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

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

Estimating Microbial Protein Synthesis in the Rumen-Can 'Omics' Methods Provide New Insights into a Long-Standing Question?

Rumen microbial protein synthesis (MPS) provides at least half of the amino acids for the synthesis of milk and meat protein in ruminants. As such, it is fundamental to global food protein security. Estimating microbial protein is central to diet formulation, maximising nitrogen (N)-use efficiency and reducing N losses to the environment. Whilst factors influencing MPS are well established in vitro, techniques for in vivo estimates, including older techniques with cannulated animals and the more recent technique based on urinary purine derivative (UPD) excretion, are subject to large experimental errors. Consequently, models of MPS used in protein rationing are imprecise, resulting in wasted feed protein and unnecessary N losses to the environment. Newer 'omics' techniques are used to characterise microbial communities, their genes and resultant proteins and metabolites. An analysis of microbial communities and genes has recently been used successfully to model complex rumen-related traits, including feed conversion efficiency and methane emissions. Since microbial proteins are more directly related to microbial genes, we expect a strong relationship between rumen metataxonomics/metagenomics and MPS. The main aims of this review are to gauge the understanding of factors affecting MPS, including the use of the UPD technique, and explore whether omics-focused studies could improve the predictability of MPS, with a focus on beef cattle.

Identification of intestinal and fecal microbial biomarkers using a porcine social stress model

Understanding the relationships between social stress and the gastrointestinal microbiota, and how they influence host health and performance is expected to have many scientific and commercial implementations in different species, including identification and improvement of challenges to animal welfare and health. In particular, the study of the stress impact on the gastrointestinal microbiota of pigs may be of interest as a model for human health. A porcine stress model based on repeated regrouping and reduced space allowance during the last 4 weeks of the finishing period was developed to identify stress-induced changes in the gut microbiome composition. The application of the porcine stress model resulted in a significant increase in salivary cortisol concentration over the course of the trial and decreased growth performance and appetite. The applied social stress resulted in 32 bacteria being either enriched (13) or depleted (19) in the intestine and feces. Fecal samples showed a greater number of microbial genera influenced by stress than caecum or colon samples. Our trial revealed that the opportunistic pathogens Treponema and Clostridium were enriched in colonic and fecal samples from stressed pigs. Additionally, genera such as Streptococcus, Parabacteroides, Desulfovibrio, Terrisporobacter, Marvinbryantia, and Romboutsia were found to be enriched in response to social stress. In contrast, the genera Prevotella, Faecalibacterium, Butyricicoccus, Dialister, Alloprevotella, Megasphaera, and Mitsuokella were depleted. These depleted bacteria are of great interest because they synthesize metabolites [e.g., short-chain fatty acids (SCFA), in particular, butyrate] showing beneficial health benefits due to inhibitory effects on pathogenic bacteria in different animal species. Of particular interest are Dialister and Faecalibacterium, as their depletion was identified in a human study to be associated with inferior quality of life and depression. We also revealed that some pigs were more susceptible to pathogens as indicated by large enrichments of opportunistic pathogens of Clostridium, Treponema, Streptococcus and Campylobacter. Generally, our results provide further evidence for the microbiota-gut-brain axis as indicated by an increase in cortisol concentration due to social stress regulated by the hypothalamic-pituitary-adrenal axis, and a change in microbiota composition, particularly of bacteria known to be associated with pathogenicity and mental health diseases.

KOunt: a reproducible KEGG orthologue abundance workflow

SUMMARY

Accurate gene prediction is essential for successful metagenome analysis. We present KOunt, a Snakemake pipeline, that precisely quantifies KEGG orthologue abundance.

AVAILABILITY AND IMPLEMENTATION

KOunt is available on GitHub: https://github.com/WatsonLab/KOunt. The KOunt reference database is available on figshare: https://doi.org/10.6084/m9.figshare.21269715. Test data are available at https://doi.org/10.6084/m9.figshare.22250152 and version 1.2.0 of KOunt at https://doi.org/10.6084/m9.figshare.23607834.

Genetic associations between human-directed behavior and intraspecific social aggression in growing pigs

This study estimated the genetic parameters for human-directed behavior and intraspecific social aggression traits in growing pigs, and explored the phenotypic correlations among them. Data on 2,413 growing pigs were available. Pigs were mixed into new social groups of 18 animals, at 69 ± 5.2 d of age and skin lesions (SL) were counted 24 h (SL24h) post-mixing. Individual behavioral responses to isolation in a weighing crate (CRATE) or when alone in an arena while a human directly approached them (IHAT) were assessed within 48 h post-mixing. Additionally, pigs were tested for behavioral responses to the presence of a single human observer walking in their home pen in a circular motion (WTP) within one (T1) and 4 wk post-mixing (T2) noting pigs that followed, nosed or bit the observer. Animal models were used to estimate genetic and phenotypic parameters for all studied traits. Heritabilities (h2) for SL, CRATE and IHAT responses were low to moderate (0.07 to 0.29), with the highest h2 estimated for speed of moving away from the approaching observer. Low but significant h2 were estimated for nosing (0.09) and biting (0.11) the observer at T2. Positive high genetic correlations (rg) were observed between CRATE and IHAT responses (0.52 to 0.93), and within SL traits (0.79 to 0.91) while positive low to high correlations between the estimated breeding values (rEBV) were estimated within the WTP test (0.24 to 0.59) traits. Positive moderate rg were observed between CRATE and central and posterior SL24h. The rEBV of CRATE and IHAT test responses and WTP test traits were low, mostly negative (-0.21 to 0.05) and not significant. Low positive rEBV (0.06 to 0.24) were observed between SL and the WTP test traits. Phenotypic correlations between CRATE and IHAT responses and SL or WTP test traits were mostly low and not significant. Under the conditions of this study, h2 estimates for all studied traits suggest they could be suitable as a method of phenotyping aggression and fear/boldness for genetic selection purposes. Additionally, genetic correlations between aggression and fear indicators were observed. These findings suggest selection to reduce the accumulation of lesions is likely to make pigs more relaxed in a crate environment, but to alter the engagement with humans in other contexts that depends on the location of the lesions under selection.

Different microbial genera drive methane emissions in beef cattle fed with two extreme diets

The ratio of forage to concentrate in cattle feeding has a major influence on the composition of the microbiota in the rumen and on the mass of methane produced. Using methane measurements and microbiota data from 26 cattle we aimed to investigate the relationships between microbial relative abundances and methane emissions, and identify potential biomarkers, in animals fed two extreme diets - a poor quality fresh cut grass diet (GRASS) or a high concentrate total mixed ration (TMR). Direct comparisons of the effects of such extreme diets on the composition of rumen microbiota have rarely been studied. Data were analyzed considering their multivariate and compositional nature. Diet had a relevant effect on methane yield of +10.6 g of methane/kg of dry matter intake for GRASS with respect to TMR, and on the centered log-ratio transformed abundance of 22 microbial genera. When predicting methane yield based on the abundance of 28 and 25 selected microbial genera in GRASS and TMR, respectively, we achieved cross-validation prediction accuracies of 66.5 ± 9% and 85 ± 8%. Only the abundance of Fibrobacter had a consistent negative association with methane yield in both diets, whereas most microbial genera were associated with methane yield in only one of the two diets. This study highlights the stark contrast in the microbiota controlling methane yield between animals fed a high concentrate diet, such as that found on intensive finishing units, and a low-quality grass forage that is often found in extensive grazing systems. This contrast must be taken into consideration when developing strategies to reduce methane emissions by manipulation of the rumen microbial composition.

