Identification of rumen microbial biomarkers linked to methane emission in Holstein dairy cows

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.

Life at the borderlands: microbiomes of interfaces critical to One Health

Microbiomes are foundational components of the environment that provide essential services relating to food security, carbon sequestration, human health, and the overall well-being of ecosystems. Microbiota exert their effects primarily through complex interactions at interfaces with their plant, animal, and human hosts, as well as within the soil environment. This review aims to explore the ecological, evolutionary, and molecular processes governing the establishment and function of microbiome-host relationships, specifically at interfaces critical to One Health-a transdisciplinary framework that recognizes that the health outcomes of people, animals, plants, and the environment are tightly interconnected. Within the context of One Health, the core principles underpinning microbiome assembly will be discussed in detail, including biofilm formation, microbial recruitment strategies, mechanisms of microbial attachment, community succession, and the effect these processes have on host function and health. Finally, this review will catalogue recent advances in microbiology and microbial ecology methods that can be used to profile microbial interfaces, with particular attention to multi-omic, advanced imaging, and modelling approaches. These technologies are essential for delineating the general and specific principles governing microbiome assembly and functions, mapping microbial interconnectivity across varying spatial and temporal scales, and for the establishment of predictive frameworks that will guide the development of targeted microbiome-interventions to deliver One Health outcomes.

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

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

Unveiling microbial biomarkers of ruminant methane emission through machine learning

Background

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

Methods

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

Results

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

Conclusion

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

Effect of castration timing and weaning strategy on the taxonomic and functional profile of ruminal bacteria and archaea of beef calves

BACKGROUND

Beef cattle experience several management challenges across their lifecycle. Castration and weaning, two major interventions in the early life of beef cattle, can have a substantial impact on animal performance. Despite the key role of the rumen microbiome on productive traits of beef cattle, the effect of castration timing and weaning strategy on this microbial community has not been formally described. We assessed the effect of four castration time windows (at birth, turnout, pre-weaning and weaning) and two weaning strategies (fence-line and truck transportation) on the rumen microbiome in a randomized controlled study with 32 male calves across 3 collection days (i.e., time points). Ruminal fluid samples were submitted to shotgun metagenomic sequencing and changes in the taxonomic (microbiota) and functional profile (metagenome) of the rumen microbiome were described.

RESULTS

Using a comprehensive yet stringent taxonomic classification approach, we identified 10,238 unique taxa classified under 40 bacterial and 7 archaeal phyla across all samples. Castration timing had a limited long-term impact on the rumen microbiota and was not associated with changes in alpha and beta diversity. The interaction of collection day and weaning strategy was associated with changes in the rumen microbiota, which experienced a significant decrease in alpha diversity and shifts in beta diversity within 48 h post-weaning, especially in calves abruptly weaned by truck transportation. Calves weaned using a fence-line weaning strategy had lower relative abundance of Bacteroides, Lachnospira, Fibrobacter and Ruminococcus genera compared to calves weaned by truck transportation. Some genes involved in the hydrogenotrophic methanogenesis pathway (fwdB and fwdF) had higher relative abundance in fence-line-weaned calves post-weaning. The antimicrobial resistance gene tetW consistently represented more than 50% of the resistome across time, weaning and castration groups, without significant changes in relative abundance.

CONCLUSIONS

Within the context of this study, castration timing had limited long-term effects on the rumen microbiota, while weaning strategy had short-term effects on the rumen microbiota and methane-associated metagenome, but not on the rumen resistome.

Ruminal and feces metabolites associated with feed efficiency, water intake and methane emission in Nelore bulls

The objectives of this study were twofold: (1) to identify potential differences in the ruminal and fecal metabolite profiles of Nelore bulls under different nutritional interventions; and (2) to identify metabolites associated with cattle sustainability related-traits. We used different nutritional interventions in the feedlot: conventional (Conv; n = 26), and by-product (ByPr, n = 26). Thirty-eight ruminal fluid and 27 fecal metabolites were significantly different (P < 0.05) between the ByPr and Conv groups. Individual dry matter intake (DMI), residual feed intake (RFI), observed water intake (OWI), predicted water intake (WI), and residual water intake (RWI) phenotypes were lower (P < 0.05) in the Conv group, while the ByPr group exhibited lower methane emission (ME) (P < 0.05). Ruminal fluid dimethylamine was significantly associated (P < 0.05) with DMI, RFI, FE (feed efficiency), OWI and WI. Aspartate was associated (P < 0.05) with DMI, RFI, FE and WI. Fecal C22:1n9 was significantly associated with OWI and RWI (P < 0.05). Fatty acid C14:0 and hypoxanthine were significantly associated with DMI and RFI (P < 0.05). The results demonstrated that different nutritional interventions alter ruminal and fecal metabolites and provided new insights into the relationship of these metabolites with feed efficiency and water intake traits in Nelore bulls.

Bacillus subtilis and Macleaya cordata extract regulate the rumen microbiota associated with enteric methane emission in dairy cows

BACKGROUND

Ruminant livestock production is a considerable source of enteric methane (CH4) emissions. In a previous study, we found that dietary inclusions of Bacillus subtilis (BS) and Macleaya cordata extract (MCE) increased dry matter intake and milk production, while reduced enteric CH4 emission in dairy cows. The objective of this study was to further elucidate the impact of feeding BS and MCE on rumen methanogenesis in dairy cows using rumen metagenomics techniques.

RESULTS

Sixty dairy cows were blocked in 20 groups of 3 cows accordingly to their live weight, milk yield, and days in milk, and within each group, the 3 cows were randomly allocated to 1 of 3 treatments: control diet (CON), control diet plus BS (BS), and control diet plus MCE (MCE). After 75 days of feeding experimental diets, 12 cows were selected from each treatment for collection of rumen samples for the metagenomic sequencing. Results showed that BS decreased ruminal acetate and butyrate, while increased propionate concentrations, resulting in decreased acetate:propionate ratio. The metagenomics analysis revealed that MCE reduced relative abundances of Methanobrevibacter wolinii, Methanobrevibacter sp. AbM4, Candidatus Methanomassiliicoccus intestinalis, Methanobrevibacter cuticularis, Methanomicrobium mobile, Methanobacterium formicicum, and Methanobacterium congolense. Both BS and MCE reduced relative abundances of Methanosphaera sp. WGK6 and Methanosphaera stadtmanae. The co-occurrence network analysis of rumen bacteria and archaea revealed that dietary treatments influenced microbial interaction patterns, with BS and MCE cows having more and stronger associations than CON cows. The random forest and heatmaps analysis demonstrated that the Halopenitus persicus was positively correlated with fat- and protein-corrected milk yield; Clostridium sp. CAG 269, Clostridium sp. 27 14, Haloarcula rubripromontorii, and Methanobrevibacter curvatus were negatively correlated with rumen acetate and butyrate concentrations, and acetate:propionate ratio, whereas Selenomonas rumiantium was positively correlated with those variables.

