3.1 Effect of BS-Z15 secondary metabolites on innate immune function in mice
Compared with the control group, NK cell activity was significantly enhanced by administration of BS-Z15 secondary metabolites in both the low and high-dose groups, with increases of 24.99% (P < 0.05) and 41.81% (P < 0.05) respectively, and no significant difference (P > 0.05) between the two groups (Figure 1A).
Compared with the control group, there was no significant change in carbon contour phagocytosis index in the low-dose group (P > 0.05). However, a significant increase of 12.77% was observed in the high-dose group (P < 0.05) (Figure 1B), indicating that BS-Z15 secondary metabolite treatment enhanced the ability of NK cells to kill target cells and phagocytosis of monocytes-macrophages in mice, thereby improving innate immune function in mice.
3.1.3 Effect of BS-Z15 secondary metabolites on blood physiological parameters in mice
Compared to the control group, the number of monocytes in the blood of mice in both the low and high-dose groups increased significantly, by 6.45% (P < 0.05) and 12.9% (P < 0.05), respectively, (Table 1). In addition, BS-Z15 secondary metabolites significantly increased the monocyte and platelet counts in the blood of mice, with increases of 2.7% (P < 0.05) and 10.59% (P < 0.05) observed in the low and high-dose groups, respectively.
3.2 Effect of BS-Z15 secondary metabolites on adaptive immunity
3.2.1 Effect of BS-Z15 secondary metabolites on cellular immune function in mice
Compared to the control group, there was a dose-dependent increase in T cell conversion in the BS-Z15 secondary metabolite treated groups, with a significant increase of 7.17% observed in the high-dose group (P < 0.05), although the 4.7% increase in the low-dose group (P > 0.05) did not reach the level of statistical significance (Figure 2A).
Compared to the control group, the delayed type hypersensitivity-mediated swelling of the left hind foot and metatarsal area of mice was in by 23.54% in the low-dose group (P < 0.05) and by 70.54% in the high-dose group (P < 0.05) (Figure 2B), indicating that treatment with BS-Z15 secondary metabolites enhanced cellular immunity in mice.
3.2.2 Effect of BS-Z15 secondary metabolites on humoral immune function in mice
Compared to the control group, the ability of antibody-forming cells to produce antibodies in the spleen was significantly increased in both the low (22.77%, P > 0.05) and high (29.79%, P < 0.05) groups (Figure 2C), with no significant difference between the two groups.
Compared to the control group, there was no significant difference in the hemolytic function of the plasma of the low-dose group (P > 0.05), although there was a significant increase of 16.06% in the high-dose group (P < 0.05) (Figure 2D), indicating that BS-Z15 secondary metabolite treatment improved antibody production and enhanced humoral immune function in mice.
3.2.3 Effect of BS-Z15 secondary metabolites on blood biochemical parameters in mice
Figure 3 shows the effect of BS-Z15 secondary metabolites treatment on blood biochemical parameters in mice. Compared to the control group, the plasma IFN-γ content was significantly increased by 73.33% in the high-dose group (P < 0.05), while the growth rate of 20% in the low-dose group (P > 0.05) was not significant (Figure 3A). The plasma IL-6 levels were also significantly increased in both the low (174.2%, P < 0.05) and high (389.26%, P < 0.05) dose groups (Figure 3B). Furthermore, the plasma IgG content increased significantly in both the low (3.44%, P < 0.05) and high (12.85%, P < 0.05) dose groups (Figure 3C). While the plasma IgM content increased by 25.57% in the low-dose group (P > 0.05), the levels increased significantly by 58.84% in the high-dose group (P < 0.05) (Figure 3D). These results showed that 90 mg/kg BS-Z15 secondary metabolite treatment increased the levels of INF-γ, IL-6, IgG and IgM in the blood of mice.
3.3 Transcriptome sequencing-based analysis of the effects of BS-Z15 secondary metabolites treatment on the expression of immune-related genes in mice
3.3.1 Transcriptome data quality control and differentially expressed gene analysis
The raw sequencing data from each sample were assessed for sequencing-related quality using fastp software, resulting in 53.77 million and 47.35 million high-quality pure reads (clean reads) for the control and treatment groups, respectively, for assembly and further downstream analysis. As shown in Table 2, all samples exceeded 98.75% for Q20 and 96.13% for Q30, with a good distribution of GC content, indicating that the RNA sequence data for all samples were of high quality and suitable for use in subsequent bioinformatics analysis.
