Bacterial quorum sensing signals reshape phycosphere functions to regulate colony morphology in Phaeocystis globosa

Bacterial quorum sensing signals reshape phycosphere functions to regulate colony morphology in Phaeocystis globosa | 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 Article Bacterial quorum sensing signals reshape phycosphere functions to regulate colony morphology in Phaeocystis globosa Jin Zhou, Jianming Zhu, Yuelu Jiang, Si Tang, Xiaobing Wen, Shuo Han, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8771247/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Phaeocystis globosa exhibits a complex life cycle alternating between solitary cells and colonial forms. However, the factors that mediate bacterial behaviour to influence colony formation, as well as how bacterial quorum sensing signals regulate colony morphology and density in P. globosa , remain poorly understood. In this study, we used metagenomic approach to investigate bacterial QS profiles and metabolic potential in intra- and extra-colonies of P. globosa and we detected notable enrichment of acyl-homoserine lactone (AHL)-based QS genes and intensified intra-specific bacterial communication within colonies. To test whether these field observed QS signals causally regulate colony development of P. globosa , we performed controlled AHL exposure experiments. Exogenous exposure of P. globosa to AHL signal induced a strategic shift in colonial development, resulting in significantly larger colonies; however, with a reduced colony number (p < 0.05). Metagenomic analysis revealed that AHL reshaped the bacterial community by enriching the populations of polysaccharide degraders and vitamin producers. In addition, exposure to AHL upregulated fatty acid and terpenoid synthesis, carbon fixation, nitrogen recycling, and phagosome ability in the host algae. Collectively, these bacterial QS induced metabolic shifts enhance resource recycling and biosynthetic capacity within colonies, facilitating colony expansion despite reduced colony frequency. Biological sciences/Ecology/Microbial ecology Biological sciences/Microbiology/Microbial communities/Microbial ecology Phaeocystis globosa Harmful algal bloom Phycosphere bacteria Quorum sensing Colony dynamics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Phaeocystis globosa is a key primary producer in marine ecosystems and plays a substantial role in the global cycling of carbon and sulfur 1 . However, P. globosa can also form large-scale harmful algal blooms under favourable environmental conditions, causing serious damage to marine ecosystems and fisheries 2 , 3 . A distinct feature of Phaeocystis species is their complex polymorphic life cycle, which alternates between free-living, flagellate, solitary and colonial cells. Colonies, featuring thousands of non-flagellate solitary cells, represent the most dominant morphotype during blooms 4 . The gelatinous colonies of Phaeocystis are composed of a polysaccharide gel matrix containing numerous mucopolysaccharides 5 . The formation of mucilaginous colonies is considered a hallmark of bloom initiation and provides P. globosa with a competitive ecological advantage 6 . These spherical colonies are enclosed by a semi-permeable and elastic gel-like membrane that protects the cells from grazing by protozoa and zooplankton, as well as from pathogens and viruses 7 . The colony structure also helps regulate buoyancy, allowing the algae to remain in the euphotic zone for extended periods to maximise photosynthesis and store energy and nutrients 8 , 9 . However, the regulatory mechanisms underlying colony formation and development has yet to be elucidated 10 . Previously, comparative studies have revealed different bacterial community structures and metabolic activities when compared between areas with and without algal blooms 2 , 11 , 12 , thus suggesting that variability in bacterial structure and function influences the fate of P. globosa blooms 13 . Some bacterial groups, such as Roseobacter clusters, are more abundant during P. globosa blooms 12 . Phylogenetic analysis has also shown that the composition of free-living bacteria varies in different bloom stages 2 . Our previous studies have highlighted the potential importance of microorganisms in the phycosphere in shaping colony dynamics 13 , thus implying that colony status can be influenced by bacterial behaviour. Over recent years, emerging evidence has indicated that chemical signalling constitutes a critical dimension of bacterial behaviour and algal-bacterial interactions 14 , 15 . Among these molecular signals, quorum sensing (QS) is a key microbial communication mechanism that regulates collective bacterial behaviours 16 , 17 . QS signalling molecules coordinate gene expression among bacteria, affecting a number of processes, including biofilm formation, metabolic activity and inter-species interactions 18 – 20 . It is therefore plausible that QS plays a pivotal role in structuring algal-bacterial relationships during bloom events 15 , 21 . Investigating the QS systems of microbes inside and outside of colonies may help to identify the molecular mechanisms underlying algal-bacterial interactions and how P. globosa modulates its microbial community to enhance ecological competitiveness. Although recent studies have reported the presence of QS genes in bacterial communities associated with algal blooms, functional evidence linking QS to colony formation and bloom maintenance remains limited 22 . Most existing research is correlative, and the causal mechanisms through which QS signals might influence the plasticity of algal phenotypes and microbial assembly have not been experimentally validated 23 . Furthermore, it is unclear as to whether algal hosts can perceive or respond to bacterial QS cues, representing a substantial gap in our understanding of cross-kingdom communication in the phycosphere 21 , 24 . To address these questions, we undertook an integrated study combining field observations of a natural P. globosa bloom with laboratory-based manipulation experiments. We used metagenomic sequencing to characterise in situ bacterial communities associated with colonies and free-living fractions, specifically probing the distribution and potential functionality of QS systems. Then, we performed exposure experiments to test the effects of acyl-homoserine lactone (AHL)-type QS signals on colony morphology under controlled conditions. Through parallel metagenomic and host transcriptomic analyses, we sought to uncover how QS signals might regulate bacterial behaviour (composition, assembly and function) and influence the phenotypic plasticity of P. globosa colonies. Our ultimate aim was establishing a mechanistic link between bacterial signalling and the formation of algal colonies, thus providing new insights into the characteristics of P. globosa that lead to ecological success. 2. Results 2.1 Bacterial composition diversity of intra- and extra-colonies in situ Field-collected samples exhibited distinct differences in bacterial communities and diversity when compared between intra- and extra-colony environments. The intra-colony environment was predominantly enriched with taxa such as Lentilitoribacter, Alteromonadaceae, Rhodobacteraceae, and Micavibrionaceae. In stark contrast, the extra-colony (seawater) environment was characterised by a high relative abundance of genera including Pseudoalteromonas, Polaribacter, Formosa and families such as Cryomorphaceae (Fig. 1 b). Factors of bacterial diversity (including the Ace, Chao 1, Shannon and Simpson indices) within the colonies was also significantly lower ( P < 0.05) than that in the surrounding seawater (Fig. 1 c), indicating a less rich and less even microbial community within the colonies. 2.2 Bacterial co-occurrence networks and community assembly mechanisms To elucidate the interactions among bacterial taxa and the underlying forces structuring the communities inside and outside of P. globosa colonies, we next constructed co-occurrence networks and quantified the relative contributions of different community assembly processes. Analysis revealed that intra-colony bacteria exhibited higher network complexity and tighter connectivity than their extra-colony counterparts (Fig. 2 a). The intra-colony network possessed 292 nodes, 3,176 connections and a significantly higher average degree (avgK = 25.75) than the extra-colony network (93 nodes, 1,484 links, avgK = 21.45). After quantifying the relative contributions of deterministic and stochastic processes, we identified a fundamental dichotomy assembly mechanism between the two niches (Fig. 2 b). The extra-colony community was dominated by heterogeneous selection, while the intra-colony community was primarily governed by stochastic processes, with dispersal limitation representing the largest contributor. 2.3 Functional divergence of the bacterial communities within intra- and extra-colonies Metagenomic analysis, based on KEGG pathway annotation, revealed a profound functional divergence between the two niches (Fig. 3 ). Some key metabolic and cellular processes were significantly enriched (P < 0.05) within the colony microenvironment, including energy production and conversion (e.g., oxidative phosphorylation, carbon fixation pathways), nutrient transport and assimilation (e.g., ABC transporters, nitrogen metabolism, valine, leucine and isoleucine degradation), cellular motility and coordination (e.g., bacterial chemotaxis, flagellar assembly, two-component system) and bacterial communication and biofilm dynamics (e.g., QS signals, bacterial secretion system, biofilm formation). In addition, pathways responsible for the biosynthesis of various amino acids and secondary metabolites were also more prevalent in the intra-colony environment (P < 0.05). 2.4 The profiles of QS encoding genes in intra- and extra-colonies We constructed and compared QS interaction networks and quantified the relative abundance of intra- and interspecific signalling genes between the two niches to directly assess the potential profiles of QS functional genes. The QS interaction network within colonies was more complex and tightly connected than that in the surrounding seawater (Fig. S1 a). Key network parameters, including total edges (4,572 vs. 1,998), average clustering coefficient (0.71 vs. 0.57) and network density (0.29 vs. 0.17), were all significantly higher in the intra-colony environment ( P < 0.05). This indicates that bacterial QS was far more active inside the colonies. We further dissected the QS communication patterns by differentiating between intra-specific (AI-1, primarily AHLs) and interspecific (AI-2) signalling. Analysis revealed that intra-specific communication was the predominant mode in both niches. However, the relative proportion of intra-specific communication was significantly greater ( P < 0.05) inside the colonies than outside (Fig. S1 b). Analysis of QS encoding genes further revealed that the intra-colony environment was highly enriched with genes specifically related to AHL-based signalling (Fig. S2). This included genes for AHL synthesis (e.g., K00655: hdtS ; K13062: ainS ), AHL receptor proteins (e.g., K15852: luxR ; K07782: sdiA ) and even genes for a Type IV secretion system (K20532, K20533, K20527) known to be regulated by AHLs and involved in effector secretion and DNA exchange. In contrast, the extra-colony environment was enriched for a more diverse set of signalling molecules, including those involved in the AI-2 system (K07173: luxS ; K11531: lsrR ), PQS (K01658: trpG ) and DSF (K01897: ACSL ). The pronounced enrichment of the AHL-based QS system inside the colonies provided a critical theory basis for our subsequent validation experiment, guiding us to utilise AHL molecules (specifically 3-OH-C 4 -HSL) to functionally test their role in mediating the shape and size of colonies. 