Transmission electron microscope observation
The surface of NZnO is granular and easy to agglomerate (Fig. 1A). Go is lamellar, and the surface of the lamellar is very smooth, but it is easy to form agglomeration between the lamellar (Fig. 1B). The loading of ZnO onto the GO surface does not change the lamellar structure of GO, and the agglomeration phenomenon of GO is improved. At the same time, it can be seen that ZnO is granular on the GO surface and uniformly distributed on the GO surface (Fig. 1C).
Growth performance
The effects of NZnOGO on growth performance are presented in Table 3. Dietary NZnOGO supplementation significantly increased ADG (P<0.05), and significantly decreased F:G (P<0.05). However, there was no significant difference in initial weight, final weight, and ADFI between the 2 groups (P>0.05).
Microbiota diversity analysis
The relative abundance of bacteria at the phylum level is presented in Fig. 2A. In CON group, the top 6 dominant phyla (>1% at least) were Firmicutes (50.07%), Bacteroidetes (36.99%), Spirochaetes (5.24%), unclassified_d_Bacteria (1.70%), Proteobacteria (1.43%), Tenericutes (1.02%). In NZnOGO group, the top 7 dominant phyla (>1% at least) were Firmicutes (62.08%), Bacteroidetes (26.32%), Spirochaetes (1.14%), unclassified_d_Bacteria (1.61%), Proteobacteria (1.49%), Tenericutes (1.30%), Verrucomicrobia (1.60%).
At the genus level (Fig. 2B), 15 dominant genera were identifified (> 1% at least in both treatment groups). The top 5 dominant genera are unclassified_o_Clostridiales (14.21%), Bacteroides (8.62%), unclassified_p_Firmicutes (6.00%), Alistipes (6.92%), unclassified_o_Bacteroidales (5.37%) at CON group. The top 5 dominant genera in NZnOGO group are unclassified_o_Clostridiales (18.80%), Bacteroides (5.71%), unclassified_p_Firmicutes (7.58%), unclassified_f_Ruminococcaceae (5.52%), Alistipes (4.97%).
Principal co-ordinates analysis (PCoA) of the colon chyme microbiota community in fattening cattle fed the NZnOGO vs. CON diet. The results showed that the microbiota clustered separately and axes accounted for 85.07% of the total variation detected for 2 groups, suggesting that certain key bacterial species may characterize microbiota of NZnOGO group (Fig. 2C).
Secondary BA synthesis microbiota analysis
To identify the taxon had the great impact on microbiota community, LEfSe were analyzed differences in the relative abundances of the microbiota community components between the 2 groups (Fig. 3A). Compared with the CON group, including unknown bacteria, 46 microbiota phlotypes were lower and 70 were higher in the NZnOGO group. The cattle fed NZnOGO had increased genera unclassified_o__Clostridiales, unclassified_f__Ruminococcaceae, Ruminococcus, unclassified_p__Firmicutes, Clostridium, Akkermansia, Evtepia, Oscillibacter, unclassified_c__Clostridia, unclassified_f__Clostridiaceae, Romboutsia, Pseudoflavonifractor, unclassified_p_Candidatus_Saccharibacteria, Brevirhabdus, Faecalibacterium, unclassified_p__Lentisphaerae, unclassified_p__Lentisphaerae, Paenibacillus, unclassified_c__Mollicutes, unclassified_f__Eubacteriaceae, Flavonifractor, unclassified_f__Erysipelotrichaceae, unclassified_p__Planctomycetes, unclassified_p_Verrucomicrobia and orders unclassified_p_Planctomycetes, Clostridiales, Verrucomicrobiales, unclassified_p_Firmicutes, Planctomycetales, o__Bacillales, unclassified_c__Clostridia, unclassified_p_Candidatus_Saccharibacteria, unclassified_c_Mollicutes, unclassified_p_Verrucomicrobia, unclassified_p_Lentisphaerae and phyla Firmicutes, Tenericutes, Verrucomicrobia, Candidatus_Saccharibacteria, Lentisphaerae (P<0.05).
To identify the secondary BA synthesis microbiota differs between the 2 groups, LEfSe analysis was performed and differentially abundant taxa were found in both CON and NZnOGO groups. The phyla Firmicutes, Tenericutes, Euryarchaeota and Actinobacteria in the NZnOGO group had significantly higher than the CON group (P<0.05; Fig. 3B). At the genus level (Fig. 3C), the abundance of Clostridium (P=0.001031), Ruminococcus (P=0.00147), Eubacterium (P=0.001917), Enterococcus (P=0.01628), Collinsella (P=0.03172), Fusobacterium (P=0.0133), Eggerthella (P=0.004785), Brevibacillus (P=0.000149), and Peptostreptococcus (P=0.02368) in the NZnOGO group had significantly higher than the CON group (P<0.05).
