Fungal and ciliate protozoa are the main rumen microbes associated with methane emissions in dairy cattle

Feed efficiency and enteric methane emissions indices are inconsistent with the outcomes of the rumen microbiome composition

The correlation between enteric methane emissions (eME) and feed efficiency (FE) in cattle is linked to the anaerobic fermentation of feedstuffs that occurs in the rumen. Several mathematical indices have been developed to predict feed efficiency and identify low methane emitters in herds. To investigate this, the current study aimed to evaluate the rumen microbial composition in the same group of animals ranked according to six different indices (three indices for FE and three for eME). Thirty-three heifers were ranked into three groups, each consisting of 11 animals, based on FE (feed conversion efficiency - FCE, residual weight gain - RG, and residual feed intake - RFI) and eME indices (production, yield, and intensity). Rumen fluids were collected using a stomach tube and analyzed using 16S rRNA and 18S rRNA, targeting rumen bacteria, archaea, and protozoa. The sequencing analysis revealed that the presence of unique microbial species in the rumen varies across animals ranked by the FE and eME indices. The High RG group harbored 17 unique prokaryotic taxa, while the High FCE group contained only seven. Significant differences existed in the microbial profiles of the animals based on the FE and eME indices. For instance, Raoultibacter was more abundant in the Intermediate RFI group but less so in the Intermediate RG and Intermediate FCE groups. The abundance of Entodinium was higher while Diplodinium was lower in the High FCE group, in contrast to the High RG and High RFI groups. Methanobrevibacter exhibited similar abundances across eME indices. However, the heifers did not demonstrate the same production, yield, and intensity of eME. The present findings underscore the importance of standardizing the FE and eME indices. This standardization is crucial for ensuring consistent and reliable assessments of the composition and function of the rumen microbiome across different herds.

The oral microbiome as a proxy for feed intake in dairy cattle

Genetic material from rumen microorganisms can be found within the oral cavity, and hence there is potential in using the oral microbiome as a proxy of the ruminal microbiome. Feed intake (FI) influences the composition of rumen microbiota and might directly influence the oral microbiome in dairy cattle. Ruminal content samples (RS) from 29 cows were collected at the beginning of the study and also 42 d later (RS0 and RS42, respectively). Additionally, 18 oral samples were collected through buccal swabbing at d 42 (OS42) from randomly selected cows. Samples were used to characterize and compare the taxonomy and functionality of the oral microbiome using nanopore sequencing and to evaluate the feasibility of using the oral microbiome to estimate FI. Up to 186 taxonomical features were found differentially abundant (DA) between RS and OS42. Similar results were observed when comparing OS42 to RS collected on different days. Microorganisms associated with the liquid fraction of the rumen were less abundant in OS42 because these were probably swallowed after regurgitation. Up to 1,102 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were found to be DA between RS and OS42, and these results differed when comparing time of collection, but DA KEGG pathways were mainly associated with metabolism in both situations. Models based on the oral microbiome and rumen microbiome differed in their selection of microbial groups and biological pathways to predict FI. In the rumen, fiber-associated microorganisms are considered suitable indicators of FI. In contrast, biofilm formers like Gammaproteobacteria or Bacteroidia classes are deemed appropriate proxies for predicting FI from oral samples. Models from RS exhibited some predictive ability to estimate FI, but oral samples substantially outperformed them. The best lineal model to estimate FI was obtained with the relative abundance of taxonomical feature at genera level, achieving an average R2 = 0.88 within the training data, and a root mean square error of 3.46 ± 0.83 (±SD) kg of DM, as well as a Pearson correlation coefficient between observed and estimated FI of 0.48 ± 0.30 in the test data. The results from this study suggest that oral microbiome has potential to predict FI in dairy cattle, and it encourages validating this potential in other populations.