2.5 Distinct QS systems regulated divergent functional pathways Next, Mantel test analysis was performed to link potential relationship between QS genes and bacterial metabolic functions. This analysis revealed a highly interconnected and coordinated functional landscape within colonies that was closely associated with QS signalling (Fig. 4 ). A greater number of functional pathways exhibited stronger correlations with QS genes inside the colonies compared to the external environment, thus reinforcing the identification of a more active and complex QS network. Distinct QS systems regulated different functional suites. Specifically, within colonies, AHL signals exhibited the strongest positive correlations with key pathways, including oxidative phosphorylation, DNA replication, ABC transporters, the two-component system and biofilm formation (Fig. 4 a). Furthermore, these AHL-correlated functions exhibited strong positive correlations with each other, thus suggesting potential functional coupling mediated by the same QS system and forming a coordinated module that was essential for colonial status. In contrast to the enclosed colony, the surrounding seawater was a more variable, dilute and competitive environment. Reflecting this, the functional landscape outside of colonies was correlated with a broader array of QS signals, with autoinducer-2 (AI-2) and other systems (e.g., c-di-GMP) playing prominent roles (Fig. 4 b). Pathways for bacterial chemotaxis and flagellar assembly were significantly associated with AI-2 and cyclic di-GMP signalling. Functions related to the bacterial secretion system exhibited stronger correlations with extra-colonial QS profiles. Unlike the focused polysaccharide metabolism in the intra-colony environment, broader xenobiotic biodegradation pathways were more linked to external QS. 2.6 AHL signalling influenced the appearance and dimensions of colonies Metagenomic data from field studies identified significant differences in bacterial QS signals within and outside of algal colonies, thus suggesting that QS signals may be involved in the colony formation process. To extend beyond correlation and establish causality, we conducted a laboratory-based manipulation experiment to test the role of AHL signals in regulating the dynamics of P. globosa colonies. Analysis revealed that AHL-treated algae developed significantly larger colonies size (diameter > 3 mm) and an increased ratio of large colonies to small colonies ( P < 0.05); however, the total number of colonies was markedly reduced when compared to control and solvent-control groups (Fig. 5 a-c). We performed transcriptomic analysis on P. globosa to decipher the response of an algal host to AHL exposure. Principal component analysis (PCA) of global gene expression profiles revealed a clear and robust separation between groups (Fig. 5 d). This was further quantified using Venn diagram analysis, which showed that the number of DEGs between the two control groups was an order of magnitude smaller than the number of DEGs between either control group (Fig. S3). A volcano plot visualising this comparison (B vs C) confirmed the identification of these 2,861 significantly upregulated genes in the AHL-treated group (Fig. 5 e). Subsequent functional enrichment analysis of these DEGs revealed that P. globosa underwent extensive metabolic reprogramming. Genes and pathways related to structure biosynthesis (fatty acid, terpenoid backbone), energy production (carbon fixation, glycolysis), nutrient scavenging and recycling (nitrogen metabolism, valine/leucine/isoleucine degradation), antioxidant defence (glutathione metabolism) and phagosome pathway were significantly upregulated (P < 0.05) (Fig. 5 f). Metagenomic analysis further revealed that this phenotypic shift underpinned the profound restructuring of the associated bacterial community. Exposure to AHL significantly altered the microbial composition and community assembly process ( P < 0.05) (Fig. 6 a, b). Some populations were notably enriched in the AHL-treated group, including Marivita , Limnobacter , Roseicyclus , Rhodopirellula , Tenacibaculum , Polaribacter , Flavobacterium and Mameliella (Fig. 6 a). The NST value of the AHL-treated group (Group C) was significantly lower than that of the control groups (Groups A and B) ( P < 0.05), falling below the 50% threshold (Fig. 6 b). This indicated that the AHL signal changed the bacterial community from stochastic to deterministic processes. Meanwhile, compared to both control groups (A and B), the group exposed to AHL also showed a significant reduction in alpha diversity indices ( P < 0.05) (Fig. S4). Functionally, the AHL-treated cultures showed significant enrichment for pathways critical for metabolic exchange and interaction, including polysaccharide degradation (CAZy), vitamin B 12 synthesis, nitrogen cycling, oxidative phosphorylation and ABC transporters (Fig. 6 c). Network analysis further suggested that QS signalling potentially regulated the functional output of key taxa, such as Flavobacterium , the main contributor for vitamin B 12 biosynthesis (Fig. S5). 3. Discussion In this work, we integrated metagenomics and laboratory exposure experiments and demonstrated that bacterial QS signals represent a potential regulator governing the trade-off between colony size and number; we also propose a novel cue for how a QS-recruited functional consortium facilitates this process. 3.1 The colonial niche possessed a specific bacterial consortium and signal molecules We detected a considerably lower diversity yet more complex co-occurrence network within colonies (Fig. 1 c, 2 a), thus indicating that colonies exerted a strong selective filtering effect on the bacterial consortium. This reduction in diversity was likely attributable to two primary factors: first, the colony matrix created a semi-enclosed habitat that limited microbial dispersal and exchange; and second, the relatively high density of organic matter within the colony exerted a strong selective pressure, filtering for a specialised bacterial consortium capable of thriving in this unique microenvironment 8 , 13 . Moreover, the reduced biodiversity within intra-colonies may reduce functional redundancy in community composition, thereby enhancing resource allocation and interspecific cooperation 25 . The enrichment of taxa such as Rhodobacteraceae and Alteromonadaceae inside colonies, coupled with stochastic and dispersal-limited assembly processes (Fig. 1 b) supports the hypothesis that the gelatinous matrix of a colony creates a semi-enclosed environment that traps initial colonisers and fosters intense inter-species interactions 25 , 26 . This concept was supported by Brisbin et al. 25 who reported that P. globosa exhibit a consistent core colonial microbiome, including Alteromonadales , Burkholderiales and Rhizobiales . P. globosa microbiomes are stable-state systems and there are specific and beneficial interactions between Phaeocystis and bacteria. Based on these results and our field data, we speculated that the P. globosa colony is not merely a collection of cells but a unique microbial habitat. The dominance of stochastic assembly processes further suggested that an important factor is which form of bacteria arrives first 27 . However, once established, the enclosed environment promotes the development of a cooperative network, a finding that aligns with previous observations of colony-specific microbiomes 25 . In addition, we must consider whether other factors might contribute to observed differences in microbial profile between the inside and outside of a colony. Considering that microbial behaviour is regulated by multiple signals, we deemed that QS plays a significant role. Our analysis revealed that the intra-colony environment was highly enriched with AI-2 and AHL related genes (Fig. 3 ). One possible reason for this is that the enclosed environment of the colony matrix likely provides a stable microenvironment that facilitates the accumulation of signalling molecules such as AHLs, allowing these molecules to reach the critical threshold concentration required to activate QS-regulated behaviours more readily than in the open water 6 . In a previous study, Morinaga, et al. 28 confirmed that QS signals regulated cell aggregation in Paracoccus denitrificans , thus promoting access to nutrients and providing an ecological advantage with a spatially confined environment. We supposed that the bacterial consortium within a colony is not merely a random subset but a highly active and coordinated community. Under these circumstances, the colonial microenvironment represents a sophisticated signalling incubator that alters bacterial communication strategies. This result provided a functional basis for the more complex co-occurrence network we observed (Fig. S2) and the tightly connected QS networks inside colonies (Fig. S1 a). 3.2 The potential mechanisms responsible for increased colony size under AHL mediation Adding exogenous AHLs resulted in significantly larger size colonies (Fig. 5 a-c). Metagenomic data showed that AHLs recruited some specific bacteria ( Polaribacter , Mameliella , Marivita ) (Fig. 6 a) to act as a powerful ecological filter 29 . This role was also demonstrated by the significant reduction in microbial richness (Chao and Ace indices) and the shift from stochastic to deterministic assembly (NST index) in the AHL-treated group. Signal molecules did not merely modestly adjust the community; rather, these signals dramatically simplified the community by imposing selective pressure 30 . This simplification meant that the diversity generalist community was replaced by a less diverse and more specialist consortium (e.g., Polaribacter , Tenacibaculum , Flavobacterium and Rhodopirellula ) 29 . This data demonstrated that the recruitment of specialised and multi-functional bacterial is an AHL-selected behaviour which coordinated functionality to facilitate colony expansion (Fig. 6 b). In addition to the role of AHL in shaping microbial architecture, functional alterations also contributed to the morphological transformation of colonies. Exposure to AHL increased the activity of certain enzymes, including glycoside hydrolases and polysaccharide lyases within the inside of colonies. The proposed function of these enzymes is to remodel the colony matrix by degrading complex algal polysaccharides 31 . Previously, Li, et al. 32 demonstrated that nano-plastics stimulated colony formation in Phaeocystis globosa by increasing colonial diameter and density. These enhancements were primarily driven by elevated levels of extracellular polysaccharides as well as key substrates involved in extracellular polymeric substances (EPS) synthesis. According to our multi-omics findings, the increase in morphological size in response to AHL stimuli related to the upregulation of bacterial EPS production or metabolic pathways. This activity not only created physical space for expansion but also liberated smaller sugar molecules that served as prefabricated building blocks and an energy source, thereby efficiently fuelling the biosynthetic processes required for growth. Except for carbohydrate metabolism, other metabolic processes were also upregulated in colonies, including nitrogen cycling, dimethyl sulphide (DMS) metabolism and the production of vitamin B, which is supported by the Rhodobacteraceae family (e.g., Mameliella , Roseicyclus , Marivita ) and Methylophaga . Previously, Zhu, et al. 33 reported that bacterial functionality differed significantly between colony and solitary strains. Bacteria in colonies exhibited stronger abilities for carbon and sulfur metabolism, energy metabolism, vitamin B synthesis and signal transduction, thus providing inorganic and organic nutrients and facilitating tight communication with the host algae, thereby promoting growth and bloom development. Based on this, we assumed that these multi-functional partners provided essential micronutrients, thus supporting the high metabolic demands of the algal host and increasing their size 34 – 36 . Algal transcriptomics further revealed the significant upregulation of pathways related to fatty acid and terpenoid synthesis ( P < 0.05) (Fig. 5 f); these factors provide the fundamental building blocks for new membrane and matrix material 6 , 37 , 38 . The concurrent enhancement of carbon fixation, glycolysis and nitrogen metabolism supplied the necessary energy and biochemical precursors 10 , 39 . This indicated a wholesale shift from a maintenance state to an investment