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Seasonal variations of microbial communities and viral diversity in fishery-enhanced marine ranching sediments: insights into metabolic potentials and ecological interactions

Microbiome volume 12, Article number: 209 (2024) Cite this article

Abstract

Background

The ecosystems of marine ranching have enhanced marine biodiversity and ecological balance and have promoted the natural recovery and enhancement of fishery resources. The microbial communities of these ecosystems, including bacteria, fungi, protists, and viruses, are the drivers of biogeochemical cycles. Although seasonal changes in microbial communities are critical for ecosystem functioning, the current understanding of microbial-driven metabolic properties and their viral communities in marine sediments remains limited. Here, we employed amplicon (16S and 18S) and metagenomic approaches aiming to reveal the seasonal patterns of microbial communities, bacterial-eukaryotic interactions, whole metabolic potential, and their coupling mechanisms with carbon (C), nitrogen (N), and sulfur (S) cycling in marine ranching sediments. Additionally, the characterization and diversity of viral communities in different seasons were explored in marine ranching sediments.

Results

The current study demonstrated that seasonal variations dramatically affected the diversity of microbial communities in marine ranching sediments and the bacterial-eukaryotic interkingdom co-occurrence networks. Metabolic reconstruction of the 113 medium to high-quality metagenome-assembled genomes (MAGs) was conducted, and a total of 8 MAGs involved in key metabolic genes and pathways (methane oxidation - denitrification - S oxidation), suggesting a possible coupling effect between the C, N, and S cycles. In total, 338 viral operational taxonomic units (vOTUs) were identified, all possessing specific ecological characteristics in different seasons and primarily belonging to Caudoviricetes, revealing their widespread distribution and variety in marine sediment ecosystems. In addition, predicted virus-host linkages showed that high host specificity was observed, with few viruses associated with specific hosts.

Conclusions

This finding deepens our knowledge of element cycling and viral diversity in fisheries enrichment ecosystems, providing insights into microbial-virus interactions in marine sediments and their effects on biogeochemical cycling. These findings have potential applications in marine ranching management and ecological conservation.

Video Abstract

Background

Offshore coastal ecosystems are one of the important ecological environments on earth, and marine ranching ecosystems, as representatives of offshore coastal ecosystems, play an important role in maintaining biodiversity, providing ecological services, and increasing resource availability [1,2,3]. Various microorganisms (bacteria, fungi, protists, viruses, etc.) inhabiting the sediments form the basic components of marine ecosystems [4]. Microorganisms play a vital role in ecosystems, contributing to the biogeochemical cycling of key elements such as carbon (C), nitrogen (N), and sulfur (S) by breaking down organic matter and transforming inorganic compounds [5, 6]. For example, in the C-cycle, microbes release organic carbon into carbon dioxide by decomposing it. Some S-reducing bacteria can participate in the S cycle by using S compounds in organic matter for reduction, releasing S compounds such as hydrogen sulfide [7]. In addition, the N-cycle is also regulated by sediment microorganisms, which can utilize organic N to carry out nitrification and denitrification processes of N, thus affecting the N cycling process [8]. The vast majority of viruses in marine sediments belong to prokaryotic phages, which are ubiquitous, highly abundant, and diverse, constituting a highly dynamic biological component [9, 10]. These viruses play a major role in microbial-driven biogeochemical cycles by infecting and lysing bacteria and activating the expression of their auxiliary metabolic genes (AMGs) [11]. Compared to the wealth of knowledge on bacterial communities in offshore sediments, viral communities remain largely underexplored.

Seasonality is an important theme in marine ecology and offshore ecosystems. In particular, it is prone to seasonal variations, which can significantly affect the diversity and function of microbial communities [12]. In recent decades, researchers have increasingly explored seasonal microbial community variation in different ecosystems, such as in river-bay systems [13], marine sediment systems [14], and freshwater lake systems [15]. Although the microbial communities in these different types of natural ecosystems have been well studied, the seasonal variation characteristics of microbial communities under different types of environmental conditions remain controversial [16,17,18,19,20]. Most studies are limited to the characterization of microbial communities based on 16S/18S/ITS rRNA gene amplicon sequencing, and the whole metabolic characteristics and ecological diversity of microbial/viral communities remain unanswered.

As high-throughput sequencing methods have advanced, metagenomic sequencing and its associated analytical methods and computational tools provide new strategies for studying the metabolic potential, microbial interaction, and viral diversity of microbial communities [21,22,23,24]. The whole metabolic potential of microbial communities has been extensively studied in many natural ecologies, such as mangrove ecosystems [25, 26], glacial ecosystems [27], and underground estuaries [28]. The ecology of viral communities has also been extensively studied in extreme environments, such as deep-sea cold seep ecosystems [29]. However, poorly understood are the microbial-driven metabolic characteristics and viral communities in marine ranching ecosystems. Therefore, understanding microbial-driven elemental cycling and viral diversity has important implications for understanding ecosystem function and biogeochemical processes.

In this study, seasonal variations of bacteria/eukaryotes, metabolic characteristics, and viral communities in fishery-enhanced marine ranching sediments were examined by using 16S/18S rRNA gene amplicon and metagenomic sequencing (Fig. 1). Herein, we aimed to (1) investigate the seasonal patterns of microbial communities inhabiting the sediment of marine ranching, (2) reveal patterns of bacterial-eukaryotic interactions in the sediment microbiome under different seasons, and (3) identify key nutrient metabolic genes/pathways and coupling mechanisms of C, N, and S cycling genes/pathways under season dynamics, and (4) characterize the diversity of viral communities in marine ranching under different seasons based on metagenomic data.

