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, pH and short chain fatty acid (SCFA) in GI, 16S rRNA gene sequencing and metagenome sequencing are summarized in additional files (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), indicating that the nutritionally optimized feed resulted in significant better animal growth. 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). The main metabolites of GIT microbiota, SCFAs can affect GIT mucosal immune responses. They can bind Toll-like receptors, activate G-protein coupled receptors, and inhibit histone deacetylase activity by affecting the function of different immune cells in the walls of the GIT . Gluconeogenesis in the GIT has been demonstrated to mediate beneficial metabolic effects through the intermediary’s butyrate and propionate. Propionate has been described as an efficient hepatic gluconeogenic substrate, it also serves as a gluconeogenic substrate in intestines before reaching the liver [17, 18]. These results indicate that the diet composition is likely to significantly alter the growth performance by influencing bacterial metabolites within the GIT. Since significant differences in SCFAs were observed between the two feeding treatments, it was assumed that the composition and function potential of the rumen and intestinal microbes were affected by different diets.
Microbiota composition and function of grazing and drylot goats
To determine how the feeding conditions altered the global microbiota structures of different GIT compartments, 60 luminal and mucosal samples from 3 compartments (rumen, cecum, and colon) were collected from grazing and drylot raised groups, as well as 5 soil samples from where the goats resided, negative control (DNA-free water and buffer; n = 3) were used for DNA extraction and sequencing, after using “decontam”  to remove the negative control contamination, 2,098,000 clean reads were obtained. 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 including Unclassified_O_Bacteroidales, Norank_C_Cyanobacteria, Lachnoclostridium_1, Ruminiclostridium, Lactobacillus and Staphylococcus (Table 1). Additionally, shotgun 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 open reading frame (ORF) length of 478 bp.
Based on the read abundances at the level of phylum, eggNOG orthologous groups (OGs), and gene levels (Fig. 2a), were investigated for microbial diversity (Shannon index) in different compartments. For phyla and COG level, it was observed that the microbial diversity of hindgut was lower than was observed in rumen (Fig. 2a). However, at the gene level, the hindgut diversity was higher compared with the rumen (Fig. 2a). These results are in agreement with a previous study reporting that the rumen bacterial community had greater diversity compared to the hindgut in ruminants . 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 of 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 functions potential for nutrient metabolism due to community structure differences. Next, PCoA was conducted on the lumen and mucosa samples separately. These data suggested that the feeding condition significantly altered the community structure of the rumen lumen (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). Previous study found that grain-rich diets altered the colonic fermentation and mucosa-associated bacterial communities and induced mucosal injuries in goats , compared with the present study, no high proportion of concentrate was added to the diet of the drylot group, which resulted in less difference in the composition of mucosal microbiota. In addition, a previous study compared the gut microbial communities of wild and captive black rhinos, and found that there was no significant difference in alpha diversity levels between wild and captive black rhinos, but significant differences in beta diversity, this study also found that bacterial groups traditionally associated with the ruminant gut of domestic animals have a higher relative abundance in captive rhinos. Functional profiling results showed greater abundance of glycolysis and amino acid synthesis pathways in captive rhino microbiomes, representing an animal receiving sub-optimal nutrition with a readily available source of glucose but possibly an imbalance of necessary macro and micronutrients .
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 S3), whereas Spirochaetae was prevalent and highly specific for the cecum mucosa (average abundance 26.98% and 27.46%; Fig. 2d and Table S3). These results are consistent with a previous study showing Bacteroidetes was the second most prevalent phyla in the colonic mucosa, whereas Proteobacteria was the second most prevalent in the ruminal mucosa. Firmicutes and Spirochaetae were the second most dominant phyla in mucosal samples of the cecum .
At the genus level, the predominant members in the hindgut were Treponema_2, Ruminococcaceae_UCG-005, Ruminococcaceae_UCG-010, Alistipes, Bacteroides, Prevotellaceae_UCG-004, 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 S4). These bacteria are crucial for the degradation and metabolism of plant structural carbohydrates, especially Prevotella, Bacteroidales, Ruminococcaceae, and Butyrivibrio . To determine relationships between the differential abundances of the gut bacteria with pH and SCFA, a correlation analysis was conducted (Fig. S3). Clostridium, Alistipes, Ruminiclostridium were positively correlated with pH and acetic acid production, whereas Methanobrevibacter and Barnesiella were positively correlated with propionic acid production, and Prevotella and Butyrivibrio have positively correlated with butyric acid production (Fig. S3). These findings provided new insights into the relationship between SCFAs, intestinal microbiota, and intestinal mucosal immune-related diseases . Linear discriminant analysis effect size (LEfSe) was used to determine the top genus-level biomarkers distinguishing lumen and mucosa of different compartments from the two feeding groups (Fig. S4). We found Bacteroidetes, Prevotellaceae and Prevotella_1 were biomarker in the rumen lumen f goats in the grazing group, Lachnospiraceae, Butyrivibrio_2 were biomarker in the rumen mucosa of goats in the grazing group, Ruminococcaceae_UCG_005, Rikenellaceae were biomarker in the colon lumen of goats in the grazing group, Ruminococcaceae_UCG_013, Verrucomicrobia and Akkermansia were biomarker in the colon mucosa of goats in the grazing group (Fig. S4).