state, channelling resources into biomass production and structural expansion. The co-upregulation of glutathione metabolism further indicated the preparation for the enhanced oxidative stress associated with rapid growth 40 , 41 . Meanwhile, the significant upregulation of phagosome pathways ( P < 0.05) indicated that P. globosa may obtain nutrition by adopting a mixotrophic strategy 42 , 43 . This mixotrophic strategy would provide a direct and highly efficient source of nutrients (N, P, vitamins) from the bacterial consortium it harbours, thus offering a potential explanation for bloom persistence 8 . In addition to broad metabolic reprogramming, our transcriptomic analysis also revealed high enrichment in the plant hormone signal transduction pathway in P. globosa after AHL treatment (Fig. 5 f). We propose that this finding offers a possible clue for the mechanism of cross-kingdom ‘eavesdropping’ 15,21,44 . Specifically, AHLs could potentially interface with conserved receptor systems that normally respond to plant hormones such as auxins or cytokinin, which are known to regulate cell division, differentiation and stress responses in a wide range of eukaryotes 24 . In this situation, AHL-induced algal-hormones increasingly participate in altering the morphology of colonies. Based on our results, we synthesised a conceptual model in that bacterial AHL QS signals that are relatively enriched within the colonial niche allow for the restructuring of a mutualistic consortium that provides key services, including nutrient provisioning, matrix modification and vitamin synthesis. In response, algae can reprogram its metabolism to invest resources into the expansion of existing colonies. The resulting ‘fewer-but-larger’ colony strategy provides ecological advantages to blooms of P. globosa . Larger colonies exhibit better defence strategies against grazers, possess more stable and optimised physicochemical conditions and likely have higher levels of buoyancy, allowing them to persist longer in the photic zone 9 , 45 , 46 . This strategy represents a highly effective adaptation for monopolising resources and dominating the phytoplankton community, particularly in the aftermath of other blooms, thus aligning its common characterisation as a secondary bloom species. 4. Conclusion After combining field metagenomics with laboratory experimentation, we demonstrated that bacterial AHL-type QS signals resulted in the characteristic phenotypic plasticity of larger colonies of P. globosa . This transition was mediated through a multifaceted mechanism: AHLs recruited and enriched a specific functional consortium that provided essential services such as nutrient remineralisation, matrix modification and vitamin supply. In response, the algal host underwent extensive metabolic reprogramming, upregulating pathways involved in structure biosynthesis, energy production and nutrient recycling, thereby channelling resources into colony expansion. Nonetheless, our findings indicated that AHL-mediated algal-bacterial cooperation enhanced the fitness of P. globosa by optimising resource allocation and enhancing colony robustness, ultimately contributing to the formation and persistence of colonies. In future studies, genetic knockdowns, metabolite tracing and advanced microscopy will be necessary to decipher the molecular mechanisms underlying cross-talk communications in algal-bacterial interactions across the entire life cycle of algal colonies. 5. Materials and methods 5.1 Collection and processing of algal bloom samples from the field Samples were obtained from a coastal area of Dapeng Bay (114°28′30″ E, 22°32′6″ N) (Fig. 1 a) in Shenzhen, China, during a natural bloom of P. globosa (22nd of January 2021 to 7th of February 2021). Nine parallel biological replicates (10.0 L of seawater per replicate sample) were obtained at each time point from the in situ environment. Colony samples (Fig. 1 a) were separated from seawater using 300-mesh sieves and washed three times with sterile seawater. These samples served as source material for extra-colony samples. The filtered samples were re-filtered using sterile Millipore filters (diameter: 47 mm; pore size: 0.22 µm; Billerica, MA, USA) to obtain extra-colony bacteria. Internal fluids from each 1.0 L sample were aseptically extracted using a sterile syringe equipped with a fine-gauge needle and designated as intra-colony samples; the remaining portion was filtered through a 0.22 µm filter to isolate bacterial strains. 5.2 Metagenomic sequencing, bioinformatic analysis and QS genes profiling DNA extraction from extra- and intra-colony microbial samples was performed using commercial kits (Powerwater, USA). Metagenomic sequencing of these samples was conducted on the Illumina platform to obtain more comprehensive microbial community information and functional gene data. All sequence data generated as part of this project have been deposited in the NCBI Short Read Archive database under accession number: PRJNA1335765. The initial metagenome sequencing dataset was subjected to a series of reprocessing steps to ensure data quality. First, adaptor sequences were removed and low-quality reads trimmed using fastp on the Majorbio Cloud Platform (cloud.majorbio.com). Subsequently, high-quality reads were employed for contig assembly using MEGAHIT (version 1.1.2); this assembly technique relies on succinct de Bruijn graphs 47 . The final assembly exclusively included contigs > 300 bp. CD-HIT (version 4.6.1) was used to construct a non-redundant gene catalog applying stringent criteria of 90% sequence identity and 90% coverage 48 . Following quality control procedures, reads were aligned to the non-redundant gene catalog using SOAPaligner (version 2.21), with a 95% identity threshold. Subsequently, gene abundance in each sample was quantified and normalised using the RPKM method 49 . The MetaGene tool was then employed to identify open reading frames in contigs 50 . Representative sequences from the non-redundant gene catalog were annotated using blastp, implemented using DIAMOND version 0.9.19, with a stringent e-value cutoff of 1e − 5 for taxonomic annotations 51 based on the NCBI NR database. In addition, KEGG annotations were executed using Diamond (version 0.8.35) against the KEGG database ( http://www.genome.jp/keeg/ , version 94.2) 51 . Carbohydrate-active enzyme annotation was performed using hmmscan and the CAZy database, with an e-value cutoff of 1e − 5 . To identify QS genes, we utilised eight sub-databases: AHLs (acyl-homoserine lactones), AI-2 (autoinducer-2), AIP (autoinducing peptides), PQS (quinolone-like2-heptyl-3-hydroxy-4-quinolone), DSF (diffusible signal factor), c-di-GMP (second messenger cyclic dimeric (3–5) GMP), and others; representative sequences were aligned with the QSDB gene database 23 using DIAMOND (BLASTP option) with specific criteria (top hit: 50% identity; 50% alignment length, and e-value 10 − 5 ) 51 . 5.3 Experimental validation of the effect of QS signals on colonies To experimentally demonstrate the role of bacterial QS in facilitating colony formation and enlargement in P. globosa , we designed a laboratory validation experiment. The algal strain used in this experiment was isolated from an in situ bloom sample that had been purified and maintained in our laboratory by serial sub-culturing in f/2 medium under colonial morphology. The molecular characterisation of this strain is provided in the Supplementary Materials. A fresh subculture of P. globosa was filtered through a sterile mesh to standardise colonies to < 1 mm diameter, diluted to a density of 30 colonies per litre, and aliquoted into nine flasks to form three treatment groups in triplicate: Group A (Blank control) with no additives, Group B (Solvent control) with an equivalent volume of DMSO (the AHL solvent) and Group C (AHL treatment) supplemented with 30 µg/L 29 of N-3-Hydroxybutyryl-L-homoserine lactone (3-OH-C 4 -HSL, CAS: 1325550-06-8). Cultures were maintained under standard growth conditions (25°C, 12:12 h L:D, f/2 medium), and daily counts were performed to track total colony count, the number of small (≤ 3 mm) and large (> 3 mm) colonies and the large-to-small colony ratio. 5.4 Microbial sampling and multi-omics analysis of colonies following exposure to QS To investigate how AHL signalling reshapes the structure of the microbial community and its functional potential, and to elucidate the molecular response mechanisms of P. globosa , we next performed integrated metagenomic and host transcriptomic sequencing on collected samples. An mBio® Water DNA Kit (USA) was used, to extract genomic DNA from the microbial communities, according to the manufacturer’s guidelines. Metagenomic sequencing and subsequent bioinformatic analysis, including taxonomic profiling, functional annotation and differential abundance analysis, were performed as previously described for the field samples in section 2.2 . For host transcriptomic analysis, total RNA was extracted from the other half of the filter using Trizol reagent. Only RNA samples meeting the following criteria were used for library construction: total amount > 1 µg, concentration > 50 ng/µL and an OD 260/280 ratio between 1.8 and 2.2. Sequencing libraries were prepared using an Illumina TruSeqTM RNA Sample Prep Kit. In brief, mRNA was enriched from total RNA using Oligo dT magnetic beads, which bound to the poly-A tail. The resulting cDNA libraries were then sequenced on an Illumina NovaSeq 6000 platform. Raw transcriptomic reads were first quality-controlled and filtered using fastp to remove low-quality reads, reads with an excessive number of unknown bases (N) and short reads after quality trimming, resulting in high-quality clean reads. Due to the lack of a high-quality reference genome for P. globosa , de novo transcriptome assembly was performed on clean reads from all samples using Trinity 52 . Gene expression levels were estimated and quantified using RNA-Seq by Expectation-Maximisation, which calculates normalised expression values in Transcripts Per Million to enable cross-sample comparison 53 . Differential gene expression analysis between the AHL-treated and control groups was conducted using DESeq2 54 . Genes with an adjusted p-value (FDR) 1 were identified as significantly differentially expressed genes (DEGs). The metagenomic and algal transcriptome sequence data generated in this section of the experiment have been submitted to the NCBI Short Read Archive database, with accession numbers PRJNA1335830 and PRJNA1335853 respectively. 5.5 Statistical analysis To investigate the assembly mechanisms of QS genes, the tNST function in the NST package of R ( www.r-project.org ) was applied to calculate the relative position of observed values between extreme values under pure deterministic and pure random assembly, thus reflecting the contribution of the stochastic or deterministic processes 55 . To identify stochastic processes, including homogenising dispersal, dispersal limitation and undominated processes, a Bray-Curtis-based Raup-Crick metric (RCbray) was calculated with RCbray > 0.95, RCbray < -0.95 and |RCbray| < 0.95 being interpreted as the gene assembly being determined by homogenising dispersal, dispersal limitation and undominated processes, respectively 56 . Microbial network analysis was conducted using the MENAP and MEN analysis modules in the iNAP2 platform ( https://inap.denglab.org.cn/ ) with a random matrix theory-based correlation method 57 . The results of network visualisation and modular analyses were analysed using Gephi 0.9.2 ( https://gephi.org ) 58 . Differential core pathway analysis between colony and seawater bacteria was conducted with a two-tailed t -test, using STAMP software (v.2.1.3), with an adjusted P threshold of < 0.05 59 . Differences in various parameters were assessed by analysis of variance (ANOVA) at a significance level of P < 0.05. Data analyses were conducted using SPSS 13.0 software (Armonk, NY, United States). Declarations Competing interests The authors declare no competing interests. Additional information Supplementary information The online version contains supplementary material available at Author Contributions Conceptualization: J.Z., Methodology: JM. Z., Y.J., S.T., and X.W. Data Analysis: X. W, S.H., and M.W., Writing - Original Draft: JM.Z., and J.Z., Writing - Review and Editing: JM.Z. and Z.C., Funding acquisition: J.Z. and JM.Z. Acknowledgements This work was supported by the NSFC (42506124, 41976126), Shenzhen Science and Technology Program (RCJC20200714114433069, KCXFZ20230731093402005, SGDX20220530111204028, ZDCYKCX202509011092659002), the Natural Science Foundation of Guangdong Province (2025A1515010643), as well as the Project of Department of Education in Guangdong Province (2025KTSCX179). References Schoemann, V., Becquevort, S., Stefels, J., Rousseau, V. & Lancelot, C. Phaeocystis blooms in the global ocean and their controlling mechanisms: a review. J. Sea Res. 53, 43–66 (2005). Li, N. et al. Phylogenetic responses of marine free-living bacterial community to Phaeocystis globosa bloom in Beibu Gulf, China. Front. Microbiol. 