Salinity-Driven Dynamics of the Ulva-Associated Virome and Their Implications for Holobiont Adaptation

Salinity-Driven Dynamics of the Ulva-Associated Virome and Their Implications for Holobiont Adaptation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Salinity-Driven Dynamics of the Ulva-Associated Virome and Their Implications for Holobiont Adaptation Shujie Tian, Jinlong Ru, Zezhong Wang, Xiuli Chen, Li Deng, Xia Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7989466/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The role of viruses in mediating microbial community adaptation to environmental stress remains a critical frontier in host-microbe interactions. While the structure and function of bacterial communities associated with the green alga Ulva are known to respond to salinity, the involvement of the algal virome in this process is entirely unexplored. Results Using metagenomic sequencing of 89 Ulva samples along a salinity gradient (5–35 PSU), we demonstrate that salinity is the dominant factor shaping the viral community, explaining 73% of the taxonomic and 34% of the functional variation. Viral richness and diversity declined significantly with increasing salinity. The response of viruses to salinity was highly host-specific, with those infecting Rhodobacteraceae exhibiting the strongest correlations. Crucially, we identified a suite of phage-encoded auxiliary metabolic genes (AMGs) that were both differentially abundant and enriched under high-salinity stress. These AMGs are functionally predicted to facilitate bacterial osmoadaptation through synergistic mechanisms including cell wall modification ( glmS , glf ), osmolyte synthesis ( preT ), epigenetic regulation via folate-dependent DNA methylation ( DNMT1 , DHFR ), and antioxidant defense ( dfrB , mec ). Conclusions Our findings reveal that phages are not passive followers of their bacterial hosts but active contributors to holobiont resilience by deploying a targeted genetic toolkit for salinity adaptation. This study provides a mechanistic model for viral-mediated environmental adaptation in a key marine holobiont, fundamentally advancing our understanding of tripartite "host-bacteria-phage" interactions in a changing ocean. Ulva Virome Osmoadaptation Auxiliary metabolic genes Host-virus interactions Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Microbial communities engage in complex, dynamic symbioses with eukaryotic hosts, playing critical roles in host development, health, and environmental adaptation. In marine environments, macroalgae, such as the genus Ulva , host structured bacterial consortia that are integral to algal physiology and ecological function. Owing to its rapid growth and euryhaline adaptations [ 1 , 2 ] , Ulva is widely used as a canonical model system for investigating algal-bacterial interactions. The Ulva -microbe assemblage constitutes a holobiont, a cohesive entity characterized by a high degree of metabolic interdependence. The associated bacterial biofilm acts as a "second skin," facilitating the exchange of nutrients and vitamins, providing physical protection, defending against pathogens, and mitigating environmental stress [ 3 , 4 ] . Critically, the structure of the Ulva bacteriome is highly dynamic and profoundly influenced by environmental factors, including temperature, light, and salinity [ 5 , 6 , 7 , 8 ] . However, the mechanisms through which these factors, particularly salinity, mediate bacterial community composition and evolution to impact host fitness remain elusive. Viruses, especially phages, are now recognized as master regulators of bacterial communities in aquatic ecosystems [ 9 , 10 ] . Through lysis, lysogeny, and horizontal gene transfer [ 11 ] , phages shape the structure, function, and evolutionary trajectories of their bacterial hosts. A key mechanism of this influence is through virally-encoded auxiliary metabolic genes (AMGs), which can reprogram host metabolism to enhance environmental adaptability [ 11 ] . These AMGs, implicated in diverse processes from nutrient cycling to biofilm formation [ 12 , 13 ] , have been identified in a range of environments, including those infecting key phytoplankton [ 14 ] . Notably, viral communities themselves exhibit distinct structural and functional profiles across environmental gradients, with salinity being a major determinant in estuarine systems [ 15 , 16 , 17 ] . This suggests that environmental shifts may directly alter the phage community, which in turn modulates bacterial-host interactions. Consequently, a comprehensive understanding of holobiont resilience demands a tripartite framework encompassing "host ( Ulva )-bacteria-phage" interactions. Despite this, the response of the Ulva -associated virome to salinity fluctuation and its subsequent impact on the host holobiont are virtually unknown. To address this critical gap, we employed metagenomic sequencing of a large-scale dataset (n = 89) to systematically investigate the viral community associated with Ulva across a salinity gradient. Our study aims to: (i) characterize the dynamics of salinity in shaping the diversity and structure of the Ulva virome; (ii) identify key phage-encoded AMGs, particularly those implicated in osmoregulation and holobiont salinity adaptation; and (iii) infer shifts in viral life strategies (lytic vs. lysogenic modes) in response to salinity stress. Our findings indicate that the functional profile of the Ulva virome is enriched in genes related to osmoprotection and stress response. Furthermore, we propose that salinity stress induces a shift towards lysogeny, a strategy that may promote bacterial host survival under such stressful conditions. This study unveils the mechanisms by which viruses contribute to microbe-host environmental adaptation, providing novel insights into holobiont resilience in a changing world. Results 1. Viral Contig Assembly, Quality Assessment, and Taxonomic Profiling Metagenomic assembly of 89 ulva samples yielded 5683 quality-filtered viral contigs. Among these, we identified 651 high-confidence contigs (completeness ≥ 50%) with a mean length of 13,025.66 bp (N50 = 15713 bp). Clustering at 95% average nucleotide identity (ANI) produced 4,888 viral operational taxonomic units (vOTUs), including 432 with ≥ 50% completeness (Fig. 1 a). The quality assessment revealed that 8.08% of the viral genomes were medium or high quality (contamination < 5%), while the quality of 13.33% remained undetermined (Fig. 1 a). Taxonomic and functional characterization of the medium-to-high-quality genomes unveiled a diverse viral community (Fig. 1 b). All classified genomes (100%) belonged to the tailed virus class Caudoviricetes , consistent with their dominance in marine environments. However, the vast majority (98.27%) could not be assigned to a known family, underscoring the extensive uncharacterized diversity of algae-associated viruses. Among the classified families, Autographiviridae (1.08%) was the most prevalent, followed by Schitoviridae ( splitsaviruses , 0.43%), Zobellviridae (0.11%), and Demerecviridae (0.11%). Analysis of viral life strategies indicated a community dominated by virulent (lytic) phages (81.3%), with temperate (lysogenic) phages representing for a smaller proportion (18.7%). The genome sizes were predominantly clustered within 20–40 kbp range(Fig. 1 b), aligning with typical bacteriophage genomes. Host prediction analysis revealed that the vOTUs primarily targeted key bacterial phyla within the Ulva microbiome, with Pseudomonadota (30.7%) being the most prevalent, followed by Bacteroidota (17.62%) and Bacillota (1.84%) (Fig. 1 b). This wide phylogenetic range of predicted hosts suggests that Ulva -associated phages infect a diverse array of bacteria, positioning them as potential key regulators of microbiome stability and function under environmental fluctuations. 2. Salinity is the Dominant Factor Structuring Viral Community Composition and Functional Potential To investigate the influence of environmental factors on Ulva -associated virome, we conducted non-metric multidimensional scaling (NMDS) analyses based on viral taxonomic composition (15,324 vOTUs) and functional potential (2,817 KEGG-annotated auxiliary metabolic genes, AMGs) across a salinity gradient of 5–35 practical salinity unit (PSU), while concurrently evaluating the effects of temperature, oxygen, and nutrients (Fig. 2 a & b). Salinity emerged as the primary and strongest driver of viral taxonomic composition, explaining a substantial proportion of the variation (R² = 0.73, p = 0.001, Fig. 2 a), while temperature exerted a secondary, yet detectable influence (R² = 0.10, p = 0.01), while the effects of nitrite (NO₂) (R² = 0.07, p = 0.04) and silicate (R² = 0.08, p = 0.03) were minor. Other factors, including oxygen and the majority of nutrients (NOx, NO₃, PO₄³⁻), showed no significant correlation ( p > 0.05, Fig. 2 a). A parallel pattern was observed for the viral functional profile. Salinity again represented the most significant environmental filter (R² = 0.34, p = 0.001; Fig. 2 B), with temperature as the only other factor demonstrating a statistically significant effect (R² = 0.10, p = 0.003). In contrast, oxygen and nutrient concentrations (including NO₂, NO₃, NOx, silicate, and PO₄³⁻) had negligible impacts on functional composition (Fig. 2 b). In summary, our analyses unequivocally identify salinity as the principal environmental factor shaping both the taxonomic structure and functional capacity of the Ulva -associated phage community, whereas dissolved nutrients and oxygen concentrations play subordinate roles. 3. Bacteriophage Diversity and Community Structure Shift Across Salinity Gradients To further delineate the impact of salinity on the viral community, we analyzed phage diversity and composition across a defined salinity gradient: horohalinicum (5–8 PSU), mesohaline (8–18 PSU), polyhaline (18–30 PSU), and euhaline (30–35 PSU). Phage richness and diversity varied significantly across these salinity regimes. The horohalinicum and mesohaline communities exhibited significantly higher richness and Shannon diversity compared to the polyhaline and euhaline groups, with the euhaline group showing the lowest values (Fig. 3 a, b). This pattern in richness was corroborated by the Chao1 index (Fig. 3 d). In contrast, the Simpson index revealed no significant differences, indicating consistent community evenness across all salinity levels (Fig. 3 c). Community composition was also strongly influenced by salinity. NMDS ordination, supported by PERMANOVA and ANOSIM tests, confirmed significant compositional differences among the salinity groups (Fig. 3 e). The horohalinicum and mesohaline groups formed closely clustered assemblages, whereas the polyhaline and euhaline groups were more distinct. Pairwise comparisons further validated that the taxonomic composition of the Ulva -associated bacteriophage community was significantly different between every salinity region (p < 0.05 for all comparisons; pairwise Adonis test) (Fig. 3 f). 4. Host-Linked vOTU Dynamics Across Salinity Gradients Building on the finding that salinity is the primary driver of viral community composition (Fig. 2 ), we investigated its specific impact on viral operational taxonomic units (vOTUs). Differential abundance analysis (ANCOM-BC2, p < 0.05) revealed that vOTUs formed two distinct, salinity-dependent response patterns relative to the horohalinicum reference (Fig. 4 a, Fig. S1 a). One group increased in abundance with rising salinity, peaking in mesohaline conditions (e.g., vOTU_20393, vOTU_20175, and vOTU_19318). The other group decreased most markedly in euhaline waters, as exemplified by vOTU_06052 (log₂FC = − 1.37, p < 0.05) and vOTU_20194. Taxonomic profiling mapped these salinity-responsive vOTUs to three key bacterial phyla, revealing phylum-specific response patterns (Fig. 4 c). Planctomycetota -associated vOTUs (e.g., vOTU_20393) showed moderate positive correlations with salinity (R = 0.31–0.46, p < 0.05). Pseudomonadota -associated vOTUs (primarily infecting Rhodobacteraceae ), such as vOTU_06052, vOTU_07412, vOTU_20168, vOTU_19466, and vOTU_19457, exhibited the strongest salinity responses, spanning both positive and negative correlations (R = -0.45–0.46, p 0.05), suggesting their dynamics are driven by other factors. 