Park JY et al. (2023), Alterations in sea urchin (Mesocentrotus nudus)...

ECB-ART-52605

NPJ Biofilms Microbiomes 2023 Oct 31;91:83. doi: 10.1038/s41522-023-00450-z.

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Alterations in sea urchin (Mesocentrotus nudus) microbiota and their potential contributions to host according to barren severity.

Park JY , Jo JW , An YJ , Lee JJ , Kim BS .

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Sea urchins are biotic factors driving the decline of kelp forests in marine ecosystems. However, few studies have analyzed the microbiota of surviving sea urchins in barren regions with scarce diet resources. Here, we analyzed the microbiota in the pharynx and gut of the sea urchin Mesocentrotus nudus located along the coast of an expanding barren region in South Korea. The ecological adaptation of genera in sea urchins was predicted using the neutral assembly model. The pharynx and gut microbiota were different, and microbes in the surrounding habitats dispersed more to the pharynx than to the gut. The gut microbiota in sea urchins is altered by barren severity and plays different roles in host energy metabolism. These findings help to understand the microbiota in sea urchins according to urchin barren and its contribution to the survival of sea urchins in severe barren regions with limited macroalgae.

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	Fig. 1. Sampling sites of sea urchins.Samples were collected from five mild barren regions (grass green circles; A–E) and three severe barren regions (brown circles; F, G, and H). Sampling sites were selected based on the annual report about population dynamics data of sea urchins and the survey report of urchin barren located in the South Korean coast. A, Taean; B, Tongyoung; C, Yeosoo; D, Ulleng do; E, Dokdo; F, Goseong; G, Homigot; H, Gooryongpo.
	Fig. 2. Comparison of microbiota among samples.a Bacterial compositions in the pharynx (n = 49) and gut (n = 49) of sea urchins, seawater (n = 3), and sand (n = 4) were compared using the NMDS plot. The p value was calculated using PERMANOVA. b The diversity of microbiota and bacterial amounts were compared among samples. Bounds of boxes represent the first quartiles (Q1) and the third quartiles (Q3), center lines represent the median values, and the whiskers stretch to 1.5 times. The significance was adjusted using Benjamini-Hochberg false discovery rate (FDR) multiple testing correction with Dunn’s test. q < 0.05, q < 0.01, **q < 0.001. c Composition of the microbiota was compared among samples at the phylum level. Phyla with relative abundance < 1% in every sample group were combined with the “others”. Bar plots show the mean relative abundances of phyla in each group. d Dominant genera in each sample group were compared using the heatmap analysis. Different colors indicate the relative abundance of each genus. e Comparison of fit to the neutral model among samples. The neutral model plots show predicted occurrence frequencies for the pharynx and gut microbiota. Genera detected more frequently than that predicted by the model are shown in light green, while genera detected less frequently than that predicted are shown in blue. Dashed lines represent 95% CIs around the model prediction (gray line). The comparison of dominant genera deviated from the neutral model among samples. Light green circles indicate above the neutral prediction, blue circles indicate below prediction, black circles indicate in the prediction, and light gray circles indicate no genus detected in the sample. f Compositions of predicted functional features at the KEGG 3rd category level were compared among samples in the NMDS plots. g Averages of normalized counts for each KEGG 1st category level were compared among samples. Bar plots show mean ± S.D. The significance was calculated using the Wilcoxon-rank sum test. M Metabolism; GIP Genetic Information Processing; EIP Environmental Information Processing; CP Cellular Processes; OS Organismal Systems; HD Human Diseases; UC Unclassified. h The number of shared functional features between samples was shown in the Venn diagram.
	Fig. 3. Correlations between microbiota and seawater temperature were compared between the pharynx and gut of sea urchins.a The microbiota diversity in the pharynx was positively correlated with seawater temperature, whereas bacterial amounts were not significantly correlated with seawater temperature. Microbiota dissimilarities in the pharynx were correlated with differences in seawater temperature. b The microbiota diversity in the gut was negatively correlated with seawater temperature, whereas bacterial amounts were positively correlated with seawater temperature. Microbiota dissimilarities in the gut were correlated with differences in seawater temperature. c Dissimilarity of algal composition was correlated with differences in seawater temperature. Dissimilarities in the microbiota of the pharynx and gut were correlated with dissimilarities of algal composition. The dissimilarity of composition was based on the Bray-Curtis distance. The correlation and significance were determined using the Spearman correlation analysis.
