Bayesian modeling reveals host genetics associated with rumen microbiota jointly influence methane emission in dairy cows

Network analyses unraveled the complex interactions in the rumen microbiota associated with methane emission in dairy cattle

BACKGROUND

Methane emissions from livestock, particularly from dairy cattle, represent a significant source of greenhouse gas, contributing to the global climate crisis. Understanding the complex interactions within the rumen microbiota that influence methane emissions is crucial for developing effective mitigation strategies.

RESULTS

This study employed Weighted Gene Co-expression Network Analysis to investigate the complex interactions within the rumen microbiota that influence methane emissions. By integrating extensive rumen microbiota sequencing data with precise methane emission measurements in 750 Holstein dairy cattle, our research identified distinct microbial communities and their associations with methane production. Key findings revealed that the blue module from network analysis was significantly correlated (0.45) with methane emissions. In this module, taxa included the genera Prevotella and Methanobrevibactor, along with species such as Prevotella brevis, Prevotella ruminicola, Prevotella baroniae, Prevotella bryantii, Lachnobacterium bovis, and Methanomassiliicoccus luminyensis are the key components to drive the complex networks. However, the absence of metagenomics sequencing is difficult to reveal the deeper taxa level and functional profiles.

CONCLUSIONS

The application of Weighted Gene Co-expression Network Analysis provided a comprehensive understanding of the microbiota-methane emission relationship, serving as an innovative approach for microbiota-phenotype association studies in cattle. Our findings underscore the importance of microbiota-trait and microbiota-microbiota associations related to methane emission in dairy cattle, contributing to a systematic understanding of methane production in cattle. This research offers key information on microbial management for mitigating environmental impact on the cattle population.

Loss of Myostatin leads to low production of CH4 by altering rumen microbiota and metabolome in cattle

Myostatin (MSTN) is a protein that plays a crucial role in regulating skeletal muscle development. Despite the known benefits of MSTN mutant cattle for increasing beef production, their potential impact on CH4 emissions has not been quantified. The study comparing wild-type (WT) cattle to MSTN-knockout (MSTN-KO) cattle revealed that CH4 production was lower. Macrogenomic analysis revealed a significant decrease in rumen archaea, with reduced Richness indices (P = 0.036). The MSTN-KO cattle also showed altered archaea distribution and composition at different taxonomic levels. LEfSe results showed changes in 21 methanogenic archaea clades, with obligately hydrogen (H2)-dependent methylotrophs Candidatus Methanoplasma termitum species belonging to Methanomassiliicoccales order demonstrating the most significant decrease. Rumen metabolites revealed a decrease in the ratio of acetate to propionate, indicating a shift in rumen fermentation pattern towards propionate fermentation. Additionally, the changing trend of methanogenic archaea is consistent with the evolution of methanogens, and this is correlated with the higher levels of linoleic acid in the rumen of MSTN-KO cattle. Linoleic acid affects the utilization of H2 by methanogenic archaea, leading to a reduction in obligately H2-dependent methylotrophs. Our study suggests that MSTN-KO cattle have potential as an economically and ecologically benign breed for reducing methane emissions.

Methane production related to microbiota in dairy cattle feces

Methane (CH4) emission from livestock feces, led by ruminants, shows a profound impact on global warming. Despite this, we have almost no information on the syntrophy of the intact microbiome metabolisms, from carbohydrates to the one-carbon units, covering multiple stages of ruminant development. In this study, syntrophic effects of polysaccharide degradation and acetate-producing bacteria, and methanogenic archaea were revealed through metagenome-assembled genomes from water saturated dairy cattle feces. Although CH4 is thought to be produced by archaea, more edges, nodes, and balanced interaction types revealed by network analysis provided a closed bacteria-archaea network. The CH4 production potential and pathways were further evaluated through dynamic, thermodynamic and 13C stable isotope analysis. The powerful CH4 production potential benefited from the metabolic flux: classical polysaccharides, soluble sugar (glucose, galactose, lactose), acetate, and CH4 produced via typical acetoclastic methanogenesis. In comparison, a cooperative model dominated by hydrogenotrophic methanogenic archaea presented a weak ability to generate CH4. Our findings comprehensively link carbon and CH4 metabolism paradigm to specific microbial lineages which are shaped related to developmental stages of the dairy cattle, directing influencing global warming from livestock and waste treatment.

A metagenomic catalogue of the ruminant gut archaeome

While the ruminant gut archaeome regulates the gut microbiota and hydrogen balance, it is also a major producer of the greenhouse gas methane. However, ruminant gut archaeome diversity within the gastrointestinal tract (GIT) of ruminant animals worldwide remains largely underexplored. Here, we construct a catalogue of 998 unique archaeal genomes recovered from the GITs of ruminants, utilizing 2270 metagenomic samples across 10 different ruminant species. Most of the archaeal genomes (669/998 = 67.03%) belong to Methanobacteriaceae and Methanomethylophilaceae (198/998 = 19.84%). We recover 47/279 previously undescribed archaeal genomes at the strain level with completeness of >80% and contamination of <5%. We also investigate the archaeal gut biogeography across various ruminants and demonstrate that archaeal compositional similarities vary significantly by breed and gut location. The catalogue contains 42,691 protein clusters, and the clustering and methanogenic pathway analysis reveal strain- and host-specific dependencies among ruminant animals. We also find that archaea potentially carry antibiotic and metal resistance genes, mobile genetic elements, virulence factors, quorum sensors, and complex archaeal viromes. Overall, this catalogue is a substantial repository for ruminant archaeal recourses, providing potential for advancing our understanding of archaeal ecology and discovering strategies to regulate methane production in ruminants.

Unraveling the genetic basis of methane emission in dairy cattle: a comprehensive exploration and breeding approach to lower methane emissions

Ruminant animals, such as dairy cattle, produce CH4, which contributes to global warming emissions and reduces dietary energy for the cows. While the carbon foot print of milk production varies based on production systems, milk yield and farm management practices, enteric fermentation, and manure management are major contributors togreenhouse gas emissions from dairy cattle. Recent emerging evidence has revealed the existence of genetic variation for CH4 emission traits among dairy cattle, suggests their potential inclusion in breeding goals and genetic selection programs. Advancements in high-throughput sequencing technologies and analytical techniques have enabled the identification of potential metabolic biomarkers, candidate genes, and SNPs linked to methane emissions. Indeed, this review critically examines our current understanding of carbon foot print in milk production, major emission sources, rumen microbial community and enteric fermentation, and the genetic architecture of methane emission traits in dairy cattle. It also emphasizes important implications for breeding strategies aimed at halting methane emissions through selective breeding, microbiome driven breeding, breeding for feed efficiency, and breeding by gene editing.

Revealing host genome-microbiome networks underlying feed efficiency in dairy cows

Ruminants have the ability to digest human-inedible plant materials, due to the symbiotic relationship with the rumen microbiota. Rumen microbes supply short chain fatty acids, amino acids, and vitamins to dairy cows that are used for maintenance, growth, and lactation functions. The main goal of this study was to investigate gene-microbiome networks underlying feed efficiency traits by integrating genotypic, microbial, and phenotypic data from lactating dairy cows. Data consisted of dry matter intake (DMI), net energy secreted in milk, and residual feed intake (RFI) records, SNP genotype, and 16S rRNA rumen microbial abundances from 448 mid-lactation Holstein cows. We first assessed marginal associations between genotypes and phenotypic and microbial traits through genomic scans, and then, in regions with multiple significant hits, we assessed gene-microbiome-phenotype networks using causal structural learning algorithms. We found significant regions co-localizing the rumen microbiome and feed efficiency traits. Interestingly, we found three types of network relationships: (1) the cow genome directly affects both rumen microbial abundances and feed efficiency traits; (2) the cow genome (Chr3: 116.5 Mb) indirectly affects RFI, mediated by the abundance of Syntrophococcus, Prevotella, and an unknown genus of Class Bacilli; and (3) the cow genome (Chr7: 52.8 Mb and Chr11: 6.1-6.2 Mb) affects the abundance of Rikenellaceae RC9 gut group mediated by DMI. Our findings shed light on how the host genome acts directly and indirectly on the rumen microbiome and feed efficiency traits and the potential benefits of the inclusion of specific microbes in selection indexes or as correlated traits in breeding programs. Overall, the multistep approach described here, combining whole-genome scans and causal network reconstruction, allows us to reveal the relationship between genome and microbiome underlying dairy cow feed efficiency.

