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The rumen microbiome is a complex microbial network critical to the health and nutrition of its host, due to their inherent ability to convert low-quality feedstuffs into energy. In rumen microbiome studies, samples from the ventral sac are most often collected because of the ease of access and repeatability. However, anatomical musculature demarcates the rumen into five sacs (biogeographical regions), which may support distinct microbial communities. The distinction among the microbes may generate functional variation among the rumen microbiome, thus, specialized tasks within different sacs. The objective of this study was to determine the rumen liquid metabolome and epimural, planktonic, and fiber-adherent bacterial communities among each rumen biogeographical region. It was hypothesized that differences in bacterial species and metabolome would occur due to differing anatomy and physiology associated with the respective regions. To assess this variation, epithelial and content microbial-associated communities were evaluated, as well as the metabolites among various rumen biogeographical regions. A total of 17 cannulated Angus cows were utilized to examine the fiber-adherent (solid fraction), planktonic (liquid fraction), and epimural microbial communities from the cranial, dorsal, caudodorsal blind, caudoventral blind, and ventral sacs. Metagenomic DNA was extracted and sequenced from the hypervariable V4 region of the 16S rRNA gene. Reads were processed using packages ‘phyloseq’ and ‘dada2’ in R. Untargeted metabolomics were conducted on rumen liquid from each sac using UHPLC-HRMS and analyzed in MetaboAnalyst 5.0. An analysis of variance (ANOVA) revealed 13 significant differentially abundant metabolites with pairwise comparisons against the five rumen sacs (P < 0.05). Within the bacterial communities, neither alpha nor beta diversity determined significance against the rumen sacs (P > 0.05), although there was significance against the fraction types (P < 0.05). Utilizing multivariable association analysis with MaAslin2, there were significant differential abundances found in fraction type × location (P < 0.05). Knowledge of similarities among fiber-adherent microbial communities provides evidence that single sac sampling is sufficient for this fraction. However, future projects focusing on either planktonic or epimural fractions may need to consider multiple rumen sac sampling to obtain the most comprehensive analysis of the rumen. Defining these variabilities, especially among the rumen epimural microbiome, are critical to define host-microbiome interactions.

Bacterial communities play major roles in rumen and uterine function toward optimal animal performance and may be affected by changes occurring during heifer development such as nutritional supplementation for optimal growth and the attainment of puberty. The effect of different levels of protein supplementation on ruminal and uterine bacterial communities following weaning was examined through first breeding of heifers. Angus heifers (n = 39) were blocked by initial body weight (BW) and randomly assigned to one of three 163-day (d) crude protein (CP) supplementation diets including control (10% CP, n = 14), 20% CP (n = 11), or 40% CP (n = 14) treatment groups. Growth and development were monitored by body weight, with blood progesterone concentration determined every 14 d to determine pubertal status. Uterine flush and rumen fluid were collected on d 56, 112, and 163 relative to the start of supplementation. Bacterial DNA was extracted from fluid samples, the V1–V3 hypervariable region of the 16S rRNA gene was amplified, and amplicons were sequenced then processed in R 4.1. Statistical analyses were performed in SAS 9.4 with a GLIMMIX procedure utilizing fixed effects of protein, month, pubertal status, and interactions, with random effects including BW, interaction of BW and protein, and heifer within the interaction, and repeated measures of day. In the uterus, pubertal status and day of supplementation affected the observed amplicon sequence variants (ASVs) and led to clustering of samples in a principal coordinate analysis (PCoA; P < 0.05), but no effect of protein supplementation was observed. Ruminal samples clustered in PCoA (P = 0.001), and observed ASVs were impacted over time (P < 0.0001), but no effect of protein supplementation was detected. In contrast, protein supplementation, pubertal status, and day of supplementation affected the abundance of multiple phyla and genera in the uterus and rumen (P < 0.05). Temporal and pubertal status effects on the heifer’s uterine bacterial communities potentially indicate a maturing uterine microbiome. Protein supplementation did not impact microbial diversity measures but did affect the abundance of individual bacterial phyla and genera that may provide future opportunities to manipulate bacterial community composition and maximize productivity.

