Microbial communities associated with the nursery stage of commercially cultivated seaweed Saccharina japonica in Southern China

doi:10.21203/rs.3.rs-4182832/v1

Epimicrobiota associated with seaweeds are crucial for the health and development of their hosts due to their ability to produce phytohormones and vitamins etc. However, there is limited knowledge related to the microbiota of commercially cultivated seaweed Saccharina japonica. In this study, we investigated the dynamics of microbiota associated with S. japonica at nursery stage using Illumina sequencing of the V3-V4 hypervariable region of 16S rRNA gene. The composition and structure of epimicrobiota showed significant differences at the transition time (from mature sporophytes to sporelings). While, the epimicrobiota were relatively stable during the development of sporelings. Blastopirellula and Pseudoalteromonas were the dominant genera of the community of mature sporophytes and 6-week-old sporelings, respectively. Rubritalea was the most dominant genus for both 7 and 8-week-old sporelings. These three genera were also part of the core microbiota, suggesting that they may play an essential function within the S. japonica holobiont. In addition, members of the Planctomicrobium and Roseibacillus were identified as both drivers and keystone species, which might be responsible for the epimicrobiota shifts from 7-week-old sporelings to 8-week-old sporelings and were fundamental for the newly assembled epimicrobiota. Our results enrich the baseline data related to the microbiota of the commercially cultivated S. japonica.

epimicrobiota

kelp

temporal dynamics

16S rRNA gene amplicon sequencing

The epimicrobiota of seaweeds form complex and dynamic biofilms, in which seaweeds exchange nutrients and signal molecules with bacteria and the surrounding environment (Juhmani et al. 2020). It is well-accepted that epimicrobiota are critical for the development, chemical defense and metabolic activity of seaweeds (Egan et al. 2013; Saha & Weinberger 2019). As such, dysbiosis or instability of epimicrobiota may cause diseases of their hosts (Egan & Gardiner 2016; Li et al. 2022).

Generally, epimicrobiota display a high degree of variation at different developmental stages (Glasl et al. 2021). It has been found that the assemblage and diversity of epimicrobiota are affected by the development of thallus tissue (Lemay et al. 2018a). For instance, epimicrobial communities associated with Saccharina latissimi (Staufenberger et al. 2008) and Macrocystis pyrifera (Weigel & Pfister 2019) exhibited higher cell density and diversity on developed tissue (i.e., tip tissue of the thalli) compared with the emerging tissue (i.e., basal meristematic tissue). Meanwhile, the composition of epimicrobial communities often display temporal dynamics (Comba González et al. 2021). The dominant genera (the most abundant bacterial genera, reflecting the characteristic of the bacterial community composition) of epimicrobiota associated with seaweeds, including Sargassum muticum (Serebryakova et al. 2018), Agarophyton vermiculophyllum (Saha et al. 2020) and Ulva lactuca (Comba González et al. 2021), exhibit temporal dynamics. Despite these recent studies, there is still a limited understanding of the dynamics of epimicrobiota across the development of seaweeds.

Keystone species are defined as highly connected bacterial taxa which are critical for the stability and function of bacterial communities regardless of their abundance (Banerjee et al. 2018; Röttjers & Faust 2018; Mikhailov et al. 2019). Thus, analyzing keystone taxa will be helpful to understand the assemblage and dynamics of the bacterial communities. Co-occurrence network analysis has become a welcomed technique to identify keystone species and has been applied across a number of microbiomes (Xiao et al. 2022; Dong et al. 2022; Liu et al. 2023). In addition, comparing co-occurrence networks of host microbiota can also provide insight into the “drivers” of microbial community changes (Kuntal et al. 2019).

Brown seaweed Saccharina japonica (synonymous of Laminaria japonica) is one of the most important commercially cultivated seaweeds worldwide due to its high edible and medical values (Ke et al. 2021; Reda et al. 2022) and plays a crucial role in seawater remediation (Tang et al. 2021). However, only a few documents have been reported on the dynamics of epimicrobiota associated with the northern S. japonica. Interestingly, Proteobacteria and Verrucomicrobia were the dominant phyla common to both nursery stage (from August to October) (Han et al. 2021) and harvest season (from April to June) (Zhang et al. 2020). While, the dominant genera were distinct between the nursery (Han et al. 2021) and harvest season (Zhang et al. 2020). In addition, Persicirhabdus, Loktanella and Rubritalea were identified as the core bacterial taxa of cultivated S. japonica at nursery stage (Han et al. 2021). To date, little is known about the dynamics of the epimicrobiota of southern S. japonica at nursery stage.

