Validating the Use of Bovine Buccal Sampling as a Proxy for the Rumen Microbiota by Using a Time Course and Random Forest Classification Approach

Buccal Swab Samples from Japanese Brown Cattle Fed with Limonite Reveal Altered Rumen Microbiome

The areas of the Mount Aso grasslands in Kumamoto, Japan, are the primary location for the breeding of the Kumamoto strain of Japanese Brown cattle (JBRK). Although Aso limonite, deposited by volcanic ash and magma, has been commonly fed to pregnant JBRK in this area, the mechanisms of its salutary effects on pregnant JBRK have not yet been elucidated. Approximately 100 days before the expected day of calf delivery, seven JBRK (four supplemented with limonite and three controls without limonite) were assigned to this study, from which a buccal swab was collected at the highest rumination every 30 days for 90 days. DNA extracted from these swabs was then analyzed using a 16S rRNA gene amplicon sequence analysis. Statistically significant differences between the two groups were discovered through beta-diversity analysis, though results from alpha-diversity analysis were inconclusive. The microbiota identified were classified into six clusters, and three of the main clusters were core-rumen bacteria, primarily cellulose digestion in cluster 1, oral bacteria in cluster 2, and non-core-rumen bacteria in cluster 3. In the limonite group, core-rumen bacteria decreased while non-core-rumen bacteria increased, suggesting that limonite feeding alters rumen microbiota, particularly activation of non-core-rumen microbiota.

Influence of empirically derived filtering parameters, ASV, and OTU pipelines on assessing rumen microbial diversity

Microbes play an important role in human and animal health as well as animal productivity. The host microbial interactions within ruminants play a critical role in animal health and productivity and provide up to 70% of the animal's energy need in the form of fermentation products. As such, many studies have investigated microbial community composition to understand microbial community changes and factors that affect microbial colonization and persistence. The advances in next generation sequencing (NGS) technologies and low cost of sequencing have gravitated many studies to utilize 16S rDNA-based analysis tools for interrogation of microbiomes at a much finer scale than traditional culturing. However, such methods that rely on single base pair differences for bacterial taxa clustering may inflate or underestimate diversity leading to inaccurate identification of bacterial diversity. Therefore, in this study, we sequenced mock communities of known membership and abundance to establish filtration parameters to reduce inflation of microbial diversity due to PCR and sequencing errors. Additionally, we evaluated the effect of the resulting filtering parameters proposed using established bioinformatic pipelines on a study consisting of Holstein and Jersey cattle to identify bread and treatment effects on the bacterial community composition and the impact of the filtering on global microbial community structure analysis and results. Filtration resulted in a sharp reduction in bacterial taxa identified, yet retain most sequencing data (retaining > 79% of sequencing reads) when analyzed using 3 different microbial analysis pipelines (DADA2, Mothur, USEARCH). After filtration, conclusions from α and β-diversity tests show very similar results across all analysis methods. The mock community-based filtering parameters proposed in this study help provide a more realistic estimation of bacterial diversity. Additionally, the filtration reduced the variation between microbiome analysis methods and help identify microbial community differences that could have been missed due to large animal to animal variation observed in the unfiltered data. As such, we believe, the new filtering parameters described in this study will help obtain diversity estimates closer to realistic values and will improve the ability of detecting microbial community differences and help better understand microbial community changes in 16S rDNA-based studies.

An artificial intelligence approach of feature engineering and ensemble methods depicts the rumen microbiome contribution to feed efficiency in dairy cows