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

BACKGROUND

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

RESULTS

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

CONCLUSION

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

Genetic Associations of Novel Behaviour Traits Derived from Social Network Analysis with Growth, Feed Efficiency, and Carcass Characteristics in Pigs

Reducing harmful aggressive behaviour remains a major challenge in pig production. Social network analysis (SNA) showed the potential in providing novel behavioural traits that describe the direct and indirect role of individual pigs in pen-level aggression. Our objectives were to (1) estimate the genetic parameters of these SNA traits, and (2) quantify the genetic associations between the SNA traits and commonly used performance measures: growth, feed intake, feed efficiency, and carcass traits. The animals were video recorded for 24 h post-mixing. The observed fighting behaviour of each animal was used as input for the SNA. A Bayesian approach was performed to estimate the genetic parameters of SNA traits and their association with the performance traits. The heritability estimates for all SNA traits ranged from 0.01 to 0.35. The genetic correlations between SNA and performance traits were non-significant, except for weighted degree with hot carcass weight, and for both betweenness and closeness centrality with test daily gain, final body weight, and hot carcass weight. Our results suggest that SNA traits are amenable for selective breeding. Integrating these traits with other behaviour and performance traits may potentially help in building up future strategies for simultaneously improving welfare and performance in commercial pig farms.

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

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

Concepts and Consequences of a Core Gut Microbiota for Animal Growth and Development

Animal microbiomes are occasionally considered as an extension of host anatomy, physiology, and even their genomic architecture. Their compositions encompass variable and constant portions when examined across multiple hosts. The latter, termed the core microbiome, is viewed as more accommodated to its host environment and suggested to benefit host fitness. Nevertheless, discrepancies in its definitions, characteristics, and importance to its hosts exist across studies. We survey studies that characterize the core microbiome, detail its current definitions and available methods to identify it, and emphasize the crucial need to upgrade and standardize the methodologies among studies. We highlight ruminants as a case study and discuss the link between the core microbiome and host physiology and genetics, as well as potential factors that shape it. We conclude with main directives of action to better understand the host-core microbiome axis and acquire the necessary insights into its controlled modulation. Expected final online publication date for the Annual Review of Animal Biosciences, Volume 10 is February 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Comparison of HPLC and NMR for quantification of the main volatile fatty acids in rumen digesta

Accurate quantification of volatile fatty acid (VFA) concentrations in rumen fluid are essential for research on rumen metabolism. The study comprehensively investigated the pros and cons of High-performance liquid chromatography (HPLC) and 1H Nuclear magnetic resonance (1H-NMR) analysis methods for rumen VFAs quantification. We also investigated the performance of several commonly used data pre-treatments for the two sets of data using correlation analysis, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The molar proportion and reliability analysis demonstrated that the two approaches produce highly consistent VFA concentrations. In the pre-processing of NMR spectra, line broadening and shim correction may reduce estimated concentrations of metabolites. We observed differences in results using multiplet of different protons from one compound and identified "handle signals" that provided the most consistent concentrations. Different data pre-treatment strategies tested with both HPLC and NMR significantly affected the results of downstream data analysis. "Normalized by sum" pre-treatment can eliminate a large number of positive correlations between NMR-based VFA. A "Combine" strategy should be the first choice when calculating the correlation between metabolites or between samples. The PCA and PLS-DA suggest that except for "Normalize by sum", pre-treatments should be used with caution.

Evaluation of direct and maternal responses in reproduction traits based on different selection strategies for postnatal piglet survival in a selection experiment

BACKGROUND

Postnatal piglet survival is important both in economic and animal welfare terms. It is influenced by the piglet's own direct genetic effects and by maternal genetic effects of the dam, associated with milk production and mothering abilities. These genetic effects might be correlated, affected by other non-genetic factors and unfavourably associated with other reproduction traits such as litter size, which makes the development of optimal breeding strategies a challenge. To identify the optimum selection strategy for piglet survival, a selection experiment was carried out to compare responses in survival and reproduction traits to selection on only direct, only maternal, or both genetic effects of postnatal survival. The data of the experiment were recorded from outdoor reared pigs, with first- and second-generation sires selected based on their estimated breeding values for maternal and direct effects of postnatal survival of indoor reared offspring, respectively, with the opportunity to identify potential genotype-by-environment interaction.

RESULTS

A Bayesian multivariate threshold-linear model that was fitted to data on 22,483 piglets resulted in significant (Pr(h2 > 0) = 1.00) estimates of maternal and direct heritabilities between 0.12 and 0.18 for survival traits and between 0.29 and 0.36 for birth weight, respectively. Selection for direct genetic effects resulted in direct and maternal responses in postnatal survival of 1.11% ± 0.17 and - 0.49% ± 0.10, respectively, while selection for maternal genetic effects led to greater direct and maternal responses, of 5.20% ± 0.34 and 1.29% ± 0.20, respectively, in part due to unintentional within-litter selection. Selection for both direct and maternal effects revealed a significant lower direct response (- 1.04% ± 0.12) in comparison to its expected response from single-effect selection, caused by interactions between direct and maternal effects.

CONCLUSIONS

Selection successfully improved post- and perinatal survival and birth weight, which indicates that they are genetically determined and that genotype-by-environment interactions between outdoor (experimental data) and indoor (selection data) housed pigs were not important for these traits. A substantially increased overall (direct plus maternal) response was obtained using selection for maternal versus direct or both direct and maternal effects, suggesting that the maternal genetic effects are the main limiting factor for improving piglet survival on which selection pressure should be emphasized.

Unravelling the Role of Rumen Microbial Communities, Genes, and Activities on Milk Fatty Acid Profile Using a Combination of Omics Approaches

Milk products are an important component of human diets, with beneficial effects for human health, but also one of the major sources of nutritionally undesirable saturated fatty acids (SFA). Recent discoveries showing the importance of the rumen microbiome on dairy cattle health, metabolism and performance highlight that milk composition, and potentially milk SFA content, may also be associated with microorganisms, their genes and their activities. Understanding these mechanisms can be used for the development of cost-effective strategies for the production of milk with less SFA. This work aimed to compare the rumen microbiome between cows producing milk with contrasting FA profile and identify potentially responsible metabolic-related microbial mechanisms. Forty eight Holstein dairy cows were fed the same total mixed ration under the same housing conditions. Milk and rumen fluid samples were collected from all cows for the analysis of fatty acid profiles (by gas chromatography), the abundances of rumen microbiome communities and genes (by whole-genome-shotgun metagenomics), and rumen metabolome (using 500 MHz nuclear magnetic resonance). The following groups: (i) 24 High-SFA (66.9-74.4% total FA) vs. 24 Low-SFA (60.2-66.6%% total FA) cows, and (ii) 8 extreme High-SFA (69.9-74.4% total FA) vs. 8 extreme Low-SFA (60.2-64.0% total FA) were compared. Rumen of cows producing milk with more SFA were characterized by higher abundances of the lactic acid bacteria Lactobacillus, Leuconostoc, and Weissella, the acetogenic Proteobacteria Acetobacter and Kozakia, Mycobacterium, two fungi (Cutaneotrichosporon and Cyphellophora), and at a lesser extent Methanobrevibacter and the protist Nannochloropsis. Cows carrying genes correlated with milk FA also had higher concentrations of butyrate, propionate and tyrosine and lower concentrations of xanthine and hypoxanthine in the rumen. Abundances of rumen microbial genes were able to explain between 76 and 94% on the variation of the most abundant milk FA. Metagenomics and metabolomics analyses highlighted that cows producing milk with contrasting FA profile under the same diet, also differ in their rumen metabolic activities in relation to adaptation to reduced rumen pH, carbohydrate fermentation, and protein synthesis and metabolism.