Unique rumen micromorphology and microbiota-metabolite interactions: features and strategies for Tibetan sheep adaptation to the plateau

The rumen microbiota-a symbiont to its host and consists of critical functional substances-plays a vital role in the animal body and represents a new perspective in the study of adaptive evolution in animals. This study used Slide Viewer slicing analysis system, gas chromatography, RT-qPCR and other technologies, as well as 16S and metabolomics determination methods, to measure and analyze the microstructure of rumen epithelium, rumen fermentation parameters, rumen transport genes, rumen microbiota and metabolites in Tibetan sheep and Hu sheep. The results indicate that the rumen nipple height and cuticle thickness of Tibetan sheep are significantly greater than those of Hu sheep (p < 0.01) and that the digestion and absorption of forage are greater. The levels of carbohydrate metabolism, lipid metabolism, and protein turnover were increased in Tibetan sheep, which enabled them to ferment efficiently, utilize forage, and absorb metabolic volatile fatty acids (VFAs). Tibetan sheep rumen metabolites are related to immune function and energy metabolism, which regulate rumen growth and development and gastrointestinal homeostasis. Thus, compared with Hu sheep, Tibetan sheep have more rumen papilla and cuticle corneum, and the synergistic effect of the microbiota and its metabolites is a characteristic and strategy for adapting to high-altitude environments.

Integrating genome- and transcriptome-wide association studies to uncover the host-microbiome interactions in bovine rumen methanogenesis

The ruminal microbiota generates biogenic methane in ruminants. However, the role of host genetics in modifying ruminal microbiota-mediated methane emissions remains mysterious, which has severely hindered the emission control of this notorious greenhouse gas. Here, we uncover the host genetic basis of rumen microorganisms by genome- and transcriptome-wide association studies with matched genome, rumen transcriptome, and microbiome data from a cohort of 574 Holstein cattle. Heritability estimation revealed that approximately 70% of microbial taxa had significant heritability, but only 43 genetic variants with significant association with 22 microbial taxa were identified through a genome-wide association study (GWAS). In contrast, the transcriptome-wide association study (TWAS) of rumen microbiota detected 28,260 significant gene-microbe associations, involving 210 taxa and 4652 unique genes. On average, host genetic factors explained approximately 28% of the microbial abundance variance, while rumen gene expression explained 43%. In addition, we highlighted that TWAS exhibits a strong advantage in detecting gene expression and phenotypic trait associations in direct effector organs. For methanogenic archaea, only one significant signal was detected by GWAS, whereas the TWAS obtained 1703 significant associated host genes. By combining multiple correlation analyses based on these host TWAS genes, rumen microbiota, and volatile fatty acids, we observed that substrate hydrogen metabolism is an essential factor linking host-microbe interactions in methanogenesis. Overall, these findings provide valuable guidelines for mitigating methane emissions through genetic regulation and microbial management strategies in ruminants.

The fecal microbiota of Holstein cows is heritable and genetically correlated to dairy performances

The fecal microbiota of ruminants constitutes a diversified community that has been phenotypically associated with a variety of host phenotypes, such as production and health. To gain a better understanding of the complex and interconnected factors that drive the fecal bacterial community, we have aimed to estimate the genetic parameters of the diversity and composition of the fecal microbiota, including heritabilities, genetic correlations among taxa, and genetic correlations between fecal microbiota features and host phenotypes. To achieve this, we analyzed a large population of 1,875 Holstein cows originating from 144 French commercial herds and routinely recorded for production, somatic cell score, and fertility traits. Fecal samples were collected from the animals and subjected to 16S rRNA gene sequencing, with reads classified into Amplicon Sequence Variants (ASVs). The estimated α- and β-diversity indices (i.e., Observed Richness, Shannon index, Bray-Curtis and Jaccard dissimilarity matrices) and the abundances of ASVs, genera, families and phyla, normalized by centered-log ratio (CLR), were considered as phenotypes. Genetic parameters were calculated using either univariate or bivariate animal models. Heritabilities estimates, ranging from 0.08 to 0.31 for taxa abundances and β-diversity indices, highlight the influence of the host genetics on the composition of the fecal microbiota. Furthermore, genetic correlations estimated within the microbial community and between microbiota features and host traits reveal the complex networks linking all components of the fecal microbiota together and to their host, thus strengthening the holobiont concept. By estimating the heritabilities of microbiota-associated phenotypes, our study quantifies the impact of the host genetics on the fecal microbiota composition. In addition, genetic correlations between taxonomic groups and between taxa abundances and host performance suggest potential applications for selective breeding to improve host traits or promote a healthier microbiota.

Exploration of mobile genetic elements in the ruminal microbiome of Nellore cattle

Metagenomics has made it feasible to elucidate the intricacies of the ruminal microbiome and its role in the differentiation of animal production phenotypes of significance. The search for mobile genetic elements (MGEs) has taken on great importance, as they play a critical role in the transfer of genetic material between organisms. Furthermore, these elements serve a dual purpose by controlling populations through lytic bacteriophages, thereby maintaining ecological equilibrium and driving the evolutionary progress of host microorganisms. In this study, we aimed to identify the association between ruminal bacteria and their MGEs in Nellore cattle using physical chromosomal links through the Hi-C method. Shotgun metagenomic sequencing and the proximity ligation method ProxiMeta were used to analyze DNA, getting 1,713,111,307 bp, which gave rise to 107 metagenome-assembled genomes from rumen samples of four Nellore cows maintained on pasture. Taxonomic analysis revealed that most of the bacterial genomes belonged to the families Lachnospiraceae, Bacteroidaceae, Ruminococcaceae, Saccharofermentanaceae, and Treponemataceae and mostly encoded pathways for central carbon and other carbohydrate metabolisms. A total of 31 associations between host bacteria and MGE were identified, including 17 links to viruses and 14 links to plasmids. Additionally, we found 12 antibiotic resistance genes. To our knowledge, this is the first study in Brazilian cattle that connect MGEs with their microbial hosts. It identifies MGEs present in the rumen of pasture-raised Nellore cattle, offering insights that could advance biotechnology for food digestion and improve ruminant performance in production systems.

Age-related compositional and functional changes in the adult and breastfed buffalo rumen microbiome

Introduction

The buffalo is an important domestic animal globally, providing milk, meat, and labor to more than 2 billion people in 67 countries. The rumen microorganisms of buffaloes play an indispensable role in enabling the healthy functionality and digestive function of buffalo organisms. Currently, there is a lack of clarity regarding the differences in the composition and function of rumen microorganisms among buffaloes at different growth stages.

Methods

In this study, metagenomics sequencing technology was applied to examine the compositional and functional differences of rumen microorganisms in adult and breastfed buffaloes.

Results

The results revealed that the rumen of adult buffaloes had significantly higher levels of the following dominant genera: Prevotella, UBA1711, RF16, Saccharofermentans, F23-D06, UBA1777, RUG472, and Methanobrevibacter_A. Interestingly, the dominant genera specific to the rumen of adult buffaloes showed a significant positive correlation (correlation>0.5, p-value<0.05) with both lignocellulose degradation-related carbohydrate-active enzymes (CAZymes) and immune signaling pathways activated by antigenic stimulation. The rumen of breastfed buffaloes had significantly higher levels of the following dominant genera: UBA629, CAG- 791, Selenomonas_C, Treponema_D, Succinivibrio, and RC9. Simultaneously, the rumen-dominant genera specific to breastfed buffaloes were significantly positively correlated (correlation>0.5, p-value<0.05) with CAZymes associated with lactose degradation, amino acid synthesis pathways, and antibiotic-producing pathways.

Discussion

This indicates that rumen microorganisms in adult buffaloes are more engaged in lignocellulose degradation, whereas rumen microorganisms in breastfed buffaloes are more involved in lactose and amino acid degradation, as well as antibiotic production. In conclusion, these findings suggest a close relationship between differences in rumen microbes and the survival needs of buffaloes at different growth stages.

Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle

BACKGROUND

Growth rate is an important component of feed conversion efficiency in cattle and varies across the different stages of the finishing period. The metabolic effect of the rumen microbiome is essential for cattle growth, and investigating the genomic and microbial factors that underlie this temporal variation can help maximize feed conversion efficiency at each growth stage.

RESULTS

By analysing longitudinal body weights during the finishing period and genomic and metagenomic data from 359 beef cattle, our study demonstrates that the influence of the host genome on the functional rumen microbiome contributes to the temporal variation in average daily gain (ADG) in different months (ADG1, ADG2, ADG3, ADG4). Five hundred and thirty-three additive log-ratio transformed microbial genes (alr-MG) had non-zero genomic correlations (rg) with at least one ADG-trait (ranging from |0.21| to |0.42|). Only a few alr-MG correlated with more than one ADG-trait, which suggests that a differential host-microbiome determinism underlies ADG at different stages. These alr-MG were involved in ribosomal biosynthesis, energy processes, sulphur and aminoacid metabolism and transport, or lipopolysaccharide signalling, among others. We selected two alternative subsets of 32 alr-MG that had a non-uniform or a uniform rg sign with all the ADG-traits, regardless of the rg magnitude, and used them to develop a microbiome-driven breeding strategy based on alr-MG only, or combined with ADG-traits, which was aimed at shaping the rumen microbiome towards increased ADG at all finishing stages. Combining alr-MG information with ADG records increased prediction accuracy of genomic estimated breeding values (GEBV) by 11 to 22% relative to the direct breeding strategy (using ADG-traits only), whereas using microbiome information, only, achieved lower accuracies (from 7 to 41%). Predicted selection responses varied consistently with accuracies. Restricting alr-MG based on their rg sign (uniform subset) did not yield a gain in the predicted response compared to the non-uniform subset, which is explained by the absence of alr-MG showing non-zero rg at least with more than one of the ADG-traits.

CONCLUSIONS

Our work sheds light on the role of the microbial metabolism in the growth trajectory of beef cattle at the genomic level and provides insights into the potential benefits of using microbiome information in future genomic breeding programs to accurately estimate GEBV and increase ADG at each finishing stage in beef cattle.

Multi-omics analyses reveal rumen microbes and secondary metabolites that are unique to livestock species

Ruminant livestock, including cattle, sheep, goats, and camels, possess a distinctive digestive system with complex microbiota communities critical for feed conversion and secondary metabolite production, including greenhouse gases. Yet, there is limited knowledge regarding the diversity of rumen microbes and metabolites benefiting livestock physiology, productivity, climate impact, and defense mechanisms across ruminant species. In this study, we utilized metataxonomics and metabolomics data from four evolutionarily distinct livestock species, which had fed on diverse plant materials like grass, shrubs, and acacia trees, to uncover the unique signature microbes and secondary metabolites. We established the presence of a distinctive anaerobic fungus called Oontomyces in camels, while cattle exhibited a higher prevalence of unique microbes like Psychrobacter, Anaeromyces, Cyllamyces, and Orpinomyces. Goats hosted Cleistothelebolus, and Liebetanzomyces was unique to sheep. Furthermore, we identified a set of conserved core microbes, including Prevotella, Rickenellaceae, Cladosporium, and Pecoramyces, present in all the ruminants, irrespective of host genetics and dietary composition. This underscores their indispensable role in maintaining crucial physiological functions. Regarding secondary metabolites, camel's rumen is rich in organic acids, goat's rumen is rich in alcohols and hydrocarbons, sheep's rumen is rich in indoles, and cattle's rumen is rich in sesquiterpenes. Additionally, linalool propionate and terpinolene were uniquely found in sheep rumen, while valencene was exclusive to cattle. This may suggest the existence of species-specific microbes and metabolites that require host rumen-microbes' environment balance. These results have implications for manipulating the rumen environment to target specific microbes and secondary metabolite networks, thereby enhancing livestock productivity, resilience, reducing susceptibility to vectors, and environmentally preferred livestock husbandry.IMPORTANCERumen fermentation, which depends on feed components and rumen microbes, plays a crucial role in feed conversion and the production of various metabolites important for the physiological functions, health, and environmental smartness of ruminant livestock, in addition to providing food for humans. However, given the complexity and variation of the rumen ecosystem and feed of these various livestock species, combined with inter-individual differences between gut microbial communities, how they influence the rumen secondary metabolites remains elusive. Using metagenomics and metabolomics approaches, we show that each livestock species has a signature microbe(s) and secondary metabolites. These findings may contribute toward understanding the rumen ecosystem, microbiome and metabolite networks, which may provide a gateway to manipulating rumen ecosystem pathways toward making livestock production efficient, sustainable, and environmentally friendly.

Approaches for predicting dairy cattle methane emissions: from traditional methods to machine learning

Measuring dairy cattle methane (CH4) emissions using traditional recording technologies is complicated and expensive. Prediction models, which estimate CH4 emissions based on proxy information, provide an accessible alternative. This review covers the different modeling approaches taken in the prediction of dairy cattle CH4 emissions and highlights their individual strengths and limitations. Following the guidelines set out by the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA); Scopus, EBSCO, Web of Science, PubMed and PubAg were each queried for papers with titles that contained search terms related to a population of "Bovine," exposure of "Statistical Analysis or Machine Learning," and outcome of "Methane Emissions". The search was executed in December 2022 with no publication date range set. Eligible papers were those that investigated the prediction of CH4 emissions in dairy cattle via statistical or machine learning (ML) methods and were available in English. 299 papers were returned from the initial search, 55 of which, were eligible for inclusion in the discussion. Data from the 55 papers was synthesized by the CH4 emission prediction approach explored, including mechanistic modeling, empirical modeling, and machine learning. Mechanistic models were found to be highly accurate, yet they require difficult-to-obtain input data, which, if imprecise, can produce misleading results. Empirical models remain more versatile by comparison, yet suffer greatly when applied outside of their original developmental range. The prediction of CH4 emissions on commercial dairy farms can utilize any approach, however, the traits they use must be procurable in a commercial farm setting. Milk fatty acids (MFA) appear to be the most popular commercially accessible trait under investigation, however, MFA-based models have produced ambivalent results and should be consolidated before robust accuracies can be achieved. ML models provide a novel methodology for the prediction of dairy cattle CH4 emissions through a diverse range of advanced algorithms, and can facilitate the combination of heterogenous data types via hybridization or stacking techniques. In addition to this, they also offer the ability to improve dataset complexity through imputation strategies. These opportunities allow ML models to address the limitations faced by traditional prediction approaches, as well as enhance prediction on commercial farms.

Alterations of rumen and fecal microbiome in growing beef and dairy steers fed rumen-protected Capsicum oleoresin

The microbiome has been linked to animal health and productivity, and thus, modulating animal microbiomes is becoming of increasing interest. Antimicrobial growth promoters (AGP) were once a common technology used to modulate the microbiome, but regulation and consumer pressure have decreased AGP use in food animals. One alternative to antimicrobial growth promoters are phytotherapeutics, compounds derived from plants. Capsaicin is a compound from the Capsicum genus, which includes chili peppers. Capsaicin has antimicrobial properties and could be used to manipulate the gastrointestinal microbiome of cattle. Both the rumen and fecal microbiomes are essential to cattle health and production, and modulation of either microbiome can affect both cattle health and productivity. We hypothesized that the addition of rumen-protected capsaicin to the diet of cattle would alter the composition of the fecal microbiome, but not the rumen microbiome. To determine the impact of rumen-protected capsaicin in cattle, four Holstein and four Angus steers were fed rumen-protected Capsicum oleoresin at 0 (Control), 5, 10, or 15 mg kg-1 diet dry matter. Cattle were fed in treatment groups in a 4 × 4 Latin Square design with a 21-d adaptation phase and a 7-d sample collection phase. Rumen samples were collected on day 22 at 0-, 2-, 6-, 12-, and 18-h post-feeding, and fecal swabs were collected on the last day of sample collection, day 28, within 1 h of feeding. Sequencing data of the 16s rRNA gene was analyzed using the dada2 pipeline and taxa were assigned using the SILVA database. No differences were observed in alpha diversity among fecal or rumen samples for either breed (P > 0.08) and no difference between groups was detected for either breed in rumen samples or for Angus steers in fecal samples (P > 0.42). There was a difference in beta diversity between treatments in fecal samples of Holstein steers (P < 0.01), however, a pairwise comparison of the treatment groups suggests no difference between treatments after adjusting for multiple comparisons. Therefore, we were unable to observe substantial overall variation in the rumen or fecal microbiomes of steers due to increasing concentrations of rumen-protected capsaicin. We do, however, see a trend toward increased concentrations of capsaicin influencing the fecal microbiome structure of Holstein steers despite this lack of significance.

