(2023) 5:54
Yu et al. Animal Microbiome
https://doi.org/10.1186/s42523-023-00276-2

Animal Microbiome

Open Access

RESEARCH

Unveiling the early life core microbiomeof the sea cucumber Apostichopus
japonicus and the unexpected abundanceof the growth‑promoting Sulfitobacter
Juanwen Yu1*, Chunqi Jiang1,3, Ryota Yamano1, Shotaro Koike1, Yuichi Sakai2, Sayaka Mino1 andTomoo Sawabe1*

Abstract
Background Microbiome in early life has long-term effects on the host’s immunological and physiological development and its disturbance is known to trigger various diseases in host Deuterostome animals. The sea cucumber
Apostichopus japonicus is one of the most valuable marine Deuterostome invertebrates in Asia and a model animal
in regeneration studies. To understand factors that impact on host development and holobiont maintenance, hostmicrobiome association has been actively studied in the last decade. However, we currently lack knowledge of earlylife core microbiome during its ontogenesis and how it benefits the host’s growth.
Results We analyzed the microbial community in 28 sea cucumber samples from a laboratory breeding system,
designed to replicate aquaculture environments, across six developmental stages (fertilized eggs to the juvenilestage) over a three years-period to examine the microbiomes’ dynamics and stability. Microbiome shifts occurred
during sea cucumber larval ontogenesis in every case. Application of the most sophisticated core microbiome extraction methodology, a hybrid approach with abundance-occupancy core microbiome analyses (top 75% of total reads
and > 70% occupation) and core index calculation, first revealed early life core microbiome consisted of Alteromonadaceae and Rhodobacteraceae, as well as a stage core microbiome consisting of pioneer core microbe Pseudoalteromonadaceae in A. japonicus, suggesting a stepwise establishment of microbiome related to ontogenesis and feeding
behavior in A. japonicus. More interestingly, four ASVs affiliated to Alteromonadaceae and Rhodobacteraceae wereextracted as early life core microbiome. One of the ASV (ASV0007) was affiliated to the Sulfitobactor strain BL28
(Rhodobacteraceae), isolated from blastula larvae in the 2019 raring batch. Unexpectedly, a bioassay revealed the BL28
strain retains a host growth-promoting ability. Further meta-pangenomics approach revealed the BL28 genome reads
were abundant in the metagenomic sequence pool, in particular, in that of post-gut development in early life stagesof A. japonicus.
Conclusion Repeated rearing efforts of A. japonicus using laboratory aquaculture replicating aquaculture environments and hybrid core microbiome extraction approach first revealed particular ASVs affiliated to Alteromonadaceae
and Rhodobacteraceae as the A. japonicus early life core microbiome. Further bioassay revealed the growth promoting*Correspondence:
Juanwen Yu
juanwen.yu@fish.hokudai.ac.jp
Tomoo Sawabe
sawabe@fish.hokudai.ac.jp
Full list of author information is available at the end of the article© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/.

Yu et al. Animal Microbiome(2023) 5:54

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ability to the host sea cucumber in one of the core microbes, the Sulfitobactor strain BL28 identified as ASV0007.
Genome reads of the BL28 were abundant in post-gut development of A. japonicus, which makes us consider effective probiotic uses of those core microbiome for sea cucumber resource production and conservation. The study
also emphasizes the importance of the core microbiome in influencing early life stages in marine invertebrates.
Understanding these dynamics could offer pathways to improve growth, immunity, and disease resistance in marine
invertebrates.
Keywords Core microbiome, Apostichopus japonicus, Larval ontogenesis, Host-microbe interaction, Metapangenomics

Background
Early life microbiome has been intensively studied inrecent decades since the establishment of gut microbiota
has long-term effects on immunological and physiological development [1]. Diseases including obesity, diabetes,
inflammatory bowel disease, cancer, allergies, asthma,and neurological diseases are all linked with gut homeostasis during early life [2–4]. The dynamic changes in
infants’ microbiome during the first year after birth are
influenced by delivery mode and feeding, indicating earlylife gut microbiome gradually develops and matures toadult-like microbiomes with core bacteria Firmicutes
and Bacteroidetes, followed by Actinobacteria and Proteobacteria [5]. Core microbiome is defined as a groupof microbes that are shared within host populations,which are particularly essential to the host biological
functions such as health maintenance, disease resistanceand nutrient intake [6, 7]. With the development of studies in host-associated microbiome, the definition of coremicrobiome has evolved to a more complicated andadvanced explanation: common core, temporary core,ecological core, functional core and host-adapted coremicrobiome [8]. The established core microbiome isused as a reference to identify diseased organisms and an
indicator of good health [9, 10]. In the marine environment, research has identified a core microbiome withinaquatic organisms that is influenced by geographic location, climate change, and seasonal changes [11–14]. This
core microbiome plays a crucial role in maintaining thehealth of the host organism and facilitating adaptations
to its environment [11–14]. However, our knowledge ofcore microbiome during host ontogenesis and microbial
establishment is very limited.
Sea cucumber A. japonicus (Selenka, 1867), belonging
to Echinodermata, Holothuroidea, is one of the most valuable marine invertebrates in Asia for its biological factors, economic benefits and phylogenetic position [15].
Since the exclusive application of A. japonicus in the food,cosmetic and medical industries, A. japonicus has beenplaced on the red list of endangered species due to theover-exploitation of wild sea cucumber [16]. Increasingstudies investigating factors impact on sea cucumber’saquaculture include animal density, culture condition,
diets, genetic factors and host-associated microbiome,in which the study on gut microbiome of sea cucumber give us new insights into how microbes contributeto its growth, immunity and disease [17–20]. For example, the probiotic dietary supplements of Pseudoalteromonas elyakovii HS1, Shewanella japonica HS7, andVibrio tasmaniensis HS10 can improve growth, immune
responses and survival rate of sea cucumber [21]. Bacillus subtilis T13, as a dietary probiotic, is able to decrease
the infection of Vibrio splendidus, which highly improved
the resistance to V. splendidus related diseases such as
skin ulceration syndrome [16, 22]. Comparison of individual sea cucumber with different growth gap revealed
Rhodobacteraceae related with polyhydroxybutyrate(PHB) metabolism gene in A. japonicus are significantly
abundant in larger individuals, suggesting the Rhodobacteraceae may benefit to host growth [23]. In addition, studies on the re-assembly of the gut microbiome
after evisceration emphasize host-associated microbiome
plays an important role in gut regeneration and functional recovery [24, 25]. Previous study on the establishment of early life microbiome during larval development
in sea cucumber revealed dynamic changes of microbiota through seven developmental stages: fertilized egg(FE) and blastula (BL), gastrula (GL), early auricularia(EA), late auricularia (LA), pentactula (PT) and juvenile(JN), which is probably related to larval ontogenesis [26].
However, due to the limitation of sample size in laboratory settings and microbial functional analysis, the earlylife core microbiome and their related functions in host
biology are still poorly understood.
Meta-pangenomics is a novel approach combining
pangenomes and metagenomes to investigate bacterial genomes recruited reads in their environmental
metagenomes, providing broader perspectives of understanding the roles of its distribution and biogeographical patterns through the microbial population [27, 28].
Meta-pangenomics have been applied to investigate thehuman microbiome, revealing the distribution of identical genes in pangenomes across populations in different locations and ages [28, 29]. The adaptation of

Yu et al. Animal Microbiome(2023) 5:54

microbial populations in extreme environment fromdeep sea microbiome also has been investigated by metapangenomics [30, 31]. However, there has been no study
applied meta-pangenomics on the distribution of hostassociated microbes in host microbiome during early life
development.
Here, we performed metagenomic analysis to assess the
dynamics of microbiome during the larval ontogenesis insea cucumber and to detect the core microbiome in earlylife stages. We also applied meta-pangenomics to underscore how specific bacteria and genes are distributed indifferent developmental stages, and discovery probiotic
bacteria promote host growth from early stages. Our
results demonstrate the detection of early life core microbiome and stage core microbiome in A. japonicus using
an abundance-occupancy core microbiome analysis,
which is significantly related to ontogenesis and feeding
behavior, suggesting a stepwise established microbiome
associated with the A. japonicus host.