A heat diffusion multilayer network approach for the identification of functional biomarkers in rumen methane emissions

A better understanding of rumen microbial interactions is crucial for the study of rumen metabolism and methane emissions. Metagenomics-based methods can explore the relationship between microbial genes and metabolites to clarify the effect of microbial function on the host phenotype. This study investigated the rumen microbial mechanisms of methane metabolism in cattle by combining metagenomic data and network-based methods. Based on the relative abundance of 1461 rumen microbial genes and the main volatile fatty acids (VFAs), a multilayer heterogeneous network was constructed, and the functional modules associated with metabolite-microbial genes were obtained by heat diffusion algorithm. The PLS model by integrating data from VFAs and microbial genes explained 72.98% variation of methane emissions. Compared with single-layer networks, more previously reported biomarkers of methane prediction can be captured by the multilayer network. More biomarkers with the rank of top 20 topological centralities were from the PLS models of diffusion subsets. The heat diffusion algorithm is different from the strategy used by the microbial metabolic system to understand methane phenotype. It inferred 24 novel biomarkers that were preferentially affected by changes in specific VFAs. Results showed that the heat diffusion multilayer network approach improved the understanding of the microbial patterns of VFAs affecting methane emissions which represented by the functional microbial genes.

MAGpy: a reproducible pipeline for the downstream analysis of metagenome-assembled genomes (MAGs)

Motivation

Metagenomics is a powerful tool for assaying the DNA from every genome present in an environment. Recent advances in bioinformatics have enabled the rapid assembly of near-complete metagenome-assembled genomes (MAGs), and there is a need for reproducible pipelines that can annotate and characterize thousands of genomes simultaneously, to enable identification and functional characterization.

Results

Here we present MAGpy, a scalable and reproducible pipeline that takes multiple genome assemblies as FASTA and compares them to several public databases, checks quality, suggests a taxonomy and draws a phylogenetic tree.

Availability and implementation

MAGpy is available on github: https://github.com/WatsonLab/MAGpy.

Supplementary information

Supplementary data are available at Bioinformatics online.

Identification of Microbial Genetic Capacities and Potential Mechanisms Within the Rumen Microbiome Explaining Differences in Beef Cattle Feed Efficiency

In this study, Bos Taurus cattle offered one high concentrate diet (92% concentrate-8% straw) during two independent trials allowed us to classify 72 animals comprising of two cattle breeds as "Low" or "High" feed efficiency groups. Digesta samples were taken from individual beef cattle at the abattoir. After metagenomic sequencing, the rumen microbiome composition and genes were determined. Applying a targeted approach based on current biological evidence, 27 genes associated with host-microbiome interaction activities were selected. Partial least square analysis enabled the identification of the most significant genes and genera of feed efficiency (VIP > 0.8) across years of the trial and breeds when comparing all potential genes or genera together. As a result, limited number of genes explained about 40% of the variability in both feed efficiency indicators. Combining information from rumen metagenome-assembled genomes and partial least square analysis results, microbial genera carrying these genes were determined and indicated that a limited number of important genera impacting on feed efficiency. In addition, potential mechanisms explaining significant difference between Low and High feed efficiency animals were analyzed considering, based on the literature, their gastrointestinal location of action. High feed efficiency animals were associated with microbial species including several Eubacterium having the genetic capacity to form biofilm or releasing metabolites like butyrate or propionate known to provide a greater contribution to cattle energy requirements compared to acetate. Populations associated with fucose sensing or hemolysin production, both mechanisms specifically described in the lower gut by activating the immune system to compete with pathogenic colonizers, were also identified to affect feed efficiency using rumen microbiome information. Microbial mechanisms associated with low feed efficiency animals involved potential pathogens within Proteobacteria and Spirochaetales, releasing less energetic substrates (e.g., acetate) or producing sialic acid to avoid the host immune system. Therefore, this study focusing on genes known to be involved in host-microbiome interaction improved the identification of rumen microbial genetic capacities and potential mechanisms significantly impacting on feed efficiency in beef cattle fed high concentrate diet.

Improving the Inference of Co-Occurrence Networks in the Bovine Rumen Microbiome

The importance of the composition and signature of rumen microbial communities has gained increasing attention. One of the key techniques was to infer co-abundance networks through correlation analysis based on relative abundances. While substantial insights and progress have been made, it has been found that due to the compositional nature of data, correlation analysis derived from relative abundance could produce misleading results and spurious associations. In this study, we proposed the use of a framework including a compendium of two correlation measures and three dissimilarity metrics in an attempt to mitigate the compositional effect in the inference of significant associations in the bovine rumen microbiome. We tested the framework on rumen microbiome data including both 16S rRNA and KEGG genes associated with methane production in cattle. Based on the identification of significant positive and negative associations supported by multiple metrics, two co-occurrence networks, e.g., co-presence and mutual-exclusion networks, were constructed. Significant modules associated with methane emissions were identified. In comparison to previous studies, our analysis demonstrates that deriving microbial associations based on the correlations between relative abundances may not only lead to missing information but also produce spurious associations. To bridge together different co-presence and mutual-exclusion relations, a multiplex network model has been proposed for integrative analysis of co-occurrence networks which has great potential to support the prediction of animal phytotypes and to provide additional insights into biological mechanisms of the microbiome associated with the traits.

Identification of Complex Rumen Microbiome Interaction Within Diverse Functional Niches as Mechanisms Affecting the Variation of Methane Emissions in Bovine

A network analysis including relative abundances of all ruminal microbial genera (archaea, bacteria, fungi, and protists) and their genes was performed to improve our understanding of how the interactions within the ruminal microbiome affects methane emissions (CH4). Metagenomics and CH4 data were available from 63 bovines of a two-breed rotational cross, offered two basal diets. Co-abundance network analysis revealed 10 clusters of functional niches. The most abundant hydrogenotrophic Methanobacteriales with key microbial genes involved in methanogenesis occupied a different functional niche (i.e., "methanogenesis" cluster) than methylotrophic Methanomassiliicoccales (Candidatus Methanomethylophylus) and acetogens (Blautia). Fungi and protists clustered together and other plant fiber degraders like Fibrobacter occupied a seperate cluster. A Partial Least Squares analysis approach to predict CH4 variation in each cluster showed the methanogenesis cluster had the best prediction ability (57.3%). However, the most important explanatory variables in this cluster were genes involved in complex carbohydrate degradation, metabolism of sugars and amino acids and Candidatus Azobacteroides carrying nitrogen fixation genes, but not methanogenic archaea and their genes. The cluster containing Fibrobacter, isolated from other microorganisms, was positively associated with CH4 and explained 49.8% of its variability, showing fermentative advantages compared to other bacteria and fungi in providing substrates (e.g., formate) for methanogenesis. In other clusters, genes with enhancing effect on CH4 were related to lactate and butyrate (Butyrivibrio and Pseudobutyrivibrio) production and simple amino acids metabolism. In comparison, ruminal genes negatively related to CH4 were involved in carbohydrate degradation via lactate and succinate and synthesis of more complex amino acids by γ-Proteobacteria. When analyzing low- and high-methane emitters data in separate networks, competition between methanogens in the methanogenesis cluster was uncovered by a broader diversity of methanogens involved in the three methanogenesis pathways and larger interactions within and between communities in low compared to high emitters. Generally, our results suggest that differences in CH4 are mainly explained by other microbial communities and their activities rather than being only methanogens-driven. Our study provides insight into the interactions of the rumen microbial communities and their genes by uncovering functional niches affecting CH4, which will benefit the development of efficient CH4 mitigation strategies.