	Fig. 4. Analysis and comparison of the microbiota and increasing seawater temperature between the pharynx and gut of sea urchins.Bar plots show the difference in microbiota composition according to seawater temperature at the phylum level. Bar plots show the mean relative abundances of phyla in each group. Phyla with relative abundance < 1% in every sample group were combined with the “others”. Smooth line curves show the changes in phyla along with increasing seawater temperature. The shifts of phyla were compared by using the calculation z-score for the relative abundance of each phylum along with seawater temperature. The significance was calculated using the Spearman correlation analysis. Only significantly changed phyla according to seawater temperature were shown in the plot. The heatmap compares significantly changed genera according to seawater temperature between the pharynx and the gut. Color codes for seawater temperature are listed above the heatmap. Clustering was performed using Spearman’s rank correlation. The genera that showed decreasing proportions with an increase in seawater temperature are shown above the black dashed line; the genera with increasing proportions are shown below the dashed line. The significance was calculated after adjusting for sampling site variation using the MaAsLin2. The phylum of each genus is displayed in front of the genus name. P Proteobacteria; B Bacteroidetes; A Actinobacteria; Pl Planctomycetes; V Verrucomicrobia; C Cyanobacteria; L Lentisphaerota; S Spirochaetes; Pe Peregrinibacteria. p < 0.05, p < 0.01, **p < 0.001.
	Fig. 5. Comparison of the microbiota in the pharynx and gut of sea urchins between mild and severe barren regions.The diversity and bacterial amounts (a) in the pharynx and (b) in the gut were compared between mild and severe barren regions. Bounds of boxes represent the first quartiles (Q1) and the third quartiles (Q3), center lines represent the median values, and the whiskers stretch to 1.5 times. The significance was calculated using the Wilcoxon-rank sum test. The taxonomic features and predicted functional features of microbiota (c) in the pharynx and (d) in the gut were compared between mild and severe barren regions in NMDS plots. The p-value was calculated using PERMANOVA. Comparison of fit to the neutral model for (e) pharynx microbiota and (f) gut microbiota between the mild and severe barren regions. The fit to the neutral models was higher in the mild barren region than in the severe barren regions for both the microbiota in the pharynx and gut. The fitness to the model (R2) was higher in the pharynx microbiota than in the gut microbiota in both the mild and severe barren regions. Dashed lines represent 95% CIs around the model prediction (gray line). Light green circles indicate above the neutral prediction, blue circles indicate below the prediction, and black circle indicate in the prediction.
	Fig. 6. Substantially different genera and functional features in the gut microbiota according to barren severity.a Importance of genera in distinguishing the gut microbiota between the mild and severe barren regions. The 12 genera with the most discriminating power were selected using the lowest cross-validation error (inner graph). Relative abundance of selected genera was compared between the mild and severe barren regions in box plots. Bounds of boxes represent the first quartiles (Q1) and the third quartiles (Q3), center lines represent the median values, and the whiskers stretch to 1.5 times. The significance was calculated using the Wilcoxon-rank sum test. p < 0.05, p < 0.01, **p < 0.001. The fit to the neutral models for selected genera was compared between the mild and severe barren regions. An AUROC curve validated the discriminating power of selected genera. The performance during cross-validation and 95% CIs are shown. Overall accuracy was 84.0%. b The volcano plot shows the different functional features of gut microbiota between mild and severe barren regions. Functional features with fold changes > \|1.5\| and p < 0.05 (gray dashed line) were considered significant features. The p-value was calculated using the Wilcoxon-rank sum test. The lowest cross-validation error selected 77 features with the most discriminating power among significant features. An AUROC curve validated the discriminating power of selected features. The overall accuracy was 80.0%. c The contributions of the selected genera to the significant functional features were compared between the mild and severe barren regions. The width of rectangles indicates the frequency of contribution for each genus to gene families in the Sankey diagram.

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