In this study, we focused on the dynamics of the epimicrobiota of S. japonica, cultivated in Fujian province, southern China, using Illumina sequencing of the V3-V4 hypervariable region of 16S rRNA gene. The aims of this study were (1) to illustrate the dynamics of the epimicrobiota during the development of southern S. japonica at nursery stage; and (2) to analyze the dominant species, core species (OTUs presented in all of the samples and are essential for community assemblage and can support key ecological functions of epimicrobiota), keystone species and drivers. Understanding the dynamics of epimicrobiota will contribute to develop tools for monitoring the health status of S. japonica and forecast the disease outbreaks.

Sample collection

S. japonica was collected from the nursery farm, located at Guanwu, Liangjiang, Fujian province, China (26˚19′18.61″ N, 119˚49′18.61″ E). To investigate the dynamics of bacterial communities, four sampling times were designed (Fig. 1): 21 September 2019 (mature sporophytes with sporangia, MS), 7 November 2019 (6-week-old sporelings, S-1), 14 November 2019 (7-week-old sporelings, S-2), and 20 November 2019 (8-week-old sporelings, S-3). During the sampling period, sporelings were cultivated in the nursery ponds with rearing conditions at 10 ± 2 °C, 30 ‰ of salinity, 10:14-hour light-dark photoperiod and 50-80 µE∙m²∙s^-1of light intensity. At the nursery stage, according to the cultivation practice, mature sporophytes with sporangia were collected for releasing zoospores. Since zoospores need to be attached for further development, substratum curtains (57.5 cm in length and 28.0 cm in width, Fig. S1) are usually used as the substratum for zoospores to attach. It usually takes 6 weeks for zoospores to develop into sporelings (1-3 mm in length), which cover the substratum curtains. After 8 weeks of growth in the greenhouses, the sporelings usually reach 10-20 mm in length and then are transferred into the sea for temporary cultivation. In this study, epimicrobiota of mature sporophytes were collected by swabbing 50 cm² of the surface of sporangia with sterile cotton swabs near the middle part of each mature sporophyte. Additionally, the epimicrobiota of 6, 7 and 8-week-old sporelings were sampled by swabbing the substratum curtains with sterile cotton swabs (Han et al. 2021). At each sampling time point, six replicates of epimicrobial samples were collected randomly and were put into 50 mL sterile centrifuge tubes, respectively. All samples were kept frozen at -80 °C until DNA extraction.

DNA extraction, amplification and high-throughput sequencing

DNA of epimicrobiota was extracted using the HiPure soil DNA kits (Magen, Guangzhou, China) according to manufacturer’s protocols. The V3-V4 regions of the bacterial 16S rRNA gene were amplified with the primers 341F (5' -CCTACGGGNGGCWGCAG-3') and 806R (5' -GGACTACHVGGGTATCTAAT-3') (Guo et al. 2017). PCR reactions were conducted with the following procedures: 95 °C for 2 min, followed by 27 cycles at 98 °C for 10 s, 62 °C for 30 s, and 68 °C for 30 s and a final extension at 68 °C for 10 min in 50 μL mixture, containing KOD Buffer, dNTPs, primers, KOD Polymerase, and 100 ng of template DNA (Guo et al. 2017). Amplicons were extracted from 2% agarose gels and purified using the AxyPrep DNA gel extraction kit (Axygen Biosciences, Union City, CA, U.S.) according to the manufacturer’s instructions and quantified using ABI StepOnePlus Real-Time PCR System (Life Technologies Foster City, USA). Purified amplicons with adaptors were sequenced on the Illumina Novaseq6000 platform to generate 2*250 bp paired-end reads by Guangzhou Genedenovo Biotechnology (China) (Guo et al. 2017).

