Host genetics and the rumen microbiome jointly associate with methane emissions in dairy cows

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.

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.

Effects of 3-nitrooxypropanol (3-NOP, Bovaer®10) and whole cottonseed on milk production and enteric methane emissions from dairy cows under Swiss management conditions

The objective of this study was to determine the potential effect and interaction of 3- nitrooxypropanol (3-NOP; Bovaer®) and whole cottonseed (WCS) on lactational performance, and enteric methane (CH4) emission of dairy cows. A total of 16 multiparous cows, including 8 Holstein Friesian (HF) and 8 Brown Swiss (BS) [224 ± 36 d in milk, 26 ± 3.7 kg milk yield], were used in a split-plot design, where the main plot was the breed of cows. Within each subplot, cows were randomly assigned to a treatment sequence in a replicated 4 × 4 Latin Square design with 2 × 2 factorial arrangements of treatments with 4, 24-d periods. The experimental treatments were: 1) Control (basal TMR), 2) 3-NOP (60 mg/kg TMR DM), 3) WCS (5% TMR DM), and 4) 3-NOP + WCS. The treatment diets were balanced for ether extract, crude protein, and NDF contents (4%, 16%, and 43% of TMR DM, respectively). The basal diets were fed twice daily at 0800 and 1800 h. Dry matter intake (DMI) and milk yield were measured daily, and enteric gas emissions were measured (using the GreenFeed system) during the last 3 d of each 24-d experimental period when animals were housed in tie stalls. There was no difference in DMI on treatment level, whereas the WCS treatment increased ECM yield and milk fat yield. There was no interaction of 3-NOP and WCS for any of the enteric gas emission parameters, but 3-NOP decreased CH4 production (g/d), CH4 yield (g/kg DMI), and CH4 intensity (g/kg ECM) by 13, 14 and 13%, respectively. Further, an unexpected interaction of breed by 3-NOP was observed for different enteric CH4 emission metrics: HF cows had a greater CH4 mitigation effect compared with BS cows for CH4 production (g/d; 18 vs. 8%), CH4 intensity (g/kg MY; 19% vs. 3%) and CH4 intensity (g/kg ECM; 19 vs. 4%). Hydrogen production was increased by 2.85 folds in HF and 1.53 folds in BS cows receiving 3-NOP. Further, there was a 3-NOP ' Time interaction for both breeds. In BS cows, 3-NOP tended to reduce CH4 production by 18% at around 4 h after morning feeding but no effect was observed at other time points. In HF cows, the greatest mitigation effect of 3-NOP (29.6%) was observed immediately after morning feeding and it persisted at around 23% to 26% for 10 h until the second feed provision, and 3 h thereafter, in the evening. In conclusion, supplementing 3-NOP at 60 mg/kg DM to a high fiber diet resulted in 18 to 19% reduction in enteric CH4 emission in Swiss Holstein Friesian cows. The lower response to 3-NOP by BS cows was unexpected and has not been observed in other studies. These results should be interpreted with caution due to low number of cows per breed. Lastly, supplementing WCS at 5% of DM improved ECM and milk fat yield but did not enhance CH4 inhibition effect of 3-NOP of dairy cows.

Fecal Microbial Communities of Nellore and Crossbred Beef Calves Raised at Pasture

This study aimed to investigate the effect of age and genetics on the fecal microbiota of beef calves. Ten purebred Nellore (Bos taurus indicus) and ten crossbreed 50% Nellore-50% European breed (Bos taurus taurus) calves co-habiting on the same pasture paddock had fecal samples collected on days five (5 d), 14 d, 28 d, 60 d, 90 d, 180 d, 245 d (weaning) and 260 d after birth. All calves were kept with their mothers, and six Nellore dams were also sampled at weaning. Microbiota analysis was carried out by amplification of the V4 region of the 16S rRNA gene following high-throughput sequencing with a MiSeq Illumina platform. Results revealed that bacterial richness increased with age and became more similar to adults near weaning. Differences in microbiota membership between breeds were found at 60 d and 90 d and for structure at 60 d, 90 d, 245 d, and 260 d (p < 0.05). In addition, crossbreed calves presented less variability in their microbiota. In conclusion, the genetic composition significantly impacted the distal gut microbiota of calves co-habiting in the same environment, and further studies investigating food intake can reveal possible associations between microbiota composition and performance.

The importance of host physical niches for the stability of gut microbiome composition

Gut bacteria are prevalent throughout the Metazoa and form complex microbial communities associated with food breakdown, nutrient provision and disease prevention. How hosts acquire and maintain a consistent bacterial flora remains mysterious even in the best-studied animals, including humans, mice, fishes, squid, bugs, worms and flies. This essay visits the evidence that hosts have co-evolved relationships with specific bacteria and that some of these relationships are supported by specialized physical niches that select, sequester and maintain microbial symbionts. Genetics approaches could uncover the mechanisms for recruiting and maintaining the stable and consistent members of the microbiome. This article is part of the theme issue 'Sculpting the microbiome: how host factors determine and respond to microbial colonization'.

Host and rumen microbiome contributions to feed efficiency traits in Holstein cows

It is now widely accepted that dairy cow performance is influenced by both the host genome and rumen microbiome composition. The contributions of the genome and the microbiome to the phenotypes of interest are quantified by heritability (h2) and microbiability (m2), respectively. However, if the genome and microbiome are included in the model, then the h2 reflects only the contribution of the direct genetic effects quantified as direct heritability (hd2), and the holobiont effect reflects the joint action of the genome and the microbiome, quantified as the holobiability (ho2). The objectives of this study were to estimate h2, hd2,m2, and ho2 for dry matter intake, milk energy, and residual feed intake; and to evaluate the predictive ability of different models, including genome, microbiome, and their interaction. Data consisted of feed efficiency records, SNP genotype data, and 16S rRNA rumen microbial abundances from 448 mid-lactation Holstein cows from 2 research farms. Three kernel models were fit to each trait: one with only the genomic effect (model G), one with the genomic and microbiome effects (model GM), and one with the genomic, microbiome, and interaction effects (model GMO). The model GMO, or holobiont model, showed the best goodness-of-fit. The hd2 estimates were always 10% to 15% lower than h2 estimates for all traits, suggesting a mediated genetic effect through the rumen microbiome, and m2 estimates were moderate for all traits, and up to 26% for milk energy. The ho2 was greater than the sum of hd2 and m2, suggesting that the genome-by-microbiome interaction had a sizable effect on feed efficiency. Kernel models fitting the rumen microbiome (i.e., models GM and GMO) showed larger predictive correlations and smaller prediction bias than the model G. These findings reveal a moderate contribution of the rumen microbiome to feed efficiency traits in lactating Holstein cows and strongly suggest that the rumen microbiome mediates part of the host genetic effect.

Host genetics drives differences in cecal microbiota composition and immune traits of laying hens raised in the same environment

Vaccination is one of the most effective strategies for preventing infectious diseases but individual vaccine responses are highly heterogeneous. Host genetics and gut microbiota composition are 2 likely drivers of this heterogeneity. We studied 94 animals belonging to 4 lines of laying hens: a White Leghorn experimental line genetically selected for a high antibody response against the Newcastle Disease Virus (NDV) vaccine (ND3) and its unselected control line (CTR), and 2 commercial lines (White Leghorn [LEG] and Rhode Island Red [RIR]). Animals were reared in the same conditions from hatching to 42 d of age, and animals from different genetic lines were mixed. Animals were vaccinated at 22 d of age and their humoral vaccine response against NDV was assessed by hemagglutination inhibition assay and ELISA from blood samples collected at 15, 19, and 21 d after vaccination. The immune parameters studied were the 3 immunoglobulins subtypes A, M, and Y and the blood cell composition was assessed by flow cytometry. The composition of the cecal microbiota was assessed at the end of the experiment by analyzing amplified 16S rRNA gene sequences to obtain amplicon sequence variants (ASV). The 4 lines showed significantly different levels of NDV vaccine response at the 3 measured points, with, logically, a higher response of the genetically selected ND3 line, and intermediate and low responses for the unselected CTR control line and for the 2 commercial lines, respectively. The ND3 line displayed also a higher proportion of immunoglobulins (IgA, IgM, and IgY). The RIR line showed the most different blood cell composition. The 4 lines showed significantly different microbiota characteristics: composition, abundances at all taxonomic levels, and correlations between genera and vaccine response. The tested genetic lines differ for immune parameters and gut microbiota composition and functions. These phenotypic differences can be attributed to genetic differences between lines. Causal relationships between both types of parameters are discussed and will be investigated in further studies.