Temporal stability of the rumen microbiota in beef cattle, and response to diet and supplements

BACKGROUND

Dietary intake is known to be a driver of microbial community dynamics in ruminants. Beef cattle go through a finishing phase that typically includes very high concentrate ratios in their feed, with consequent effects on rumen metabolism including methane production. This longitudinal study was designed to measure dynamics of the rumen microbial community in response to the introduction of high concentrate diets fed to beef cattle during the finishing period. A cohort of 50 beef steers were fed either of two basal diet formulations consisting of approximately 10:90 or 50:50 forage:concentrate ratios respectively. Nitrate and oil rich supplements were also added either individually or in combination. Digesta samples were taken at time points over ~ 200 days during the finishing period of the cattle to measure the adaptation to the basal diet and long-term stability of the rumen microbiota.

RESULTS

16S rRNA gene amplicon libraries were prepared from 313 rumen digesta samples and analysed at a depth of 20,000 sequences per library. Bray Curtis dissimilarity with analysis of molecular variance (AMOVA) revealed highly significant (p < 0.001) differences in microbiota composition between cattle fed different basal diets, largely driven by reduction of fibre degrading microbial groups and increased relative abundance of an unclassified Gammaproteobacteria OTU in the high concentrate fed animals. Conversely, the forage-based diet was significantly associated with methanogenic archaea. Within basal diet groups, addition of the nitrate and combined supplements had lesser, although still significant, impacts on microbiota dissimilarity compared to pre-treatment time points and controls. Measurements of the response and stability of the microbial community over the time course of the experiment showed continuing adaptation up to 25 days in the high concentrate groups. After this time point, however, no significant variability was detected.

CONCLUSIONS

High concentrate diets that are typically fed to finishing beef cattle can have a significant effect on the microbial community in the rumen. Inferred metabolic activity of the different microbial communities associated with each of the respective basal diets explained differences in methane and short chain fatty acid production between cattle. Longitudinal sampling revealed that once adapted to a change in diet, the rumen microbial community remains in a relatively stable alternate state.

Correction to: The rumen microbiome as a reservoir of antimicrobial resistance and pathogenicity genes is directly affected by diet in beef cattle

Following publication of the original article [1], the authors reported an error in the Additional file 1.

Identification of Rumen Microbial Genes Involved in Pathways Linked to Appetite, Growth, and Feed Conversion Efficiency in Cattle

The rumen microbiome is essential for the biological processes involved in the conversion of feed into nutrients that can be utilized by the host animal. In the present research, the influence of the rumen microbiome on feed conversion efficiency, growth rate, and appetite of beef cattle was investigated using metagenomic data. Our aim was to explore the associations between microbial genes and functional pathways, to shed light on the influence of bacterial enzyme expression on host phenotypes. Two groups of cattle were selected on the basis of their high and low feed conversion ratio. Microbial DNA was extracted from rumen samples, and the relative abundances of microbial genes were determined via shotgun metagenomic sequencing. Using partial least squares analyses, we identified sets of 20, 14, 17, and 18 microbial genes whose relative abundances explained 63, 65, 66, and 73% of the variation of feed conversion efficiency, average daily weight gain, residual feed intake, and daily feed intake, respectively. The microbial genes associated with each of these traits were mostly different, but highly correlated traits such as feed conversion ratio and growth rate showed some overlapping genes. Consistent with this result, distinct clusters of a coabundance network were enriched with microbial genes identified to be related with feed conversion ratio and growth rate or daily feed intake and residual feed intake. Microbial genes encoding for proteins related to cell wall biosynthesis, hemicellulose, and cellulose degradation and host–microbiome crosstalk (e.g., aguA, ptb, K01188, and murD) were associated with feed conversion ratio and/or average daily gain. Genes related to vitamin B12 biosynthesis, environmental information processing, and bacterial mobility (e.g., cobD, tolC, and fliN) were associated with residual feed intake and/or daily feed intake. This research highlights the association of the microbiome with feed conversion processes, influencing growth rate and appetite, and it emphasizes the opportunity to use relative abundances of microbial genes in the prediction of these performance traits, with potential implementation in animal breeding programs and dietary interventions.

Addressing Global Ruminant Agricultural Challenges Through Understanding the Rumen Microbiome: Past, Present, and Future

The rumen is a complex ecosystem composed of anaerobic bacteria, protozoa, fungi, methanogenic archaea and phages. These microbes interact closely to breakdown plant material that cannot be digested by humans, whilst providing metabolic energy to the host and, in the case of archaea, producing methane. Consequently, ruminants produce meat and milk, which are rich in high-quality protein, vitamins and minerals, and therefore contribute to food security. As the world population is predicted to reach approximately 9.7 billion by 2050, an increase in ruminant production to satisfy global protein demand is necessary, despite limited land availability, and whilst ensuring environmental impact is minimized. Although challenging, these goals can be met, but depend on our understanding of the rumen microbiome. Attempts to manipulate the rumen microbiome to benefit global agricultural challenges have been ongoing for decades with limited success, mostly due to the lack of a detailed understanding of this microbiome and our limited ability to culture most of these microbes outside the rumen. The potential to manipulate the rumen microbiome and meet global livestock challenges through animal breeding and introduction of dietary interventions during early life have recently emerged as promising new technologies. Our inability to phenotype ruminants in a high-throughput manner has also hampered progress, although the recent increase in “omic” data may allow further development of mathematical models and rumen microbial gene biomarkers as proxies. Advances in computational tools, high-throughput sequencing technologies and cultivation-independent “omics” approaches continue to revolutionize our understanding of the rumen microbiome. This will ultimately provide the knowledge framework needed to solve current and future ruminant livestock challenges.

Fat accretion measurements strengthen the relationship between feed conversion efficiency and Nitrogen isotopic discrimination while rumen microbial genes contribute little

The use of biomarkers for feed conversion efficiency (FCE), such as Nitrogen isotopic discrimination (Δ15N), facilitates easier measurement and may be useful in breeding strategies. However, we need to better understand the relationship between FCE and Δ15N, particularly the effects of differences in the composition of liveweight gain and rumen N metabolism. Alongside measurements of FCE and Δ15N, we estimated changes in body composition and used dietary treatments with and without nitrates, and rumen metagenomics to explore these effects. Nitrate fed steers had reduced FCE and higher Δ15N in plasma compared to steers offered non-nitrate containing diets. The negative relationship between FCE and Δ15N was strengthened with the inclusion of fat depth change at the 3rd lumbar vertebrae, but not with average daily gain. We identified 1,700 microbial genes with a relative abundance >0.01% of which, 26 were associated with Δ15N. These genes explained 69% of variation in Δ15N and showed clustering in two distinct functional networks. However, there was no clear relationship between their relative abundances and Δ15N, suggesting that rumen microbial genes contribute little to Δ15N. Conversely, we show that changes in the composition of gain (fat accretion) provide additional strength to the relationship between FCE and Δ15N.

Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen

The cow rumen is adapted for the breakdown of plant material into energy and nutrients, a task largely performed by enzymes encoded by the rumen microbiome. Here we present 913 draft bacterial and archaeal genomes assembled from over 800 Gb of rumen metagenomic sequence data derived from 43 Scottish cattle, using both metagenomic binning and Hi-C-based proximity-guided assembly. Most of these genomes represent previously unsequenced strains and species. The draft genomes contain over 69,000 proteins predicted to be involved in carbohydrate metabolism, over 90% of which do not have a good match in public databases. Inclusion of the 913 genomes presented here improves metagenomic read classification by sevenfold against our own data, and by fivefold against other publicly available rumen datasets. Thus, our dataset substantially improves the coverage of rumen microbial genomes in the public databases and represents a valuable resource for biomass-degrading enzyme discovery and studies of the rumen microbiome.

