Zootechnical performance and parameters of microbial fermentation
To determine the relationship between the GIT microbiota and feeding condition of animal growth performance, phenotypic data for animals from the two feeding groups were collected. An overview of the analyses of birth weight, yearling weight, and pH measurements, as well as SCFA in GIT, 16S rRNA gene sequencing, and metagenome sequencing are summarized in Table S2. No differences in birth weights of kids were observed between the grazing and drylot groups (Fig. 1b). However, the average yearling weight (at day 365) of the drylot group was significantly higher than that of the grazing group (p = 0.007, Fig. 1c), increased by 48.5%. To further investigate the effects of the environment on rumen, cecum, and colon fermentation patterns, the pH and SCFA levels were determined in the rumen, cecum, and colon fluids. It was observed that the pH in the drylot group was significantly decreased in the rumen (p = 0.001, Fig. 1d) and significantly increased in colons (p = 0.03, Fig. 1d). However, the pH in cecum was not altered (p > 0.05, Fig. 1d). Additionally, it was observed that drylot feeding significantly decreased acetic acid levels (p < 0.001, Fig. 1e) and significantly increased the concentration of propionic acid (p < 0.001, Fig. 1e). The concentrations of butyric acid in the rumen, cecum, and colon were not affected by the animal’s diet (p > 0.05, Fig. 1g).
Microbiota composition and function feature change in foregut and hindgut
We obtained a total number of 2,098,000 clean reads from metagenomic sequencing data. These sequences included an average of 32,276 reads per sample. Further analysis revealed that removal of the contaminating bacteria had a large effect on the samples with low microbial abundance. The contaminating bacteria included Unclassified_O_Bacteroidales, Norank_C_Cyanobacteria, Lachnoclostridium_1, Ruminiclostridium, Lactobacillus and Staphylococcus (Table S3). Additionally, metagenomic sequencing of 30 luminal samples generated a total of 289.7 Gb of Illumina HiSeq clean metagenomic data after removing low-quality reads and host contaminants, with an average of 9.65 Gb per sample. Based on the assembled contigs with an N50 contig length of 790.93 bp, a total of 3.78 million non-redundant genes were identified, with an average ORF length of 478 bp.
Based on the read abundances at the level of phylum, eggNOG orthologous groups (OG) and gene levels (Fig. 2a) were investigated for microbial diversity (Shannon index) in different compartments. For phyla and cluster of OG level, it was observed that the luminal microbial diversity of hindgut was lower than that observed in rumen (Fig. 2a). However, at the gene level, the hindgut diversity was higher compared with the rumen (Fig. 2a). In addition, we observed that drylot feeding improved hindgut microbial diversity and reducing rumen microbial diversity (Fig. 2a). The total number of bacteria in the lumen was significantly higher than that of the mucosa (Fig. 2b, P < 0.05), indicating that drylot feeding significantly increased bacterial numbers in the hindgut (Fig. 2b, P < 0.05). The principal coordinates analysis (PCoA) of operational taxonomic units (OTUs) indicated that the microbiota is significantly different between the rumen and hindgut (ANOSIM, Bray-Curtis metric: R2 = 0.64, p = 0.001; Fig. 2c). Interestingly, the mucosa and lumen microbiota of the hindgut formed 2 distinct clusters (ANOSIM, Bray-Curtis metric: R2 = 0.43, p = 0.001; Fig. 2c). These results suggested that the hindgut lumen and mucosa microbiota may have different functional potentials for nutrient metabolism due to community structure differences. Next, PCoA was conducted on the lumen and mucosa samples separately (ANOSIM, Bray-Curtis metric: R2 = 0.67, p = 0.001; Fig. S1a). Interestingly, we observed that the feeding system had little influence on the mucosa microbial structure in the same compartment (ANOSIM, Bray-Curtis metric: R2 = 0.57, p = 0.001. Fig. S1b).