Genetic selection has remarkably helped U.S. dairy farms to decrease their carbon footprint by more than doubling milk production per cow over time. Despite the environmental and economic benefits of improved feed and milk production efficiency, there is a critical need to explore phenotypical variance for feed utilization to advance the long-term sustainability of dairy farms. Feed is a major expense in dairy operations, and their enteric fermentation is a major source of greenhouse gases in agriculture. The challenges to expanding the phenotypic database, especially for feed efficiency predictions, and the lack of understanding of its drivers limit its utilization. Herein, we leveraged an artificial intelligence approach with feature engineering and ensemble methods to explore the predictive power of the rumen microbiome for feed and milk production efficiency traits, as rumen microbes play a central role in physiological responses in dairy cows. The novel ensemble method allowed to further identify key microbes linked to the efficiency measures. We used a population of 454 genotyped Holstein cows in the U.S. and Canada with individually measured feed and milk production efficiency phenotypes. The study underscored that the rumen microbiome is a major driver of residual feed intake (RFI), the most robust feed efficiency measure evaluated in the study, accounting for 36% of its variation. Further analyses showed that several alpha-diversity metrics were lower in more feed-efficient cows. For RFI, [Ruminococcus] gauvreauii group was the only genus positively associated with an improved feed efficiency status while seven other taxa were associated with inefficiency. The study also highlights that the rumen microbiome is pivotal for the unexplained variance in milk fat and protein production efficiency. Estimation of the carbon footprint of these cows shows that selection for better RFI could reduce up to 5 kg of diet consumed per cow daily, potentially reducing up to 37.5% of CH4. These findings shed light that the integration of artificial intelligence approaches, microbiology, and ruminant nutrition can be a path to further advance our understanding of the rumen microbiome on nutrient requirements and lactation performance of dairy cows to support the long-term sustainability of the dairy community.

Gut microbiome biogeography in reindeer supersedes millennia of ecological and evolutionary separation

Ruminants are dependent on their gut microbiomes for nutrient extraction from plant diets. However, knowledge about the composition, diversity, function, and spatial structure of gut microbiomes, especially in wild ruminants, is limited, largely because analysis has been restricted to faeces or the rumen. In two geographically separated reindeer subspecies, 16S rRNA gene amplicon sequencing revealed strong spatial structuring, and pronounced differences in microbial diversity of at least 33 phyla across the stomach, small intestine, and large intestine (including faeces). The main structural feature was the Bacteroidota to Firmicutes ratio, which declined from the stomach to the large intestine, likely reflecting functional adaptation. Metagenome shotgun sequencing also revealed highly significant structuring in the relative occurrence of carbohydrate-active enzymes (CAZymes). CAZymes were enriched in the rumen relative to the small and large intestines. Interestingly, taxonomic diversity was highest in the large intestine, suggesting an important and understudied role for this organ. Despite the two study populations being separated by an ocean and six millennia of evolutionary history, gut microbiome structuring was remarkably consistent. Our study suggests a strong selection for gut microbiome biogeography along the gastrointestinal tract in reindeer subspecies.

Ruminant Salivary Microbes: Passenger or Player in the Rumen?

Sampling of ruminant saliva has gained interest as a non-invasive proxy for exploring the structure of the rumen microbiome. However, the subsequent data analysis assumes that bacteria originating from the oral cavity are merely passengers in the rumen and play no active role. In this study, it was hypothesised that metabolically active oral bacteria present in the salivary microbiome play a role in the ruminal degradation of plant material. In vitro cultivation-based enumeration confirmed that the ruminant oral cavity harbours a significant number of anaerobic and cellulolytic bacteria that are metabolically active under ruminal conditions. Bacterial 16S rRNA gene profiling of in vitro enrichments also confirmed that oral-derived bacteria were capable of colonising plant material. Preliminary analysis of the colonising bacteria indicated that bacteria belonging to the genus Streptococcus were of particular interest. In conclusion, the findings of the current study clearly indicate that bolus-associated bacteria have the potential to play a metabolically active role in terms of ruminal colonisation and the degradation of plant material. This evidence confirms the merit of the hypothesis that the metabolically active oral bacteria present in the salivary microbiome may play a role in the ruminal degradation of plant material.

