Effect of bacillus subtilis strain Z15 secondary metabolites on immune function in mice

Abstract

Background: Previous studies have shown that secondary metabolites of Bacillus subtilis strain Z15 (BS-Z15) are effective in treating fungal infections in mice. To evaluate whether it also modulates immune function in mice to exert antifungal effects, we investigated the effect of BS-Z15 secondary metabolites on both the innate and adaptive immune functions of mice, and explored its molecular mechanism through blood transcriptome analysis.

Results: The study showed that BS-Z15 secondary metabolites increased the number of monocytes and platelets in the blood, improved natural killer (NK) cell activity and phagocytosis of monocytes-macrophages, increased the conversion rate of lymphocytes in the spleen, the number of T lymphocytes and the antibody production capacity of mice, and increased the levels of Interferon gamma (IFN-γ), Interleukin-6 (IL-6), Immunoglobulin G (IgG) and Immunoglobulin M (IgM) in plasma. The blood transcriptome analysis revealed 608 differentially expressed genes following treatment with BS-Z15 secondary metabolites, all of which were significantly enriched in the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms for immune-related entries and pathways such as Tumor Necrosis Factor (TNF) and Toll-like receptor (TLR) signaling pathways, and upregulated expression levels of immune-related genes such as Complement 1q B chain (C1qb), Complement 4B (C4b), Tetracyclin Resistant (TCR) and Regulatory Factor X, 5 (RFX5).

Conclusions: BS-Z15 secondary metabolites were shown to enhance innate and adaptive immune function in mice, laying a theoretical foundation for its development and application in the field of immunity.

Keywords: Animals; Bacillus subtilis; Immune function; Secondary metabolites; Transcriptome.

Fig. 1

Effects of BS-Z15 secondary metabolites on innate immunity (A) (B)in mice. (A) Effects of BS-Z15 secondary metabolites on mouse NK cell activity (n = 8–11). (B) Effects of BS-Z15 secondary metabolites on phagocytosis of mouse monocyte-macrophages (n = 3)

Fig. 2

Effects of BS-Z15 secondary metabolites on cellular immunity (A) (B), and humoral immunity (C) (D)in mice. (A) Effect of BS-Z15 secondary metabolites on T-lymphocyte proliferative in mice (n = 4). (B) Effects of BS-Z15 secondary metabolites on delayed-type hypersensitivity in mice (n = 8–9). (C) Effect of BS-Z15 secondary metabolites on the activity of mouse antibody-producing cells (n = 10). (D) Effects of BS-Z15 secondary metabolites on serum hemolysin levels in mouse cells (n = 10). Bar graphs are expressed as mean ± SEM. P-values of less than 0.05 were considered statistically significant, different letters were considered significant differences, and the same letters were not significantly different

Fig. 3

Effects of BS-Z15 secondary metabolites on immune cytokine (A) (B) and immunoglobulin (C) (D) levels in mouse plasma. (A) IFN-γ (B) IL-6 (C) IgG (D) IgM (n = 10). Bar graphs are expressed as mean ± SEM. P-values of less than 0.05 were considered statistically significant, different letters were considered significant differences, and the same letters were not significantly different

Fig. 4

Analysis results of differentially expressed genes. (A) Distribution of DEGs volcanoes. X-axis is the display of log2 fold change, Y-axis is -log10Pvalue; Red represents upregulation gene, green represents downregulation gene, gray represents non differentiation gene. (B) Statistical chart of the number of DEGs. The abscissa is DEGs, and the ordinate is the number of difference genes; Red represents upregulation gene, green represents downregulation gene. (C) Clustering diagram of different groups. Red indicates high expression of gene and blue indicates low expression of gene

Fig. 5

DEGs were analyzed by GO enrichment. (A) GO enrichment analysis of DEGs. The horizontal axis represents -log10 P-value, and the vertical axis represents the GO term. Red represents cellular component (CC), blue represents molecular function (MF), green represents biological process (BP). (B) Comparison of upregulation and downregulation number of gene at GO term. Red indicates up regulation of GO term enriched by DEGs, blue indicates down regulation of GO term enriched by DEGs, the horizontal axis is the GO term name, and the vertical axis is the number of gene of corresponding terms. Immune-related items are marked with black boxes

Fig. 6

DEGs were analyzed by KEGG enrichment. (A) KEGG enrichment analysis of DEGs. The horizontal axis represents Rich Factor, and the vertical axis represents the KEGG pathway. The size of the point represents the number of genes in this KEGG pathway, and the color of the point corresponds to different P-value ranges. (B) Comparison of upregulation and downregulation number of gene at KEGG pathway. Red indicates up regulation of KEGG pathway enriched by DEGs, blue indicates down regulation of KEGG pathway enriched by DEGs, the horizontal axis is the KEGG pathway name, and the vertical axis is the number of gene of corresponding pathways. Immune-related pathways are marked with black boxes

Fig. 7

(A) Clustering heatmap of immune-related DEGs sorted by high Log FC. Red indicates high expression of gene and blue indicates low expression of gene.(B) Log2 fold change of 6 immune-related DEGs screened by qPCR and transcriptome. The vertical axis represents log2 fold change and the horizontal axis represents the name of genes, black represents qPCR data and grey represents transcriptome data results

Fig. 8

Mechanism of BS-Z15 secondary metabolites in regulating immune function in mice