Results
The dynamics of microbial diversity and composition
during larval development

A total of 2,337,684 Meta16S sequence reads wereobtained from 28 sea cucumber and 26 seawater samples
collected from six stages of FE, GL, EA, LA, PT and JN,
in 2019, 2020 and 2021. Reads passed quality control andremoved eukaryotic reads (e.g., mitochondria and chloroplast) were used for microbial diversity analyses andtaxonomic assignments; 465,273 and 599,246 qualified
reads were generated from sea cucumber samples andseawater samples, respectively.
In order to understand the fluctuations of microbialcommunities during sea cucumber’s larval development, a microbial diversity analysis was performed.
Although alpha diversity based on evenness significantly changed within developmental stages, the initialstages before gut development (FE to EA) are generally
higher than later stages, especially increasing at the FE
and GL stages (Fig. 1a). Unweighted and weighted UniFrac distance analysis revealed the microbial community changed along with the larval developmental stages

Page 3 of 22(Fig. 1b and Additional file 2: Fig. S6). In particular, themicrobiota is significantly different between the EA and
LA stages (p < 0.01), indicating microbiota changes before
and after the gut developed at that point (Fig. 1c–e andh). Additionally, it is worth noting that the microbiota
compositions of larvae from different years fluctuated, as
the results of beta diversity analyses (Fig. 1f and g, andAdditional file 2: Fig. S6). Following the gut development,
we observed a decrease in alpha diversity alongside anincrease in beta diversity of the microbiotas (Fig. 1a andb).
The microbial composition of early life sea cucumber
at different stages

A total of 2,237 ASVs from sea cucumber samples was
obtained and further assigned to 93 orders, 152 families and 332 genera using a similarity threshold of 99%
sequence identity. On order-level taxonomic rank, Alteromonadales (31.2 ± 23.3%) ([mean ± SD]) and Rhodobacteriales (22.9 ± 17.9%) are the most dominant bacterial
taxa in early life stages, followed with Oceanospirillales(12.8 ± 17.1%) and Flavobacteriales (7.4 ± 8.3%) (Fig. 2aand b, and Additional file 1: Table S1.
On family level, Rhodobacteraceae (22.9 ± 17.9%)
and Alteromonadaceae (22.0 ± 19.6%) were the mostdominant bacterial taxa during larval development, followed by Nitrincolaceae (7.7 ± 13.6%), Flavobacteraceae(6.2 ± 7.9%), Marinobacteriaceae (3.0 ± 3.5%) and Methylophagaceae (2.8 ± 6.2%). Before gut development, Pseudoalteromonadacea was significantly abundant withrelative abundance of 9.9 ± 11.3% but rare in later stages
with 0.2 ± 0.4%. Besides, Colwelliaceae, Fusobacteriaceae,
Saccharospirillaceae and Moraxellaceae and Idionarinaceae were also prevalent in initial stages and decreased
after gut development. In later stages, Microbacteriaceae
was significantly increased from below detectable limit to
0.3 ± 0.3% before LA stage (Figs. 2c and 3). Furthermore,
specific bacterial taxa at the family level that exhibited
significant increases in abundance during specific developmental stages were observed (Figs. 2d and 3). Pseudoalteromonadacea, Colwelliaceae and Shewanellaceae
were significantly abundant at FE stage; Vibrionacaeae,(See figure on next page.)
Fig. 1 Alpha and beta diversity among sea cucumber samples. a Box-plot based on evenness index (Kruskal-Wallis, p < 0.05). b Unweighted
UniFrac distance plot between sea cucumber larvae at different developmental stage (PERMANOVA, p < 0.05, q < 0.05). FE: Fertilized Egg; GL:
Gastrula; EA: Early Auricularia; LA: Late Auricularia; PT: Pentactula; JN: Juvenile. c PCoA plot based on Bray–Curtis distance and d PCoA plot basedon Unweighted UniFrac distance. Different colors represent different years; closed circles represent larvae samples before gut development,
triangles represent larvae samples post gut development. e NMDS (non-metric multidimensional scaling) ordination based on Bray–Curtis distanceshowing the differences of microbiome both pre and post gut development. preGD: pre gut development; postGD: post gut development. f
NMDS ordination based on Bray–Curtis distance showing the differences of microbiome between years. g Unweighted UniFrac distance plot
between different years ((PERMANOVA, p = 0.001, q < 0.05) and h Unweighted Unifrac distance plot between sea cucumber larvae before and postgut development (PERMANOVA, p = 0.001, q < 0.05)

Yu et al. Animal Microbiome(2023) 5:54

Fig. 1 (See legend on previous page.)

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Yu et al. Animal Microbiome(2023) 5:54

Alcanivoracaceae and Saccharosorollaceae were significantly abundant at the GL stage; Rhodobacteraceae
and Stappiaceae significantly increased at the LA stage;
Bdellovibrionaceae and Nannocystaceae significantly
increased at the PT stage; Hyphomonadaceae and Saprospiraceae were significantly abundant at the JN stage.
The impact of environmental microbiome on the dynamics
of larval microbiome

In order to understand if the rearing seawater is related
to the dynamics of microbiome during larval development, the microbial structure and composition of rearing
seawater were also assessed. Even though 64.2% bacterial taxa were shared between the rearing seawater andsea cucumber, the beta diversity based on unweighted
and weighted UniFrac distance indicated the microbiota
was significantly different between sea cucumber larvae and their rearing seawater (PERMANOVA, p < 0.01)(Fig. 4a–d and Additional file 2: Fig. S6). Particularly,
SAR11 clade with relative abundance of 9.6 ± 20.0% wassignificantly dominant in rearing seawater but very rare
in sea cucumber. SAR86 clade and Rickettsiales were alsosignificantly abundant in rearing seawater with relative
abundance of 0.2 ± 0.4% and 0.6 ± 0.9% but not prevalent
in sea cucumber (Fig. 5a and b, and Additional file 1:
Table S1). On a family level, 189 (60.8%) bacterial taxa
were shared between rearing seawater and sea cucumber, however, 71 and 51 bacterial taxa were only detected
in rearing seawater and sea cucumber, respectively. In
which, some significant differences were also observed(Figs. 4a and 5c). Such as SAR11 Clade I (7.8 ± 16.0%),
Clade II (1.6 ± 4.1%) and Litoricolaceae (1.5 ± 2.9%)
were significantly abundant in rearing seawater but notdetected or very rare in sea cucumber (Fig. 5c and Additional file 1: Table S2). The same tendency could also be
observed in Sphingomonadaceae, Pseudohongiellaceae,
Methylophilaceae and Nitratiruptoraceae (relative abundance in seawater > 0.1%).
Since the microbial composition of seawater is partly
different from those to that of sea cucumber, we were
speculating if the key bacteria taxa, which changed
significantly along with the larval development, alsochanged in seawater. These bacteria taxa were evaluated