Analysis of sequencing data

Quality control was performed with raw reads by using FASTP (version 0.18.0) to remove low-quality reads (containing more than 10% of unknown nucleotides or less than 60% of bases with Q-value > 20). Tags with minimum overlap more than 10 bp or mismatch error rates more than 2% were eliminated by using FLSAH (version 1.2.11) and clean paired end reads were merged as raw tags. Noisy sequences of raw tags were filtered using QIIME2 to obtain the high-quality clean tags. The clean tags with similarity ≥ 97% were clustered into operational taxonomic units (OTUs) using UPARSE (version 9.2.64) (Edgar and Robert 2013). The OTUs were classified into organisms by a naive Bayesian model using RDP classifier (version 2.2) (Wang et al. 2007) based on SILVA database (version 132; Pruesse et al. 2007). The confidence threshold values ranged from 0.8 to 1.0. Finally, sequences of chloroplast, mitochondria and archaea origin were removed from the dataset. The raw reads were uploaded to the NCBI Sequence Read Archive (SRA) database (Accession Number PRJNA874852).

Indicator species analysis

Indicator species were analyzed to exhibit the different structure of the epimicrobiota. These indicator species were identified by using labdsv package (version2.0-1) in R project. IndVal was calculated based on the frequency and relative abundance of the bacterial genera followed by performing post-hoc test (Roberts et al. 2016). Indicator species were identified with IndVal ≥ 0.7 and p < 0.01 (Glasl et al. 2019).

Core species analysis

OTUs presented in 100% of samples were defined as core OTUs (Bonthond et al. 2020). Firstly, core OTUs were analyzed using VennDiagrams package (version 1.7.3) in R project (Chen 2022). Further, top core OTUs were identified by screening the core OTUs with relative abundance more than 1.0%.

Keystone and driver taxa identified by co-occurrence network analysis

Keystone taxa were identified by co-occurrence network analysis. OTUs with relative abundance more than 0.02% in epimicrobiota were selected to construct co-occurrence networks. Co-occurrence network analysis was performed by online software MENA (http://ieg2.ou.edu/MENA) (Deng et al. 2012). In brief, the relative abundance of OTUs were submitted to calculate the Pearson correlation matrix and construct the networks. Subsequently, the network topology characterization was computed in the downstream processes, including “Global Network properties”, “Individual nodes’ centrality” and “Module Separation and modularity calculation” (Deng et al. 2012). Cytoscape v3.8.2 was used for the visualization of the networks.

Keystone species were screened from the OTU nodes in the co-occurrence networks as proposed previously (Deng et al. 2012; Olesen et al. 2006). All nodes were categorized into four topological categories based on the value of among-module connectivity (Pi) and within-module connectivity (Zi) (Shannon et al. 2003). These four topological categories included peripheral nodes (Pi < 0.62 and Zi < 2.5), module hubs (Pi < 0.62 and Zi > 2.5), connectors (Pi > 0.62 and Zi < 2.5) and network hubs (Pi > 0.62 and Zi > 2.5). The nodes with Pi > 0.62 and/or Zi > 2.5 were defined as keystone species (Zhou et al. 2011; Deng et al. 2012).

Drivers are defined as the bacterial taxa contributing to the dynamics of adjacent bacterial communities. To identify the drivers, the most common subnetworks were constructed using the online software NetShift (https://web.rniapps.net/netshift/). Drivers were determined by the threshold of neighbor shift score (NESH-score) ≥ 2.0 and positive delta betweenness (ΔB) value (Kuntal et al. 2019).

Statistical analysis

Alpha diversity indices (Chao1 and Shannon) were calculated in QIIME (version 1.9.1) and the comparison between groups was computed by Tukey-HSD test in R project. To illustrate the variability of the overall microbial communities between groups, PCoA (principal coordinate analysis) plot and permutational multivariate analysis of variance (PERMANOVA) were performed based on Bray-Curtis dissimilarity with 9999 random permutations using Vegan package 2.5.3. Relative abundance bar plots were visualized with ggplot2 package 2.2.1.