The pathway analysis of secondary BA synthesis
Based on KEGG pathway database information, ko00121 pathway is a secondary BA synthesis pathway. Welch T test was used to analyze the abundance difference of enzymes in ko00121 pathway between CON group and NZnOGO group (Fig. 4A). NZnOGO group had significantly higher 7α-hydroxysteroid dehydrogenase (EC:1.1.1.159) and bile acid-CoA ligase BaiB (baiB; EC:6.2.1.7) than the CON group (P<0.05; Fig. 4B).
BA concentration analysis
Dietary NZnOGO supplementation significantly decreased primary BA (TCA, TCDCA, and TDCA) concentration (P<0.05), and significantly increased secondary BA (DCA, β-MCA, 12-KLCA, HDCA, LCA, isoLCA, UDCA, and apoCA) concentration (P<0.05; Table 4).
Correlation analysis for secondary BA concentration and the relative abundance of secondary BA synthesis microbiota
A Pearson’s correlation matrix was generated to explore the correlation between the secondary BA synthesis bacterial genera and secondary BA concentration that were affected by dietary NZnOGO supplementation (Fig. 5). The concentration of DCA was positively correlated with the genera Clostridium, Ruminococcus, Eubacterium, Enterococcus, Collinsella, Peptostreptococcus, Eggerthella and Brevibacillus (P<0.05). The concentration of β-MCA was positively correlated with the genera Clostridium, Ruminococcus, Eubacterium, Enterococcus, Collinsella, Eggerthella and Brevibacillus (P<0.05). The concentration of 12-KLCA was positively associated with the genera Clostridium, Ruminococcus, Eggerthella and Brevibacillus. The concentration of LCA was positively correlated with the genera Clostridium, Ruminococcus, Eubacterium, Enterococcus, Peptostreptococcus, Eggerthella and Brevibacillus (P<0.05). The concentration of isoLCA was positively correlated with the genera Eggerthella (P<0.05). The concentration of UDCA was positively correlated with the genera Eggerthella and Brevibacillus (P<0.05). The concentration of apoCA was positively correlated with the genera Eggerthella (P<0.05).
Microbiota function prediction
Compare the non-redundant gene set sequence with the eggNOG database using BLASTP (BLAST Version 2.2.28+, http://blast.ncbi.nlm.nih.gov/Blast.cgi), obtain the clusters of orthogenetic groups of proteins (COG) corresponding to the gene, and then calculate the abundance of the COG using the sum of gene abundances corresponding to the COG. According to the COG abundance of the top 30, Welch T test was used to analyze the category and function between 2 groups. Compared with the CON group, NZnOGO group significantly increased the abundance of genes in category at metabolism, information storage and processing (P<0.05), and significantly decreased the abundance of genes in category at poor characterized, cellular processes and signaling (P<0.05; Fig. 6A).
At function level, NZnOGO group had significantly higher carbohydrate transport and metabolism (G), amino acid transport and metabolism (E), translation, ribosomal structure and biogenesi (J), energy production and conversion (C), transcription (K), defense mechanisms (V), cell cycle control, cell division, chromosome p (D), intracellular trafficking, secretion, and vesicular transport (U), secondary metabolites biosynthesis, transport (Q), cytoskeleton (Z), chromation structure and dynamics (B), extracellular structures (W) and RNA processing and modification (A) than the CON group (P<0.05; Fig. 6B).
Intestinal immune function analysis
Dietary NZnOGO supplementation significantly increased the expression of TGR5, PKA, CREB and IL-10 genes, while the expression of NF-κB, TNF-α and IL-1β genes were significantly decreased (P<0.05; Fig. 7A). IL-10 concentration was increased by dietary NZnOGO supplementation, whereas TNF-α and IL-1β contents were significantly decreased (P<0.05; Fig. 7B).
Pearson correlation analysis between secondary BA concentration and gene expression was presented in Fig. 7C. TGR5 gene’s expression was positively correlated with the DCA, β-MCA and LCA (P<0.05). PKA gene’s expression was positively correlated with the β-MCA, 12-KLCA, HDCA, isoLCA, LCA, UDCA and apoCA (P<0.05). CREB gene’s expression was positively correlated with the DCA, β-MCA, HDCA and UDCA (P<0.05). NF-κB gene’s expression was negatively correlated with the 12-KLCA, HDCA, isoLCA, LCA and apoCA (P<0.05). TNF-α gene’s expression was negatively correlated with the DCA, β-MCA, 12-KLCA, LCA and UDCA (P<0.05). IL-1β gene’s expression was negatively correlated with the 12-KLCA, HDCA, isoLCA and LCA (P<0.05). IL-10 gene’s expression was positively correlated with the DCA, β-MCA, 12-KLCA and apoCA (P<0.05).