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to reveal whether the dynamics of microbiome in seawater is related to larval microbiome. Six bacterial taxa
at order level in seawater were commensurate with thechanges in sea cucumber larval microbiome, Alteromonadales (57.5 ± 34.9%) was significantly abundant before
gut development and then decreased to 19.4 ± 18.5% inlater stages, whereas Rhodobacterales (5.4 ± 8.0%), Exiguobacterales (0.2 ± 0.3%), Micrococcales (1.7 ± 1.2%),
Rickettsiales (1.0 ± 1.0%) and Blfdi19 (0.1 ± 0.1%) significantly increased in later stages (Figs. 2a and 5d).
Moreover, SourceTracker analysis further unveiled the
proportions of sources contributing to the compositionof the larval microbiome (Additional file 2: Fig. S3). In
both pre- and post-gut development stages, the influence of seawater accounted for 52% and 58% of the larval
microbiome, respectively, whereas factors of unidentified
origin affected 41% and 28% of the larval microbiome,
respectively. These results underscore the impact of environmental seawater on the larval microbiome to a moderate extent, concurrently hinting at the involvement ofother unidentified factors that drive alterations in thelarval microbiome. According to the above findings, it
becomes apparent that seawater alone might not solely
determine the composition of the sea cucumber larval
microbiome.
In addition, feeding is also considered as a possible factor that may affect microbiome of rearing seawater followed with the change larval microbiome, especiallyafter gut development. At LA stage when feeding process
started, bacteria taxa Marivita, Kordia and Oceanicaulis
were significantly enriched in rearing seawater may influenced by input diets, which could explain the significant
increase on the genus Kordia and Marivita at LA stage
in sea cucumber larvae. However, other significantly
increased bacteria in rearing seawater after diets induced
were likely not related with the changes in larval microbiome at each developmental stage (Fig. 2e).
Early life core microbiome and stage‑core microbiome
during larval development

Since the dynamics of microbial structure and significantly prevalent bacteria were encountered during larval
development, we are wondering whether there is core

(See figure on next page.)
Fig. 2 The linear discriminant analysis effect size (LEfSe) analysis of microbial abundance among sea cucumber larvae samples at different
developmental stage. a Taxa until order level with significant differences pre- and post-gut development were detected by LEfSe analysis
with a LDA threshold score of 3.5 and a p-value of 0.05. b The cladogram of detected prokaryotic taxa for microbial community pre- and post-gut
development. c Only family level taxa with significant differences pre- and post-gut development were detected by LEfSe analysis with a LDA
threshold score of 3.5 and a p-value of 0.05. d Only family level taxa with significant differences at each developmental stage were detectedby LEfSe analysis with a LDA threshold score of 3.5 and a p-value of 0.05. e Only genus level taxa with significant differences at each developmental
stage were detected by LEfSe analysis with a LDA threshold score of 3.5 and a p-value of 0.05

Yu et al. Animal Microbiome(2023) 5:54

Fig. 2 (See legend on previous page.)

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Yu et al. Animal Microbiome(2023) 5:54

microbiome shared during the early life of sea cucumber larvae. To define the core microbiome in our study,
we employed a hybrid approach based on abundance
and occupancy-based core microbiome analyses withcore index (CI) calculation [32]. Specifically, we set up
thresholds that collectively represented 75% of the totalfrequency (i.e., core taxa assigned to all taxa accounting
for a substantial portion of the top 75% of total reads) andexhibited a presence in at least 70% of the total samples
analyzed for abundance and occupancy-based analyses,
respectively (Fig. 6a) [7]. For CI, we calculated CI in the
top 20 most abundant families across all samples and thenormalized index (NorCI) according to Zhang et al. [32].
This allowed us to extract more reliable core microbiome
during the initial phases of larval development. Notably,families such as Alteromonadaceae and Rhodobacteraceae, exhibiting a > 0.3 of NorCI, were detected in over
93% of larval samples and collectively constituted 42.1%of the microbiome (Fig. 6b). These taxa were thereby
identified as the early life core microbiome during the sea
cucumber larval developmental stages.
Abundant taxa including Vibrionaceae, Pseudoalteromonadaceae and Alcanivoracaceae were also designatedas the initial stage core microbiome (Additional file 2:
Fig. S4). Conversely, Nitrincolaceae were recognized aslater stage core microbiome constituents (Additional
file 2: Fig. S4). Noteworthy bacterial families, such as
Pseudoalteromonadaceae, Colwelliaceae, and Shewanellaceae, demonstrated significant enrichment at the FE
stage through LEfSe analysis, while three additionalfamilies exclusively appeared at the FE stage, Idiomarinaceae, Fusobacteriaceae, and Moraxellaceae, and were
subsequently subjected to CI analysis for pioneer coremicrobiome determination. Among these, Pseudoalteromonadaceae with > 0.3 NorCI were defined as pioneer
core microbes (Figs. 2d and 6). Similarly, the JN stage
exhibited significantly enriched bacterial families, Hyphomonadaceae and Saprosiraceae, along with two exclusive
families, Methyloligellaceae and Phycisphaeraceae, all ofwhich were selected for CI analysis (Figs. 2d and 6). In
this context, Methyloligellaceae with > 0.3 of NorCI were
designated as the juvenile core microbiome.
To gain deeper insights into the core microbiome contributing to host physiology, we conducted amore detailed analysis at both the genus and amplicon

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sequence variant (ASV) levels. Utilizing abundance andoccupancy-based analyses, we identified the genera
Sulfitobacter, Marinobacter, Kordia, and Alteromonas ascore members at ASV levels (Additional file 1: Tables S3and S4, Additional file 2: Fig. S2). The NorCI further validated ASV level core microbiome definition. Core microbiome ASV0004, ASV0005, ASV0007, and AS0010 weredetected in fertilized eggs, gastrula and early auricularia,
which were animal stages in pre-gut-development before
start-feeding with a diatom (Additional file 2: Fig. S2).
Meta‑pangenomics of growth promoting bacteria
Sulfitobacter BL28

Given that the results revealed the presence of universal
core microbiome associated with larval developmental
stage, it was of interest to investigate whether there were
specific bacteria playing a vital role in the host biology
during the early life stage. To enhance our understanding, we used bacterial isolates with sequences matched to
key ASVs isolated from the 2019 rearing batch for testing host sea cucumber growth promotion [26] (Fig. 7aand Additional file 2: Fig. S2). Remarkably, laboratory
experimental feeding tests revealed that BL28 showedapparent growth promotion of host sea cucumber,which was strongly supported based on Bayes factors­(BF10 = 654.777) (Fig. 7b). The complete genome sequencing revealed that Sulfitobacter strain BL28 possesses the
PHB metabolism gene cluster (Additional file 2: Fig. S1).
To further investigate the role of Sulfitobacter during
the larval development stage, the complete genome of
BL28 and 23 metagenomes obtained in this study, as
well as three reference genomes collected from NCBI,
were used for meta-pangenomic analyses (Fig. 8).
Firstly, a pangenome using four genomes of genus
Sulfitobacter was constructed, which consists of 7,
695 gene clusters with 16,463 genes, in which 7, 915
genes were recognized as core genes of Sulfitobacter
pangenome and 817 genes were identified as uniquegenes of BL28 (Fig. 8A). The functional annotation for
unique genes in BL28 showed that these unique genes
were related to functions of amino acid biosynthesis,
fatty acid biosynthesis, carbohydrate transport andmetabolism, and cobalamin/B12 biosynthesis. Then,the pangenome was linked into the 23 environmental
metagenomes, which were divided into two groups,(See figure on next page.)
Fig. 3 Family-level taxonomic distribution among sea cucumber and seawater samples. a Family-level taxonomic distribution among sea
cucumber larvae at different developmental stage. Bars represent the relative percentage of each bacterial family. FE: Fertilized Egg; GL: Gastrula; EA:
Early Auricularia; LA: Late Auricularia; PT: Pentactula; JN: Juvenile. b Family-level taxonomic distribution among rearing seawater of sea cucumber
larvae at different developmental stage. FESW: Fertilized egg rearing seawater; GLSW: Gastrula rearing seawater; EASW: Early Auricularia rearing
seawater; LASW: Late Auricularia rearing seawater; PTSW: Pentactula rearing seawater; JNSW: Juvenile rearing seawaterYu et al. Animal Microbiome(2023) 5:54

Fig. 3 (See legend on previous page.)