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.

Forage lignocellulose is an important factor in driving the seasonal dynamics of rumen anaerobic fungi in grazing yak and cattle

Anaerobic fungi (AF) inhabit the gastrointestinal tract of ruminants and play an important role in the degradation of fiber feed. However, limited knowledge is available on seasonal dynamics and inter-species differences in rumen AF community in yak and cattle under natural grazing systems. Using the random forests model, the null model, and structural equation model, we investigated the seasonal dynamics and key driving factors of fiber-associated rumen AF in grazing yak and cattle throughout the year on the Qinghai-Tibet Plateau (QTP). We found that the richness and diversity of rumen AF of grazing yak and cattle in cold season were significantly higher than those in warm season (P < 0.05). We identified 12 rumen AF genera, among which , Cyllamyces, and Orpinomyces were predominant in the rumen of both grazing yak and cattle. LEfSe and random forest analysis showed that Feramyces, Tahromyces, and Buwchfawromyces were important seasonal indicator of rumen AF in grazing yak (P < 0.05), and Caecomyces, Cyllamyces, and Piromyces in grazing cattle (P < 0.05). Null model analysis revealed that the dynamic changes of rumen AF community structure were mainly affected by deterministic factors. Notably, mantel test and structural equation model revealed that forage physical-chemical properties, including dry matter (DM), neutral detergent fiber (NDF), and hemicellulose contents (HC) were the key factors driving the seasonal variations of the rumen AF community (P < 0.05). The results revealed that forage lignocellulose was probably an important factor affecting the seasonal dynamics and inter-species differences of the rumen AF community under natural grazing conditions. IMPORTANCE The seasonal dynamics of rumen anaerobic fungi in nature grazing yak and cattle were determined during cold and warm seasons based on pasture nutritional quality and environmental data sets. The main driving factors of anaerobic fungi in yak and cattle rumen were explored by combining random forest and structural equation models. In addition, the dynamic differences in the composition of the anaerobic fungi community in the yak and cattle in different seasons were characterized. It was found that some rumen anaerobic fungi have contributed to high fiber degradation rate in yak. These novel findings improve our understanding of the association of environmental and dietary seasonal variations with anaerobic fungal community, facilitating yak adaptation to high altitude.

Heritability and recursive influence of host genetics on the rumen microbiota drive body weight variance in male Hu sheep lambs

BACKGROUND

Heritable rumen microbiota is an important modulator of ruminant growth performance. However, no information exists to date on host genetics-rumen microbiota interactions and their association with phenotype in sheep. To solve this, we curated and analyzed whole-genome resequencing genotypes, 16S rumen-microbiota data, and longitudinal body weight (BW) phenotypes from 1150 sheep.

RESULTS

A variance component model indicated significant heritability of rumen microbial community diversity. Genome-wide association studies (GWAS) using microbial features as traits identified 411 loci-taxon significant associations (P < 10-8). We found a heritability of 39% for 180-day-old BW, while also the rumen microbiota likely played a significant role, explaining that 20% of the phenotypic variation. Microbiota-wide association studies (MWAS) and GWAS identified four marker genera (Bonferroni corrected P < 0.05) and five novel genetic variants (P < 10-8) that were significantly associated with BW. Integrative analysis identified the mediating role of marker genera in genotype influencing phenotype and unravelled that the same genetic markers have direct and indirect effects on sheep weight.

CONCLUSIONS

This study reveals a reciprocal interplay among host genetic variations, the rumen microbiota and the body weight traits of sheep. The information obtained provide insights into the diverse microbiota characteristics of rumen and may help in designing precision microbiota management strategies for controlling and manipulating sheep rumen microbiota to increase productivity. Video Abstract.

Combining host and rumen metagenome profiling for selection in sheep: prediction of methane, feed efficiency, production, and health traits

BACKGROUND

Rumen microbes break down complex dietary carbohydrates into energy sources for the host and are increasingly shown to be a key aspect of animal performance. Host genotypes can be combined with microbial DNA sequencing to predict performance traits or traits related to environmental impact, such as enteric methane emissions. Metagenome profiles were generated from 3139 rumen samples, collected from 1200 dual purpose ewes, using restriction enzyme-reduced representation sequencing (RE-RRS). Phenotypes were available for methane (CH4) and carbon dioxide (CO2) emissions, the ratio of CH4 to CH4 plus CO2 (CH4Ratio), feed efficiency (residual feed intake: RFI), liveweight at the time of methane collection (LW), liveweight at 8 months (LW8), fleece weight at 12 months (FW12) and parasite resistance measured by faecal egg count (FEC1). We estimated the proportion of phenotypic variance explained by host genetics and the rumen microbiome, as well as prediction accuracies for each of these traits.

RESULTS

Incorporating metagenome profiles increased the variance explained and prediction accuracy compared to fitting only genomics for all traits except for CO2 emissions when animals were on a grass diet. Combining the metagenome profile with host genotype from lambs explained more than 70% of the variation in methane emissions and residual feed intake. Predictions were generally more accurate when incorporating metagenome profiles compared to genetics alone, even when considering profiles collected at different ages (lamb vs adult), or on different feeds (grass vs lucerne pellet). A reference-free approach to metagenome profiling performed better than metagenome profiles that were restricted to capturing genera from a reference database. We hypothesise that our reference-free approach is likely to outperform other reference-based approaches such as 16S rRNA gene sequencing for use in prediction of individual animal performance.

CONCLUSIONS

This paper shows the potential of using RE-RRS as a low-cost, high-throughput approach for generating metagenome profiles on thousands of animals for improved prediction of economically and environmentally important traits. A reference-free approach using a microbial relationship matrix from log₁₀ proportions of each tag normalized within cohort (i.e., the group of animals sampled at the same time) is recommended for future predictions using RE-RRS metagenome profiles.

Microbiota members from body sites of dairy cows are largely shared within individual hosts throughout lactation but sharing is limited in the herd

BACKGROUND

Host-associated microbes are major determinants of the host phenotypes. In the present study, we used dairy cows with different scores of susceptibility to mastitis with the aim to explore the relationships between microbiota composition and different factors in various body sites throughout lactation as well as the intra- and inter-animal microbial sharing.

RESULTS

Microbiotas from the mouth, nose, vagina and milk of 45 lactating dairy cows were characterized by metataxonomics at four time points during the first lactation, from 1-week pre-partum to 7 months post-partum. Each site harbored a specific community that changed with time, likely reflecting physiological changes in the transition period and changes in diet and housing. Importantly, we found a significant number of microbes shared among different anatomical sites within each animal. This was between nearby anatomic sites, with up to 32% of the total number of Amplicon Sequence Variants (ASVs) of the oral microbiota shared with the nasal microbiota but also between distant ones (e.g. milk with nasal and vaginal microbiotas). In contrast, the share of microbes between animals was limited (< 7% of ASVs shared by more than 50% of the herd for a given site and time point). The latter widely shared ASVs were mainly found in the oral and nasal microbiotas. These results thus indicate that despite a common environment and diet, each animal hosted a specific set of bacteria, supporting a tight interplay between each animal and its microbiota. The score of susceptibility to mastitis was slightly but significantly related to the microbiota associated to milk suggesting a link between host genetics and microbiota.

CONCLUSIONS

This work highlights an important sharing of microbes between relevant microbiotas involved in health and production at the animal level, whereas the presence of common microbes was limited between animals of the herd. This suggests a host regulation of body-associated microbiotas that seems to be differently expressed depending on the body site, as suggested by changes in the milk microbiota that were associated to genotypes of susceptibility to mastitis.