5. Functional Diversity of Ulva Virome Across Salinity Gradients To investigate the functional potential of the Ulva -associated virome, we annotated phage-encoded AMGs against the KEGG database. These AMGs were implicated in diverse metabolic pathways, including carbohydrate and energy metabolism, metabolism of cofactors and vitamins, lipid metabolism, and metabolism of amino acids, among others (Fig. S2). Given that salinity was identified as the primary driver of viral functional composition (Fig. 2 b), we further assessed its impact on functional diversity. The richness of phage-encoded AMGs (based on KEGG Orthology, KO) was significantly higher in horohalinicum and mesohaline environments than in polyhaline and euhaline conditions (Fig. 5 a). This pattern of enhanced functional diversity and richness in lower salinity regimes was consistently supported by the Shannon diversity (Fig. 5 b) and Chao1 indices (Fig. 5 d). The Simpson index mirrored this trend, showing higher diversity in horohalinicum and mesohaline groups (Fig. 5 c). NMDS revealed distinct clustering of viral functional profiles across the salinity gradient (stress = 0.186; Fig. 5 e), a finding corroborated by significant PERMANOVA (R² = 0.078, p < 0.001) and ANOSIM (R = 0.129, p < 0.01) results. Network analysis further illustrated the relationships among salinity conditions, highlighting the particularly significant roles of horohalinicum and euhaline environments in shaping the overall functional diversity structure (R²=0.1, p = 0.002; Fig. 5 f). 6. Phage-encoded AMGs Enriched in Salt Tolerance Pathways and Linked to Key Bacterial Hosts To identify phage-encoded AMGs responsive to salinity, we performed differential abundance analysis (Kruskal-Wallis test, p < 0.05) followed by KEGG enrichment analysis ( p < 0.05). This approach revealed 15 differentially abundant AMGs which are significantly enriched in salt tolerance-associated pathways, including amino sugar and nucleotide sugar metabolism, cofactor biosynthesis, and sulfur metabolism (Fig. 6 a). Specifically, these AMGs have putative roles in diverse processes, including cell wall modification ( glmS , glf ), osmolyte synthesis ( preT ), folate metabolism ( DHFR , queE , DNMT1 , DNMT3A ), antioxidant defense ( dfrB , mec ), and cofactor synthesis ( cobS , rhnA-cobC , ubiG ). Functional annotation indicates these pathways form an integrated network to support bacterial osmoadaptation, suggesting these genes contribute to host bacterial salt tolerance through synergistic mechanisms. In addition, we predicted the protein structures of these phage-encoded AMGs. Among them, nine AMGs (Fig. S3) showed high similarity to bacterial-derived homologous protein. Host prediction analysis linked these salinity-responsive AMGs to three dominant bacterial lineages within the Ulva microbiome: Pseudomonadota (specifically Rhodobacteraceae ), Cyanobacteriota ( Cyanobacteriales ), and Bacteroidota ( Flavobacteriaceae ) (Fig. 6 b). Notably, these predicted host associations align with established ecological functions, organic sulfur metabolism in Rhodobacteraceae , nitrogen fixation in Cyanobacteriales , and polysaccharide degradation in Flavobacteriaceae , suggesting that phages may modulate core microbial processes critical for holobiont salinity adaptation. Discussion Our metagenomic investigation provides compelling evidence that salinity acts as a paramount environmental filter structuring the Ulva -associated viral community, with bacteriophages serving as active contributors to holobiont adaptation. We demonstrate this through a consistent pattern of declining viral diversity along the salinity gradient, host-specific viral dynamics, and a marked enrichment of phage-encoded AMGs functionally linked to osmoprotection. These findings illuminate the complex role of viruses in mediating environmental adaptation in marine holobionts. Our findings robustly identify salinity as the dominant factor governing both the taxonomic composition and functional potential of the Ulva virome. This strong salinity-driven signal aligns with the well-established role of salinity in shaping microbial and viral assemblages in transitional ecosystems like the Baltic Sea [ 15 , 17 , 18 ] . The significantly stronger explanatory power of salinity on taxonomy (R² = 0.73) compared to function (R² = 0.34) suggests that while salinity imposes a strong filter on which viruses are present, a degree of functional redundancy may be preserved across diverse viral taxa. The observed decline in viral diversity from brackish to marine conditions likely reflects the heightened environmental variability and niche heterogeneity in lower-salinity waters, which can support a more diverse array of bacterial hosts and their associated phages [ 19 ] . This environmental filtering is further reflected in viral life strategy. The overwhelming dominance of lytic phages across all salinities underscores their role in regulating bacterial populations and driving nutrient turnover. However, the subtle increase in temperate phages under high-salinity conditions hints at a strategic shift [ 20 , 21 ] . In these stable, yet osmotically challenging environments, lysogeny may be favored as a “piggyback-the-winner” strategy, allowing phages to persist while potentially conferring adaptive genes, such as the AMGs we identified, that enhance host survival under stress [ 22 , 23 , 24 , 25 ] . A key insight from our study is that viral response to salinity is not uniform but is intrinsically linked to host identity. We found that vOTUs infecting Rhodobacteraceae , keystone symbionts of Ulva [ 26 , 27 ] , exhibited the strongest correlations with salinity, displaying both positive (e.g., vOTU_07412, R = 0.43) and negative (e.g., vOTU_19466, R = -0.45) abundance shifts. This positions these phages as sensitive bioindicators of holobiont restructuring. In stark contrast, viruses targeting Bacteroidota showed no significant response to salinity, likely because their dynamics are governed by substrate availability (e.g., algal polysaccharides) rather than ionic strength [ 26 ] . This host-specific filtering effect demonstrates that salinity-driven changes in the viral community directly mirror, and may even amplify, restructuring within the bacterial microbiome, with cascading consequences for holobiont function. The most mechanistically significant finding of our study is the identification of a suite of phage-encoded AMGs that are enriched across the salinity gradient and are predicted to form an integrated defense network against osmotic stress. These AMGs appear to synergistically enhance bacterial resilience through several interconnected mechanisms. (1) Cellular Integrity: Genes like glmS and glf facilitate cell wall remodeling and exopolysaccharide production [ 28 ] , providing critical physical resistance to osmotic pressure. (2) Osmotic Balance: The AMG preT is implicated in the synthesis of compatible solutes like betaine, key intracellular osmoprotectants [ 29 , 30 ] . (3) Epigenetic Regulation & Redox Homeostasis: A cluster of genes involved in folate metabolism ( DHFR , queE ) and methylation ( DNMT1 , DNMT3A ) suggests phages can influence host epigenetics. By supporting the production of S-adenosylmethionine (SAM), these AMGs may enable stress-responsive gene regulation via DNA methylation [ 31 , 32 ] . Concurrently, these pathways contribute to redox balance, as reduced folates can scavenge reactive oxygen species (ROS) [ 33 , 34 , 35 , 36 ] , a critical function under salinity-induced oxidative stress [ 37 ] . (4) Sulfur and Cofactor Metabolism: The enrichment of sulfur metabolism genes (e.g., mec ) is particularly relevant given Ulva ’s high production of dimethylsulfoniopropionate (DMSP) [ 27 , 38 ] . Phages may thereby augment the host's capacity to cycle sulfur, contributing to osmolyte production and antioxidant defense. Similarly, AMGs for cofactor synthesis ( cobS , ubiG ) ensure the availability of essential enzyme cofactors under metabolically demanding conditions [ 38 , 39 , 40 ] . The linkage of these AMGs to hosts like Rhodobacteraceae , Cyanobacteriales , and Flavobacteriaceae indicates a targeted viral strategy. Phages are not merely random passengers; they appear to selectively augment the metabolic capabilities of ecologically critical taxa, thereby elevating the entire holobiont's adaptive capacity. The integration of viral AMGs into bacterial metabolic networks represents a powerful mechanism for rapid holobiont adaptation. Phages act as horizontal gene transfer vectors, providing a dynamic reservoir of adaptive functions that can be rapidly deployed in response to environmental change, bypassing the slower pace of mutation and selection [ 41 , 42 ] . This is crucial for sessile hosts like Ulva inhabiting dynamic coastal zones [ 43 ] . The coexistence of lytic and temperate strategies creates a dual viral influence: lytic phages control population dynamics and nutrient cycling, while temperate phages can stabilize beneficial traits within the microbiome. Beyond the holobiont, by modulating key processes like sulfur and nitrogen metabolism, antioxidant defense, and carbon turnover, Ulva -associated phages likely play an underappreciated role in shaping broader coastal biogeochemical cycles. Methods Data collection In this study, we reanalyzed public metagenomic data (BioProject PRJNA1040445) derived from 89 Ulva sensu lato samples collected across a salinity gradient (5.1–34.3 PSU) in the Baltic Sea and adjacent waters. The original dataset includes host species information (tufA-based identification) and associated microbial community sequences generated by Illumina NovaSeq 6000 paired-end sequencing, along with in situ measurements of temperature, oxygen, and nutrient concentrations (NO 3− , NO 2− , silicate, and PO 4 3− in µM) [ 44 ] . Metagenomic virome analysis: Phage identification, classification, lifestyle prediction and host relationship study and AMGs identification The viral community within the metagenomic assemblies was profiled through a comprehensive analytical workflow. Phage identification was initiated by screening assembled contigs with geNomad (v1.8.0) [ 45 ] under a lenient threshold (–min-score = 0.75) to maximize the recovery of putative viral sequences. These initial predictions were subsequently refined and annotated using our in-house ViroProfiler pipeline [ 46 ] . This pipeline first employs CheckV (v0.9.0) [ 47 ] to excise host-associated regions from contigs identified as proviruses, after which definitive viral contigs are discerned using a combination of VirSorter2 (v2.2.4) [ 48 ] and VIBRANT (v1.2.1) [ 49 ] . For functional annotation, the identification of Auxiliary Metabolic Genes (AMGs) was carried out by integrating the results from VIBRANT (v1.4.4). In terms of classification, taxonomic labels were assigned to viral contigs using a consensus approach from geNomad and VITAP (v1.7.1) [ 50 ] . Precedence was given to VITAP annotations for their higher resolution, often reaching genus or species level, while geNomad provided classifications for contigs that VITAP failed to annotate. To establish a non-redundant viral catalog, contigs were clustered into viral Operational Taxonomic Units (vOTUs) at the species level using the CheckV clustering algorithm, with thresholds of ≥ 95% Average Nucleotide Identity (ANI) and ≥ 85% Alignment Fraction (AF). The longest contig in each cluster was designated as the representative vOTU sequence. Lifestyle prediction (temperate vs. lytic) for the viruses was conducted based on a consensus from multiple tools: contigs flagged as proviruses by geNomad, VIBRANT, or CheckV, or classified as temperate by BACPHLIP (v0.9.6) [ 51 ] , were categorized as temperate phages. The host relationship study involved predicting the putative hosts for the vOTUs using iPHoP (v1.2.0) [ 52 ] , retaining only predictions with a confidence score of ≥ 90. To elucidate evolutionary relationships and uncover novel viral taxa, a gene-sharing network was constructed with vConTACT3 ( https://bitbucket.org/MAVERICLab/vcontact3 ), comparing our vOTUs against NCBI