CONCLUSIONS

The present results provided new information for mitigation of enteric methane emissions of dairy cows by feeding BS and MCE to influence rumen microbial activities. This fundamental knowledge is essential for developing enteric CH4 reduction strategies to mitigate climate change and reduce dietary energy waste. Video Abstract.

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

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

Review: Effect of Experimental Diets on the Microbiome of Productive Animals

The microorganisms that inhabit the gastrointestinal tract are responsible for multiple chains of reactions that affect their environment and modify the internal metabolism, their study receives the name of microbiome, which has become more relevant in recent years. In the near future, the challenges related to feeding are anticipated to escalate, encompassing the nutritional needs to sustain an overpopulated world. Therefore, it is expected that a better understanding of the interactions between microorganisms within the digestive tract will allow their modulation in order to provide an improvement in the immune system, feed efficiency or the promotion of nutritional characteristics in production animals, among others. In the present study, the main effects of experimental diets in production animals were described, emphasizing the diversity of the bacterial populations found in response to the diets, ordering them between polygastric and monogastric animals, and then describing the experimental diets used and their effect on the microorganisms. It is hoped that this study will help as a first general approach to the study of the role of the microbiome in production animals under different diets.

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

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

Effect of a garlic and citrus extract supplement on performance, rumen fermentation, methane production, and rumen microbiome of dairy cows

The aim of this trial was to determine the effect of a garlic and citrus extract supplement (GCE) on the performance, rumen fermentation, methane emissions, and rumen microbiome of dairy cows. Fourteen multiparous Nordic Red cows in mid-lactation from the research herd of Luke (Jokioinen, Finland) were allocated to 7 blocks in a complete randomized block design based on body weight, days in milk, dry matter intake (DMI), and milk yield. Animals within each block were randomly allocated to a diet with or without GCE. The experimental period for each block of cows (one for each of the control and GCE groups) consisted of 14 d of adaptation followed by 4 d of methane measurements inside the open circuit respiration chambers, with the first day being considered as acclimatization. Data were analyzed using the GLM procedure of SAS (SAS Institute Inc.). Methane production (g/d) and methane intensity (g/kg of energy-corrected milk) were lower by 10.3 and 11.7%, respectively, and methane yield (g/kg of DMI) tended to be lower by 9.7% in cows fed GCE compared with the control. Dry matter intake, milk production, and milk composition were similar between treatments. Rumen pH and total volatile fatty acid concentrations in rumen fluid were similar, whereas GCE tended to increase molar propionate concentration and decrease the molar ratio of acetate to propionate. Supplementation with GCE resulted in greater abundance of Succinivibrionaceae, which was associated with reduced methane. The relative abundance of the strict anaerobic Methanobrevibacter genus was reduced by GCE. The change in microbial community and rumen propionate proportion may explain the decrease in enteric methane emissions. In conclusion, feeding GCE to dairy cows for 18 d modified rumen fermentation and microbiota, leading to reduced methane production and intensity without compromising DMI or milk production in dairy cows. This could be an effective strategy for enteric methane mitigation of dairy cows.

Technology for Carbon Neutral Animal Breeding

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

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

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

Understanding variability and repeatability of enteric methane production in feedlot cattle

Breeding ruminants for low methane (CH4) emissions can be permanent and cumulative, but requires a better understanding of the variability of CH4 production among animals to accurately assess low-CH4 phenotypes. Our objectives were to: 1) investigate the variation in CH4 production among and within growing beef cattle, 2) identify low-CH4 emitters, and 3) examine relationships between CH4 production and intake, feeding behavior, growth, and rumen fermentation. Crossbred beef heifers (n=77; body weight=450 kg) were allocated to 3 pens and offered a finishing diet of 90% concentrate and 10% silage (dry matter (DM) basis). The study was conducted over 3 consecutive 6-week periods (126 days). GrowSafe bunks measured individual animal DM intake (DMI) and rumen fluid was sampled orally each period. A GreenFeed system measured individual animal emissions for 2 weeks/period. Methane production was calculated by animal within period using visits that were ≥3 min with fluxes compiled into six 4-h blocks corresponding to time of day, and averaged over blocks to obtain an average daily emission for the period. Animals with &lt;12 visits and &lt;5 blocks were omitted for the period and animals with ≥2 periods of complete CH4 data were used in the final analysis (n=52). Animals were ranked based on CH4 yield (g/kg DMI) from low to high, and grouped as Very-low (≤10% of animals), Low (11-25%), Intermediate (26-74%), High (75-89%), and Very high (≥90%) emitters (mean ± SD, 12.6 ± 2.16). The CH4 yield was 16% less (P&lt;0.05) for Very-low compared with Intermediate animals due to lower CH4 production (g/d, P&lt;0.05), with no differences in DMI (P&gt;0.05). However, the period × grouping interaction (P&lt;0.001) for CH4 yield indicated that the ranking of animals changed over time, although there were no extreme changes in rankings. Total VFA concentration decreased as CH4 yield decreased, but molar proportions of VFA remained unchanged, suggesting lower extent of ruminal digestion rather than a shift in fermentation. There were no differences in feeding behavior or average daily gain among groupings (P&gt;0.05). The between-animal coefficient of variation in CH4 yield of 17.3% enabled identification of low CH4-emmitting finishing beef cattle. However, accurate selection of low CH4-emitting animals should be based on repeated CH4 measurements over the production cycle.

Fermented soybean meal modified the rumen microbiome to enhance the yield of milk components in Holstein cows

The study was conducted to evaluate the rumen microbiota as well as the milk composition and milk component yields of Holstein cows supplemented with fermented soybean meal (FSBM). Eighteen Holstein cows in their 2nd parity with 54.38 ± 11.12 SD days in milking (DIM) were divided into two dietary groups (CON and TRT) of nine cows per group. The cows in the TRT group received 300 g of FSBM per cow per day in addition to the conventional diet, while each cow in the CON group was supplemented with 350 g of soybean meal (SBM) in their diet daily throughout the 28-day feeding trial. Rumen bacterial composition was detected via 16S rRNA sequencing, and the functional profiles of bacterial communities were predicted. Milk composition, milk yield, as well as rumen fermentation parameters, and serum biochemistry were also recorded. The inclusion of FSBM into the diets of Holstein cows increased the milk urea nitrogen (MUN), milk protein yield, fat corrected milk (FCM), and milk fat yield while the milk somatic cell count (SCC) was decreased. In the rumen, the relative abundances of Fibrobacterota, and Spirochaetota phyla were increased in the TRT group, while the percentage of Proteobacteria was lower. In addition, the supplementation of FSBM to Holstein cows increased the acetate percentage, rumen pH, and acetate to propionate ratio, while the proportion of propionate and propionate % was observed to decrease in the TRT group. The KEGG pathway and functional prediction revealed an upregulation in the functional genes associated with the biosynthesis of amino acids in the TRT group. This enrichment in functional genes resulted in an improved synthesis of several essential amino acids including lysine, methionine, and branch chain amino acids (BCAA) which might be responsible for the increased milk protein yield. Future studies should employ shotgun metagenomics, transcriptomics, and metabolomics technology to investigate the effects of FSBM on other rumen microbiomes and milk protein synthesis in the mammary gland in Holstein cows. KEY POINTS: • The supplementation of fermented soybean meal (FSBM) to Holstein cows modified the proportion of rumen bacteria. • Predicted metabolic pathways and functional genes of rumen bacteria revealed an enrichment in pathway and genes associated with biosynthesis of amino acids in the group fed FSBM. • The cows supplemented with FSBM record an improved rumen fermentation. • Cows supplemented with FSBM recorded an increased yield of milk protein and milk fat.

