. Using an integrated approach combining multidomain quantitative metatranscriptomics with gas and volatile fatty acid (VFA) profiling, we aimed at obtaining the most comprehensive picture of the active rumen microbiome during feed degradation to date. Bacterial, archaeal, and eukaryotic biomass, but also methane emissions and VFA concentrations, increased drastically within an hour after feed intake. mRNA profiling revealed a dynamic response of carbohydrate-active enzyme transcripts, transcripts involved in VFA production and methanogenesis. While the relative abundances of functional transcripts did not mirror observed processes, such as methane emissions, transformation to mRNA abundance per gram of rumen fluid echoed ruminant processes. The microbiome composition was highly individual, with, e.g., ciliate, Neocallimastigaceae, Prevotellaceae, Succinivibrionaceae, and Fibrobacteraceae abundances differing between cows. Microbiome individuality was accompanied by inter- and intradomain multifunctional redundancy among microbiome members during feed degradation. This likely enabled the robust performance of the anaerobic degradation process in each rumen. Neocallimastigaceae and ciliates contributed an unexpectedly large share of transcripts for cellulose- and hemicellulose-degrading enzymes, respectively. Methyl-reducing but not CO₂-reducing methanogens were positively correlated with methane emissions. While Methanomassiliicoccales switched from methanol to methylamines as electron acceptors, Methanosphaera became the dominating methanol-reducing methanogen. This study for the first time linked rumen meta-omics with processes and enabled holistic insights into the contribution of all microbiome members to feed degradation. IMPORTANCE Ruminant animals, such as cows, live in a tight symbiotic association with microorganisms, allowing them to feed on otherwise indigestible plant biomass as food sources. Methane is produced as an end product of the anaerobic feed degradation in ruminants and is emitted to the atmosphere, making ruminant animals among the major anthropogenic sources of the potent greenhouse gas methane. Using newly developed quantitative metatranscriptomics for holistic microbiome analysis, we here identified bacterial, archaeal, and eukaryotic key players and the short-term dynamics of the rumen microbiome during anaerobic plant biomass degradation and subsequent methane emissions. These novel insights might pave the way for novel ecologically and economically sustainable methane mitigation strategies, much needed in times of global climate change.