A first characterization of the microbiota-resilience link in swine

BACKGROUND

The gut microbiome plays a crucial role in understanding complex biological mechanisms, including host resilience to stressors. Investigating the microbiota-resilience link in animals and plants holds relevance in addressing challenges like adaptation of agricultural species to a warming environment. This study aims to characterize the microbiota-resilience connection in swine. As resilience is not directly observable, we estimated it using four distinct indicators based on daily feed consumption variability, assuming animals with greater intake variation may face challenges in maintaining stable physiological status. These indicators were analyzed both as linear and categorical variables. In our first set of analyses, we explored the microbiota-resilience link using PERMANOVA, α-diversity analysis, and discriminant analysis. Additionally, we quantified the ratio of estimated microbiota variance to total phenotypic variance (microbiability). Finally, we conducted a Partial Least Squares-Discriminant Analysis (PLS-DA) to assess the classification performance of the microbiota with indicators expressed in classes.

RESULTS

This study offers four key insights. Firstly, among all indicators, two effectively captured resilience. Secondly, our analyses revealed robust relationship between microbial composition and resilience in terms of both composition and richness. We found decreased α-diversity in less-resilient animals, while specific amplicon sequence variants (ASVs) and KEGG pathways associated with inflammatory responses were negatively linked to resilience. Thirdly, considering resilience indicators in classes, we observed significant differences in microbial composition primarily in animals with lower resilience. Lastly, our study indicates that gut microbial composition can serve as a reliable biomarker for distinguishing individuals with lower resilience.

CONCLUSION

Our comprehensive analyses have highlighted the host-microbiota and resilience connection, contributing valuable insights to the existing scientific knowledge. The practical implications of PLS-DA and microbiability results are noteworthy. PLS-DA suggests that host-microbiota interactions could be utilized as biomarkers for monitoring resilience. Furthermore, the microbiability findings show that leveraging host-microbiota insights may improve the identification of resilient animals, supporting their adaptive capacity in response to changing environmental conditions. These practical implications offer promising avenues for enhancing animal well-being and adaptation strategies in the context of environmental challenges faced by livestock populations. Video Abstract.

Multi-omics reveals the mechanism of rumen microbiome and its metabolome together with host metabolome participating in the regulation of milk production traits in dairy buffaloes

Recently, it has been discovered that certain dairy buffaloes can produce higher milk yield and milk fat yield under the same feeding management conditions, which is a potential new trait. It is unknown to what extent, the rumen microbiome and its metabolites, as well as the host metabolism, contribute to milk yield and milk fat yield. Therefore, we will analyze the rumen microbiome and host-level potential regulatory mechanisms on milk yield and milk fat yield through rumen metagenomics, rumen metabolomics, and serum metabolomics experiments. Microbial metagenomics analysis revealed a significantly higher abundance of several species in the rumen of high-yield dairy buffaloes, which mainly belonged to genera, such as Prevotella, Butyrivibrio, Barnesiella, Lachnospiraceae, Ruminococcus, and Bacteroides. These species contribute to the degradation of diets and improve functions related to fatty acid biosynthesis and lipid metabolism. Furthermore, the rumen of high-yield dairy buffaloes exhibited a lower abundance of methanogenic bacteria and functions, which may produce less methane. Rumen metabolome analysis showed that high-yield dairy buffaloes had significantly higher concentrations of metabolites, including lipids, carbohydrates, and organic acids, as well as volatile fatty acids (VFAs), such as acetic acid and butyric acid. Meanwhile, several Prevotella, Butyrivibrio, Barnesiella, and Bacteroides species were significantly positively correlated with these metabolites. Serum metabolome analysis showed that high-yield dairy buffaloes had significantly higher concentrations of metabolites, mainly lipids and organic acids. Meanwhile, several Prevotella, Bacteroides, Barnesiella, Ruminococcus, and Butyrivibrio species were significantly positively correlated with these metabolites. The combined analysis showed that several species were present, including Prevotella.sp.CAG1031, Prevotella.sp.HUN102, Prevotella.sp.KHD1, Prevotella.phocaeensis, Butyrivibrio.sp.AE3009, Barnesiella.sp.An22, Bacteroides.sp.CAG927, and Bacteroidales.bacterium.52-46, which may play a crucial role in rumen and host lipid metabolism, contributing to milk yield and milk fat yield. The "omics-explainability" analysis revealed that the rumen microbial composition, functions, metabolites, and serum metabolites contributed 34.04, 47.13, 39.09, and 50.14%, respectively, to milk yield and milk fat yield. These findings demonstrate how the rumen microbiota and host jointly affect milk production traits in dairy buffaloes. This information is essential for developing targeted feeding management strategies to improve the quality and yield of buffalo milk.

Multi-omics reveal mechanisms of high enteral starch diet mediated colonic dysbiosis via microbiome-host interactions in young ruminant

BACKGROUND

Although rumen development is crucial, hindgut undertakes a significant role in young ruminants' physiological development. High-starch diet is usually used to accelerate rumen development for young ruminants, but always leading to the enteral starch overload and hindgut dysbiosis. However, the mechanism behind remains unclear. The combination of colonic transcriptome, colonic luminal metabolome, and metagenome together with histological analysis was conducted using a goat model, with the aim to identify the potential molecular mechanisms behind the disrupted hindgut homeostasis by overload starch in young ruminants.

RESULT

Compared with low enteral starch diet (LES), high enteral starch diet (HES)-fed goats had significantly higher colonic pathology scores, and serum diamine oxidase activity, and meanwhile significantly decreased colonic mucosal Mucin-2 (MUC2) protein expression and fecal scores, evidencing the HES-triggered colonic systemic inflammation. The bacterial taxa Prevotella sp. P4-67, Prevotella sp. PINT, and Bacteroides sp. CAG:927, together with fungal taxa Fusarium vanettenii, Neocallimastix californiae, Fusarium sp. AF-8, Hypoxylon sp. EC38, and Fusarium pseudograminearum, and the involved microbial immune pathways including the "T cell receptor signaling pathway" were higher in the colon of HES goats. The integrated metagenome and host transcriptome analysis revealed that these taxa were associated with enhanced pathogenic ability, antigen processing and presentation, and stimulated T helper 2 cell (TH2)-mediated cytokine secretion functions in the colon of HES goats. Further luminal metabolomics analysis showed increased relative content of chenodeoxycholic acid (CDCA) and deoxycholic acid (DCA), and decreased the relative content of hypoxanthine in colonic digesta of HES goats. These altered metabolites contributed to enhancing the expression of TH2-mediated inflammatory-related cytokine secretion including GATA Binding Protein 3 (GATA3), IL-5, and IL-13. Using the linear mixed effect model, the variation of MUC2 biosynthesis explained by the colonic bacteria, bacterial functions, fungi, fungal functions, and metabolites were 21.92, 20.76, 19.43, 12.08, and 44.22%, respectively. The variation of pathology scores explained by the colonic bacterial functions, fungal functions, and metabolites were 15.35, 17.61, and 57.06%.

CONCLUSIONS

Our findings revealed that enteral starch overload can trigger interrupted hindgut host-microbiome homeostasis that led to impaired mucosal, destroyed colonic water absorption, and TH2-mediated inflammatory process. Except for the colonic metabolites mostly contribute to the impaired mucosa, the nonnegligible contribution from fungi deserves more future studies focused on the fungal functions in hindgut dysbiosis of young ruminants. Video Abstract.

Recent Advances in Enteric Methane Mitigation and the Long Road to Sustainable Ruminant Production

Mitigation of methane emission, a potent greenhouse gas, is a worldwide priority to limit global warming. A substantial part of anthropogenic methane is emitted by the livestock sector, as methane is a normal product of ruminant digestion. We present the latest developments and challenges ahead of the main efficient mitigation strategies of enteric methane production in ruminants. Numerous mitigation strategies have been developed in the last decades, from dietary manipulation and breeding to targeting of methanogens, the microbes that produce methane. The most recent advances focus on specific inhibition of key enzymes involved in methanogenesis. But these inhibitors, although efficient, are not affordable and not adapted to the extensive farming systems prevalent in low- and middle-income countries. Effective global mitigation of methane emissions from livestock should be based not only on scientific progress but also on the feasibility and accessibility of mitigation strategies.