Contrasting Fecal Methanogenic and Bacterial Profiles of Organic Dairy Cows Located in Northwest Washington Receiving Either a Mixed Diet of Pasture and TMR or Solely TMR

Currently, little is known regarding fecal microbial populations and their associations with methanogenic archaea in pasture-based dairy cattle. In this study, we assessed the fecal microbiome of organic dairy cows across different time points receiving a mixed diet of pasture and total mixed ration (TMR) or TMR only. We hypothesized that the fecal methanogenic community, as well as co-occurrence patterns with bacteria, change across diets. To test these hypotheses, we analyzed TMR and pasture samples, as well as the V3-V4 region of 16S rRNA of fecal samples collected over the course of a one-year study period from 209 cows located on an organic dairy in Northwest Washington. The inherent variability in pasture quality, quantity, availability, and animal preference can lead to diverse dietary intakes. Therefore, we conducted a k-means clustering analysis to identify samples from cows that were associated with either a pasture-based diet or a solely TMR diet. A total of 4 clusters were identified. Clusters 1 and 3 were mainly associated with samples primarily collected from cows with access to pasture of varying quality and TMR, cluster 2 was formed by samples from cows receiving only TMR, and cluster 4 was a mix of samples from cows receiving high-quality pasture and TMR or TMR only. Interestingly, we found little difference in the relative abundance of methanogens between the community clusters. There was evidence of differences in diversity between pasture associated bacterial communities and those associated with TMR. Cluster 4 had higher diversity and a less robust co-occurrence network based on Spearman correlations than communities representing TMR only or lower-quality pasture samples. These findings indicate that varied bacterial communities are correlated with the metabolic characteristics of different diets. The overall good pasture and TMR quality in this study, combined with the organic allowance for feeding high levels of TMR even during the grazing season, might have contributed to the lack of differences in the fecal archaeal community from samples associated with a mixed pasture and TMR diet, and a TMR only diet. Mitigation strategies to decrease methane emissions such as increasing concentrate to forage ratio, decreasing pasture maturity and adopting grazing systems targeting high quality pasture have been shown to be efficient for pasture-based systems. However, the allowance for organic dairy producers to provide up to an average of 70% of a ruminant's dry matter demand from dry matter fed (e.g., TMR), suggests that reducing enteric methane emissions may require the development of novel dietary strategies independent of pasture management.

Cecal microbiota of feedlot cattle fed a four-species Bacillus supplement

As commercial fed cattle consume large amounts of concentrate feedstuffs, hindgut health can be challenged. The objective of this study was to evaluate the effects of a commercially available Bacillus feed additive on cattle health outcomes and cecal microbiota of fed cattle at the time of harvest. Commercial cattle from a single feedlot were identified for characterization of cecal microbial communities using 16S ribosomal ribonucleic acid gene sequencing. All cattle were fed a common corn-based finishing diet. Control cattle (CON) were administered no treatment while treated cattle (TRT) were supplemented daily with 0.050 g of MicroSaf 4C 40 (2 billion colony forming units of Bacillus spp.; Phileo by Lesaffre, Milwaukee, WI). Immediately after harvest and evisceration, the cecal contents of cattle were sampled. After DNA extraction, amplification, and sequencing, reads from CON samples (N = 12) and TRT samples (N = 12) were assigned taxonomy using the SILVA 138 database. Total morbidity, first treatment of atypical interstitial pneumonia, and early shipments for harvest were decreased among TRT cattle compared to CON cattle (P ≤ 0.021). On average, cecal microbiota from TRT cattle had greater alpha diversity than microbiota from CON cattle as measured by Shannon diversity, Pielou's evenness, and feature richness (P < 0.010). Additionally, TRT microbial communities were different (P = 0.001) and less variable (P < 0.001) than CON microbial communities when evaluated by unweighted UniFrac distances. By relative abundance across all samples, the most prevalent phyla were Firmicutes (55.40%, SD = 15.97) and Bacteroidetes (28.17%, SD = 17.74) followed by Proteobacteria (6.75%, SD = 10.98), Spirochaetes (4.54%, SD = 4.85), and Euryarchaeota (1.77%, SD = 3.00). Spirochaetes relative abundance in TRT communities was greater than that in CON communities and was differentially abundant between treatments by ANCOM testing (W = 11); Monoglobaceae was the only family-level taxon identified as differentially abundant (W = 59; greater mean relative abundance in TRT group by 2.12 percentage points). Half (N = 6) of the CON samples clustered away from all other samples based on principal coordinates and represented cecal dysbiosis among CON cattle. The results of this study indicated that administering a four-species blend of Bacillus positively supported the cecal microbial communities of finishing cattle. Further research is needed to explore potential mechanisms of action of Bacillus DFM products in feedlot cattle.

First Steps into Ruminal Microbiota Robustness

Despite its central role in ruminant nutrition, little is known about ruminal microbiota robustness, which is understood as the ability of the microbiota to cope with disturbances. The aim of the present review is to offer a comprehensive description of microbial robustness, as well as its potential drivers, with special focus on ruminal microbiota. First, we provide a briefing on the current knowledge about ruminal microbiota. Second, we define the concept of disturbance (any discrete event that disrupts the structure of a community and changes either the resource availability or the physical environment). Third, we discuss community resistance (the ability to remain unchanged in the face of a disturbance), resilience (the ability to return to the initial structure following a disturbance) and functional redundancy (the ability to maintain or recover initial function despite compositional changes), all of which are considered to be key properties of robust microbial communities. Then, we provide an overview of the currently available methodologies to assess community robustness, as well as its drivers (microbial diversity and network complexity) and its potential modulation through diet. Finally, we propose future lines of research on ruminal microbiota robustness.