Identification, Comparison, and Validation of Robust Rumen Microbial Biomarkers for Methane Emissions Using Diverse Bos Taurus Breeds and Basal Diets

Previous shotgun metagenomic analyses of ruminal digesta identified some microbial information that might be useful as biomarkers to select cattle that emit less methane (CH4), which is a potent greenhouse gas. It is known that methane production (g/kgDMI) and to an extent the microbial community is heritable and therefore biomarkers can offer a method of selecting cattle for low methane emitting phenotypes. In this study a wider range of Bos Taurus cattle, varying in breed and diet, was investigated to determine microbial communities and genetic markers associated with high/low CH4 emissions. Digesta samples were taken from 50 beef cattle, comprising four cattle breeds, receiving two basal diets containing different proportions of concentrate and also including feed additives (nitrate or lipid), that may influence methane emissions. A combination of partial least square analysis and network analysis enabled the identification of the most significant and robust biomarkers of CH4 emissions (VIP > 0.8) across diets and breeds when comparing all potential biomarkers together. Genes associated with the hydrogenotrophic methanogenesis pathway converting carbon dioxide to methane, provided the dominant biomarkers of CH4 emissions and methanogens were the microbial populations most closely correlated with CH4 emissions and identified by metagenomics. Moreover, these genes grouped together as confirmed by network analysis for each independent experiment and when combined. Finally, the genes involved in the methane synthesis pathway explained a higher proportion of variation in CH4 emissions by PLS analysis compared to phylogenetic parameters or functional genes. These results confirmed the reproducibility of the analysis and the advantage to use these genes as robust biomarkers of CH4 emissions. Volatile fatty acid concentrations and ratios were significantly correlated with CH4, but these factors were not identified as robust enough for predictive purposes. Moreover, the methanotrophic Methylomonas genus was found to be negatively correlated with CH4. Finally, this study confirmed the importance of using robust and applicable biomarkers from the microbiome as a proxy of CH4 emissions across diverse production systems and environments.

Changes in feed intake during isolation stress in respiration chambers may impact methane emissions assessment

Respiration chambers are considered the ‘gold standard’ technique for measuring in vivo methane (CH4) emissions in live animals. However, the imposed isolation required may alter feeding behaviour and intake, which ultimately impact CH4 emissions. The aim of this study was to assess the impact of isolation within respiration chambers on feed intake and CH4 emissions with two different diets and breeds of beef cattle. In addition, a routine stressor (transport) was used to examine the relationship between individual stress responsiveness and changes in feed intake during isolation. Eighty-four steers (castrated males) (569 ± 5.7 kg bodyweight, BW) were divided into two groups and each group fed with one of two basal diets consisting of (g/kg dry matter, DM) either 50 : 50 (Mixed) or 8 : 92 (Concentrate) forage to concentrate ratios. Within each basal diet there were three supplementation treatments: (1) control, (2) calcium nitrate, and (3) rapeseed cake. The stress biomarkers plasma cortisol, creatine kinase (CK), and free fatty acids (FFA) were determined before (0 h) and after (30 min, 3 h, 6 h and 9 h) a 30-min journey, when steers were transported to the respiration chamber facilities. Methane emissions were measured over a 3-day period using individual respiration chambers. Dry matter intake (DMI) was assessed within the group-housed pens (4 weeks before entry to training pen), in the training pens and the chambers. Cortisol, FFA and CK increased (P < 0.05) after transport confirming a stress response. DMI (g/kg BW) decreased (P < 0.001) during isolation in the training pens (14.7 ± 0.28) and the chambers (14.3 ± 0.26) compared with that of the same animals in the group pens (16.8 ± 0.23). DMI during isolation decreased more in those animals which had an increased (P < 0.05) stress response during transport as measured by cortisol, FFA and CK. With the Mixed diet, the decline in DMI was estimated to result in an increase in CH4 (g/kg DMI) (r = 0.25, P = 0.001), which did not occur with the Concentrate diet. According to the results of this experiment, the stress associated with isolation reduces the DMI resulting in an increase in g CH4/kg DMI in fibrous diets. Habituation to isolation needs refinement in order to reduce the impact of stress on intake and therefore achieve more accurate estimates of CH4 emissions. Alternatively, modelling CH4 estimations according to behavioural and physiological changes associated with isolation stress would improve accuracy of CH4 estimations.

The rumen microbiome as a reservoir of antimicrobial resistance and pathogenicity genes is directly affected by diet in beef cattle

BACKGROUND

The emergence and spread of antimicrobial resistance is the most urgent current threat to human and animal health. An improved understanding of the abundance of antimicrobial resistance genes and genes associated with microbial colonisation and pathogenicity in the animal gut will have a major role in reducing the contribution of animal production to this problem. Here, the influence of diet on the ruminal resistome and abundance of pathogenicity genes was assessed in ruminal digesta samples taken from 50 antibiotic-free beef cattle, comprising four cattle breeds receiving two diets containing different proportions of concentrate.

RESULTS

Two hundred and four genes associated with antimicrobial resistance (AMR), colonisation, communication or pathogenicity functions were identified from 4966 metagenomic genes using KEGG identification. Both the diversity and abundance of these genes were higher in concentrate-fed animals. Chloramphenicol and microcin resistance genes were dominant in samples from forage-fed animals (P < 0.001), while aminoglycoside and streptomycin resistances were enriched in concentrate-fed animals. The concentrate-based diet also increased the relative abundance of Proteobacteria, which includes many animal and zoonotic pathogens. A high ratio of Proteobacteria to (Firmicutes + Bacteroidetes) was confirmed as a good indicator for rumen dysbiosis, with eight cases all from concentrate-fed animals. Finally, network analysis demonstrated that the resistance/pathogenicity genes are potentially useful as biomarkers for health risk assessment of the ruminal microbiome.

CONCLUSIONS

Diet has important effects on the complement of AMR genes in the rumen microbial community, with potential implications for human and animal health.

Integrated metagenomic analysis of the rumen microbiome of cattle reveals key biological mechanisms associated with methane traits

Methane is one of the major contributors to global warming. The rumen microbiota is directly involved in methane production in cattle. The link between variation in rumen microbial communities and host genetics has important applications and implications in bioscience. Having the potential to reveal the full extent of microbial gene diversity and complex microbial interactions, integrated metagenomics and network analysis holds great promise in this endeavour. This study investigates the rumen microbial community in cattle through the integration of metagenomic and network-based approaches. Based on the relative abundance of 1570 microbial genes identified in a metagenomics analysis, the co-abundance network was constructed and functional modules of microbial genes were identified. One of the main contributions is to develop a random matrix theory-based approach to automatically determining the correlation threshold used to construct the co-abundance network. The resulting network, consisting of 549 microbial genes and 3349 connections, exhibits a clear modular structure with certain trait-specific genes highly over-represented in modules. More specifically, all the 20 genes previously identified to be associated with methane emissions are found in a module (hypergeometric test, p<10^-11). One third of genes are involved in methane metabolism pathways. The further examination of abundance profiles across 8 samples of genes highlights that the revealed pattern of metagenomics abundance has a strong association with methane emissions. Furthermore, the module is significantly enriched with microbial genes encoding enzymes that are directly involved in methanogenesis (hypergeometric test, p<10^-9).