Summary of the sequencing data

To investigate the dynamics of epimicrobiota of S. japonica at nursery stage, a total of 24 epimicrobial samples were collected at 4 time points (Fig. 1). After quality control and removal of chloroplast (254,271 reads), mitochondria (937 reads) and archaea origin (121 reads) sequences, a total of 2,236,531 clean reads with an average of 93,189 (SD = 16,453; min = 59,843; max = 119,370) reads per sample were obtained from 24 samples. A total of 2,862 OTUs were classified into 32 phyla, 78 classes, 172 orders, 275 families and 527 genera with the entire dataset. Rarefaction curves indicated that the sequencing reached saturation in all bacterial communities (Fig. S2), and this was further supported by Good’s coverage higher than 99.5%.

Composition and diversity of epimicrobiota

The dominant bacteria of epimicrobiota shifted with the development of S. japonica (Fig. 2a, Fig. S3). Planctomycetes (mean relative abundance: 87.1%) was the most dominant phylum for MS. Proteobacteria was the most dominant for both S-1 (64.5%) and S-2 (57.9%). As for S-3, the most dominant phylum was Verrucomicrobia (69.9%) (Fig. S3). Blastopirellula (86.0%) and Pseudoalteromonas (44.3%) were the most abundant genera for both MS and S-1, respectively. While, Rubritalea was the most dominant genus for S-2 (30.6%) and S-3 (67.1%). Specifically, the relative abundance of Rubritalea in the epimicrobial communities gradually increased (ranging from 0.008% to 67.1%) over the sampling time (Fig. 2a).

Regarding the alpha diversity, Chao1 index of MS was significantly different from other samples (Tukey-HSD test: p < 0.001, Fig. S4). While for Shannon index, significant difference was only found between MS and S-2 (Tukey-HSD test: p = 0.016, Fig. S4). As for the Beta diversity, the epimicrobial communities of S. japonica were significantly clustered by sampling time (Epimicrobiota by time: PERMANOVA F = 9.5809, R²= 0.5897, p = 0.001, Table 1) according to PCoA ordination (Fig. 2b) and PERMANOVA analysis. The community structure of MS and S-1 was significantly different from other samples based on pairwise comparison (PERMANOVA: F = 11.1103, R²= 0.5263, p = 0.005, Table 1). While no significant difference was observed in the community structure between S-2 and S-3 samples (PERMANOVA: F = 2.4851, R²= 0.199, p = 0.124, Table 1).

Table 1 Results of permutational multivariate analysis of variance (PERMANOVA) with adonis based on Bray-Curtis dissimilarity of epimicrobiota.

Groups	df	F	R²	P
Epimicrobiota by time	3	9.5809	0.5897	0.001
MS vs. S-1	1	11.1103	0.5263	0.005
MS vs. S-2	1	21.8481	0.686	0.001
MS vs. S-3	1	14.7187	0.5954	0.004
S-1 vs. S-2	1	7.2898	0.4216	0.003
S-1 vs. S-3	1	6.3122	0.387	0.001
S-2 vs. S-3	1	2.4851	0.199	0.124

MS, mature sporophytes (n = 6); S-1, 6-week-old sporelings (n = 6); S-2, 7-week-old sporelings (n = 6); S-3, 8-week-old sporelings (n = 6).

There were 13 indicator species at four sampling points (Fig. 2c). Blastopirellula, Phycisphaera and Granulosicoccus were the indicator species for MS. Pseudoalteromonas, Psychrobacter, Cobetia, Halomonas, Idiomarina and Alcanivorax were the indicator species for S-1. Loktanella, Thalassobius and Parasphingopyxis were the indicator species for S-2. Planctomicrobium was the only indicator species for S-3.

Core OTUs of the epimicrobial communities

OTUs presented in 100% of samples were defined as core OTUs. Among these core OTUs, the top core OTUs were defined as those with relative abundance more than 1.0%. A total of 130 core OTUs, belonging to 7 phyla, 35 order, 68 genera, were found within the epimicrobial communities (Fig. S5 and Table S1) with 8 top core OTUs (Table 2). These top core OTUs included Pseudoalteromonas OTU1, Rubritalea OTU2, Blastopirellula OTU3, Loktanella OTU9, Sulfitobacter OTU12, Litoreibacter OTU13, Psychrobacter OTU23 and Unclassified Rhodobacterales OTU24 (Table 2). The relative abundance of these top core OTUs fluctuated with the development of S. japonica (Table 2).