- Invited Review - The role of rumen microbiota in enteric methane mitigation for sustainable ruminant production

Ruminal methane production functions as the main sink for metabolic hydrogen generated through rumen fermentation and is recognized as a considerable source of greenhouse gas emissions. Methane production is a complex trait affected by dry matter intake, feed composition, rumen microbiota and their fermentation, lactation stage, host genetics, and environmental factors. Various mitigation approaches have been proposed. Because individual ruminants exhibit different methane conversion efficiencies, the microbial characteristics of low-methane-emitting animals can be essential for successful rumen manipulation and environment-friendly methane mitigation. Several bacterial species, including Sharpea, uncharacterized Succinivibrionaceae, and certain Prevotella phylotypes have been listed as key players in low-methane-emitting sheep and cows. The functional characteristics of the unclassified bacteria remain unclear, as they are yet to be cultured. Here, we review ruminal methane production and mitigation strategies, focusing on rumen fermentation and the functional role of rumen microbiota, and describe the phylogenetic and physiological characteristics of a novel Prevotella species recently isolated from low methane-emitting and high propionate-producing cows. This review may help to provide a better understanding of the ruminal digestion process and rumen function to identify holistic and environmentally friendly methane mitigation approaches for sustainable ruminant production.

Use of methane production data for genetic prediction in beef cattle: A review

Methane (CH4) is a greenhouse gas that is produced and emitted from ruminant animals through enteric fermentation. Methane production from cattle has an environmental impact and is an energetic inefficiency. In the beef industry, CH4 production from enteric fermentation impacts all three pillars of sustainability: environmental, social, and economic. A variety of factors influence the quantity of CH4 produced during enteric fermentation, including characteristics of the rumen and feed composition. There are several methodologies available to either quantify or estimate CH4 production from cattle, all with distinct advantages and disadvantages. Methodologies include respiration calorimetry, the sulfur-hexafluoride tracer technique, infrared spectroscopy, prediction models, and the GreenFeed system. Published studies assess the accuracy of the various methodologies and compare estimates from different methods. There are advantages and disadvantages of each technology as they relate to the use of these phenotypes in genetic evaluation systems. Heritability and variance components of CH4 production have been estimated using the different CH4 quantification methods. Agreement in both the amounts of CH4 emitted and heritability estimates of CH4 emissions between various measurement methodologies varies in the literature. Using greenhouse gas traits in selection indices along with relevant output traits could provide producers with a tool to make selection decisions on environmental sustainability while also considering productivity. The objective of this review was to discuss factors that influence CH4 production, methods to quantify CH4 production for genetic evaluation, and genetic parameters of CH4 production in beef cattle.

Linkages between rumen microbiome, host, and environment in yaks, and their implications for understanding animal production and management

Livestock on the Qinghai-Tibetan Plateau is of great importance for the livelihood of the local inhabitants and the ecosystem of the plateau. The natural, harsh environment has shaped the adaptations of local livestock while providing them with requisite eco-services. Over time, unique genes and metabolic mechanisms (nitrogen and energy) have evolved which enabled the yaks to adapt morphologically and physiologically to the Qinghai-Tibetan Plateau. The rumen microbiota has also co-evolved with the host and contributed to the host's adaptation to the environment. Understanding the complex linkages between the rumen microbiota, the host, and the environment is essential to optimizing the rumen function to meet the growing demands for animal products while minimizing the environmental impact of ruminant production. However, little is known about the mechanisms of host-rumen microbiome-environment linkages and how they ultimately benefit the animal in adapting to the environment. In this review, we pieced together the yak's adaptation to the Qinghai-Tibetan Plateau ecosystem by summarizing the natural selection and nutritional features of yaks and integrating the key aspects of its rumen microbiome with the host metabolic efficiency and homeostasis. We found that this homeostasis results in higher feed digestibility, higher rumen microbial protein production, higher short-chain fatty acid (SCFA) concentrations, and lower methane emissions in yaks when compared with other low-altitude ruminants. The rumen microbiome forms a multi-synergistic relationship among the rumen microbiota services, their communities, genes, and enzymes. The rumen microbial proteins and SCFAs act as precursors that directly impact the milk composition or adipose accumulation, improving the milk or meat quality, resulting in a higher protein and fat content in yak milk and a higher percentage of protein and abundant fatty acids in yak meat when compared to dairy cow or cattle. The hierarchical interactions between the climate, forage, rumen microorganisms, and host genes have reshaped the animal's survival and performance. In this review, an integrating and interactive understanding of the host-rumen microbiome environment was established. The understanding of these concepts is valuable for agriculture and our environment. It also contributes to a better understanding of microbial ecology and evolution in anaerobic ecosystems and the host-environment linkages to improve animal production.

Mechanisms of hepatic steatosis in chickens: integrated analysis of the host genome, molecular phenomics and gut microbiome

Hepatic steatosis is the initial manifestation of abnormal liver functions and often leads to liver diseases such as nonalcoholic fatty liver disease in humans and fatty liver syndrome in animals. In this study, we conducted a comprehensive analysis of a large chicken population consisting of 705 adult hens by combining host genome resequencing; liver transcriptome, proteome, and metabolome analysis; and microbial 16S ribosomal RNA gene sequencing of each gut segment. The results showed the heritability (h2 = 0.25) and duodenal microbiability (m2 = 0.26) of hepatic steatosis were relatively high, indicating a large effect of host genetics and duodenal microbiota on chicken hepatic steatosis. Individuals with hepatic steatosis had low microbiota diversity and a decreased genetic potential to process triglyceride output from hepatocytes, fatty acid β-oxidation activity, and resistance to fatty acid peroxidation. Furthermore, we revealed a molecular network linking host genomic variants (GGA6: 5.59-5.69 Mb), hepatic gene/protein expression (PEMT, phosphatidyl-ethanolamine N-methyltransferase), metabolite abundances (folate, S-adenosylmethionine, homocysteine, phosphatidyl-ethanolamine, and phosphatidylcholine), and duodenal microbes (genus Lactobacillus) to hepatic steatosis, which could provide new insights into the regulatory mechanism of fatty liver development.

Unveiling microbial biomarkers of ruminant methane emission through machine learning

Background

Enteric methane from cow burps, which results from microbial fermentation of high-fiber feed in the rumen, is a significant contributor to greenhouse gas emissions. A promising strategy to address this problem is microbiome-based precision feed, which involves identifying key microorganisms for methane production. While machine learning algorithms have shown success in associating human gut microbiome with various human diseases, there have been limited efforts to employ these algorithms to establish microbial biomarkers for methane emissions in ruminants.

Methods

In this study, we aim to identify potential methane biomarkers for methane emission from ruminants by employing regression algorithms commonly used in human microbiome studies, coupled with different feature selection methods. To achieve this, we analyzed the microbiome compositions and identified possible confounding metadata variables in two large public datasets of Holstein cows. Using both the microbiome features and identified metadata variables, we trained different regressors to predict methane emission. With the optimized models, permutation tests were used to determine feature importance to find informative microbial features.

Results

Among the regression algorithms tested, random forest regression outperformed others and allowed the identification of several crucial microbial taxa for methane emission as members of the native rumen microbiome, including the genera Piromyces, Succinivibrionaceae UCG-002, and Acetobacter. Additionally, our results revealed that certain herd locations and feed composition markers, such as the lipid intake and neutral-detergent fiber intake, are also predictive features for methane emissions.

Conclusion

We demonstrated that machine learning, particularly regression algorithms, can effectively predict cow methane emissions and identify relevant rumen microorganisms. Our findings offer valuable insights for the development of microbiome-based precision feed strategies aiming at reducing methane emissions.

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.