Fig. 1
figure 1

Study design and bioinformatics workflow. Bioinformatics workflow consists of three main parts: amplicon analysis (blue dashed box), metagenomic analysis (red dashed box), and virus analysis (purple dashed box). Gold, data collection; green, data analysis; gray, software or equipment used

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Materials and methods

Sample collection and sediment physicochemical data measurements

Sediment samples were collected in four seasons from May 2022 to January 2023 from the Tian coastal marine ranching (36°91′ N and 122°15′ E) located along Jinghai Bay in Weihai city, Shandong province, China. Details of the sampling locations are shown in Additional file 1: Fig. S1. Specifically, samples were collected in the spring (May 2022), summer (August 2022), autumn (November 2022), and winter (January 2023) seasons. Tian coastal marine ranching is a typical fishery-enhanced marine ranching, and the climate of the area is characterized by a warm-temperate oceanic monsoon climate with marked seasonal variations. At each sampling site, three parallel surface sediment samples were collected using a Peterson sampler to collect surface sediment (0–10 cm; approximately 1 kg per sample). Samples were mixed and sealed in sterile bags and transported to the laboratory on dry ice. A portion of the sediment samples were stored at 4 °C for analysis of physicochemical parameters, and the other sediment samples were stored at −80 °C for DNA extraction. Physicochemical data, including salinity, electrical conductivity (EC), oxidation-reduction potential (ORP), pH, Bulk density (BD), water content (WC), total carbon (TC), total nitrogen (TN), and total sulfur (TS) were measured. The detailed method was described in Additional file 1: Materials and methods. Sediment physicochemical parameters in samples from each season were presented in Additional file 2: Table S1.

DNA extraction, PCR amplification, amplicon sequencing, and data analysis

Total genomic DNA was extracted from sediment samples using the DNeasy PowerMax Soil kit (Qiagen, Germany) according to the manufacturer’s instructions. For PCR amplification, the V3–V4 region of bacterial 16S rRNA and the V4 region of eukaryotic 18S rRNA were amplified with particular primers (341F and 806R) and (528F and 706R), respectively. The library was constructed using the NEBNext® Ultra™ II DNA Library Preparation Kit before sequencing on the Illumina NovaSeq 6000 platform (Novogene, Co., Ltd., China) to generate 250-bp paired-end reads. The raw data obtained from sequencing were filtered, spliced, and de-chimerized, and then sequences with 100% similarity were generated into amplicon sequence variants (ASVs) via the QIIME2 platform (Version QIIME2-202006) [30]. Taxonomic identification of bacterial and eukaryotic sequences was conducted using the SILVA (v138.1) [31] database. Finally, 3167 bacterial ASVs and 2002 eukaryotic ASVs were retained for subsequent analysis. Full details on the amplicon sequencing process, bioinformatics analysis, and sequencing data statistical analysis (habitat classification, microbial α-diversity, β-diversity, random forest, and network analysis) were detailed in Additional file 1: Materials and methods.

Metagenomic sequencing and data processing

Extracted sediment DNA for amplicon sequencing was also subjected to metagenomic sequencing (4 sediment samples were selected for each season, totaling 16 metagenome samples). Library construction and quality control were performed according to the manufacturer’s experimental protocols. Metagenomic sequencing was performed on the Illumina HiSeq PE150 platform in Novogene Co. Ltd. (Tianjin, China) with a paired-end protocol (2 × 150 bp). Metagenomic sequences were preprocessed using fastp (v23.4) [32, 33] with a minimum Q-score of 38 and a minimum sequence length of 40 bp to obtain clean reads for subsequent analysis.

Metagenome assembly and functional annotation

Single assembly of each metagenomic sample was processed by MEGAHIT (v1.2.9) using the option “--min-count 2 --k-min 29 --k-max 141 --k-step 20 -presets meta-large --min-contig-len 500” for processing [34]. Clean reads from all samples were co-assembled using MEGAHIT (v1.2.9) with the same assembly parameters as single samples for subsequent metagenomic bins (or metagenome-assembled genomes, MAGs). Open reading frames (ORFs) prediction of contigs (≥ 500 bp) of each sample was employed using MetaGeneMark (v2.10) [35] with default parameters. The ORFs prediction results were de-redundant using CD-HIT (v4.5.8) [36, 37] software with the parameters “-c 0.95, -G 0, -aS 0.9, -g 1, -d 0”. For overall functional annotation, the predicted gene fragments were functionally annotated (options: -e 1e-5) using DIAMOND (v0.9.9) software [38]. Comparison of predicted gene fragments with the KEGG database yielded a functional map of KO items. Genes were searched using the MCycDB [39], NCycDB [40], and SCycDB [41] databases for the CH4-cycle, N-cycle, and S-cycle, respectively.

Genome binning and recruitment

Clean reads were mapped to the co-assembled contigs with Bowtie 2 (v2.3.2) [42]. The genome binning was conducted using the MetaBAT2 (v 2.12.1) [43], MaxBin2 (v2.2.6) [44], and SemiBin2 (v2.2) [45]. In total, 2874 bins were produced by the 3 binning methods. The bins generated by the three binning methods were dereplicated and aggregated to create nonredundant bins (total 289 MAGs) by using the DAS_Tools (v1.1.6) [46]. The integrity and contamination of each bin were evaluated using the lineage_wf workflow in CheckM (v1.2.2) [47]. De-replication of MAGs was performed using dRep (v3.4.5) [48] with an average nucleotide identity (ANI) threshold of 99% and at least 30% overlap between genomes (options: -sa 0.99 -nc 0.30 -p 64 -pa 0.9 -comp 50 -con 5). Finally, 113 MAGs of medium to high quality (≥ 50% completeness and ≤ 5% contamination) were recovered for subsequent analysis. The bins abundance was calculated using the TPM method in CoverM (v0.6.1 https://github.com/wwood/CoverM).