In addition, the effects of pasture soil microbes on the GIT microbial structure of grazing goats were investigated. A total of 20 soil samples were randomly collected from grazing areas. Every 4 samples were pooled for sequencing. Ternary Plot analysis indicated that the soil microbiota had no influence on mature GIT microbiota for grazing goats (Fig. S5).
Furthermore, we determined that rumen and hindgut have distinct function 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. Interestingly, these pathways were enriched at extremely low levels in the hindgut (Fig. 2e), studies of these pathways related to rumen physiology need further validation 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). These results are indicative of the specialized roles of the rumen and hindgut microbiomes play in metabolism and immunity potential.
Unique composition and functions of the rumen microbiota of grazing and drylot goats
To investigate the effect of different feeding systems on the rumen microbial communities and their functions potential, the PCoA of the OTU level revealed significant differences in the microbiota between the lumen and mucosa in rumen of the two feeding conditions (ANOSIM, Bray-Curtis metric: R2 = 0.46, p = 0.001. Fig. 3a). The total number of rumen bacteria was significantly lower in drylot goats (Fig. 3). Firmicutes, Bacteroidetes, Spirochaetes, and Proteobacteria were observed to be the dominant phyla in the ruminal lumens from both groups (Table S5). In the ruminal mucosa, Proteobacteria was more abundant than Spirochaetes (Fig. 2d). These results corroborate previous reports as these being the predominant phyla in the rumen [25, 26]. 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, KEGG pathway analysis discriminated the ruminal lumen metagenomes (Fig. S6), 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 and were differentially enriched included Methanobrevibacter and Selenomonas (Fig. 3d). Methanobrevibacter is a hydrogenotrophic methanogen , and the hydrogenotrophic methane production pathway for enzymes are involved in formate dehydrogenase (EC 126.96.36.199), formylmethanofuran dehydrogenase (EC 188.8.131.52), formylmethanofuran-tetrahydromethanopterin N-formyltransferase (EC 184.108.40.206), methenyltetrahydromethanopterin cyclohydrolase (EC220.127.116.11), methylenetetrahydromethanopterin dehydrogenase (EC 18.104.22.168), 5,10-methylenetetrahydromethanopterin reductase (EC22.214.171.124), tetrahydromethanopterin S-methyltransferase (EC 126.96.36.199) and coenzyme-B sulfoethylthiotransferase (EC 188.8.131.52) . It was also observed that an 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). Unfortunately, methane emissions were unable to be measured in this study. Previous study comparisons of gene and transcript abundance for enzymes involved in methanogenesis between high and low CH4 yield sheep, found that similar abundance of methanogens and methanogenesis pathway genes in high and low methane emitters. However, transcription of methanogenesis pathway genes was substantially increased in sheep with high methane yields . These gene are consistent with our result.
In grazing goats, Ruminococcus was determined to be a core genus that positively facilitated two different clusters in the rumen. Treponema, on the other hand, 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 simple in the drylot group, and was not as complicated as was observed in the rumen of grazing goats . Ruminiclostridium and Clostridium were core genera, actively promoting interactions between different clusters (Fig. 3f). Interestingly, Alistipes appeared to actively restrain the relative abundance of Methanobrevibacter, which may be responsible for the observed differences in the methane pathway between the grazing and drylot groups.
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 families include endoglucanase (EC 184.108.40.206), beta-glucosidase (EC 220.127.116.11). xylan 1,4-beta-xylosidase (EC 18.104.22.168), beta-glucosylceramidase (EC 22.214.171.124), and exo-beta-glycosaminidase (EC 126.96.36.199) . Of these enzymes involved in plant cell wall degradation, EC 188.8.131.52 has been demonstrated to promote cellulose degradation to cellodextrin, and EC 184.108.40.206 promotes the degradation on cellodextrin to cellobiose and D-Glucose. 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 220.127.116.11), oligo-alpha-glucosidase (EC 18.104.22.168), and alpha-glucosidase (EC 22.214.171.124). It has been demonstrated that EC 126.96.36.199 promotes starch and glycogen transformed to dextrin, and dextrin uses EC188.8.131.52 to further break the molecule down to transformed into D-Glucose. In addition, EC 184.108.40.206 promoted 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. There is a clear pattern of gene abundance reflecting microbial crosstalk with the host, highlighting the different nutrition metabolism in grazing and drylot goats.