Estimation of Complex-Trait Prediction Accuracy from the Different Holo-Omics Interaction Models

Statistical models play a significant role in designing competent breeding programs related to complex traits. Recently; the holo-omics framework has been productively utilized in trait prediction; but it contains many complexities. Therefore; it is desirable to establish prediction accuracy while combining the host’s genome and microbiome data. Several methods can be used to combine the two data in the model and study their effectiveness by estimating the prediction accuracy. We validate our holo-omics interaction models with analysis from two publicly available datasets and compare them with genomic and microbiome prediction models. We illustrate that the holo-omics interactive models achieved the highest prediction accuracy in ten out of eleven traits. In particular; the holo-omics interaction matrix estimated using the Hadamard product displayed the highest accuracy in nine out of eleven traits, with the direct holo-omics model and microbiome model showing the highest prediction accuracy in the remaining two traits. We conclude that comparing prediction accuracy in different traits using real data showed important intuitions into the holo-omics architecture of complex traits.

Gut microbiota and host genetics contribute to the phenotypic variation of digestive and feed efficiency traits in growing pigs fed a conventional and a high fiber diet

Background

Breeding pigs that can efficiently digest alternative diets with increased fiber content is a viable strategy to mitigate the feed cost in pig production. This study aimed at determining the contribution of the gut microbiota and host genetics to the phenotypic variability of digestive efficiency (DE) traits, such as digestibility coefficients of energy, organic matter and nitrogen, feed efficiency (FE) traits (feed conversion ratio and residual feed intake) and growth traits (average daily gain and daily feed intake). Data were available for 791 pigs fed a conventional diet and 735 of their full-sibs fed a high-fiber diet. Fecal samples were collected at 16 weeks of age to sequence the V3–V4 regions of the 16S ribosomal RNA gene and predict DE with near-infrared spectrometry. The proportions of phenotypic variance explained by the microbiota (microbiability) were estimated under three OTU filtering scenarios. Then, microbiability and heritability were estimated independently (models Micro and Gen) and jointly (model Micro+Gen) using a Bayesian approach for all traits. Breeding values were estimated in models Gen and Micro+Gen.

Results

Differences in microbiability estimates were significant between the two extreme filtering scenarios (14,366 and 803 OTU) within diets, but only for all DE. With the intermediate filtering scenario (2399 OTU) and for DE, microbiability was higher (> 0.44) than heritability (< 0.32) under both diets. For two of the DE traits, microbiability was significantly higher under the high-fiber diet (0.67 ± 0.06 and 0.68 ± 0.06) than under the conventional diet (0.44 ± 0.06). For growth and FE, heritability was higher (from 0.26 ± 0.06 to 0.44 ± 0.07) than microbiability (from 0.17 ± 0.05 to 0.35 ± 0.06). Microbiability and heritability estimates obtained with the Micro+Gen model did not significantly differ from those with the Micro and Gen models for all traits. Finally, based on their estimated breeding values, pigs ranked differently between the Gen and Micro+Gen models, only for the DE traits under both diets.

Conclusions

The microbiota explained a significant proportion of the phenotypic variance of the DE traits, which was even larger than that explained by the host genetics. Thus, the use of microbiota information could improve the selection of DE traits, and to a lesser extent, of growth and FE traits. In addition, our results show that, at least for DE traits, filtering OTU is an important step and influences the microbiability.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-022-00742-6.

Host genetics associated with gut microbiota and methane emission in cattle

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

Stool and Ruminal Microbiome Components Associated With Methane Emission and Feed Efficiency in Nelore Beef Cattle

Background: The impact of extreme changes in weather patterns on the economy and human welfare is one of the biggest challenges our civilization faces. From anthropogenic contributions to climate change, reducing the impact of farming activities is a priority since it is responsible for up to 18% of global greenhouse gas emissions. To this end, we tested whether ruminal and stool microbiome components could be used as biomarkers for methane emission and feed efficiency in bovine by studying 52 Brazilian Nelore bulls belonging to two feed intervention treatment groups, that is, conventional and by-product-based diets.

Results: We identified a total of 5,693 amplicon sequence variants (ASVs) in the Nelore bulls’ microbiomes. A Differential abundance analysis with the ANCOM approach identified 30 bacterial and 15 archaeal ASVs as differentially abundant (DA) among treatment groups. An association analysis using Maaslin2 software and a linear mixed model indicated that bacterial ASVs are linked to the host’s residual methane emission (RCH₄) and residual feed intake (RFI) phenotype variation, suggesting their potential as targets for interventions or biomarkers.

Conclusion: The feed composition induced significant differences in both abundance and richness of ruminal and stool microbial populations in ruminants of the Nelore breed. The industrial by-product-based dietary treatment applied to our experimental groups influenced the microbiome diversity of bacteria and archaea but not of protozoa. ASVs were associated with RCH₄ emission and RFI in ruminal and stool microbiomes. While ruminal ASVs were expected to influence CH₄ emission and RFI, the relationship of stool taxa, such as Alistipes and Rikenellaceae (gut group RC9), with these traits was not reported before and might be associated with host health due to their link to anti-inflammatory compounds. Overall, the ASVs associated here have the potential to be used as biomarkers for these complex phenotypes.

Application of 3-nitrooxypropanol and canola oil to mitigate enteric methane emissions of beef cattle results in distinctly different effects on the rumen microbial community

Background

The major greenhouse gas from ruminants is enteric methane (CH₄) which in 2010, was estimated at 2.1 Gt of CO₂ equivalent, accounting for 4.3% of global anthropogenic greenhouse gas emissions. There are extensive efforts being made around the world to develop CH₄ mitigating inhibitors that specifically target rumen methanogens with the ultimate goal of reducing the environmental footprint of ruminant livestock production. This study examined the individual and combined effects of supplementing a high-forage diet (90% barley silage) fed to beef cattle with the investigational CH₄ inhibitor 3-nitrooxypropanol (3-NOP) and canola oil (OIL) on the rumen microbial community in relation to enteric CH₄ emissions and ruminal fermentation.

Results

3-NOP and OIL individually reduced enteric CH₄ yield (g/kg dry matter intake) by 28.2% and 24.0%, respectively, and the effects were additive when used in combination (51.3% reduction). 3-NOP increased H₂ emissions 37-fold, while co-administering 3-NOP and OIL increased H₂ in the rumen 20-fold relative to the control diet. The inclusion of 3-NOP or OIL significantly reduced the diversity of the rumen microbiome. 3-NOP resulted in targeted changes in the microbiome decreasing the relative abundance of Methanobrevibacter and increasing the relative abundance of Bacteroidetes. The inclusion of OIL resulted in substantial changes to the microbial community that were associated with changes in ruminal volatile fatty acid concentration and gas production. OIL significantly reduced the abundance of protozoa and fiber-degrading microbes in the rumen but it did not selectively alter the abundance of rumen methanogens.

Conclusions

Our data provide a mechanistic understanding of CH₄ inhibition by 3-NOP and OIL when offered alone and in combination to cattle fed a high forage diet. 3-NOP specifically targeted rumen methanogens and partly inhibited the hydrogenotrophic methanogenesis pathway, which increased H₂ emissions and propionate molar proportion in rumen fluid. In contrast, OIL caused substantial changes in the rumen microbial community by indiscriminately altering the abundance of a range of rumen microbes, reducing the abundance of fibrolytic bacteria and protozoa, resulting in altered rumen fermentation. Importantly, our data suggest that co-administering CH₄ inhibitors with distinct mechanisms of action can both enhance CH₄ inhibition and provide alternative sinks to prevent excessive accumulation of ruminal H₂.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42523-022-00179-8.

