Holobiont effect accounts for more methane emission variance than the additive and microbiome effects on dairy cattle

Is early life programming a promising strategy for methane mitigation and sustainable intensification in ruminants?

Sustainable animal production requires lowering emissions and adapting to climate change. Numerous nutritional and management interventions that enhance adult ruminants' efficiency and resilience produce only temporary results, reducing the sustainability of the programs. This is because only short-lived changes in the host and rumen microbiome occur, which revert to the original levels when the intervention ceases. Early life programming (ELP) is a promising approach to increase sustainable livestock production, enhance efficiency and reduce greenhouse gas emissions. Early influences using ELP have profound and enduring effects on molecular pathways, physiological adaptations, and long-term phenotypic consequences later in life. These effects occur from the embryonic stage to birth (foetal programming, FP), birth to weaning, and beyond. The underlying mechanisms of ELP include the sequential development of the rumen and microbial colonisation in the rumen, orchestrated through molecular changes, including transcriptomic and epigenetic modifications. This review highlights the key mechanisms behind ELP and explores strategies across different production systems that can improve livestock performance while helping to achieve net-zero emissions. Management strategies like step-down weaning, dietary modifications including increasing solid feed and high-fibre diets and adding anti-methanogenic agents and other feed additives to target the desired rumen microbial community, such as propionate-producing Prevotella, Sharpea, Coprococcus and Megasphaera, are promising strategies for implementing ELP. Creating alternate hydrogen sinks through ELP by favouring metabolic pathways that enhance propionate production can also be targeted. Furthermore, recent innovative strategies, such as using methanotroph-methylotroph consortium as probiotics and oxidising feed additives, are worth researching for ELP.

Combining genomics and semen microbiome increases the accuracy of predicting bull prolificacy

Commercial livestock producers need to prioritize genetic progress for health and efficiency traits to address productivity, welfare, and environmental concerns but face challenges due to limited pedigree information in extensive multi-sire breeding scenarios. Utilizing pooled DNA for genotyping and integrating seminal microbiome information into genomic models could enhance predictions of male fertility traits, thus addressing complexities in reproductive performance and inbreeding effects. Using the Angus Australia database comprising genotypes and pedigree data for 78,555 animals, we simulated percentage of normal sperm (PNS) and prolificacy of sires, resulting in 713 sires and 27,557 progeny in the final dataset. Publicly available microbiome data from 45 bulls was used to simulate data for the 713 sires. By incorporating both genomic and microbiome information our models were able to explain a larger proportion of phenotypic variation in both PNS (0.94) and prolificacy (0.56) compared to models using a single data source (e.g., 0.36 and 0.41, respectively, using only genomic information). Additionally, models containing both genomic and microbiome data revealed larger phenotypic differences between animals in the top and bottom quartile of predictions, indicating potential for improved productivity and sustainability in livestock farming systems. Inbreeding depression was observed to affect fertility traits, which makes the incorporation of microbiome information on the prediction of fertility traits even more actionable. Crucially, our inferences demonstrate the potential of the semen microbiome to contribute to the improvement of fertility traits in cattle and pave the way for the development of targeted microbiome interventions to improve reproductive performance in livestock.

A Recursive Model Approach to Include Epigenetic Effects in Genetic Evaluations Using Simulated DNA Methylation Effects

The advancement of epigenetics has highlighted DNA methylation as an intermediate-omic influencing gene regulation and phenotypic expression. With emerging technologies enabling the large-scale and affordable capture of methylation data, there is growing interest in integrating this information into genetic evaluation models for animal breeding. This study used methylome information from six dairy cows to simulate the methylation profile of 13,183 genotyped animals. The liability to methylation was treated as an additive trait, while a trait moderated by methylation effects was also simulated. A multiomic model (GOBLUP) was adapted to incorporate methylation data in genomic and genetic evaluations, using the traditional BLUP method as a benchmark. The GOBLUP accurately recovered heritability estimates for the liability to methylation in all low, medium and high heritability scenarios and was consistent at estimating the heritability for the epigenetics-moderated trait of interest at a low-medium heritability of 0.14. The genetic variance recovered by the BLUP model was influenced by the h2 of the liability to methylation, and a part of the methylation variance for the phenotypic trait was captured as additive. The h2 of the phenotypic trait partially relies on the h2 value for the methylation windows in the traditional model. A newly proposed estimated epigenetic value (EEV) combines the traditional additive genetic information from genotyping arrays with epigenetic information. The correlation between the traditional estimated breeding value (EBV) and EEV was high (0.92-0.99 depending on the scenario), but the correlation of the EEV with the true breeding value was higher than the correlation between the traditional EBV and the TBV (0.85 vs. 0.75, 0.71 vs. 0.66 and 0.61 vs. 0.62 depending on the scenario). This study demonstrates that the GOBLUP multiomic recursive model can effectively separates additive and epigenetic variances, enabling improved breeding decisions by accounting for genetic liability to DNA methylation. This enables more informed breeding decisions, optimising selection for desired traits. Emerging sequencing techniques offer new opportunities for cost-effective simultaneous acquisition of genetic and epigenetic data, further enhancing breeding accuracy.

