Sample collection
The living adults of ascidian (H. roretzi) were collected in four distinct months (January, April, July, and October 2018) that briefly represent four main seasons. No apparent morphological changes among animals from different seasons were observed (Fig. 1A and B). Usually the peritrophic membranes of ascidians formed long stringy shape twist filled with dark fecal materials (red arrow, Fig. 1C). Following starvation, however, most of fecal materials were evaluated; the peritrophic membranes became lighter and slimmer but were covered with more sticky secretions (Fig. 1D-F).
Stool samples were collected to delineate the changes of gut microbiota by season and starvation using 16S rRNA gene amplicon sequencing (Fig. 1G). In order to further understand the host-microbe interaction, gut microbiota in Winter season (January 2018) were isolated for metabolite profiling. Meanwhile, stool samples and ascidian peritrophic tissue samples before (day 0) and after starvation (day 2, 4, and 6) in Winter season were conducted with shotgun metagenomic and transcriptomic sequencing for bacterial and host gene metabolic functional analysis, respectively (Fig. 1G).
Ascidian Gut Microbiota Compared With That Of Marine Environment
We first used 16S rRNA hypervariable V4 region amplicon sequencing to compare the difference of microbial communities between ascidian gut and marine environment. Four seawater samples in each season (n = 16) and five stool samples at each day timepoint of starvation (n = 80) were surveyed, with a total of 4,813,906 high-quality sequences generated from 96 samples (mean ± s.d. of 50,144 ± 9,682). A rarefaction analysis of 20,000 reads per sample clustered short reads into 20,992 amplicon sequence variants (ASVs) that represented 54 bacterial phyla. Among them, 16 phyla were detectable at ≥1% relative abundance in at least one sample (Table S1). Proteobacteria (mean relative abundance of 61.1%) was the most predominant bacterial phylum in the surveyed samples, followed by Bacteroidetes (11.2%) and Firmicutes (6.5%) (Fig. 2A).
As expected, we observed differential bacterial communities between samples from seawater and ascidian peritrophic membranes, as discriminated by a principal coordinate analysis (PCoA) using either UniFrac distances or Bray-Curtis dissimilarities (Fig. 2B and Fig. S1). A permutational multivariate analysis of variance (PERMANOVA) using the adonis2 function in R’s package ‘vegan’ based on unweighted UniFrac distances (mean distance between seawater and stool = 0.0531; p < 0.001) found a more distinct discrimination in microbial community composition when compared with the weighted UniFrac distances (0.0497; p = 0.003) (Figure S1), indicating that the clustering between ascidian gut microbiota and marine microbiota was driven more by the presence/absence of bacterial ASVs (unweighted) rather than the proportion of microbial community members (weighted). For example, a significant increase of the relative abundance of Bacteroidetes and Epsilonbacteraeota were observed in seawater (Fig. 2C, Table S1) whereas Firmicutes was more common in ascidian stool samples (Fig. 2C). When ASVs were summarized at the order levels, Flavobacteriales, Oceanospirillales, Alteromonadales, and Campylobacterales were largely observed in seawater (mean relative abundance > 5%, MWU p < 0.002), while ascidian stool samples were mainly dominated by Xanthomonadales, Rhizobiales, Legionellales, and Bacteroidales (Table S2), indicating the bacterial communities may form the strong niche adaptation. In line with differential compositions and abundances, the microbial community of ascidian stool samples showed higher alpha diversities when compared with the seawater (Fig. 2D and Figure S2).
Ascidian Gut Microbiota Changed By Season And Starvation Stress
In order to elucidate the changes of ascidian gut microbiota by season and starvation stress, we refined the ASV table by excluding the seawater samples. Overall, ascidian gut microbiota was mainly dominated by Proteobacteria (mean relative abundance of 46%, represented by Rhodobacterales, Xanthomonadales, Rhizobiales, and Legionellales), followed by Bacteroidetes (8%, represented by Bacteroidales) and Firmicutes (5%, represented by Clostridiales) (Table S3). A PERMANOVA test using Bray-Curtis dissimilarities based on the ASV table indicated that approximately 54% of variation in microbial community composition could be attributed to season (Df = 3, R2 = 0.359, pseudo F = 18.843, p < 0.001), starvation (Df = 1, R2 = 0.080, pseudo F = 12.609, p < 0.001) and the combination of season and starvation (Df = 3, R2 = 0.103, pseudo F = 5.384, p < 0.001), which was supported by the PCoA analysis that the majority of microbial variability was associated with differences between seasons (Fig. 3A). Similarly, we found significant changes of the alpha diversities of gut microbial communities across season (Fig. 3B) and starvation (Fig. 3C).