The importance of hindgut microbiota for growth between grazing and drylot goats
Although the microbial composition in cecum has been thoroughly reported on [25, 32, 33], the function of this microbiota remains poorly understood. 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. S7a). Specifically, Spirochaetes and Fibrobacteres were significantly higher (p = 0.03. Fig. S7b), and Firmicutes were significantly lower in cecum lumen of drylot goats (p = 0.03. Fig. S7b). 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. S7b). The core genera of Clostridium, Prevotella and Treponema were observed in significantly different proportions between the two groups (Fig. 4a). This finding is in agreement with a previous study in which Prevotella, Bacteroides, Ruminococcus, and Clostridium were consistently identified in hindgut samples, and were therefore considered to be part of the core microbiota [34, 35]. 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, and 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). Prevotella has more diverse functional isomers than Clostridium in genes involved in specific metabolic processes . Furthermore, Prevotella possess a greater diversity of functional isoforms than Clostridium for peptide digestion, which may be related to the essential production of the SCFAs propionate and butyrate used as nutrients by the host . Clostridium has significantly higher functional diversity than Prevotella, involving a range of metabolic processes including cysteine biosynthesis and formaldehyde assimilation/serine pathways . In general, the differences in bacterial metabolites are directly related to the differences in abundance of the core genera of the hindgut, which also leads to differences in pathways associated with nutrition metabolism.
Of particular interest is the difference of intestinal antibiotic resistance genes (ARGs) between the free-range grazing and drylot goats. The PCoA revealed a list of significantly expressed ARGs in each of the two feeding systems Additional files 2: Fig. S8). The grazing goats harbored lower abundances of ARGs. It is possible, even likely that the administration of antibiotics in the feed is associated with a significant increase in microbiota richness in the drylot goats. These results were confirmed by the ARGs levels, in which bacitracin resistance genes was significantly higher (average abundance 97.84%, p = 0.01, Fig. 4e) in the grazing group, whereas the resistance genes of tetracycline, macrolide cephalosporin and streptomycin were significantly enriched in the drylot group (Fig. 4e). Bacitracin is a mixture of high molecular weight polypeptides that possess antimicrobial activity against gram-positive microorganisms by interfering with bacterial cell wall formation and peptidoglycan synthesis. Bacitracin may also interfere with additional cellular processes [36, 37]. Previous studies demonstrated that bacitracin-treated chickens had significant changes in their cecum microbiota. Of particular interest was the significant increase in abundance of Clostridium [38, 39]. In order to improve immune function and adapt to harsh environments, grazing goats produce high levels bacitracin (by Bacillus sp), thereby promoting the healthy growth of the body and achieving the goal of adapting to the environment. In addition, antimicrobial resistance in bacteria was significantly correlated with feeding conditions. Importantly, pasturing did not lead to an increase in the abundance of tetracycline, macrolide cephalosporin and streptomycin antibiotic resistance genes.
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. S9). However, in the drylot group, 30 genera are complex 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 S9). Interestingly, Eubacterium and Butyrivibrio have the ability to ferment SCFA in the animal gut . These results suggest that the diet provided in the drylot feeding strategy results in more diverse and complex cecum microbial communities, but more independent and simple rumen microbial communities.
Similar core genera patterns were 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. S10). As a result, alanine, aspartate and glutamate metabolism and glyoxylate and dicarboxylate metabolism were highly enriched in the cecum of grazing goats (Fig. S11a). 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. S11b). These genes encode for enzymes involved in plant cell wall degradation, such as endo-1,4-beta-xylanase (EC 220.127.116.11), endoglucanase (EC 18.104.22.168) and sucrose synthase (EC 22.214.171.124). In addition, the genes GH109, GH78, CE3, GH29, GH28, GH127 and CE9 were significantly enriched in the grazing group (Fig. S11b). Interestingly, GH109 and GH 29 were reported to be involved in mucin synthesis . Among the CAZy genes, beta-L-arabinofuranosidase (EC 126.96.36.199); 3-C-carboxy-5-deoxy-L-xylose hydrolase polygalacturonase (EC 188.8.131.52) and alpha-L-rhamnosidase (EC 184.108.40.206) have been implicated as those contributing to the metabolism of different carbohydrate substrates . These results suggest that the cecum microbes had a completely different metabolic pattern. For example, the cecum microbes in drylot goats were mainly involved in plant cell wall degradation, whereas in the grazing goats, the cecum microbes primarily contributed to the immune functions of the host. The pasture conditions significantly enhanced the metabolic interaction of the rumen strains, weakened the microbial metabolism interactions of the hindgut. The opposite was true under the drylot feeding conditions. Compared with single stomach animals (e.g. humans, chicken, and pigs), hindgut microbial fermentation was positively correlated with the growth rate of individuals.