viral RefSeq database (v230). The phylogenetic tree of Duplodnaviria produced by vConTACT3 was visualized using iTOL [ 53 ] . The resulting Viral Clusters (VCs) classified by vConTACT3 represent groups of phylogenetically related genomes, offering insights into genus-level affiliations. Finally, viral abundance was estimated by mapping quality-filtered metagenomic reads to the non-redundant vOTU catalog using Bowtie2, with quantification performed by CoverM (v0.7.0) [ 54 ] . Statistical analyses All statistical analyses were conducted using R (version 4.1.0). Data processing and visualization were performed with the tidyverse package suite (version 1.3.0). Alpha diversity indices, including Richness, Shannon, Simpson, and Chao1, were compared across salinity gradients using Student’s t-test. Non-metric multidimensional scaling (NMDS) based on Bray–Curtis distances was applied to assess community structure differences. Differential abundance analysis at the viral vOTU level was performed using ANCOM-BC [ 55 ] . Differentially abundant auxiliary metabolic genes (AMGs) were identified with the Kruskal–Wallis test, and KEGG pathway enrichment analysis of these AMGs was conducted using ClusterProfiler (version 4.0) [57] . Spearman’s rank correlation was employed to evaluate relationships between differentially abundant vOTUs, AMGs, and environmental factors. A significance threshold of p < 0.05 was used for all statistical tests. Network analyses were conducted based on pairwise PERMANOVA results (using Bray-Curtis distance) to visualize the significant dissimilarities between groups. Declarations Supplementary information Supplementary Fig.S1: Differential Viral Populations and Their Environmental Correlates. Supplementary Fig.S2: Functional distribution of Ulva-associated bacterial phage AMGs based on KEGG Orthologs (KO). Supplementary Fig.S3: Structural models of phage‑encoded AMGs. Data availability The raw data for this study were sourced from a public metagenomic dataset with the accession number PRJNA1040445.The code used for data analysis is available at the GitHub repository https://github.com/wanglabx/UlvaVirome. Funding This study was supported by the open fund of China (Guangxi)-ASEAN Key Laboratory of Comprehensive Exploitation and Utilization of Aquatic Germplasm Resources, Ministry of Agriculture and Rural Affairs (No. TG20231119). Guangxi Natural Science Foundation (2025GXNSFAA069905, 2025GXNSFAA069175) and the earmarked fund for CARS (CARS-48-02). Authors' contributions All authors contributed intellectually to and agreed to this submission. SJT and JLR conducted the experiments, analyzed and interpreted the data. SJT and JLR prepared figures. SJT interpreted the data and wrote the original draft, while JLR, XW, ZZW, LD provided substantial feedback. XW contributed to the conceptual design and supervision of the study. XW and XLC provided funding support. All authors read and approved the final manuscript. Ethics declarations The animal data utilized in this study were obtained from public databases, as clearly stated in the Materials section. Hence, ethical review and approval are not applicable. Competing interests The authors declare no competing interests. Acknowledgements The authors would like to thank the computing platform support from High-Performance Computing of NWAFU. References Simon, C., McHale, M. & Sulpice, R. Applications of Ulva Biomass and Strategies to Improve Its Yield and Composition: A Perspective for Ulva Aquaculture. Biology 11, 1593 (2022). Steinhagen, S., Hoffmann, S., Pavia, H. & Toth, G. B. Molecular identification of the ubiquitous green algae Ulva reveals high biodiversity, crypticity, and invasive species in the Atlantic-Baltic Sea region. Algal Research 73, 103132 (2023). Egan, S. et al. The seaweed holobiont: understanding seaweed–bacteria interactions. FEMS Microbiology Reviews 37, 462–476 (2013). van der Loos, L. M., Eriksson, B. K. & Falcão Salles, J. The Macroalgal Holobiont in a Changing Sea. Trends in Microbiology 27, 635–650 (2019). Liu, C., Zhang, Z. & Lei, Y. Effects of salinity, light and nutrients on photosynthesis of sterile Ulva pertusa. AES 21, 795–798 (2012). Jun, Z., Ruijia, Z., Sheng, Z., Jiye, L., Haiyan, Y., Donghui, X., & Longjiang, H. (2021). Research on the Effects of Different Salinity,Temperature and Illumination for Enteromorpha prolifera Physiological Ecology. Haiyang Kaifa Yu Guanli , 38 (3), 55 ~ 60. Düsedau, L. et al. Elevated Temperature-Induced Epimicrobiome Shifts in an Invasive Seaweed Gracilaria vermiculophylla. Microorganisms 11, 599 (2023). Paix, B. et al. Synergistic effects of temperature and light affect the relationship between Taonia atomaria and its epibacterial community: a controlled conditions study. Environmental Microbiology 23, 6777–6797 (2021). Fuhrman, J. A. Marine viruses and their biogeochemical and ecological effects. Nature 399, 541–548 (1999). Middelboe, M., Jacquet, S. & Weinbauer, M. Viruses in freshwater ecosystems: an introduction to the exploration of viruses in new aquatic habitats. Freshwater Biology 53, 1069–1075 (2008). Chevallereau, A., Pons, B. J., van Houte, S. & Westra, E. R. Interactions between bacterial and phage communities in natural environments. Nat Rev Microbiol 20, 49–62 (2022). Rosenwasser, S., Ziv, C., Creveld, S. G. van & Vardi, A. Virocell Metabolism: Metabolic Innovations During Host–Virus Interactions in the Ocean. Trends in Microbiology 24, 821–832 (2016). Hurwitz, B. L. & U’Ren, J. M. Viral metabolic reprogramming in marine ecosystems. Current Opinion in Microbiology 31, 161–168 (2016). Tee, H. S., Waite, D., Lear, G. & Handley, K. M. Microbial river-to-sea continuum: gradients in benthic and planktonic diversity, osmoregulation and nutrient cycling. Microbiome 9, 190 (2021). Hussain, S. et al. Salinity-driven shifts in estuarine viral community composition and diversity near the Shenzhen coast. Appl Environ Microbiol 91, e0040725 (2025). Wang, C. et al. Divergent Patterns of Bacterial Community Structure and Function in Response to Estuarine Output in the Middle of the Bohai Sea. Front Microbiol 12, 630741 (2021). Zhang, C. et al. The communities and functional profiles of virioplankton along a salinity gradient in a subtropical estuary. Science of The Total Environment 759, 143499 (2021). Herlemann, D. P. et al. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J 5, 1571–1579 (2011). Muscatt, G., Cook, R., Millard, A., Bending, G. D. & Jameson, E. Viral metagenomics reveals diverse virus-host interactions throughout the soil depth profile. mBio 14, e02246-23 (2023). Sime-Ngando, T. Environmental bacteriophages: viruses of microbes in aquatic ecosystems. Front Microbiol 5, 355 (2014). Paul, J. H. Prophages in marine bacteria: dangerous molecular time bombs or the key to survival in the seas? ISME J 2, 579–589 (2008). Chibani-Chennoufi, S., Bruttin, A., Dillmann, M.-L. & Brüssow, H. Phage-Host Interaction: an Ecological Perspective. Journal of Bacteriology 186, 3677–3686 (2004). Abdulrahman Ashy, R. & Agustí, S. Low Host Abundance and High Temperature Determine Switching from Lytic to Lysogenic Cycles in Planktonic Microbial Communities in a Tropical Sea (Red Sea). Viruses 12, 761 (2020). Howard-Varona, C., Hargreaves, K. R., Abedon, S. T. & Sullivan, M. B. Lysogeny in nature: mechanisms, impact and ecology of temperate phages. ISME J 11, 1511–1520 (2017). Sinha, V., Goyal, A., Svenningsen, S. L., Semsey, S. & Krishna, S. In silico Evolution of Lysis-Lysogeny Strategies Reproduces Observed Lysogeny Propensities in Temperate Bacteriophages. Front. Microbiol . 8, (2017). Califano, G., Kwantes, M., Abreu, M. H., Costa, R. & Wichard, T. Cultivating the Macroalgal Holobiont: Effects of Integrated Multi-Trophic Aquaculture on the Microbiome of Ulva rigida (Chlorophyta). Frontiers in Marine Science 7, (2020). Kessler, R. W., Weiss, A., Kuegler, S., Hermes, C. & Wichard, T. Macroalgal–bacterial interactions: Role of dimethylsulfoniopropionate in microbial gardening by Ulva (Chlorophyta). Molecular Ecology 27, 1808–1819 (2018). Parada, V., Sintes, E., van Aken, H. M., Weinbauer, M. G. & Herndl, G. J. Viral Abundance, Decay, and Diversity in the Meso- and Bathypelagic Waters of the North Atlantic. Appl Environ Microbiol 73, 4429–4438 (2007). Omari Alzahrani, F. Metabolic engineering of osmoprotectants to elucidate the mechanism(s) of salt stress tolerance in crop plants. Planta 253, 24 (2021). Zou, H. et al. The metabolism and biotechnological application of betaine in microorganism. Appl Microbiol Biotechnol 100, 3865–3876 (2016). Zhaxybayeva, O. & Nesbø, C. L. Impact of Horizontal Gene Transfer on Adaptations to Extreme Environments. Journal of Molecular Biology 169403 (2025) doi: 10.1016/j.jmb.2025.169403 . Beaulaurier, J., Schadt, E. E. & Fang, G. Deciphering bacterial epigenomes using modern sequencing technologies. Nat Rev Genet 20, 157–172 (2019). Huang, D. et al. The association of prokaryotic antiviral systems and symbiotic phage communities in drinking water microbiomes. ISME COMMUN . 3, 46 (2023). Asbaghi, O. et al. Effects of Folic Acid Supplementation on Oxidative Stress Markers: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Antioxidants (Basel) 10, 871 (2021). Joshi, R., Adhikari, S., Patro, B. S., Chattopadhyay, S. & Mukherjee, T. Free radical scavenging behavior of folic acid: evidence for possible antioxidant activity. Free Radical Biology and Medicine 30, 1390–1399 (2001). Liu, C., Zhang, Z. & Lei, Y. Effects of salinity, light and nutrients on photosynthesis of sterile Ulva pertusa. AES 21, 795–798 (2012). Dvořák, P., Krasylenko, Y., Zeiner, A., Šamaj, J. & Takáč, T. Signaling Toward Reactive Oxygen Species-Scavenging Enzymes in Plants. Front Plant Sci 11, 618835 (2021). Kieft, K. et al. Ecology of inorganic sulfur auxiliary metabolism in widespread bacteriophages. Nat Commun 12, 3503 (2021). Lan, M. A. & Chao-zhou, L. I. Effects of cobalt and DFMA on polyamine content and membrane-lipid peroxidation in wheat seedling under osmotic stresses. Ganhan Diqu Nongye Yanjiu 28, 136–141 (2010). Mao, C. et al. DHODH-mediated ferroptosis defense is a targetable vulnerability in cancer. Nature 593, 586–590 (2021). Varble, A., Meaden, S., Barrangou, R., Westra, E. R. & Marraffini, L. A. Recombination between phages and CRISPR-cas loci facilitates horizontal gene transfer in staphylococci. Nat Microbiol 4, 956–963 (2019). Thomas, C. M. & Nielsen, K. M. Mechanisms of, and Barriers to, Horizontal Gene Transfer between Bacteria. Nat Rev Microbiol 3, 711–721 (2005). Wichard, T. et al. The green seaweed Ulva: a model system to study morphogenesis. Front. Plant Sci . 6, (2015). van der Loos, L. M. et al. Low functional change despite high taxonomic turnover characterizes the Ulva microbiome across a 2000-km salinity gradient. Science Advances 11, eadr6070 (2025). Camargo, A. P. et al. Identification of mobile genetic elements with geNomad. Nat Biotechnol 42, 1303–1312 (2024). Ru, J., Khan Mirzaei, M., Xue, J., Peng, X. & Deng, L. ViroProfiler: a containerized bioinformatics pipeline for viral metagenomic data analysis. Gut Microbes 15, 2192522 (2023). Nayfach, S. et al. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nat Biotechnol 39, 578–585 (2021). Guo, J. et al. VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses. Microbiome 9, 37 (2021). Kieft, K., Zhou, Z. & Anantharaman, K. VIBRANT: automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences. Microbiome 8, 90 (2020). Zheng, K. et al. VITAP: a high precision tool for DNA and RNA viral classification based on meta-omic data. Nat Commun 16, 2226 (2025). Hockenberry A.J., Wilke C.O. BACPHLIP: predicting bacteriophage lifestyle from conserved protein domains. PeerJ 9, e11396 (2021). Roux, S. et al. iPHoP: An integrated machine learning framework to maximize host prediction for metagenome-derived viruses of archaea and bacteria. PLoS Biol 21, e3002083 (2023). Letunic, I. & Bork, P. Interactive Tree of Life (iTOL) v6: recent updates to the phylogenetic tree display and annotation tool. Nucleic Acids Res 52, W78–W82 (2024). Aroney S.T.N., Newell R.J.P., Nissen J.N., Camargo A.P., Tyson G.W., Woodcroft B.J. CoverM: read alignment statistics for metagenomics. Bioinformatics 41, btaf147 (2025). Lin, H. & Peddada, S. D. Analysis of compositions of microbiomes with bias correction. Nat Commun 11, 3514 (2020). Wu, T. et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Camb) 2, 100141 (2021). Additional Declarations No competing interests reported. Supplementary Files supplemental10.30.