Phylogenetic analysis and genome annotation of MAGs

The phylogenetic trees of bacterial and archaeal MAGs were reconstructed according to the annotation and comparison of the BAC 120 [49] and AR 53 [50] protein marker sets in the “infer” command in GTDB-Tk (v2.3.2) [51], respectively. In particular, the phylogenetic inference was conducted with the maximum likelihood approximation method in FastTree (v2.1.11) [52] to infer homology from alignment. The template files for iTOL (v6) [53] were generated using the R package “itol.toolkit” [54]. The final trees were visualized in the iTOL (v6) [53]. Taxonomic information was determined for a total of 113 representative MAGs using GTDB-Tk (v2.3.2) with the reference GTDB release 214 [51]. The metabolic and biogeochemical functional trait profiles of the involvement of MAGs were characterized by employing the METABOLIC (v4.0) [55].

Viral identification, clustering, and viral genome analysis

Potential viral contigs were identified from 16 metagenomic co-assembly contigs (> 5 kb) using three tools, VIBRANT (v1.2.1) [56], Virsorter2 (v2.2.4; maximum scores ≥ 0.8) [57], and DeepVirFinder (v1.0; scores ≥ 0.9 and P < 0.05) [58]. The combination of candidate viral contigs obtained by the three tools generates the final viral contigs for further analysis. The quality and completeness of final viral contigs (n = 5,853) were evaluated by CheckV (v1.0.1) [59]. Viral contigs with ≥ 50% estimated completeness (n = 338) were de-duplicated and clustered into viral operational taxonomic units (vOTUs, n = 338) by MMseqs2 (v15-6f452) [60] according to the MIUViG guidelines (average ANI of 95%; comparison rate of 85%). The MMseqs2 pipeline runs with parameters “--min-seq-id 0.95 -c 0.85 --cov-mode 1 -e 1E-05 --cluster-mode 2 --cluster-reassign”. The taxonomic classification of virus contigs was annotated with geNomad (v1.3.3) [61] following the International Committee on Taxonomy of Viruses (ICTV). The vOTUs were clustered into viral clusters (VCs) using the recommended parameters based on the gene-sharing network vConTACT (v.2.0) [62]. Viral lifestyle was predicted using DeePhage (v1.0) [63] using default parameters, sequences with scores not higher than 0.3 will be considered “temperate (lysogenic)”, sequences with scores higher than or equal to 0.7 will be considered “virulent (lytic)”, and the others will be considered as “uncertain” according to the scoring system. Higher scores indicate greater virulence. Viral genomes were analyzed for AMGs using VIBRANT (v1.2.1) [56].

Virus-host prediction and viral diversity analysis

The automated command line pipeline iPHoP (v1.3.2) [64] was employed to predict the host of the virus (confidence score threshold of 90). The default iPHoP database was used, and the recovered 113 MAGs were used as the host reference database. Multiple sequence alignments were performed using MAFFT (v7.526) [65] based on the viral sequences obtained at the representative level of the host genome. A phylogenetic tree was then constructed with the maximum likelihood approximation method in FastTree (v2.1.11) [52], visualization and annotation of phylogenetic trees via iTol [53], and viral diversity analysis of 338 vOTUs using MetaPop multifunctional bioinformatics pipeline [66]. The MetaPop output consists mainly of microdiversity and macrodiversity. Microdiversity includes novel codon-constrained linkage of single-nucleotide polymorphisms (SNPs), Pi nucleotide diversity (π), Watterson’s theta nucleotide diversity (θ), gene-specific selection pressure (Tajima’s D), and fixation index (Fst; between samples where the same genome). Macrodiversity includes raw population abundance/normalized population abundance, α-diversity (within community), and β-diversity (between community) indices.

Statistical analysis

For sediment physicochemical parameters, α-diversity, and genes/KO/MAGs abundances, Shapiro-Wilk was used for normal distribution tests. One-way analysis of variance (ANOVA) with Tukey’s HSD multiple range test was used to determine significant differences between seasons. For the α-diversity (Shannon’s index) of the whole metagenomes, KO items and key metabolic genes (CH4, N, S cycling genes) were calculated using the diversity function in the “vegan” R package. β-diversity analysis of the whole KO items and key metabolic genes (CH4, N, S cycling genes) were analyzed using nonmetric multidimensional scaling (NMDS) based on Bray-Curtis distances.

Results and discussion

Marine ranching ecosystems play an essential role in maintaining marine ecological balance and biodiversity and protecting coastal habitats [67]. Marine ranching sediments contain rich organic matter and nutrients and usually contain highly diverse microbial communities, including complex viral communities. However, our understanding of the overall metabolic potential in marine ranching ecosystems has been limited by the diversity of microbial communities, their novelty, and the uncertainty of associated metabolic functions. Here, we used amplicon (16S and 18S) and metagenomic techniques to study how seasonal variations affect microbial/viral communities in marine ranching sediments.

