Sang X et al. (2024), Sulfated Polysaccharides from Sea Cucumber Cook...

ECB-ART-53141

Foods 2024 Jun 26;1313:. doi: 10.3390/foods13132017.

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Sulfated Polysaccharides from Sea Cucumber Cooking Liquid Prevents Obesity by Modulating Gut Microbiome, Transcriptome, and Metabolite Profiles in Mice Fed a High-Fat Diet.

Sang X , Guan X , Tong Y , Wang F , Zhou B , Li Y , Zhao Q .

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We aimed to explore the anti-obesity mechanism from the microbiome, metabolome, and transcriptome viewpoints, focusing on the sulfated polysaccharides found in the cooking liquid of Apostichopus japonicus (CLSPAJ) to explore the potential mediators of the anti-obesity effects in mice fed a high-fat diet (HFD). The mice treated with CLSPAJ showed a decrease in obesity and blood lipid levels. Gut microbiome dysbiosis caused by the HFD was reversed after CLSPAJ supplementation, along with increased levels of indole-3-ethanol, N-2-succinyl-L-glutamic acid 5-semialdehyde, and urocanic acid. These increases were positively related to the increased Akkermansia, Lactobacillus, Roseburia, and Phascolarctobacterium. Transcriptome analysis showed that B cell receptor signaling and cytochrome P450 xenobiotic metabolism were the main contributors to the improvement in obesity. Metabolome-transcriptome analysis revealed that CLSPAJ reversal of obesity was mainly due to amino acid metabolism. These findings suggest that CLSPAJ could be a valuable prebiotic preparation for preventing obesity-related diseases.