The phenotypic differences between the feeding systems examined here were primarily affected by the GIT lumen microbial structure. The relative abundances of phyla and genera showed distinct microbial structures between the lumen and mucosa in both the rumen and hindgut (Fig. 2d, Fig. S2). In addition, Bacteroidetes and Firmicutes were the advantage phyla. In the rumen mucosa, Proteobacteria was in high abundance for both the grazing and drylot environments (average abundance 6.62% and 8.44%; Fig. 2d and Table S4), whereas Spirochaetae was prevalent and highly specific for the cecum mucosa (average abundance 26.98% and 27.46%; Fig. 2d and Table S4). At the genus level, the predominant members in the hindgut were Treponema_2, Ruminococcaceae_UCG-005, Ruminococcaceae_UCG-010, Alistipes, Bacteroides, Prevotellaceae_UCG-004, and Ruminococcaceae_UCG-013. However, the predominant members of the rumen were Prevotella_1, Bacteroidales_BS11, Butyrivibrio_2, and Prevotellaceae_UCG-001 (Fig. S2 and Table S5). To determine relationships between the differential abundances of the gut bacteria with pH and SCFA, a correlation analysis was conducted (Fig. S3). Clostridium, Alistipes, and Ruminiclostridium were positively correlated with pH and acetic acid production, whereas Methanobrevibacter and Barnesiella were positively correlated with propionic acid production, as well as Prevotella and Butyrivibrio have positively correlated with butyric acid production (Fig. S3).
Furthermore, we determined that rumen and hindgut have distinct functional potential. Specifically, those involving peptidases, arginine and proline metabolism, oxidative phosphorylation, cysteine and methionine metabolism, energy metabolism and other ion-coupled transporters were highly enriched in the rumen microbiome relative to that of the hindgut (Fig. 2e). In contrast to the rumen, pathways involved in chloroalkane and chloroalkene degradation, peroxisome, lysosome, ethylbenzene degradation, pertussis, neurotrophin signaling, TGF-beta signaling, focal adhesion, vascular smooth muscle contraction, clavulanic acid biosynthesis, and leukocyte transendothelial migration were highly enriched in the hindgut microbiome (Fig. 2e).
The composition and functions of the rumen microbiota of grazing and drylot goats
The PCoA revealed significant differences in the microbiota between the lumen and mucosa in rumen of the two feeding conditions in the OTU level (ANOSIM, Bray-Curtis metric: R2 = 0.46, p = 0.001. Figure 3a). At the genus level, for the relative abundances of the core genera, Methanobrevibacter was significantly higher in the drylot group (p = 0.01, Fig. 3b), while Alistipes was significantly lower (p = 0.01, Fig. 3b). Furthermore, Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis discriminated the ruminal lumen metagenomes (Fig. S5) and it was observed that methane metabolism was significantly enriched in the drylot group (p = 0.03, Fig. 3c). Furthermore, the core genera that are significant contributors to the methane pathway were differentially enriched, including Methanobrevibacter and Selenomonas (Fig. 3d). Methanobrevibacter is mainly involved in hydrogenotrophic methane production pathway [31]. The increased number of Methanobrevibacter genes in drylot goats prompted an examination of the enzyme abundance for each of the enzymes involved in hydrogenotrophic methanogenesis (Fig. 3e). We determined that the enzymes involved in the hydrogenotrophic methane production pathway were significantly enriched in the drylot group (Fig. 3e).
In grazing goats, Ruminococcus was determined to be a core genus that positively facilitated two different clusters in the rumen. On the other hand, Treponema was a competitively inhibited cluster of bacteria, with negative correlations calculated for these genera (Fig. 3f). In contrast to the grazing group, a co-occurrence network was found to be more independent and simpler in the drylot group and was not as complicated as was observed in the rumen of grazing goats (Fig. 3f).
Since ruminants require a method to efficiently digest lignocellulose in order to satisfy their energy requirements, the CAZyme profiles of different degradation efficiencies were examined in the context of varied feeding systems. The family of GH3, GH2, GH78, and GH9 were significantly higher in grazing goats (Fig. 3g). These gene families are involved in plant cell wall degradation. In addition, the families of GH77, GH23, GH13, G32, and GH25 were significantly higher in drylot raised goats (Fig. 3g). Furthermore, the family consisted of alpha-amylase (EC 3.2.1.1), oligo-alpha-glucosidase (EC 3.2.1.10), and alpha-glucosidase (EC 3.2.1.20). These gene families promote the transformation of starch and glycogen into dextrin that uses EC3.2.1.10 to further break the molecule down to transform into D-Glucose. Additionally, EC 3.2.1.20 promotes the conversion of maltose to D-Glucose (Fig. 3g). As a result of the high-grain diets optimized to maximize growth rates and feed efficiency in the drylot, digestible carbohydrate supplementation of the diet promotes changes in the ruminal microbiome, ultimately reducing the diversity of the microbial communities.