Distinct diversity patterns of sediment microbial communities under different seasons

For bacterial communities, lower richness, Chao1, and ACE indices were presented in winter and high Shannon and Simpson indices in autumn (Additional file 1: Fig. S2A). For eukaryotic communities, lower richness, Chao1, and ACE indices were presented in the spring and high Shannon, Simpson, and phylogenetic diversity (PD) indices in the autumn (Additional file 1: Fig. S2B). To evaluate the seasonal variations of microbial community structure in different taxa (including the habitat generalists and specialists), principal coordinate analysis (PCoA) analysis was performed using the β-diversity (including three nonparametric tests) of the Bray-Curtis matrix. The results showed that bacterial and eukaryotic communities under different seasons showed significant differences, but the separation of samples in the autumn and winter was not as pronounced as in the other seasons (Additional file 1: Fig. S2C and D; Additional file 2: Table S2). Most of the bacterial phyla in the sediments are dominated by the Proteobacteria, Desulfobacterota, Bacteroidota, Firmicutes, Acidobacteriota, and Actinobacteriota (Additional file 1: Fig. S3A). Annelida (spring: 74.81%; summer: 30.00%) was the most dominant eukaryotic phyla in the spring and summer, Arthropoda (25.13%) was the most abundant eukaryotic phylum in the autumn, and Diatomea (32.30%) was the most abundant eukaryotic phylum in the winter (Additional file 1: Fig. S3B). Random forest analysis was used to investigate the influence of environmental physicochemical variables on microbial community structure between taxa (all, generalist, and specialist). The results showed that BD and ORP were the most important factors explaining PCoA1 and PCoA2 of bacterial communities, respectively (Additional file 1: Fig. S4). Similarly, in eukaryotic communities, EC was the most important factor in revealing PCoA1 for all communities, BD was the most important factor in explaining PCoA1 for habitat generalist and specialist, and ORP was the most important factor in explaining PCoA2 for communities (Additional file 1: Fig. S5).

Many previous studies have demonstrated that seasonality is the dominant factor in determining microbial community diversity and composition, such as in Laoshan Bay marine ranching [68], shallow lake ecosystems [69], and aquaculture ecosystems [70]. It is a truism that seasonal variations generally result in considerable changes in the sediment microbiome. Environmental variables can better explain seasonally predictable community change [71]. When factors such as temperature, humidity, and light change, the growth and ecological environment of microorganisms will also change. The species and number of microorganisms may fluctuate significantly in different seasons, and such changes reflect the stability and adaptability of the ecosystem to a certain extent [72].

Season dynamics of sediment microbial interkingdom co-occurrence networks

Bacterial-eukaryotic interkingdom co-occurrence networks were constructed with SparCC correlations between ASVs to investigate potential sediment microbial interconnections. The four networks showed different structural and topological properties (Fig. 2; Additional file 2: Table S3). Specifically, the proportion of bacterial taxa was higher in the spring, and network connectivity (i.e., network degree) was highest in the winter (Fig. 2A). Bacteria and eukaryotes had the most edges in summer, with negative network edges highest in winter; positive network edges in bacteria-bacteria and eukaryotes-eukaryotes were highest in spring (Fig. 2B). The potential “keystone taxa” in different seasons were defined, and the relationship between degree and betweenness centrality was compared in the microbial interkingdom networks (Fig. 2C). The modularity (0.7), average clustering coefficient (0.586), average path length (3.746), and average degree (15.146) of the winter network were significantly increased compared to other seasons. In addition, the indices of empirical networks were greater than 10,000 corresponding random networks, indicating that the constructed empirical networks were not random or accidental and were statistically significant.

Fig. 2
figure 2

Bacterial-eukaryotic interkingdom co-occurrence networks and topological features under different seasons. A Sediment symbiotic networks under different seasons. Correlation (|r|> 0.7) and statistical significance (P < 0.01) were analyzed, with node size indicating degree and color representing bacterial or eukaryotic taxa. B Number of positively or negatively correlated inter-and intra-kingdom edges for different taxa (bacterial and eukaryotic) under different seasons. BB, bacterial-bacterial; BE, bacterial-eukaryotic; EE, eukaryotic-eukaryotic. C comparison of node topological features under different seasons

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Direct or indirect interactions between microorganisms were revealed by co-occurrence networks for analysis, including positive (symbiotic) and negative (repulsive) interactions, in which the network topology directly influences the stability of microbial communities [71, 73]. In this study, the networks presented a nonrandom structure of modularity, indicating the complex network characteristics in sediment ecosystems. Networks with higher relative modularity tend to be more stable, as they typically have more persistent and stable community-level and within-community interactions, thus exhibiting higher relative modularity [19, 74]. These results indicated different roles for bacteria and eukaryotes in sediment microbial networks under changing seasonal dynamics, with more complex network structures in winter.

Overview of metagenomic characteristics and functional gene profiles of the sediment microbiome under different seasons

Metagenomic sequencing of DNA obtained from a total of 16 marine ranching sediment samples under different seasons yielded a total of about 607 Gbp of clean data, with an average number of contigs obtained from a single-sample assembly strategy yielding a mean number of 543,574 (Additional file 2: Table S4). Overall functional α-diversity (based on KO terms) and N cycle functional diversity of the microbial community were highest in winter, and CH4 and S cycle functional alpha diversity did not differ significantly under the four seasons (Additional file 1: Fig. S6A). Based on NMDS, it was shown that the functional characterization structure of sediment microbiome varied clearly with seasons (Additional file 1: Fig. S6B).