11 (2020). Song, H. et al. 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Metabolomics-derived marker metabolites to characterize Phaeocystis pouchetii physiology in natural plankton communities. Sci. Rep. 10, 20444 (2020). Ryderheim, F., Hansen, P. J. & Kiørboe, T. Predator field and colony morphology determine the defensive benefit of colony formation in marine phytoplankton. Front. Mar. Sci. 9 (2022). Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015). Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152 (2012). Li, R., Li, Y., Kristiansen, K. & Wang, J. SOAP: short oligonucleotide alignment program. Bioinformatics 24, 713–714 (2008). Noguchi, H., Park, J. & Takagi, T. MetaGene: prokaryotic gene finding from environmental genome shotgun sequences. Nucleic Acids Res. 34, 5623–5630 (2006). Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015). Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 29, 644–652 (2011). Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011). Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014). Ning, D., Deng, Y., Tiedje, J. M. & Zhou, J. A general framework for quantitatively assessing ecological stochasticity. Proc. Natl. Acad. Sci. U.S.A. 116, 16892–16898 (2019). Zhou, J. & Ning, D. Stochastic community assembly: does it matter in microbial ecology? Microbiol. Mol. Biol. Rev. 81, 10.1128/mmbr.00002–00017 (2017). Feng, K. et al. iNAP: An integrated network analysis pipeline for microbiome studies. iMeta 1, e13 (2022). Bastian, M., Heymann, S. & Jacomy, M. in Proceedings of the international AAAI conference on web and social media. 361–362. Parks, D. H., Tyson, G. W., Hugenholtz, P. & Beiko, R. G. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30, 3123–3124 (2014). Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryMaterials.docx Bacterial quorum sensing signals reshape phycosphere functions to regulate colony morphology in Phaeocystis globosa Cite Share Download PDF Status: Under Review 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. 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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-8771247","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":586562995,"identity":"66e20c2c-db50-429a-9a39-ac8b54f71352","order_by":0,"name":"Jin Zhou","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-0372-2554","institution":"Tsinghua University","correspondingAuthor":true,"prefix":"","firstName":"Jin","middleName":"","lastName":"Zhou","suffix":""},{"id":586562996,"identity":"bf07f37c-b713-44f9-ba92-f7670a28fbe0","order_by":1,"name":"Jianming Zhu","email":"","orcid":"","institution":"Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Jianming","middleName":"","lastName":"Zhu","suffix":""},{"id":586562997,"identity":"caaa5b71-8e01-4235-b1cb-f6313fe6e53b","order_by":2,"name":"Yuelu Jiang","email":"","orcid":"https://orcid.org/0000-0002-7448-4410","institution":"Shenzhen International Graduate school, Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Yuelu","middleName":"","lastName":"Jiang","suffix":""},{"id":586562998,"identity":"606adb89-0998-4bb2-8971-7605b02604ba","order_by":3,"name":"Si Tang","email":"","orcid":"","institution":"Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Si","middleName":"","lastName":"Tang","suffix":""},{"id":586562999,"identity":"8eac86ff-41d6-41a6-9551-b4f5a015525c","order_by":4,"name":"Xiaobing Wen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xiaobing","middleName":"","lastName":"Wen","suffix":""},{"id":586563000,"identity":"bac6e5be-6c47-4aec-97ca-ac2af1cfa478","order_by":5,"name":"Shuo Han","email":"","orcid":"","institution":"Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Shuo","middleName":"","lastName":"Han","suffix":""},{"id":586563001,"identity":"6fa4addd-8743-4959-8f17-9da9880703c9","order_by":6,"name":"Mengjie Wu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mengjie","middleName":"","lastName":"Wu","suffix":""},{"id":586563002,"identity":"8f593ee5-dda3-449a-9771-848d0eafc403","order_by":7,"name":"Zhonghua Cai","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zhonghua","middleName":"","lastName":"Cai","suffix":""}],"badges":[],"createdAt":"2026-02-03 05:35:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8771247/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8771247/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102398141,"identity":"0d0c1c9e-4230-433b-9a10-2b243a903330","added_by":"auto","created_at":"2026-02-11 10:21:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3896424,"visible":true,"origin":"","legend":"\u003cp\u003eSample collection and bacterial community composition analysis. (\u003cstrong\u003ea\u003c/strong\u003e) Sampling location and images of \u003cem\u003eP. globosa\u003c/em\u003e colonies. (\u003cstrong\u003eb\u003c/strong\u003e) Wilcoxon rank-sum test bar plot showing the relative abundance of significantly differentially abundant bacterial genera between the intra-colony and extra-colony environments. Genera are ordered by their differential abundance significance. (\u003cstrong\u003ec\u003c/strong\u003e) Student’s t-test comparisons of alpha diversity indices (Ace, Chao, Shannon and Simpson) between intra- and extra-colonial microbial communities. Asterisks indicate statistically significant differences (\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05). Error bars represent standard deviation.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8771247/v1/8ec370f4166d82691b5b5754.png"},{"id":102329315,"identity":"43f91b45-ac26-4b84-849d-669de51305eb","added_by":"auto","created_at":"2026-02-10 15:02:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4334321,"visible":true,"origin":"","legend":"\u003cp\u003eBacterial co-occurrence networks and community assembly processes in intra- and extra-colonial niches. (\u003cstrong\u003ea\u003c/strong\u003e) Co-occurrence networks of bacterial communities of intra- and extra-colonies. Nodes represent bacterial taxa, and edges represent closely co-occurrence relationships. (\u003cstrong\u003eb\u003c/strong\u003e) Relative contributions of different ecological processes governing bacterial community assembly.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8771247/v1/60e032c95d4bf3de617cc718.png"},{"id":102397373,"identity":"23ed882b-4d74-4a87-8d06-510744375bb1","added_by":"auto","created_at":"2026-02-11 10:16:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1954864,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional composition and differences between intra- and extra-colony microbes. These were annotated against the KEGG pathway database (at level 3). Blue and red bars represent the extra- and intra-colony groups, respectively. P-value was determined using the Student’s t-test and multiple-corrected using the Bonferroni method.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8771247/v1/37d920b3d8f1d29935b54c91.png"},{"id":102329319,"identity":"e9c9a515-c533-4e19-85a5-c69bf2ab0244","added_by":"auto","created_at":"2026-02-10 15:02:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3240371,"visible":true,"origin":"","legend":"\u003cp\u003eMantel test analysis revealed correlations between QS systems and metabolic pathways in intra- (\u003cstrong\u003ea\u003c/strong\u003e) and extra-colonial (\u003cstrong\u003eb\u003c/strong\u003e) bacterial communities. Lines indicate the correlations between different types of QS systems (left: AHL, AIP, c-di-GMP, DSF, PQS and others,) and enriched metabolic pathways (right). The edge width corresponds to Mantel’s r statistic for the corresponding distance correlation, and the edge colour indicates statistical significance based on 9,999 permutations.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8771247/v1/7b7e2af79331ad4e5237316f.png"},{"id":102329318,"identity":"d84ac51b-9659-4fcd-8c03-0cf4a465ab4b","added_by":"auto","created_at":"2026-02-10 15:02:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3062004,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of AHL addition on colony morphology and the metabolic profiles of \u003cem\u003eP. globosa\u003c/em\u003e. (\u003cstrong\u003ea\u003c/strong\u003e) Representative photographs taken on Day 8 of the experiment, showing the contrasting colony morphology between the AHL-treated group (C, left) and the solvent control group (B, right). (\u003cstrong\u003eb\u003c/strong\u003e) Temporal changes in the total number of colonies across all treatment groups throughout the experimental period. (\u003cstrong\u003ec\u003c/strong\u003e) Dynamics of the ratio of large to small colonies (size threshold: 3 mm) in different treatment groups over time. (\u003cstrong\u003ed\u003c/strong\u003e) Volcano plot of differentially expressed genes (DEGs) in \u003cem\u003eP. globosa\u003c/em\u003e between AHL-treated and control groups (C \u003cem\u003evs\u003c/em\u003e B comparison). Genes with an adjusted P-value \u0026lt; 0.05 and |log₂FC| \u0026gt; 1 were considered significantly differentially expressed. (\u003cstrong\u003ee\u003c/strong\u003e) KEGG pathway enrichment analysis of significantly upregulated genes in AHL-treated \u003cem\u003eP. globosa\u003c/em\u003e. The bubble chart shows the top significantly enriched metabolic pathways. The rich factor indicates the degree of enrichment, and the bubble size represents the number of genes enriched in each pathway. (\u003cstrong\u003ef\u003c/strong\u003e) Principal Component Analysis (PCA) of the \u003cem\u003eP. globosa\u003c/em\u003e transcriptome profiles across different treatment groups. The PCA plot illustrates the global gene expression patterns based on RNA-Seq data from the blank control (Group A), solvent control (Group B) and AHL-treated (Group C) groups, with three biological replicates per group.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8771247/v1/ef6f5d664c25cc79994f98f9.png"},{"id":102329314,"identity":"733bc301-e345-4634-8714-e6f89e685c3f","added_by":"auto","created_at":"2026-02-10 15:02:55","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":967920,"visible":true,"origin":"","legend":"\u003cp\u003eBacterial community in response to AHL exposure. (\u003cstrong\u003ea\u003c/strong\u003e) Heatmap showing the relative abundance of significantly differentially abundant bacterial genera between the AHL-treated (Group C) and control groups (Group A and B). Colour intensity indicates Z-score normalised abundance. (\u003cstrong\u003eb\u003c/strong\u003e) Shift in microbial community assembly processes driven by AHL addition. An NST value of 50% served as a threshold, with values \u0026gt; 50% and \u0026lt; 50% indicating the dominance of stochastic and deterministic processes, respectively. (\u003cstrong\u003ec\u003c/strong\u003e) Functional enrichment of the bacterial microbiome in response to AHL treatment. Heatmap showing the relative abundance of KEGG pathways at Level 3 between the AHL-treated (Group C) and control groups. Each row represents a metabolic pathway or cellular process, and each column represents a sample group. Colour intensity indicates Z-score normalised abundance of functional genes, with red representing higher abundance and blue representing lower abundance. Pathways are clustered based on abundance patterns.