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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.

Animal manure can be a source of antibiotic resistant genes (ARG) and pharmaceutical residues; however, few studies have evaluated the presence of ARG in pasture-raised animal production systems. The objective of this study was to examine changes in microbiome diversity and the presence of antibiotic residues (ABR) on three farms that contained a diverse range of animal species: pasture-raised poultry (broiler and layer), swine and beef cattle. Total bacterial communities were determined using 16S rRNA microbiome analysis, while specific ARG (sulfonamide [Sul; Sul1] and tetracycline [Tet; TetA]) were enumerated by qPCR. Results indicated that the ARG abundances (Sul1 [P < 0.05] and TetA [P < 0.001]) were higher in layer hen manures (16.5 x 10 -4 and 1.4 x 10 -4 µg kg -1, respectively) followed by broiler chickens (2.9 x 10 -4 and 1.7 x 10 -4 µg kg -1, respectively), swine (0.22 x 10 -4 and 0.20 x 10 -4 µg kg -1, respectively) and beef cattle (0.19 x 10 -4 and 0.02 x 10 -4 µg kg -1, respectively). Average fecal TetA ABR tended to be greater (P = 0.09) for broiler chickens (11.4 µg kg -1) than for other animal species (1.8-0.06 µg kg -1), while chlortetracycline, lincomycin, and oxytetracycline ABR were similar among animal species. Furthermore, fecal microbial richness and abundances differed significantly (P < 0.01) both among farms and specific species of animal. This study indicated that the microbial diversity, ABR, ARG concentrations, and types in feces varied from farm-to-farm and from animal species-to-animal species. Future studies are necessary to perform detailed investigations of the horizontal transfer mechanism of antibiotic resistant microorganisms (ARM) and ARG.

The objective of this study was to explore the relationships between ruminal microbial populations from Angus steers that were divergent in carcass traits related to adipose accumulation. Twenty-four feedlot-finished Angus steers (age: 538 ± 21 d; body weight following lairage: 593.9 ± 43.7 kg) were slaughtered, and ruminal contents and carcass data were collected. Ruminal microbial deoxyribonucleic acid (DNA) extraction and 16S ribosomal ribonucleic acid (rRNA) gene sequencing were performed to determine microbial relative abundances, to estimate microbial diversity, and to predict microbial metabolic pathways. A variety of correlation analyses and one-way ANOVA were performed to investigate the relationships between the rumen microbiome and carcass traits. Marbling score (P = 0.001) and longissimus lipid content (P = 0.009) were positively correlated to Chao1 Richness Index, suggesting that increased intramuscular fat was associated with increased numbers of ruminal microbial species. The phyla Tenericutes and TM7 were negatively correlated (P ≤ 0.05) to marbling score and longissimus lipid content, indicating that lower abundances of these phyla may be associated with improvements in intramuscular fat content. Greater abundance of the bacterial family S24-7 was positively correlated (P = 0.002) to marbling score. Analysis by marbling classification revealed further linkages to microbial richness (P ≤ 0.063), diversity (P = 0.044), and S24-7 (P < 0.001) populations. Computational prediction of the microbial metabolic pathways revealed no differences (P ≥ 0.05) in metabolic pathway expression in rumen microbes between steers in the high- and low-marbling classes. Several phyla, families, and genera were positively correlated (P ≤ 0.05) to both rib fat thickness and yield grade. Collectively, our results suggest that microbial composition is associated to differing performance in carcass adipose traits. Overall, most of the bacterial taxa correlated to the intramuscular and subcutaneous fat depots did not overlap, suggesting the microbial population end products likely impacted adipose accumulation largely via separate adipogenic pathways of the host animal.