Genetic associations of short- and long-term aggressiveness identified by skin lesion with growth, feed efficiency, and carcass characteristics in growing pigs

The objective of this study was to investigate the genetic relationships between skin lesion traits in group housed growing pigs as a measure of short- (in a newly mixed group) and long- (in a socially stable group) term aggression and commonly used commercial performance measures: growth, feed intake, feed efficiency, and carcass traits. Data on 2,413 growing pigs (138 groups) were available. Pigs were mixed into new social groups of 18 animals, and skin lesions were counted 24 h (SL24h) and 5 wk (SL5wk) postmixing. The animal model was used to estimate genetic parameters for skin lesion traits, test daily gain, lifetime daily gain, daily feed intake, feed efficiency (calculated as test daily gain divided by daily feed intake), loin depth, back fat, and HCW. Skin lesions had a heritable component, ranging from 0.08 for anterior SL24h to 0.22 for central SL5wk and would, therefore, be suitable as a method of phenotyping aggression for selection purposes. Significant positive genetic correlations were found between SL24h and SL5wk (0.46 to 0.81). Positive genetic correlations were also found between SL24h (central and posterior body regions) or SL5wk (all body regions) and the production traits lifetime daily gain, test daily gain, and HCW (0.29 to 0.54). Central SL24h, anterior SL5wk, and posterior SL5wk were found to correlate positively with feed efficiency (0.39 to 0.50), suggesting that pigs with more lesions convert feed more efficiently. Where significant, the magnitude of phenotypic correlations was low but positive (0.07 to 0.10). These results suggest that, genetically, animals that receive many lesions show improved performance compared to those with few lesions, except for anterior SL24h, which had previously been shown to be genetically positively correlated with the initiation of nonreciprocal attacks. It may, therefore, be possible, via selection against anterior skin lesions at mixing, to reduce this form of 1-sided aggression without adversely affecting production traits.

Association of Temperament and Acute Stress Responsiveness with Productivity, Feed Efficiency, and Methane Emissions in Beef Cattle: An Observational Study

The aim of this study was to assess individual differences in temperament and stress response and quantify their impact on feed efficiency, performance, and methane (CH₄) emissions in beef cattle. Eighty-four steers (castrated males) (Charolais or Luing) were used. Temperament was assessed using two standardized tests: restlessness when restrained [crush score (CS)] and flight speed (FS) on release from restraint. Over a 56-day period individual animal dry matter intake (DMI) and weekly body weight was measured. Ultrasound fat depth was measured at the end of 56 days. Average daily gain (ADG), feed conversion ratio (FCR), and residual feed intake (RFI) were calculated. After the 56-day test period, animals were transported in groups of six/week to respiration chamber facilities. Blood samples were taken before and 0, 3, 6, and 9 h after transport. Plasma cortisol, creatine kinase (CK), glucose, and free fatty acids (FFA) were determined to assess physiological stress response. Subsequently, CH₄ emissions were measured over a 3-day period in individual respiration chambers. CS (1.7 ± 0.09) and FS (1.6 ± 0.60 m/s) were repeatable (0.63 and 0.51, respectively) and correlated (r = 0.36, P < 0.001). Plasma cortisol, CK, and FFA concentrations increased after transport (P = 0.038, P = 0.006, and P < 0.001, respectively). Temperament (CS) and CK concentration were correlated (r = 0.29; P = 0.015). The extreme group analysis reveals that excitable animals (FS; P = 0.032) and higher stress response (cortisol, P = 0.007; FFA, P = 0.007; and CK, P = 0.003) were associated with lower DMI. ADG was lower in more temperamental animals (CS, P = 0.097, and FS, P = 0.030). Fat depth was greater in steers showing calmer CS (P = 0.026) and lower plasma CK (P = 0.058). Temperament did not show any relationship with RFI or CH₄ emissions. However, steers with higher cortisol showed improved feed efficiency (lower FCR and RFI) (P < 0.05) and greater CH₄ emissions (P = 0.017). In conclusion, agitated temperament and higher stress responsiveness is detrimental to productivity. A greater stress response is associated with a reduction in feed intake that may both increase the efficiency of consumed feed and the ratio of CH₄ emissions/unit of feed. Therefore, temperament and stress response should be considered when designing strategies to improve efficiency and mitigate CH₄ emissions in beef cattle.

Effectiveness of nitrate addition and increased oil content as methane mitigation strategies for beef cattle fed two contrasting basal diets

The objectives of this study were to investigate the effects of (1) the addition of nitrate and (2) an increase in dietary oil on methane (CH4) and hydrogen (H2) emissions from 2 breeds (cross-bred Charolais and purebred Luing) of finishing beef cattle receiving 2 contrasting basal diets consisting (grams per kilogram DM) of 500:500 (Mixed) and 80:920 (Concentrate) forage to concentrate ratios. Within each basal diet there were 3 treatments: (i) control treatments (mixed-CTL and concentrate-CTL) contained rapeseed meal as the protein source, which was replaced with either (ii) calcium nitrate (mixed-NIT and concentrate-NIT) supplying 21.5 g nitrate/kg DM, or (iii) rapeseed cake (mixed-RSC and concentrate-RSC) to increase dietary oil from 27 (CTL) to 53 g/kg DM (RSC). Following adaption to diets, CH4 and H2 emissions were measured on 1 occasion from each of the 76 steers over a 13-wk period. Dry matter intakes tended (P = 0.051) to be greater for the concentrate diet than the mixed diet; however, when expressed as grams DMI per kilogram BW, there was no difference between diets (P = 0.41). Dry matter intakes for NIT or RSC did not differ from CTL. Steers fed a concentrate diet produced less CH4 and H2 than those fed a mixed diet (P < 0.001). Molar proportions of acetate (P < 0.001) and butyrate (P < 0.01) were lower and propionate (P < 0.001) and valerate (P < 0.05) higher in the rumen fluid from steers fed the concentrate diet. For the mixed diet, CH4 yield (grams per kilogram DMI) was decreased by 17% when nitrate was added (P < 0.01), while H2 yield increased by 160% (P < 0.001). The addition of RSC to the mixed diet decreased CH4 yield by 7.5% (P = 0.18). However, for the concentrate diet neither addition of nitrate (P = 0.65) nor increasing dietary oil content (P = 0.46) decreased CH4 yield compared to concentrate-CTL. Molar proportions of acetate were higher (P < 0.001) and those of propionate lower (P < 0.01) in rumen fluid from NIT treatments compared to respective CTL treatments. Overall, reductions in CH4 emissions from adding nitrate or increasing the oil content of the mixed diet were similar to those expected from previous reports. However, the lack of an effect of these mitigation strategies when used with high concentrate diets has not been previously reported. This study shows that the effect of CH4 mitigation strategies is basal diet-dependent.

Bovine Host Genetic Variation Influences Rumen Microbial Methane Production with Best Selection Criterion for Low Methane Emitting and Efficiently Feed Converting Hosts Based on Metagenomic Gene Abundance

Methane produced by methanogenic archaea in ruminants contributes significantly to anthropogenic greenhouse gas emissions. The host genetic link controlling microbial methane production is unknown and appropriate genetic selection strategies are not developed. We used sire progeny group differences to estimate the host genetic influence on rumen microbial methane production in a factorial experiment consisting of crossbred breed types and diets. Rumen metagenomic profiling was undertaken to investigate links between microbial genes and methane emissions or feed conversion efficiency. Sire progeny groups differed significantly in their methane emissions measured in respiration chambers. Ranking of the sire progeny groups based on methane emissions or relative archaeal abundance was consistent overall and within diet, suggesting that archaeal abundance in ruminal digesta is under host genetic control and can be used to genetically select animals without measuring methane directly. In the metagenomic analysis of rumen contents, we identified 3970 microbial genes of which 20 and 49 genes were significantly associated with methane emissions and feed conversion efficiency respectively. These explained 81% and 86% of the respective variation and were clustered in distinct functional gene networks. Methanogenesis genes (e.g. mcrA and fmdB) were associated with methane emissions, whilst host-microbiome cross talk genes (e.g. TSTA3 and FucI) were associated with feed conversion efficiency. These results strengthen the idea that the host animal controls its own microbiota to a significant extent and open up the implementation of effective breeding strategies using rumen microbial gene abundance as a predictor for difficult-to-measure traits on a large number of hosts. Generally, the results provide a proof of principle to use the relative abundance of microbial genes in the gastrointestinal tract of different species to predict their influence on traits e.g. human metabolism, health and behaviour, as well as to understand the genetic link between host and microbiome.

