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Bacterial quorum sensing orchestrates longitudinal interactions to shape microbiota assembly



The mechanism of microbiota assembly is one of the main problems in microbiome research, which is also the primary theoretical basis for precise manipulation of microbial communities. Bacterial quorum sensing (QS), as the most common means for bacteria to exchange information and interactions, is characterized by universality, specificity, and regulatory power, which therefore may influence the assembly processes of human microbiota. However, the regulating role of QS in microbiota assembly is rarely reported. In this study, we developed an optimized in vitro oral biofilm microbiota assembling (OBMA) model to simulate the time-series assembly of oral biofilm microbiota (OBM), by which to excavate the QS network and its regulating power in the process.


By using the optimized OBMA model, we were able to restore the assembly process of OBM and generate time-series OBM metagenomes of each day. We discovered a total of 2291 QS protein homologues related to 21 QS pathways. Most of these pathways were newly reported and sequentially enriched during OBM assembling. These QS pathways formed a comprehensive longitudinal QS network that included successively enriched QS hubs, such as Streptococcus, Veillonella-Megasphaera group, and Prevotella-Fusobacteria group, for information delivery. Bidirectional cross-talk among the QS hubs was found to play critical role in the directional turnover of microbiota structure, which in turn, influenced the assembly process. Subsequent QS-interfering experiments accurately predicted and experimentally verified the directional shaping power of the longitudinal QS network in the assembly process. As a result, the QS-interfered OBM exhibited delayed and fragile maturity with prolonged membership of Streptococcus and impeded membership of Prevotella and Fusobacterium.


Our results revealed an unprecedented longitudinal QS network during OBM assembly and experimentally verified its power in predicting and manipulating the assembling process. Our work provides a new perspective to uncover underlying mechanism in natural complex microbiota assembling and a theoretical basis for ultimately precisely manipulating human microbiota through intervention in the QS network.

Video Abstract


Human microbiomes have been empirically revealed to undergo nonrandom and repeatable community assembly and succession, such as the trait-based community assembly revealed in infant gut [1, 2]; as well as the quick recoverable assembly of human oral biofilm microbiome (OBM) after clinical scaling [3]. Mechanisms underlying microbiota assembly are one of the main problems in microbiome research, which is also the primary theoretical basis for precise manipulation of microbial communities [4, 5]. Until now, several theories, such as priority effects [6], metabolic cooperation [7,8,9], interspecies bacterial competition [10], and hydrodynamic disturbance [11], have been proposed to uncover rules underlying community assembly from the perspective of metabolic interaction. However, metabolism in bacterial flora is extremely complex and redundant, so it is a big challenge to map the metabolic network for precise manipulation on both community structure and function [4]. In this case, excavating an applicable ecological network with more universality, specificity, and regulatory power is of great significance for understanding assembly rules and realizing precise manipulation of bacterial flora.

Bacterial quorum sensing (QS), as the most common means for bacteria to exchange information and interact, is characterized by universality, specificity, and regulatory power and potentially influences the assembly processes of human microbiota. QS has been widely found from human microbiota such as oral cavity [12, 13], skin [14], lung [15], and gut [16, 17]. Among all the above flora, the assembly of OBM is more likely to be regulated by QS due to its compact spatial structure [18, 19] and the hydrophobic glycoproteins coat [20, 21], which promote a higher transmission efficiency of QS signals within it [22]. Combined with its fast and repeatable assembly characteristics [3], OBM is an ideal target to study the role of QS in controlling microbiome assembly.

Due to current cultivation limitations, only three kinds of QS pathways have been experimentally identified in OBM, namely autoinducer peptide (AIP)-based QS represented by Streptococcus [23, 24], autoinducer-2 (AI-2)-based QS found in Fusobacterium nucleatum [25, 26], and recently discovered (acylated homoserine lactone) AHL-based QS pathway [27, 28]. High-throughput sequencing of target microbiome is beneficial to uncover QS pathways in uncultured microbes from human microflora [29]. However, most studies are based on cross-sectional analysis, ignoring the potential changes and evolution of QS regulation, which limits our understanding of the true role of QS in community assembly. This is a key reason why QS manipulation is studied in simple synthetic microbial communities but is difficult to be widely utilized in larger multispecies communities.