Animal manure can be a source of antibiotic resistant genes (ARGs) and pharmaceutical residues; however, few studies have been conducted to evaluate the presence of ARGs in pastured livestock systems. The objective was to determine the effects of four pasture-raised livestock species (broiler and layer chickens, swine, and beef cattle) on three different farms on fecal antibiotic residues (AR), microbial community diversity (MCD), and ARGs. Fecal DNA was collected (25 g each), homogenized, and extracted (0.2 g) using a Fecal/Soil Microbe DNA MimiPrep Kit. 16S rRNA, an estimate of the total MCD, was sequenced. Sulfonamide (Sul; Sul1) and tetracycline (Tet; TetA) ARGs were enumerated by qPCR. The results indicated that average fecal Tet AR tended to be greater (P = 0.09) for broiler chickens (11.4 µg kg-1) than for other livestock species (1.8-0.06 µg kg1), while chlortetracycline, lincomycine, oxytetracycline, and sulfachloropyridazine AR were similar among livestock species. There were interactions (P < 0.01) between farms and animal species for fecal pH and concentrations of total carbon, magnesium, sulfur, copper, and lead across animal species. In addition, cattle and swine samples showed a high degree of variability (α- and □-diversities) in fecal MCD, compared to broiler and layer fecal samples (P < 0.01). Firmicutes were the most abundant bacterial phylum in broiler (85.3%) and layer (80.0%) chickens (P < 0.01), as compared to swine (64.4%) and cattle (62.1%). The Sul1 (P < 0.05) and TetA (P < 0.001) ARGs in layer hens were the highest (16.5 x 10–4 and 1.4 x 10–4), followed by broiler chickens (2.9 x 10–4 and 1.7 x 10–4), swine (0.22 x 10–4 and 0.20 x 10–4), and beef cattle (0.19 x 10–4 and 0.02 x 10–4). This study indicated that individual farm management practices and specific animal species affected content of fecal AR, the fecal MCD, and ARG status in pasture-raised animals.

Feed is the greatest cost of animal production, so reducing it is critical to increase producer profits. In ruminants, the microbial population within the gastrointestinal tract (GIT) is critical to nutrient digestion and absorption in both the rumen and the hindgut. The objective of this study was to determine the bacterial taxonomic profile of the rumen, cecum, and feces of feedlot steers at slaughter in order to link feed efficiency and the GIT bacterial populations from these three locations. Twenty commercial Angus steers were selected and divided into two groups according to their residual feed intake (RFI) classification determined during the feedlot-finishing period: high-RFI (n = 10) and low-RFI (n = 10). After the ruminal, cecal, and fecal samples were collected at slaughter, DNA extraction and 16S rRNA gene sequencing were performed on them to determine their bacterial composition. One-way ANOVA was performed on the animal performance data, alpha diversities, and bacterial abundances using RFI classification as the fixed effect. Overall, the ruminal bacterial population was the most different in terms of taxonomic profile compared with the cecal and fecal populations as revealed by beta diversity analysis (P < 0.001). Moreover, bacterial richness (Chao1) was greatest (P = 0.01) in the rumen of the high-RFI group compared with the low-RFI group. In contrast, bacterial richness and diversity in the intestinal environment showed that Chao1 was greater (P = 0.01) in the cecum, and the Shannon diversity index was greater in both the cecum and feces of low-RFI compared with high-RFI steers (P = 0.01 and P < 0.001, respectively). Ruminococcaceae was more abundant in the low-RFI group in the cecum and feces (P = 0.01); fecal Bifidobacteriaceae was more abundant in high-RFI steers (P = 0.03). No correlations (P ≥ 0.13) between any ruminal bacterial family and RFI were detected; however, Ruminococcaceae, Mogibacteriaceae, Christensenellaceae, and BS11 were negatively correlated with RFI (P < 0.05) in the cecum and feces. Succinivibrionaceae in the cecum was positively correlated with RFI (P = 0.05), and fecal Bifidobacteriaceae was positively correlated with RFI (P = 0.03). Results collectively indicate that in addition to the ruminal bacteria, the lower gut bacterial population has a significant impact on feed efficiency and nutrient utilization in feedlot steers; therefore, the intestinal bacteria should also be considered when examining the basis of ruminant feed efficiency.