A Heterogeneous Codonopsis pilosula Polysaccharide Preparation Attenuates Doxorubicin-Induced Cardiotoxicity in Mice: Associations with Oxidative Stress, Mitochondrial Function, and Gut Microbiota

Figures

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  Figure 1. Physicochemical characterization of the Codonopsis pilosula polysaccharide preparation (CPP). (A) Fourier-transform infrared (FT-IR) spectrum of CPP. (B) Molecular-weight distribution curve of CPP analyzed by gel permeation chromatography (GPC). (C) Chromatogram and calibration curve of CPP showing the retention times of pullulan standards used for molecular-weight calibration. (D) High-performance liquid chromatography (HPLC) chromatogram of standard monosaccharides. (E) HPLC chromatogram of CPP hydrolysate.

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  Figure 2. CPP attenuated doxorubicin-induced cardiac dysfunction in mice. (A) Experimental timeline and treatment schedule. (B) Changes in body weight during the 4-week intervention period. (C) Kaplan–Meier survival curves; for each group, n = 14. (D) Representative echocardiographic M-mode images. (E–L) Quantitative analysis of cardiac morphometric and functional parameters, including heart weight/tibia length (HW/TL) (E), heart weight/body weight (HW/BW) (F), ejection fraction (EF) (G), fractional shortening (FS) (H), cardiac output (CO) (I), stroke volume (SV) (J), left ventricular end-systolic volume (LVESV) (K), and left ventricular internal dimension at end-systole (LVIDs) (L); for each group, n = 6. For panels (E–L), group differences were analyzed using one-way analysis of variance (ANOVA), followed by Tukey’s multiple-comparisons test. Survival curves in panel C were analyzed using the Kaplan–Meier method with the log-rank test. Data are presented as mean ± SD. * p < 0.05, ** p < 0.01, *** p < 0.001; ns, not significant.

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  Figure 3. CPP ameliorated doxorubicin-induced cardiac pathological damage and biochemical dysfunction. (A) Hematoxylin–eosin (H&E) staining of heart. Scale bar = 20 μm. (B) Wheat germ agglutinin (WGA) staining of heart. Scale bar = 20 μm. (C) Quantitative analysis of cardiomyocyte cross-sectional area. (D,E) mRNA expression of cardiac dysfunction markers ANP (D) and BNP (E). (F–J) Serum biochemical indices, including AST (F), CK (G), CK-MB (H), LDH (I), and LDH-1 (J). (K–M) Serum levels of oxidative-stress markers, including MDA (K), SOD (L), and GSH-PX (M). For each group, n = 6. LCPP, low-dose CPP (100 mg/kg/day); HCPP, high-dose CPP (300 mg/kg/day). One-way analysis of variance (ANOVA) was used to compare the group differences. Significant differences between groups were indicated by *, where * indicates p < 0.05; ** indicates p < 0.01; and *** indicates p < 0.001; ns indicates no significant difference.

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  Figure 4. CPP reduced doxorubicin-induced myocardial fibrosis. (A) Sirius Red staining of cardiac tissue. Scale bar = 50 μm. (B) Quantitative analysis of fibrotic area (%). (C–E) Relative mRNA expression levels of collagen-related genes Col1a1 (C), Col5a1 (D), and MMP-2 (E). For each group, n = 6. One-way analysis of variance (ANOVA) was used to compare the group differences. Significant differences between groups were indicated by *, where * indicates p < 0.05; ** indicates p < 0.01; and *** indicates p < 0.001; ns indicates no significant difference.

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  Figure 5. CPP preserved mitochondrial structure and function in doxorubicin-treated mice. (A) Representative transmission electron microscopy (TEM) images. Scale bar = 1 μm (upper), 500 nm (lower). (B–D) Quantitative analysis of mitochondrial area, cristae/mitochondrial area ratio, and cristae density. (E–I) Relative mRNA expression levels of mitochondrial respiratory-chain complex genes Ndufb8 (E), Sdhb (F), Uqcrc1 (G), Cox5b (H), and Atp5f1 (I). (J) Determination of the relative content of ATP in cardiac tissue. For each group, n = 6. One-way analysis of variance (ANOVA) was used to compare the group differences. Significant differences between groups were indicated by *, where * indicates p < 0.05; ** indicates p < 0.01; and *** indicates p < 0.001; ns indicates no significant difference.

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  Figure 6. CPP restored gut-microbiota diversity and altered gut-microbial composition in doxorubicin-treated mice. (A–C) Alpha diversity of the gut microbiota, including the Chao1 index (A), Shannon index (B), and Simpson index (C). Group differences were analyzed using the Kruskal–Wallis test, followed by Dunn’s multiple-comparisons test. (D) Principal coordinates analysis (PCoA) based on Bray–Curtis distances at the genus level; differences in community structure among groups were assessed by PERMANOVA. (E) Relative abundance of the top genera in each group. (F–H) Relative abundances of norank_f__Muribaculaceae (F), Escherichia-Shigella (G), and Ligilactobacillus (H). For each group, n = 4. Group differences were analyzed using the Kruskal–Wallis test, followed by Dunn’s multiple-comparisons test. Data are presented as median with interquartile range. * p < 0.05, ** p < 0.01, *** p < 0.001; ns, not significant.

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  Figure 7. Correlation analysis of gut microbiota with cardiac indicators and predicted microbial functions. (A) Heatmap showing correlations between differential gut genera and cardiac/oxidative-stress indices. Correlations were evaluated using Spearman’s rank correlation analysis. (B–D) Predicted microbial functional pathways based on PICRUSt analysis at KEGG level 1 (B), level 2 (C), and level 3 (D). (E) Heatmap showing correlations between differential bacterial genera and KEGG level 3 pathways, analyzed using Spearman’s rank correlation analysis. For each group, n = 4. Red indicates positive correlation and blue indicates negative correlation. * p < 0.05, ** p < 0.01, *** p < 0.001.