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.

On the holobiont 'predictome' of immunocompetence in pigs

BACKGROUND

Gut microbial composition plays an important role in numerous traits, including immune response. Integration of host genomic information with microbiome data is a natural step in the prediction of complex traits, although methods to optimize this are still largely unexplored. In this paper, we assess the impact of different modelling strategies on the predictive capacity for six porcine immunocompetence traits when both genotype and microbiota data are available.

METHODS

We used phenotypic data on six immunity traits and the relative abundance of gut bacterial communities on 400 Duroc pigs that were genotyped for 70 k SNPs. We compared the predictive accuracy, defined as the correlation between predicted and observed phenotypes, of a wide catalogue of models: reproducing kernel Hilbert space (RKHS), Bayes C, and an ensemble method, using a range of priors and microbial clustering strategies. Combined (holobiont) models that include both genotype and microbiome data were compared with partial models that use one source of variation only.

RESULTS

Overall, holobiont models performed better than partial models. Host genotype was especially relevant for predicting adaptive immunity traits (i.e., concentration of immunoglobulins M and G), whereas microbial composition was important for predicting innate immunity traits (i.e., concentration of haptoglobin and C-reactive protein and lymphocyte phagocytic capacity). None of the models was uniformly best across all traits. We observed a greater variability in predictive accuracies across models when microbiability (the variance explained by the microbiome) was high. Clustering microbial abundances did not necessarily increase predictive accuracy.

CONCLUSIONS

Gut microbiota information is useful for predicting immunocompetence traits, especially those related to innate immunity. Modelling microbiome abundances deserves special attention when microbiability is high. Clustering microbial data for prediction is not recommended by default.

Technology for Carbon Neutral Animal Breeding

Animal breeding techniques are to genetically select highly productive animals with less GHG emission intensity, thereby reducing the number of animals required to produce the same amount of food. Shotgun metagenomics provides a platform to identify rumen microbial communities and genetic markers associated with CH4 emissions, allowing the selection of cattle with less CH4 emissions. Moreover, breeding is a viable option to make real progress towards carbon neutrality with a very high rate of return on investment and a very modest cost per tonne of CO2 equivalents saved regardless of the accounting method. Other high technologies include the use of cloned livestock animals and the manipulation of traits by controlling target genes with improved productivity.

Maternal Rumen Bacteriota Shapes the Offspring Rumen Bacteriota, Affecting the Development of Young Ruminants

The maternal rumen microbiota can affect the infantile rumen microbiota and likely offspring growth, and some rumen microbes are heritable and are associated with host traits. However, little is known about the heritable microbes of the maternal rumen microbiota and their role in and effect on the growth of young ruminants. From analyzing the ruminal bacteriota from 128 Hu sheep dams and their 179 offspring lambs, we identified the potential heritable rumen bacteria and developed random forest prediction models to predict birth weight, weaning weight, and preweaning gain of the young ruminants using rumen bacteria as predictors. We showed that the dams tended to shape the bacteriota of the offspring. About 4.0% of the prevalent amplicon sequence variants (ASVs) of rumen bacteria were heritable (h2 > 0.2 and P < 0.05), and together they accounted for 4.8% and 31.5% of the rumen bacteria in relative abundance in the dams and the lambs, respectively. Heritable bacteria classified to Prevotellaceae appeared to play a key role in the rumen niche and contribute to rumen fermentation and the growth performance of lambs. Lamb growth traits could be successfully predicted using some maternal ASVs, and the accuracy of the predictive models was improved when some ASVs from both dams and their offspring were included. IMPORTANCE Using a study design that enabled direct comparison of the rumen microbiota between sheep dams and their lambs, between littermates, and between sheep dams and lambs from other mothers, we identified the heritable subsets of rumen bacteriota in Hu sheep, some of which may play important roles in affecting the growth traits of young lambs. Some maternal rumen bacteria could help predict the growth traits of the young offspring, and they may assist in breeding of and selection for high-performance sheep.

Prevotella: A Key Player in Ruminal Metabolism

Ruminants are foregut fermenters that have the remarkable ability of converting plant polymers that are indigestible to humans into assimilable comestibles like meat and milk, which are cornerstones of human nutrition. Ruminants establish a symbiotic relationship with their microbiome, and the latter is the workhorse of carbohydrate fermentation. On the other hand, during carbohydrate fermentation, synthesis of propionate sequesters H, thus reducing its availability for the ultimate production of methane (CH4) by methanogenic archaea. Biochemically, methane is the simplest alkane and represents a downturn in energetic efficiency in ruminants; environmentally, it constitutes a potent greenhouse gas that negatively affects climate change. Prevotella is a very versatile microbe capable of processing a wide range of proteins and polysaccharides, and one of its fermentation products is propionate, a trait that appears conspicuous in P. ruminicola strain 23. Since propionate, but not acetate or butyrate, constitutes an H sink, propionate-producing microbes have the potential to reduce methane production. Accordingly, numerous studies suggest that members of the genus Prevotella have the ability to divert the hydrogen flow in glycolysis away from methanogenesis and in favor of propionic acid production. Intended for a broad audience in microbiology, our review summarizes the biochemistry of carbohydrate fermentation and subsequently discusses the evidence supporting the essential role of Prevotella in lignocellulose processing and its association with reduced methane emissions. We hope this article will serve as an introduction to novice Prevotella researchers and as an update to others more conversant with the topic.

Host genetic control on rumen microbiota and its impact on dairy traits in sheep

BACKGROUND

Milk yield and fine composition in sheep depend on the volatile and long-chain fatty acids, microbial proteins, vitamins produced through feedstuff digestion by the rumen microbiota. In cattle, the host genome has been shown to have a low to moderate genetic control on rumen microbiota abundance but a high control on dairy traits with heritabilities higher than 0.30. There is little information on the genetic correlations and quantitative trait loci (QTL) that simultaneously affect rumen microbiota abundance and dairy traits in ruminants, especially in sheep. Thus, our aim was to quantify the effect of the host genetics on rumen bacterial abundance and the genetic correlations between rumen bacterial abundance and several dairy traits, and to identify QTL that are associated with both rumen bacterial abundance and milk traits.

RESULTS

Our results in Lacaune sheep show that the heritability of rumen bacterial abundance ranges from 0 to 0.29 and that the heritability of 306 operational taxonomic units (OTU) is significantly different from 0. Of these 306 OTU, 96 that belong mainly to the Prevotellaceae, Lachnospiraceae and Ruminococcaceae bacterial families show strong genetic correlations with milk fatty acids and proteins (absolute values ranging from 0.33 to 0.99). Genome-wide association studies revealed a QTL for alpha-lactalbumin concentration in milk on Ovis aries chromosome (OAR) 11, and six QTL for rumen bacterial abundances i.e., for two OTU belonging to the genera Prevotella (OAR3 and 5), Rikeneleaceae_RC9_gut_group (OAR5), Ruminococcus (OAR5), an unknown genus of order Clostridia UCG-014 (OAR10), and CAG-352 (OAR11). None of these detected regions are simultaneously associated with rumen bacterial abundance and dairy traits, but the bacterial families Prevotellaceae, Lachnospiraceae and F082 show colocalized signals on OAR3, 5, 15 and 26.

CONCLUSIONS

In Lacaune dairy sheep, rumen microbiota abundance is partially controlled by the host genetics and is poorly genetically linked with milk protein and fatty acid compositions, and three main bacterial families, Prevotellaceae, Lachnospiraceae and F082, show specific associations with OAR3, 5, 15 and 26.