Evolutions in Commercial Meat Poultry Breeding

This paper provides a comprehensive overview of the history of commercial poultry breeding, from domestication to the development of science and commercial breeding structures. The development of breeding goals over time, from mainly focusing on production to broad goals, including bird welfare and health, robustness, environmental impact, biological efficiency and reproduction, is detailed. The paper outlines current breeding goals, including traits (e.g., on foot and leg health, contact dermatitis, gait, cardiovascular health, robustness and livability), recording techniques, their genetic basis and how trait these antagonisms, for example, between welfare and production, are managed. Novel areas like genomic selection and gut health research and their current and potential impact on breeding are highlighted. The environmental impact differences of various genotypes are explained. A future outlook shows that balanced, holistic breeding will continue to enable affordable lean animal protein to feed the world, with a focus on the welfare of the birds and a diversity of choice for the various preferences and cultures across the world.

Leveraging microbiome information for animal genetic improvement

There is growing evidence that the microbiome influences host phenotypic variation. Incorporating information about the holobiont - the host and its microbiome - into genomic prediction models may accelerate genetic improvements in farmed animal populations. Importantly, these models must account for the indirect effects of the host genome on microbiome-mediated phenotypes.

Multi-breed host rumen epithelium transcriptome and microbiome associations and their relationship with beef cattle feed efficiency

Understanding host-microbial interactions in the rumen and its influence on desirable production traits may lead to potential microbiota manipulation or genetic selection for improved cattle feed efficiency. This study investigated the host transcriptome and its correlation with the rumen archaea and bacteria differential abundance of two pure beef cattle breeds (Angus and Charolais) and one composite beef hybrid (Kinsella) divergent for residual feed intake (RFI; low-RFI vs. high-RFI). Using RNA-Sequencing of rumen tissue and 16S rRNA gene amplicon sequencing, differentially expressed genes (FDR ≤ 0.05, |log2(Fold-change) >|2) and differentially abundant (p-value < 0.05) archaea and bacteria amplicon sequence variants (ASV) were determined. Significant correlations between gene expression and ASVs (p-value < 0.05) were determine using Spearman correlation. Interesting associations with muscle contraction and the modulation of the immune system were observed for the genes correlated with bacterial ASVs. Potential functional candidate genes for feed efficiency status were identified for Angus (CCL17, CCR3, and CXCL10), Charolais (KCNK9, GGT1 and IL6), and Kinsella breed (ESR2). The results obtained here provide more insights regarding the applicability of target host and rumen microbial traits for the selection and breeding of more feed efficient beef cattle.

Dynamic changes of rumen bacteria and their fermentative ability in high-producing dairy cows during the late perinatal period

Background

High-producing dairy cows face varying degrees of metabolic stress and challenges during the late perinatal period, resulting in ruminal bacteria abundance and their fermentative ability occurring as a series of changes. However, the dynamic changes are still not clear.

Aims/methods

Ten healthy, high-producing Holstein dairy cows with similar body conditions and the same parity were selected, and ruminal fluid from the dairy cows at postpartum 0, 7, 14, and 21 d was collected before morning feeding. 16S rRNA high-throughput sequencing, GC-MS/MS targeted metabolomics, and UPLC-MS/MS untargeted metabolomics were applied in the study to investigate the dynamic changes within 21 d postpartum.

Results

The results displayed that the structures of ruminal bacteria were significantly altered from 0 to 7 d postpartum (R = 0.486, P = 0.002), reflecting the significantly declining abundances of Euryarchaeota and Chloroflexi phyla and Christensenellaceae, Methanobrevibacter, and Flexilinea genera (P < 0.05) and the obviously ascending abundances of Ruminococcaceae, Moryella, Pseudobutyrivibrio, and Prevotellaceae genera at 7 d postpartum (P < 0.05). The structures of ruminal bacteria also varied significantly from 7 to 14 d postpartum (R = 0.125, P = 0.022), reflecting the reducing abundances of Christensenellaceae, Ruminococcaceae, and Moryella genera (P < 0.05), and the elevating abundances of Sharpea and Olsenella genera at 14 d postpartum (P < 0.05). The metabolic profiles of ruminal SCFAs were obviously varied from 0 to 7 d postpartum, resulting in higher levels of propionic acid, butyric acid, and valeric acid at 7 d postpartum (P < 0.05); the metabolic profiles of other ruminal metabolites were significantly shifted from 0 to 7 d postpartum, with 27 significantly elevated metabolites and 35 apparently reduced metabolites (P < 0.05). The correlation analysis indicated that propionic acid was positively correlated with Prevotellaceae and Ruminococcaceae (P < 0.05), negatively correlated with Methanobrevibacter (P < 0.01); butyric acid was positively associated with Prevotellaceae, Ruminococcaceae, and Pseudobutyrivibrio (P < 0.05), negatively associated with Christensenellaceae (P < 0.01); valeric acid was positively linked with Prevotellaceae and Ruminococcaceae (P < 0.05); pyridoxal was positively correlated with Flexilinea and Methanobrevibacter (P < 0.05) and negatively correlated with Ruminococcaceae (P < 0.01); tyramine was negatively linked with Ruminococcaceae (P < 0.01).

Conclusion

The findings contribute to the decision of nutritional management and prevention of metabolic diseases in high-producing dairy cows during the late perinatal period.

Differences in Donor Animal Production Stage Affect Repeatability of In Vitro Rumen Fermentation Kinetics

In vitro gas production techniques (IVGPT) are widely used to screen feeds and feed additives to reduce the number of animals needed for experiments, which in turn, reduces costs and increases animal welfare. However, information about repeatability is scarce. The objective of this study was to evaluate the variation from in vitro gas production fermentations in the same laboratory using the same feed substrate. The source of rumen fluid used in the fermentations was from two different farms with either cannulated lactating dairy cows or cannulated fasting heifers, representing two distinct stages of production (donor types). Seventeen 24 h fermentations, undertaken during a year, were used to evaluate the variation between the following parameters: gas curve parameters, baseline-corrected total gas production (TGP (mL at Standard Temperature and Pressure (STP))/g incubated dry matter (DM)), methane concentration (%) and yield (mL gas at STP/g DM), pH and degraded dry matter (dDM). Significant differences between donor types were found for the pH of the rumen fluid from individual animals and pH of fermented fluid. However, no significant differences were observed within donor type. The means for methane concentration and yield, after 24 h of fermentation, were not significantly different between or within donor types. Rate of early gas production was significantly different between donor types, but baseline-corrected TGP was not significantly different at 24 h. No dDM differences after 24 h of fermentation between or within donor types were detected. Gas production curves were different between donor types, being either a monophasic version of the sigmoidal model or an exponential curve for the heifers and the production animals, respectively. No differences were observed within type. Repeatability of rumen fluid (CVRF), calculated as the coefficient of variation, and the associated parameters, which were investigated, was best for methane yield (CVRFALL = 0.3%) and least for TGP at 3 h (CVRFALL = 3%). Repeatability was dependent on donor type.

Large-scale analysis of sheep rumen metagenome profiles captured by reduced representation sequencing reveals individual profiles are influenced by the environment and genetics of the host

BACKGROUND

Producing animal protein while reducing the animal's impact on the environment, e.g., through improved feed efficiency and lowered methane emissions, has gained interest in recent years. Genetic selection is one possible path to reduce the environmental impact of livestock production, but these traits are difficult and expensive to measure on many animals. The rumen microbiome may serve as a proxy for these traits due to its role in feed digestion. Restriction enzyme-reduced representation sequencing (RE-RRS) is a high-throughput and cost-effective approach to rumen metagenome profiling, but the systematic (e.g., sequencing) and biological factors influencing the resulting reference based (RB) and reference free (RF) profiles need to be explored before widespread industry adoption is possible.

RESULTS

Metagenome profiles were generated by RE-RRS of 4,479 rumen samples collected from 1,708 sheep, and assigned to eight groups based on diet, age, time off feed, and country (New Zealand or Australia) at the time of sample collection. Systematic effects were found to have minimal influence on metagenome profiles. Diet was a major driver of differences between samples, followed by time off feed, then age of the sheep. The RF approach resulted in more reads being assigned per sample and afforded greater resolution when distinguishing between groups than the RB approach. Normalizing relative abundances within the sampling Cohort abolished structures related to age, diet, and time off feed, allowing a clear signal based on methane emissions to be elucidated. Genus-level abundances of rumen microbes showed low-to-moderate heritability and repeatability and were consistent between diets.