Variation in gene length and total read distribution in gene expression levels was eliminated by FPKM calculations. Differentially expressed genes were screened using an absolute value of log2 fold change ≥1 (i.e., the change in gene expression in the treated groups compared with the control group was more than 2-fold, with a statistical significance threshold of P < 0.05). A volcano plot showing the distribution of DEGs is shown in Figure 4A. As shown in Figure 4B, 608 genes showed significant differential expression in the blood of the BS-Z15 secondary metabolites treated group compared to the control group. Of these DEGs, 315 were upregulated and 293 were downregulated). A heat map of cluster analysis of these DEGs is shown in Figure 4C.
3.3.2 GO database analysis of differentially expressed genes
To illustrate the functions of these DEGs, GO enrichment analysis was performed in terms of biological processes (BP), cellular component (CC) and molecular function (MF). In total, 866 GO terms were located in the GO enrichment analysis, with the top 25 GO terms in terms of abundance shown in Figure 5A. Sixteen of the GO terms were located in the BP category, three in MF and one in CC. The main immune-related terms were cellular response to IFN-b, response to IFN-b, immune response, and response to tumor necrosis factor. The number of differentially up- and downregulated genes in the top twenty-five GO terms in terms of abundance are shown in Figure 5B. In terms of differential gene expression trends, five DEGs were upregulated and three were downregulated in cellular response to IFN-b, five DEGs were upregulated and four downregulated in response to IFN-b, 24 DEGS were upregulated and 28 were downregulated in immune response, and eight DEGs were up-regulated and four were down-regulated in response to tumor necrosis factor. These findings suggested that BS-Z15 secondary metabolites have immunomodulatory effects.
3.3.3 KEGG analysis pathway enrichment of the differentially expressed genes
KEGG pathway enrichment analysis revealed involvement of the significant DEGs in a total of 276 metabolic pathways. Among the top 25 metabolic pathways shown in Figure 6A, systemic lupus erythematosus, viral carcinogenesis and alcoholism pathways were the most significantly expressed. In addition, immune pathways such as primary immunodeficiency, tumour necrosis factor (TNF) signaling pathway and TLR signaling pathway, were significantly differentially expressed.
The number of DEGs upregulated and downregulated in the top 25 KEGG pathways in terms of abundance are shown in Figure 6B. In terms of differential gene expression trends, 11 DEGs were upregulated and nine were downregulated in systemic lupus erythematosus, seven DEGs were upregulated and four were downregulated in primary immunodeficiency, five DEGs were upregulated and one was downregulated in TNF signaling pathway, and five were upregulated and none were downregulated in Toll-like receptor signaling pathway. The DEGs in the Toll-like receptor signaling pathway included Gm17809, encoding IKKα, Gm49320, encoding MMK7, and the transcription factors Jun and Fos. The TNF signaling pathway negative regulatory protein synthesis death domain silencer (SODD) Bag4, and the chemokine Ccl3. Thus, These findings provided further evidence that BS-Z15 secondary metabolites have immunomodulatory effects.
3.3.4 Immune-related gene analysis and real-time fluorescence quantitative PCR validation
The GO and KEGG enrichment analyses of immune-related entries and pathways revealed that 74 immune-related genes were differentially expressed in the treated group compared to the control group, including 39 upregulated DEGs and 35 downregulated DEGs (Figure 3C). Upregulated genes included those encoding interleukin-7 (Il7) and complement components (C1qb, C4b), which are widely involved in non-specific and specific immune responses, and those encoding transmembrane protein 98 (Tmem98), the T cell antigen receptor (Trav16d-dv11), and the transcription factor RFX5 (B230398E01Rik), as well as transforming factor-related genes (Tgfb2, Fstl3), which regulate the innate immune response. The main genes downregulated were those encoding chemokine (Ccr5), CD40 ligand (CD40lg), and histone H4 (Gm20634). These findings indicated that BS-Z15 secondary metabolite treatment modulated the expression of immune-related genes in mice.
To verify the reliability of the RNA-seq transcriptomic data, six immune-related genes (Trav16d-dv11, MKK7, Fos, Lrrc32, Ccl3, and Ccr5) were selected for qPCR validation, with β-actin as an internal reference gene. As shown in Figure 7, Trav16d-dv11, MKK7, Fos, Lrrc32 and Ccl3 were upregulated after BS-Z15 secondary metabolite treatment, while Ccr5 was downregulated. The qPCR validation was consistent with the expression patterns of the six DEGs obtained by RNA-seq, indicating that the RNA-seq results reliably reflected the gene expression trends.