  • CH4 cycling: Genes mapping to the CH4 cycle were mainly associated with anaerobic oxidation of methane (AOM), acetoclastic methanogenesis (AM), and serine cycle (Additional file 1: Fig. S7).

  • N cycling: The N cycle was mainly related to organic degradation and synthesis (ODS), dissimilatory nitrate reduction (DNRA), and assimilatory nitrate reduction (ANRA), in which the pathways of anammox, ODS, and others were significantly different (Additional file 1: Fig. S8).

  • S cycling: Genes involved in the S cycle mainly mapped to organic sulfur transformation (OST), linkages between inorganic and organic sulfur transformation (LBIOST), and sulfur reduction (Additional file 1: Fig. S9).

These results revealed a holistic functional landscape of microbially mediated genes/pathways of the C, N, and S cycles under seasonal variations. The CH4 cycle is an important process in the C cycle, with methanogenesis and methane oxidation being two key processes [25, 75]. Previous studies have shown that hydrogenotrophic methanogenesis dominates in subseafloor sediments [76]. The N cycle involves the processes of N fixation, nitrification, and denitrification, among which denitrification is the main pathway of N loss in sediments, and narG and nosZ play a vital role in denitrification [8]. The S cycle involves sulfide reduction, S oxidation, and sulfate reduction, and the S reduction gene family is highly abundant in marine sediments [41].

Metabolic and biogeochemical cycling potential of the MAGs

In total, 113 medium- to high-quality MAGs were recovered from all co-assembled contigs after de-replication (Additional file 2: Table S5). These MAGs were taxonomically classified to 16 phyla, and 88.80% of the genomes were unclassified at the species level, of which 108 were bacteria and another 5 were archaea (Fig. 3). Among the 113 MAGs, 30 (26.55%) were assigned to Pseudomonadota. Other phyla, represented by more than 10 MAGs, include Acidobacteriota, Bacteroidota, and Desulfobacterota (Fig. 3A). The five archaeal MAGs were categorized as Thermoplasmatota (1), Micrarchaeota (1), and Thermoproteota (3) (Fig. 3B). The transfer pathways, number, and coverage of MAGs involved in the cycling of N, C, S, and other elements highlight the potential of nutrient cycling pathways in the overall microbial communities (Additional file 1: Fig. S10). For example, the highest number of MAGs involved in organic carbon oxidation was found in the C cycle, nitrite ammonification in the N cycle, S oxidation in the S cycle, and iron reduction in the other cycles. The metabolic weight score (MW-score) was used to quantify the “functional weights” within a microbial community to represent potential metabolic interactions within the microbial communities [55]. The results showed that the top-ranked pathways were amino acid utilization (MW-score = 6.6), complex carbon degradation (MW-score = 6.3), and fatty acid degradation (MW-score = 5.8) (Additional file 1: Fig. S11). The contribution of the microbiota to the MW-score showed that Pseudomonadota contributed most significantly to function within the whole community. A previous study of metagenomic analysis of hadal sediments showed high MW-score in pathways related to the degradation of dissolved organic matter, with Thaumarchaeota contributing to the MW-score for ammonia oxidation, sulfide oxidation, acetate oxidation, and amino acid utilization [77].

Fig. 3
figure 3

Phylogenetic trees constructed from the retrieved medium- to high-quality metagenome-assembled genomes (MAGs). A Phylogenetic tree of bacterial MAGs. B Phylogenetic tree of archaeal MAGs. The outer layers of the tree, L1 to L6, indicated the coloration of the bacterial and archaeal phyla, the completeness of each MAGs, the degree of contamination, the genome size, the GC content, and the relative abundance of MAGs, respectively. Data were provided in Additional file 2: Table S5

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Many of these recovered 113 MAGs were involved in encoding genes/traits related to key metabolic pathways and different biogeochemical cycles. Genes involved in complex functional pathways are usually not restricted to only a single MAG but are distributed in several MAGs (Additional file 2: Table S6 and S7). Among bacterial MAGs, we have retrieved eight MAGs carrying methane metabolism genes (mmoB) co-mediating the transformation pathways of denitrification and S oxidation (Fig. 4). Four of these MAGs were affiliated with Pseudomonadota (bin.207, bin.231, bin.29, and bin. 6), 2 with Desulfobacterota (bin.250 and bin.287), 1 with Chloroflexota (bin.286), and 1 with Bacteroidota (bin.8). Several key genes were involved in the denitrification reactions of nitrate reduction to nitrite, nitrite reduction to nitric oxide, and nitric oxide reduction to nitrous oxide and the S oxidation reactions of S to sulfite, sulfite to sulfate, thiosulfate to sulfite, and sulfate. The relative abundance of these MAGs exhibits distinct seasonal variations, with bin.207, bin.250, and bin.286 being most prevalent during the spring (Additional file 1: Fig. S12). mmoB gene encoding methane monooxygenase, an enzyme that catalyzes the oxidation of methane to formaldehyde, representing a key step in the methane metabolic pathway [78]. These MAGs involved in methane oxidation share metabolic potential with several S cycle pathways and denitrification, demonstrating a potential coupled cycling effect. Previous studies have shown a strong coupling between N and S cycling in other sediment ecosystems (mangroves), and 34 MAGs were also identified in trophic lake sediments to indicate the coupling of N reduction and S oxidation [79]. One view is that denitrification in combination with S oxidation is also essential for the detoxification of sulfides in sediments [80]. Additionally, key genes involved in iron oxidation, iron reduction, arsenite methylation, arsenate reduction for detoxification, and selenate reduction were detected in these genomes co-driving methane metabolism mediating denitrification and S oxidation (Additional file 1: Fig. S13). Overall, these results emphasize the driving role of methane metabolism for denitrification and S oxidation to identify MAGs and their involved genes/pathways. This finding provides important clues to gain insights into the complex interactions and coupling effects among microorganisms in marine sediments and helps to reveal the linkages between microbial activities and biogeochemical cycles in marine sediment, as well as the functions and stability of marine benthic communities and ecosystems [81].