CONCLUSIONS

Variation in rumen metagenomic profiles was influenced by diet, age, time off feed and genetics. Not accounting for environmental factors may limit the ability to associate the profile with traits of interest. However, these differences can be accounted for by adjusting for Cohort effects, revealing robust biological signals. The abundances of some genera were consistently heritable and repeatable across different environments, suggesting that metagenomic profiles could be used to predict an individual's future performance, or performance of its offspring, in a range of environments. These results highlight the potential of using rumen metagenomic profiles for selection purposes in a practical, agricultural setting.

Applying assisted reproductive technology and reproductive management to reduce CO2-equivalent emission in dairy and beef cattle: a review

Methane emission from beef and dairy cattle combined contributes around 4.5-5.0% of total anthropogenic global methane. In addition to enteric methane (CH4) produced by the rumen, cattle production also contributes carbon dioxide (CO2) (feed), nitrous oxide (N2O) (feed production, manure) and other CH4 (manure) to the total greenhouse gas (GHG) budget of beef and dairy production systems. The relative contribution in standard dairy systems is typically enteric CH4 58%, feed 29% and manure 10%. Herds with low production efficiency can have an enteric CH4 contribution up to 90%. Digestibility of feed can impact CH4 emission intensity. Low fertility herds also have a greater enteric CH4 contribution. Animals with good feed conversion efficiency have a lower emission intensity of CH4/kg of meat or milk. Feed efficient heifers tend to be lean and have delayed puberty. Fertility is a major driver of profit in both beef and dairy cattle, and it is highly important to apply multi-trait selection when shifting herds towards improved efficiency and reduced CH4. Single nucleotide polymorphisms (SNPs) have been identified for feed efficiency in cattle and are used in genomic selection. SNPs can be utilized in artificial insemination and embryo transfer to increase the proportion of cattle that have the attributes of efficiency, fertility and reduced enteric CH4. Prepubertal heifers genomically selected for favourable traits can have oocytes recovered to produce IVF embryos. Reproductive technology is predicted to be increasingly adopted to reduce generation interval and accelerate the rate of genetic gain for efficiency, fertility and low CH4 in cattle. The relatively high contribution of cattle to anthropogenic global methane has focussed attention on strategies to reduce enteric CH4 without compromising efficiency and fertility. Assisted reproductive technology has an important role in achieving the goal of multiplying and distributing cattle that have good efficiency, fertility and low CH4.

Microbiability of milk composition and genetic control of microbiota effects in sheep

Recently, high-dimensional omics data are becoming available in larger quantities, and models have been developed that integrate them with genomics to understand in finer detail the relationship between genotype and phenotype, and thus improve the performance of genetic evaluations. Our objectives are to quantify the effect of the inclusion of microbiome data in the genetic evaluation for dairy traits in sheep, through the estimation of the heritability, microbiability, and how the microbiome effect on dairy traits decomposes into genetic and nongenetic parts. In this study we analyzed milk and rumen samples of 795 Lacaune dairy ewes. We included, as phenotype, dairy traits and milk fatty acids and proteins composition; as omics measurements, 16S rRNA rumen bacterial abundances; and as genotyping, 54K SNP chip for all ewes. Two nested genomic models were used: a first model to predict the individual contributions of the genetic and microbial abundances to phenotypes, and a second model to predict the additive genetic effect of the microbial community. In addition, microbiome-wide association studies for all dairy traits were applied using the 2,059 rumen bacterial abundances, and the genetic correlations between microbiome principal components and dairy traits were estimated. Results showed that in general the inclusion of both genetic and microbiome effect did not improve the fit of the model compared with the model with the genetic effect only. In addition, for all dairy traits the total heritability was equal to the direct heritability after fitting microbiota effects, due to a microbiability being almost zero for most dairy traits and heritability of the microbial community was very close to zero. Microbiome-wide association studies did not show operational taxonomic units with major effect for any of the dairy traits evaluated, and the genetic correlations between the first 5 principal components and dairy traits were low to moderate. So far, we can conclude that, using a substantial data set of 795 Lacaune dairy ewes, rumen bacterial abundances do not provide improved genetic evaluation for dairy traits in sheep.

Integrating Omics Technologies for a Comprehensive Understanding of the Microbiome and Its Impact on Cattle Production

Ruminant production holds a pivotal position within the global animal production and agricultural sectors. As population growth escalates, posing environmental challenges, a heightened emphasis is directed toward refining ruminant production systems. Recent investigations underscore the connection between the composition and functionality of the rumen microbiome and economically advantageous traits in cattle. Consequently, the development of innovative strategies to enhance cattle feed efficiency, while curbing environmental and financial burdens, becomes imperative. The advent of omics technologies has yielded fresh insights into metabolic health fluctuations in dairy cattle, consequently enhancing nutritional management practices. The pivotal role of the rumen microbiome in augmenting feeding efficiency by transforming low-quality feedstuffs into energy substrates for the host is underscored. This microbial community assumes focal importance within gut microbiome studies, contributing indispensably to plant fiber digestion, as well as influencing production and health variability in ruminants. Instances of compromised animal welfare can substantially modulate the microbiological composition of the rumen, thereby influencing production rates. A comprehensive global approach that targets both cattle and their rumen microbiota is paramount for enhancing feed efficiency and optimizing rumen fermentation processes. This review article underscores the factors that contribute to the establishment or restoration of the rumen microbiome post perturbations and the intricacies of host-microbiome interactions. We accentuate the elements responsible for responsible host-microbiome interactions and practical applications in the domains of animal health and production. Moreover, meticulous scrutiny of the microbiome and its consequential effects on cattle production systems greatly contributes to forging more sustainable and resilient food production systems, thereby mitigating the adverse environmental impact.

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.

Enteric methane emission of dairy cows supplemented with iodoform in a dose-response study

Enteric methane (CH4) emission is one of the major greenhouse gasses originating from cattle. Iodoform has in studies been found to be a potent mitigator of rumen CH4 formation in vitro. This study aimed to quantify potential of iodoform as an anti-methanogenic feed additive for dairy cows and investigate effects on feed intake, milk production, feed digestibility, rumen microbiome, and animal health indicators. The experiment was conducted as a 4 × 4 Latin square design using four lactating rumen, duodenal, and ileal cannulated Danish Holstein dairy cows. The treatments consisted of four different doses of iodoform (1) 0 mg/day, (2) 320 mg/day, (3) 640 mg/day, and (4) 800 mg/day. Iodoform was supplemented intra-ruminally twice daily. Each period consisted of 7-days of adaptation, 3-days of digesta and blood sampling, and 4-days of gas exchange measurements using respiration chambers. Milk yield and dry matter intake (DMI) were recorded daily. Rumen samples were collected for microbial analyses and investigated for fermentation parameters. Blood was sampled and analyzed for metabolic and health status indicators. Dry matter intake and milk production decreased linearly by maximum of 48% and 33%, respectively, with increasing dose. Methane yield (g CH4/kg DMI) decreased by maximum of 66%, while up to 125-fold increases were observed in hydrogen yield (g H2/kg DMI) with increasing dose of iodoform. Total tract digestibility of DM, OM, CP, C, NDF, and starch were unaffected by treatments, but large shifts, except for NDF, were observed for ruminal to small intestinal digestion of the nutrients. Some indicators of disturbed rumen microbial activity and fermentation dynamics were observed with increasing dose, but total number of ruminal bacteria was unaffected by treatment. Serum and plasma biomarkers did not indicate negative effects of iodoform on cow health. In conclusion, iodoform was a potent mitigator of CH4 emission. However, DMI and milk production were negatively affected and associated with indications of depressed ruminal fermentation. Future studies might reveal if depression of milk yield and feed intake can be avoided if iodoform is continuously administered by mixing it into a total mixed ration.

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.