Rumen Microbiome Reveals the Differential Response of CO2 and CH4 Emissions of Yaks to Feeding Regimes on the Qinghai-Tibet Plateau

Shifts in feeding regimes are important factors affecting greenhouse gas (GHG) emissions from livestock farming. However, the quantitative values and associated drivers of GHG emissions from yaks (Bos grunniens) following shifts in feeding regimes have yet to be fully described. In this study, we aimed to investigate CH4 and CO2 emissions differences of yaks under different feeding regimes and their potential microbial mechanisms. Using static breathing chamber and Picarro G2508 gas concentration analyzer, we measured the CO2 and CH4 emissions from yaks under traditional grazing (TG) and warm-grazing and cold-indoor feeding (WGCF) regimes. Microbial inventories from the ruminal fluid of the yaks were determined via Illumina 16S rRNA and ITS sequencing. Results showed that implementing the TG regime in yaks decreased their CO2 and CH4 emissions compared to the WGCF regime. The alpha diversity of ruminal archaeal community was higher in the TG regime than in the WGCF regime. The beta diversity showed that significant differences in the rumen microbial composition of the TG regime and the WGCF regime. Changes in the rumen microbiota of the yaks were driven by differences in dietary nutritional parameters. The relative abundances of the phyla Neocallimastigomycota and Euryarchaeota and the functional genera Prevotella, Ruminococcus, Orpinomyces, and Methanobrevibacter were significantly higher in the WGCF regime than in the TG regime. CO2 and CH4 emissions from yaks differed mainly because of the enrichment relationship of functional H2- and CO2-producing microorganisms, hydrogen-consuming microbiota, and hydrogenotrophic methanogenic microbiota. Our results provided a view that it is ecologically important to develop GHG emissions reduction strategies for yaks on the Qinghai-Tibet Plateau based on traditional grazing regime.

Heritable and Nonheritable Rumen Bacteria Are Associated with Different Characters of Lactation Performance of Dairy Cows

Recent studies have reported that some rumen microbes are heritable. However, it is necessary to clarify the functions and specific contributions of the heritable rumen microbes to cattle phenotypes (microbiability) in comparison with those that are nonheritable. This study aimed to identify the distribution and predicted functions of heritable and nonheritable bacterial taxa at species level in the rumen of dairy cows and their respective contributions to energy-corrected milk yield, protein content and yield, and fat content and yield in milk. Thirty-two heritable and 674 nonheritable bacterial taxa were identified at species level, and the functional analysis revealed that predicted microbial functions for both groups were mainly enriched for energy, amino acid, and ribonucleotide metabolism. The mean microbiability (to reflect a single taxon's contribution) of heritable bacteria was found to range from 0.16% to 0.33% for the different milk traits, whereas the range for nonheritable bacteria was 0.03% to 0.06%. These findings suggest a strong contribution by host genetics in shaping the rumen microbiota, which contribute significantly to milk production traits. Therefore, there is an opportunity to further improve milk production traits through attention to host genetics and the interaction with the rumen microbiota. IMPORTANCE Rumen bacteria produce volatile fatty acids which exert a far-reaching influence on hepatic metabolism, mammary gland metabolism, and animal production. In the current study, 32 heritable and 674 nonheritable bacterial taxa at species level were identified, and shown to have different microbiability (overall community contribution) and mean microbiability (the average of a single taxon's contribution) for lactation performance. The predicted functions of heritable and nonheritable bacterial taxa also differed, suggesting that targeted nutritional and genetic breeding approaches could be used to manipulate them to improve dairy cow performance.

Microbiome-driven breeding strategy potentially improves beef fatty acid profile benefiting human health and reduces methane emissions

BACKGROUND

Healthier ruminant products can be achieved by adequate manipulation of the rumen microbiota to increase the flux of beneficial fatty acids reaching host tissues. Genomic selection to modify the microbiome function provides a permanent and accumulative solution, which may have also favourable consequences in other traits of interest (e.g. methane emissions). Possibly due to a lack of data, this strategy has never been explored.

RESULTS

This study provides a comprehensive identification of ruminal microbial mechanisms under host genomic influence that directly or indirectly affect the content of unsaturated fatty acids in beef associated with human dietary health benefits C18:3n-3, C20:5n-3, C22:5n-3, C22:6n-3 or cis-9, trans-11 C18:2 and trans-11 C18:1 in relation to hypercholesterolemic saturated fatty acids C12:0, C14:0 and C16:0, referred to as N3 and CLA indices. We first identified that ~27.6% (1002/3633) of the functional core additive log-ratio transformed microbial gene abundances (alr-MG) in the rumen were at least moderately host-genomically influenced (HGFC). Of these, 372 alr-MG were host-genomically correlated with the N3 index (n=290), CLA index (n=66) or with both (n=16), indicating that the HGFC influence on beef fatty acid composition is much more complex than the direct regulation of microbial lipolysis and biohydrogenation of dietary lipids and that N3 index variation is more strongly subjected to variations in the HGFC than CLA. Of these 372 alr-MG, 110 were correlated with the N3 and/or CLA index in the same direction, suggesting the opportunity for enhancement of both indices simultaneously through a microbiome-driven breeding strategy. These microbial genes were involved in microbial protein synthesis (aroF and serA), carbohydrate metabolism and transport (galT, msmX), lipopolysaccharide biosynthesis (kdsA, lpxD, lpxB), or flagellar synthesis (flgB, fliN) in certain genera within the Proteobacteria phyla (e.g. Serratia, Aeromonas). A microbiome-driven breeding strategy based on these microbial mechanisms as sole information criteria resulted in a positive selection response for both indices (1.36±0.24 and 0.79±0.21 sd of N3 and CLA indices, at 2.06 selection intensity). When evaluating the impact of our microbiome-driven breeding strategy to increase N3 and CLA indices on the environmental trait methane emissions (g/kg of dry matter intake), we obtained a correlated mitigation response of -0.41±0.12 sd.

CONCLUSION

This research provides insight on the possibility of using the ruminal functional microbiome as information for host genomic selection, which could simultaneously improve several microbiome-driven traits of interest, in this study exemplified with meat quality traits and methane emissions. Video Abstract.

Metagenomic Predictions: A Review 10 years on

Metagenomic predictions use variation in the metagenome (microbiome profile) to predict the unknown phenotype of the associated host. Metagenomic predictions were first developed 10 years ago, where they were used to predict which cattle would produce high or low levels of enteric methane. Since then, the approach has been applied to several traits and species including residual feed intake in cattle, and carcass traits, body mass index and disease state in pigs. Additionally, the method has been extended to include predictions based on other multi-dimensional data such as the metabolome, as well to combine genomic and metagenomic information. While there is still substantial optimisation required, the use of metagenomic predictions is expanding as DNA sequencing costs continue to fall and shows great promise particularly for traits heavily influenced by the microbiome such as feed efficiency and methane emissions.

Host genetics associated with gut microbiota and methane emission in cattle

In livestock sector, dairy animals alone produce 18% of the total greenhouse gas emissions globally as methane (CH₄). This Enteric methane is the largest component of total carbon footprints produced by livestock production system and its reduction is today's new challenge to make livestock farming sustainable for earth's environment. The production of enteric methane in ruminants is a complex phenomena involving different host factors like host genotype, rumen microbiome, host physiology along with dietary factors. Efforts have been made to reduce methane emissions largely through nutritional interventions and dietary supplements, but permanent reductions can be obtained through genetic means by selecting and breeding of low methane emitting animals. From genome-wide association studies, many important genomic QTL regions and single nucleotide polymorphisms involved in shaping the composition of the ruminal microbiome and thus their carbon footprints have been recognised, implying that methane emission traits are quantitative traits. The major bottleneck in implementation of reduced methane emission traits in the breeding programs is wide variation at phenotypic level, lack of precise methane measurements at individual level. Overall, the heritability for CH₄ production traits is moderate, and it can be used in breeding programmes to target changes in microbial composition to reduce CH₄ emission in the dairy industry for far-reaching environmental benefits at the cost of a minor reduction in genetic gain in production traits.

Use of Bayes factors to evaluate the effects of host genetics, litter and cage on the rabbit cecal microbiota

Background

The rabbit cecum hosts and interacts with a complex microbial ecosystem that contributes to the variation of traits of economic interest. Although the influence of host genetics on microbial diversity and specific microbial taxa has been studied in several species (e.g., humans, pigs, or cattle), it has not been investigated in rabbits. Using a Bayes factor approach, the aim of this study was to dissect the effects of host genetics, litter and cage on 984 microbial traits that are representative of the rabbit microbiota.