The relative abundance analysis of bacterial order revealed that ascidian gut microbiota presented seasonal variation (Fig. 3D and Figure S3, Table S3). For example, Rhizobiales was highly abundant in stool samples collected in January but rarely observed in other seasons (Fig. 4A). Babeliales, Vibrionales, and Xanthomonadales seemed to uniquely form dominant population in April, July, and October, respectively (Fig. 4A). In contrast, the colonization of some bacterial orders might be season-specific. For example, stool samples collected in January and October contained extremely low proportion of Clostridiales and Microtrichales, respectively (Fig. 4A). Bacteroidales and Saccharimonadales were rarely found in Jan/Apr and Jul/Oct, respectively. Interestingly, Xanthomonadales was commonly found in both ascidian stool samples (46.2% vs 0.1%, p < 0.001) and seawater (6.7% vs 0.1%, p < 0.001) collected in October but not in other seasons, implying that gut bacterial transmission from marine environment is possible (Figure S4, Table S4).
Consistent with the decreased alpha diversity of gut microbiota during starvation (Fig. 3C), a number of microbes largely changed in the relative abundances (Figure S5, Table S3). We found 13 bacterial orders prevalently decreased across starvation while another 11 becoming more resistant, with statistical significance in at least one season. As shown in Fig. 4B, for example, Synechococcales and Pirellulales, two predominate gut bacterial orders in aquafarm condition in most of seasons, were dramatically depressed when food and nutrition elements were lacking (mean relative abundance of 9.9% vs 0.4%, q < 0.001; 4.7% vs 0.9%, q < 0.001). In contrast, some rare bacteria in certain seasons, such as Xanthomonadales, Legionellales, Alteromonadales, and Corynebacteriales, became booming in starvation condition.
Functional profile of ascidian gut microbiota based on 16S rRNA amplicon sequencing
Differential gut microbial communities observed between habitats, seasons, and starvation conditions indicates that these factors may enrich for functionally different microbial communities. Hence, we used PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) to predict functional pathways based on the composition of the microbial communities and produced Kyoto Encyclopaedia of Genes and Genomes (KEGG) Orthology (KO) abundance profiles. Results of the summarized KO pathways were supported by spare partial least squares discriminant analysis (sPLSDA) using the first three ordination components that show clustering of samples mainly by seasons and habitats (Figure S6).
Next, we attempted to identify the metabolic functions that discriminated the ascidian gut microbial communities before and after starvation (Fig. 5A). As shown in Table S5, we found 26 up- and 22 down-regulated pathways across starvation, with statistical significance in one season and more. Among them, the functions involving photosynthesis (ko00195, ko00196) and its related biosynthesis (ko00710, ko00906) were dramatically depressed (baseMean > 1000, |log2FoldChange| > 1, q < 0.001) (Fig. 5A), probably a result of the reduced colonization of Synechococcales in starvation condition (Fig. 4B). In contrast, the enrichments of Xanthomonadales and Legionellales in starvation condition might facilitate bile acid biosynthesis (ko00120, ko00121) (Fig. 4B and Fig. 5B). Xanthomonadales and Corynebacteriales might also contribute linoleic acid metabolism (ko00591) and biosynthesis of siderophore group nonribosomal peptides (ko01053). The moderately increased metabolism pathway involving bacterial secretion system (ko03070, baseMean = 25600, log2FoldChange = 0.36, q = 0.001) might explain in part the observation of sticky secretions covering the surface of ascidian peritrophic membranes during the starvation (Table S5). It is worth noting, however, that the limited resolution of partial 16S rRNA gene in discriminating bacterial phylotypes, as well as a possible lack of marine animal PICRUST2 reference microbial genomes may have limited resolution of functional prediction, given the relatively high scores of the weighted Nearest Sequenced Taxon Index (0.17 ± 0.10).