Differences in the Composition of the Rumen Microbiota of Finishing Beef Cattle Divergently Ranked for Residual Methane Emissions

With the advent of high throughput technology, it is now feasible to study the complex relationship of the rumen microbiota with methanogenesis in large populations of ruminant livestock divergently ranked for enteric emissions. Recently, the residual methane emissions (RME) concept has been identified as the optimal phenotype for assessing the methanogenic potential of ruminant livestock due to the trait's independence from animal productivity but strong correlation with daily methane emissions. However, there is currently a dearth of data available on the bacterial and archaeal microbial communities residing in the rumens of animals divergently ranked for RME. Therefore, the objective of this study was to investigate the relationship between the rumen microbiota and RME in a population of finishing beef cattle. Methane emissions were estimated from individual animals using the GreenFeed Emissions Monitoring system for 21 days over a mean feed intake measurement period of 91 days. Residual methane emissions were calculated for 282 crossbred finishing beef cattle, following which a ∼30% difference in all expressions of methane emissions was observed between high and low RME ranked animals. Rumen fluid samples were successfully obtained from 268 animals during the final week of the methane measurement period using a trans-oesophageal sampling device. Rumen microbial DNA was extracted and subjected to 16S rRNA amplicon sequencing. Animals ranked as low RME had the highest relative abundances (P < 0.05) of lactic-acid-producing bacteria (Intestinibaculum, Sharpea, and Olsenella) and Selenomonas, and the lowest (P < 0.05) proportions of Pseudobutyrivibrio, Butyrivibrio, and Mogibacterium. Within the rumen methanogen community, an increased abundance (P < 0.05) of the genus Methanosphaera and Methanobrevibacter RO clade was observed in low RME animals. The relative abundances of both Intestinibaculum and Olsenella were negatively correlated (P < 0.05) with RME and positively correlated with ruminal propionate. A similar relationship was observed for the abundance of Methanosphaera and the Methanobrevibacter RO clade. Findings from this study highlight the ruminal abundance of bacterial genera associated with the synthesis of propionate via the acrylate pathway, as well as the methanogens Methanosphaera and members of the Methanobrevibacter RO clade as potential microbial biomarkers of the methanogenic potential of beef cattle.

Application of MinION Amplicon Sequencing to Buccal Swab Samples for Improving Resolution and Throughput of Rumen Microbiota Analysis

The Illumina MiSeq platform has been widely used as a standard method for studying the rumen microbiota. However, the low resolution of taxonomic identification is the only disadvantage of MiSeq amplicon sequencing, as it targets a part of the 16S rRNA gene. In the present study, we performed three experiments to establish a high-resolution and high-throughput rumen microbial profiling approach using a combination of MinION platform and buccal swab sample, which is a proxy for rumen contents. In experiment 1, rumen contents and buccal swab samples were collected simultaneously from cannulated cattle (n = 6) and used for microbiota analysis using three different analytical workflows: amplicon sequencing of the V3–V4 region of the 16S rRNA gene using MiSeq and amplicon sequencing of near full-length 16S rRNA gene using MinION or PacBio Sequel II. All reads derived from the MinION and PacBio platforms were classified at the species-level. In experiment 2, rumen fluid samples were collected from beef cattle (n = 28) and used for 16S rRNA gene amplicon sequencing using the MinION platform to evaluate this sequencing platform for rumen microbiota analysis. We confirmed that the MinION platform allowed species-level taxa assignment for the predominant bacterial groups, which were previously identified at the family- and genus-level using the MiSeq platform. In experiment 3, buccal swab samples were collected from beef cattle (n = 30) and used for 16S rRNA gene amplicon sequencing using the MinION platform to validate the applicability of a combination of the MinION platform and buccal swab samples for rumen microbiota analysis. The distribution of predominant bacterial taxa in the buccal swab samples was similar to that in the rumen samples observed in experiment 2. Based on these results, we concluded that the combination of the MinION platform and buccal swab samples may be potentially applied for rumen microbial analysis in large-scale studies.

Acremonium terricola Culture’s Dose–Response Effects on Lactational Performance, Antioxidant Capacity, and Ruminal Characteristics in Holstein Dairy Cows

Acremonium terricola culture (ATC) has similar bioactive constituents to Cordyceps and is known for its nutrient and pharmacological value, indicating the potential of ATC as a new feed additive in dairy cow feeding. The primary aim of this experiment was to investigate the effects of increasing amounts of ATC in diets on milk performance, antioxidant capacity, and rumen fermentation, and the secondary aim was to evaluate the potential effects of high doses of ATC. A total of 60 multiparous Holstein cows (110 ± 21 days in milk; 2.53 ± 0.82 parity) were assigned into 15 blocks and randomly assigned to one of four groups: 0, 30, 60, or 300 g/d of ATC per cow for 97 days. Data were analyzed using repeated measures in the Mixed procedure. Dry-matter intake was not changed (p > 0.05), while energy-corrected milk and fat-corrected milk yields increased linearly and quadratically, and somatic cell count in milk decreased linearly and quadratically (p < 0.05). The lactation efficiency and the yields of milk fat and protein increased linearly (p < 0.05). On day 90, serum catalase level, total oxidative capacity, glutathione peroxidase, immunoglobulin A, and immunoglobulin M concentrations were significantly higher in the 60 and 300 g/d groups than in the 0 g/d group (p < 0.05). ATC addition showed linear effects on total volatile fatty acid (VFA), acetate, branched VFA concentrations, and rumen pH (p < 0.05). Supplementing 60 and 300 g/d ATC significantly affected the bacterial composition (p < 0.05). The relative abundance of Christensenellaceae_R–7_group and Lachnospiraceae_NK3A20_group were significantly increased by 60 g/d supplementation, and the relative abundance of Erysipelotrichaceae_UCG_002, Acetitomaculum, Olsenella, and Syntrophococcus were significantly increased by 300 g/d supplementation (p < 0.05). ATC was effective in enhancing rumen fermentation and reducing somatic cell count in milk, thereby improving milk yield. The optimized dose of ATC was 60 g/d for lactating cows, and there were no risks associated with high doses of ATC.

Alternative pathways for hydrogen sink originated from the ruminal fermentation of carbohydrates: Which microorganisms are involved in lowering methane emission?

