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Microorganisms
2023 Feb 07;112:. doi: 10.3390/microorganisms11020416.
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Gut Microbiota and Metabolites May Play a Crucial Role in Sea Cucumber Apostichopus japonicus Aestivation.
Kang YH
,
Yang BT
,
Hu RG
,
Zhang P
,
Gu M
,
Cong W
.
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The constant increase in temperatures under global warming has led to a prolonged aestivation period for Apostichopus japonicus, resulting in considerable losses in production and economic benefits. However, the specific mechanism of aestivation has not been fully elucidated. In this study, we first tried to illustrate the biological mechanisms of aestivation from the perspective of the gut microbiota and metabolites. Significant differences were found in the gut microbiota of aestivating adult A. japonicus (AAJSD group) compared with nonaestivating adult A. japonicus (AAJRT group) and young A. japonicus (YAJRT and YAJSD groups) based on 16S rRNA gene high-throughput sequencing analysis. The abundances of Desulfobacterota, Myxococcota, Bdellovibrionota, and Firmicutes (4 phyla) in the AAJSD group significantly increased. Moreover, the levels of Pseudoalteromonas, Fusibacter, Labilibacter, Litorilituus, Flammeovirga, Polaribacter, Ferrimonas, PB19, and Blfdi19 genera were significantly higher in the AAJSD group than in the other three groups. Further analysis of the LDA effect size showed that species with significant variation in abundance in the AAJSD group, including the phylum Firmicutes and the genera Litorilituus, Fusibacter, and Abilibacter, might be important biomarkers for aestivating adult A. japonicus. In addition, the results of metabolomics analysis showed that there were three distinct metabolic pathways, namely biosynthesis of secondary metabolites, tryptophan metabolism, and sesquiterpenoid and triterpenoid biosynthesis in the AAJSD group compared with the other three groups. Notably, 5-hydroxytryptophan was significantly upregulated in the AAJSD group in the tryptophan metabolism pathway. Moreover, the genera Labilibacter, Litorilituus, Ferrimonas, Flammeovirga, Blfdi19, Fusibacter, Pseudoalteromonas, and PB19 with high abundance in the gut of aestivating adult A. japonicus were positively correlated with the metabolite 5-HTP. These findings suggest that there may be potential biological associations among the gut microbiota, metabolites, and aestivation in A. japonicus. This work may provide a new perspective for further understanding the aestivation mechanism of A. japonicus.
Figure 1. The effect of temperature on the growth of A. japonicus. (A) Effects of aestivation on the growth of adult A. japonicus (AAJ): AAJRT group, nonaestivating group (growth temperature 15 °C); AAJSD group, aestivation group (growth temperature 26 °C). (B) Effects of different temperatures on the growth of young A. japonicus (YAJ): YAJRT group, growth temperature 15 °C; YAJSD group, growth temperature 26 °C. *: p-value < 0.05; **: p-value < 0.01; ***: p-value < 0.001.
Figure 2. Analysis of the amplicon sequence variants (ASVs) in the gut microbiota of A. japonicus from different groups. (A) Quantitative analysis of shared and specific ASVs of the gut microbiota among different groups. (B) Phylogenetic relationships of species at the genus level; the representative sequences of the top 100 genera were obtained by multiple sequence alignment, and a phylogenetic tree of the representative sequences of the species at the genus level was constructed. The colors of the sectors and branches represent their corresponding phyla, and the stacked bar chart outside the sector ring represents the abundance distribution information of the genus in different samples. (The legend on the left is the sample information and the legend on the right is the classification information at the phylum level corresponding to the species at the genus level.) (C) Top 10 relative abundances of gut microbiota members in each group at the phylum level. (D) Top 30 relative abundances of gut microbiota members in each group at the genus level. (E) Cluster analysis of the species abundance of the gut microbiota at the phylum level. (F) Cluster analysis of the species abundance of the gut microbiota at the genus level.
Figure 3. Analysis of microbial community diversity and differences in the abundance of species among different groups. Analysis of microbial community diversity. (A) Alpha diversity analysis of the gut microbiota (Shannon index). (B) Alpha diversity analysis of the gut microbiota (Simpson index). (C) Beta diversity analysis of the gut microbiota (PCoA, principal coordinates analysis using weighted UniFrac distance). (D) Beta diversity analysis of the gut microbiota (NMDS, nonmetric multidimensional scaling). Analysis of differences in species abundance by LefSe. (E) Histogram of LDA value distribution: the length of the bar graph represents the impact of different species (LDA score). Biomarkers with an LDA score greater than the set value (the default value was 4) are shown. (F) Evolutionary branching diagram: the circles radiating from inside to outside represent taxonomic levels from phylum to genus. Each small circle at a different taxonomic level represents a classification at that level, and the size of the small circle diameter is proportional to the relative abundance size. The species with no significant difference are uniformly colored yellow, and the different biomarker species followed the group to color. The red nodes represent microbial groups that play an important role in the red group, and the green nodes represent microbial groups that play an important role in the green group. If a group is missing in the figure, it indicates that there were no significant differentially abundant species in the group. Species names indicated by English letters are shown in the legend on the right. Different letters in Figure 3A,B indicate that the differences between the groups were statistically significant (Tukey’s test, p-value < 0.05).
Figure 4. Difference analysis of the gut microbiota host cometabolites among different groups. (A) Differences in gut microbiota host cometabolites between the AAJRT and AAJSD groups. (B) Differences in gut microbiota host cometabolites between the YAJRT and YAJSD groups. (C) Differences in gut microbiota host cometabolites between the YAJSD and AAJSD groups. (D) Differences in gut microbiota host cometabolites between the YAJRT and AAJSD groups. (E,F) Clustering heatmap of the differential metabolites, negative ion mode (E), and positive ion mode. (F) Longitudinal is the clustering of samples, horizontal is the clustering of metabolites, and shorter clustering branches represent higher similarity.
Figure 5. KEGG analysis of differential metabolites among different groups (top 20). (A) Results of KEGG analysis of differential metabolites between the AAJSD and AAJRT groups. (B) Results of KEGG analysis of differential metabolites between the AAJSD and YAJRT groups. (C) Results of KEGG analysis of differential metabolites between the AAJSD and YAJSD groups. (D) Results of KEGG analysis of differential metabolites between the YAJRT and YAJSD groups.
Figure 6. Correlation analysis between differentially abundant microbes and differential metabolites. (A) Correlation analysis was conducted based on the Pearson correlation coefficient between the significant differentially abundant genera obtained by high-throughput sequencing analysis of the 16S rRNA gene and the significant differential metabolites obtained by metabolomics analysis (AAJRT vs. AAJSD group). In the figure, the horizontal rows represent the differentially abundant genera, the vertical columns represent the differential metabolites, and the legend on the right is the correlation coefficient. Red indicates a positive correlation, blue indicates a negative correlation, and asterisk (*) marks indicate statistical significance, a p-value < 0.05. (B) Analysis of the association network of the dominant gut microbes in the AAJSD group. Different nodes represent different genera, node size represents the average relative abundance of the genus, nodes of the same clade have the same color (as shown in the legend), the thickness of the line between nodes is positively correlated with the absolute value of the correlation coefficient of species interactions, red lines indicate a positive correlation, and blue lines indicate a negative correlation.
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