Metagenomic approach to infer rumen microbiome derived traits of cattle

Ruminants enable the conversion of indigestible plant material into animal consumables, including dairy products, meat, and valuable fibers. Microbiome research is gaining popularity in livestock species because it aids in the knowledge of illnesses and efficiency processes in animals. In this study, we use WGS metagenomic data to thoroughly characterize the ruminal ecosystem of cows to infer positive and negative livestock traits determined by the microbiome. The rumen of cows from Argentina were described by combining different gene biomarkers, pathways composition and taxonomic information. Taxonomic characterization indicated that the two major phyla were Bacteroidetes and Firmicutes; in third place, Proteobacteria was highly represented followed by Actinobacteria; Prevotella, and Bacteroides were the most abundant genera. Functional profiling of carbohydrate-active enzymes indicated that members of the Glycoside Hydrolase (GH) class accounted for 52.2 to 55.6% of the total CAZymes detected, among them the most abundant were the oligosaccharide degrading enzymes. The diversity of GH families found suggested efficient hydrolysis of complex biomass. Genes of multidrug, macrolides, polymyxins, beta-lactams, rifamycins, tetracyclines, and bacitracin resistance were found below 0.12% of relative abundance. Furthermore, the clustering analysis of genera and genes that correlated to methane emissions or feed efficiency, suggested that the cows analysed could be regarded as low methane emitters and clustered with high feed efficiency reference animals. Finally, the combination of bioinformatic analyses used in this study can be applied to assess cattle traits difficult to measure and guide enhanced nutrition and breeding methods.

Integrative meta-omics in Galaxy and beyond

BACKGROUND

'Omics methods have empowered scientists to tackle the complexity of microbial communities on a scale not attainable before. Individually, omics analyses can provide great insight; while combined as "meta-omics", they enhance the understanding of which organisms occupy specific metabolic niches, how they interact, and how they utilize environmental nutrients. Here we present three integrative meta-omics workflows, developed in Galaxy, for enhanced analysis and integration of metagenomics, metatranscriptomics, and metaproteomics, combined with our newly developed web-application, ViMO (Visualizer for Meta-Omics) to analyse metabolisms in complex microbial communities.

RESULTS

In this study, we applied the workflows on a highly efficient cellulose-degrading minimal consortium enriched from a biogas reactor to analyse the key roles of uncultured microorganisms in complex biomass degradation processes. Metagenomic analysis recovered metagenome-assembled genomes (MAGs) for several constituent populations including Hungateiclostridium thermocellum, Thermoclostridium stercorarium and multiple heterogenic strains affiliated to Coprothermobacter proteolyticus. The metagenomics workflow was developed as two modules, one standard, and one optimized for improving the MAG quality in complex samples by implementing a combination of single- and co-assembly, and dereplication after binning. The exploration of the active pathways within the recovered MAGs can be visualized in ViMO, which also provides an overview of the MAG taxonomy and quality (contamination and completeness), and information about carbohydrate-active enzymes (CAZymes), as well as KEGG annotations and pathways, with counts and abundances at both mRNA and protein level. To achieve this, the metatranscriptomic reads and metaproteomic mass-spectrometry spectra are mapped onto predicted genes from the metagenome to analyse the functional potential of MAGs, as well as the actual expressed proteins and functions of the microbiome, all visualized in ViMO.

CONCLUSION

Our three workflows for integrative meta-omics in combination with ViMO presents a progression in the analysis of 'omics data, particularly within Galaxy, but also beyond. The optimized metagenomics workflow allows for detailed reconstruction of microbial community consisting of MAGs with high quality, and thus improves analyses of the metabolism of the microbiome, using the metatranscriptomics and metaproteomics workflows.

Selection for environmental variance shifted the gut microbiome composition driving animal resilience

BACKGROUND

Understanding how the host's microbiome shapes phenotypes and participates in the host response to selection is fundamental for evolutionists and animal and plant breeders. Currently, selection for resilience is considered a critical step in improving the sustainability of livestock systems. Environmental variance (V _E), the within-individual variance of a trait, has been successfully used as a proxy for animal resilience. Selection for reduced V _E could effectively shift gut microbiome composition; reshape the inflammatory response, triglyceride, and cholesterol levels; and drive animal resilience. This study aimed to determine the gut microbiome composition underlying the V _E of litter size (LS), for which we performed a metagenomic analysis in two rabbit populations divergently selected for low (n = 36) and high (n = 34) V _E of LS. Partial least square-discriminant analysis and alpha- and beta-diversity were computed to determine the differences in gut microbiome composition among the rabbit populations.

RESULTS

We identified 116 KEGG IDs, 164 COG IDs, and 32 species with differences in abundance between the two rabbit populations studied. These variables achieved a classification performance of the V _E rabbit populations of over than 80%. Compared to the high V _E population, the low V _E (resilient) population was characterized by an underrepresentation of Megasphaera sp., Acetatifactor muris, Bacteroidetes rodentium, Ruminococcus bromii, Bacteroidetes togonis, and Eggerthella sp. and greater abundances of Alistipes shahii, Alistipes putredinis, Odoribacter splanchnicus, Limosilactobacillus fermentum, and Sutterella, among others. Differences in abundance were also found in pathways related to biofilm formation, quorum sensing, glutamate, and amino acid aromatic metabolism. All these results suggest differences in gut immunity modulation, closely related to resilience.

CONCLUSIONS

This is the first study to show that selection for V _E of LS can shift the gut microbiome composition. The results revealed differences in microbiome composition related to gut immunity modulation, which could contribute to the differences in resilience among rabbit populations. The selection-driven shifts in gut microbiome composition should make a substantial contribution to the remarkable genetic response observed in the V _E rabbit populations. Video Abstract.

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.

The effects of host quantitative genetic architecture on the gut microbiota composition of Chinook salmon (Oncorhynchus tshawytscha)

The microbiota consists of microbes living in or on an organism and has been implicated in host health and function. Environmental and host-related factors were shown to shape host microbiota composition and diversity in many fish species, but the role of host quantitative architecture across populations and among families within a population is not fully characterized. Here, Chinook salmon were used to determine if inter-population differences and additive genetic variation within populations influenced the gut microbiota diversity and composition. Specifically, hybrid stocks of Chinook salmon were created by crossing males from eight populations with eggs from an inbred line created from self-fertilized hermaphrodite salmon. Based on high-throughput sequencing of the 16S rRNA gene, significant gut microbial community diversity and composition differences were found among the hybrid stocks. Furthermore, additive genetic variance components varied among hybrid stocks, indicative of population-specific heritability patterns, suggesting the potential to select for specific gut microbiota composition for aquaculture purposes. Determining the role of host genetics in shaping their gut microbiota has important implications for predicting population responses to environmental changes and will thus impact conservation efforts for declining populations of Chinook salmon.

Microbial signature inferred from genomic breeding selection on milk urea concentration and its relation to proxies of nitrogen-utilization efficiency in Holsteins

Increasing the nitrogen-utilization efficiency (NUE) of dairy cows by breeding selection would offer advantages from nutritional, environmental, and economic perspectives. Because data collection of NUE phenotypes is not feasible in large cow cohorts, the cow individual milk urea concentration (MU) has been suggested as an indicator trait. Considering the symbiotic interplay between dairy cows and their rumen microbiome, individual MU was thought to be influenced by host genetics and by the rumen microbiome, the latter in turn being partly attributed to host genetics. To enhance our knowledge of MU as an indicator trait for NUE, we aimed to identify differential abundant rumen microbial genera between Holstein cows with divergent genomic breeding values for MU (GBVMU; GBV_HMU vs. GBV_LMU, where H and L indicate high and low MU phenotypes, respectively). The microbial genera identified were further investigated for their correlations with MU and 7 additional NUE-associated traits in urine, milk, and feces in 358 lactating Holsteins. Statistical analysis of microbial 16S rRNA amplicon sequencing data revealed significantly higher abundances of the ureolytic genus Succinivibrionaceae UCG-002 in GBV_LMU cows, whereas GBV_HMU animals hosted higher abundances of Clostridia unclassified and Desulfovibrio. The entire discriminating ruminal signature of 24 microbial taxa included a further 3 genera of the Lachnospiraceae family that revealed significant correlations to MU values and were therefore proposed as considerable players in the GBVMU-microbiome-MU axis. The significant correlations of Prevotellaceae UCG-003, Anaerovibrio, Blautia, and Butyrivibrio abundances with MU measurements, milk nitrogen, and N content in feces suggested their contribution to genetically determined N-utilization in Holstein cows. The microbial genera identified might be considered for future breeding programs to enhance NUE in dairy herds.

