Effects of CB on the biochemical index
As shown in Table 3, CB did not have much effect on the liver antioxidant properties of spotted sea bass (P > 0.05). ALT and AST are important indicators of liver damage, as shown in Fig. 1. There was no significant difference in ALT in the liver. Still, AST decreased significantly (P < 0.05), indicating that CB has a protective effect on liver damage. It has a protective effect.
Table 3
Effect of CB on the activity of liver Non-specific immune and antioxidant capacity
GROUP | SOD (u/mL) | MDA (nmoL/ml) | CAT (U/mg prot) | AKP (U/100 mL) | ACP (U/100 mL) |
CC | 286.9 ± 100.71a | 1.02 ± 0.02a | 8.09 ± 0.62a | 60.89 ± 0.91a | 5.36 ± 0.1a |
CB1 | 325.61 ± 68.19a | 0.8 ± 0.07a | 7.14 ± 0.79a | 62.51 ± 2.3ab | 5.51 ± 0.22ab |
CB2 | 274.35 ± 16.14a | 1.04 ± 0.2a | 7.06 ± 1.27a | 65.31 ± 3.6b | 5.82 ± 0.38b |
CB3 | 307.8 ± 44.64a | 1.03 ± 0.22a | 7.34 ± 0.81a | 65.5 ± 1.8b | 5.84 ± 0.19b |
CB4 | 322.09 ± 54.26a | 1.13 ± 0.12a | 6.34 ± 0.55a | 62.5 ± 0.96ab | 5.52 ± 0.09ab |
CB5 | 242.11 ± 53.31a | 1 ± 0.26a | 6.13 ± 0.79a | 63.11 ± 0.55ab | 5.58 ± 0.04ab |
Note: Different superscript letters in a column indicate significant differences (p < 0.05) |
Effect of CB on the activity of liver digestive enzymes
The impact of CB supplementation in the diet on the activity of liver digestive enzymes is shown in Table 4. One-way ANOVA showed a significant increase in AMS activity at CB4 and TRS activity at CB5 (P < 0.05), while LPS did not differ significantly between groups (P > 0.05).
Table 4
Effect of CB on the activity of liver digestive enzymes
GROUP | AMS (U/mg prot) | LPS (U/g prot) | TRS (U/mg prot) |
CC | 0.11 ± 0.01a | 0.36 ± 0.06a | 800.53 ± 60.12a |
CB1 | 0.12 ± 0.01a | 0.42 ± 0.1a | 1011.16 ± 143.39b |
CB2 | 0.13 ± 0.03a | 0.39 ± 0.05a | 859.1 ± 139.41a |
CB3 | 0.12 ± 0.02a | 0.46 ± 0.03a | 851.74 ± 72.48a |
CB4 | 0.2 ± 0.03b | 0.43 ± 0.08a | 941.27 ± 32.59a |
CB5 | 0.13 ± 0.04a | 0.41 ± 0.12a | 828.44 ± 81.35a |
Note: Different superscript letters in a column indicate significant differences (p < 0.05) |
Effect of CB supplementation on the liver transcriptomics
RNA Seq analysis was applied to liver samples from the CC and CB3 groups. A total of 5.79 clean data points were obtained in each sample after quality checks. By selecting BLAST parameters with E-values no greater than 1e-5 and HMMER parameters with E-values no greater than 1e-10, 21,383 unigenes with annotation information were finally obtained. These transcripts were compared with the NR, Swiss-Prot, COG, KOG, eggNOG4.5, and KEGG databases, with 20785, 10447, 3809, 12722, 18023, and 17012 unigenes annotated, respectively, among which 2366 unigenes were co-annotated. The total number of genes annotated in the four databases was 21383 (Table 5) (Fig. 2).