Agriculture is responsible for a great share of the anthropogenic sources of greenhouse gases that, by warming the earth, threaten its biodiversity. Among greenhouse gas emissions, enteric CH4 from livestock is an important target to slow down climate changes. The CH4 is originated from rumen fermentation and its concentration is affected by several factors, including genetics and nutrition. Ruminants have an extraordinary symbiosis with microorganisms (bacteria, fungi, and protozoa) that ferment otherwise indigestible carbohydrates, from which they obtain energy to grow and continue actively producing, among other products, volatile fatty acids, CO2 and H2. Detrimental ruminal accumulation of H2 is avoided by methanogenesis carried out by Archaea methanogens. Importantly, methanogenesis is not the only H2 sink pathway. In fact, other bacteria can reduce substrates using metabolic hydrogen formed during carbohydrate fermentation, namely propionate production and reductive acetogenesis, thus lowering the CH4 produced. Although the complexity of rumen poses challenges to mitigate CH4 production, the emergence of sequencing techniques that allow the study of microbial communities, gene expression, and metabolome are largely contributing to unravel pathways and key players in the rumen. Indeed, it is now recognized that in vivo emissions of CH4 are correlated to microbial communities, and particularly with the abundance of methanogens, several bacterial groups, and  their genes. The goal of CH4 mitigation is to work in favor of the natural processes, without compromising rumen function, animal health, and productivity. Notwithstanding, the major challenge continues to be the feasibility and affordability of the proposed solutions.

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

Background

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

Methods

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

Results

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

Conclusions

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

Supplementary Information

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

Herd-level versus animal-level variation in methane emission prediction in grazing dairy cattle

In response to the increased concern over agriculture's contribution to greenhouse gas (GHG) emissions, more detailed assessments of current methane emissions and their variation, within and across individual dairy farms and cattle, are of interest for research and policy development. This assessment will provide insights into possible changes needed to reduce GHG emissions, the nature and direction of these changes, ways to influence farmer behavior and areas to maximize the adoption of emerging mitigation technologies. The objectives of this study were to (1) quantify the variation in enteric fermentation methane emissions within and among seasonal calving dairy farms with the majority of nutritional requirements met through grazed pasture; (2) use this variation to assess the potential of new individual animal emission monitoring technologies and their impact on mitigation policy. We used a large database of cow performance records for milk production and survival from 2 398 herds in New Zealand, and simulation to account for unobserved variation in feed efficiency and methane emissions per unit of feed. Results showed an average of 120 ± 31.4 kg predicted methane (CH₄) per cow per year after accounting for replacement costs, ranging 8.9-323 kg CH₄/cow per year. Whereas milk production, survival and predicted live weight were reasonably effective at predicting both individual and herd average levels of per cow feed intake, substantial within animal variation in emissions per unit of feed reduced the ability of these variables to predict variation in per animal methane output. Animal-level measurement technologies predicting only feed intake but not emissions per unit of feed are unlikely to be effective for advancing national policy goals of reducing dairy farming enteric methane output. This is because farmers seek to profitably utilize all farm feed resources available, so improvements in feed efficiency will not result in the reduction in feed utilization required to reduce methane emissions. At a herd level, average per cow milk production and live weight could form the basis of assigning a farm-level point of obligation for methane emissions. In conclusion, a comprehensive national database infrastructure that was tightly linked to animal identification and movement systems, and captured live weight data from existing farm-level recording systems, would be required to make this effective. Additional policy and incentivization mechanisms would still be required to encourage farmer uptake of mitigation interventions, such as novel feed supplements or vaccines that reduce methane emissions per unit of feed.

The Airway Pathobiome in Complex Respiratory Diseases: A Perspective in Domestic Animals

Respiratory infections in domestic animals are a major issue for veterinary and livestock industry. Pathogens in the respiratory tract share their habitat with a myriad of commensal microorganisms. Increasing evidence points towards a respiratory pathobiome concept, integrating the dysbiotic bacterial communities, the host and the environment in a new understanding of respiratory disease etiology. During the infection, the airway microbiota likely regulates and is regulated by pathogens through diverse mechanisms, thereby acting either as a gatekeeper that provides resistance to pathogen colonization or enhancing their prevalence and bacterial co-infectivity, which often results in disease exacerbation. Insight into the complex interplay taking place in the respiratory tract between the pathogens, microbiota, the host and its environment during infection in domestic animals is a research field in its infancy in which most studies are focused on infections from enteric pathogens and gut microbiota. However, its understanding may improve pathogen control and reduce the severity of microbial-related diseases, including those with zoonotic potential.

Microbial Communities in Methane Cycle: Modern Molecular Methods Gain Insights into Their Global Ecology

The role of methane as a greenhouse gas in the concept of global climate changes is well known. Methanogens and methanotrophs are two microbial groups which contribute to the biogeochemical methane cycle in soil, so that the total emission of CH4 is the balance between its production and oxidation by microbial communities. Traditional identification techniques, such as selective enrichment and pure-culture isolation, have been used for a long time to study diversity of methanogens and methanotrophs. However, these techniques are characterized by significant limitations, since only a relatively small fraction of the microbial community could be cultured. Modern molecular methods for quantitative analysis of the microbial community such as real-time PCR (Polymerase chain reaction), DNA fingerprints and methods based on high-throughput sequencing together with different “omics” techniques overcome the limitations imposed by culture-dependent approaches and provide new insights into the diversity and ecology of microbial communities in the methane cycle. Here, we review available knowledge concerning the abundances, composition, and activity of methanogenic and methanotrophic communities in a wide range of natural and anthropogenic environments. We suggest that incorporation of microbial data could fill the existing microbiological gaps in methane flux modeling, and significantly increase the predictive power of models for different environments.

Early life dietary intervention in dairy calves results in a long-term reduction in methane emissions

Recent evidence suggests that changes in microbial colonization of the rumen prior to weaning may imprint the rumen microbiome and impact phenotypes later in life. We investigated how dietary manipulation from birth influences growth, methane production, and gastrointestinal microbial ecology. At birth, 18 female Holstein and Montbéliarde calves were randomly assigned to either treatment or control (CONT). Treatment was 3-nitrooxypropanol (3-NOP), an investigational anti-methanogenic compound that was administered daily from birth until three weeks post-weaning (week 14). Samples of rumen fluid and faecal content were collected at weeks 1, 4, 11, 14, 23, and 60 of life. Calves were tested for methane emissions using the GreenFeed system during the post-weaning period (week 11–23 and week 56–60 of life). Calf physiological parameters (BW, ADG and individual VFA) were similar across groups throughout the trial. Treated calves showed a persistent reduction in methane emissions (g CH₄/d) throughout the post-weaning period up to at least 1 year of life, despite treatment ceasing three weeks post-weaning. Similarly, despite variability in the abundance of individual taxa across weeks, the rumen bacterial, archaeal and fungal structure differed between CONT and 3-NOP calves across all weeks, as visualised using sparse-PLS-DA. Similar separation was also observed in the faecal bacterial community. Interestingly, despite modest modifications to the abundance of rumen microbes, the reductive effect of 3-NOP on methane production persisted following cessation of the treatment period, perhaps indicating a differentiation of the ruminal microbial ecosystem or a host response triggered by the treatment in the early development phase.