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8771247/v1/95437389675776cecc777a6b.png"},{"id":102399103,"identity":"3776eb57-dd56-4a57-931e-447f929aca24","added_by":"auto","created_at":"2026-02-11 10:32:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":18197726,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8771247/v1/750a81a3-667d-4a6d-8154-4395b8ad18ca.pdf"},{"id":102329321,"identity":"8f3b065e-8f24-4f0e-b2ad-623f8ca9ffa1","added_by":"auto","created_at":"2026-02-10 15:02:56","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1669534,"visible":true,"origin":"","legend":"Bacterial quorum sensing signals reshape phycosphere functions to regulate colony morphology in Phaeocystis globosa","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8771247/v1/d560d22cfabf80fe05a6fec8.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Bacterial quorum sensing signals reshape phycosphere functions to regulate colony morphology in Phaeocystis globosa","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cem\u003ePhaeocystis globosa\u003c/em\u003e is a key primary producer in marine ecosystems and plays a substantial role in the global cycling of carbon and sulfur\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. However, \u003cem\u003eP. globosa\u003c/em\u003e can also form large-scale harmful algal blooms under favourable environmental conditions, causing serious damage to marine ecosystems and fisheries\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. A distinct feature of \u003cem\u003ePhaeocystis\u003c/em\u003e species is their complex polymorphic life cycle, which alternates between free-living, flagellate, solitary and colonial cells. Colonies, featuring thousands of non-flagellate solitary cells, represent the most dominant morphotype during blooms\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The gelatinous colonies of \u003cem\u003ePhaeocystis\u003c/em\u003e are composed of a polysaccharide gel matrix containing numerous mucopolysaccharides\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The formation of mucilaginous colonies is considered a hallmark of bloom initiation and provides \u003cem\u003eP. globosa\u003c/em\u003e with a competitive ecological advantage\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. These spherical colonies are enclosed by a semi-permeable and elastic gel-like membrane that protects the cells from grazing by protozoa and zooplankton, as well as from pathogens and viruses\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The colony structure also helps regulate buoyancy, allowing the algae to remain in the euphotic zone for extended periods to maximise photosynthesis and store energy and nutrients\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. However, the regulatory mechanisms underlying colony formation and development has yet to be elucidated\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePreviously, comparative studies have revealed different bacterial community structures and metabolic activities when compared between areas with and without algal blooms\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, thus suggesting that variability in bacterial structure and function influences the fate of \u003cem\u003eP. globosa\u003c/em\u003e blooms\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Some bacterial groups, such as \u003cem\u003eRoseobacter\u003c/em\u003e clusters, are more abundant during \u003cem\u003eP. globosa\u003c/em\u003e blooms\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Phylogenetic analysis has also shown that the composition of free-living bacteria varies in different bloom stages\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Our previous studies have highlighted the potential importance of microorganisms in the phycosphere in shaping colony dynamics\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, thus implying that colony status can be influenced by bacterial behaviour.\u003c/p\u003e \u003cp\u003eOver recent years, emerging evidence has indicated that chemical signalling constitutes a critical dimension of bacterial behaviour and algal-bacterial interactions\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Among these molecular signals, quorum sensing (QS) is a key microbial communication mechanism that regulates collective bacterial behaviours\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. QS signalling molecules coordinate gene expression among bacteria, affecting a number of processes, including biofilm formation, metabolic activity and inter-species interactions\u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. It is therefore plausible that QS plays a pivotal role in structuring algal-bacterial relationships during bloom events\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Investigating the QS systems of microbes inside and outside of colonies may help to identify the molecular mechanisms underlying algal-bacterial interactions and how \u003cem\u003eP. globosa\u003c/em\u003e modulates its microbial community to enhance ecological competitiveness.\u003c/p\u003e \u003cp\u003eAlthough recent studies have reported the presence of QS genes in bacterial communities associated with algal blooms, functional evidence linking QS to colony formation and bloom maintenance remains limited\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Most existing research is correlative, and the causal mechanisms through which QS signals might influence the plasticity of algal phenotypes and microbial assembly have not been experimentally validated\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Furthermore, it is unclear as to whether algal hosts can perceive or respond to bacterial QS cues, representing a substantial gap in our understanding of cross-kingdom communication in the phycosphere\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo address these questions, we undertook an integrated study combining field observations of a natural \u003cem\u003eP. globosa\u003c/em\u003e bloom with laboratory-based manipulation experiments. We used metagenomic sequencing to characterise \u003cem\u003ein situ\u003c/em\u003e bacterial communities associated with colonies and free-living fractions, specifically probing the distribution and potential functionality of QS systems. Then, we performed exposure experiments to test the effects of acyl-homoserine lactone (AHL)-type QS signals on colony morphology under controlled conditions. Through parallel metagenomic and host transcriptomic analyses, we sought to uncover how QS signals might regulate bacterial behaviour (composition, assembly and function) and influence the phenotypic plasticity of \u003cem\u003eP. globosa\u003c/em\u003e colonies. Our ultimate aim was establishing a mechanistic link between bacterial signalling and the formation of algal colonies, thus providing new insights into the characteristics of \u003cem\u003eP. globosa\u003c/em\u003e that lead to ecological success.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Bacterial composition diversity of intra- and extra-colonies \u003cem\u003ein situ\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eField-collected samples exhibited distinct differences in bacterial communities and diversity when compared between intra- and extra-colony environments. The intra-colony environment was predominantly enriched with taxa such as Lentilitoribacter, Alteromonadaceae, Rhodobacteraceae, and Micavibrionaceae. In stark contrast, the extra-colony (seawater) environment was characterised by a high relative abundance of genera including Pseudoalteromonas, Polaribacter, Formosa and families such as Cryomorphaceae (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Factors of bacterial diversity (including the Ace, Chao 1, Shannon and Simpson indices) within the colonies was also significantly lower (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) than that in the surrounding seawater (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec), indicating a less rich and less even microbial community within the colonies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Bacterial co-occurrence networks and community assembly mechanisms\u003c/h2\u003e \u003cp\u003eTo elucidate the interactions among bacterial taxa and the underlying forces structuring the communities inside and outside of \u003cem\u003eP. globosa\u003c/em\u003e colonies, we next constructed co-occurrence networks and quantified the relative contributions of different community assembly processes.\u003c/p\u003e \u003cp\u003eAnalysis revealed that intra-colony bacteria exhibited higher network complexity and tighter connectivity than their extra-colony counterparts (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The intra-colony network possessed 292 nodes, 3,176 connections and a significantly higher average degree (avgK\u0026thinsp;=\u0026thinsp;25.75) than the extra-colony network (93 nodes, 1,484 links, avgK\u0026thinsp;=\u0026thinsp;21.45). After quantifying the relative contributions of deterministic and stochastic processes, we identified a fundamental dichotomy assembly mechanism between the two niches (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The extra-colony community was dominated by heterogeneous selection, while the intra-colony community was primarily governed by stochastic processes, with dispersal limitation representing the largest contributor.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Functional divergence of the bacterial communities within intra- and extra-colonies\u003c/h2\u003e \u003cp\u003eMetagenomic analysis, based on KEGG pathway annotation, revealed a profound functional divergence between the two niches (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Some key metabolic and cellular processes were significantly enriched (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) within the colony microenvironment, including energy production and conversion (e.g., oxidative phosphorylation, carbon fixation pathways), nutrient transport and assimilation (e.g., ABC transporters, nitrogen metabolism, valine, leucine and isoleucine degradation), cellular motility and coordination (e.g., bacterial chemotaxis, flagellar assembly, two-component system) and bacterial communication and biofilm dynamics (e.g., QS signals, bacterial secretion system, biofilm formation). In addition, pathways responsible for the biosynthesis of various amino acids and secondary metabolites were also more prevalent in the intra-colony environment (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 The profiles of QS encoding genes in intra- and extra-colonies\u003c/h2\u003e \u003cp\u003eWe constructed and compared QS interaction networks and quantified the relative abundance of intra- and interspecific signalling genes between the two niches to directly assess the potential profiles of QS functional genes. The QS interaction network within colonies was more complex and tightly connected than that in the surrounding seawater (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea). Key network parameters, including total edges (4,572 \u003cem\u003evs.\u003c/em\u003e 1,998), average clustering coefficient (0.71 \u003cem\u003evs.\u003c/em\u003e 0.57) and network density (0.29 \u003cem\u003evs.\u003c/em\u003e 0.17), were all significantly higher in the intra-colony environment (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This indicates that bacterial QS was far more active inside the colonies.