Fig. 4
figure 4

Metabolic characterization of eight MAGs involved in methane oxidation, denitrification, and sulfur (S) oxidation identified in marine ranching sediments. Yellow, genes related to the nitrogen (N) cycle; orange, genes related to the S cycle; green, genes related to the methane (CH4) cycle. A detailed list of genes/pathways involved in these MAGs was presented in Additional file 2: Tables S6 and Table S7

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Overview and ecological characteristics of virus communities in marine ranching sediments

In this study, 338 single-virus contigs with ≥ 50% completeness were identified using multiple virus-identification tools for sediment samples from four seasons of marine ranching. Three-hundred thirty-eight vOTUs were finally recovered after de-replication and clustering. Of these 338 viral genomes, 4 vOTUs were annotated at the class level, 14 were annotated at the order level, 32 vOTUs had host predictions, and 19 vOTUs were predicted to the host genus level (Fig. 5A). For virus lifestyle, 88 and 73 vOTUs were “virulent” and “temperate”, respectively, while the others were uncertain, and these vOTUs belonged to 161 clusters of viruses (roughly equivalent to the ICTV genera) based on the gene-sharing network (Additional file 2: Table S8). Among the 338 vOTUs, 335 belonged to Caudoviricetes, 1 to Arfiviricetes, 1 to Malgrandaviricetes, and 1 to Maveriviricetes in terms of taxonomic affiliation (Fig. 5B). Most virome studies indicated that Caudoviricetes were ubiquitous in marine sediment ecosystems and were a class of tailed dsDNA phages that dominate prokaryotic dsDNA viruses [83, 84]. It is important to note that one view is that the predicted dominance of Caudoviricetes in the virome may be due to limitations in the current database annotations [83]. Viral infection can affect host metabolism through viral AMGs expression. Based on KEGG annotations, 238 AMGs were identified in these viral contigs, which were widely involved in the metabolism of carbohydrates, energy metabolism, amino acids, nucleotides, cofactors, vitamins, and so on (Fig. 5C). This finding probably reflects the extensive involvement of viruses in these pathways by facilitating host adaptation to ecosystem fluctuations, indicating that viruses play an active role in shaping microbial metabolic processes [85].

Fig. 5
figure 5

Information on viral data identified in marine ranching sediments. A Number of viral contigs, number of viral operational taxonomic units (vOTUs), number on class level, number on order, number of viral clusters (VCs), number of viruses with host predictions, and number on predictions to genus level. B Taxonomic annotations on 338 vOTUs. C Number of auxiliary metabolic genes (AMGs) at each metabolic category. D Predicting the phylogenetic tree of hosts of bacteria/archaea obtained. Branches were colored according to phylum, with the outermost indicating the number of host genomes; yellow triangles indicated the number of viruses infecting the host at phylum level. Detailed statistics on the classification of vOTUs, lifestyles, information on VCs, and virus-host linkages were presented in Additional file 2: Table S8, Table S9, and Table S10

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Based on the host prediction tool iPHoP pipeline, only a small fraction (n = 31, ~0.09%) of the 338 vOTUs were predicted to be putative hosts, and 111 prokaryotes were predicted to be potential hosts for vOTUs, most of which (n = 109, ~98%) were predicted by GTDB-Tk (Additional file 2: Table S9). Interestingly, only two potential hosts were predicted from our assembled MAGs. Most of these viruses have a narrow host range, consistent with previous observations in mariculture sediments [86]. Phylogenetic analysis showed that virally infected hosts were detected in eight phyla (bacteria and archaea) (Fig. 5D). The most common phylum among the predicted hosts was Proteobacteria (n = 12), followed by Bacteroidota (n = 9), and Actinobacteriota (n = 5). Based on the results of iPHoP, 19 viral genomes were classified at the host genus level, including 3 archaea phyla (Additional file 2: Table S10). Overall, these results follow the prevailing view and previous findings [82, 85, 87], implying that marine sediment virus communities are highly specific and closely linked to the environment and host.