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Fig. 4 Microbiota shared within sea cucumber and seawater samples. a Venn diagram depicting unique and shared bacteria orders and families
among sea cucumber larvae and their rearing seawater. b Boxplot based on unweighted UniFrac distance of atrophy larvae and their rearing
seawater (PERMANOVA, p < 0.05, q < 0.05). Scale represents similarity within samples. c PCoA plot based on Unweighted UniFrac distance. Colors
represent different developmental stages. Closed circles represent larvae samples and open circles represent seawater samples. d NMDS ordination
based on Bray-Curtis distance showing the differences of microbiome between sea cucumber larvae and seawater

pre-gut development (preGD) and post-gut development (postGD), by checking each gene’s median coverage across the metagenomes. Results showed that the
total coverage of each Sulfitobacter genome in postGD

environments (average 7.7) was significantly higher
than preGD environments (average 0.8), in particular, the highest coverage was identified in the genomeof BL28 in postGD (around 20). Meanwhile, the core

(See figure on next page.)
Fig. 5 The linear discriminant analysis effect size (LEfSe) analysis of microbial abundance among sea cucumber larvae samples and their rearing
seawater. a Taxa with significant differences in larvae and rearing seawater were detected by LEfSe analysis with a LDA threshold score of 3.5
and a p-value of 0.05. b The cladogram of detected prokaryotic taxa for microbial community of larvae and rearing seawater. c Only family level taxa
with significant differences in larvae and rearing seawater detected by LEfSe analysis with a LDA threshold score of 3.5 and a p-value of 0.05. d Taxa
with significant differences in rearing seawater pre- and post-gut development were detected by LEfSe analysis with a LDA threshold score of 3.5
and a p-value of 0.05

Yu et al. Animal Microbiome(2023) 5:54

Fig. 5 (See legend on previous page.)

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Fig. 6 Detection of early life core microbiome and stage core microbiome. a Heatmap of core microbiome and stage core microbiome. Scale
represents relative abundance. Scale represents relative abundance. Legend bar shows the sample developmental stage and gut development
status. Stage shows the samples collected from different developmental stage. Dark yellow: fertilized egg, purple: gastrula, turquoise: early
auricularia, dark green: late auricularia, pink: pentactula, and blue: juvenile, respectively. GutDev shows the gut development status of samples, lightyellow represents samples before gut developed and light green represents samples post gut developed. FE: Fertilized Egg; GL: Gastrula; EA: Early
Auricularia; LA: Late Auricularia; PT: Pentactula; JN: Juvenile. GutDev shows the gut development status of samples, light yellow represents samples
before the gut developed and light green represents samples post-gut development. b CI for core bacteria (at the family level) in the sea cucumber
larvae and NorCI, the normalized core index

Yu et al. Animal Microbiome(2023) 5:54

genes of pangenome and unique genes of BL28 wereidentified as environmental core genes (ECGs) since
they were covered in both preGD and postGD environments, which means the origination of BL28 environmental and further on/in hosts. Furthermore, eachgene of not only BL28 but also the other three genomesof Sulfitobacter showed higher coverage in postGD
compared to preGD environments, which may explain
the abundant taxa in the later stage (Fig. 8B). The present findings suggest that Sulfitobacter BL28 may havea specific function in the early stages of larval development and host growth in sea cucumbers than the others. Additionally, BL28 belongs to Rhodobacterales may
be particularly influential in promoting host growthfrom the initial stages in sea cucumber.
Functional profile of early life core microbiome
and stage‑core microbiome

To better understand the role of the microbiome anddetermine how the functional capacity of the larval
microbiome develops during early life stages, microbial functions at different developmental stages wereanalyzed. The top 10 core microbial functions are respiration, based subsystems, carbohydrates, amino acids
and derivatives, protein metabolism, miscellaneous,cofactors, vitamins, prosthetic groups, pigments, RNAmetabolism, DNA metabolism and membrane transport, in which the functions related to respiration is relatively higher at the FE stage (Fig. 9a and b). Whereas
after gut development, feeding behavior and nutrient
intake enriched functions belong to amino acid metabolisms, such as Glutamine and Asparagine biosynthesis, Cysteine biosynthesis, Histidine Biosynthesis andGlycine and Serine utilization, significantly increasein later stages. Pyruvate metabolism, sugar utilization
and Glycolysis and Gluconeogenesis related to fats,proteins and carbohydrates metabolisms also increasein later stages after the feeding process. In addition,some metabolisms which are essential for carbon andenergy intake, such as Polyhydroxybutyrate metabolism and Peptidoglycan biosynthesis, are enriched aftergut development. The functional capacity of larval

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microbiome also varies from the developmental stage
and developed with the organogenesis and feeding
behavior during microbiome establishment.

Discussion
Core microbiome plays a crucial role in maintaining
normal host development and host health and facilitating adaptations to its environment [11–14]. However, ambiguous definition of core microbiome remains
an obstacle to discovering beneficial microbes to hostorganisms, in particular, in marine invertebrates whichshare evolutionary history with humans and contribute to fisheries and aquaculture industries. Our study
adopted a sophisticated abundance and occupancy-based
core microbiome mining approach with 75% representation of total reads abundance and > 70% occupationthresholds [7] and further supported by core index [32].
Moreover, we expanded our fertilized egg sample diversity obtained from different years and sites, aiming tofind a robust core microbiome unaffected by temporaland spatial variations, which is a less-explored aspect inprevious core microbiome research. This approach successfully revealed ASV0007, which is affiliated to thehost growth-promoting S. pontiacus strain BL28 isolated from A. japonicus blastula, as one of the cores of
early life sea cucumber A. japonicus. Furthermore, ourpioneering use of meta-pangenomics examined specificbacteria-host associations in the early life microbiome of
28 sea cucumber larval samples, shedding light on their
metabolic roles in marine invertebrates. The study also
highlighted the power of meta-pangenomics in understanding bacterial contributions to host physiology during early life development.
Sea cucumber (Echinodermata, Holothuroidea) is amember of the Deuterostomia clade, which also includes
humans and other mammals. The shared ancestry of sea
cucumbers and humans in the early deuterostome lineage underscores the significance of comprehending theevolutionary history and origin of Deuterostomia. The
dynamics of gut microbiome in early life infants, miceand other marine organisms has been investigated intensively in recent years and is mainly shaped by host genetics, feeding mode, early environmental exposure, and(See figure on next page.)
Fig. 7 Growth promoting ability of Sulfitobacter sp. BL28. a A phylogenetic tree based on ASVs having > 500 reads with relative abundance.
Inner circles represent taxonomic analysis at genus level. Red colored heatmap represent relative abundance of ASVs in sea cucumber samples.
FE: Fertilized egg; GL: Gastrula; EA: Early auricularia; LA: Late auricularia; PT: Pentactula; JN: Juvenile. Star represents the key feature at different
developmental stage. Red: fertilized egg; yellow: stages before gut developed; green: late auricularia; purple: stages post gut development;orange: Sulfitobacter. b Growth performance of juvenile sea cucumber with diet supplementary BL28. Bayes t-test with independent samples
was performed using the JASP version 0.17.0 and growth with BL28 > those of control was set as an alternative hypothesis (H1), respectively.
Alternative hypothesis was more likely to be occurred by 655 folds, respectively (n = 15). The error bar indicates standard errorYu et al. Animal Microbiome(2023) 5:54

Fig. 7 (See legend on previous page.)