\u003c/p\u003e \u003cp\u003eWe further dissected the QS communication patterns by differentiating between intra-specific (AI-1, primarily AHLs) and interspecific (AI-2) signalling. Analysis revealed that intra-specific communication was the predominant mode in both niches. However, the relative proportion of intra-specific communication was significantly greater (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) inside the colonies than outside (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eAnalysis of QS encoding genes further revealed that the intra-colony environment was highly enriched with genes specifically related to AHL-based signalling (Fig. S2). This included genes for AHL synthesis (e.g., K00655: \u003cem\u003ehdtS\u003c/em\u003e; K13062: \u003cem\u003eainS\u003c/em\u003e), AHL receptor proteins (e.g., K15852: \u003cem\u003eluxR\u003c/em\u003e; K07782: \u003cem\u003esdiA\u003c/em\u003e) and even genes for a Type IV secretion system (K20532, K20533, K20527) known to be regulated by AHLs and involved in effector secretion and DNA exchange. In contrast, the extra-colony environment was enriched for a more diverse set of signalling molecules, including those involved in the AI-2 system (K07173: \u003cem\u003eluxS\u003c/em\u003e; K11531: \u003cem\u003elsrR\u003c/em\u003e), PQS (K01658: \u003cem\u003etrpG\u003c/em\u003e) and DSF (K01897: \u003cem\u003eACSL\u003c/em\u003e). The pronounced enrichment of the AHL-based QS system inside the colonies provided a critical theory basis for our subsequent validation experiment, guiding us to utilise AHL molecules (specifically 3-OH-C\u003csub\u003e4\u003c/sub\u003e-HSL) to functionally test their role in mediating the shape and size of colonies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Distinct QS systems regulated divergent functional pathways\u003c/h2\u003e \u003cp\u003eNext, Mantel test analysis was performed to link potential relationship between QS genes and bacterial metabolic functions. This analysis revealed a highly interconnected and coordinated functional landscape within colonies that was closely associated with QS signalling (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A greater number of functional pathways exhibited stronger correlations with QS genes inside the colonies compared to the external environment, thus reinforcing the identification of a more active and complex QS network.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDistinct QS systems regulated different functional suites. Specifically, within colonies, AHL signals exhibited the strongest positive correlations with key pathways, including oxidative phosphorylation, DNA replication, ABC transporters, the two-component system and biofilm formation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Furthermore, these AHL-correlated functions exhibited strong positive correlations with each other, thus suggesting potential functional coupling mediated by the same QS system and forming a coordinated module that was essential for colonial status.\u003c/p\u003e \u003cp\u003eIn contrast to the enclosed colony, the surrounding seawater was a more variable, dilute and competitive environment. Reflecting this, the functional landscape outside of colonies was correlated with a broader array of QS signals, with autoinducer-2 (AI-2) and other systems (e.g., c-di-GMP) playing prominent roles (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Pathways for bacterial chemotaxis and flagellar assembly were significantly associated with AI-2 and cyclic di-GMP signalling. Functions related to the bacterial secretion system exhibited stronger correlations with extra-colonial QS profiles. Unlike the focused polysaccharide metabolism in the intra-colony environment, broader xenobiotic biodegradation pathways were more linked to external QS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 AHL signalling influenced the appearance and dimensions of colonies\u003c/h2\u003e \u003cp\u003eMetagenomic data from field studies identified significant differences in bacterial QS signals within and outside of algal colonies, thus suggesting that QS signals may be involved in the colony formation process. To extend beyond correlation and establish causality, we conducted a laboratory-based manipulation experiment to test the role of AHL signals in regulating the dynamics of \u003cem\u003eP. globosa\u003c/em\u003e colonies. Analysis revealed that AHL-treated algae developed significantly larger colonies size (diameter\u0026thinsp;\u0026gt;\u0026thinsp;3 mm) and an increased ratio of large colonies to small colonies (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); however, the total number of colonies was markedly reduced when compared to control and solvent-control groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-c).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe performed transcriptomic analysis on \u003cem\u003eP. globosa\u003c/em\u003e to decipher the response of an algal host to AHL exposure. Principal component analysis (PCA) of global gene expression profiles revealed a clear and robust separation between groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). This was further quantified using Venn diagram analysis, which showed that the number of DEGs between the two control groups was an order of magnitude smaller than the number of DEGs between either control group (Fig. S3). A volcano plot visualising this comparison (B \u003cem\u003evs\u003c/em\u003e C) confirmed the identification of these 2,861 significantly upregulated genes in the AHL-treated group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee).\u003c/p\u003e \u003cp\u003eSubsequent functional enrichment analysis of these DEGs revealed that \u003cem\u003eP. globosa\u003c/em\u003e underwent extensive metabolic reprogramming. Genes and pathways related to structure biosynthesis (fatty acid, terpenoid backbone), energy production (carbon fixation, glycolysis), nutrient scavenging and recycling (nitrogen metabolism, valine/leucine/isoleucine degradation), antioxidant defence (glutathione metabolism) and phagosome pathway were significantly upregulated (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef).\u003c/p\u003e \u003cp\u003eMetagenomic analysis further revealed that this phenotypic shift underpinned the profound restructuring of the associated bacterial community. Exposure to AHL significantly altered the microbial composition and community assembly process (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, b). Some populations were notably enriched in the AHL-treated group, including \u003cem\u003eMarivita\u003c/em\u003e, \u003cem\u003eLimnobacter\u003c/em\u003e, \u003cem\u003eRoseicyclus\u003c/em\u003e, \u003cem\u003eRhodopirellula\u003c/em\u003e, \u003cem\u003eTenacibaculum\u003c/em\u003e, \u003cem\u003ePolaribacter\u003c/em\u003e, \u003cem\u003eFlavobacterium\u003c/em\u003e and \u003cem\u003eMameliella\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). The NST value of the AHL-treated group (Group C) was significantly lower than that of the control groups (Groups A and B) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), falling below the 50% threshold (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). This indicated that the AHL signal changed the bacterial community from stochastic to deterministic processes. Meanwhile, compared to both control groups (A and B), the group exposed to AHL also showed a significant reduction in alpha diversity indices (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig. S4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFunctionally, the AHL-treated cultures showed significant enrichment for pathways critical for metabolic exchange and interaction, including polysaccharide degradation (CAZy), vitamin B\u003csub\u003e12\u003c/sub\u003e synthesis, nitrogen cycling, oxidative phosphorylation and ABC transporters (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). Network analysis further suggested that QS signalling potentially regulated the functional output of key taxa, such as \u003cem\u003eFlavobacterium\u003c/em\u003e, the main contributor for vitamin B\u003csub\u003e12\u003c/sub\u003e biosynthesis (Fig. S5).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eIn this work, we integrated metagenomics and laboratory exposure experiments and demonstrated that bacterial QS signals represent a potential regulator governing the trade-off between colony size and number; we also propose a novel cue for how a QS-recruited functional consortium facilitates this process.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The colonial niche possessed a specific bacterial consortium and signal molecules\u003c/h2\u003e \u003cp\u003eWe detected a considerably lower diversity yet more complex co-occurrence network within colonies (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), thus indicating that colonies exerted a strong selective filtering effect on the bacterial consortium. This reduction in diversity was likely attributable to two primary factors: first, the colony matrix created a semi-enclosed habitat that limited microbial dispersal and exchange; and second, the relatively high density of organic matter within the colony exerted a strong selective pressure, filtering for a specialised bacterial consortium capable of thriving in this unique microenvironment\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Moreover, the reduced biodiversity within intra-colonies may reduce functional redundancy in community composition, thereby enhancing resource allocation and interspecific cooperation\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The enrichment of taxa such as \u003cem\u003eRhodobacteraceae\u003c/em\u003e and \u003cem\u003eAlteromonadaceae\u003c/em\u003e inside colonies, coupled with stochastic and dispersal-limited assembly processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) supports the hypothesis that the gelatinous matrix of a colony creates a semi-enclosed environment that traps initial colonisers and fosters intense inter-species interactions\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. This concept was supported by Brisbin et al.\u003csup\u003e25\u003c/sup\u003e who reported that \u003cem\u003eP. globosa\u003c/em\u003e exhibit a consistent core colonial microbiome, including \u003cem\u003eAlteromonadales\u003c/em\u003e, \u003cem\u003eBurkholderiales\u003c/em\u003e and \u003cem\u003eRhizobiales\u003c/em\u003e. \u003cem\u003eP. globosa\u003c/em\u003e microbiomes are stable-state systems and there are specific and beneficial interactions between Phaeocystis and bacteria. Based on these results and our field data, we speculated that the \u003cem\u003eP. globosa\u003c/em\u003e colony is not merely a collection of cells but a unique microbial habitat. The dominance of stochastic assembly processes further suggested that an important factor is which form of bacteria arrives first\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. However, once established, the enclosed environment promotes the development of a cooperative network, a finding that aligns with previous observations of colony-specific microbiomes\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition, we must consider whether other factors might contribute to observed differences in microbial profile between the inside and outside of a colony. Considering that microbial behaviour is regulated by multiple signals, we deemed that QS plays a significant role. Our analysis revealed that the intra-colony environment was highly enriched with AI-2 and AHL related genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). One possible reason for this is that the enclosed environment of the colony matrix likely provides a stable microenvironment that facilitates the accumulation of signalling molecules such as AHLs, allowing these molecules to reach the critical threshold concentration required to activate QS-regulated behaviours more readily than in the open water\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In a previous study, Morinaga, et al.