Table 2 Taxonomy and mean relative abundances of the top core OTUs in epimicrobiota.

ID	Order	Genus	MS	S-1	S-2	S-3
OTU1	Alteromonadales	Pseudoalteromonas	0.3	42.7	0.7	0.4
OTU2	Verrucomicrobiales	Rubritalea	0.004	0.7	17.5	55.9
OTU3	Pirellulales	Blastopirellula	45.0	0.01	0.01	0.01
OTU9	Rhodobacterales	Loktanella	0.01	0.4	19.4	2.5
OTU12	Rhodobacterales	Sulfitobacter	0.02	0.9	2.1	0.8
OTU13	Rhodobacterales	Litoreibacter	0.005	0.2	4.9	3.5
OTU23	Moraxellales	Psychrobacter	0.003	5.2	0.04	0.02
OTU24	Rhodobacterales	Unclassified	0.003	0.003	4.0	2.1

MS, mature sporophytes (n = 6); S-1, 6-week-old sporelings (n = 6); S-2, 7-week-old sporelings (n = 6); S-3, 8-week-old sporelings (n = 6).

Analysis of co-occurrence networks and identification of keystone species

To illustrate the interspecies interactions between the four sampling times, co-occurrence networks of the epimicrobiota were constructed (Fig. 3). The topological properties of each network were summarized in Table 3 and Table S2. The average path distance (GD) values and average clustering coefficient (avgCC) were different from randomized networks with the same number of edges and nodes (Table S2). Simultaneously, modularity (M) values of these networks were higher than randomized networks, indicating that the networks were modular. With the exception of the S-2 network, the interactions within epimicrobiota became more closely along with the sampling time according to the rising average clustering coefficient (Fig. 3 and Table S2).

Table 3 Network characteristics of epimicrobiota at nursery stage

	Number of nodes	Number of edges	Number of positive links	Number of negative links
MS	41	126	125	1
S-1	108	515	442	73
S-2	109	262	246	16
S-3	78	481	416	65

MS, mature sporophytes (n = 6); S-1, 6-week-old sporelings (n = 6); S-2, 7-week-old sporelings (n = 6); S-3, 8-week-old sporelings (n = 6).

Based on the values of Pi and Zi, all nodes in our networks were categorized into connectors and peripheral nodes. Among these two topological categories, connectors were defined as keystone species (Fig. S6 and Table S3). There were no keystone species at MS. Loktanella OTU9 and unclassified Rhodobacteraceae OTU192 were the keystone species for S-1. Unclassified Alphaproteobacteria OTU324 were keystone species for S-2. For S-3, the keystone species were Planctomicrobium OTU128, unclassified Rubinisphaeraceae OTU728 and Roseibacillus OTU375.

To illustrate the community dynamics in epimicrobiota over the sampling time, NetShift analysis was used to establish the most common subnetwork and to find the drivers during the development at nursery stage (Fig. 4 and Table S4). These drivers were further confirmed by high NESH-score and positive DelBet value. We failed to construct the common subnetwork between MS and S1 because the co-occurrence network of MS was fragmented (Fig. 3a). There were 5 drivers between S-1 and S-2, in which 1 belonged to Planctomycetes and others corresponded to Proteobacteria (Fig. 4A and Table S4). 14 drivers were identified between S-2 and S-3, including 3 Verrucomicrobia, 8 Proteobacteria, 2 Planctomycetes and 1 Bacteroidetes (Fig. 4b and Table S4). 8 drivers were classified into Proteobacteria between S-1 and S-3 (Fig. 4c and Table S4).

Epimicrobiota associated with seaweed surfaces play a crucial role in the development of their hosts (Comba González et al. 2021; Shi et al. 2023). In this study, we investigated the dynamics of epimicrobiota of cultivated S. japonica at nursery stage, i.e., from mature sporophytes to sporelings. The change of epimicrobiota was more dramatic at the developmental transition time point, i.e., from mature sporophytes to sporelings. Epimicrobial communities were relatively stable during the development of sporelings. In addition, we further analyzed the core bacteria, keystone species and drivers of epimicrobiota associated with S. japonica.