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Yu et al. Animal Microbiome(2023) 5:54

Page 14 of 22

Fig. 8 Meta-pangenomic analysis of Sulfitobacter sp. reveals its coverage in microbiota before and post gut development. a Pangenomics
of Sulfitobacter sp. indicated the unique and core genes in BL28, and the higher total genome coverage in the post gut development. The
inner radial dendrogram shows the gene clusters in the pangenome, clustered by presence/absence across genomes. The four genomesof Sulfitobacter strains are plotted on the innermost four layers, spaced to reflect discernable groups based on genomic composition. The genome
pointed in orange is BL28 isolated from sea cucumber microbiome. Gene clusters within a given genome are filled in with black or dark orange;
gene clusters do not present remain unfilled or light orange. Core gene across the Sulfitobacter strains were pointed in red and unique genedetected in BL28 were pointed in orange. Above the genome content summaries, each genome’s median coverage across larval metagenomes
with different gut development status is shown in the colored bar graph. ANI value of genome is shown in the heatmap above, where each row
represents a different sample, and cell color intensity reflects the ANI value. The colored two layers show the proportion of genes within each genecluster determined to be environmental accessory or core genes: EAGs (green) and ECGs (blue) with 23 metagenomes. b Mean coverage of eachgene in the pangenome of Sulfitobacter sp. within two developments indicated higher coverage in metagenomes of post-gut development
environment. Outer layers show the four genomes of Sulfitobacter spp.and the coverage in environmental metagenomes. PGD, post gut
development; BGD, before gut development(See figure on next page.)
Fig. 9 Microbial function analysis of larvae among different developmental stages. a Distribution of microbial functions among larvae samples
before and post gut developed. Bars represent the relative percentage of each microbial functions. b Dynamics of significantly changed microbial
functions pre- and post-gut development. Scale represents relative abundance. Legend bar shows the sample developmental stage and gut
development status. Stage shows the samples collected from different developmental stages. Dark yellow: fertilized egg, purple: gastrula, turquoise:
early auricularia, dark green: late auricularia, pink: pentactula, and blue: juvenile, respectively. GutDev shows the gut development status of samples,light yellow represents samples before gut developed and light green represents samples post gut developedYu et al. Animal Microbiome(2023) 5:54

Fig. 9 (See legend on previous page.)

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Yu et al. Animal Microbiome(2023) 5:54

birth mode, which has profound effects on the immune
system, resistance to pathogens and absorption of nutrients [33–35]. Our study revealed the dynamics of larval
microbiome during early colonization in sea cucumber
related to host development, feeding behavior andenvironmental factors: (1) there is a significant change
occurring at the late auricularia stage when the gut developed and feeding started; (2) bacteria that significantly
increased at specific stages were detected; (3) microbiome of environmental rearing seawater is significantly
different to larval microbiome; (4) temporal dynamics of
key ASV using the growth-promoting Sulfitobacter strain
BL28 genome. Studies in newborns revealed a large alteration in gut microbiota occurred during the transition in
the diet from milk to solid food, in which the dominantbacteria Bifidobacteria and Lactobacillus shift to Bacteroides and Firmicutes [1]. The change in juvenile diets
also altered the gut microbiome especially reducing therelative abundance of Muribaculum intestinale in mice[3]. Other studies of larval microbiome during early life
stages of lake sturgeon and catfish indicate dynamic relationships between the gut microbiota composition andhost ontogenesis such as gastrointestinal physiology [36,37]. According to these studies, the significantly changed
microbiome at the late auricularia stage after feeding isprobably related to diets, development of gastrointestinal (GI) tracts and maturation of microbial functions
for nutrition intake. Interestingly, we also demonstrated
significantly increased bacteria at specific developmental
stages indicating not only feeding behavior impacts on
the larval microbiome, but also ontogenesis and metamorphosis shifts in the bacterial community.
Research in microbial colonization in infant revealed
the establishment of microbiome during early life are
essential to immune development and neurodevelopment
and is related with diseases including IBD (Inflammatory
Bowel Disease), obesity and asthma [2, 38]. As the pioneer microbiome is considered a crucial period for host
biological development, extensive research has been dedicated to studying its composition, colonization mechanisms, and the various factors influencing this process[39]. Studies of Amphibian larvae have demonstrated thatmicrobial colonization at hatching is essential for composition and function of the microbiome, which similar to
the effects of birth mode on the human microbiome [40].
Pseudoalteromonas has been reported as potentially promoting larval settlement and metamorphosis in marine
invertebrates through production of inductive cues consisting of molecular domains of polysaccharides, proteins,and lipids [41]. Pseudoalteromonas (Pseudoalteromonadacea) was extracted as a member of the pioneer core ofsea cucumber microbiome. These significantly enrichedpioneering bacteria on fertilized eggs are probably related

Page 16 of 22

to the metamorphosis of fertilized eggs in later developmental stages. Three members belonging to initial stage
core microbiome Vibrionaceae were also regarded as coremicrobiome in early larval stages of Pacific blue shrimp,
indicating these bacteria may play an important role inearly life development and establishment of microbiotas[42]. The stage core microbiome during the sea cucumber larval development provides new insights into interactions of host microbiome and its physiology including
ontogenesis and metamorphosis.
Core microbiomes are the key microbes shared acrosshost populations and contribute to their host function
and fitness, which is one of the best ways to understandthe microbial composition and host-microbe interactions
in early life stages [6–8]. In this study, we demonstrated
early life core microbiome and stage-core microbiome
during larval ontogenesis, enabling us to advance ourunderstanding of the stability and dynamics of early life
microbiome in sea cucumber and the further approaches
of probiotics could benefit aspects of sea cucumber
aquaculture such as pathogen resistance and microbiome mediation. Other studies on core microbiome insea cucumber also indicated the change of core bacteria
affiliated to hosts’ ecological and physiological events.
A study revealed the seasonal core microbiome whose
abundance are temporally stable across the host population during the events, in which Proteobacteria and Bacteroidetes increased in autumn and Firmicutes dominant
in spring [13]. Studies of sea cucumber gut regeneration
reported the temporary core microbiome at each generation stage [24, 25, 43]. In which, Yamazaki et al. [24]revealed the core microbiome during gut regeneration
process include Colwelliaceae, Flavobacteraceae, Rhodobacteraceae, Alteromonadaceae and Oceanospirillaceae.
Our studies and other studies in sea cucumber A. japonicus emphasized Rhodobacteraceae, a member of early life
core microbiome, significantly increased after gut development during larval ontogenesis. Rhodobacteraceae
has been widely studied for its potential positive effects
on host immunity, growth, and pathogen defense [23, 44,45]. In experiments of feeding behavior on growth rateof A. japonicus, Rhodobacteraceae are significantly abundant in fast-growing individuals related to capacity ofalgal polysaccharide degradation [46]. Paracoccus belonging to Rhodobacteraceae could promote sea cucumber
intestinal microbiota homeostasis through improving
modularity, enhancing species-species interactions andincreasing the number of connecters within the network[47]. The study of dynamics of gut microbiota during gut
regeneration by Yamazaki et al. pointed out Sulfitobacter belonging to Rhodobacteraceae significantly changed,
highlighting the importance of Sulfitobacter in intestinal
function recovery [23].