Diversity of virus communities in marine ranching sediments

The clustered heatmap reflected the normalized abundance of 338 vOTUs recovered from the metagenomes of all sediment samples (Additional file 1: Fig. S14; Additional file 2: Table S11). Macrodiversity analysis of 338 recovered viral genomes showed that α-diversity (Shannon index) was significantly lower in spring than in other seasons, and β-diversity indicated that seasonal changes shaped the structure of the viral communities (Fig. 6A and B). In a study of viral diversity in deep-sea cold seep sediments, it was shown that the sampling site and the geological state of the cold seep shaped the structure of the viral communities [29]. The results of the SNPs in the microdiversity module revealed 338 viral genomes across all samples with viral genetic diversity and genetic differences between samples (Fig. 6C and D). SNPs reflect differences between individuals at the genetic level, and the presence of SNPs increases genetic diversity within a population [88]. Intrapopulation variation (i.e., microdiversity) has enabled viruses to rapidly adapt to different host and environmental stresses [89]. Higher microbial diversity allows viruses to be produced and maintained in a disturbance-prone environment, reflecting increased microbial adaptability to environmental fluctuations and increased microbial stress responses [90]. Nucleotide diversity (π and θ) did not show significant differences across seasons (P > 0.05; Fig. 6E and F). Tajima’s D values for 338 viral genomes ranged from −9.05 to 0 across samples, with 84.90% of Tajima’s D values being 0 and no SNPs detected, with significant differences across seasonal samples (P = 1.03e-12; Additional file 1: Fig. S15; Additional file 2: Table S12). The average Fst value of the viral genomes between different sediment samples was 0.023, 77.63% of the pairwise fixation indices were none, and 21.09% were 0 (Additional file 2: Table S13). These results revealed the genetic diversity, adaptation, and population dynamics of viruses in microbial communities and the impact of microbial diversity on the ecological roles of viruses. These findings may have important implications for understanding viral adaptation and evolution under different environmental conditions.

Fig. 6
figure 6

Diversity analysis of viral communities (macrodiversity and microdiversity) in marine ranching sediments based on the MetaPop pipeline [66]. A α-diversity (Shannon’s index) of vOTUs under different seasons. The labels with different letters represent significant differences (P < 0.05). B β-diversity (nonmetric multidimensional scaling, NMDS) of vOTUs under different seasons. C Distribution of single-nucleotide polymorphisms (SNPs) at codon positions on each sediment sample. D Total number of SNPs on each sediment sample. E Pi nucleotide diversity analysis of vOTUs under different seasons. F Theta nucleotide diversity analysis of vOTUs under different seasons

Full size image

Conclusions

Here, we presented the first comprehensive characterization of the metabolic capacity, biogeochemical cycling potential, and viral communities in fishery-enhanced marine ranching sediments. The results indicated that seasonal variations significantly influence the diversity and function of these sediment microbial communities. The constructed bacterial-eukaryotic interaction networks have revealed complex ecological linkages among sediment microorganisms, with notable structural variations across seasons, particularly a more intricate network in winter. A total of eight MAGs were identified involving key metabolic genes and pathways (methane oxidation - denitrification - S oxidation), suggesting a possible coupling effect between C, N, and S cycles. Analysis of viral communities has identified 338 vOTUs with distinct ecological traits across different seasons, predominantly belonging to Caudoviricetes, indicating their broad presence and diversity in marine sediment ecosystems. Predicted virus-host linkages showed that only a few viruses were associated with specific hosts, which indicated that the viral communities were highly host specific. Analyses of genetic diversity and viral population dynamics have exposed a swift adaptation to varying host and environmental pressures.

Overall, this study emphasized the importance of understanding microbial-driven elemental cycling and viral diversity in marine ranching ecosystems for grasping ecosystem function and biogeochemical processes. However, there are limitations to this study, such as the molecular translational mechanisms involved in metabolic pathways are not well understood and the viral communities extracted from the metagenomics data represent only double-stranded DNA viruses. Future research needs to be directed towards an in-depth exploration of microbial-viral interactions in marine ranching sediments and their impact on biogeochemical cycling, to characterize the tightly coupled biogeochemical processes and viral communities in marine ranching sediments in their entirety.

Data availability

The authors declare that all data generated or analyzed during this study are included in this published article and its Supplementary Information (Additional file 1 and Additional file 2). All other data and codes supporting the findings of this study are available from the corresponding authors upon reasonable request. The raw sequencing data have been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database under the accession numbers PRJNA1124225 (16S), PRJNA1124233 (18S), and PRJNA1124343 (Metagenomic). The presented datasets (mainly including rarefied ASV tables generated by amplicon sequencing, corresponding taxonomic classification tables, metagenome assembly, MAGs sequences, and vOTUs sequences) have been archived to Zenodo (https://doiorg.publicaciones.saludcastillayleon.es/10.5281/zenodo.12632097).

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Acknowledgements

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Funding

This work was financially supported by the National Natural Science Foundation of China (42277269) and Key Laboratory of Marine Ranching, Ministry of Agriculture and Rural Affairs, China (KLMR-2022-03).

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  1. Marine College, Shandong University, Weihai, 264209, Shandong, China

    Cheng-Zhuang Chen, Ping Li, Ling Liu & Zhi-Hua Li

  2. Homey Group Co. Ltd., Rongcheng, 264306, Shandong, China

    Yong-Jun Sun & Wen-Ming Ju

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  3. Ling Liu
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  4. Yong-Jun Sun
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  5. Wen-Ming Ju
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  6. Zhi-Hua Li
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Contributions

CZC and ZHL designed the study. CZC, YJS, and WMJ collected the sediment samples. PL, LL, and ZHL participated in project design and management. CZC carried out the bioinformatics statistical analysis, visualization, and wrote the paper. All authors read and approved the final paper.

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Correspondence to Zhi-Hua Li.