Ruminal microbiome changes across lactation in primiparous Holstein cows with varying methane intensity: Heritability assessment

Methane is a potent greenhouse gas produced during the ruminal fermentation and is associated with a loss of feed energy. Therefore, efforts to reduce methane emissions have been ongoing in the last decades. Methane production is highly influenced by factors such as the ruminal microbiome and host genetics. Previous studies have proposed to use the ruminal microbiome to reduce long-term methane emissions, as ruminal microbiome composition is a moderately heritable trait and genetic improvement accumulates over time. Lactation stage is another important factor that might influence methane production but potential associations with the ruminal microbiome have not been evaluated previously. This study sought to examine the changes in ruminal microbiome over the lactation period of primiparous Holstein cows differing in methane intensity and estimate the heritability of the abundance of relevant microorganisms. Ruminal content samples from 349 primiparous Holstein cows with 14 - 378 d in milk were collected from May 2018 to June 2019. Methane intensity (MI) of each cow was calculated as methane concentration/milk yield. Up to 64 taxonomic features (TF) from 20 phyla had a significant differential abundance between cows with low and high MI early in lactation, 16 TF during mid lactation, and none late in lactation. Taxonomical features within the Firmicutes, Proteobacteria, Melainabacteria, Cyanobacteria, Bacteroidetes and Actinobacteria phyla were associated to low MI, whereas eukaryotic TF and those within the Euryarchaeota, Verrucomicrobia, Kiritimatiellaeota, Lentisphaerae phyla were associated to high MI. Out of the 60 TF that were found to be differentially abundant between early and late lactation in cows with low MI, 56 TF were also significant when cows with low and high MI were compared in the first third of the lactation. In general, microbes associated with low MI were more abundant early in lactation (e.g., Acidaminococcus, Aeromonas and Weimeria genera) and showed low to moderate heritabilities (0.03 to 0.33). These results suggest some potential to modulate the rumen microbiome composition through selective breeding for lower MI. Differences in the ruminal microbiome of cows with extreme MI levels likely result from variations in the ruminal physiology of these cows and were more noticeable early in lactation probably due to important interactions between the host phenotype and environmental factors associated to that period. Our results suggest that the ruminal microbiome evaluated early in lactation may be more precise for MI difference, and hence, this should be considered to optimize sampling periods to establish a reference population in genomic selection scenarios.

Rumen microbes, enzymes, metabolisms, and application in lignocellulosic waste conversion - A comprehensive review

The rumen of ruminants is a natural anaerobic fermentation system that efficiently degrades lignocellulosic biomass and mainly depends on synergistic interactions between multiple microbes and their secreted enzymes. Ruminal microbes have been employed as biomass waste converters and are receiving increasing attention because of their degradation performance. To explore the application of ruminal microbes and their secreted enzymes in biomass waste, a comprehensive understanding of these processes is required. Based on the degradation capacity and mechanism of ruminal microbes and their secreted lignocellulose enzymes, this review concentrates on elucidating the main enzymatic strategies that ruminal microbes use for lignocellulose degradation, focusing mainly on polysaccharide metabolism-related gene loci and cellulosomes. Hydrolysis, acidification, methanogenesis, interspecific H2 transfer, and urea cycling in ruminal metabolism are also discussed. Finally, we review the research progress on the conversion of biomass waste into biofuels (bioethanol, biohydrogen, and biomethane) and value-added chemicals (organic acids) by ruminal microbes. This review aims to provide new ideas and methods for ruminal microbe and enzyme applications, biomass waste conversion, and global energy shortage alleviation.

Methane prediction equations including genera of rumen bacteria as predictor variables improve prediction accuracy

Methane (CH4) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH4. To address this limitation, we developed novel CH4 prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH4 production (g CH4/animal·d, ANIM-B models) and CH4 yield (g CH4/kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lin's concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH4 prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH4 emissions from sheep, providing valuable insights for future research and mitigation strategies.