To gain a better understanding of the role of bacterial QS in the assembly of microbiota, we developed an in vitro model that mimics the process of OBM assembly. We conducted a thorough screening of QS pathways in OBM and analyzed their patterns of succession during assembly. We also proposed a longitudinal QS communicating network that deciphers the flow of QS signals. Based on this network, we experimentally verified the predictability and feasibility of interfering the QS network to control OBM assembly. These findings reveal an unprecedented QS network that drives microbiota assembly and provide novel perspectives on the precise manipulation of natural, multispecies microbiota.


In vitro modelling of Oral Biofilm Microbiota Assembling (OBMA)

Saliva collection

Saliva samples were collected from 12 dentally healthy subjects, avoiding from antibiotic intakes for at least 3 months. Subjects were required to refrain from food or drink 1 h before saliva donation. Saliva samples were pooled together and processed for subsequent two purposes: as inoculating seeds and media component, separately. For the use as inoculating seeds, around 10 ml pooled saliva was centrifuged at 2600g for 10 min at 4 ℃ to remove large debris and eukaryotic cells, and the remaining supernatant with oral microbiota was mixed with same volume of 50% glycerol and stored at − 80 ℃ before use. For the use as media component, the rest of pooled saliva was treated with dithiothreitol (2.5 mM final concentration) [30] for 10 min and centrifuged at 17,500g for 30 min at 4 ℃. The supernatant was collected, filter-sterilized, and stored at − 20 ℃ before use.

Cultivation conditions

The device for oral biofilm microbiota (OBM) assembling consisted of a 12-well polystyrene cell culture plate, optimized growth medium (OGM), and hydroxyapatite (HA) disks as previously described [31] as biofilm growth substrata (Fig. 1). HA discs were pre-coated with sterile saliva 1 day before OBM assembling (day − 1). To initiate assembly of OMB, the model was inoculated with 1% seeds in an optimized growth medium consisting of 30% sterile saliva, 60% modified mFUM (Guggenheim B et al., 2001) (supplemented with 1 mM L-arginine, 10 mg/L N-acetylmuramic acid, 1 mg/L hemin, 0.2 mg/L vitamin K, 0.1% sucrose, 0.1% glucose), 10% FBS, and 50 mmol/L PIPIES at pH 7.0 (day 0). Cultures were incubated with 8 replicates at 37 °C for 11 days in an anaerobic chamber (90% N2, 5% CO2 and 5% H2; Don Whitley Scientific Limited, UK) to favor the assembly of OBM. The culture medium was refreshed with OGM daily. Disks without inoculating treatment were conducted as negative controls.

Fig. 1
figure 1

In vitro modelling of oral biofilm microbiota assembly (OBMA) and the study design


OBM samples developed on the HA discs were collected daily. Briefly, the collected samples were “dip-washed” three times in sterile phosphate-buffered saline to remove the culturing suspension and the intact biofilm-discs were frozen at 80°C for further DNA extraction with the DNeasy PowerBiofilm Kit (Qiagen).


Quantitative PCR (qPCR) was performed as previously described [32] to assess the abundance of bacteria in OBM samples. qPCR reactions were performed in triplicate in a 20-µl system including 10 µl of 2 × SYBR Premix ExTaqII (TakaraBioInc), 0.2–0.4 µM of primer sets Eub338F/Eub518R, and 2 µl of 1/10 diluted template DNA. qPCR was performed with the CFX96 Touch (Bio-Rad) using the standard reaction conditions according to the standard operating manual.

Shotgun sequencing and bioinformatics

The DNA extracted from the OBM samples and control samples was quantified using a Qubit 4.0 Fluorometer (Life Technologies, Grand Island, NY). High-quality DNA samples from OBM were used for subsequent metagenomic sequencing. Sequencing of paired-end libraries was generated using the Next®Ultra™ DNA Library Prep Kit for Illumina ® (New England Biolabs, MA, USA) following the manufacturer’s recommendations. The resulting libraries were then sequenced on one 2 × 150 bp lane of an Illumina NovaSeq 6000 platform.