\u003csup\u003e28\u003c/sup\u003e confirmed that QS signals regulated cell aggregation in \u003cem\u003eParacoccus denitrificans\u003c/em\u003e, thus promoting access to nutrients and providing an ecological advantage with a spatially confined environment. We supposed that the bacterial consortium within a colony is not merely a random subset but a highly active and coordinated community. Under these circumstances, the colonial microenvironment represents a sophisticated signalling incubator that alters bacterial communication strategies. This result provided a functional basis for the more complex co-occurrence network we observed (Fig. S2) and the tightly connected QS networks inside colonies (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The potential mechanisms responsible for increased colony size under AHL mediation\u003c/h2\u003e \u003cp\u003eAdding exogenous AHLs resulted in significantly larger size colonies (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-c). Metagenomic data showed that AHLs recruited some specific bacteria (\u003cem\u003ePolaribacter\u003c/em\u003e, \u003cem\u003eMameliella\u003c/em\u003e, \u003cem\u003eMarivita\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea) to act as a powerful ecological filter\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. This role was also demonstrated by the significant reduction in microbial richness (Chao and Ace indices) and the shift from stochastic to deterministic assembly (NST index) in the AHL-treated group. Signal molecules did not merely modestly adjust the community; rather, these signals dramatically simplified the community by imposing selective pressure\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. This simplification meant that the diversity generalist community was replaced by a less diverse and more specialist consortium (e.g., \u003cem\u003ePolaribacter\u003c/em\u003e, \u003cem\u003eTenacibaculum\u003c/em\u003e, \u003cem\u003eFlavobacterium\u003c/em\u003e and \u003cem\u003eRhodopirellula\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. This data demonstrated that the recruitment of specialised and multi-functional bacterial is an AHL-selected behaviour which coordinated functionality to facilitate colony expansion (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eIn addition to the role of AHL in shaping microbial architecture, functional alterations also contributed to the morphological transformation of colonies. Exposure to AHL increased the activity of certain enzymes, including glycoside hydrolases and polysaccharide lyases within the inside of colonies. The proposed function of these enzymes is to remodel the colony matrix by degrading complex algal polysaccharides\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Previously, Li, et al.\u003csup\u003e32\u003c/sup\u003e demonstrated that nano-plastics stimulated colony formation in \u003cem\u003ePhaeocystis globosa\u003c/em\u003e by increasing colonial diameter and density. These enhancements were primarily driven by elevated levels of extracellular polysaccharides as well as key substrates involved in extracellular polymeric substances (EPS) synthesis. According to our multi-omics findings, the increase in morphological size in response to AHL stimuli related to the upregulation of bacterial EPS production or metabolic pathways. This activity not only created physical space for expansion but also liberated smaller sugar molecules that served as prefabricated building blocks and an energy source, thereby efficiently fuelling the biosynthetic processes required for growth. Except for carbohydrate metabolism, other metabolic processes were also upregulated in colonies, including nitrogen cycling, dimethyl sulphide (DMS) metabolism and the production of vitamin B, which is supported by the \u003cem\u003eRhodobacteraceae\u003c/em\u003e family (e.g., \u003cem\u003eMameliella\u003c/em\u003e, \u003cem\u003eRoseicyclus\u003c/em\u003e, \u003cem\u003eMarivita\u003c/em\u003e) and \u003cem\u003eMethylophaga\u003c/em\u003e. Previously, Zhu, et al.\u003csup\u003e33\u003c/sup\u003e reported that bacterial functionality differed significantly between colony and solitary strains. Bacteria in colonies exhibited stronger abilities for carbon and sulfur metabolism, energy metabolism, vitamin B synthesis and signal transduction, thus providing inorganic and organic nutrients and facilitating tight communication with the host algae, thereby promoting growth and bloom development. Based on this, we assumed that these multi-functional partners provided essential micronutrients, thus supporting the high metabolic demands of the algal host and increasing their size\u003csup\u003e\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlgal transcriptomics further revealed the significant upregulation of pathways related to fatty acid and terpenoid synthesis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef); these factors provide the fundamental building blocks for new membrane and matrix material\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. The concurrent enhancement of carbon fixation, glycolysis and nitrogen metabolism supplied the necessary energy and biochemical precursors\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. This indicated a wholesale shift from a maintenance state to an investment state, channelling resources into biomass production and structural expansion. The co-upregulation of glutathione metabolism further indicated the preparation for the enhanced oxidative stress associated with rapid growth\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Meanwhile, the significant upregulation of phagosome pathways (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) indicated that \u003cem\u003eP. globosa\u003c/em\u003e may obtain nutrition by adopting a mixotrophic strategy\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. This mixotrophic strategy would provide a direct and highly efficient source of nutrients (N, P, vitamins) from the bacterial consortium it harbours, thus offering a potential explanation for bloom persistence\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition to broad metabolic reprogramming, our transcriptomic analysis also revealed high enrichment in the plant hormone signal transduction pathway in \u003cem\u003eP. globosa\u003c/em\u003e after AHL treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef). We propose that this finding offers a possible clue for the mechanism of cross-kingdom \u0026lsquo;eavesdropping\u0026rsquo;\u003csup\u003e15,21,44\u003c/sup\u003e. Specifically, AHLs could potentially interface with conserved receptor systems that normally respond to plant hormones such as auxins or cytokinin, which are known to regulate cell division, differentiation and stress responses in a wide range of eukaryotes\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. In this situation, AHL-induced algal-hormones increasingly participate in altering the morphology of colonies.\u003c/p\u003e \u003cp\u003eBased on our results, we synthesised a conceptual model in that bacterial AHL QS signals that are relatively enriched within the colonial niche allow for the restructuring of a mutualistic consortium that provides key services, including nutrient provisioning, matrix modification and vitamin synthesis. In response, algae can reprogram its metabolism to invest resources into the expansion of existing colonies. The resulting \u0026lsquo;fewer-but-larger\u0026rsquo; colony strategy provides ecological advantages to blooms of \u003cem\u003eP. globosa\u003c/em\u003e. Larger colonies exhibit better defence strategies against grazers, possess more stable and optimised physicochemical conditions and likely have higher levels of buoyancy, allowing them to persist longer in the photic zone\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. This strategy represents a highly effective adaptation for monopolising resources and dominating the phytoplankton community, particularly in the aftermath of other blooms, thus aligning its common characterisation as a secondary bloom species.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eAfter combining field metagenomics with laboratory experimentation, we demonstrated that bacterial AHL-type QS signals resulted in the characteristic phenotypic plasticity of larger colonies of \u003cem\u003eP. globosa\u003c/em\u003e. This transition was mediated through a multifaceted mechanism: AHLs recruited and enriched a specific functional consortium that provided essential services such as nutrient remineralisation, matrix modification and vitamin supply. In response, the algal host underwent extensive metabolic reprogramming, upregulating pathways involved in structure biosynthesis, energy production and nutrient recycling, thereby channelling resources into colony expansion. Nonetheless, our findings indicated that AHL-mediated algal-bacterial cooperation enhanced the fitness of \u003cem\u003eP. globosa\u003c/em\u003e by optimising resource allocation and enhancing colony robustness, ultimately contributing to the formation and persistence of colonies. In future studies, genetic knockdowns, metabolite tracing and advanced microscopy will be necessary to decipher the molecular mechanisms underlying cross-talk communications in algal-bacterial interactions across the entire life cycle of algal colonies.\u003c/p\u003e"},{"header":"5. Materials and methods","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Collection and processing of algal bloom samples from the field\u003c/h2\u003e \u003cp\u003eSamples were obtained from a coastal area of Dapeng Bay (114\u0026deg;28\u0026prime;30\u0026Prime; E, 22\u0026deg;32\u0026prime;6\u0026Prime; N) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) in Shenzhen, China, during a natural bloom of \u003cem\u003eP. globosa\u003c/em\u003e (22nd of January 2021 to 7th of February 2021). Nine parallel biological replicates (10.0 L of seawater per replicate sample) were obtained at each time point from the \u003cem\u003ein situ\u003c/em\u003e environment. Colony samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) were separated from seawater using 300-mesh sieves and washed three times with sterile seawater. These samples served as source material for extra-colony samples. The filtered samples were re-filtered using sterile Millipore filters (diameter: 47 mm; pore size: 0.22 \u0026micro;m; Billerica, MA, USA) to obtain extra-colony bacteria. Internal fluids from each 1.0 L sample were aseptically extracted using a sterile syringe equipped with a fine-gauge needle and designated as intra-colony samples; the remaining portion was filtered through a 0.22 \u0026micro;m filter to isolate bacterial strains.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Metagenomic sequencing, bioinformatic analysis and QS genes profiling\u003c/h2\u003e \u003cp\u003eDNA extraction from extra- and intra-colony microbial samples was performed using commercial kits (Powerwater, USA). Metagenomic sequencing of these samples was conducted on the Illumina platform to obtain more comprehensive microbial community information and functional gene data. All sequence data generated as part of this project have been deposited in the NCBI Short Read Archive database under accession number: PRJNA1335765.