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7989466","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":594213246,"identity":"9d13f4cb-58e6-4985-967b-b05acc4361bc","order_by":0,"name":"Shujie Tian","email":"","orcid":"","institution":"Northwest A\u0026F University","correspondingAuthor":false,"prefix":"","firstName":"Shujie","middleName":"","lastName":"Tian","suffix":""},{"id":594213247,"identity":"ff94632a-d61d-47d1-a937-75837cd8cacc","order_by":1,"name":"Jinlong Ru","email":"","orcid":"","institution":"Helmholtz Centre Munich - German Research Centre for Environmental Health","correspondingAuthor":false,"prefix":"","firstName":"Jinlong","middleName":"","lastName":"Ru","suffix":""},{"id":594213248,"identity":"962ab563-6305-4e56-b740-37cf190c54ea","order_by":2,"name":"Zezhong Wang","email":"","orcid":"","institution":"Northwest A\u0026F University","correspondingAuthor":false,"prefix":"","firstName":"Zezhong","middleName":"","lastName":"Wang","suffix":""},{"id":594213249,"identity":"ce5a2356-1efd-48bd-8db8-aa6013443ce8","order_by":3,"name":"Xiuli Chen","email":"","orcid":"","institution":"Guangxi Academy of Fishery Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xiuli","middleName":"","lastName":"Chen","suffix":""},{"id":594213253,"identity":"b9e4e28f-fdb0-45b4-9293-1bfa61afcd4e","order_by":4,"name":"Li Deng","email":"","orcid":"","institution":"Helmholtz Centre Munich - German Research Centre for Environmental Health","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Deng","suffix":""},{"id":594213254,"identity":"35ffc50c-6182-48ea-b47f-6b0517182c64","order_by":5,"name":"Xia Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYDACZgglByI+ADFjA7FajEGqZxCnBQoSG4jWYnCc+ZnEzx216dslkh828zDYyG44wPzsAT4tks1sZpK9Z47n7pyRZgjUkma84QCbuQE+LfzMDGY3eNuO5W64nWD+mIfhcOKGAzxsEvi0sDGzf7v5t+1YusHt9I9AW/4T1sLPzGN2m7etJsHgdg7IYQcIa5Fs5in/Ldt2wHDD/TeFjXMMko1nHmYzw6vF4PzxzYZv2+rkDc4c39jwpsJOtu948zO8WqDgMMwEBnjkEgJ1xCkbBaNgFIyCkQkArI1KrmJ1gq8AAAAASUVORK5CYII=","orcid":"","institution":"Northwest A\u0026F University","correspondingAuthor":true,"prefix":"","firstName":"Xia","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-10-30 12:38:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7989466/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7989466/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103224266,"identity":"64625e4a-f52d-40c1-aef3-41267cb44b49","added_by":"auto","created_at":"2026-02-23 10:47:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":385038,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuality Assessment of Viral Genomes and Viral Classification and Characteristics. a. \u003c/strong\u003eQuality Distribution of Viral Genomes. This pie chart illustrates the distribution of viral genomes across different quality categories. \u003cstrong\u003eb. \u003c/strong\u003eClassification and Characteristics of viral Genomes. This circular plot provides a detailed classification of viral genomes based on various characteristics, with outer rings displaying class taxonomy of vOTUs, family taxonomy of vOTUs, life-cycle types of vOTUs, phylum taxonomy of vOTUs’ host, and genome size distribution. The inner rings show the distribution of these characteristics within the viral genomes.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7989466/v1/e8e8835e78bc0fcf078f0224.png"},{"id":103224265,"identity":"7e5951b4-8765-4bd0-8abe-07052acb27dc","added_by":"auto","created_at":"2026-02-23 10:47:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":90298,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNMDS Ordination of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eUlva\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e-Associated Bacterial Communities.Samples are colored according to salinity.\u003c/strong\u003e Environmental vectors were fitted onto the NMDS scores of the microbial community by the R-function envfit (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, permutation = 999). \u003cstrong\u003ea.\u003c/strong\u003e NMDS of Taxonomic Composition (vOTU-based ordination). NMDS ordination of taxonomic composition (vOTU-based ordination) based on Bray – Curtis dissimilarities. \u003cstrong\u003eb.\u003c/strong\u003e NMDS of Functional Composition (Amg-based ordination). NMDS ordination of functional composition (Amg-based ordination) based on Bray – Curtis dissimilarities.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7989466/v1/62261ee9fd7f3fc88d39a9a7.png"},{"id":103224269,"identity":"b8b85afb-35e4-4d63-b1c4-45b8b5731ee1","added_by":"auto","created_at":"2026-02-23 10:47:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":132773,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSalinity-Driven Variations in Bacteriophage Diversity and Community Composition.\u003c/strong\u003e \u003cstrong\u003ea-d.\u003c/strong\u003e Phage diversity indices across four salinity gradients (Horohalinicum, Mesohaline, Polyhaline, and Euhaline): richness (\u003cstrong\u003ea\u003c/strong\u003e), Shannon index (\u003cstrong\u003eb\u003c/strong\u003e), Simpson index (\u003cstrong\u003ec\u003c/strong\u003e), and Chao1 index (\u003cstrong\u003ed\u003c/strong\u003e). Boxplots display median values with interquartile range (IQR) whiskers. Statistical significance for group-wise comparisons was determined using t-test. Significant differences are indicated by asterisks (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001). \u003cstrong\u003ef.\u003c/strong\u003e Network analysis of inter-salinity bacteriophage community relationships, based on pairwise PERMANOVA results (Bray-Curtis distance), visualizes the significant dissimilarities between salinity groups. The width of the edges in the network is proportional to the R² values, with significant edges (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) highlighted in red and non-significant edges in gray. Additionally, the length of the edges is inversely proportional to the R² values, meaning that shorter edges represent stronger relationships.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7989466/v1/911ecef5575ccbd59d1a8916.png"},{"id":103224267,"identity":"0c55f22c-b10f-4eb4-9c7f-0ff3ff12a9e8","added_by":"auto","created_at":"2026-02-23 10:47:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":289855,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSalinity-Driven vOTU Dynamics and Host-Specific Responses.\u003c/strong\u003e \u003cstrong\u003ea.\u003c/strong\u003e Heatmap of differential vOTUs identified using the ANCOM-BC2 trend test (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). The log2 fold change values represent the relative abundance changes of vOTUs compared to the Horohalinicum group across mesohaline, polyhaline, and euhaline conditions. Red indicates upregulation, while blue indicates downregulation. \u003cstrong\u003eb.\u003c/strong\u003e Spearman correlation analysis of differential vOTUs with salinity. The scatter plots show the significant positive and negative correlations between specific vOTUs and salinity levels, with the correlation coefficients (R) and p-values indicated for each relationship. \u003cstrong\u003ec.\u003c/strong\u003e Taxonomic classification of differential vOTUs at the phylum, class, order, and family levels.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7989466/v1/d777a02be956a06d2c9585c4.png"},{"id":103506069,"identity":"e6b8f238-e041-400d-989f-921362e8bf55","added_by":"auto","created_at":"2026-02-26 13:33:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":155475,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional Diversity of Ulva-Associated Phages Across Different Salinity Conditions. a-d:\u003c/strong\u003e Box plots showing the richness \u003cstrong\u003e(a)\u003c/strong\u003e, Shannon diversity \u003cstrong\u003e(b)\u003c/strong\u003e, Simpson diversity \u003cstrong\u003e(c)\u003c/strong\u003e, and Chao1 index \u003cstrong\u003e(d)\u003c/strong\u003e of phage-encoded amg (based on KO) in \u003cem\u003eUlva\u003c/em\u003e-associated phages across different salinity conditions (horohaline, mesohaline, polyhaline, and euhaline). Boxplots display median values with interquartile range (IQR) whiskers. Statistical significance for group-wise comparisons was determined using t-test. Significant differences are indicated by asterisks (*\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001), and \"ns\" denotes non-significant differences. \u003cstrong\u003ee.\u003c/strong\u003e Non-metric multidimensional scaling (NMDS) plot illustrating the community composition of phage-encoded AMG in Ulva-associated bacteria across different salinity conditions. \u003cstrong\u003ef.\u003c/strong\u003e Network analysis, based on pairwise PERMANOVA results (Bray-Curtis distance), visualizes the significant dissimilarities and depicts the contributions of different salinity conditions to the overall phage functional diversity. The width of the edges in the network is proportional to the R² values, with significant edges (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) highlighted in red and non-significant edges in gray. Additionally, the length of the edges is inversely proportional to the R² values, meaning that shorter edges represent stronger relationships.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7989466/v1/3b0b963af5924aad58bcb3f1.png"},{"id":103224272,"identity":"c52687a5-3a06-472b-a3cd-8f863bd38aee","added_by":"auto","created_at":"2026-02-23 10:47:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":438245,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional Distribution and Host Association of Ulva-Associated Bacterial Phage AMGs.\u003c/strong\u003e \u003cstrong\u003ea.\u003c/strong\u003eDifferentially enriched KEGG pathways based on KO, using the Kruskal-Wallis test (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). \u003cstrong\u003eb.\u003c/strong\u003e Host distribution of differentially enriched AMGs at various taxonomic levels, including phylum, class, order and family level.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7989466/v1/10247e2ab164d3b7ea8eb085.png"},{"id":106774701,"identity":"51de6759-96c5-4c65-aa89-68e81e468bbb","added_by":"auto","created_at":"2026-04-13 10:43:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2271672,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7989466/v1/cd842e4d-b8bd-4d6c-b53e-672b501ecc4b.pdf"},{"id":103224268,"identity":"67f6901d-f570-47e4-b6bd-936bdb325cd0","added_by":"auto","created_at":"2026-02-23 10:47:06","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2267732,"visible":true,"origin":"","legend":"","description":"","filename":"supplemental10.30.docx","url":"https://assets-eu.researchsquare.com/files/rs-7989466/v1/5914d59628e9fd1be924b6d8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Salinity-Driven Dynamics of the Ulva-Associated Virome and Their Implications for Holobiont Adaptation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMicrobial communities engage in complex, dynamic symbioses with eukaryotic hosts, playing critical roles in host development, health, and environmental adaptation. In marine environments, macroalgae, such as the genus \u003cem\u003eUlva\u003c/em\u003e, host structured bacterial consortia that are integral to algal physiology and ecological function. Owing to its rapid growth and euryhaline adaptations \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e, \u003cem\u003eUlva\u003c/em\u003e is widely used as a canonical model system for investigating algal-bacterial interactions. The \u003cem\u003eUlva\u003c/em\u003e-microbe assemblage constitutes a holobiont, a cohesive entity characterized by a high degree of metabolic interdependence. The associated bacterial biofilm acts as a \"second skin,\" facilitating the exchange of nutrients and vitamins, providing physical protection, defending against pathogens, and mitigating environmental stress \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCritically, the structure of the \u003cem\u003eUlva\u003c/em\u003e bacteriome is highly dynamic and profoundly influenced by environmental factors, including temperature, light, and salinity \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. However, the mechanisms through which these factors, particularly salinity, mediate bacterial community composition and evolution to impact host fitness remain elusive.