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	Figure 1. Dietary CLSPAJ improves body weight gain, fat accumulation, and obesity-associated metabolic diseases in HFD-fed mice. (A) Study design of in vivo mice experiment, (B) average food intake, (C) mice morphology, (D) body weight gain, (E–K) serum TG, T-CHO, HDL-c, LDL-c, AST, ALT, and MDA contents. Data are expressed as mean ± SD (n = 9). Graph bars marked by asterisks represent statistically significant differences (p < 0.05) based on one-way analysis of variance (ANOVA) with Tukey’s multiple comparisons test. : p < 0.05, : p < 0.01, **: p < 0.001.
	Figure 2. CLSPAJ reduced lipid accumulation and adipocyte enlargement. (A) Representative colon length, (B) kidney mass, (C) liver morphology, (D) representative H and E staining images of the liver (200×), (E) liver mass, (F) epididymal fat morphology, (G) representative H and E staining images of the epididymal fat (200×), (H) epididymal fat mass. Data are expressed as mean ± SD (n = 9). Graph bars marked by asterisks represent statistically significant differences (p < 0.05) based on one-way analysis of variance. Different superscripts (a, b) represent significant differences from each other; p < 0.05. : p < 0.01, *: p < 0.001.
	Figure 3. CLSPAJ intervention alters the composition of gut microbiota in HFD-fed mice. (A) PLS-DA, (B) coverage analysis, (C) Chao index, (D) ACE index, (E) Shannon index, (F) Simpson index, (G) relative abundance at phylum level and the ratio of Firmicutes to Bacteroidetes, (H) Kruskal-Wallis H test analysis at phylum level. Data are expressed as mean ± SD (n = 3). Graph bars marked with ‘’ in the graph denote statistically significant differences (p < 0.05), as determined by one-way analysis of variance (ANOVA) followed by Tukey’s multiple comparison test. : p < 0.05, : p < 0.01, *: p < 0.001.
	Figure 4. Effects of CLSPAJ on the correlation network diagram and circos analysis at the genus level. (A) Circos sample-species relationship diagram. The left part of the inner semicircle represents the species composition in the sample, with the colors of the outer box line representing the subgroup from which it came, the color of the inner box line representing the species, and the length of the color block representing the relative abundance of the species in the corresponding sample. The right part of the outer semicircle represents the proportion of the species distribution in different samples at this taxonomic level, with the outer box line representing the species, the colors of the inner box line representing different subgroups, and the length of the color block representing the distribution of the particular species in the sample. (B–J) Relative abundances of nine key bacterial genera enriched by CLSPAJ. (K) The species relevance network map. The figure displays the leading 50 species characterized by p < 0.05 and the absolute correlation coefficient ≥0.5. Node size reflects each species’ relative abundance, with varying colors distinguishing the various species. Red lines denote positive correlations, while green lines signify negative correlations. The thicker the line, the stronger the correlation. Additionally, a greater number of lines between species implies a closer relationship. : p < 0.05, : p < 0.01, **: p < 0.001.
	Figure 5. CLSPAJ supplementation modifies gut metabolite levels in cecal content samples (n = 6). PLS-DA score plot among the four experimental groups in (A) cationic and (B) anionic mode. (C) Differences in KEGG functional pathways between HFD and HFD_SP_H. (D) CLSPAJ regulates the metabolic pathways in HFD-induced mice. (E,F) Volcano plots of altered metabolites with p < 0.05 and \|log2FC\| > 1.2 between HFD and HFD_SP_H (E) and control and HFD (F). The size and color of each circle were based on the pathway impact value and the p-value. (G) CLSPAJ regulates the metabolic pathways in HFD-induced mice with p < 0.05 and \|log2FC\| > 1.2 between HFD and HFD_SP_H. (H) Heatmap of the top 16 differential metabolites between HFD and HFD_SP_H. (I) Heatmap of the metabolites in the control group altered by HFD responding to HFD_SP_H treatment; colored from blue for low levels to red for high levels.
	Figure 6. Analysis of transcriptome between CLSPAJ and HFD groups. (A) Volcano map for gene expression differences. (B) Statistical histograms of up-regulated and down-regulated genes. (C) Functional annotation of DEGs. (D) Enrichment analysis of KEGG pathways in down-regulated Genes. (E) Enrichment analysis of KEGG pathways in up-regulated genes. KEGG, Kyoto Encyclopedia of Genes and Genomes; HFD, high-fat diet; CLSPAJ, sulfated polysaccharides from Apostichopus japonicus cooking liquid.
	Figure 7. Analysis of the association between gut microbiome and metabolites with obesity-related indices following CLSPAJ supplementation. (A) Analysis of the Spearman’s correlation between the gut microbiome and indices related to obesity. (B) Analysis of the Spearman’s correlation between the gut metabolites and indices related to obesity. (C) Analysis of the Spearman’s correlation between the obesity-related gut microbiome and metabolites. The different colors indicate different correlations: blue denotes a negative correlation, whereas red signifies a positive one. Correlations of significance are highlighted with asterisks. : p < 0.05, : p < 0.01, **: p < 0.001.
	Figure 8. Analysis of the association between gut microbiome and amino acid metabolites with obesity-related phenotypes following CLSPAJ supplementation. (A) Analysis of the Spearman’s correlation between the amino acid metabolites and phenotypes related to obesity. The different colors indicate different correlations: blue denotes a negative correlation, whereas red signifies a positive one. Correlations of significance are highlighted with asterisks. : p < 0.05, : p < 0.01, **: p < 0.001. (B) Correlation networks showing associations between the obesity-related gut microbiome and amino acid metabolites. The strength of the correlation is represented proportionately by the width and color of the edges, with red indicating a positive correlation and green indicating a negative one. Only strong (Spearman \|r\| ≥ 0.5) and significant (p < 0.05) correlations were displayed.
	Figure 9. Correlation analysis among the microbiome, metabolome, and transcriptome. (A) Spearman’s correlation between genus-level gut microbiota and differential genes in obesity-related pathways. (B) Conjoint analysis of the metabolome and transcriptome. (C) Schematic of CLSPAJ resistance to the mechanism of high-fat-induced obesity. The strength of the correlation is represented proportionately by the width and color of the edges, with red indicating a positive correlation and blue indicating a negative one. : p < 0.05, : p < 0.01, **: p < 0.001.

