Predictive ability of host genetics and rumen microbiome for subclinical ketosis

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

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

Rumen microbiota succession throughout the perinatal period and its association with postpartum production traits in dairy cows: A review

The transition period for dairy cows usually refers to the 3 weeks pre-calving to the 3 weeks post-calving. During this period, dairy cows undergo metabolic and physiological adaptations because of their susceptibility to metabolic and infectious diseases. Poor feeding management under these circumstances may adversely affect the health and subsequent production performance of the cows. Owing to long-term adaptation and evolution, the rumen has become a unique ecosystem inhabited by a complex microbial community closely associated with its natural host. Dietary components are metabolized by the rumen microbiota, and volatile fatty acids and microbial protein products can be used as precursor substances for synthesizing meat and milk components. The successful transition of perinatal dairy cows includes changes in diet, physiology, and the rumen microbiota. Rumen microbial profiles have been confirmed to be heritable and repairable; however, adverse circumstances affect rumen microbial composition, host digestion and metabolism, as well as postpartum production traits of dairy cows for a certain period. Preliminary evidence indicates a close relationship between the rumen microbiota and animal performance. Therefore, changes in rumen microbes during the transition period and the intrinsic links between the microbiota and host postpartum phenotypic traits need to be better understood to optimize production performance in ruminants.

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

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

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.

Integrated meta-omics analyses reveal a role of ruminal microorganisms in ketone body accumulation and ketosis in lactating dairy cows

The extent to which a nutrition-related disorder such as ketosis alters the ruminal microbiota or whether microbiota composition is related to ketosis and potential associations with host metabolism is unknown. We aimed to evaluate variations occurring in the ruminal microbiota of ketotic and nonketotic cows in the early postpartum period, and how those changes may affect the risk of developing the disease. Data on milk yield, dry matter intake (DMI), body condition score, and blood β-hydroxybutyrate (BHB) concentrations at 21 d postpartum were used to select 27 cows, which were assigned (n = 9 per group) to a clinical ketotic (CK, 4.10 ± 0.72 mmol BHB/L, DMI 11.61 ± 0.49 kg/d, ruminal pH 7.55 ± 0.07), subclinical ketotic (SK, 1.36 ± 0.12 mmol BHB/L, DMI 15.24 ± 0.34 kg/d, ruminal pH 7.58 ± 0.08), or control (NK, 0.88 ± 0.14 mmol BHB/L, DMI 16.74 ± 0.67/d, ruminal pH 7.61 ± 0.03) group. Cows averaged 3.6 ± 0.5 lactations and a body condition score of 3.11 ± 0.34 at the time of sampling. After blood serum collection for metabolomics analysis (¹H nuclear magnetic resonance spectra), 150 mL of ruminal digesta was collected from each cow using an esophageal tube, paired-end (2 × 300 bp) sequencing of isolated DNA from ruminal digesta was performed via Illumina MiSeq, and sequencing data were analyzed using QIIME2 (v 2020.6) to measure the ruminal microbiota composition and relative abundance. Spearman correlation coefficients were used to evaluate relationships between relative abundance of bacterial genera and concentrations of serum metabolites. There were more than 200 genera, with approximately 30 being significant between NK and CK cows. Succinivibrionaceae UCG 1 taxa decreased in CK compared with NK cows. Christensenellaceae (Spearman correlation coefficient = 0.6), Ruminococcaceae (Spearman correlation coefficient = 0.6), Lachnospiraceae (Spearman correlation coefficient = 0.5), and Prevotellaceae (Spearman correlation coefficient = 0.6) genera were more abundant in the CK group and were highly positively correlated with plasma BHB. Metagenomic analysis indicated a high abundance of predicted functions related to metabolism (37.7%), genetic information processing (33.4%), and Brite hierarchies (16.3%) in the CK group. The 2 most important metabolic pathways for butyrate and propionate production were enriched in CK cows, suggesting increased production of acetyl coenzyme A and butyrate and decreased production of propionate. Overall, the combined data suggested that microbial populations may be related to ketosis by affecting short-chain fatty acid metabolism and BHB accumulation even in cows with adequate feed intake in the early postpartum period.

On the holobiont 'predictome' of immunocompetence in pigs

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

The association of hyperketonemia with fecal and rumen microbiota at time of diagnosis in a case-control cohort of early lactation cows

BACKGROUND

Many dairy cows experience a state of energy deficit as they transition from late gestation to early lactation. The aims of this study were to 1) determine if the development of hyperketonemia in early lactation dairy cows is indicated by their gut microbiome, and 2) to identify microbial features which may inform health status. We conducted a prospective nested case-control study in which cows were enrolled 14 to 7 days before calving and followed through their first 14 days in milk (DIM). Hyperketonemic cows (HYK, n = 10) were classified based on a blood β-hydroxybutyrate (BHB) concentration 1.2 mmol/L within their first 14 DIM. For each HYK cow, two non-HYK (CON, n = 20) cows were matched by parity and 3 DIM, with BHB < 1.2 mmol/L. Daily blood BHB measures were used to confirm CON cows maintained their healthy status; some CON cows displayed BHB 1.2 mmol/L after matching and these cows were reclassified as control-HYK (C-HYK, n = 9). Rumen and fecal samples were collected on the day of diagnosis or matching and subjected to 16S rRNA profiling.