\u003c/p\u003e \u003cp\u003eViruses, especially phages, are now recognized as master regulators of bacterial communities in aquatic ecosystems \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Through lysis, lysogeny, and horizontal gene transfer \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, phages shape the structure, function, and evolutionary trajectories of their bacterial hosts. A key mechanism of this influence is through virally-encoded auxiliary metabolic genes (AMGs), which can reprogram host metabolism to enhance environmental adaptability \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. These AMGs, implicated in diverse processes from nutrient cycling to biofilm formation \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, have been identified in a range of environments, including those infecting key phytoplankton \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Notably, viral communities themselves exhibit distinct structural and functional profiles across environmental gradients, with salinity being a major determinant in estuarine systems \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. This suggests that environmental shifts may directly alter the phage community, which in turn modulates bacterial-host interactions. Consequently, a comprehensive understanding of holobiont resilience demands a tripartite framework encompassing \"host (\u003cem\u003eUlva\u003c/em\u003e)-bacteria-phage\" interactions.\u003c/p\u003e \u003cp\u003eDespite this, the response of the \u003cem\u003eUlva\u003c/em\u003e-associated virome to salinity fluctuation and its subsequent impact on the host holobiont are virtually unknown. To address this critical gap, we employed metagenomic sequencing of a large-scale dataset (n\u0026thinsp;=\u0026thinsp;89) to systematically investigate the viral community associated with \u003cem\u003eUlva\u003c/em\u003e across a salinity gradient. Our study aims to: (i) characterize the dynamics of salinity in shaping the diversity and structure of the \u003cem\u003eUlva\u003c/em\u003e virome; (ii) identify key phage-encoded AMGs, particularly those implicated in osmoregulation and holobiont salinity adaptation; and (iii) infer shifts in viral life strategies (lytic vs. lysogenic modes) in response to salinity stress. Our findings indicate that the functional profile of the \u003cem\u003eUlva\u003c/em\u003e virome is enriched in genes related to osmoprotection and stress response. Furthermore, we propose that salinity stress induces a shift towards lysogeny, a strategy that may promote bacterial host survival under such stressful conditions. This study unveils the mechanisms by which viruses contribute to microbe-host environmental adaptation, providing novel insights into holobiont resilience in a changing world.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003e1. Viral Contig Assembly, Quality Assessment, and Taxonomic Profiling\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMetagenomic assembly of 89 \u003cem\u003eulva\u003c/em\u003e samples yielded 5683 quality-filtered viral contigs. Among these, we identified 651 high-confidence contigs (completeness\u0026thinsp;\u0026ge;\u0026thinsp;50%) with a mean length of 13,025.66 bp (N50\u0026thinsp;=\u0026thinsp;15713 bp). Clustering at 95% average nucleotide identity (ANI) produced 4,888 viral operational taxonomic units (vOTUs), including 432 with \u0026ge;\u0026thinsp;50% completeness (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The quality assessment revealed that 8.08% of the viral genomes were medium or high quality (contamination\u0026thinsp;\u0026lt;\u0026thinsp;5%), while the quality of 13.33% remained undetermined (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eTaxonomic and functional characterization of the medium-to-high-quality genomes unveiled a diverse viral community (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). All classified genomes (100%) belonged to the tailed virus class \u003cem\u003eCaudoviricetes\u003c/em\u003e, consistent with their dominance in marine environments. However, the vast majority (98.27%) could not be assigned to a known family, underscoring the extensive uncharacterized diversity of algae-associated viruses. Among the classified families, \u003cem\u003eAutographiviridae\u003c/em\u003e (1.08%) was the most prevalent, followed by \u003cem\u003eSchitoviridae\u003c/em\u003e (\u003cem\u003esplitsaviruses\u003c/em\u003e, 0.43%), \u003cem\u003eZobellviridae\u003c/em\u003e (0.11%), and \u003cem\u003eDemerecviridae\u003c/em\u003e (0.11%).\u003c/p\u003e \u003cp\u003eAnalysis of viral life strategies indicated a community dominated by virulent (lytic) phages (81.3%), with temperate (lysogenic) phages representing for a smaller proportion (18.7%). The genome sizes were predominantly clustered within 20\u0026ndash;40 kbp range(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), aligning with typical bacteriophage genomes.\u003c/p\u003e \u003cp\u003eHost prediction analysis revealed that the vOTUs primarily targeted key bacterial phyla within the Ulva microbiome, with \u003cem\u003ePseudomonadota\u003c/em\u003e (30.7%) being the most prevalent, followed by \u003cem\u003eBacteroidota\u003c/em\u003e (17.62%) and \u003cem\u003eBacillota\u003c/em\u003e (1.84%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). This wide phylogenetic range of predicted hosts suggests that \u003cem\u003eUlva\u003c/em\u003e-associated phages infect a diverse array of bacteria, positioning them as potential key regulators of microbiome stability and function under environmental fluctuations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e2. Salinity is the Dominant Factor Structuring Viral Community Composition and Functional Potential\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo investigate the influence of environmental factors on \u003cem\u003eUlva\u003c/em\u003e-associated virome, we conducted non-metric multidimensional scaling (NMDS) analyses based on viral taxonomic composition (15,324 vOTUs) and functional potential (2,817 KEGG-annotated auxiliary metabolic genes, AMGs) across a salinity gradient of 5\u0026ndash;35 practical salinity unit (PSU), while concurrently evaluating the effects of temperature, oxygen, and nutrients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea \u0026amp; b).\u003c/p\u003e \u003cp\u003eSalinity emerged as the primary and strongest driver of viral taxonomic composition, explaining a substantial proportion of the variation (R\u0026sup2; = 0.73, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), while temperature exerted a secondary, yet detectable influence (R\u0026sup2; = 0.10, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), while the effects of nitrite (NO₂) (R\u0026sup2; = 0.07, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04) and silicate (R\u0026sup2; = 0.08, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) were minor. Other factors, including oxygen and the majority of nutrients (NOx, NO₃, PO₄\u0026sup3;⁻), showed no significant correlation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eA parallel pattern was observed for the viral functional profile. Salinity again represented the most significant environmental filter (R\u0026sup2; = 0.34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), with temperature as the only other factor demonstrating a statistically significant effect (R\u0026sup2; = 0.10, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). In contrast, oxygen and nutrient concentrations (including NO₂, NO₃, NOx, silicate, and PO₄\u0026sup3;⁻) had negligible impacts on functional composition (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eIn summary, our analyses unequivocally identify salinity as the principal environmental factor shaping both the taxonomic structure and functional capacity of the \u003cem\u003eUlva\u003c/em\u003e-associated phage community, whereas dissolved nutrients and oxygen concentrations play subordinate roles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3. Bacteriophage Diversity and Community Structure Shift Across Salinity Gradients\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo further delineate the impact of salinity on the viral community, we analyzed phage diversity and composition across a defined salinity gradient: horohalinicum (5\u0026ndash;8 PSU), mesohaline (8\u0026ndash;18 PSU), polyhaline (18\u0026ndash;30 PSU), and euhaline (30\u0026ndash;35 PSU).\u003c/p\u003e \u003cp\u003ePhage richness and diversity varied significantly across these salinity regimes. The horohalinicum and mesohaline communities exhibited significantly higher richness and Shannon diversity compared to the polyhaline and euhaline groups, with the euhaline group showing the lowest values (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, b). This pattern in richness was corroborated by the Chao1 index (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). In contrast, the Simpson index revealed no significant differences, indicating consistent community evenness across all salinity levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eCommunity composition was also strongly influenced by salinity. NMDS ordination, supported by PERMANOVA and ANOSIM tests, confirmed significant compositional differences among the salinity groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). The horohalinicum and mesohaline groups formed closely clustered assemblages, whereas the polyhaline and euhaline groups were more distinct. Pairwise comparisons further validated that the taxonomic composition of the \u003cem\u003eUlva\u003c/em\u003e-associated bacteriophage community was significantly different between every salinity region (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all comparisons; pairwise Adonis test) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e4. Host-Linked vOTU Dynamics Across Salinity Gradients\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBuilding on the finding that salinity is the primary driver of viral community composition (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), we investigated its specific impact on viral operational taxonomic units (vOTUs). Differential abundance analysis (ANCOM-BC2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) revealed that vOTUs formed two distinct, salinity-dependent response patterns relative to the horohalinicum reference (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea). One group increased in abundance with rising salinity, peaking in mesohaline conditions (e.g., vOTU_20393, vOTU_20175, and vOTU_19318). The other group decreased most markedly in euhaline waters, as exemplified by vOTU_06052 (log₂FC = \u0026minus;\u0026thinsp;1.37, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and vOTU_20194.\u003c/p\u003e \u003cp\u003eTaxonomic profiling mapped these salinity-responsive vOTUs to three key bacterial phyla, revealing phylum-specific response patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). \u003cem\u003ePlanctomycetota\u003c/em\u003e-associated vOTUs (e.g., vOTU_20393) showed moderate positive correlations with salinity (R\u0026thinsp;=\u0026thinsp;0.31\u0026ndash;0.46, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). \u003cem\u003ePseudomonadota\u003c/em\u003e-associated vOTUs (primarily infecting \u003cem\u003eRhodobacteraceae\u003c/em\u003e), such as vOTU_06052, vOTU_07412, vOTU_20168, vOTU_19466, and vOTU_19457, exhibited the strongest salinity responses, spanning both positive and negative correlations (R = -0.45\u0026ndash;0.46, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). \u003cem\u003eBacteroidota\u003c/em\u003e-associated vOTUs (e.g., vOTU_06029) showed no significant correlation with salinity (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting their dynamics are driven by other factors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e5. Functional Diversity of\u003c/b\u003e \u003cb\u003eUlva\u003c/b\u003e \u003cb\u003eVirome Across Salinity Gradients\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo investigate the functional potential of the \u003cem\u003eUlva\u003c/em\u003e-associated virome, we annotated phage-encoded AMGs against the KEGG database. These AMGs were implicated in diverse metabolic pathways, including carbohydrate and energy metabolism, metabolism of cofactors and vitamins, lipid metabolism, and metabolism of amino acids, among others (Fig. S2).