Yu et al. Animal Microbiome(2023) 5:54

Sulfitobacter related genomes were dramaticallyincreased after gut development in sea cucumber earlylife microbiome in our meta-pangenomic analyses, which
is probably related to functional development such as
nutrient intake that is also observed in infants. Lactic
acid metabolisms were prevalent in infants during breast
feeding but developing to an adult-like microbiota withhigher nutrient availability when solid food was introduced [5]. The microbial functions of sugar utilization,
fatty acid metabolism, amino acid metabolism, PHB
metabolism and peptidoglycan biosynthesis are significantly enriched in later developmental stages. Sulfitobacter as a PHB producer, the increased function of PHB
metabolism might coincide with the relative abundantSulfitobacter in later developmental stages [48]. Besides,
Sulfitobacter could be used as probiotics to inhibit thegrowth of fish pathogen Vibrio anguillarum [49]. The
sea cucumber under antibiotics exposure showed a significantly changed microbiota with decreased abundanceof Sulfitobacter, increasing the risk of infection [50]. The
results of our study suggest that S. pontiacus BL28 has
potential as a probiotic for improving the seed production of A. japonicus and contributing to its aquaculture.
Further investigation of its probiotic ability to promote
host growth and resistance against pathogens may lead tomore effective application in aquaculture practices.
This study also firstly applied meta-pangenomics thatcombined both metagenomic and pangenomic methods
to study the specific bacteria in early life microbiome inmarine invertebrates. The unique genes of S. pontiacus
BL28 were affiliated to functions of higher availabilityof nutrient intake and metabolism of essential vitamins,
playing an important role in host oncogenesis and healthmaintenance. Meta-pangenomics studies have advanced
our understanding of microbial populations and theirfunctions. For example, research on Prochlorococcus
revealed core gene clusters related to sugar metabolism,
offering insights into their ecological roles [27]. Investigations into the oral microbiome demonstrated thatdifferent oral habitats are associated with specific functions, such as oxaloacetate decarboxylase metabolism ina tongue-enriched subgroup of H. parainfluenzae [28].
In humans, meta-pangenomics of Escherichia in older
Chinese individuals highlighted higher abundance in thegut microbiome, with implications for dietary habits andgut health during aging [29]. The meta-pangenomics give
us new insights into bacteria genome in its host and/or
environmental metagenomes and its microbial functions related to host physiology and habitat preferences.
Our meta-pangenomics of Sulfitobacter BL28 is just a
beginning in the approach to understanding the roleof specific bacteria in larval development [51], further
exploration of other stage denoted microbes applied with

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meta-pangenomics could contribute to a more comprehensive and deep-seated knowledge of early life microbiome during sea cucumber ontogenesis.

Conclusion
Our study applied metagenomic analysis on the early life
microbiome using replicated larvae samples from different years and host populations, revealed the dynamics
and stability of early life microbiome during sea cucumber larval development. Gut development and feeding
behavior are the major factors impacting the alteration ofearly life microbial composition and functions occurringat the late auricularia stage, linked to the higher availability of nutrient digestive in gut during larval development.
The early life core microbiome and stage core microbiome, including pioneer core microbes on fertilized egg,
initial and later stage core microbiome, detected in larval microbiome might play an important role in larval
ontogenesis and microbial homeostasis in sea cucumber,providing candidates to investigate potential probiotics
contributing to improving sea cucumber seed production
and aquaculture. Further studies using omics-approachof holobionts and visualization of core microbes on hostand in environments are promoted to a deeper understanding of the interaction between bacteria and the hostsea cucumber.
Methods
Sample collection and rearing conditions under laboratory
conditions

Fertilized egg or gastrula samples of the sea cucumber
A. japonicus were collected five times in 2019, 2020 and
2021. Fertilized eggs or gastrula were prepared at 18.7 °C
at a farm of Hokkaido Aquaculture Promotion Corporation Kumaishi Branch, located in Kumaishi, Hokkaido,
Japan (Latitude: 42.12574, Longitude: 139.99966) at 11:00
am on 5th August 2019, 4th August 2020, 30th June 2021
and 12th July 2021. Another fertilized egg sample wasprepared at 18.7 °C at a farm in Hokkaido Aquaculture
Promotion Corporation Shiriuchi Branch, located in
Shiriuchi, Hokkaido, Japan (Latitude: 41.61463, Longitude: 140.38335) at 11:00 am on 30th July 2021. These
eggs or gastrulas were transferred to the Laboratory ofMicrobiology, Faculty of Fisheries Sciences, Hokkaido
University, whilst being kept at 18 °C for 2 h, and thenused immediately for experiments. Density of the fertilized eggs or gastrula was set at 7500 eggs/L in an 8 L
volume aquarium after manual counting of these eggs or
gastrulas in 0.1 mL seawater using a microscope (AxioImager Z2, Zeiss, Oberkochen, Germany) and reared at
18 °C. The aquarium was prepared using a sterilized 8 L
glass bottle (Ishizuka glass Co. Ltd., Aichi, Japan) set in
an incubator (MLR-352-PJ, PHC Corp., Tokyo, Japan).

Yu et al. Animal Microbiome(2023) 5:54

In 2019, each rearing bottle was filled with 7.5 L natural filtrated seawater using a 50 μm mesh cartridge filter (SWP50P10, AS One, Osaka, Tokyo) used in the
Kumaishi farm. In 2020 and 2021, each rearing bottle
was filled with 7.5 L artificial sterilized seawater (SeaLife, Nihonkaisui Co., Ltd., Tokyo). Each 80 mL (7500
individuals) of fertilized egg or gastrula suspensions, corresponding to the final density of around 1 individual/
mL, was added to each bottle, and rearing was started
with aeration (SPP-25GA, Techno Takatsuki Co., Ltd.,
Osaka, Japan). Feeding started at 48 h after fertilization
when early auricularia morphogenesis was observed inover 80% of individuals. A commercially available diatom, Chaetoceros gracilis (Hakodate Fisheries Research,
Japan), was fed to the sea cucumber larvae daily. The
diatoms used for feeding were alive and suspended inrearing seawater, allowing the free-swimming larvae toconsume them naturally. We provided 8000 live diatom
cells per individual larva per day. The rearing seawater forindividual groups was not changed throughout the larval
development stages.
Subsampling of sea cucumber larvae and microbes
in rearing waterSea cucumber larvae at five major developmental stages(GL, EA, LA, PT and JN), were used for characterizationof microbiomes after being confirmed by microscopic
observation. Five liters of rearing seawater containing
sea cucumber larvae were passed through a sterilized
40 μm nylon mesh (Falcon Cell Strainer, Durham, USA)to selectively isolate the larvae while effectively excluding
diatoms, which are approximately 5 μm in size. The isolated larvae were subsequently rinsed once with 0.22 μm
filter-sterilized seawater to ensure purity. Sterivex filter(SterivexTM-GV Sterile Vented Filter Unit 0.22 μm, EMD
Millipore, Billerica, USA) was used to prepare this sterilized seawater. The larvae on nylon filters were immediately frozen at − 80 °C until DNA extraction.
To prepare microbial fractions in rearing water, fiveliters of rearing water after passing through nylon mesh
was filtered through a 0.22 μm Sterivex filter by positive
pressure using filtered (0.22 μm) N­ 2 gas. These Sterivex
filters were preserved at − 80 °C until DNA extraction.
Microbial DNA extraction and 16 S rRNA gene sequencing