Metabolic Changes Of Gut Microbiome And Host Across Starvation
In order to further understand the host-microbe interaction, ascidian stool samples and the ascidian whole tissues collected in January were conducted for metabolic profiling using high-performance liquid chromatography. Among 37,538 identified metabolites, 1,157 of them could be annotated as known ones using mass spectrometry data (MS2 spectrum) and metabolic reaction network (MRN)-based recursive algorithm (MetDNA) (Table S6). The PCoA analysis based on the abundance of all the identified metabolites clearly discriminated stool samples from the ascidian tissues (Fig. 6A), implying differential metabolic profiles between microbiota and host. We also observed distinct separation of stool samples before (Day0) and after starvation (Day246). However, the metabolic profiles of the ascidian tissues were not significant different upon the starvation. In line with these observations, the differential analysis revealed metabolites with significant differences in abundance between stools and tissues, and between stool samples before and after starvation, but not between tissue samples before and after starvation (Fig. 6B, Table S7). When the abundances of metabolites were visualized in a heatmap, we observed a pattern of metabolites highly expressed in stool samples across starvation when compared with those in aquafarm condition (see green rectangle in Fig. 6C), such as the pathways involving linolenic acid metabolism, methane metabolism, and cyanoamino acid metabolism (Fig. 6D). In contrast, a number of abundant metabolites in aquafarm condition were dramatically depressed (see red rectangle in Fig. 6C), such as phenylalanine metabolism, phenylalanine tyrosine, tryptophan biosynthesis, and D-glutamine and D-glutamate metabolism (Fig. 6D). Some metabolites might be host- or bacteria-specific, given the differentially expressed metabolites between stool and tissue samples. For example, linoleic acid were regarded as production of plants and green algae before [32], but in recent years, bacteria have been identified as linoleic acid producers [33, 34]. Interestingly, limited impact of starvation on regulating metabolites of tissue samples implies that the gut microbiome dysbiosis rather than host may mainly contribute the global changes of metabolites across starvation.
Contribution Of Gut Microbiome In Metabolite Changes
To determine the gut bacterial contributors influencing the changes of metabolic pathways across starvation, we first performed the abundance correlation analysis between the bacteria and stool metabolites, and revealed that many bacteria and metabolites have highly relevance in abundance (Figure S7), suggesting these metabolites were from bacteria. For example, phosphatidylcholine lyso and arachidonate in arachidonic acid metabolism pathway were highly correlative with Rhodobacteriales, Flavobacteriales, vibrionales, and Spirochaetales etc. (Figure S7).
To further reveal the relationship between ascidian gut microbiota and tissue metabolites, we performed transcriptome sequencing (Table S8) for gut tissues of adult H. roretzi and metagenomic sequencing (Table S9) for gut bacteria before and after starvation, respectively, to track the origin of the metabolites identified in tissues and gut bacteria. We calculated the KEGG pathway annotation overlaps among ascidian tissue RNA-Seq data, stool metagenome data, and stool metabolome data. The results showed that eight pathways annotated in tissue metabolome appeared only in stool metagenome annotation but not in tissue RNA-Seq annotation. 56 pathways annotated in both tissue and stool metabolome appeared in stool metagenome annotation but not in tissue RNA-Seq annotation (Figure S8A). Many genes that were responsible for the synthesis and decomposition of metabolites were specifically enriched in gut bacteria but not in ascidian tissues, such as alox15 and beta-carotene 3-hydroxylase (Figure S8B and S8C), indicating that the bacteria are important sources for the gut metabolites.
We next analyzed the involved pathways of gut abundance metabolites and their potential connection with host tissues. Based on above data, the pigment compounds (such as astaxanthin and Xanthophyll), plant-like polyunsaturated fatty acids and esters, hormone signal substance, plant hormones (such as salicylic acid and stearidonic acid), C18 unsaturated fatty acids (such as oleic acid, linoleic acid, and linolenic), phenylalanine, benzoate, salicylic acid, and stearidonic acid were enriched in the gut (Fig. 7A). These gut bacteria-originated metabolites were likely absorbed and played crucial roles on host energy supports, inflammation balancing, and body defense through glucose and lipid metabolism pathways. For example, plant hormones and C18 unsaturated fatty acids are common signaling substances constituting the systemic acquired resistance (SAR) immune system in ascidian gut (Fig. 7A).
Furthermore, we performed the pathway enrichment analysis between Day0 and Day246 stool groups and revealed that unsaturated fatty acid-related metabolism including arachidonic acid and linoleic acid was significantly enhanced (Fig. 7A), suggesting that they might contribute to the regulation of host physiology when ascidian is under the starvation stress.
Moreover, the source bacteria species that produced the metabolites were deduced through the combination with the 16S rRNA sequencing data (Fig. 7B). For example, Rhodobacterales, Xanthomondadales were deduced to be the source bacteria that produced carnitine, cholic acid (CA), and branched-chain-amino-acids (BCAA), contributing to the regulation of glucose and lipid metabolism for energy maintenance; Solirubrobacterales and Rhodobacterales were deduced to be the source bacteria, which produce amino acid for inflammation balancing and systemic immunity in host tissues (Fig. 7B).
Taken together, our data reveal that a large number of metabolites are synthesized in gut bacteria but contribute to host immune, physical and chemical defense, the color of tunic, and maintenance of energy supply of host. The results suggest the existence of the interaction and communication of gut and tissue metabolites, which play a mutual beneficial for both gut bacteria and host physiology and regulation of metabolism.