Effects of dosing non-toxigenic Clostridia on the bacterial populations and immunological responses in the intestinal tract of lactating dairy cows

Understanding the effects of dosing non-toxigenic Clostridia to cows is rare and has received little attention so far. In the present study, a total of eight lactating dairy cows were divided in two groups: control (n = 4) or Clostridia challenged (oral supplementation of five diverse strains of Paraclostridium bifermentans, n = 4). Bacterial communities were analyzed by qPCR and next-generation sequencing (NGS) in the buccal mucosa as well as digesta and mucosal samples of the gastrointestinal (GI) tract from rumen to rectum (10 compartments), as well as fecal samples. Transcriptomic analysis of barrier and immune-related gene expression was performed on rumen, jejunum, and liver samples. We observed increased microbial populations with the Clostridial challenge in the buccal tissues and the proximal GI tract (forestomach), correlating with Clostridial loads in the feed. Otherwise, there were no significant differences in microbial populations (p > 0.05) throughout the distal part of the GI tract. The NGS approach, however, revealed that the Clostridial challenge changed the relative abundance of gut and fecal microbiota. In particular, in the challenge group, no Bifidobacterium was observed in the mucosa-associated microbiota and abundance of Pseudomonadota increased in the feces. These results indicated potential adverse effects of Clostridia to cow health. In general, immune responses to the Clostridial challenge were weak. However, transcriptional analysis revealed the down-regulation of junction adhesion molecule encoding gene (-1.44 of log₂ fold-change), which might impact intestinal permeability.

Microbiota members from body sites of dairy cows are largely shared within individual hosts throughout lactation but sharing is limited in the herd

BACKGROUND

Host-associated microbes are major determinants of the host phenotypes. In the present study, we used dairy cows with different scores of susceptibility to mastitis with the aim to explore the relationships between microbiota composition and different factors in various body sites throughout lactation as well as the intra- and inter-animal microbial sharing.

RESULTS

Microbiotas from the mouth, nose, vagina and milk of 45 lactating dairy cows were characterized by metataxonomics at four time points during the first lactation, from 1-week pre-partum to 7 months post-partum. Each site harbored a specific community that changed with time, likely reflecting physiological changes in the transition period and changes in diet and housing. Importantly, we found a significant number of microbes shared among different anatomical sites within each animal. This was between nearby anatomic sites, with up to 32% of the total number of Amplicon Sequence Variants (ASVs) of the oral microbiota shared with the nasal microbiota but also between distant ones (e.g. milk with nasal and vaginal microbiotas). In contrast, the share of microbes between animals was limited (< 7% of ASVs shared by more than 50% of the herd for a given site and time point). The latter widely shared ASVs were mainly found in the oral and nasal microbiotas. These results thus indicate that despite a common environment and diet, each animal hosted a specific set of bacteria, supporting a tight interplay between each animal and its microbiota. The score of susceptibility to mastitis was slightly but significantly related to the microbiota associated to milk suggesting a link between host genetics and microbiota.

CONCLUSIONS

This work highlights an important sharing of microbes between relevant microbiotas involved in health and production at the animal level, whereas the presence of common microbes was limited between animals of the herd. This suggests a host regulation of body-associated microbiotas that seems to be differently expressed depending on the body site, as suggested by changes in the milk microbiota that were associated to genotypes of susceptibility to mastitis.

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.

Boosting the potential of cattle breeding using molecular biology, genetics, and bioinformatics approaches – a review

Cattle are among the most important farm animals that underwent an intense selection with the aim to increase milk production and to improve growth and meat properties, meanwhile reducing the generation interval allowing for a faster herd turnover. Recently, a shift from traditional breeding methods to breeding based on genetic testing has been observed. In this perspective, we review the techniques of molecular biology, genetics, and bioinformatics that are expected to further boost the agricultural potential of cattle. We discuss embryo selection based on next-generation and Nanopore sequencing and in vitro embryo production, boosting the potential of genetically superior animals. Gene editing of embryos could further speed up the selection process, essentially introducing a change in a single generation. Lastly, we discuss the host-microbiome co-evolution and adaptation. For example, cattle already adapted to low-quality low-cost fodder could be bred to achieve desired properties for the beef and dairy industry. The challenge of breeding and genetic editing is to accompany the selection on desired consumer-oriented traits with the push for sustainability and the adaptation to a changing climate while remaining economically viable. We propose that we are yet to see the limits of what is possible to achieve with modern technology for the cattle of the future; the ultimate goal will be to produce and maintain genetically elite individuals that can sustain the growing demands on the production.