Microbial DNA extraction from sea cucumber was
performed using the NucleoSpin Soil Kit (MACHEREY-NAGEL, Düren, Germany), according to the manufacturer’s protocol. Microbial DNA extraction fromseawater was performed using the NucleoSpin Tissue
kit (MACHEREY-NAGEL), according to the modified
manufacturer’s protocol. In brief, seawater samples were
heated at 55 °C for 1 h to add an active cell lysis process

Page 18 of 22

in TE buffer (10 mM Tris-HCl, 1 mM EDTA) containing
20% SDS and proteinase K (20 mg/mL) instead of buffer
T1. In the third step Lyse Sample, 1 mL buffer B3 wasused instead of 200 µL amount of the buffer.
The bacterial community was specifically targeted byamplifying the hypervariable V1-V2 region of the 16 S
rRNA gene and accomplished using PCR with barcoded
27Fmod and 338R primers, which were also affixed with
Illumina adaptor sequences [23]. Thermal cycling consisted of initial denaturation at 96 °C for 2 min, followed
by 25 cycles of denaturation at 96 °C for 30 s, annealing at
55 °C for 45 s and extension at 72 °C for 1 min, and final
extension at 72 °C on a 9700 PCR system (Life Technologies Japan, Tokyo, Japan). PCR amplicons were purified
using AMPure XP magnetic purification beads (Beckman Coulter, Brea, CA, USA), and quantified using the
Quant-iT PicoGreen dsDNA Assay Kit (Life Technologies Japan). Equal amount of each PCR amplicon was
mixed and then sequenced using MiSeq Reagent Kit v3(600-cycles) with the MiSeq Illumina platform. Based on
sample specific barcodes, obtained reads were assigned
to each sample.
16 S rRNA gene analysis and core microbiome analysis

The paired-end sequence data with quality scores (i.e.,
Fastq files) was analyzed using Quantitative Insights
Into Microbial Ecology 2 (QIIME 2, version 2022.2) [52].
Quality controls (e.g., trimming primers and denoising
sequences, removing chimeric sequences) and merging
paired-end sequences were performed using DADA2[53]. Reads with 100% similarity constituted an ampliconsequence variance (ASV). Unlike the method to cluster
sequences into operational taxonomic units (OTUs) with
fixed threshold (usually 97%), this quality control method
using DADA2 allows us to detect even a single nucleotide
difference. Taxonomic assignments for the ASVs werecarried out using a Naive Bayes classifier trained on theGreengenes database. To enhance the specificity of our
microbial community analyses, we systematically identified and excluded sequences corresponding to mitochondrial and chloroplast. For downstream diversity analyses,
we subsampled reads to a minimum depth of 5683 per
sample, which was enough number diversity saturated(Additional file 2: Fig. S5).
To dissect the complex interplay of microbial communities, multiple analytical methods were applied.
Unweighted UniFrac distances served as beta-diversity
metrics and were visualized using Principal Coordinate Analysis (PCoA) plots [54]. In addition, Bray–
Curtis distance metrics were calculated to assess
microbial community dissimilarities [55], and visualized through Non-metric Multidimensional Scaling(NMDS) using the ‘vegan’ and ‘ggplot2’ R packages

Yu et al. Animal Microbiome(2023) 5:54

[56, 57]. The potential influence of rearing seawater on
the larval microbiome was assessed using the SourceTracker package [58].
Permutational Multivariate Analysis of Variance(PERMANOVA) was conducted to statistically validate
the differences in UniFrac distances, with significanceset at an FDR-corrected p-value of less than 0.05. Phylogenetic relationships were explored using FastTree[59]. Z-scores were calculated using the ‘genefilter’
R package [60]. Both ‘Phyloseq` and ‘DESeq2’ were
employed for heatmap construction and subsequentstatistical analyses [61, 62].
For identifying taxonomic differences, we used Linear Discriminant Analysis Effect Size (LEfSe), setting
the p-value at 0.05 and LDA score at 3.5, per established guidelines [63]. To visualize shared Amplicon
Sequence Variants (ASVs) between sea cucumber
and seawater samples, Venn diagrams were generatedusing Venny 2.1 [64].
Given the absence of a consensus definition for thecore microbiome, our study aligned with the latest
findings [6, 7]. We opted for a hybrid definition, incorporating both occupancy and abundance-based criteria. In our analysis, core microbiome members wereidentified if they were present in over 70% of the total
samples and accounted for a substantial portion, specifically 75%, of the total reads.
For a more refined understanding, we incorporated the Core Index (CI) and Normalized Core Index(NorCI) metrics, as introduced by Zhang et al. [32].
The CI is calculated using the formula:

CoreIndex(CI) =f ∗n∗sN ∗S

Where f represents the frequency of each family
within the dataset, n is the number of samples containing each family, s is the sequence length for each family across all samples, N is the total number of samples,and S is the total sequence length from all samples.
The NorCI is then computed as:

NorCI =CI ′ − CI min
CI max − CI min

Here, CI’ is the specific CI for each taxon, while
CImin and CImax are the minimum and maximum CI
values within the dataset, respectively.
For the purpose of this study, taxa with a NorCI
greater than 0.3 were deemed to be part of the coremicrobiome in each taxon. This approach offers arobust and nuanced framework for defining the coremicrobiome, combining both ubiquity and relative
abundance across samples.

Page 19 of 22

Metagenomic shotgun sequencing and functional profile

A total of 23 templates with ≥ 300 ng DNA material
extracted from sea cucumber larvae collected in 2019and 2021 were used for paired-end shotgun metagenomic sequencing on the HiSeq platform. For analysis ofsea cucumber larval metagenome samples, the metagenomics RAST server (MG-RAST) was used for microbial
functional annotation [65]. The low-quality regions were
trimmed using SolexaQA then followed with dereplication by k-mer approach [66]. Duplicate Read Inferred
Sequencing Error Estimation (DRISEE) was used to analyze the sets of Artificial Duplicate Reads (ADRs) [67, 68].
Reads with 97% identity were clustered and the longest
sequence was picked as the cluster representative. The
cluster representative was further assigned to taxonomy
using a BLAT similarity search which integrates SILVA,
Greengenes and RDP [69–71]. After the abundance profile generated, the SEED subsystem was used for functional profile [72].
Probiotic experiment with Sulfitobacter BL28

