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. 2018 Aug 23;8(1):12680.
doi: 10.1038/s41598-018-30881-0.

The Effect of Ganoderma Lucidum Extract on Immunological Function and Identify Its Anti-Tumor Immunostimulatory Activity Based on the Biological Network

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Free PMC article

The Effect of Ganoderma Lucidum Extract on Immunological Function and Identify Its Anti-Tumor Immunostimulatory Activity Based on the Biological Network

Ruolin Zhao et al. Sci Rep. .
Free PMC article

Abstract

Ganoderma lucidum extract (GLE) has shown positive effects for tumor treatment. However, the molecular mechanism of GLE treatment is unknown. In this study, a Hepa1-6-bearing C57 BL/6 mouse model was established to explore the anti-tumor and immunostimulatory activity of GLE treatment. The results showed that GLE effectively inhibited tumor growth without hepatic/renal toxicity and bone marrow suppression, and might enhancing immunological function. Based on the mRNA profiles of GLE treated and untreated mice, 302 differentially expressed (DE) mRNAs were identified and 6 kernel mRNAs were identified from the established protein-protein interaction (PPI) network. Quantitative RT-PCR and western-blot analysis indicated that 6 mRNAs have had statistically significant differences between the GLE treated and untreated mice. Furthermore, four kernel pathways were isolated from the KEGG-Target network, including the Jak-STAT signaling pathway, T cell receptor signaling pathway, PI3K-Akt signaling pathway, and cytokine-cytokine receptor interaction. Western-blot and cytokine detection results demonstrated that GLE suppressed growth and proliferation of tumors by the Jak-STAT signaling pathway, T cell receptor signaling pathway and PI3K-Akt signaling pathway, but also regulated the expression levels of serum immune cytokines and improved the anti-tumor immunostimulatory activity.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
GLE effectively inhibited tumor growth in vivo. (A) The weight of mice was significantly reduced in cisplatin group from the 10th day (P < 0.01) compared with those in model group and control group. The difference was no statistically significant in GLE treatment group. (B) The average tumor weight in GLE treatment and cisplatin groups decreased significantly compared with those in model group (P < 0.01). Representative results of three independent experiments are shown. Error bars, SD; *P < 0.05; **P < 0.01, versus control values; ΔP < 0.05; ΔΔP < 0.01, versus model values.
Figure 2
Figure 2
The toxicological impact of GLE treated tumor-bearing mice. (A) Pathological examination was used to detect toxic damage of liver and renal tissue in all groups. Sections of liver and renal tissue in mice were stained using HE followed by observation on a phase-contrast microscope (×400). Obvious changes are highlighted by arrows. The structure of the hepatic lobule was intact, and no inflammatory cell infiltration was found in the portal area and the liver proper. Six hours prior to sacrifice, mice had not been fasted, thus each group showed vacuoles in the liver cells. This simply indicated glycogen accumulation in the liver cells was not associated with pathology. Likewise, hypertrophy of hepatocytes around the central vein of the liver tissue in cisplatin group was attributed to damage to liver cells caused by cisplatin. (B,C) Effects of GLE on liver and renal indexes were analyzed in tumor-bearing mice. (D,E) GLE improves the suppression of bone marrow cells in tumor-bearing mice. FACS was used to detect the effects of GLE on the cell cycle progression of bone marrow. Representative results of three independent experiments are shown. Error bars, SD; *P < 0.05; **P < 0.01, versus control values; ΔP < 0.05; ΔΔP < 0.01, versus model values.
Figure 3
Figure 3
Effect of GLE on immunostimulatory activity of tumor-bearing mice was determined. (A,D) GLE improved the suppression of thymus cells in tumor-bearing mice. Cell cycle distribution was analyzed by FACS, in order to detect the effect of GLE on the cell cycle progression of thymus. (B,F,G) Effects of GLE on T cell subsets in peripheral blood in tumor-bearing mice were detected. The lymphocyte subsets were detected by flow cytometry. The lymphocyte subset analysis included CD3+ (T lymphocyte), CD4+ (T-helper cells) and CD8+ (T-suppressor cells). Representative results of three independent experiments are shown. (C,E) Effects of GLE on NK cell activity in tumor-bearing mice. The percentage of NK cells were quantified by FACS. Error bars, SD; *P < 0.05; **P < 0.01, versus control values; ΔP < 0.05; ΔΔP < 0.01, versus model values.
Figure 4
Figure 4
The differential expressed mRNAs were identified between the GLE treated and untreated Hepa1-6-bearing C57 BL/6 mice. (A) Heat-map of DE mRNAs between GLE treated and untreated Hepa1-6-bearing C57 BL/6 mice. The red color represents up-expression of mRNA and green color represents down-expression of mRNA. The relationship among the samples was divided by binary tree classification and was shown at the upper portion. Hierarchical cluster of mRNAs were displayed at nearside. (BD) GO (Gene Ontology) terms in differentially expressed mRNAs. (E) KEGG pathways in differentially expressed mRNAs.
Figure 5
Figure 5
The protein-protein interaction (PPI) network was constructed based on DE mRNAs, the related clusters were identified using ClusterONE algorithm. (A) The global profiles of GLE treatment related PPI network was built. The red nodes represent the differential expressed mRNAs. (B) The topological profile of GLE treatment related Cluster-PPI network, the red nodes represent the differential expressed mRNAs. (C) The kernel mRNA clusters isolated from Cluster-PPI network. (D) The qRT-PCR was used to validate 6 potential kernel mRNAs, and the expression levels of 6 kernel mRNAs have statistically significant difference in GLE treated Hepa1-6-bearing C57 BL/6 mice. (E) The expression tendency of 6 kernel mRNAs in the microarray. (F,G) Western-blot assay was used to further validate the 6 kernel mRNAs. Representative results of three independent experiments are shown. Error bars, *P < 0.01 versus model values. Representative results of three independent experiments are shown. The β-actin was used as a loading control. Error bars, *P < 0.01 versus model values.
Figure 6
Figure 6
The kernel biological pathway screening and validating was based on the KEGG-Target network and Western-blot. (A) The KEGG-Target pathway network was constructed based on the differential expressed genes and their KEGG pathways. The red dot represents each KEGG pathway, the green dot represents target genes, and the red triangles represent the selected important pathways in network, including Jak-STAT signaling pathway, T cell receptor signaling pathway, PI3K-Akt signaling pathway, and cytokine-cytokine receptor interaction. The yellow dot represents the target genes of four selected pathways. (B) Four kernel pathways selected from the KEGG-Target network. (C,D) The effects of GLE were validated on the Jak/Stat signaling pathway. Compared with those in the model group, the expression levels of JAK3, p-Stat1, p-Stat3, p-Stat5 and p-Stat6 were significantly inhibited by GLE in vivo (P < 0.05). (E,F) The effects of GLE were validated on the T cell receptor signaling pathway. The expression levels of p-Lck and p-Zap-70 were down-regulated (P < 0.05). (G,H) The effects of GLE were validated on the PI3K/Akt/mTOR signaling pathway for proliferation inhibition. Compared with those in model group, the expression levels of p-Akt and p-mTOR were down-regulated (P < 0.05), and PI3K was up-regulated (P < 0.05). Densitometry analysis of the levels of these proteins relative to actin was performed. Representative results of three independent experiments are shown. The β-actin was used as a loading control. Error bars, SD; *P < 0.05, versus model values.

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