This study determined potential microbial and metabolic biomarkers of feed efficiency in Angus heifers. Seventeen ruminal cannulated Angus heifers underwent a 70-day feed efficiency trial. Residual feed intake was used to determine high and low feed efficient heifers. On day 70 of the trial, rumen content and blood were collected and used for microbial and metabolomic analyses, respectively. Bacterial populations were examined by targeting the V4 region of the 16S rRNA gene and analyzed using QIIME and SAS. Rumen fluid and serum metabolites were analyzed using MetaboAnalyst. No microbial taxa differed after false discovery rate correction, but seven did differ (p ≤ 0.05) prior to correction, including Lachnospiraceae (Other), Desulfobulbaceae, Neisseriaceae, Shuttleworthia, Corynebacterium, p-75-a5, and L7A-E11. No differences were observed in alpha diversity metrics. Beta diversity utilizing unweighted UniFrac distances analyzed via PERMANOVA was significant (p = 0.03). Several metabolites in rumen fluid metabolites were correlated with bacteria that differed by feed efficiency phenotype. The metabolites correlated with bacteria were primarily involved in nutrient signaling and microbial crude protein availability. These data suggest variation in the availability of nutrients, primarily amino acids, as well as a relationship among microbiota, metabolome, and host feed efficiency phenotypes in heifers.

Protein supplementation to replacement heifers is utilized to reach targeted body weights, which may affect the animal’s gut and reproductive microbiomes and potentially impact breeding outcomes. The objective of the study was to evaluate the effects of protein supplementation on the ruminal and uterine bacterial communities of developing heifers. Thirty-nine pre-pubertal commercial Angus heifers were blocked by body weight (BW) and randomly assigned to one of three supplementation groups: 10% crude protein (CP), 20% CP, or 40% CP. Supplements were provided four times weekly for 140d. Blood and BW were taken every 14d to monitor development. Every 56d, uterine flushes and rumen content were collected for bacterial identification. Uterine flushes were collected via 20 mL sterile saline flushed through a Foley catheter, and rumen content by esophageal tubing. Bacterial DNA was extracted and amplified targeting the V1-V3 region of the 16S rRNA gene. Microbiome analyses were performed in R 4.1 with dada2 and phyloseq packages. Statistical analyses were performed in R and SAS 9.4. The GLIMMIX procedure was used including fixed effects of protein, month, and pubertal status. Random effects included BW, interaction of BW and protein, and heifer within the interaction, with repeated measures of month. Alpha diversity differed by month in the rumen, and by month and pubertal status in the uterus (P < 0.05). Bray Curtis analyses indicated clustering by month and pubertal status (P < 0.001) in the uterus, and by month in the rumen (P < 0.001). Multiple uterine and ruminal bacteria abundances differed over time and by protein supplementation and pubertal status (P < 0.05). The impact of pubertal status and month of development may indicate maturation of the uterine bacterial communities through development. Protein supplementation affecting the uterine bacterial communities may provide opportunity to manipulate the uterine microbiome, potentially affecting future reproductive success.

Microbiome studies in animal science using 16S rRNA gene sequencing have become increasingly common in recent years as sequencing costs continue to fall and bioinformatic tools become more powerful and user-friendly. The combination of molecular biology, microbiology, microbial ecology, computer science, and bioinformatics—in addition to the traditional considerations when conducting an animal science study—makes microbiome studies sometimes intimidating due to the intersection of different fields. The objective of this review is to serve as a jumping-off point for those animal scientists less familiar with 16S rRNA gene sequencing and analyses and to bring up common issues and concerns that arise when planning an animal microbiome study from design through analysis. This review includes an overview of 16S rRNA gene sequencing, its advantages, and its limitations; experimental design considerations such as study design, sample size, sample pooling, and sample locations; wet lab considerations such as field handing, microbial cell lysis, low biomass samples, library preparation, and sequencing controls; and computational considerations such as identification of contamination, accounting for uneven sequencing depth, constructing diversity metrics, assigning taxonomy, differential abundance testing, and, finally, data availability. In addition to general considerations, we highlight some special considerations by species and sample type.

