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. 2023 Nov 3:14:1280049.
doi: 10.3389/fimmu.2023.1280049. eCollection 2023.

Serum, spleen metabolomics and gut microbiota reveals effect of catalpol on blood deficiency syndrome caused by cyclophosphamide and acetylphenylhydrazine

Wensen Zhang  1 , Na Cui  1 , Fazhi Su  1 , Yanping Sun  1 , Biao Li  1 , Yupeng Ren  1 , Ping Wang  1 , Haodong Bai  1 , Wei Guan  1 , Bingyou Yang  1 , Qiuhong Wang  2 , Haixue Kuang  1
Affiliations

Serum, spleen metabolomics and gut microbiota reveals effect of catalpol on blood deficiency syndrome caused by cyclophosphamide and acetylphenylhydrazine

Wensen Zhang et al. Front Immunol. .

Abstract

Catalpol (CA), extracted from Rehmannia Radix, holds extensive promise as a natural medicinal compound. This study employed 16S rRNA gene sequencing and combined serum and spleen metabolomics to profoundly investigate the therapeutic effects of CA on blood deficiency syndrome (BDS) and the underlying mechanisms. Notably, CA exhibited effectiveness against BDS induced by cyclophosphamide (CP) and acetylphenylhydrazine (APH) in rats-CA substantially elevated levels of crucial indicators such as erythropoietin (EPO), granulocyte colony-stimulating factor (G-CSF), tumor necrosis factor-alpha (TNF-a), and interleukin-6 (IL-6). Additionally, CA could alleviate peripheral blood cytopenia. Furthermore, the analysis of 16S rRNA revealed that CA had the potential to reverse the Firmicutes/Bacteroidetes (F/B) ratio associated with BDS. Through comprehensive serum and spleen metabolomic profiling, we successfully identified 22 significant biomarkers in the serum and 23 in the spleen, respectively. Enrichment analysis underscored Glycerophospholipid metabolism and Sphingolipid metabolism as potential pathways through which CA exerts its therapeutic effects on BDS.

Keywords: catalpol; cyclophosphamide; gut microbiota; metabolomics; rehmannia radix.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Modeling method in BDS rats.
Figure 2
Figure 2
The effect of CA on physiological and biochemical indicators in BDS rats. (A) Rat spleen coefficient; (B) The level of WBC; (C) The level of HCT; (D) The level of RBC; (E) The level of HGB; (F) The level of EPO; (G) The level of TNF-α; (H) The level of G-CSF; (I) The level of IL-6; (J) HE staining (200 × and 20 ×), CA treatment of CP and APH induced spleen injury. Each value represents the mean ± SD (n = 3). ## p < 0.01 or # p < 0.05, compared with the control group; *p < 0.05 or **p < 0.01, compared with the model group.
Figure 3
Figure 3
Serum metabolomics. (A, D) The PCA analysis represents the positive and negative ion modes of the ESI; (B, E) The PLS-DA analysis represents the positive and negative ion modes of the ESI (n = 6); (C, F) The permutation test analysis represents the positive and negative ion modes; (G) The heatmap and cluster analysis for the potential biomarker; (H) Folding change chart; (I) Bubble chart.
Figure 4
Figure 4
Spleen metabolomics. (A, D) The PCA analysis represents the positive and negative ion modes of the ESI; (B, E) The PLS-DA analysis represents the positive and negative ion modes of the ESI (n = 6); (C, F) The permutation test analysis represents the positive and negative ion modes; (G) The heatmap and cluster analysis for the potential biomarker; (H) Folding change chart; (I) Bubble chart.
Figure 5
Figure 5
Effect of CA on the richness and diversity of colonic contents microbiota in rats. (A) Rarefaction curves. (B) Rank abundance curves. (C) OTU Venn diagram. (D) Chao index. (E) Simpson index. (F) Shannon index. (G, H) The colonic contents bacterial community at the phylum, and genus levels. Less than 0.5% abundance of the genus was merged into others. Each value represents the mean ± SD (n = 3). # p < 0.05 or ## p < 0.01, compared with the control group; *p < 0.05 or **p < 0.01, compared with the model group.
Figure 6
Figure 6
β-diversity analysis of gut microbiota. (A) PCA analysis; (B) PCoA analysis; (C) NMDS analysis; (D) PLS-DA analysis; (E) PCA box diagram; (F) PCoA box diagram; (G) β-diversity heatmap; (H) The indicator bacteria with an LDA score of 2 or higher in the bacterial community were associated with the five groups of rats; (I) The areas with different colors represent different components (red, control group; blue, model group; green, CA-H group; pink, CA-M group; purple, CA-L group). The circle indicates the level of phylogeny from phylum to genus.
Figure 7
Figure 7
Functional prediction of altered gut microbiota by PICRUSt2 analysis based on the rat level II KEGG pathway. (A) Control group vs model group; (B) Model group vs CA-H group. (C) Spearman correlation heatmap between the top 10 important serum metabolite concentrations and the relative abundance of gut microbiota genus levels in the CA-H group; (D) The top 10 important spleen essential metabolites and gut microbiota in the CA-H group relative abundance at the genus level. R values are represented by gradient colors, where red and blue cells indicate positive and negative correlations. *p < 0.05 or **p < 0.01.
Figure 8
Figure 8
Effect of each group on the key target expression. (A) The level of S1P; (B) The level of GAPDH; (C) The level of PLD. ## p < 0.01 or# p < 0.05, compared with the control group; *p < 0.05 and **p < 0.01, compared with the model group.
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