Recent studies have shown that the epimicrobiota associated with certain seaweeds exhibit a dynamic with the age of the thallus (Saha et al. 2014; Lemay et al. 2018b; Weigel and Pfister 2019). Similarly, in the epimicrobiota associated with S. japonica, the relative abundance of Rubritalea was gradually increased while the relative abundance of Blastopirellula and Pseudoalteromonas were gradually decreased during the development of sporelings. Such variation in dominant genera might be influenced by the specific metabolites of S. japonica at different developmental time points. Seaweeds may rely on these metabolites to promote the growth of specific bacterial species and to regulate the composition of epimicrobiota (Paix et al. 2019; Glasl et al. 2021). With respect to the epimicrobial community structure, significant difference only occurred at the developmental transition time point, i.e., from mature sporophytes to sporelings. While the epimicrobiota were relatively stable during the development of sporelings, except for S-1. Based on these findings, we suggest that the dynamics of epimicrobiota associated with S. japonica maybe driven by developmental transition time points.

It is well-accepted that the dominant bacterial taxa of epimicrobiota associated with seaweeds are common at the phylum level (Florez et al. 2017; Weigel and Pfister 2019) but species-specific at the genus level (Lemay et al. 2018a; Morrissey et al. 2019). We found that Planctomycetes, Verrucomicrobia and Proteobacteria were the dominant phyla at nursery stage. These phyla were also identified as the dominant phyla for S. japonica cultivated in the northern China (Zhang et al. 2020; Han et al. 2021) and other seaweeds, including Laminaria digitata (Ihua et al. 2020), Caulerpa lentillifera (Kopprio et al. 2021) and Pyropia yezoensis (Ahmed et al. 2021). However, at the genus level, Rubritalea was the dominant genus of S. japonica cultivated in both southern (in this study) and northern China (Han et al. 2021). These results imply that S. japonica recruits similar epimicrobial taxa irrespective of the cultivation site, and we speculate that Rubritalea may represent a core taxon of the cultivated S. japonica.

Core bacteria are often considered essential for community assemblage and can support key ecological functions (Shade and Handelsman 2012; Rubio-Portillo et al. 2018; Bonthond et al. 2020). In this study, Rubritalea OTU2, Pseudoalteromonas OTU1 and Blastopirellula OTU3 were the top three core bacteria of epimicrobial communities. Rubritalea and Blastopirellula were also reported as core bacteria for S. japonica cultivated in northern China (Han et al. 2021) and other seaweeds like Agarophyton vermiculophyllum (Bonthond et al. 2020) and Phyllospora comosa (Wood et al. 2022). This suggests that members of Rubritalea and Blastopirellula may play a general functional role in seaweeds. Rubritalea is a widely distributed bacterium in the marine environment (Hedlund et al. 2015) and serves as symbiont for sponges (Axinella polypoides and Halichondria okadai) and sea hares (Scheuermayer et al. 2006; Yoon et al. 2007; Kasai et al. 2007). It has been reported that Rubritalea can produce potent antioxidants to scavenge reactive oxygen species (Shindo et al. 2008; Yoon et al. 2008) and produce antibiotics to protect seabass (Dicentrarchus labrax) from the infection of Vibrio harveyi (Cámara-Ruiz et al. 2021). Besides, during the development of sporelings, the relative abundance of Rubritalea were gradually increased in epimicrobiota. Thus, our study suggest that Rubritalea may be beneficial in maintaining the health and promoting the development of S. japonica. However, the role and function of Rubritalea needs to be investigated by isolating pure bacterial strains. Blastopirellula was isolated from the surface of Fucus spiralis (Lage and Bondoso 2011) and is known to decompose polysaccharides secreted by seaweeds (Bondoso et al. 2017). It has been found that Blastopirellula could regulate the assemblage of epimicrobiota associated with seaweeds like Fucus vesiculosus (Goecke et al. 2010; Lachnit et al. 2010). Hence, Blastopirellula may influence the composition of epimicrobiota of S. japonica. Pseudoalteromonas is a common genus present in various marine environments (Chen et al. 2020). Most pigmented and a few non-pigmented Pseudoalteromonas can produce antibiotics (Bowman and McMeekin 2015) and serve as beneficial bacteria for cultivated shrimp (Litopenaeus vananmei) (Wang et al. 2020). Due to the antibiotic and anti-fouling ability of Pseudoalteromonas (Tangestani et al. 2021), we speculate that Pseudoalteromonas might play the protective role in resisting the pathogen colonization on S. japonica surface. In addition to being core genera, Blastopirellula and Pseudoalteromonas were also indicator species with high relative abundance at MS and S-1, respectively. These two bacterial taxa were replaced by Rubritalea at S-2. Therefore, Blastopirellula and Pseudoalteromonas might be development-specific bacteria associated with mature sporophyte and 6-week-old sporelings, respectively.