Evolving understanding of rumen methanogen ecophysiology

Production of methane by methanogenic archaea, or methanogens, in the rumen of ruminants is a thermodynamic necessity for microbial conversion of feed to volatile fatty acids, which are essential nutrients for the animals. On the other hand, methane is a greenhouse gas and its production causes energy loss for the animal. Accordingly, there are ongoing efforts toward developing effective strategies for mitigating methane emissions from ruminant livestock that require a detailed understanding of the diversity and ecophysiology of rumen methanogens. Rumen methanogens evolved from free-living autotrophic ancestors through genome streamlining involving gene loss and acquisition. The process yielded an oligotrophic lifestyle, and metabolically efficient and ecologically adapted descendants. This specialization poses serious challenges to the efforts of obtaining axenic cultures of rumen methanogens, and consequently, the information on their physiological properties remains in most part inferred from those of their non-rumen representatives. This review presents the current knowledge of rumen methanogens and their metabolic contributions to enteric methane production. It also identifies the respective critical gaps that need to be filled for aiding the efforts to mitigate methane emission from livestock operations and at the same time increasing the productivity in this critical agriculture sector.

The effects of Selenohomolanthionine supplementation on the rumen eukaryotic diversity of Shaanbei white cashmere wether goats

Selenium (Se) is an important microelement for animal health. However, the knowledge about the effects of Se supplementation on rumen eukaryotic community remains less explored. In this study, the ruminal eukaryotic diversity in three months old Shaanbei white cashmere wether goats, with body weight (26.18 ± 2.71) kg, fed a basal diet [0.016 mg/kg Se dry matter (DM), control group (CG)] were compared to those animals given basal diet supplemented with different levels of organic Se in the form of Selenohomolanthionine (SeHLan), namely low Se group (LSE, 0.3 mg/kg DM), medium Se group (MSE, 0.6 mg/kg Se DM) and high Se group (HSE, 1.2 mg/kg DM) using 18S rRNA amplicon sequencing. Illumina sequencing generated 2,623,541 reads corresponding to 3123 operational taxonomic units (OTUs). Taxonomic analysis revealed that Eukaryota (77.95%) and Fungi (14.10%) were the dominant eukaryotic kingdom in all samples. The predominant rumen eukaryotic phylum was found to be Ciliophora (92.14%), while fungal phyla were dominated by Ascomycota (40.77%), Basidiomycota (23.77%), Mucoromycota (18.32%) and unidentified_Fungi (13.89%). The dominant eukaryotic genera were found to be Entodinium (55.44%), Ophryoscolex (10.51%) and Polyplastron (10.19%), while the fungal genera were dominanted by Mucor (15.39%), Pichia (9.88%), Aspergillu (8.24%), Malassezia (7.73%) and unidentified_Neocallimastigaceae (7.72%). The relative abundance of eukaryotic genera Ophryoscolex, Enoploplastron and fungal genus Mucor were found to differ significantly among the four treatment groups (P < 0.05). Moreover, Spearman correlation analysis revealed that the ciliate protozoa and fungi were negatively correlated with each other. The results of this study provided newer information about the effects of Se on rumen eukaryotic diversity patterns using 18s rRNA high-throughput sequencing technology.

Review: Diving into the cow hologenome to reduce methane emissions and increase sustainability

Interest on methane emissions from livestock has increased in later years as it is an anthropogenic greenhouse gas with an important warming potential. The rumen microbiota has a large influence on the production of enteric methane. Animals harbour a second genome consisting of microbes, collectively referred to as the "microbiome". The rumen microbial community plays an important role in feed digestion, feed efficiency, methane emission and health status. This review recaps the current knowledge on the genetic control that the cow exerts on the rumen microbiota composition. Heritability estimates for the rumen microbiota composition range between 0.05 and 0.40 in the literature, depending on the taxonomical group or microbial gene function. Variables depicting microbial diversity or aggregating microbial information are also heritable within the same range. This study includes a genome-wide association analysis on the microbiota composition, considering the relative abundance of some microbial taxa previously associated to enteric methane in dairy cattle (Archaea, Dialister, Entodinium, Eukaryota, Lentisphaerae, Methanobrevibacter, Neocallimastix, Prevotella and Stentor). Host genomic regions associated with the relative abundance of these microbial taxa were identified after Benjamini-Hoschberg correction (Padj < 0.05). An in-silico functional analysis using FUMA and DAVID online tools revealed that these gene sets were enriched in tissues like brain cortex, brain amigdala, pituitary, salivary glands and other parts of the digestive system, and are related to appetite, satiety and digestion. These results allow us to have greater knowledge about the composition and function of the rumen microbiome in cattle. The state-of-the art strategies to include methane traits in the selection indices in dairy cattle populations is reviewed. Several strategies to include methane traits in the selection indices have been studied worldwide, using bioeconomical models or economic functions under theoretical frameworks. However, their incorporation in the breeding programmes is still scarce. Some potential strategies to include methane traits in the selection indices of dairy cattle population are presented. Future selection indices will need to increase the weight of traits related to methane emissions and sustainability. This review will serve as a compendium of the current state of the art in genetic strategies to reduce methane emissions in dairy cattle.