Table 5
Statistical table of Unigene annotations
#Anno_Database | Annotated_Number | 300 < = length < 1000 | length > = 1000 |
COG_Annotation | 3809 | 522 | 3176 |
GO_Annotation | 18034 | 4250 | 12031 |
KEGG_Annotation | 17012 | 3891 | 11542 |
KOG_Annotation | 12722 | 2433 | 9386 |
Pfam_Annotation | 15075 | 2815 | 11401 |
Swissprot_Annotation | 10447 | 1976 | 7778 |
TrEMBL_Annotation | 20896 | 5243 | 13424 |
eggNOG_Annotation | 18023 | 4247 | 12030 |
nr_Annotation | 20785 | 5182 | 13398 |
All_Annotated | 21383 | 5478 | 13542 |
Note: (1) Annotated databases: indicates each functional database; |
(2) Annotated_Number: indicates the number of unigenes annotated to this database; |
(3) 300 ≤ length < 1000: indicates the number of unigenes whose length is greater than or equal to 300 and less than 1000 bases annotated to this database; |
(4) Length ≥ 1000: indicates the number of unigenes annotated to this database with lengths greater than 1000 bases. |
The homology of the assembled unigene was compared with that of other species. The homology of spotted sea bass with Morone saxatillis and Larimichthys crocea was closer than that of other species (Fig. 3). According to the sample correlation heatmap analysis, the R2 between different samples of the same group was greater than 0.8, indicating that the experimental biological replication was reliable (Fig. 4).
The differentially expressed genes of the two groups were taken together, and the FPKM values of the genes were clustered to homogenize the rows (Z score). The heatmap was plotted as shown in Fig. 5A. The results of clustering among the samples indicated that there were significant differences in gene expression between the different groups. For the same group, the differentially expressed genes between different samples in the same group were basically consistent.
To verify the effect of Clostridium butyricum addition on the liver of spotted sea bass, FDR and FC were used to screen for differentially expressed genes with FC ≥ 1.5 and FDR < 0.05. A total of 149 differentially expressed genes were obtained in the liver tissue. Among them, 84 were down-regulated genes, and 65 were up-regulated genes. As clearly shown in the volcano plot, the left and upper dots are the down-regulated DEGs. As clearly shown in the volcano plot, the points on the left and above are the down-regulated DEGs. The volcano plot also shows the main changes in DEGs in the CB3 group of flowering bass compared to the control group (Fig. 5B).
We performed GO function enrichment analysis. Three major categories were included, namely, biological process, cell component, and molecular function. A total of 112 terms were enriched. As shown in Fig. 6, in the biological process category, the main enrichment was in the single-organism process, cellular process, metabolic process, and biological regulation. In the cellular component category, the main enrichment was in the cell, cell part, organelle, and membrane. In the cellular component category, the main enrichment was in binding, catalytic activity, and molecular function regulator. KEGG enrichment analysis of differentially expressed genes was performed on 6 samples between the CC and CB3 groups.4 − 2 C shows that we selected the top 20 pathways for differential gene enrichment; the larger the rich factor, the higher the enrichment, and the lower the q value, the more significant the enrichment. The analysis revealed that the pathways of CC and CB3 with up-regulated differential genes were mainly enriched in metabolism-related pathways (biosynthesis of amino acids, arginine biosynthesis, arginine, and proline metabolism, D-glutamine and D-glutamate metabolism, glycine, serine and threonine metabolism, alanine, aspartate and glutamate metabolism, alpha-linolenic acid metabolism, etc.) (Fig. 7A). The pathways that were mainly enriched for the upregulation of differentially expressed genes were hepatitis C, necroptosis, Huntington disease, aldosterone synthesis, and secretion, etc. (Fig. 7B).