The Holobiont concept in ruminant physiology - more of the same, or something new and meaningful to food quality, food security, and animal health?

The holobiont concept has emerged as an attempt to recognize and describe the myriad interactions and physiological signatures inherent to a host organism, as impacted by the microbial communities that colonize and/or co-inhabit the environment within which the host resides. The field acknowledges and draws upon principles from evolution, ecology, genetics, and biology, and in many respects has been "pushed" by the advent of high throughput DNA sequencing and, to a lesser extent, other "omics"-based technologies. Despite the explosion in data generation and analyses, much of our current understanding of the human and ruminant "holobiont" is based on compositional forms of data and thereby, restricted to describing host phenotypes via associative or correlative studies. So, where to from here? We will discuss some past findings arising from ruminant and human gut microbiota research and seek to evaluate the rationale, progress, and opportunities that might arise from the "holobiont" approach to the ruminant and human host. In particular, we will consider what is a "good" or "bad" host gastrointestinalmicrobiome in different scenarios, as well as potential avenues to sustain or alter the holobiont. While the holobiont approach might improve food quality, food security and animal health, these benefits will be most likely achieved via a judicious and pragmatic compromise in data generation, both in terms of its scale, as well as its generation in context with the "forgotten" knowledge of ruminant and human physiology.

Correlated Responses to Selection for Intramuscular Fat on the Gut Microbiome in Rabbits

Intramuscular fat (IMF) content is important for meat production and human health, where the host genetics and its microbiome greatly contribute to its variation. The aim of this study is to describe the consequences of the genetic modification of IMF by selecting the taxonomic composition of the microbiome, using rabbits from the 10th generation of a divergent selection experiment for IMF (high (H) and low (L) lines differ by 3.8 standard deviations). The selection altered the composition of the gut microbiota. Correlated responses were better distinguished at the genus level (51 genera) than at the phylum level (10 phyla). The H-line was enriched in Hungateiclostridium, Limosilactobacillus, Legionella, Lysinibacillus, Phorphyromonas, Methanosphaera, Desulfovibrio, and Akkermansia, while the L-line was enriched in Escherichia, Methanobrevibacter, Fonticella, Candidatus Amulumruptor, Methanobrevibacter, Exiguobacterium, Flintibacter, and Coprococcus, among other genera with smaller line differences. A microbial biomarker generated from the abundance of four of these genera classified the lines with 78% accuracy in a logit regression. Our results demonstrate different gut microbiome compositions in hosts with divergent IMF genotypes. Furthermore, we provide a microbial biomarker to be used as an indicator of hosts genetically predisposed to accumulate muscle lipids, which opens up the opportunity for research to develop probiotics or microbiome-based breeding strategies targeting IMF.

Exploring the Interplay between the Hologenome and Complex Traits in Bovine and Porcine Animals Using Genome-Wide Association Analysis