\u003c/p\u003e \u003cp\u003eThe initial metagenome sequencing dataset was subjected to a series of reprocessing steps to ensure data quality. First, adaptor sequences were removed and low-quality reads trimmed using fastp on the Majorbio Cloud Platform (cloud.majorbio.com). Subsequently, high-quality reads were employed for contig assembly using MEGAHIT (version 1.1.2); this assembly technique relies on succinct de Bruijn graphs\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. The final assembly exclusively included contigs\u0026thinsp;\u0026gt;\u0026thinsp;300 bp. CD-HIT (version 4.6.1) was used to construct a non-redundant gene catalog applying stringent criteria of 90% sequence identity and 90% coverage\u003csup\u003e48\u003c/sup\u003e. Following quality control procedures, reads were aligned to the non-redundant gene catalog using SOAPaligner (version 2.21), with a 95% identity threshold. Subsequently, gene abundance in each sample was quantified and normalised using the RPKM method\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. The MetaGene tool was then employed to identify open reading frames in contigs\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRepresentative sequences from the non-redundant gene catalog were annotated using blastp, implemented using DIAMOND version 0.9.19, with a stringent e-value cutoff of 1e\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e for taxonomic annotations\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e based on the NCBI NR database. In addition, KEGG annotations were executed using Diamond (version 0.8.35) against the KEGG database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genome.jp/keeg/\u003c/span\u003e\u003cspan address=\"http://www.genome.jp/keeg/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, version 94.2)\u003csup\u003e51\u003c/sup\u003e. Carbohydrate-active enzyme annotation was performed using hmmscan and the CAZy database, with an e-value cutoff of 1e\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e. To identify QS genes, we utilised eight sub-databases: AHLs (acyl-homoserine lactones), AI-2 (autoinducer-2), AIP (autoinducing peptides), PQS (quinolone-like2-heptyl-3-hydroxy-4-quinolone), DSF (diffusible signal factor), c-di-GMP (second messenger cyclic dimeric (3\u0026ndash;5) GMP), and others; representative sequences were aligned with the QSDB gene database\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e using DIAMOND (BLASTP option) with specific criteria (top hit: 50% identity; 50% alignment length, and e-value 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e)\u003csup\u003e51\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Experimental validation of the effect of QS signals on colonies\u003c/h2\u003e \u003cp\u003eTo experimentally demonstrate the role of bacterial QS in facilitating colony formation and enlargement in \u003cem\u003eP. globosa\u003c/em\u003e, we designed a laboratory validation experiment. The algal strain used in this experiment was isolated from an \u003cem\u003ein situ\u003c/em\u003e bloom sample that had been purified and maintained in our laboratory by serial sub-culturing in f/2 medium under colonial morphology. The molecular characterisation of this strain is provided in the Supplementary Materials. A fresh subculture of \u003cem\u003eP. globosa\u003c/em\u003e was filtered through a sterile mesh to standardise colonies to \u0026lt;\u0026thinsp;1 mm diameter, diluted to a density of 30 colonies per litre, and aliquoted into nine flasks to form three treatment groups in triplicate: Group A (Blank control) with no additives, Group B (Solvent control) with an equivalent volume of DMSO (the AHL solvent) and Group C (AHL treatment) supplemented with 30 \u0026micro;g/L\u003csup\u003e29\u003c/sup\u003e of N-3-Hydroxybutyryl-L-homoserine lactone (3-OH-C\u003csub\u003e4\u003c/sub\u003e-HSL, CAS: 1325550-06-8).\u003c/p\u003e \u003cp\u003eCultures were maintained under standard growth conditions (25\u0026deg;C, 12:12 h L:D, f/2 medium), and daily counts were performed to track total colony count, the number of small (\u0026le;\u0026thinsp;3 mm) and large (\u0026gt;\u0026thinsp;3 mm) colonies and the large-to-small colony ratio.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Microbial sampling and multi-omics analysis of colonies following exposure to QS\u003c/h2\u003e \u003cp\u003eTo investigate how AHL signalling reshapes the structure of the microbial community and its functional potential, and to elucidate the molecular response mechanisms of \u003cem\u003eP. globosa\u003c/em\u003e, we next performed integrated metagenomic and host transcriptomic sequencing on collected samples. An mBio\u0026reg; Water DNA Kit (USA) was used, to extract genomic DNA from the microbial communities, according to the manufacturer\u0026rsquo;s guidelines. Metagenomic sequencing and subsequent bioinformatic analysis, including taxonomic profiling, functional annotation and differential abundance analysis, were performed as previously described for the field samples in section \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFor host transcriptomic analysis, total RNA was extracted from the other half of the filter using Trizol reagent. Only RNA samples meeting the following criteria were used for library construction: total amount\u0026thinsp;\u0026gt;\u0026thinsp;1 \u0026micro;g, concentration\u0026thinsp;\u0026gt;\u0026thinsp;50 ng/\u0026micro;L and an OD\u003csub\u003e260/280\u003c/sub\u003e ratio between 1.8 and 2.2. Sequencing libraries were prepared using an Illumina TruSeqTM RNA Sample Prep Kit. In brief, mRNA was enriched from total RNA using Oligo dT magnetic beads, which bound to the poly-A tail. The resulting cDNA libraries were then sequenced on an Illumina NovaSeq 6000 platform.\u003c/p\u003e \u003cp\u003eRaw transcriptomic reads were first quality-controlled and filtered using fastp to remove low-quality reads, reads with an excessive number of unknown bases (N) and short reads after quality trimming, resulting in high-quality clean reads. Due to the lack of a high-quality reference genome for \u003cem\u003eP. globosa\u003c/em\u003e, \u003cem\u003ede novo\u003c/em\u003e transcriptome assembly was performed on clean reads from all samples using Trinity\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Gene expression levels were estimated and quantified using RNA-Seq by Expectation-Maximisation, which calculates normalised expression values in Transcripts Per Million to enable cross-sample comparison\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Differential gene expression analysis between the AHL-treated and control groups was conducted using DESeq2\u003csup\u003e54\u003c/sup\u003e. Genes with an adjusted p-value (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and an absolute log2 fold change (|log\u003csub\u003e2\u003c/sub\u003eFC|)\u0026thinsp;\u0026gt;\u0026thinsp;1 were identified as significantly differentially expressed genes (DEGs). The metagenomic and algal transcriptome sequence data generated in this section of the experiment have been submitted to the NCBI Short Read Archive database, with accession numbers PRJNA1335830 and PRJNA1335853 respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eTo investigate the assembly mechanisms of QS genes, the tNST function in the NST package of R (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.r-project.org\u003c/span\u003e\u003cspan address=\"http://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was applied to calculate the relative position of observed values between extreme values under pure deterministic and pure random assembly, thus reflecting the contribution of the stochastic or deterministic processes\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. To identify stochastic processes, including homogenising dispersal, dispersal limitation and undominated processes, a Bray-Curtis-based Raup-Crick metric (RCbray) was calculated with RCbray\u0026thinsp;\u0026gt;\u0026thinsp;0.95, RCbray \u0026lt; -0.95 and |RCbray| \u0026lt; 0.95 being interpreted as the gene assembly being determined by homogenising dispersal, dispersal limitation and undominated processes, respectively\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Microbial network analysis was conducted using the MENAP and MEN analysis modules in the iNAP2 platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://inap.denglab.org.cn/\u003c/span\u003e\u003cspan address=\"https://inap.denglab.org.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with a random matrix theory-based correlation method\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. The results of network visualisation and modular analyses were analysed using Gephi 0.9.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gephi.org\u003c/span\u003e\u003cspan address=\"https://gephi.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e58\u003c/sup\u003e. Differential core pathway analysis between colony and seawater bacteria was conducted with a two-tailed \u003cem\u003et\u003c/em\u003e-test, using STAMP software (v.2.1.3), with an adjusted \u003cem\u003eP\u003c/em\u003e threshold of \u0026lt;\u0026thinsp;0.05\u003csup\u003e59\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDifferences in various parameters were assessed by analysis of variance (ANOVA) at a significance level of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Data analyses were conducted using SPSS 13.0 software (Armonk, NY, United States).\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003e \u003cb\u003eAdditional information\u003c/b\u003e \u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eSupplementary information\u003c/strong\u003e \u003cp\u003eThe online version contains supplementary material available at\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eConceptualization: J.Z., Methodology: JM. Z., Y.J., S.T., and X.W. Data Analysis: X. W, S.H., and M.W., Writing - Original Draft: JM.Z., and J.Z., Writing - Review and Editing: JM.Z. and Z.C., Funding acquisition: J.Z. and JM.Z.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis work was supported by the NSFC (42506124, 41976126), Shenzhen Science and Technology Program (RCJC20200714114433069, KCXFZ20230731093402005, SGDX20220530111204028, ZDCYKCX202509011092659002), the Natural Science Foundation of Guangdong Province (2025A1515010643), as well as the Project of Department of Education in Guangdong Province (2025KTSCX179).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSchoemann, V., Becquevort, S., Stefels, J., Rousseau, V. \u0026amp; Lancelot, C. \u003cem\u003ePhaeocystis\u003c/em\u003e blooms in the global ocean and their controlling mechanisms: a review. \u003cem\u003eJ. 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STAMP: statistical analysis of taxonomic and functional profiles. \u003cem\u003eBioinformatics\u003c/em\u003e 30, 3123\u0026ndash;3124 (2014).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Phaeocystis globosa, Harmful algal bloom, Phycosphere bacteria, Quorum sensing, Colony dynamics","lastPublishedDoi":"10.21203/rs.3.rs-8771247/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8771247/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003ePhaeocystis globosa\u003c/em\u003e exhibits a complex life cycle alternating between solitary cells and colonial forms. However, the factors that mediate bacterial behaviour to influence colony formation, as well as how bacterial quorum sensing signals regulate colony morphology and density in \u003cem\u003eP. globosa\u003c/em\u003e, remain poorly understood. In this study, we used metagenomic approach to investigate bacterial QS profiles and metabolic potential in intra- and extra-colonies of \u003cem\u003eP. globosa\u003c/em\u003e and we detected notable enrichment of acyl-homoserine lactone (AHL)-based QS genes and intensified intra-specific bacterial communication within colonies. To test whether these field observed QS signals causally regulate colony development of \u003cem\u003eP. globosa\u003c/em\u003e, we performed controlled AHL exposure experiments. Exogenous exposure of \u003cem\u003eP. globosa\u003c/em\u003e to AHL signal induced a strategic shift in colonial development, resulting in significantly larger colonies; however, with a reduced colony number (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Metagenomic analysis revealed that AHL reshaped the bacterial community by enriching the populations of polysaccharide degraders and vitamin producers. In addition, exposure to AHL upregulated fatty acid and terpenoid synthesis, carbon fixation, nitrogen recycling, and phagosome ability in the host algae. Collectively, these bacterial QS induced metabolic shifts enhance resource recycling and biosynthetic capacity within colonies, facilitating colony expansion despite reduced colony frequency.\u003c/p\u003e","manuscriptTitle":"Bacterial quorum sensing signals reshape phycosphere functions to regulate colony morphology in Phaeocystis globosa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 15:02:50","doi":"10.21203/rs.3.rs-8771247/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-earth-and-environment","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsenv","sideBox":"Learn more about [Communications Earth and Environment](https://www.nature.com/commsenv/)","snPcode":"","submissionUrl":"","title":"Communications Earth \u0026 Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3a5681d2-a8bd-45d1-beea-c4e909fc91fc","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62409225,"name":"Biological sciences/Ecology/Microbial ecology"},{"id":62409226,"name":"Biological sciences/Microbiology/Microbial communities/Microbial ecology"}],"tags":[],"updatedAt":"2026-04-03T18:10:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 15:02:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8771247","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8771247","identity":"rs-8771247","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}