RESULTS

No differences in taxa abundance, or alpha and beta diversity, were observed among CON, C-HYK, and HYK health groups for fecal microbiomes. Similar microbiome composition based on beta diversity analysis was detected for all health statuses, however the rumen microbiome of CON and HYK cows were found to be significantly different. Interestingly, highly similar microbiome composition was observed among C-HYK cow rumen and fecal microbiomes, suggesting that these individual animals which initially appear healthy with late onset of hyperketonemia were highly similar to each other. These C-HYK cows had significantly lower abundance of Ruminococcus 2 in their rumen microbiome compared to CON and HYK groups. Multinomial regressions used to compute log-fold changes in microbial abundance relative to health status were not found to have predictive value, therefore were not useful to identify the role of certain microbial features in predicting health status.

CONCLUSIONS

Lower relative abundance of Ruminococcus 2 in C-HYK cow rumens was observed, suggesting these cows may be less efficient at degrading cellulose although the mechanistic role of Ruminococcus spp. in rumen metabolism is not completely understood. Substantial differences in fecal or rumen microbiomes among cows experiencing different levels of energy deficit were not observed, suggesting that hyperketonemia may not be greatly influenced by gut microbial composition, and vice versa. Further studies using higher resolution -omics approaches like meta-transcriptomics or meta-proteomics are needed to decipher the exact mechanisms at play.

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

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

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.

Changes in the rumen microbiota community in ketosis cows during propylene glycol treatment

Ketosis, a common metabolic disorder in dairy cattle, occurs during early lactation and leads to higher concentrations of non-esterified fatty acids (NEFAs) and β-hydroxybutyrate (BHBA), and is generally believed to be caused by excessive negative energy balance (NEB). Propylene glycol (PG), a gluconeogenic precursor, has been proved to promote gluconeogenesis and alleviate NEB. Oral administration of PG is widely considered one of the most effective therapeutic options for treating ketosis. Thus, in this study, we assessed the effects of PG on rumen microbiota via 16S rDNA analysis. The results show that one dose (500 mL) of PG treatment could rapidly reduce the blood BHBA level in ketosis cows by increasing the level and proportion of propionate in the rumen. Meanwhile, PG also had certain effects on the rumen bacterial community. Compared with before treatment, the relative abundances of Prevotella, Succinivibrionaceae_UCG-001 and Prevotellaceae_UCG-001 increased significantly, while those of Christensenellaceae_R-7_group, Butyrivibrio and Saccharofermentans significantly decreased. LEfSe analysis revealed that after PG treatment, only Rikenellaceae_RC9_gut_group was enriched in the rumen fluid at the genus level. In conclusion, the present study indicates that ketosis is accompanied by alterations in the rumen microbiota community. PG treatment changes the composition of rumen microbiota to a healthier state and contributes to rapid recovery from ketosis. These results support the usage of PG for treating such metabolic diseases that challenge high-yield cows due to their minimized cost and maximized safety without any adverse events.

The structure and functional profile of ruminal microbiota in young and adult reindeers (Rangifer tarandus) consuming natural winter-spring and summer-autumn seasonal diets

Background

The key natural area of Russian reindeer (Rangifer tarandus, Nenets breed) is arctic zones, with severe climatic conditions and scarce feed resources, especially in the cold winter season. The adaptation of reindeer to these conditions is associated not only with the genetic potential of the animal itself. The rumen microbiome provides significant assistance in adapting animals to difficult conditions by participating in the fiber digestion. The aim of our study is to investigate the taxonomy and predicted metabolic pathways of the ruminal microbiota (RM) during the winter–spring (WS) and summer–autumn (SA) seasons, in calves and adult reindeer inhabiting the natural pastures of the Yamalo-Nenetsky Autonomous District of the Russian Federation.

Methods

The RM in reindeer was studied using the Next Generation Sequencing method with the MiSeq (Illumina, San Diego, CA, USA) platform. Reconstruction and prediction of functional profiles of the metagenome, gene families, and enzymes were performed using the software package PICRUSt2 (v.2.3.0).

Results

The nutritional value of WS and SA diets significantly differed. Crude fiber content in the WS diet was higher by 22.4% (p < 0.05), compared to SA, indicating possibly poorer digestibility and necessity of the adaptation of the RM to this seasonal change. A total of 22 bacterial superphyla and phyla were found in the rumen, superphylum Bacteroidota and phylum Firmicutes being the dominating taxa (up to 48.1% ± 4.30% and 46.1% ± 4.80%, respectively); while only two archaeal phyla presented as minor communities (no more then 0.54% ± 0.14% totally). The percentages of the dominating taxa were not affected by age or season. However, significant changes in certain minor communities were found, with seasonal changes being more significant than age-related ones. The percentage of phylum Actinobacteriota significantly increased (19.3-fold) in SA, compared to WS (p = 0.02) in adults, and the percentage of phylum Cyanobacteria increased up to seven-fold (p = 0.002) in adults and calves. Seasonal changes in RM can improve the ability of reindeer to withstand the seasons characterized by a low availability of nutrients. The PICRUSt2 results revealed 257 predicted metabolic pathways in RM: 41 pathways were significantly (p < 0.05) influenced by season and/or age, including the processes of synthesis of vitamins, volatile fatty acids, and pigments; metabolism of protein, lipids, and energy; pathogenesis, methanogenesis, butanediol to pyruvate biosynthesis, cell wall biosynthesis, degradation of neurotransmitters, lactic acid fermentation, and biosynthesis of nucleic acids. A large part of these changeable pathways (13 of 41) was related to the synthesis of vitamin K homologues.

Conclusion

The results obtained improve our knowledge on the structure and possible metabolic pathways of the RM in reindeer, in relation to seasonal changes.