Modulation of milking performance, methane emissions, and rumen microbiome on dairy cows by dietary supplementation of a blend of essential oils

Cattle represent a high contribution of the livestock's greenhouse gas emissions, mainly in the form of methane. Essential oils are a group of plant secondary metabolites obtained from volatile fractions of plants that have been shown to exert changes in the rumen fermentation and may alter feed efficiency and to reduce methane production. The objective of this study was to investigate the effect on rumen microbial population, CH₄ emissions and milking performance of a mixture of essential oils (Agolin Ruminant, Switzerland) incorporated daily in the ration of dairy cattle. Forty Holstein cows (644 ± 63.5 kg of BW producing 41.2 ± 6.44 kg/d of milk with 190 ± 28.3 DIM) were divided into two treatments (n = 20) for 13 wk and housed in a single pen equipped with electronic feeding gates to control access to feed and monitor individual DM intake (DMI) on a daily basis. Treatments consisted of no supplementation (Control) or supplementation of 1 g/d of a blend of essential oils (BEOs) fed in the TMR. Individual milk production was recorded using electronic milk meters on a daily basis. Methane emissions were recorded using sniffers at the exit of the milking parlour. At day 64 of the study, a sample of rumen fluid was collected from 12 cows per treatment after the morning feeding using a stomach tube. There were no differences in DMI, milk yield, or milk composition between the two treatments. However, cows on BEO exhaled less CH₄ (444 ± 12.5 l/d) than cows on Control (479 ± 12.5 l/d), and exhaled less (P < 0.05) CH₄/kg of DM consumed (17.6 vs 20.1 ± 0.53 l/kg, respectively) from the first week of study, with no interaction with time, which suggests a fast action of BEO of CH₄ emissions. Rumen relative abundance of Entodonium increased, and those of Fusobacteria, Chytridiomycota, Epidinium, and Mogibacterium decreased in BEO compared with Control cows. Supplementing 1 g/d of BEO reduces CH₄ emissions on absolute terms (l/d) and diminishes the amount of CH₄ produced by unit of DM consumed by cows relatively soon after the first supplementation, and the effect is sustained over time without impacting intake or milking performance.

Could propionate formation be used to reduce enteric methane emission in ruminants?

To meet the increasing demand for meat and milk, the livestock industry has to increase its production. Without improving its efficiency, increased livestock, especially ruminant animals, will worsen the environmental damage, mainly from enteric CH₄ emission. Enteric CH₄ emission from ruminants not only exacerbates the global greenhouse effect but also reduces feed energy efficiency for the animals. The rumen disposes of metabolic hydrogen ([H]) primarily through methanogenesis and propionate formation. Theoretically, redirecting [H] from methanogenesis to propionate formation to reduce CH₄ production could be a promising method for reducing greenhouse gas emission from ruminants, and may also increase animal productivity. However, the feasibility of such a shifting has never been synthetically discussed. Thus, the objectives of this review are to provide a brief overview of the biochemical pathways for disposal of H₂ in the rumen, to analyze current feeding strategies that potentially promote propionate formation and their effects on methanogenesis, and to deliberate the challenge and opportunity associated with propionate formation as a sink to store the [H] shifting from enteric CH₄ inhibition.