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Supplementary Information

40168_2024_1922_MOESM1_ESM.docx

Additional file 1: Compilation of supplementary materials and methods used in this study and supplementary results. Fig. S1. Map of the study area and sampling sites, with ArcGIS (Version 10.8; http://desktop.arcgis.com/en/arcmap/) created. Fig. S2. Diversity of microbial communities (bacterial communities and eukaryotic communities) in marine ranching sediments under different seasons (α-diversity and β-diversity). A: α-diversity included Richness, Chao1 index, ACE index, Shannon index, Simpson index, and phylogenetic diversity (PD) index for bacteria communities. B: α-diversity included Richness, Chao1 index, ACE index, Shannon index, Simpson index, and phylogenetic diversity (PD) index for eukaryotic communities. C: β-diversity of bacterial communities in different seasonal marine ranching sediments, based on Bray-Curtis distance. D: β-diversity of eukaryotic communities in different seasonal marine ranching sediments, based on Bray-Curtis distance. All: all taxa; Generalists: generalist taxa; Specialist: specialist taxa. Fig. S3 Composition of microbial communities in marine ranching sediments under different seasons at the phylum level. A: Bacterial communities. B: Eukaryotic communities. Fig. S4. Random Forest (RF) model showing the importance of environmental factors on the Beta diversity of bacterial communities. A: Predicted by Principal Coordinate Analysis (PCoA) axis 1. B: Predicted by PCoA axis 2. BD: bulk density; WC: water content; EC: electrical conductivity; ORP: oxidation reduction potential; TN: total nitrogen; TC: total carbon; TP: total phosphorus. Fig. S5. Random Forest (RF) model showing the importance of environmental factors on the Beta diversity of eukaryotic communities. A: Predicted by PCoA axis 1. B: Predicted by PCoA axis 2. BD: bulk density; WC: water content; EC: electrical conductivity; ORP: oxidation reduction potential; TN: total nitrogen; TC: total carbon; TP: total phosphorus. Fig. S6. Diversity analysis of the microbial functional potential of marine ranching sediments under different seasons. A: α-diversity (Shannon's index) of functional genes under different seasons. Different letters indicated significant differences (P < 0.05). B: β-diversity (Non-metric multidimensional scaling, NMDS) of functional genes under different seasons. Fig. S7. Relative abundance of methane (CH4) cycle gene families in marine ranching sediments under different seasons. AM: Aceticlastic methanogenesis; AOM: Anaerobic oxidation of methane; CMP: Central methanogenic pathway; HM: Hydrogenotrophic methanogenesis; MM: Methylotrophic methanogenesis; OMCC: Oxidation of methane and C1 compounds. Fig. S8. Relative abundance of nitrogen (N) cycle gene families in marine ranching sediments under different seasons. ANRA: Assimilatory nitrate reduction; DNRA: Dissimilatory nitrate reduction; ODS: Organic degradation and synthesis. Fig. S9. Relative abundance of sulfur (S) cycle gene families in marine ranching sediments under different seasons. ASR: Assimilatory sulphate reduction; DSRO: Dissimilatory sulfur reduction and oxidation; OST: Organic sulfur transformation; SD: Sulfur disproportionation. Fig. S10. The number of genomes and genome coverage of microorganisms involved in the sequential transformation of relevant inorganic elements and organic compounds. Fig. S11. The term metabolic weight score (MW-score) was calculated for each function based on the METABOLIC (v4.0) [55], which represents the functional weights of the entire community, and the percentage of term contribution for each MW-score reflects the contribution of each gate to the function within the entire community. Fig. S12. Relative abundance of 8 MAGs involved in methane oxidation, denitrification, and sulfur oxidation identified in marine ranching sediments. Different letters indicated significant differences (P < 0.05). Fig. S13. Metabolic characterization of key genes involved in iron oxidation, iron reduction, arsenite methylation, arsenate reduction for detoxification, and selenate reduction to co-drive methane metabolism, denitrification, and sulfur oxidation in 8 MAGs in marine ranching sediments. Fig. S14. The color intensity of each cell reflects the normalized abundance of a particular genome in a given sample. Detailed statistics were provided in Additional file 2: Table S11. Fig. S15. Tajima’s D of viral genes across 16 sediment samples from marine ranching. Detailed statistics were provided in Additional file 2: Table S12.

40168_2024_1922_MOESM2_ESM.xlsx

Additional file 2: Table S1. Sediment physicochemical parameters in marine ranching sediments under different seasons. Table S2. Analysis of structural differences in microbial communities (Bacteria and Eukaryota) of different taxa. Table S3. Topological features of sediment microbial interkingdom networks under different seasons and their corresponding random networks. Table S4. Characterization of metagenome sequencing of sediments under different seasons. Table S5. Summary of metagenome-assembled genomes (MAGs) in marine ranching sediments. Table S6. Detailed genetic markers used to infer functional traits of MAGs in marine ranching sediments. "Present" = present in the MAG; "Absent" = not present in the MAG. Table S7. Genetic markers used to infer functional traits of MAGs in marine ranching sediments. "Present" = present in the MAG; "Absent" = not present in the MAG. Table S8. Summary of quality, taxonomy, lifestyle, and virus cluster (VC) for 338 viral operational taxonomic units (vOTUs) in marine ranching sediments. Table S9. Summary of detectable host-virus linkages in marine ranching sediments. Table S10. Summary of detectable host-virus linkages in marine ranching sediments at the genus level. Table S11. The normalized abundances of 338 vOTUs in marine ranching sediments. Table S12. The microdiversity of viral genes in marine ranching sediments. Table S13. The fixation indexes (Fst) of viruses in marine ranching sediments.

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Chen, CZ., Li, P., Liu, L. et al. Seasonal variations of microbial communities and viral diversity in fishery-enhanced marine ranching sediments: insights into metabolic potentials and ecological interactions. Microbiome 12, 209 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40168-024-01922-w

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Keywords

Microbiome

ISSN: 2049-2618

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