Effect of CB supplementation on the metabolomics
To further investigate the effects of CB on the hepatic physiology and metabolic responses in spotted sea bass, liver metabolomic profiling was performed using UHPLC-MS/MS. A total of 11,809 peaks were detected in the metabolomic dataset (6,365 for ESI + and 5,444 for ESI-). OPLS-DA was employed to explore the differences between the CC group and the CB group (Fig. 8). The score plot demonstrated significant metabolic differentiation between the two groups. In both positive and negative ion modes, R2Y represents the model's explanation rate, while Q2Y represents the model's predictive ability. The closer R2Y and Q2Y values are to 1, the more stable and reliable the model is considered, indicating its suitability for screening differential metabolites. Generally, a model is considered effective when Q2Y > 0.5 and excellent when Q2Y > 0.9. In the positive ion mode, the R2Y and Q2Y values were 0.994 and 0.59, respectively, while in the negative ion mode, the values were 0.997 and 0.675, indicating that the constructed model has good fitness and predictive ability.
The volcano map was used to show the overall distribution of the differential metabolites, and further, the differential metabolites were screened using FC and VIP. Metabolites meeting both FC > 1 and VIP > 1 were considered differential metabolites. The results showed that there were 248 differential metabolites, among which 157 metabolites were up-regulated, and 91 metabolites were down-regulated in expression (Fig. 9). These metabolites are mainly steroids and their derivatives, lipids and lipid-like molecules, nucleotides and their analogs, amino acids, organic acids, and monosaccharides. To gain more insight into the different changes in hepatic metabolites by CB supplementation, a KEGG-based metabolic pathway enrichment analysis was performed. The DA score reflects the overall changes of all metabolites in a metabolic pathway. A score of 1 indicates upregulation of all annotated metabolites in the pathway, while − 1 indicates downregulation of all annotated metabolites in the pathway. The DA score shows that the downgraded pathways are the renin − angiotensin system and ubiquinone and other terpenoid − quinone biosynthesis. The main upward adjustment pathways were carbohydrate metabolism (starch and sucrose metabolism, amino sugar and nucleotide sugar metabolism, and galactose metabolism), lipid metabolism (fatty acid degradation and regulation of lipolysis in adipocytes), immune system (Fc gamma R-mediated phagocytosis, platelet activation and Fc epsilon RI signaling pathway), and endocrine system (oxytocin signaling pathway, GnRH signaling pathway) et.al (Fig. 10).
Combined transcriptomics and metabolomics analysis
Through the joint analysis of differential metabolites (VIP > 1, FC > 1) and DEGs (FC ≥ 2 and FDR < 0.01), 8 pathways were coenriched. They are glycolysis/gluconeogenesis, fatty acid metabolism, amino sugar and nucleotide sugar metabolism, starch and sucrose metabolism, galactose metabolism, biosynthesis of unsaturated fatty acids, necroptosis, and carbon metabolism (Fig. 11).
Considering the integrated transcriptomic and metabolomic analyses, CB was hypothesized to enhance the immunity of the flowering bass organism through pathways that regulate carbohydrate metabolism and lipid metabolism. To further validate this result, KEGG pathway xml analysis was performed. Multiple genes and multiple metabolites may exist in a pathway, and a gene or metabolite may also belong to the same pathway on multiple pathways. The relationship between each differentially grouped pathway, gene, and metabolite can be visualized using the igraph R package (Fig. 12). We can clearly see the main pathways regulated by CB in the liver metabolism of C. perfringens. Upregulation of NLRP3 through the necroptosis signaling pathway leads to increased metabolism of arachidonate, affecting biosynthesis of the unsaturated fatty acid signaling pathway. Acyl-CoA 6-desaturase upregulation affects the fatty acid metabolism pathway, leading to increased crotonyl-CoA metabolism. Carbon metabolism causes glucokinase downregulation through four glucose metabolism pathways, leading to increased D-glucose 1-phosphate metabolism. Upregulation of chitinase also causes an increase in D-glucose 1-phosphate metabolism through the amino sugar and nucleotide sugar metabolism pathways.
Real-time fluorescence quantitative PCR verification
From the differentially expressed genes of the control group and the experimental group, 10 differentially expressed genes were randomly selected for RT‒qPCR, and the Transcriptomics sequencing results were verified. The results are shown in Fig. 13. The change trend of gene expression determined by RT‒qPCR was consistent with the results of RNA-seq, indicating the reliability of the Transcriptomics sequencing analysis results.