\u003c/p\u003e \u003cp\u003eGiven that salinity was identified as the primary driver of viral functional composition (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), we further assessed its impact on functional diversity. The richness of phage-encoded AMGs (based on KEGG Orthology, KO) was significantly higher in horohalinicum and mesohaline environments than in polyhaline and euhaline conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). This pattern of enhanced functional diversity and richness in lower salinity regimes was consistently supported by the Shannon diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb) and Chao1 indices (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). The Simpson index mirrored this trend, showing higher diversity in horohalinicum and mesohaline groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eNMDS revealed distinct clustering of viral functional profiles across the salinity gradient (stress\u0026thinsp;=\u0026thinsp;0.186; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee), a finding corroborated by significant PERMANOVA (R\u0026sup2; = 0.078, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and ANOSIM (R\u0026thinsp;=\u0026thinsp;0.129, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) results. Network analysis further illustrated the relationships among salinity conditions, highlighting the particularly significant roles of horohalinicum and euhaline environments in shaping the overall functional diversity structure (R\u0026sup2;=0.1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e6. Phage-encoded AMGs Enriched in Salt Tolerance Pathways and Linked to Key Bacterial Hosts\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo identify phage-encoded AMGs responsive to salinity, we performed differential abundance analysis (Kruskal-Wallis test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) followed by KEGG enrichment analysis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This approach revealed 15 differentially abundant AMGs which are significantly enriched in salt tolerance-associated pathways, including amino sugar and nucleotide sugar metabolism, cofactor biosynthesis, and sulfur metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Specifically, these AMGs have putative roles in diverse processes, including cell wall modification (\u003cem\u003eglmS\u003c/em\u003e, \u003cem\u003eglf\u003c/em\u003e), osmolyte synthesis (\u003cem\u003epreT\u003c/em\u003e), folate metabolism (\u003cem\u003eDHFR\u003c/em\u003e, \u003cem\u003equeE\u003c/em\u003e, \u003cem\u003eDNMT1\u003c/em\u003e, \u003cem\u003eDNMT3A\u003c/em\u003e), antioxidant defense (\u003cem\u003edfrB\u003c/em\u003e, \u003cem\u003emec\u003c/em\u003e), and cofactor synthesis (\u003cem\u003ecobS\u003c/em\u003e, \u003cem\u003erhnA-cobC\u003c/em\u003e, \u003cem\u003eubiG\u003c/em\u003e). Functional annotation indicates these pathways form an integrated network to support bacterial osmoadaptation, suggesting these genes contribute to host bacterial salt tolerance through synergistic mechanisms. In addition, we predicted the protein structures of these phage-encoded AMGs. Among them, nine AMGs (Fig. S3) showed high similarity to bacterial-derived homologous protein.\u003c/p\u003e \u003cp\u003eHost prediction analysis linked these salinity-responsive AMGs to three dominant bacterial lineages within the \u003cem\u003eUlva\u003c/em\u003e microbiome: \u003cem\u003ePseudomonadota\u003c/em\u003e (specifically \u003cem\u003eRhodobacteraceae\u003c/em\u003e), \u003cem\u003eCyanobacteriota\u003c/em\u003e (\u003cem\u003eCyanobacteriales\u003c/em\u003e), and \u003cem\u003eBacteroidota\u003c/em\u003e (\u003cem\u003eFlavobacteriaceae\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Notably, these predicted host associations align with established ecological functions, organic sulfur metabolism in \u003cem\u003eRhodobacteraceae\u003c/em\u003e, nitrogen fixation in \u003cem\u003eCyanobacteriales\u003c/em\u003e, and polysaccharide degradation in \u003cem\u003eFlavobacteriaceae\u003c/em\u003e, suggesting that phages may modulate core microbial processes critical for holobiont salinity adaptation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur metagenomic investigation provides compelling evidence that salinity acts as a paramount environmental filter structuring the \u003cem\u003eUlva\u003c/em\u003e-associated viral community, with bacteriophages serving as active contributors to holobiont adaptation. We demonstrate this through a consistent pattern of declining viral diversity along the salinity gradient, host-specific viral dynamics, and a marked enrichment of phage-encoded AMGs functionally linked to osmoprotection. These findings illuminate the complex role of viruses in mediating environmental adaptation in marine holobionts.\u003c/p\u003e \u003cp\u003eOur findings robustly identify salinity as the dominant factor governing both the taxonomic composition and functional potential of the \u003cem\u003eUlva\u003c/em\u003e virome. This strong salinity-driven signal aligns with the well-established role of salinity in shaping microbial and viral assemblages in transitional ecosystems like the Baltic Sea\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. The significantly stronger explanatory power of salinity on taxonomy (R\u0026sup2; = 0.73) compared to function (R\u0026sup2; = 0.34) suggests that while salinity imposes a strong filter on which viruses are present, a degree of functional redundancy may be preserved across diverse viral taxa.\u003c/p\u003e \u003cp\u003eThe observed decline in viral diversity from brackish to marine conditions likely reflects the heightened environmental variability and niche heterogeneity in lower-salinity waters, which can support a more diverse array of bacterial hosts and their associated phages \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. This environmental filtering is further reflected in viral life strategy. The overwhelming dominance of lytic phages across all salinities underscores their role in regulating bacterial populations and driving nutrient turnover. However, the subtle increase in temperate phages under high-salinity conditions hints at a strategic shift \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. In these stable, yet osmotically challenging environments, lysogeny may be favored as a \u0026ldquo;piggyback-the-winner\u0026rdquo; strategy, allowing phages to persist while potentially conferring adaptive genes, such as the AMGs we identified, that enhance host survival under stress \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA key insight from our study is that viral response to salinity is not uniform but is intrinsically linked to host identity. We found that vOTUs infecting \u003cem\u003eRhodobacteraceae\u003c/em\u003e, keystone symbionts of \u003cem\u003eUlva\u003c/em\u003e \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e, exhibited the strongest correlations with salinity, displaying both positive (e.g., vOTU_07412, R\u0026thinsp;=\u0026thinsp;0.43) and negative (e.g., vOTU_19466, R = -0.45) abundance shifts. This positions these phages as sensitive bioindicators of holobiont restructuring. In stark contrast, viruses targeting \u003cem\u003eBacteroidota\u003c/em\u003e showed no significant response to salinity, likely because their dynamics are governed by substrate availability (e.g., algal polysaccharides) rather than ionic strength \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. This host-specific filtering effect demonstrates that salinity-driven changes in the viral community directly mirror, and may even amplify, restructuring within the bacterial microbiome, with cascading consequences for holobiont function.\u003c/p\u003e \u003cp\u003eThe most mechanistically significant finding of our study is the identification of a suite of phage-encoded AMGs that are enriched across the salinity gradient and are predicted to form an integrated defense network against osmotic stress. These AMGs appear to synergistically enhance bacterial resilience through several interconnected mechanisms. (1) Cellular Integrity: Genes like \u003cem\u003eglmS\u003c/em\u003e and \u003cem\u003eglf\u003c/em\u003e facilitate cell wall remodeling and exopolysaccharide production \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e, providing critical physical resistance to osmotic pressure. (2) Osmotic Balance: The AMG \u003cem\u003epreT\u003c/em\u003e is implicated in the synthesis of compatible solutes like betaine, key intracellular osmoprotectants \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. (3) Epigenetic Regulation \u0026amp; Redox Homeostasis: A cluster of genes involved in folate metabolism (\u003cem\u003eDHFR\u003c/em\u003e, \u003cem\u003equeE\u003c/em\u003e) and methylation (\u003cem\u003eDNMT1\u003c/em\u003e, \u003cem\u003eDNMT3A\u003c/em\u003e) suggests phages can influence host epigenetics. By supporting the production of S-adenosylmethionine (SAM), these AMGs may enable stress-responsive gene regulation via DNA methylation \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Concurrently, these pathways contribute to redox balance, as reduced folates can scavenge reactive oxygen species (ROS) \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e, a critical function under salinity-induced oxidative stress \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. (4) Sulfur and Cofactor Metabolism: The enrichment of sulfur metabolism genes (e.g., \u003cem\u003emec\u003c/em\u003e) is particularly relevant given \u003cem\u003eUlva\u003c/em\u003e\u0026rsquo;s high production of dimethylsulfoniopropionate (DMSP) \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Phages may thereby augment the host's capacity to cycle sulfur, contributing to osmolyte production and antioxidant defense. Similarly, AMGs for cofactor synthesis (\u003cem\u003ecobS\u003c/em\u003e, \u003cem\u003eubiG\u003c/em\u003e) ensure the availability of essential enzyme cofactors under metabolically demanding conditions \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. The linkage of these AMGs to hosts like \u003cem\u003eRhodobacteraceae\u003c/em\u003e, \u003cem\u003eCyanobacteriales\u003c/em\u003e, and \u003cem\u003eFlavobacteriaceae\u003c/em\u003e indicates a targeted viral strategy. Phages are not merely random passengers; they appear to selectively augment the metabolic capabilities of ecologically critical taxa, thereby elevating the entire holobiont's adaptive capacity.\u003c/p\u003e \u003cp\u003eThe integration of viral AMGs into bacterial metabolic networks represents a powerful mechanism for rapid holobiont adaptation. Phages act as horizontal gene transfer vectors, providing a dynamic reservoir of adaptive functions that can be rapidly deployed in response to environmental change, bypassing the slower pace of mutation and selection \u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. This is crucial for sessile hosts like \u003cem\u003eUlva\u003c/em\u003e inhabiting dynamic coastal zones \u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe coexistence of lytic and temperate strategies creates a dual viral influence: lytic phages control population dynamics and nutrient cycling, while temperate phages can stabilize beneficial traits within the microbiome. Beyond the holobiont, by modulating key processes like sulfur and nitrogen metabolism, antioxidant defense, and carbon turnover, \u003cem\u003eUlva\u003c/em\u003e-associated phages likely play an underappreciated role in shaping broader coastal biogeochemical cycles.