Methane is a highly potent greenhouse gas and ruminants are the major source of methane emissions from anthropogenic activities. Here we show in an experiment with cattle that genetic selection of low-emitting animals is a viable option based on a newly developed selection criterion. The experimental data provided a comprehensive insight into the host additive genetic influence on the microbiome, the impact of nutrition on genetics and the microbiome, and the effect of metagenomic microbial genes on the analysed traits. We developed a selection criterion to change those traits by evaluation of hosts based on the relative abundance of microbial genes. This criterion is shown to be highly informative and it is therefore suggested to be used in studies analysing different traits and species. This study provides a proof of principle that there is an additive genetic influence of the host on its microbiome and that selection for the desired host can be based on the abundance of a suite of genes in the ruminal metagenome associated with the trait. The use of this criterion will allow genetic analysis on a large number of hosts, previously a significant barrier to determination of host genetic effects on such traits.

Evaluation of the laser methane detector to estimate methane emissions from ewes and steers

The laser methane detector (LMD) has been proposed as a method to characterize enteric methane (CH4) emissions from animals in a natural environment. To validate LMD use, its CH4 outputs (LMD-CH4), were compared against CH4 measured with respiration chambers (chamber-CH4). The LMD was used to measure CH4 concentration (µL/L) in the exhaled air of 24 lactating ewes and 72 finishing steers. In ewes, LMD was used on 1 d for each ewe, for 2-min periods at 5 hourly observation periods (P1 to P5, respectively) after feeding. In steers fed either low- or high-concentrate diets, LMD was used once daily for a 4-min period for 3 d. The week after LMD-CH4 measurement, ewes or steers entered respiration chambers to quantify daily CH4 output (g/d). The LMD outputs consisted of periodic events of high CH4 concentrations superimposed on a background of oscillating lower CH4 concentrations. The high CH4 events were attributed to eructation and the lower background CH4 to respiration. After fitting a double normal distribution to the data set, a threshold of 99% of probability of the lower distribution was used to separate respiration from eructation events. The correlation between mean LMD-CH4 and chamber-CH4 was not high, and only improved correlations were observed after data were separated in 2 levels. In ewes, a model with LMD and DMI (adjusted R(2) = 0.92) improved the relationship between DMI and chamber-CH4 alone (adjusted R(2) = 0.79) and between LMD and chamber-CH4 alone (adjusted R(2) = 0.86). In both experiments, chamber-CH4 was best explained by models with length of eructation events (time) and maximum values of CH4 concentration during respiration events (µL/L; P < 0.01). Correlation between methods differed between observation periods, indicating the best results of the LMD were observed from 3 to 5 h after feeding. Given the short time and ease of use of LMD, there is potential for its commercial application and field-based studies. Although good indicators of quantity of CH4 were obtained with respiration and eructation CH4, the method needed to separate the data into high and low levels of CH4 was not simple to apply in practice. Further assessment of the LMD should be performed in relation to animal feeding behavior and physiology to validate assumptions of eructation and respiration levels, and other sources of variation should be tested (i.e., micrometeorology) to better investigate its potential application for CH4 testing in outdoor conditions.

The rumen microbial metagenome associated with high methane production in cattle

Background

Methane represents 16 % of total anthropogenic greenhouse gas emissions. It has been estimated that ruminant livestock produce ca. 29 % of this methane. As individual animals produce consistently different quantities of methane, understanding the basis for these differences may lead to new opportunities for mitigating ruminal methane emissions. Metagenomics is a powerful new tool for understanding the composition and function of complex microbial communities. Here we have applied metagenomics to the rumen microbial community to identify differences in the microbiota and metagenome that lead to high- and low-methane-emitting cattle phenotypes.

Methods

Four pairs of beef cattle were selected for extreme high and low methane emissions from 72 animals, matched for breed (Aberdeen-Angus or Limousin cross) and diet (high or medium concentrate). Community analysis was carried out by qPCR of 16S and 18S rRNA genes and by alignment of Illumina HiSeq reads to the GREENGENES database. Total genomic reads were aligned to the KEGG genes databasefor functional analysis.

Results

Deep sequencing produced on average 11.3 Gb per sample. 16S rRNA gene abundances indicated that archaea, predominantly Methanobrevibacter, were 2.5× more numerous (P = 0.026) in high emitters, whereas among bacteria Proteobacteria, predominantly Succinivibrionaceae, were 4-fold less abundant (2.7 vs. 11.2 %; P = 0.002). KEGG analysis revealed that archaeal genes leading directly or indirectly to methane production were 2.7-fold more abundant in high emitters. Genes less abundant in high emitters included acetate kinase, electron transport complex proteins RnfC and RnfD and glucose-6-phosphate isomerase. Sequence data were assembled de novo and over 1.5 million proteins were annotated on the subsequent metagenome scaffolds. Less than half of the predicted genes matched matched a domain within Pfam. Amongst 2774 identified proteins of the 20 KEGG orthologues that correlated with methane emissions, only 16 showed 100 % identity with a publicly available protein sequence.

Conclusions

The abundance of archaeal genes in ruminal digesta correlated strongly with differing methane emissions from individual animals, a finding useful for genetic screening purposes. Lower emissions were accompanied by higher Succinovibrionaceae abundance and changes in acetate and hydrogen production leading to less methanogenesis, as similarly postulated for Australian macropods. Large numbers of predicted protein sequences differed between high- and low-methane-emitting cattle. Ninety-nine percent were unknown, indicating a fertile area for future exploitation.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2032-0) contains supplementary material, which is available to authorized users.

Novel insight into the genomic architecture of feed and nitrogen efficiency measured by residual energy intake and nitrogen excretion in growing pigs

BACKGROUND

Improvement of feed efficiency in pigs is of great economical and environmental interest and contributes to use limited resources efficiently to feed the world population. Genome scans for feed efficiency traits are of importance to reveal the underlying biological causes and increase the rate of genetic gain. The aim of this study was to determine the genomic architecture of feed efficiency measured by residual energy intake (REI), in association with production, feed conversion ratio (FCR) and nitrogen excretion traits through the identification of quantitative trait loci (QTL) at different stages of growth using a three generation full-sib design population which originated from a cross between Pietrain and a commercial dam line.

RESULTS

Six novel QTL for REI were detected explaining 2.7-6.1% of the phenotypic variance in REI. At growth from 60-90 kg body weight (BW), a QTL with a significant dominance effect was identified for REI on SSC14, at a similar location to the QTL for feed intake and nitrogen excretion traits. At growth from 90-120 kg BW, three QTL for REI were detected on SSC2, SSC4 and SSC7 with significant additive, imprinting and additive effects, respectively. These QTL (except for the imprinted QTL) were positionally overlapping with QTL for FCR and nitrogen excretion traits. During final growth (120-140 kg BW), a further QTL for REI was identified on SSC8 with significant additive effect, which overlapped with QTL for nitrogen excretion. During entire analysed growth (60-140 kg BW), a novel additive QTL for REI on SSC4 was observed, with no overlapping with QTL for any other traits considered.

CONCLUSIONS

The occurrence of only one overlapping QTL of REI with feed intake suggests that only a small proportion of the variance in REI was explained by change in feed intake, whereas four overlapping QTL of REI with those of nitrogen excretion traits suggests that mostly underlying factors of feed utilisation such as metabolism and protein turnover were the reason for change in REI. Different QTL for REI were identified at different growth stages, indicating that different genes are responsible for efficiency in feed utilisation at different stages of growth.