Results

Analysis of 16S rDNA sequences of cecal microbiota from 425 rabbits resulted in the relative abundances of 29 genera, 951 operational taxonomic units (OTU), and four microbial alpha-diversity indices. Each of these microbial traits was adjusted with mixed linear and zero-inflated Poisson (ZIP) models, which all included additive genetic, litter and cage effects, and body weight at weaning and batch as systematic factors. The marginal posterior distributions of the model parameters were estimated using MCMC Bayesian procedures. The deviance information criterion (DIC) was used for model comparison regarding the statistical distribution of the data (normal or ZIP), and the Bayes factor was computed as a measure of the strength of evidence in favor of the host genetics, litter, and cage effects on microbial traits. According to DIC, all microbial traits were better adjusted with the linear model except for the OTU present in less than 10% of the animals, and for 25 of the 43 OTU with a frequency between 10 and 25%. On a global scale, the Bayes factor revealed substantial evidence in favor of the genetic control of the number of observed OTU and Shannon indices. At the taxon-specific level, significant proportions of the OTU and relative abundances of genera were influenced by additive genetic, litter, and cage effects. Several members of the genera Bacteroides and Parabacteroides were strongly influenced by the host genetics and nursing environment, whereas the family S24-7 and the genus Ruminococcus were strongly influenced by cage effects.

Conclusions

This study demonstrates that host genetics shapes the overall rabbit cecal microbial diversity and that a significant proportion of the taxa is influenced either by host genetics or environmental factors, such as litter and/or cage.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-022-00738-2.

Association Studies and Genomic Prediction for Genetic Improvements in Agriculture

To feed the fast growing global population with sufficient food using limited global resources, it is urgent to develop and utilize cutting-edge technologies and improve efficiency of agricultural production. In this review, we specifically introduce the concepts, theories, methods, applications and future implications of association studies and predicting unknown genetic value or future phenotypic events using genomics in the area of breeding in agriculture. Genome wide association studies can identify the quantitative genetic loci associated with phenotypes of importance in agriculture, while genomic prediction utilizes individual genetic value to rank selection candidates to improve the next generation of plants or animals. These technologies and methods have improved the efficiency of genetic improvement programs for agricultural production via elite animal breeds and plant varieties. With the development of new data acquisition technologies, there will be more and more data collected from high-through-put technologies to assist agricultural breeding. It will be crucial to extract useful information among these large amounts of data and to face this challenge, more efficient algorithms need to be developed and utilized for analyzing these data. Such development will require knowledge from multiple disciplines of research.

Bovine host genome acts on rumen microbiome function linked to methane emissions

Our study provides substantial evidence that the host genome affects the comprehensive function of the microbiome in the rumen of bovines. Of 1,107/225/1,141 rumen microbial genera/metagenome assembled uncultured genomes (RUGs)/genes identified from whole metagenomics sequencing, 194/14/337 had significant host genomic effects (heritabilities ranging from 0.13 to 0.61), revealing that substantial variation of the microbiome is under host genomic control. We found 29/22/115 microbial genera/RUGs/genes host-genomically correlated (|0.59| to |0.93|) with emissions of the potent greenhouse gas methane (CH4), highlighting the strength of a common host genomic control of specific microbial processes and CH4. Only one of these microbial genes was directly involved in methanogenesis (cofG), whereas others were involved in providing substrates for archaea (e.g. bcd and pccB), important microbial interspecies communication mechanisms (ABC.PE.P), host-microbiome interaction (TSTA3) and genetic information processes (RP-L35). In our population, selection based on abundances of the 30 most informative microbial genes provided a mitigation potential of 17% of mean CH4 emissions per generation, which is higher than for selection based on measured CH4 using respiration chambers (13%), indicating the high potential of microbiome-driven breeding to cumulatively reduce CH4 emissions and mitigate climate change.

Translational multi-omics microbiome research for strategies to improve cattle production and health

Cattle microbiome plays a vital role in cattle growth and performance and affects many economically important traits such as feed efficiency, milk/meat yield and quality, methane emission, immunity and health. To date, most cattle microbiome research has focused on metataxonomic and metagenomic characterization to reveal who are there and what they may do, preventing the determination of the active functional dynamics in vivo and their causal relationships with the traits. Therefore, there is an urgent need to combine other advanced omics approaches to improve microbiome analysis to determine their mode of actions and host-microbiome interactions in vivo. This review will critically discuss the current multi-omics microbiome research in beef and dairy cattle, aiming to provide insights on how the information generated can be applied to future strategies to improve production efficiency, health and welfare, and environment-friendliness in cattle production through microbiome manipulations.

Predicting metabolizable energy from digestible energy for growing and finishing beef cattle and relationships to the prediction of methane

Reliable predictions of metabolizable energy (ME) from digestible energy (DE) are necessary to prescribe nutrient requirements of beef cattle accurately. A previously developed database that included 87 treatment means from 23 respiration calorimetry studies has been updated to evaluate the efficiency of converting DE to ME by adding 47 treatment means from 11 additional studies. Diets were fed to growing-finishing cattle under individual feeding conditions. A citation-adjusted linear regression equation was developed where dietary ME concentration (Mcal/kg of dry matter [DM]) was the dependent variable and dietary DE concentration (Mcal/kg) was the independent variable: ME = 1.0001 × DE - 0.3926; r2 = 0.99, root mean square prediction error [RMSPE] = 0.04, and P < 0.01 for the intercept and slope. The slope did not differ from unity (95% CI = 0.936 to 1.065); therefore, the intercept (95% CI = -0.567 to -0.218) defines the value of ME predicted from DE. For practical use, we recommend ME = DE - 0.39. Based on the relationship between DE and ME, we calculated the citation-adjusted loss of methane, which yielded a value of 0.2433 Mcal/kg of dry matter intake (DMI; SE = 0.0134). This value was also adjusted for the effects of DMI above maintenance, yielding a citation-adjusted relationship: CH4, Mcal/kg = 0.3344 - 0.05639 × multiple of maintenance; r2 = 0.536, RMSPE = 0.0245, and P < 0.01 for the intercept and slope. Both the 0.2433 value and the result of the intake-adjusted equation can be multiplied by DMI to yield an estimate of methane production. These two approaches were evaluated using a second, independent database comprising 129 data points from 29 published studies. Four equations in the literature that used DMI or intake energy to predict methane production also were evaluated with the second database. The mean bias was substantially greater for the two new equations, but slope bias was substantially less than noted for the other DMI-based equations. Our results suggest that ME for growing and finishing cattle can be predicted from DE across a wide range of diets, cattle types, and intake levels by simply subtracting a constant from DE. Mean bias associated with our two new methane emission equations suggests that further research is needed to determine whether coefficients to predict methane from DMI could be developed for specific diet types, levels of DMI relative to body weight, or other variables that affect the emission of methane.

Host Genetic Determinants of the Microbiome Across Animals: From Caenorhabditis elegans to Cattle

Animals harbor diverse communities of microbes within their gastrointestinal tracts. Phylogenetic relationship, diet, gut morphology, host physiology, and ecology all influence microbiome composition within and between animal clades. Emerging evidence points to host genetics as also playing a role in determining gut microbial composition within species. Here, we discuss recent advances in the study of microbiome heritability across a variety of animal species. Candidate gene and discovery-based studies in humans, mice, Drosophila, Caenorhabditis elegans, cattle, swine, poultry, and baboons reveal trends in the types of microbes that are heritable and the host genes and pathways involved in shaping the microbiome. Heritable gut microbes within a host species tend to be phylogenetically restricted. Host genetic variation in immune- and growth-related genes drives the abundances of these heritable bacteria within the gut. With only a small slice of the metazoan branch of the tree of life explored to date, this is an area rife with opportunities to shed light into the mechanisms governing host-microbe relationships.

The microbiome of the buffalo digestive tract

Buffalo is an important livestock species. Here, we present a comprehensive metagenomic survey of the microbial communities along the buffalo digestive tract. We analysed 695 samples covering eight different sites in three compartments (four-chambered stomach, intestine, and rectum). We mapped ~85% of the raw sequence reads to 4,960 strain-level metagenome-assembled genomes (MAGs) and 3,255 species-level MAGs, 90% of which appear to correspond to new species. In addition, we annotated over 5.8 million nonredundant proteins from the MAGs. In comparison with the rumen microbiome of cattle, the buffalo microbiota seems to present greater potential for fibre degradation and less potential for methane production. Our catalogue of microbial genomes and the encoded proteins provides insights into microbial functions and interactions at distinct sites along the buffalo digestive tract.