The milk microbiome in swine has been recently demonstrated to develop and alter the growing gut microbiome in offspring. Milk is the primary nutritional source for piglets and the established microbial communities from initial diet remain stable over time, thus being critical for long-term effects on the animal. Since little is known about the ontogeny of the milk microbiome, the objective of this study was to determine differences in the sow milk microbiome among multiple parities and days in lactation. A total of 32 sows were grouped by parity, 1–4, with 8 sows per parity. Sows were on the same diet and had ad libitum access to water. At sampling, teats were cleaned and stripped. Milk samples were manually collected on one side from a fore, mid, and hind teat, then combined for one whole milk sample per animal. Milk was sampled on days 2 and 16 to determine differences between days in lactation. Samples were sequenced using an Illumina MiSeq for V1-V3 regions of the bacterial 16S rRNA gene and downstream analyses were conducted in the R environment using ‘phyloseq’ and ‘DADA2’ packages. Statistical analyzes utilized a completely randomized design and a mixed model analysis of variance, with fixed effects of parity and days in lactation and examining a parity by day interaction. The genera Corynebacterium were greater (P < 0.05) abundance in parity 4. There was an increase (P < 0.05) in Staphylococcus at day 2 compared to day 16. Core microbial communities, Lactobacillus and Romboutsia increased (P < 0.05) by day 16. Identifying microbial communities present in the milk microbiome in sows can allow producers to understand how these microbes may influence the gut microbiome in growing piglets to augment gain. USDA is an equal opportunity provider and employer.

Objectives Extensive efforts have been made to characterize the rumen microbiome under various conditions. However, few studies have addressed the long-term impacts of ruminal microbiome dysbiosis and the extent of host control over microbiome stability. These data can also inform host-microbial symbioses. The objective was to develop preliminary data to measure the changes that occur in the rumen bacterial communities following a rumen content exchange to understand the effects major perturbations may impart upon the rumen microbiome, which may be host-driven. Data description We report here an initial rumen content exchange between two SimAngus (Simmental/Angus) non-pregnant, non-lactating cows of ~ 6 years of age weighing 603.4 ± 37.5 kg. To measure bacterial community succession and acclimation following the exchange, rumen content was collected via rumen cannula at the beginning of the study immediately prior to and following the rumen content exchange, and weekly for 12 weeks. The V4 hypervariable region of the 16S rRNA gene was targeted for DNA sequencing and bacterial analysis. Over 12 weeks, numerous genera and diversity varied, before partial return to pre-exchange metrics. These preliminary data help support potential host control for the rumen microbiome, aiding in efforts to define bovine host-microbe relationships.

Surveys of microbial populations in environmental niches of interest often utilize sequence variation in the gene encoding the ribosomal small subunit (the 16S rRNA gene). Generally, these surveys target the 16S genes using semi-degenerate primers to amplify portions of a subset of bacterial species, sequence the amplicons in bulk, and assign to putative taxonomic categories by comparison to databases purporting to connect specific sequences in the main variable regions of the gene to specific organisms. Due to sequence length constraints of the most popular bulk sequencing platforms, the primers selected amplify one to three of the nine variable regions, and taxonomic assignment is based on relatively short stretches of sequence (150-500 bases). We demonstrate that taxonomic assignment is improved through reduced unassigned reads by including a survey of near-full-length sequences specific to the target environment, using a niche of interest represented by the upper respiratory tract (URT) of cattle. We created a custom Bovine URT database from these longer sequences for assignment of shorter, less expensive reads in comparisons of the upper respiratory tract among individual animals. This process improves the ability to detect changes in the microbial populations of a given environment, and the accuracy of defining the content of that environment at increasingly higher taxonomic resolution.