A multi-kingdom metabarcoding study on cattle grazing Alpine pastures discloses intra-seasonal shifts in plant selection and faecal microbiota

Diet selection by grazing livestock may affect animal performance as well as the biodiversity of grazed areas. Recent DNA barcoding techniques allow to assess dietary plant composition in faecal samples, which may be additionally integrated by the description of gut microbiota. In this high throughput metabarcoding study, we investigated the diversity of plant, fungal and bacterial taxa in faecal samples of lactating cows of two breeds grazing an Alpine semi-natural grassland during summer. The estimated plant composition of the diet comprised 67 genera and 39 species, which varied remarkably during summer, suggesting a decline of the diet forage value with the advancing of the vegetative season. The fungal community included Neocallimastigomycota gut symbionts, but also Ascomycota and Basidiomycota plant parasite and coprophilous taxa, likely ingested during grazing. The proportion of ingested fungi was remarkably higher than in other studies, and varied during summer, although less than that observed for plants. Some variation related to breed was also detected. The gut bacterial taxa remained stable through the summer but displayed a breed-specific composition. The study provided insights in the reciprocal organisms' interactions affecting, and being affected by, the foraging behaviour: plants showed a high temporal variation, fungi a smaller one, while bacteria had practically none; conversely, the same kingdoms showed the opposite gradient of variation as respect to the animal host breed, as bacteria revealed to be the group mostly characterized by host-specificity.

Genome-wide association studies for methane emission and ruminal volatile fatty acids using Holstein cattle sequence data

Background

Methane emission by ruminants has contributed considerably to the global warming and understanding the genomic architecture of methane production may help livestock producers to reduce the methane emission from the livestock production system. The goal of our study was to identify genomic regions affecting the predicted methane emission (PME) from volatile fatty acids (VFAs) indicators and VFA traits using imputed whole-genome sequence data in Iranian Holstein cattle.

Results

Based on the significant-association threshold (p < 5 × 10^− 8), 33 single nucleotide polymorphisms (SNPs) were detected for PME per kg milk (n = 2), PME per kg fat (n = 14), and valeric acid (n = 17). Besides, 69 genes were identified for valeric acid (n = 18), PME per kg milk (n = 4) and PME per kg fat (n = 47) that were located within 1 Mb of significant SNPs. Based on the gene ontology (GO) term analysis, six promising candidate genes were significantly clustered in organelle organization (GO:0004984, p = 3.9 × 10^− 2) for valeric acid, and 17 candidate genes significantly clustered in olfactory receptors activity (GO:0004984, p = 4 × 10^− 10) for PME traits. Annotation results revealed 31 quantitative trait loci (QTLs) for milk yield and its components, body weight, and residual feed intake within 1 Mb of significant SNPs.

Conclusions

Our results identified 33 SNPs associated with PME and valeric acid traits, as well as 17 olfactory receptors activity genes for PME traits related to feed intake and preference. Identified SNPs were close to 31 QTLs for milk yield and its components, body weight, and residual feed intake traits. In addition, these traits had high correlations with PME trait. Overall, our findings suggest that marker-assisted and genomic selection could be used to improve the difficult and expensive-to-measure phenotypes such as PME. Moreover, prediction of methane emission by VFA indicators could be useful for increasing the size of reference population required in genome-wide association studies and genomic selection.

Microbiability of meat quality and carcass composition traits in swine

The impact of gut microbiome composition was investigated at different stages of production (weaning, Mid‐test and Off‐test) on meat quality and carcass composition traits of 1,123 three‐way crossbred pigs. Data were analysed using linear mixed models which included the fixed effects of dam line, contemporary group and gender as well as the random effects of pen, animal and microbiome information at different stages. The contribution of the microbiome to all traits was prominent although it varied over time, increasing from weaning to Off‐test for most traits. Microbiability estimates of carcass composition traits were greater than that of meat quality traits. Among all of the traits analysed, belly weight (BEL) had a higher microbiability estimate (0.29 ± 0.04). Adding microbiome information did not affect the estimates of genomic heritability of meat quality traits but affected the estimates of carcass composition traits. Fat depth had a greater decrease (10%) in genomic heritability at Off‐test. High microbial correlations were found among different traits, particularly with traits related to fat deposition with a decrease in the genomic correlation up to 20% for loin weight and BEL. This suggested that genomic correlation was partially contributed by genetic similarity of microbiome composition. The results indicated that better understanding of microbial composition could aid the improvement of complex traits, particularly the carcass composition traits in swine by inclusion of microbiome information in the genetic evaluation process.

Link-HD: a versatile framework to explore and integrate heterogeneous microbial communities

MOTIVATION

We present Link-HD, an approach to integrate multiple datasets. Link-HD is a generalization of 'Structuration des Tableaux A Trois Indices de la Statistique-Analyse Conjointe de Tableaux', a family of methods designed to integrate information from heterogeneous data. Here, we extend the classical approach to deal with broader datasets (e.g. compositional data), methods for variable selection and taxon-set enrichment analysis.

RESULTS

The methodology is demonstrated by integrating rumen microbial communities from cows for which methane yield (CH4y) was individually measured. Our approach reproduces the significant link between rumen microbiota structure and CH4 emission. When analyzing the TARA's ocean data, Link-HD replicates published results, highlighting the relevance of temperature with members of phyla Proteobacteria on the structure and functionality of this ecosystem.

AVAILABILITY AND IMPLEMENTATION

The source code, examples and a complete manual are freely available in GitHub https://github.com/lauzingaretti/LinkHD and in Bioconductor https://bioconductor.org/packages/release/bioc/html/LinkHD.html.

Sward type alters the relative abundance of members of the rumen microbial ecosystem in dairy cows

The performance of ruminant livestock has been shown to benefit from the enhanced nutritive value and herbage yield associated with clover incorporation in the grazing sward. However, little research to date has been conducted investigating the effects of mixed swards containing white clover on the composition of the rumen microbiome. In this study, the rumen microbial composition of late lactation dairy cows grazing perennial ryegrass only (PRG; n = 20) or perennial ryegrass and white clover (WCPRG; n = 19) swards, was characterised using 16S rRNA amplicon sequencing. PERMANOVA analysis indicated diet significantly altered the composition of the rumen microbiome (P = 0.024). Subtle shifts in the relative abundance of 14 bacterial genera were apparent between diets, including an increased relative abundance of Lachnospira (0.04 vs. 0.23%) and Pseudobutyrivibrio (1.38 vs. 0.81%) in the WCPRG and PRG groups, respectively. The composition of the archaeal community was altered between dietary groups, with a minor increase in the relative abundance of Methanosphaera in the WCPRG observed. Results from this study highlight the potential for sward type to influence the composition of the rumen microbial community.