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.

Opportunities and limits of combining microbiome and genome data for complex trait prediction

Background

Analysis and prediction of complex traits using microbiome data combined with host genomic information is a topic of utmost interest. However, numerous questions remain to be answered: how useful can the microbiome be for complex trait prediction? Are estimates of microbiability reliable? Can the underlying biological links between the host’s genome, microbiome, and phenome be recovered?

Methods

Here, we address these issues by (i) developing a novel simulation strategy that uses real microbiome and genotype data as inputs, and (ii) using variance-component approaches (Bayesian Reproducing Kernel Hilbert Space (RKHS) and Bayesian variable selection methods (Bayes C)) to quantify the proportion of phenotypic variance explained by the genome and the microbiome. The proposed simulation approach can mimic genetic links between the microbiome and genotype data by a permutation procedure that retains the distributional properties of the data.

Results

Using real genotype and rumen microbiota abundances from dairy cattle, simulation results suggest that microbiome data can significantly improve the accuracy of phenotype predictions, regardless of whether some microbiota abundances are under direct genetic control by the host or not. This improvement depends logically on the microbiome being stable over time. Overall, random-effects linear methods appear robust for variance components estimation, in spite of the typically highly leptokurtic distribution of microbiota abundances. The predictive performance of Bayes C was higher but more sensitive to the number of causative effects than RKHS. Accuracy with Bayes C depended, in part, on the number of microorganisms’ taxa that influence the phenotype.

Conclusions

While we conclude that, overall, genome-microbiome-links can be characterized using variance component estimates, we are less optimistic about the possibility of identifying the causative host genetic effects that affect microbiota abundances, which would require much larger sample sizes than are typically available for genome-microbiome-phenome studies. The R code to replicate the analyses is in https://github.com/miguelperezenciso/simubiome.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-021-00658-7.

Feeding Systems and Host Breeds Influence Ruminal Fermentation, Methane Production, Microbial Diversity and Metagenomic Gene Abundance

Our previous research revealed the advantages of separate feeding (SF) systems compared to total mixed ration (TMR) in terms of ruminal methane (CH₄) production. The purpose of this experiment was to confirm the advantage of SF as a nutritional strategy for CH₄ mitigation, and to determine the effects of different feeding systems (TMR and SF) on the rumen microbiome and associated metagenome of two different breeds and on CH₄ emissions. We randomly allocated four Holstein (305 ± 29 kg) and four Hanwoo steers (292 ± 24 kg) to two groups; the steers were fed a commercial concentrate with tall fescue (75:25) as TMR or SF, in a crossover design (two successive 22-day periods). Neither feeding systems nor cattle breeds had an effect on the total tract digestibility of nutrients. The TMR feeding system and Hanwoo steers generated significantly more CH₄ (P < 0.05) and had a higher yield [g/d and g/kg dry matter intake (DMI)] compared to the SF system and Holstein steers. A larger rumen acetate:propionate ratio was observed for the TMR than the SF diet (P < 0.05), and for Hanwoo than Holstein steers (P < 0.001), clearly reflecting a shift in the ruminal H₂ sink toward CH₄ production. The linear discriminant analysis (LDA) effect size (LEfSe) revealed a greater abundance (α < 0.05 and LDA > 2.0) of operational taxonomic units (OTUs) related to methanogenesis for Hanwoo steers compared to Holstein steers. Kendall’s correlation analysis revealed wide variation of microbial co-occurrence patterns between feeding systems, indicating differential H₂ thermodynamics in the rumen. A metagenome analysis of rumen microbes revealed the presence of 430 differentially expressed genes, among which 17 and 27 genes exhibited positive and negative associations with CH₄ production, respectively (P < 0.001). A strong interaction between feeding system and breed was observed for microbial and metagenomic abundance. Overall, these results suggest that the TMR feeding system produces more CH₄, and that Hanwoo cattle are higher CH₄ emitters than SF diet and Holstein cattle, respectively. Interestingly, host-associated microbial interactions differed within each breed depending on the feeding system, which indicated that breed-specific feeding systems should be taken into account for farm management.

Host genetics exerts lifelong effects upon hindgut microbiota and its association with bovine growth and immunity

The gut microbiota is a complex ecological community that plays multiple critical roles within a host. Known intrinsic and extrinsic factors affect gut microbiota structure, but the influence of host genetics is understudied. To investigate the role of host genetics upon the gut microbiota structure, we performed a longitudinal study in which we evaluated the hindgut microbiota and its association with animal growth and immunity across life. We evaluated three different growth stages in an Angus-Brahman multibreed population with a graduated spectrum of genetic variation, raised under variable environmental conditions and diets. We found the gut microbiota structure was changed significantly during growth when preweaning, and fattening calves experienced large variations in diet and environmental changes. However, regardless of the growth stage, we found gut microbiota is significantly influenced by breed composition throughout life. Host genetics explained the relative abundances of 52.2%, 40.0%, and 37.3% of core bacterial taxa at the genus level in preweaning, postweaning, and fattening calves, respectively. Sutterella, Oscillospira, and Roseburia were consistently associated with breed composition at these three growth stages. Especially, butyrate-producing bacteria, Roseburia and Oscillospira, were associated with nine single-nucleotide polymorphisms (SNPs) located in genes involved in the regulation of host immunity and metabolism in the hindgut. Furthermore, minor allele frequency analysis found breed-associated SNPs in the short-chain fatty acids (SCFAs) receptor genes that promote anti-inflammation and enhance intestinal epithelial barrier functions. Our findings provide evidence of dynamic and lifelong host genetic effects upon gut microbiota, regardless of growth stages. We propose that diet, environmental changes, and genetic components may explain observed variation in critical hindgut microbiota throughout life.

Seaweed and Seaweed Bioactives for Mitigation of Enteric Methane: Challenges and Opportunities

Seaweeds contain a myriad of nutrients and bioactives including proteins, carbohydrates and to a lesser extent lipids as well as small molecules including peptides, saponins, alkaloids and pigments. The bioactive bromoform found in the red seaweed Asparagopsis taxiformis has been identified as an agent that can reduce enteric CH4 production from livestock significantly. However, sustainable supply of this seaweed is a problem and there are some concerns over its sustainable production and potential negative environmental impacts on the ozone layer and the health impacts of bromoform. This review collates information on seaweeds and seaweed bioactives and the documented impact on CH4 emissions in vitro and in vivo as well as associated environmental, economic and health impacts.

Anaerobic Fungi: Past, Present, and Future

Anaerobic fungi (AF) play an essential role in feed conversion due to their potent fiber degrading enzymes and invasive growth. Much has been learned about this unusual fungal phylum since the paradigm shifting work of Colin Orpin in the 1970s, when he characterized the first AF. Molecular approaches targeting specific phylogenetic marker genes have facilitated taxonomic classification of AF, which had been previously been complicated by the complex life cycles and associated morphologies. Although we now have a much better understanding of their diversity, it is believed that there are still numerous genera of AF that remain to be described in gut ecosystems. Recent marker-gene based studies have shown that fungal diversity in the herbivore gut is much like the bacterial population, driven by host phylogeny, host genetics and diet. Since AF are major contributors to the degradation of plant material ingested by the host animal, it is understandable that there has been great interest in exploring the enzymatic repertoire of these microorganisms in order to establish a better understanding of how AF, and their enzymes, can be used to improve host health and performance, while simultaneously reducing the ecological footprint of the livestock industry. A detailed understanding of AF and their interaction with other gut microbes as well as the host animal is essential, especially when production of affordable high-quality protein and other animal-based products needs to meet the demands of an increasing human population. Such a mechanistic understanding, leading to more sustainable livestock practices, will be possible with recently developed -omics technologies that have already provided first insights into the different contributions of the fungal and bacterial population in the rumen during plant cell wall hydrolysis.