Genetic associations between behavioral traits and direct-social effects of growth rate in pigs

This study examined the behavioral consequences of selecting pigs using a social genetic model for growth. Calculations enable each member of a group of pigs to be given a direct breeding value (DBV) and a social breeding value (SBV), which can be summarized into a total breeding value (TBV) for growth. Selection for growth TBV could affect animal behavior because social effects account for within-group interactions. Data were recorded from 96 groups of Yorkshire and Yorkshire × Landrace pigs in a nucleus herd. Each group contained 15 pigs fed ad libitum from 2 feeders; the space allowance was 0.85 m2/pig. Average daily gain was quantified from 35 to 100 kg of BW. Fighting and bullying activity at mixing (period 1), lying frequency 3 wk after mixing (period 2), and counts of skin lesions in periods 1 and 2 were recorded. The DBV for these traits were estimated with a classic animal model. We simulated different correlations between the direct genetic effect and the social genetic effect on growth rate (r(DS)), 2 components that respectively determine a pig's genetic capacity to grow and its genetic influence on growth of group mates: r(DS) was successively assumed to be 0 and ±0.12, ±0.20, ±0.29, and ±0.58. Finally, the correlations between DBV, SBV, and TBV for ADG, as well as the DBV for behavior and skin lesions, were calculated and tested for a level of significance at P < 0.05. The gradient from negative to positive values of r(DS) refers to a progressive path running from genetic antagonism to genetic mutualism for growth. If rDS in the population truly ranged between -0.58 and -0.20, correlations for TBV for ADG with DBV for fighting and bullying progressively increased with rDS. Consequently, if rDS was low (between -0.12 and +0.12) or positive (>+0.12), pigs with high TBV for ADG had higher DBV for bullying other pigs in the group and for fighting than pigs with lower TBV for ADG. Pigs with high TBV for ADG did not differ from other pigs in their DBV for lesions to the anterior part of the body, but they had a lower DBV for posterior lesions, whereas in period 2, they had higher DBV for posterior lesions and lower DBV for lying. Under genetic mutualism for growth and in housing conditions similar to those in the present study, selection for growth TBV would promote the rapid establishment of the dominance relationships, with more aggressive contests among group mates at mixing. Pigs would subsequently be more active but, judging by skin lesions, less willing to fight in a more stable social situation.

Index selection in terminal sires improves lamb performance at finishing

Lamb meat is often perceived by consumers as fatty, and consumption has decreased in recent decades. A lean growth index was developed in the UK for terminal sire breeds to increase carcass lean content and constrain fat content at a constant age end point. The purposes of this study were 1) to evaluate the effects of index selection of terminal sires on their crossbred offspring at finishing and 2) to evaluate its effectiveness within terminal sire breeds. Approximately 70% of lambs marketed in the UK have been sired by rams of breeds typically thought of as specialized terminal sires. The most widely used are Charollais, Suffolk, and Texel. These breeds participated in sire referencing schemes from the early 1990s by sharing rams among flocks selected on the lean growth index. From 1999 to 2002 approximately 15 "high" and 15 "low" lean growth index score rams were selected from within their sire referencing schemes and mated to Welsh and Scottish Mule ewes. Their crossbred offspring were commercially reared on 3 farms in the UK. Lambs were finished to an estimated 11% subcutaneous fat by visual evaluation. At finishing, lambs were weighed, ultrasonically scanned, and assessed for condition score and conformation. Records were obtained for 6356 lambs on finishing BW (FWT), ultrasonic muscle depth (UMD), ultrasonic fat depth, overall condition score (OCS), and conformation of gigot, loin, and shoulder. Ultrasonic fat depth was log transformed (logUFD) to approach normality. High-index-sired lambs were heavier at finishing (1.2±0.2 kg) with thicker UMD (0.7±0.2 mm) and less logUFD (0.08±0.01 mm; P<0.05). There were no differences in OCS or conformation based on the sire index or breed (P>0.08). Suffolk-sired lambs were heavier than Charollais (1.0±0.3 kg), which were heavier than Texel (0.9±0.3 kg; P<0.001). Texel-sired lambs had thicker UMD than Charollais (0.7±0.2 mm; P<0.001) but were not different than Suffolk. Charollais-sired lambs had greater logUFD than both Texel (0.098±0.016 mm) and Suffolk (0.061±0.017 mm) sired lambs (P<0.001). Within a breed, high- and low-index-sired lambs differed in performance with the exceptions of FWT and UMD in Suffolks. Index selection produced heavier and leaner lambs at finishing. Producers have flexibility in choosing the terminal sire that best fits their production system.

Efficiency of genomic selection using Bayesian multi-marker models for traits selected to reflect a wide range of heritabilities and frequencies of detected quantitative traits loci in mice

BACKGROUND

Genomic selection uses dense single nucleotide polymorphisms (SNP) markers to predict breeding values, as compared to conventional evaluations which estimate polygenic effects based on phenotypic records and pedigree information. The objective of this study was to compare polygenic, genomic and combined polygenic-genomic models, including mixture models (labelled according to the percentage of genotyped SNP markers considered to have a substantial effect, ranging from 2.5% to 100%). The data consisted of phenotypes and SNP genotypes (10,946 SNPs) of 2,188 mice. Various growth, behavioural and physiological traits were selected for the analysis to reflect a wide range of heritabilities (0.10 to 0.74) and numbers of detected quantitative traits loci (QTL) (1 to 20) affecting those traits. The analysis included estimation of variance components and cross-validation within and between families.

RESULTS

Genomic selection showed a high predictive ability (PA) in comparison to traditional polygenic selection, especially for traits of moderate heritability and when cross-validation was between families. This occurred although the proportion of genomic variance of traits using genomic models was 22 to 33% smaller than using polygenic models. Using a 2.5% mixture genomic model, the proportion of genomic variance was 79% smaller relative to the polygenic model. Although the proportion of variance explained by the markers was reduced further when a smaller number of SNPs was assumed to have a substantial effect on the trait, PA of genomic selection for most traits was little affected. These low mixture percentages resulted in improved estimates of single SNP effects. Genomic models implemented for traits with fewer QTLs showed even lower PA than the polygenic models.

CONCLUSIONS

Genomic selection generally performed better than traditional polygenic selection, especially in the context of between family cross-validation. Reducing the number of markers considered to affect the trait did not significantly change PA for most traits, particularly in the case of within family cross-validation, but increased the number of markers found to be associated with QTLs. The underlying number of QTLs affecting the trait has an effect on PA, with a smaller number of QTLs resulting in lower PA using the genomic model compared to the polygenic model.

The effect of sex on some carcass and meat quality traits in Texel ewe and ram lambs

Much of the past research into gender effects on lamb meat quality has focussed on comparing ram lambs with castrated males, but more recent comparisons between ram and ewe lambs have yielded variable results. The objective of the current research was to compare meat quality parameters of M. longissimus lumborum (LL), and M. semimembranosus (SM) from pasture-fed Texel ram (n = 94) and ewe (n = 114) lambs slaughtered at an average age of 144 days in a commercial abattoir. After aging carcasses for between 7 and 9 days, LL and SM were significantly tougher (higher shear values) for ram compared with ewe lambs (P < 0.001). LL from rams had significantly lower intramuscular fat percentage, and higher moisture content than LL from ewes. Differences in LL intramuscular fat percentage or ultimate pH did not explain the sex effect on LL shear force when tested individually or together as additional covariates in the model. Ram SM was lighter in colour (higher L*) and had a higher cooking loss than that of ewes (P < 0.001). The correlations between some of the traits within and between muscles clearly differed between the sexes. Finishing ram lambs to the specifications used in this experiment resulted in meat with relatively minor, but statistically significant differences in quality relative to that from ewe lambs.