Fungi and cercozoa regulate methane-associated prokaryotes in wetland methane emissions

Wetlands are natural sources of methane (CH₄) emissions, providing the largest contribution to the atmospheric CH₄ pool. Changes in the ecohydrological environment of coastal salt marshes, especially the surface inundation level, cause instability in the CH₄ emission levels of coastal ecosystems. Although soil methane-associated microorganisms play key roles in both CH₄ generation and metabolism, how other microorganisms regulate methane emission and their responses to inundation has not been investigated. Here, we studied the responses of prokaryotic, fungal and cercozoan communities following 5 years of inundation treatments in a wetland experimental site, and molecular ecological networks analysis (MENs) was constructed to characterize the interdomain relationship. The result showed that the degree of inundation significantly altered the CH₄ emissions, and the abundance of the pmoA gene for methanotrophs shifted more significantly than the mcrA gene for methanogens, and they both showed significant positive correlations to methane flux. Additionally, we found inundation significantly altered the diversity of the prokaryotic and fungal communities, as well as the composition of key species in interactions within prokaryotic, fungal, and cercozoan communities. Mantel tests indicated that the structure of the three communities showed significant correlations to methane emissions (p < 0.05), suggesting that all three microbial communities directly or indirectly contributed to the methane emissions of this ecosystem. Correspondingly, the interdomain networks among microbial communities revealed that methane-associated prokaryotic and cercozoan OTUs were all keystone taxa. Methane-associated OTUs were more likely to interact in pairs and correlated negatively with the fungal and cercozoan communities. In addition, the modules significantly positively correlated with methane flux were affected by environmental stress (i.e., pH) and soil nutrients (i.e., total nitrogen, total phosphorus and organic matter), suggesting that these factors tend to positively regulate methane flux by regulating microbial relationships under inundation. Our findings demonstrated that the inundation altered microbial communities in coastal wetlands, and the fungal and cercozoan communities played vital roles in regulating methane emission through microbial interactions with the methane-associated community.

Fungal and ciliate protozoa are the main rumen microbes associated with methane emissions in dairy cattle

Background

Mitigating the effects of global warming has become the main challenge for humanity in recent decades. Livestock farming contributes to greenhouse gas emissions, with an important output of methane from enteric fermentation processes, mostly in ruminants. Because ruminal microbiota is directly involved in digestive fermentation processes and methane biosynthesis, understanding the ecological relationships between rumen microorganisms and their active metabolic pathways is essential for reducing emissions. This study analysed whole rumen metagenome using long reads and considering its compositional nature in order to disentangle the role of rumen microbes in methane emissions.

Results

The β-diversity analyses suggested a subtle association between methane production and overall microbiota composition (0.01 < R² < 0.02). Differential abundance analysis identified 36 genera and 279 KEGGs as significantly associated with methane production (P_adj < 0.05). Those genera associated with high methane production were Eukaryota from Alveolata and Fungi clades, while Bacteria were associated with low methane emissions. The genus-level association network showed 2 clusters grouping Eukaryota and Bacteria, respectively. Regarding microbial gene functions, 41 KEGGs were found to be differentially abundant between low- and high-emission animals and were mainly involved in metabolic pathways. No KEGGs included in the methane metabolism pathway (ko00680) were detected as associated with high methane emissions. The KEGG network showed 3 clusters grouping KEGGs associated with high emissions, low emissions, and not differentially abundant in either. A deeper analysis of the differentially abundant KEGGs revealed that genes related with anaerobic respiration through nitrate degradation were more abundant in low-emission animals.

Conclusions

Methane emissions are largely associated with the relative abundance of ciliates and fungi. The role of nitrate electron acceptors can be particularly important because this respiration mechanism directly competes with methanogenesis. Whole metagenome sequencing is necessary to jointly consider the relative abundance of Bacteria, Archaea, and Eukaryota in the statistical analyses. Nutritional and genetic strategies to reduce CH₄ emissions should focus on reducing the relative abundance of Alveolata and Fungi in the rumen. This experiment has generated the largest ONT ruminal metagenomic dataset currently available.