Genome-wide association studies (GWAS) significantly enhance our ability to identify trait-associated genomic variants by considering the host genome. Moreover, the hologenome refers to the host organism's collective genetic material and its associated microbiome. In this study, we utilized the hologenome framework, called Hologenome-wide association studies (HWAS), to dissect the architecture of complex traits, including milk yield, methane emissions, rumen physiology in cattle, and gut microbial composition in pigs. We employed four statistical models: (1) GWAS, (2) Microbial GWAS (M-GWAS), (3) HWAS-CG (hologenome interaction estimated using COvariance between Random Effects Genome-based restricted maximum likelihood (CORE-GREML)), and (4) HWAS-H (hologenome interaction estimated using the Hadamard product method). We applied Bonferroni correction to interpret the significant associations in the complex traits. The GWAS and M-GWAS detected one and sixteen significant SNPs for milk yield traits, respectively, whereas the HWAS-CG and HWAS-H each identified eight SNPs. Moreover, HWAS-CG revealed four, and the remaining models identified three SNPs each for methane emissions traits. The GWAS and HWAS-CG detected one and three SNPs for rumen physiology traits, respectively. For the pigs' gut microbial composition traits, the GWAS, M-GWAS, HWAS-CG, and HWAS-H identified 14, 16, 13, and 12 SNPs, respectively. We further explored these associations through SNP annotation and by analyzing biological processes and functional pathways. Additionally, we integrated our GWA results with expression quantitative trait locus (eQTL) data using transcriptome-wide association studies (TWAS) and summary-based Mendelian randomization (SMR) methods for a more comprehensive understanding of SNP-trait associations. Our study revealed hologenomic variability in agriculturally important traits, enhancing our understanding of host-microbiome interactions.

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.

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.

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.

Progress and opportunities through use of genomics in animal production

The rearing of farmed animals is a vital component of global food production systems, but its impact on the environment, human health, animal welfare, and biodiversity is being increasingly challenged. Developments in genetic and genomic technologies have had a key role in improving the productivity of farmed animals for decades. Advances in genome sequencing, annotation, and editing offer a means not only to continue that trend, but also, when combined with advanced data collection, analytics, cloud computing, appropriate infrastructure, and regulation, to take precision livestock farming (PLF) and conservation to an advanced level. Such an approach could generate substantial additional benefits in terms of reducing use of resources, health treatments, and environmental impact, while also improving animal health and welfare.

Metagenomic Predictions: A Review 10 years on

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

Exploring methods to summarize gut microbiota composition for microbiability estimation and phenotypic prediction in swine

The microbial composition resemblance among individuals in a group can be summarized in a square covariance matrix and fitted in linear models. We investigated eight approaches to create the matrix that quantified the resemblance between animals based on the gut microbiota composition. We aimed to compare the performance of different methods in estimating trait microbiability and predicting growth and body composition traits in three pig breeds. This study included 651 purebred boars from either breed: Duroc (n = 205), Landrace (n = 226), and Large White (n = 220). Growth and body composition traits, including body weight (BW), ultrasound backfat thickness (BF), ultrasound loin depth (LD), and ultrasound intramuscular fat (IMF) content, were measured on live animals at the market weight (156 ± 2.5 days of age). Rectal swabs were taken from each animal at 158 ± 4 days of age and subjected to 16S rRNA gene sequencing. Eight methods were used to create the microbial similarity matrices, including four kernel functions (Linear Kernel, LK; Polynomial Kernel, PK; Gaussian Kernel, GK; Arc-cosine Kernel with one hidden layer, AK1), two dissimilarity methods (Bray-Curtis, BC; Jaccard, JA), and two ordination methods (Metric Multidimensional Scaling, MDS; Detrended Correspondence analysis, DCA). Based on the matrix used, microbiability estimates ranged from 0.07 to 0.21 and 0.12 to 0.53 for Duroc, 0.03 to 0.21 and 0.05 to 0.44 for Landrace, and 0.02 to 0.24 and 0.05 to 0.52 for Large White pigs averaged over traits in the model with sire, pen, and microbiome, and model with the only microbiome, respectively. The GK, JA, BC, and AK1 obtained greater microbiability estimates than the remaining methods across traits and breeds. Predictions were made within each breed group using four-fold cross-validation based on the relatedness of sires in each breed group. The prediction accuracy ranged from 0.03 to 0.18 for BW, 0.08 to 0.31 for BF, 0.21 to 0.48 for LD, and 0.04 to 0.16 for IMF when averaged across breeds. The BC, MDS, LK, and JA achieved better accuracy than other methods in most predictions. Overall, the PK and DCA exhibited the worst performance compared to other microbiability estimation and prediction methods. The current study shows how alternative approaches summarized the resemblance of gut microbiota composition among animals and contributed this information to variance component estimation and phenotypic prediction in swine.