The functional capacity of the rumen is central to ruminant feed efficiency, and therefore,differences in ruminal metabolism may contribute to cattle feed efficiency variations. The objective of this study was to determine differences in rumen metabolites associated withvariations in feed efficiency. Fifty steers were utilized and maintained on a backgrounding diet (11.57% CP; 76.93% TDN DM basis; 28 mg monensin/kg DM) for 70 days following a two-week diet adaptation. Feed intake was monitored using the GrowSafe system. Following the 70-day trial, steers were ranked by residual feed intake (RFI) and divided into low- (n = 14) and high-RFI (n = 15) based on RFI ±0.5 SD around the mean. On day 70, rumen fluid was collected viaorogastric tubing and filtered through a 0.22 μM syringe filter. Untargeted metabolomics wasconducted utilizing the Dionex UltiMate 3000 UPLC system and electrospray ionization used tointroduce samples into an Exactive Plus Orbitrap MS. Eighty-eight known metabolites wereidentified using MAVEN. Metabolites were median-centered, log-transformed, and pareto-scaled using Metaboanalyst 4.0. Normality was assessed using the SAS 9.4 PROC UNIVARIATE procedure. Normal distribution was considered based on a Shapiro-Wilk score of ≥ 0.85 andvisual analysis of histogram and plots of residuals. Normally distributed metabolites wereanalyzed using one-way ANOVA in SAS with Tukey’s HSD post-hoc test, whereas non-normally distributed variables were analyzed using Wilcoxon Rank Sum and Kruskal Wallis H test. Significance was set at P ≤ 0.05. Eight metabolites differed between high- and low-RFI steers. Twelve metabolites identified as having > two-fold change between low- and high-RFI steers, including 3,4-Dihydroxyphenylacetate and sulfolactate. Orthogonal PLSDA revealed overlap ofrumen fluid metabolites from high- and low-RFI steers. Understanding rumen metabolites in high- and low-RFI steers will provide researchers with information to identify more efficient cattle.

Bovine respiratory disease complex (BRDC) is caused by a variety of complex factors that involves host elements, environmental elements, inadequate management and housing, as well as viral and bacterial pathogens, such as Mannheimia haemolytica and Mycoplasma bovis. The objective of the study was to develop and evaluate a deep-nasopharyngeal bacterial community database to provide greater assignment of taxa within the bovine nasopharynx and as a site-specific tool for examining microbiome dysbioses involved in BRDC. A total of 180 cattle with an overall BW ranging from 164-269 kg were obtained from Athens, Tennessee (n = 60), Maryville, Missouri (n = 60), and Richmond, Kentucky (n = 60). Cattle were transported to a feeding facility in Kansas and housed in open air, dirt floor, group housing pens. At the first diagnosis of BRD, deep nasopharyngeal samples were collected. DNA library preparation of the bacterial 16S rRNA V1–V8 hypervariable regions was performed and sequenced on the Pacific Biosciences RSII instrument. Database construction and all sequencing data were curated using mothur (v1.39.5). For validation analyses using, a subset of cattle was sampled as described above, and three samples sequenced using the V1–V3 hypervariable 16SrRNA regions and the Illumina MiSeq. In the database, Mycoplasma accounted for over 57% of the reads and Mannheimia was present at 0.7%. When validating the database using short-reads, unassigned taxa in the Greengenes analysis was 4.22% contrasted to the 0.91% of the closed reference database analysis. The two taxonomic compositions were not correlated and the correlation was statistically significant (r = -0127; P = 0.001). All alpha diversity indices between the two analysis methods differed (P < 0.05). Development of this reference database provides researchers a tool with considerable improvements in performance for the analysis of bacterial communities in the nasopharynx relating to cattle diagnosed with BRDC.