Bacterial strain BL28 was isolated from sea cucumber
larvae at the blastula stage and subsequently identified
as S. pontiacus [26]. This strain was cultured on Marine
Agar media (DifcoTM Marine Broth, Becton Dickinson, Franklin Lake, USA) at 20 °C. Commercial feed was
obtained from Hokkaido Aquaculture Promotion Corporation, Japan. The experimental diet was prepared byadding 0.05 g of powder feeds to 1 mL of artificial seawater and supplementing it with a suspension of BL28
at a final concentration of 1
­ 04 CFU/mL. To prepare thesuspension of BL28, the strain was incubated in Marine
broth for 24 h at 20 °C, followed by centrifugation at
7000×g for 10 min at 15 °C. The resulting cell pellets
were washed three times with sterile artificial seawater,
and the suspension of BL28 was adjusted to a concentration of 1 × ­108 CFU/mL in artificial seawater.
Juvenile sea cucumbers were obtained from a farm of
Hokkaido Aquaculture Promotion Corporation Kumaishi
Branch, Japan, and acclimated to rearing conditions for
one week. Thirty sea cucumbers were randomly distributed into six aquaria. The water temperature was maintained at 18 °C, salinity at 3.3–3.5%, and pH at 7.8–8.0.
During the 30-day experiment, feeding was performed
twice a week, and the tank was cleaned twice a week
before feeding. The body length of the sea cucumber wasmeasured before and after the experimental period toevaluate changes in body length. The sea cucumber wasanesthetized using a 40% menthol solution (8 mL of 100%
menthol and 12 mL of sterilized artificial seawater) tomeasure its body length. The specific growth rate (SGR)Yu et al. Animal Microbiome(2023) 5:54

was calculated for statistical analysis using the following formula: SGR (%)=100×((Ln Lt − Ln L0)/t, where Lt
and L0 are the final and initial length of sea cucumbers,
respectively, and t is the experimental period of 30 days.
The obtained body length data were analyzed using Bayes
statistics to test whether null hypothesis (H0), which isthe effect size = 0, was more probably occurred or notthan the alternative hypothesis (H1) based on bayes factor ­(BF10) using JASP software version 0.17.0 [73].
Meta‑pangenomics workflow against Sulfitobacter isolates

Three currently existing complete genomes of genus
Sulfitobacter from RefSeq database, National Center for
Biotechnology Information (NCBI) and one assembled
genome of S. pontiacus strain BL28 from this study wereused for pan-genomic analysis, and then further combined with the above 23 metagenomes (divided into twogroups: pre and post) for meta-pangenomic analysis.
These analyses were mainly constructed using anvi’o v7.1
following methods from Delmont TO and Eren AM [27]and Utter et al. [28] with minor modifications. Briefly, thepan-genomic analysis of the four genomes was performed
as previously described [74]. The genomes were first
converted into an anvi’o-compatible database (anvi-gencontigs-db). Further HMM decoration (anvi-run-hmms),
COG annotations(anvi-run-ncbi-cogs) and KEGG annotation (anvi-run-kegg-kofams) for contig databases were
performed. The pangenome was constructed throughgenerating a genome storage (anvi-gen-genomes-storage) and followed with pangenome computing (anvipan-genome). For the metagenomes, firstly, the config
files for illumina-utils were created (iu-gen-configs) andfiltered with default (iu-filter-quality-minoche). Next,
these metagenomes were mapped against the Sulfitobacter genomes to create the bam files using bowtie2-2.3.5[75], sorted and indexed using samtools-1.16.1 [76], profiled using anvi-profile, and merged to an anvi’o profile
database (anvi-merge). Then, gene coverage values acrossmetagenomes per genome were calculated (anvi-summarize). Finally, the meta-pangenomics were generated bylinking the pangenome to the metagenomes (anvi-metapan-genome). The results were visualized (anvi-displaypan) and improved manually.
Abbreviations
ASVs	Amplicon sequence variant
LEfSe	LDA effect size
LDA	Liner discriminant analysis
PCoA	Principal coordinate analysis
BUL	below undetectable limit
FE	Fertilized egg
GL	Gastrula
EA	Early auricularia
LA	Late auricularia

Page 20 of 22

PT	Pentactula
JN	Juvenile
SC	Sea cucumber
SW	Seawater
preGD	Pre gut development
postGD	Post gut development

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s42523-​023-​00276-2.
Additional file 1: Table S1. Relative abundance of each bacteria taxon atorder level. SC: Sea cucumber; SW: Seawater; preGD: before gut development; postGD: post gut development. Table S2. Relative abundanceof each bacteria taxon at family level. SC: Sea cucumber; SW: Seawater;
preGD: before gut development; postGD: post gut development. Orange
represents the number represent samples’ occupancy >70%; yellow represents samples’ abundance in top 75% of total reads. Table S3. Relative
abundance of each bacteria taxon at genera level in sea cucumber samples. preGD: before gut development; postGD: post gut development.
Orange represents the number represent samples’ occupancy >70%;
yellow represents samples’ abundance in top 75% of total reads. Table S4.
Relative abundance of ASVs in sea cucumber samples. preGD: before gut
development; postGD: post gut development. Orange represents thenumber represent samples’ occupancy >70%; yellow represents samples’
abundance in top 75% of total reads.
Additional file 2: Fig. S1. Genome analysis revealed the pathway of PHB
metabolism.Fig. S2. Heatmap based on relative abundance of top100
ASVs.Fig. S3. The impact of seawater on microbiome of sea cucumber
larvae analyzed by SourceTracker. a Barplot of the proportion of sources in
specific sample. b Pie plot of proportion of sources in larval microbiome
before and after gut development. SW-Before: seawater before larvalgut development; SW-Post: seawater post larval gut development.Fig.
S4. Normalized core index (NorCI) for core bacteria in the sea cucumber
larvae. a NorCI of bacteria at family level in sea cucumber larvae before
gut development. b NorCI of bacteria at family level in sea cucumber
larvae post gut development. c NorCI of bacteria at genera level in sea
cucumber samples. d NorCI of ASVs in sea cucumber samples. Fig. S5.
Rarefaction curve of a sea cucumber samples and b seawater samples.Fig.
S6. Beta diversity of weighted UniFrac distance of larval microbiome. a
PCoA plot based on weighted UniFrac distance showing the microbiota
differences in different years. Different color represent different years;
closed circle represent larvae samples before gut development, triangle
represent larvae samples post gut development. b Weighted UniFrac
distance plot between different developmental stage. c Weighted UniFrac
distance plots between different years and d between sea cucumber
before and post gut development (PERMANOVA, p<0.05). e PCoA plot
based on weighted UniFrac distance of seawater and sea cucumber
microbiotas. Different color represent developmental stages; closed circle
represent larval samples; open circles represent the seawater samples. f
Weighted UniFrac distance plot between sea cucumber and seawater(PERMANOVA, p<0.05, q<0.05).
Acknowledgements
We thank the Hokkaido University DX Doctoral Fellowship for the PhD scholarship. This study was supported by Kaken 19K22262. The funders had no role instudy design, data collection and analysis, decision to publish, or preparationof the manuscript.
Author contributions
TS and JY designed the study, JY, RY, SK and YS performed experiment, JY, CJ
and SM obtained data and data analysis. JY, CJ and TS drafted the manuscript.
All authors contributed to the article and approved the submitted version.
Funding
The authors declare that this study did not receive any specific grant fromfunding agencies in the public, commercial, or not-for-profit sectors.

Yu et al. Animal Microbiome(2023) 5:54

Availability of data and materials
Sequences were deposited to DDBJ/ENA/GenBank under accession numbers
as DRA012782 and DRA015867-DRA015868 for meta16S and metagenome,
and AP027373-AP027377 for BL28 complete genome.

Declarations
Ethic approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Author details
1
Laboratory of Microbiology, Faculty of Fisheries Sciences, Hokkaido University,
Hakodate, Japan. 2 Hakodate Fisheries Research, Hokkaido Research Organization, Local Independent Administrative Agency, Hakodate, Japan. 3 Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba, Japan.
Received: 29 March 2023 Accepted: 16 October 2023

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