Multi-omics reveals that the host-microbiome metabolism crosstalk of differential rumen bacterial enterotypes can regulate the milk protein synthesis of dairy cows

BACKGROUND

Dairy cows' lactation performance is the outcome of the crosstalk between ruminal microbial metabolism and host metabolism. However, it is still unclear to what extent the rumen microbiome and its metabolites, as well as the host metabolism, contribute to regulating the milk protein yield (MPY).

METHODS

The rumen fluid, serum and milk of 12 Holstein cows with the same diet (45% coarseness ratio), parity (2-3 fetuses) and lactation days (120-150 d) were used for the microbiome and metabolome analysis. Rumen metabolism (rumen metabolome) and host metabolism (blood and milk metabolome) were connected using a weighted gene co-expression network (WGCNA) and the structural equation model (SEM) analyses.

RESULTS

Two different ruminal enterotypes, with abundant Prevotella and Ruminococcus, were identified as type1 and type2. Of these, a higher MPY was found in cows with ruminal type2. Interestingly, [Ruminococcus] gauvreauii group and norank_f_Ruminococcaceae (the differential bacteria) were the hub genera of the network. In addition, differential ruminal, serum and milk metabolome between enterotypes were identified, where the cows with type2 had higher L-tyrosine of rumen, ornithine and L-tryptophan of serum, and tetrahydroneopterin, palmitoyl-L-carnitine, S-lactoylglutathione of milk, which could provide more energy and substrate for MPY. Further, based on the identified modules of ruminal microbiome, as well as ruminal serum and milk metabolome using WGCNA, the SEM analysis indicated that the key ruminal microbial module1, which contains the hub genera of the network ([Ruminococcus] gauvreauii group and norank_f_Ruminococcaceae) and high abundance of bacteria (Prevotella and Ruminococcus), could regulate the MPY by module7 of rumen, module2 of blood, and module7 of milk, which contained L-tyrosine and L-tryptophan. Therefore, in order to more clearly reveal the process of rumen bacterial regulation of MPY, we established the path of SEM based on the L-tyrosine, L-tryptophan and related components. The SEM based on the metabolites suggested that [Ruminococcus] gauvreauii group could inhibit the energy supply of serum tryptophan to MPY by milk S-lactoylglutathione, which could enhance pyruvate metabolism. Norank_f_Ruminococcaceae could increase the ruminal L-tyrosine, which could provide the substrate for MPY.

CONCLUSION

Our results indicated that the represented enterotype genera of Prevotella and Ruminococcus, and the hub genera of [Ruminococcus] gauvreauii group and norank_f_Ruminococcaceae could regulate milk protein synthesis by affecting the ruminal L-tyrosine and L-tryptophan. Moreover, the combined analysis of enterotype, WGCNA and SEM could be used to connect rumen microbial metabolism with host metabolism, which provides a fundamental understanding of the crosstalk between host and microorganisms in regulating the synthesis of milk composition.

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.

Predicting feed efficiency traits in growing lambs from their ruminal microbiota

Selecting feed-efficient sheep could improve the sustainability of this livestock production. However, most sheep breeding companies cannot afford to record feed intake to select feed-efficient animals. Past studies underlined the potential of omics data, including microbiota metabarcoding data, as proxies for feed efficiency. The study involved 277 Romane lambs from two lines divergently selected for residual feed intake (RFI). There were two objectives: check the consequences of selecting for feed efficiency over the rumen microbiota, and assess the predictive ability of the rumen microbiota for host traits. The study assessed two contrasting diets (concentrate diet and mixed diet) and two microbial groups (prokaryotes and eukaryotes). Discriminant analyses did not highlight any significant effect of sheep selection for residual feed intake on the rumen microbiota composition. Indeed, prokaryotic and eukaryotic microbiota compositions poorly discriminated the RFI lines, with averaged balanced error rates ranging from 45% to 55%. Correlations between host traits (feed efficiency and production traits) and their predictions from microbiota data varied between -0.07 and 0.56, depending on the trait, diet and sequencing. Feed intake was the most accurately predicted trait. However, predictions from fixed effects and BW were more accurate than or as accurate as predictions from the microbiota. Environmental effects can greatly affect the variability of microbiota compositions. Considering batch and environmental effects should be paramount when the predictive ability of the microbiota is assessed. This study argues why metabarcoding the rumen microbiota is not the best way to predict meat sheep production traits: fixed effects and BW were more cost-effective proxies and they led to similar or better predictive accuracies than microbiota metabarcoding (16S and 18S sequencing).

Rumen Microbiota Predicts Feed Efficiency of Primiparous Nordic Red Dairy Cows

Efficient feed utilization in dairy cows is crucial for economic and environmental reasons. The rumen microbiota plays a significant role in feed efficiency, but studies utilizing microbial data to predict host phenotype are limited. In this study, 87 primiparous Nordic Red dairy cows were ranked for feed efficiency during their early lactation based on residual energy intake, and the rumen liquid microbial ecosystem was subsequently evaluated using 16S rRNA amplicon and metagenome sequencing. The study used amplicon data to build an extreme gradient boosting model, demonstrating that taxonomic microbial variation can predict efficiency (r_test = 0.55). Prediction interpreters and microbial network revealed that predictions were based on microbial consortia and the efficient animals had more of the highly interacting microbes and consortia. Rumen metagenome data was used to evaluate carbohydrate-active enzymes and metabolic pathway differences between efficiency phenotypes. The study showed that an efficient rumen had a higher abundance of glycoside hydrolases, while an inefficient rumen had more glycosyl transferases. Enrichment of metabolic pathways was observed in the inefficient group, while efficient animals emphasized bacterial environmental sensing and motility over microbial growth. The results suggest that inter-kingdom interactions should be further analyzed to understand their association with the feed efficiency of animals.

The hindgut microbiome contributes to host oxidative stress in postpartum dairy cows by affecting glutathione synthesis process

BACKGROUND

Dairy cows are susceptible to postpartum systemic oxidative stress (OS), which leads to significant production loss and metabolic disorders. The gut microbiota has been linked to host health and stress levels. However, to what extent the gut microbiota is associated with postpartum OS remains unknown. In this study, the contribution of the fecal microbiota to postpartum systemic OS and its underlying mechanisms were investigated by integrating 16S rRNA gene sequencing, metagenomics, and metabolomics in postpartum dairy cattle and by transplanting fecal microbiota from cattle to mice.

RESULTS

A strong link was found between fecal microbial composition and postpartum OS, with an explainability of 43.1%. A total of 17 significantly differential bacterial genera and 19 species were identified between cows with high (HOS) and low OS (LOS). Among them, 9 genera and 16 species showed significant negative correlations with OS, and Marasmitruncus and Ruminococcus_sp._CAG:724 had the strongest correlations. The microbial functional analysis showed that the fecal microbial metabolism of glutamine, glutamate, glycine, and cysteine involved in glutathione synthesis was lower in HOS cows. Moreover, 58 significantly different metabolites were identified between HOS and LOS cows, and of these metabolites, 19 were produced from microbiota or cometabolism of microbiota and host. Furthermore, these microbial metabolites were enriched in the metabolism of glutamine, glutamate, glycine, and cysteine. The mice gavaged with HOS fecal microbiota had significantly higher OS and lower plasma glutathione peroxidase and glutathione content than those orally administered saline or LOS fecal microbiota.

CONCLUSIONS

Integrated results suggest that the fecal microbiota is responsible for OS and that lower glutathione production plays a causative role in HOS. These findings provide novel insights into the mechanisms of postpartum OS and potential regulatory strategies to alleviate OS in dairy cows. Video Abstract.