Analyses of metagenomic sequencing data of hindgut microbiota between grazing and drylot goats
The PCoA of the OTU suggested significant differences between the microbiota of the cecum lumen and mucosa in the two feeding systems examined here (ANOSIM, Bray-Curtis metric: R2 = 0.45, p = 0.001. Fig. S5a). Specifically, Spirochaetes and Fibrobacteres were significantly higher (p = 0.03. Fig. S5b), and Firmicutes were significantly lower in cecum lumen of drylot goats (p = 0.03. Fig. S5b). In the cecum mucosa, the proportion of Spirochaetae accounts more than 27% of the total microbial population, but accounts for only about 1.5% in the lumen (Fig. S5b). The core genera of Clostridium, Prevotella, and Treponema were observed in significantly different proportions between the two groups (Fig. 4a). Consistently, significant differences in the top proportions of functional levels are due to difference in the abundances of the core genera (Fig. 4c). The grazing goats were enriched for several microbial pathways, including quorum sensing, aminoacyl-tRNA biosynthesis, peptidoglycan biosynthesis, carbon metabolism, pentose phosphate pathway, and propanoate metabolism. In general, these pathways are involved in translation, replication, and repair, as well as cellular processes (Fig. 4d). In comparison, the drylot group was significantly enriched for pathways related to amino acid metabolism (e.g. alanine, aspartate and glutamate metabolism, biosynthesis of amino acids, arginine biosynthesis, glyoxylate and dicarboxylate metabolism, fatty acid biosynthesis, lysine biosynthesis, and fatty acid metabolism) (Fig. 4c).
Subsequently, it was observed that the co-occurrence network was more independent of grazing group in cecum. Ruminococcus and Paenibacillus showed positive correlations with one another and demonstrated a relatively independent and stable cluster (Fig. S6). However, in the drylot group, 30 genera were complexly correlated with each other and formed a large co-occurrence network in the cecum. Eubacterium and Butyrivibrio are important nodes, suggesting that they competitively inhibit colonization by Phascolarctobacterium and Blautia (Fig S6).
Similar change of core genera patterns was observed in colons as were found in the cecum (Fig. 4b). For example, the proportions of Intestinimonas, Paenibacillus, unclassified_o_Clostridiales, unclassified_f_Ruminococcaceae, Ruminiclostridium, and Roseburia were significantly higher in grazing goats (Fig. S7). As a result, alanine, aspartate and glutamate metabolism, as well as glyoxylate and dicarboxylate metabolism were highly enriched in the colon of grazing goats (Fig. S8a). Furthermore, when focusing on the differences of the CAZy family in colon, it was observed that GT2, GT4, CE1, GH10, AA6, GH9, and GH16 were significantly enriched in the drylot group (Fig. S8b). These genes encode for enzymes involved in plant cell wall degradation, such as endo-1,4-beta-xylanase (EC 3.2.1.8), endoglucanase (EC 3.2.1.4) and sucrose synthase (EC 2.4.1.13). In addition, the genes GH109, GH78, CE3, GH29, GH28, GH127, and CE9 were significantly enriched in the grazing group (Fig. S8b).
The difference of foregut and hindgut ARGs between grazing and drylot goats
Of particular interest is the difference of rumen, cecum and, colon antibiotic type between the free-range grazing and drylot goats. The PCoA revealed a list of significantly expressed antibiotic type in each of the two feeding systems (Additional files 2: Fig. S9a). The grazing goats harbored lower abundances of ARGs. These results were confirmed by the ARGs levels, in which bacitracin were significantly higher in the grazing group (average abundance 97.84%, p = 0.01, Fig. 4e, Fig. S9), whereas the resistance genes of lincosamide, tetracycline, macrolide, cephalosporin, and streptomycin were significantly enriched in the drylot group (Fig. 4e, Fig. S9).