Stochasticity constrained by deterministic effects of diet and age drive rumen microbiome assembly dynamics

How complex communities assemble through the animal's life, and how predictable the process is remains unexplored. Here, we investigate the forces that drive the assembly of rumen microbiomes throughout a cow's life, with emphasis on the balance between stochastic and deterministic processes. We analyse the development of the rumen microbiome from birth to adulthood using 16S-rRNA amplicon sequencing data and find that the animals shared a group of core successional species that invaded early on and persisted until adulthood. Along with deterministic factors, such as age and diet, early arriving species exerted strong priority effects, whereby dynamics of late successional taxa were strongly dependent on microbiome composition at early life stages. Priority effects also manifest as dramatic changes in microbiome development dynamics between animals delivered by C-section vs. natural birth, with the former undergoing much more rapid species invasion and accelerated microbiome development. Overall, our findings show that together with strong deterministic constrains imposed by diet and age, stochastic colonization in early life has long-lasting impacts on the development of animal microbiomes.

Identification of rumen microbial biomarkers linked to methane emission in Holstein dairy cows

Mitigation of greenhouse gas emissions is relevant for reducing the environmental impact of ruminant production. In this study, the rumen microbiome from Holstein cows was characterized through a combination of 16S rRNA gene and shotgun metagenomic sequencing. Methane production (CH4 ) and dry matter intake (DMI) were individually measured over 4-6 weeks to calculate the CH4 yield (CH4 y = CH4 /DMI) per cow. We implemented a combination of clustering, multivariate and mixed model analyses to identify a set of operational taxonomic unit (OTU) jointly associated with CH4 y and the structure of ruminal microbial communities. Three ruminotype clusters (R1, R2 and R3) were identified, and R2 was associated with higher CH4 y. The taxonomic composition on R2 had lower abundance of Succinivibrionaceae and Methanosphaera, and higher abundance of Ruminococcaceae, Christensenellaceae and Lachnospiraceae. Metagenomic data confirmed the lower abundance of Succinivibrionaceae and Methanosphaera in R2 and identified genera (Fibrobacter and unclassified Bacteroidales) not highlighted by metataxonomic analysis. In addition, the functional metagenomic analysis revealed that samples classified in cluster R2 were overrepresented by genes coding for KEGG modules associated with methanogenesis, including a significant relative abundance of the methyl-coenzyme M reductase enzyme. Based on the cluster assignment, we applied a sparse partial least-squares discriminant analysis at the taxonomic and functional levels. In addition, we implemented a sPLS regression model using the phenotypic variation of CH4 y. By combining these two approaches, we identified 86 discriminant bacterial OTUs, notably including families linked to CH4 emission such as Succinivibrionaceae, Ruminococcaceae, Christensenellaceae, Lachnospiraceae and Rikenellaceae. These selected OTUs explained 24% of the CH4 y phenotypic variance, whereas the host genome contribution was ~14%. In summary, we identified rumen microbial biomarkers associated with the methane production of dairy cows; these biomarkers could be used for targeted methane-reduction selection programmes in the dairy cattle industry provided they are heritable.

Methane and Carbon Dioxide Emission of Beef Heifers in Relation with Growth and Feed Efficiency

Simple Summary

For sustainable meat production, beef farmers must make the best use of grass and roughage while limiting the carbon footprint of their herds. The genetic improvement in feed efficiency and enteric methane production of replacement heifers is possible if the recorded phenotypes are available. Intuitively, the relationship between the two traits should be negative, i.e., favorable, since the energy lost with the methane is not available for heifer metabolism. The measurement of feed efficiency requires several weeks of feed intake recording. The enteric methane emission rate can also be recorded over several weeks. The two traits of 326 beef heifers from two experimental farms were measured simultaneously for 8 to 12 weeks. The correlations between roughage intake, daily gain, and methane were all positive. The enteric methane emission rate was positively related to body weight, daily gain, and dry matter intake. The relationship with feed efficiency was slightly positive, i.e., unfavorable. Therefore, the two traits should be recorded simultaneously to evidence low-emitting and efficient heifers. This study also showed that replacing the feed intake recording with the carbon dioxide emission rate appeared potentially beneficial for selecting these low-emitting and efficient heifers.

Abstract

Reducing enteric methane production and improving the feed efficiency of heifers on roughage diets are important selection objectives for sustainable beef production. The objective of the current study was to assess the relationship between different methane production and feed efficiency criteria of beef heifers fed ad libitum roughage diets. A total of 326 Charolais heifers aged 22 months were controlled in two farms and fed either a grass silage (n = 252) or a natural meadow hay (n = 74) diet. Methane (CH₄) and carbon dioxide (CO₂) emission rates (g/day) were measured with GreenFeed systems. The dry matter intake (DMI), average daily gain (ADG), CH₄ and CO₂ were measured over 8 to 12 weeks. Positive correlations were observed among body weight, DMI, ADG, CH₄ and CO₂. The residual feed intake (r_wgDMI) was not related to CH₄ or residual methane (r_wiCH₄). It was negatively correlated with methane yield (CH₄/DMI): R_p = −0.87 and −0.83. Residual gain (r_wiADG) and ADG/DMI were weakly and positively related to residual methane (r_wiCH₄): R_p = 0.21 on average. The ratio ADG/CO₂ appeared to be a useful proxy of ADG/DMI (R_p = 0.64 and 0.97) and CH₄/CO₂ a proxy of methane yield (R_p = 0.24 and 0.33) for selecting low-emitting and efficient heifers.

Perilla frutescens Leaf Alters the Rumen Microbial Community of Lactating Dairy Cows

Perilla frutescens (L.) Britt., an annual herbaceous plant, has antibacterial, anti-inflammation, and antioxidant properties. To understand the effects of P. frutescens leaf on the ruminal microbial ecology of cattle, Illumina MiSeq 16S rRNA sequencing technology was used. Fourteen cows were used in a randomized complete block design trial. Two diets were fed to these cattle: a control diet (CON); and CON supplemented with 300 g/d P. frutescens leaf (PFL) per cow. Ruminal fluid was sampled at the end of the experiment for microbial DNA extraction. Overall, our findings revealed that supplementation with PFL could increase ruminal fluid pH value. The ruminal bacterial community of cattle was dominated by Bacteroidetes, Firmicutes, and Proteobacteria. The addition of PFL had a positive effect on Firmicutes, Actinobacteria, and Spirochaetes, but had no effect on Bacteroidetes and Proteobacteria compared with the CON. The supplementation with PFL significantly increased the abundance of Marvinbryantia, Acetitomaculum, Ruminococcus gauvreauii, Eubacterium coprostanoligenes, Selenomonas_1, Pseudoscardovia, norank_f__Muribaculaceae, and Sharpea, and decreased the abundance of Treponema_2 compared to CON. Eubacterium coprostanoligenes, and norank_f__Muribaculaceae were positively correlated with ruminal pH value. It was found that norank_f__Muribaculaceae and Acetitomaculum were positively correlated with milk yield, indicating that these different genera are PFL associated bacteria. This study suggests that PFL supplementation could increase the ruminal pH value and induce shifts in the ruminal bacterial composition.