	Figure 1. Dietary CLSPAJ improves body weight gain, fat accumulation, and obesity-associated metabolic diseases in HFD-fed mice. (A) Study design of in vivo mice experiment, (B) average food intake, (C) mice morphology, (D) body weight gain, (E–K) serum TG, T-CHO, HDL-c, LDL-c, AST, ALT, and MDA contents. Data are expressed as mean ± SD (n = 9). Graph bars marked by asterisks represent statistically significant differences (p < 0.05) based on one-way analysis of variance (ANOVA) with Tukey’s multiple comparisons test. : p < 0.05, : p < 0.01, **: p < 0.001.
	Figure 2. CLSPAJ reduced lipid accumulation and adipocyte enlargement. (A) Representative colon length, (B) kidney mass, (C) liver morphology, (D) representative H and E staining images of the liver (200×), (E) liver mass, (F) epididymal fat morphology, (G) representative H and E staining images of the epididymal fat (200×), (H) epididymal fat mass. Data are expressed as mean ± SD (n = 9). Graph bars marked by asterisks represent statistically significant differences (p < 0.05) based on one-way analysis of variance. Different superscripts (a, b) represent significant differences from each other; p < 0.05. : p < 0.01, *: p < 0.001.
	Figure 3. CLSPAJ intervention alters the composition of gut microbiota in HFD-fed mice. (A) PLS-DA, (B) coverage analysis, (C) Chao index, (D) ACE index, (E) Shannon index, (F) Simpson index, (G) relative abundance at phylum level and the ratio of Firmicutes to Bacteroidetes, (H) Kruskal-Wallis H test analysis at phylum level. Data are expressed as mean ± SD (n = 3). Graph bars marked with ‘’ in the graph denote statistically significant differences (p < 0.05), as determined by one-way analysis of variance (ANOVA) followed by Tukey’s multiple comparison test. : p < 0.05, : p < 0.01, *: p < 0.001.
	Figure 4. Effects of CLSPAJ on the correlation network diagram and circos analysis at the genus level. (A) Circos sample-species relationship diagram. The left part of the inner semicircle represents the species composition in the sample, with the colors of the outer box line representing the subgroup from which it came, the color of the inner box line representing the species, and the length of the color block representing the relative abundance of the species in the corresponding sample. The right part of the outer semicircle represents the proportion of the species distribution in different samples at this taxonomic level, with the outer box line representing the species, the colors of the inner box line representing different subgroups, and the length of the color block representing the distribution of the particular species in the sample. (B–J) Relative abundances of nine key bacterial genera enriched by CLSPAJ. (K) The species relevance network map. The figure displays the leading 50 species characterized by p < 0.05 and the absolute correlation coefficient ≥0.5. Node size reflects each species’ relative abundance, with varying colors distinguishing the various species. Red lines denote positive correlations, while green lines signify negative correlations. The thicker the line, the stronger the correlation. Additionally, a greater number of lines between species implies a closer relationship. : p < 0.05, : p < 0.01, **: p < 0.001.
	Figure 5. CLSPAJ supplementation modifies gut metabolite levels in cecal content samples (n = 6). PLS-DA score plot among the four experimental groups in (A) cationic and (B) anionic mode. (C) Differences in KEGG functional pathways between HFD and HFD_SP_H. (D) CLSPAJ regulates the metabolic pathways in HFD-induced mice. (E,F) Volcano plots of altered metabolites with p < 0.05 and \|log2FC\| > 1.2 between HFD and HFD_SP_H (E) and control and HFD (F). The size and color of each circle were based on the pathway impact value and the p-value. (G) CLSPAJ regulates the metabolic pathways in HFD-induced mice with p < 0.05 and \|log2FC\| > 1.2 between HFD and HFD_SP_H. (H) Heatmap of the top 16 differential metabolites between HFD and HFD_SP_H. (I) Heatmap of the metabolites in the control group altered by HFD responding to HFD_SP_H treatment; colored from blue for low levels to red for high levels.
	Figure 6. Analysis of transcriptome between CLSPAJ and HFD groups. (A) Volcano map for gene expression differences. (B) Statistical histograms of up-regulated and down-regulated genes. (C) Functional annotation of DEGs. (D) Enrichment analysis of KEGG pathways in down-regulated Genes. (E) Enrichment analysis of KEGG pathways in up-regulated genes. KEGG, Kyoto Encyclopedia of Genes and Genomes; HFD, high-fat diet; CLSPAJ, sulfated polysaccharides from Apostichopus japonicus cooking liquid.
	Figure 7. Analysis of the association between gut microbiome and metabolites with obesity-related indices following CLSPAJ supplementation. (A) Analysis of the Spearman’s correlation between the gut microbiome and indices related to obesity. (B) Analysis of the Spearman’s correlation between the gut metabolites and indices related to obesity. (C) Analysis of the Spearman’s correlation between the obesity-related gut microbiome and metabolites. The different colors indicate different correlations: blue denotes a negative correlation, whereas red signifies a positive one. Correlations of significance are highlighted with asterisks. : p < 0.05, : p < 0.01, **: p < 0.001.
	Figure 8. Analysis of the association between gut microbiome and amino acid metabolites with obesity-related phenotypes following CLSPAJ supplementation. (A) Analysis of the Spearman’s correlation between the amino acid metabolites and phenotypes related to obesity. The different colors indicate different correlations: blue denotes a negative correlation, whereas red signifies a positive one. Correlations of significance are highlighted with asterisks. : p < 0.05, : p < 0.01, **: p < 0.001. (B) Correlation networks showing associations between the obesity-related gut microbiome and amino acid metabolites. The strength of the correlation is represented proportionately by the width and color of the edges, with red indicating a positive correlation and green indicating a negative one. Only strong (Spearman \|r\| ≥ 0.5) and significant (p < 0.05) correlations were displayed.
	Figure 9. Correlation analysis among the microbiome, metabolome, and transcriptome. (A) Spearman’s correlation between genus-level gut microbiota and differential genes in obesity-related pathways. (B) Conjoint analysis of the metabolome and transcriptome. (C) Schematic of CLSPAJ resistance to the mechanism of high-fat-induced obesity. The strength of the correlation is represented proportionately by the width and color of the edges, with red indicating a positive correlation and blue indicating a negative one. : p < 0.05, : p < 0.01, **: p < 0.001.