Microbiability and microbiome-wide association analyses of feed efficiency and performance traits in pigs

Background

The objective of the present study was to investigate how variation in the faecal microbial composition is associated with variation in average daily gain (ADG), backfat thickness (BFT), daily feed intake (DFI), feed conversion ratio (FCR), and residual feed intake (RFI), using data from two experimental pig lines that were divergent for feed efficiency. Estimates of microbiability were obtained by a Bayesian approach using animal mixed models. Microbiome-wide association analyses (MWAS) were conducted by single-operational taxonomic units (OTU) regression and by back-solving solutions of best linear unbiased prediction using a microbiome covariance matrix. In addition, accuracy of microbiome predictions of phenotypes using the microbiome covariance matrix was evaluated.

Results

Estimates of heritability ranged from 0.31 ± 0.13 for FCR to 0.51 ± 0.10 for BFT. Estimates of microbiability were lower than those of heritability for all traits and were 0.11 ± 0.09 for RFI, 0.20 ± 0.11 for FCR, 0.04 ± 0.03 for DFI, 0.03 ± 0.03 for ADG, and 0.02 ± 0.03 for BFT. Bivariate analyses showed a high microbial correlation of 0.70 ± 0.34 between RFI and FCR. The two approaches used for MWAS showed similar results. Overall, eight OTU with significant or suggestive effects on the five traits were identified. They belonged to the genera and families that are mainly involved in producing short-chain fatty acids and digestive enzymes. Prediction accuracy of phenotypes using a full model including the genetic and microbiota components ranged from 0.60 ± 0.19 to 0.78 ± 0.05. Similar accuracies of predictions of the microbial component were observed using models that did or did not include an additive animal effect, suggesting no interaction with the genetic effect.

Conclusions

Our results showed substantial associations of the faecal microbiome with feed efficiency related traits but negligible effects with growth traits. Microbiome data incorporated as a covariance matrix can be used to predict phenotypes of animals that do not (yet) have phenotypic information. Connecting breeding environment between training sets and predicted populations could be necessary to obtain reliable microbiome predictions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-022-00717-7.

Composition of the ileum microbiota is a mediator between the host genome and phosphorus utilization and other efficiency traits in Japanese quail (Coturnix japonica)

BACKGROUND

Phosphorus is an essential nutrient in all living organisms and, currently, it is the focus of much attention due to its global scarcity, the environmental impact of phosphorus from excreta, and its low digestibility due to its storage in the form of phytates in plants. In poultry, phosphorus utilization is influenced by composition of the ileum microbiota and host genetics. In our study, we analyzed the impact of host genetics on composition of the ileum microbiota and the relationship of the relative abundance of ileal bacterial genera with phosphorus utilization and related quantitative traits in Japanese quail. An F2 cross of 758 quails was genotyped with 4k genome-wide single nucleotide polymorphisms (SNPs) and composition of the ileum microbiota was characterized using target amplicon sequencing. Heritabilities of the relative abundance of bacterial genera were estimated and quantitative trait locus (QTL) linkage mapping for the host was conducted for the heritable genera. Phenotypic and genetic correlations and recursive relationships between bacterial genera and quantitative traits were estimated using structural equation models. A genomic best linear unbiased prediction (GBLUP) and microbial (M)BLUP hologenomic selection approach was applied to assess the feasibility of breeding for improved phosphorus utilization based on the host genome and the heritable part of composition of the ileum microbiota.

RESULTS

Among the 59 bacterial genera examined, 24 showed a significant heritability (nominal p ≤ 0.05), ranging from 0.04 to 0.17. For these genera, six genome-wide significant QTL were mapped. Significant recursive effects were found, which support the indirect host genetic effects on the host's quantitative traits via microbiota composition in the ileum of quail. Cross-validated microbial and genomic prediction accuracies confirmed the strong impact of microbial composition and host genetics on the host's quantitative traits, as the GBLUP accuracies based on the heritable microbiota-mediated components of the traits were similar to the accuracies of conventional GBLUP based on genome-wide SNPs.

CONCLUSIONS

Our results revealed a significant effect of host genetics on composition of the ileal microbiota and confirmed that host genetics and composition of the ileum microbiota have an impact on the host's quantitative traits. This offers the possibility to breed for improved phosphorus utilization based on the host genome and the heritable part of composition of the ileum microbiota.

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.