Heritability and genetic correlations between enteric methane production and concentration recorded by GreenFeed and sniffers on dairy cows

To reduce methane (CH₄) emissions of dairy cows by animal breeding, CH₄ measurements have to be recorded on thousands of individual cows. Currently, several techniques are used to phenotype cows for CH₄, differing in costs and applicability. However, there is uncertainty about the agreement between techniques. To judge the similarity and repeatability between measurements of different recording techniques, the repeatability, heritability, and genetic correlation are useful metrics. Therefore, our objective was to estimate (1) the repeatability and heritability for CH₄ and carbon dioxide production recorded by GreenFeed (GF) and for CH₄ and carbon dioxide concentration measured by cost-effective but less accurate sniffers, and (2) the genetic correlation between CH₄ recorded with these 2 different on farm and high throughput techniques. Data were available from repeated measurements of CH₄ production (grams/day) by GF units and of CH₄ concentration (ppm) by sniffers, recorded on commercial dairy farms in the Netherlands. The final data comprised 24,284 GF daily means from 822 cows, 170,826 sniffer daily means from 1,800 cows, and 1,786 daily means from 75 cows by both GF and sniffer (in the same period). Additionally, CH₄ records were averaged per week. For daily and weekly mean GF CH₄ the heritabilities were 0.19 ± 0.02 and 0.33 ± 0.04, and for daily and weekly mean sniffer CH₄ the heritabilities were similar and were 0.18 ± 0.01 and 0.32 ± 0.02, respectively. Phenotypic correlations between GF CH₄ production and sniffer CH₄ concentration were moderate (0.39 ± 0.03 for daily means and 0.37 ± 0.05 for weekly means). However, genetic correlations were high; 0.71 ± 0.13 for daily means and 0.76 ± 0.15 for weekly means. The high genetic correlation indicates that selection on low CH₄ concentrations (ppm) recorded by the cost-effective sniffer method, will result in reduced CH₄ production (grams/day) as recorded with GF.

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.

Breed and ruminal fraction effects on bacterial and archaeal community composition in sheep

While the breed of cattle can impact on the composition and structure of microbial communities in the rumen, breed-specific effects on rumen microbial communities have rarely been examined in sheep. In addition, rumen microbial composition can differ between ruminal fractions, and be associated with ruminant feed efficiency and methane emissions. In this study, 16S rRNA amplicon sequencing was used to investigate the effects of breed and ruminal fraction on bacterial and archaeal communities in sheep. Solid, liquid and epithelial rumen samples were obtained from a total of 36 lambs, across 4 different sheep breeds (Cheviot (n = 10), Connemara (n = 6), Lanark (n = 10) and Perth (n = 10)), undergoing detailed measurements of feed efficiency, who were offered a nut based cereal diet ad-libitum supplemented with grass silage. Our results demonstrate that the feed conversion ratio (FCR) was lowest for the Cheviot (most efficient), and highest for the Connemara breed (least efficient). In the solid fraction, bacterial community richness was lowest in the Cheviot breed, while Sharpea azabuensis was most abundant in the Perth breed. Lanark, Cheviot and Perth breeds exhibited a significantly higher abundance of epithelial associated Succiniclasticum compared to the Connemara breed. When comparing ruminal fractions, Campylobacter, Family XIII, Mogibacterium, and Lachnospiraceae UCG-008 were most abundant in the epithelial fraction. Our findings indicate that breed can impact the abundance of specific bacterial taxa in sheep while having little effect on the overall composition of the microbial community. This finding has implications for genetic selection breeding programs aimed at improving feed conversion efficiency of sheep. Furthermore, the variations in the distribution of bacterial species identified between ruminal fractions, notably between solid and epithelial fractions, reveals a rumen fraction bias, which has implications for sheep rumen sampling techniques.

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.

Enteric methane research and mitigation strategies for pastoral-based beef cattle production systems

Ruminant livestock play a key role in global society through the conversion of lignocellulolytic plant matter into high-quality sources of protein for human consumption. However, as a consequence of the digestive physiology of ruminant species, methane (CH₄), which originates as a byproduct of enteric fermentation, is accountable for 40% of global agriculture's carbon footprint and ~6% of global greenhouse gas (GHG) emissions. Therefore, meeting the increasing demand for animal protein associated with a growing global population while reducing the GHG intensity of ruminant production will be a challenge for both the livestock industry and the research community. In recent decades, numerous strategies have been identified as having the potential to reduce the methanogenic output of livestock. Dietary supplementation with antimethanogenic compounds, targeting members of the rumen methanogen community and/or suppressing the availability of methanogenesis substrates (mainly H₂ and CO₂), may have the potential to reduce the methanogenic output of housed livestock. However, reducing the environmental impact of pasture-based beef cattle may be a challenge, but it can be achieved by enhancing the nutritional quality of grazed forage in an effort to improve animal growth rates and ultimately reduce lifetime emissions. In addition, the genetic selection of low-CH₄-emitting and/or faster-growing animals will likely benefit all beef cattle production systems by reducing the methanogenic potential of future generations of livestock. Similarly, the development of other mitigation technologies requiring minimal intervention and labor for their application, such as anti-methanogen vaccines, would likely appeal to livestock producers, with high uptake among farmers if proven effective. Therefore, the objective of this review is to give a detailed overview of the CH₄ mitigation solutions, both currently available and under development, for temperate pasture-based beef cattle production systems. A description of ruminal methanogenesis and the technologies used to estimate enteric emissions at pastures are also presented.

Disentangling the causal relationship between rabbit growth and cecal microbiota through structural equation models

BACKGROUND

The effect of the cecal microbiome on growth of rabbits that were fed under different regimes has been studied previously. However, the term "effect" carries a causal meaning that can be confounded because of potential genetic associations between the microbiome and production traits. Structural equation models (SEM) can help disentangle such a complex interplay by decomposing the effect on a production trait into direct host genetics effects and indirect host genetic effects that are exerted through microbiota effects. These indirect effects can be estimated via structural coefficients that measure the effect of the microbiota on growth while the effects of the host genetics are kept constant. In this study, we applied the SEM approach to infer causal relationships between the cecal microbiota and growth of rabbits fed under ad libitum (ADG_AL) or restricted feeding (ADG_R).

RESULTS

We identified structural coefficients that are statistically different from 0 for 138 of the 946 operational taxonomic units (OTU) analyzed. However, only 15 and 38 of these 138 OTU had an effect greater than 0.2 phenotypic standard deviations (SD) on ADG_AL and ADG_R, respectively. Many of these OTU had a negative effect on both traits. The largest effects on ADG_R were exerted by an OTU that is taxonomically assigned to the Desulfovibrio genus (- 1.929 g/d, CSS-normalized OTU units) and by an OTU that belongs to the Ruminococcaceae family (1.859 g/d, CSS-normalized OTU units). For ADG_AL, the largest effect was from OTU that belong to the S24-7 family (- 1.907 g/d, CSS-normalized OTU units). In general, OTU that had a substantial effect had low to moderate estimates of heritability.

CONCLUSIONS

Disentangling how direct and indirect effects act on production traits is relevant to fully describe the processes of mediation but also to understand how these traits change before considering the application of an external intervention aimed at changing a given microbial composition by blocking/promoting the presence of a particular microorganism.

Genetic and microbiome analysis of feed efficiency in laying hens

Improving feed efficiency is an important target for poultry breeding. Feed efficiency is affected by host genetics and the gut microbiota, but many of the mechanisms remain elusive in laying hens, especially in the late laying period. In this study, we measured feed intake, body weight, and egg mass of 714 hens from a pedigreed line from 69 to 72 wk of age and calculated the residual feed intake (RFI) and feed conversion ratio (FCR). In addition, fecal samples were also collected for 16S ribosomal RNA gene sequencing (V4 region). Genetic analysis was then conducted in DMU packages by using AI-REML with animal model. Moderate heritability estimates for FCR (h² = 0.31) and RFI (h² = 0.52) were observed, suggesting that proper selection programs can directly improve feed efficiency. Genetically, RFI was less correlated with body weight and egg mass than that of FCR. The phenotypic variance explained by gut microbial variance is defined as the microbiability (m²). The microbiability estimates for FCR (m² = 0.03) and RFI (m² = 0.16) suggested the gut microbiota was also involved in the regulation of feed efficiency. In addition, our results showed that the effect of host genetics on fecal microbiota was minor in three aspects: 1) microbial diversity indexes had low heritability estimates, and genera with heritability estimates more than 0.1 accounted for only 1.07% of the tested fecal microbiota; 2) the genetic relationship correlations between host genetics and different microbial distance were very weak, ranging from -0.0057 to -0.0003; 3) the microbial distance between different kinships showed no significant difference. Since the RFI has the highest microbiability, we further screened out three genera, including Anaerosporobacter, Candidatus Stoquefichus, and Fournierella, which were negatively correlated with RFI and played positive roles in improving the feed efficiency. These findings contribute to a great understanding of the genetic background and microbial influences on feed efficiency.