Bacterial community is a complex system with multiple species gathered to perform ecological functions (Dai et al. 2020). Within this biofilm system, keystone species are indispensable to the stability and function of the bacterial communities irrespective of their abundances (Banerjee et al. 2016; Banerjee et al. 2018). In this study, most of the keystone species (5 out of 6) belonged to Planctomycetales, Verrucomicrobiales and Rhodobacterales. Planctomycetales and Verrucomicrobiales were also keystone species of S. japonica (Ling et al. 2022) and the brown seaweed Taonia atomaria (Paix et al. 2021), respectively. Rhodobacterales has been reported as the important commensal bacteria associated with seaweeds because of its ability to promote host growth (Kurepin et al. 2014) and to prevent microalgal fouling (Zhang et al. 2018). Thus, Rhodobacterales may also be related to the growth and development of S. japonica. So far, Rhodobacterales were only reported as keystones in the gut microbiota of the cultivated shrimp (L. vananmei) (Dai et al. 2018; Yang et al. 2018; Dai et al. 2020; Huang et al. 2022). Further studies are needed to investigate the function of Rhodobacterales as keystone species in the epimicrobiota of S. japonica.

Planctomicrobium OTU128 and Roseibacillus OTU375 were found to be keystone species at 8-week-old sporelings and were also the drivers from 7- to 8-week-old sporelings. As drivers, the OTUs can contribute to the restructuring of epimicrobial communities, while as keystones species, they played a fundamental role in shaping the composition of the newly assembled bacterial communities. Such dual roles of Planctomicrobium OTU128 and Roseibacillus OTU375 may play different ecological functions from 7- to 8-week-old sporelings. However, further studies are needed to identify the ecological roles of these important bacteria.

This study examined the dynamics of the epimicrobiota during the developmental stages of cultivated S. japonica at the nursery stage using 16S rRNA gene amplicon sequencing. Our findings revealed significant changes in epimicrobiota composition at specific developmental transition points, particularly the transition from mature sporophytes to sporelings. In contrast, epimicrobiota remained relatively stable during sporeling development. Notably, Blastopirellula, Pseudoalteromonas, and Rubritalea emerged as key players in the development of cultivated S. japonica and were identified as dominant and core bacterial species. Furthermore, our analysis highlighted the dual roles of Planctomicrobium and Roseibacillus as keystone species and drivers within the epimicrobiota, potentially contributing to the stability of these microbial communities and, as a result, the overall health of S. japonica. These findings significantly expand our understanding of the bacterial communities associated with cultivated seaweeds and provide valuable insights that can aid in the prediction and management of disease outbreaks in seaweed cultivation.

Funding

This study was sponsored by the National Natural Science Foundation of China (42076106; 41576158), Sino-German Center for Research Promotion (GZ1357), National Key R & D Program of China (2018YFD0900305), and Student Research & Development Program (202310423244X), Ocean University of China.

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Availability of data and material

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article.

Authors' contributions

G Wang conceived and designed the experiments as well as involved in revising the manuscript. Y Zhuang analyzed the sequencing data and wrote the manuscript. S Egan involved in designing the experiments and gave critical English writing. Y Han collected the samples. M Saha participated in critical English writing. Q Qiu and D Chen helped to collect the samples of Saccharina japonica. All authors contributed to the article and approved the submitted version.

Acknowledgment

We are grateful to Guangzhou Genedenovo Biotechnology Co. Ltd for their kindly technical support.

Supplementary material

The Supplementary Material for this article can be found online at: Supplementary material.

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No competing interests reported.

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Microbial communities associated with the nursery stage of commercially cultivated seaweed Saccharina japonica in Southern China

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