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eIn this study, we reanalyzed public metagenomic data (BioProject PRJNA1040445) derived from 89 \u003cem\u003eUlva\u003c/em\u003e sensu lato samples collected across a salinity gradient (5.1\u0026ndash;34.3 PSU) in the Baltic Sea and adjacent waters. The original dataset includes host species information (tufA-based identification) and associated microbial community sequences generated by Illumina NovaSeq 6000 paired-end sequencing, along with in situ measurements of temperature, oxygen, and nutrient concentrations (NO\u003csup\u003e3\u0026minus;\u003c/sup\u003e, NO\u003csup\u003e2\u0026minus;\u003c/sup\u003e, silicate, and PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e in \u0026micro;M) \u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMetagenomic virome analysis: Phage identification, classification, lifestyle prediction and host relationship study and AMGs identification\u003c/h3\u003e\n\u003cp\u003eThe viral community within the metagenomic assemblies was profiled through a comprehensive analytical workflow. Phage identification was initiated by screening assembled contigs with geNomad (v1.8.0) \u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e under a lenient threshold (\u0026ndash;min-score\u0026thinsp;=\u0026thinsp;0.75) to maximize the recovery of putative viral sequences. These initial predictions were subsequently refined and annotated using our in-house ViroProfiler pipeline \u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. This pipeline first employs CheckV (v0.9.0) \u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e to excise host-associated regions from contigs identified as proviruses, after which definitive viral contigs are discerned using a combination of VirSorter2 (v2.2.4) \u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e and VIBRANT (v1.2.1) \u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor functional annotation, the identification of Auxiliary Metabolic Genes (AMGs) was carried out by integrating the results from VIBRANT (v1.4.4). In terms of classification, taxonomic labels were assigned to viral contigs using a consensus approach from geNomad and VITAP (v1.7.1) \u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e. Precedence was given to VITAP annotations for their higher resolution, often reaching genus or species level, while geNomad provided classifications for contigs that VITAP failed to annotate. To establish a non-redundant viral catalog, contigs were clustered into viral Operational Taxonomic Units (vOTUs) at the species level using the CheckV clustering algorithm, with thresholds of \u0026ge;\u0026thinsp;95% Average Nucleotide Identity (ANI) and \u0026ge;\u0026thinsp;85% Alignment Fraction (AF). The longest contig in each cluster was designated as the representative vOTU sequence.\u003c/p\u003e \u003cp\u003eLifestyle prediction (temperate vs. lytic) for the viruses was conducted based on a consensus from multiple tools: contigs flagged as proviruses by geNomad, VIBRANT, or CheckV, or classified as temperate by BACPHLIP (v0.9.6) \u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e, were categorized as temperate phages. The host relationship study involved predicting the putative hosts for the vOTUs using iPHoP (v1.2.0) \u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e, retaining only predictions with a confidence score of \u0026ge;\u0026thinsp;90. To elucidate evolutionary relationships and uncover novel viral taxa, a gene-sharing network was constructed with vConTACT3 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bitbucket.org/MAVERICLab/vcontact3\u003c/span\u003e\u003cspan address=\"https://bitbucket.org/MAVERICLab/vcontact3\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), comparing our vOTUs against NCBI viral RefSeq database (v230). The phylogenetic tree of \u003cem\u003eDuplodnaviria\u003c/em\u003e produced by vConTACT3 was visualized using iTOL \u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e. The resulting Viral Clusters (VCs) classified by vConTACT3 represent groups of phylogenetically related genomes, offering insights into genus-level affiliations.\u003c/p\u003e \u003cp\u003eFinally, viral abundance was estimated by mapping quality-filtered metagenomic reads to the non-redundant vOTU catalog using Bowtie2, with quantification performed by CoverM (v0.7.0) \u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eAll statistical analyses were conducted using R (version 4.1.0). Data processing and visualization were performed with the tidyverse package suite (version 1.3.0). Alpha diversity indices, including Richness, Shannon, Simpson, and Chao1, were compared across salinity gradients using Student\u0026rsquo;s t-test. Non-metric multidimensional scaling (NMDS) based on Bray\u0026ndash;Curtis distances was applied to assess community structure differences. Differential abundance analysis at the viral vOTU level was performed using ANCOM-BC \u003csup\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e. Differentially abundant auxiliary metabolic genes (AMGs) were identified with the Kruskal\u0026ndash;Wallis test, and KEGG pathway enrichment analysis of these AMGs was conducted using ClusterProfiler (version 4.0) \u003csup\u003e[57]\u003c/sup\u003e. Spearman\u0026rsquo;s rank correlation was employed to evaluate relationships between differentially abundant vOTUs, AMGs, and environmental factors. A significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used for all statistical tests. Network analyses were conducted based on pairwise PERMANOVA results (using Bray-Curtis distance) to visualize the significant dissimilarities between groups.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary Fig.S1: Differential Viral Populations and Their Environmental Correlates.\u003c/p\u003e\n\u003cp\u003eSupplementary Fig.S2: Functional distribution of Ulva-associated bacterial phage AMGs based on KEGG Orthologs (KO).\u003c/p\u003e\n\u003cp\u003eSupplementary Fig.S3: Structural models of phage‑encoded AMGs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data for this study were sourced from a public metagenomic dataset with the accession number PRJNA1040445.The code used for data analysis is available at the GitHub repository https://github.com/wanglabx/UlvaVirome.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the open fund of China (Guangxi)-ASEAN Key Laboratory of Comprehensive Exploitation and Utilization of Aquatic Germplasm Resources, Ministry of Agriculture and Rural Affairs (No. TG20231119).\u0026nbsp;Guangxi Natural Science Foundation (2025GXNSFAA069905, 2025GXNSFAA069175) and the earmarked fund for CARS (CARS-48-02).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed intellectually to and agreed to this submission.\u0026nbsp;SJT and JLR\u0026nbsp;conducted the experiments,\u0026nbsp;analyzed and interpreted the data.\u0026nbsp;SJT and JLR prepared figures. SJT interpreted the data and wrote the original draft,\u0026nbsp;while JLR, XW, ZZW, LD\u0026nbsp;provided substantial feedback.\u0026nbsp;XW contributed to the conceptual design and supervision of the study. XW and XLC provided funding support.\u0026nbsp;All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe animal data utilized in this study were obtained from public databases, as clearly stated in the Materials section. Hence, ethical review and approval are not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the computing platform support from High-Performance Computing of NWAFU.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSimon, C., McHale, M. \u0026amp; Sulpice, R. Applications of Ulva Biomass and Strategies to Improve Its Yield and Composition: A Perspective for Ulva Aquaculture. \u003cem\u003eBiology\u003c/em\u003e 11, 1593 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteinhagen, S., Hoffmann, S., Pavia, H. \u0026amp; Toth, G. B. Molecular identification of the ubiquitous green algae Ulva reveals high biodiversity, crypticity, and invasive species in the Atlantic-Baltic Sea region. \u003cem\u003eAlgal Research\u003c/em\u003e 73, 103132 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEgan, S. et al. The seaweed holobiont: understanding seaweed\u0026ndash;bacteria interactions. \u003cem\u003eFEMS Microbiology Reviews\u003c/em\u003e 37, 462\u0026ndash;476 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan der Loos, L. M., Eriksson, B. K. \u0026amp; Falc\u0026atilde;o Salles, J. The Macroalgal Holobiont in a Changing Sea. \u003cem\u003eTrends in Microbiology\u003c/em\u003e 27, 635\u0026ndash;650 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, C., Zhang, Z. \u0026amp; Lei, Y. Effects of salinity, light and nutrients on photosynthesis of sterile Ulva pertusa. \u003cem\u003eAES\u003c/em\u003e 21, 795\u0026ndash;798 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJun, Z., Ruijia, Z., Sheng, Z., Jiye, L., Haiyan, Y., Donghui, X., \u0026amp; Longjiang, H. (2021). Research on the Effects of Different Salinity,Temperature and Illumination for Enteromorpha prolifera Physiological Ecology. \u003cem\u003eHaiyang Kaifa Yu Guanli\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(3), 55\u0026thinsp;~\u0026thinsp;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026uuml;sedau, L. et al. Elevated Temperature-Induced Epimicrobiome Shifts in an Invasive Seaweed Gracilaria vermiculophylla. \u003cem\u003eMicroorganisms\u003c/em\u003e 11, 599 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaix, B. et al. Synergistic effects of temperature and light affect the relationship between Taonia atomaria and its epibacterial community: a controlled conditions study. \u003cem\u003eEnvironmental Microbiology\u003c/em\u003e 23, 6777\u0026ndash;6797 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFuhrman, J. A. Marine viruses and their biogeochemical and ecological effects. \u003cem\u003eNature\u003c/em\u003e 399, 541\u0026ndash;548 (1999).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiddelboe, M., Jacquet, S. \u0026amp; Weinbauer, M. Viruses in freshwater ecosystems: an introduction to the exploration of viruses in new aquatic habitats. \u003cem\u003eFreshwater Biology\u003c/em\u003e 53, 1069\u0026ndash;1075 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChevallereau, A., Pons, B. J., van Houte, S. \u0026amp; Westra, E. R. Interactions between bacterial and phage communities in natural environments. \u003cem\u003eNat Rev Microbiol\u003c/em\u003e 20, 49\u0026ndash;62 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosenwasser, S., Ziv, C., Creveld, S. G. van \u0026amp; Vardi, A. Virocell Metabolism: Metabolic Innovations During Host\u0026ndash;Virus Interactions in the Ocean. \u003cem\u003eTrends in Microbiology\u003c/em\u003e 24, 821\u0026ndash;832 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHurwitz, B. L. \u0026amp; U\u0026rsquo;Ren, J. M. Viral metabolic reprogramming in marine ecosystems. Current Opinion in Microbiology 31, 161\u0026ndash;168 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTee, H. S., Waite, D., Lear, G. \u0026amp; Handley, K. M. Microbial river-to-sea continuum: gradients in benthic and planktonic diversity, osmoregulation and nutrient cycling. \u003cem\u003eMicrobiome\u003c/em\u003e 9, 190 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHussain, S. et al. Salinity-driven shifts in estuarine viral community composition and diversity near the Shenzhen coast. \u003cem\u003eAppl Environ Microbiol\u003c/em\u003e 91, e0040725 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, C. et al. Divergent Patterns of Bacterial Community Structure and Function in Response to Estuarine Output in the Middle of the Bohai Sea. \u003cem\u003eFront Microbiol\u003c/em\u003e 12, 630741 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, C. et al. The communities and functional profiles of virioplankton along a salinity gradient in a subtropical estuary. \u003cem\u003eScience of The Total Environment\u003c/em\u003e 759, 143499 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerlemann, D. P. et al. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. \u003cem\u003eISME J\u003c/em\u003e 5, 1571\u0026ndash;1579 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuscatt, G., Cook, R., Millard, A., Bending, G. D. \u0026amp; Jameson, E. Viral metagenomics reveals diverse virus-host interactions throughout the soil depth profile. \u003cem\u003emBio\u003c/em\u003e 14, e02246-23 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSime-Ngando, T. Environmental bacteriophages: viruses of microbes in aquatic ecosystems. \u003cem\u003eFront Microbiol\u003c/em\u003e 5, 355 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaul, J. H. Prophages in marine bacteria: dangerous molecular time bombs or the key to survival in the seas? \u003cem\u003eISME J\u003c/em\u003e 2, 579\u0026ndash;589 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChibani-Chennoufi, S., Bruttin, A., Dillmann, M.-L. \u0026amp; Br\u0026uuml;ssow, H. Phage-Host Interaction: an Ecological Perspective. \u003cem\u003eJournal of Bacteriology\u003c/em\u003e 186, 3677\u0026ndash;3686 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbdulrahman Ashy, R. \u0026amp; Agust\u0026iacute;, S. Low Host Abundance and High Temperature Determine Switching from Lytic to Lysogenic Cycles in Planktonic Microbial Communities in a Tropical Sea (Red Sea). \u003cem\u003eViruses\u003c/em\u003e 12, 761 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoward-Varona, C., Hargreaves, K. R., Abedon, S. T. \u0026amp; Sullivan, M. B. Lysogeny in nature: mechanisms, impact and ecology of temperate phages. \u003cem\u003eISME J\u003c/em\u003e 11, 1511\u0026ndash;1520 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinha, V., Goyal, A., Svenningsen, S. L., Semsey, S. \u0026amp; Krishna, S. In silico Evolution of Lysis-Lysogeny Strategies Reproduces Observed Lysogeny Propensities in Temperate Bacteriophages. \u003cem\u003eFront. Microbiol\u003c/em\u003e. 8, (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalifano, G., Kwantes, M., Abreu, M. H., Costa, R. \u0026amp; Wichard, T. Cultivating the Macroalgal Holobiont: Effects of Integrated Multi-Trophic Aquaculture on the Microbiome of Ulva rigida (Chlorophyta). \u003cem\u003eFrontiers in Marine Science\u003c/em\u003e 7, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKessler, R. W., Weiss, A., Kuegler, S., Hermes, C. \u0026amp; Wichard, T. Macroalgal\u0026ndash;bacterial interactions: Role of dimethylsulfoniopropionate in microbial gardening by Ulva (Chlorophyta). \u003cem\u003eMolecular Ecology\u003c/em\u003e 27, 1808\u0026ndash;1819 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParada, V., Sintes, E., van Aken, H. M., Weinbauer, M. G. \u0026amp; Herndl, G. J. Viral Abundance, Decay, and Diversity in the Meso- and Bathypelagic Waters of the North Atlantic. \u003cem\u003eAppl Environ Microbiol\u003c/em\u003e 73, 4429\u0026ndash;4438 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOmari Alzahrani, F. Metabolic engineering of osmoprotectants to elucidate the mechanism(s) of salt stress tolerance in crop plants. \u003cem\u003ePlanta\u003c/em\u003e 253, 24 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZou, H. et al. The metabolism and biotechnological application of betaine in microorganism. \u003cem\u003eAppl Microbiol Biotechnol\u003c/em\u003e 100, 3865\u0026ndash;3876 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhaxybayeva, O. \u0026amp; Nesb\u0026oslash;, C. L. Impact of Horizontal Gene Transfer on Adaptations to Extreme Environments. Journal of Molecular Biology 169403 (2025) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jmb.2025.169403\u003c/span\u003e\u003cspan address=\"10.1016/j.jmb.2025.169403\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeaulaurier, J., Schadt, E. E. \u0026amp; Fang, G. Deciphering bacterial epigenomes using modern sequencing technologies. \u003cem\u003eNat Rev Genet\u003c/em\u003e 20, 157\u0026ndash;172 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang, D. et al. The association of prokaryotic antiviral systems and symbiotic phage communities in drinking water microbiomes. \u003cem\u003eISME COMMUN\u003c/em\u003e. 3, 46 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsbaghi, O. et al. Effects of Folic Acid Supplementation on Oxidative Stress Markers: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. \u003cem\u003eAntioxidants (Basel)\u003c/em\u003e 10, 871 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoshi, R., Adhikari, S., Patro, B. S., Chattopadhyay, S. \u0026amp; Mukherjee, T. Free radical scavenging behavior of folic acid: evidence for possible antioxidant activity. \u003cem\u003eFree Radical Biology and Medicine\u003c/em\u003e 30, 1390\u0026ndash;1399 (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, C., Zhang, Z. \u0026amp; Lei, Y. Effects of salinity, light and nutrients on photosynthesis of sterile Ulva pertusa. \u003cem\u003eAES\u003c/em\u003e 21, 795\u0026ndash;798 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDvoř\u0026aacute;k, P., Krasylenko, Y., Zeiner, A., Šamaj, J. \u0026amp; Tak\u0026aacute;č, T. Signaling Toward Reactive Oxygen Species-Scavenging Enzymes in Plants. \u003cem\u003eFront Plant Sci\u003c/em\u003e 11, 618835 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKieft, K. et al. Ecology of inorganic sulfur auxiliary metabolism in widespread bacteriophages. \u003cem\u003eNat Commun\u003c/em\u003e 12, 3503 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLan, M. A. \u0026amp; Chao-zhou, L. I. Effects of cobalt and DFMA on polyamine content and membrane-lipid peroxidation in wheat seedling under osmotic stresses. \u003cem\u003eGanhan Diqu Nongye Yanjiu\u003c/em\u003e 28, 136\u0026ndash;141 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMao, C. et al. DHODH-mediated ferroptosis defense is a targetable vulnerability in cancer. \u003cem\u003eNature\u003c/em\u003e 593, 586\u0026ndash;590 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVarble, A., Meaden, S., Barrangou, R., Westra, E. R. \u0026amp; Marraffini, L. A. Recombination between phages and CRISPR-cas loci facilitates horizontal gene transfer in staphylococci. \u003cem\u003eNat Microbiol\u003c/em\u003e 4, 956\u0026ndash;963 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas, C. M. \u0026amp; Nielsen, K. M. Mechanisms of, and Barriers to, Horizontal Gene Transfer between Bacteria. \u003cem\u003eNat Rev Microbiol\u003c/em\u003e 3, 711\u0026ndash;721 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWichard, T. et al. The green seaweed Ulva: a model system to study morphogenesis. \u003cem\u003eFront. Plant Sci\u003c/em\u003e. 6, (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan der Loos, L. M. et al. Low functional change despite high taxonomic turnover characterizes the Ulva microbiome across a 2000-km salinity gradient. \u003cem\u003eScience Advances\u003c/em\u003e 11, eadr6070 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCamargo, A. P. et al. Identification of mobile genetic elements with geNomad. Nat Biotechnol 42, 1303\u0026ndash;1312 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRu, J., Khan Mirzaei, M., Xue, J., Peng, X. \u0026amp; Deng, L. ViroProfiler: a containerized bioinformatics pipeline for viral metagenomic data analysis. Gut Microbes 15, 2192522 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNayfach, S. et al. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. \u003cem\u003eNat Biotechnol\u003c/em\u003e 39, 578\u0026ndash;585 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo, J. et al. VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses. \u003cem\u003eMicrobiome\u003c/em\u003e 9, 37 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKieft, K., Zhou, Z. \u0026amp; Anantharaman, K. VIBRANT: automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences. \u003cem\u003eMicrobiome\u003c/em\u003e 8, 90 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng, K. et al. VITAP: a high precision tool for DNA and RNA viral classification based on meta-omic data. \u003cem\u003eNat Commun\u003c/em\u003e 16, 2226 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHockenberry A.J., Wilke C.O. BACPHLIP: predicting bacteriophage lifestyle from conserved protein domains. \u003cem\u003ePeerJ\u003c/em\u003e 9, e11396 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoux, S. et al. iPHoP: An integrated machine learning framework to maximize host prediction for metagenome-derived viruses of archaea and bacteria. \u003cem\u003ePLoS Biol\u003c/em\u003e 21, e3002083 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLetunic, I. \u0026amp; Bork, P. Interactive Tree of Life (iTOL) v6: recent updates to the phylogenetic tree display and annotation tool. \u003cem\u003eNucleic Acids Res\u003c/em\u003e 52, W78\u0026ndash;W82 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAroney S.T.N., Newell R.J.P., Nissen J.N., Camargo A.P., Tyson G.W., Woodcroft B.J. CoverM: read alignment statistics for metagenomics. \u003cem\u003eBioinformatics\u003c/em\u003e 41, btaf147 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin, H. \u0026amp; Peddada, S. D. Analysis of compositions of microbiomes with bias correction. Nat Commun 11, 3514 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, T. et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. \u003cem\u003eInnovation (Camb)\u003c/em\u003e 2, 100141 (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Ulva, Virome, Osmoadaptation, Auxiliary metabolic genes, Host-virus interactions","lastPublishedDoi":"10.21203/rs.3.rs-7989466/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7989466/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe role of viruses in mediating microbial community adaptation to environmental stress remains a critical frontier in host-microbe interactions. While the structure and function of bacterial communities associated with the green alga \u003cem\u003eUlva\u003c/em\u003e are known to respond to salinity, the involvement of the algal virome in this process is entirely unexplored.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eUsing metagenomic sequencing of 89 \u003cem\u003eUlva\u003c/em\u003e samples along a salinity gradient (5\u0026ndash;35 PSU), we demonstrate that salinity is the dominant factor shaping the viral community, explaining 73% of the taxonomic and 34% of the functional variation. Viral richness and diversity declined significantly with increasing salinity. The response of viruses to salinity was highly host-specific, with those infecting \u003cem\u003eRhodobacteraceae\u003c/em\u003e exhibiting the strongest correlations. Crucially, we identified a suite of phage-encoded auxiliary metabolic genes (AMGs) that were both differentially abundant and enriched under high-salinity stress. These AMGs are functionally predicted to facilitate bacterial osmoadaptation through synergistic mechanisms including cell wall modification (\u003cem\u003eglmS\u003c/em\u003e, \u003cem\u003eglf\u003c/em\u003e), osmolyte synthesis (\u003cem\u003epreT\u003c/em\u003e), epigenetic regulation via folate-dependent DNA methylation (\u003cem\u003eDNMT1\u003c/em\u003e, \u003cem\u003eDHFR\u003c/em\u003e), and antioxidant defense (\u003cem\u003edfrB\u003c/em\u003e, \u003cem\u003emec\u003c/em\u003e).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur findings reveal that phages are not passive followers of their bacterial hosts but active contributors to holobiont resilience by deploying a targeted genetic toolkit for salinity adaptation. This study provides a mechanistic model for viral-mediated environmental adaptation in a key marine holobiont, fundamentally advancing our understanding of tripartite \"host-bacteria-phage\" interactions in a changing ocean.\u003c/p\u003e","manuscriptTitle":"Salinity-Driven Dynamics of the Ulva-Associated Virome and Their Implications for Holobiont Adaptation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 10:47:01","doi":"10.21203/rs.3.rs-7989466/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"022eca9e-c9ef-49c5-b934-b9b616e7eb2b","owner":[],"postedDate":"February 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T10:41:03+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-23 10:47:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7989466","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7989466","identity":"rs-7989466","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}