The raw data was processed using Trimmomatic (v.0.36) to acquire the clean data for subsequent analysis. Clean reads were assembled into contigs using MEGAHIT (v.1.2.9) with default parameters. Open reading frames (ORFs) were predicted based on assembled contigs using metaProdigal (v.2.6.3) with a minimum length of 100 nt. A non-redundant gene catalog was constructed using CD-HIT (v.4.8.1) with thresholds of 95% identity and 90% coverage.

The taxonomic assignment of each unigene was performed based on the result of gene annotation using MEGAN (v.6.21.7) with LCA algorithm. To calculate the relative abundance of each gene, the number of reads assigned to a specific gene was divided by the length of the gene and subsequently compared to the sum of divided read number of all genes using the BWA-MEM program [33].

Workflow for the retrieval of QS proteins from OBM metagenomes

Construction of a reference database of QS synthases and receptors

According to the Sigmol [34] and the Quorum Peps [35] databases, proteins participating in synthesizing or sensing 26 different types of QS signals have been experimentally characterized and summarized into a list of QS systems in a previous study [36]. Protein sequences of the listed QS proteins were obtained from NCBI ( and UniProt ( and were used as training sequences to create a QS reference database.

Putative homologues of QS proteins in OBM metagenomes were retrieved using the BLASTP command implemented in DIAMOND against the QS reference database with the following thresholds: sequence identity ≥ 30%; alignment coverage ≥ 50%; e-value ≤ 1e−5. All retrieved homologues were further submitted on the Conserved Domain Database (CDD) [37] and the non-reductant protein database (NR) at NCBI. Those sequences without conserved domains (CDs) and annotations similar to reference QS proteins were discarded.

Relative abundance of each QS homologues from OBM metagenomes was calculated by summing up the abundances of all sequences affiliated to the specific QS homologue, which was subsequently divided by the abundance of a single-copy housekeeping gene recA to get normalized abundance of each QS homologues [38, 39]. The normalized abundance represented the averaging copies of each homologue in an individual cell. The complete workflow for retrieval of QS proteins from the OBM metagenomes is exhibited in Fig. S1.

QS-interfering experiment

To assess the power of QS in driving the assembly of OBM, another verification experiment with a control group (3 replicates) and an AI-2 signal interfering group (3 replicates) was conducted using the OBMA model (Fig. S2). All cultures were incubated at 37 °C for 7 days to fulfill a complete assembly of OBM. Differently, the interfering group was additionally supplemented with D-ribose, a frequently used AI-2 inhibitor [40, 41], to interrupt the AI-2 based signaling from day 2 to day 7. The OBM disks were sampled to extract DNA and quantified as previously mentioned. Community structures of the collected OBM samples were analyzed by sequencing 16S rRNA gene amplicons in Magigene (Guangzhou, China). Briefly, the variable region four (V4) of bacteria 16S rRNA gene was amplified using primer set 515F/806R [42] and sequenced on one Illumina Hiseq2500 PE250 platform. All the obtained paired-end reads of 16S rRNA gene amplicons were analyzed using QIIME [43] to obtain their taxonomic assignments and calculate their relative abundances. All libraries were rarefied to an even depth based on the smallest sample.

Statistical analysis

All statistical analyses were performed in RStudio (v.1.4.1103) with R (v.4.2.1). Shannon index was calculated to characterize microbial diversity using the vegan package [44]. Differences and taxonomy classification in microbial communities were calculated and visualized by a PCA ordination plot and a histogram plot respectively using the MicrobiotaProcess package [45]. Nonparametric Spearman’s correlation test was used to test the associations between genus and annotated metabolic pathways of OBM. Time periods when key genera were differentially abundant between the AI-2 interfering and control group were evaluated using the “MetaDprof” R package with smoothing spline ANOVA (ssANOVA) [46]. All the analyses were unpaired.


In vitro OBMA model is robust to simulate the assemble process of OBM

The in vitro OBMA model uses pooled saliva as the inoculum seed and a modified OGM, which can well simulate the naturally complex conditions for OBM assembly. During 11 consecutive days of culture, we observed the complete assembly process of OBM from attachment, expansion to maturation using the OBMA model (Fig. 2A). Based on the successional characteristics of the assembled OBM, we divided the whole process into three periods: the adapting phase (AP) from day 0 to day 2, the growing phase (GP) from day 2 to day 5, and the mature phase (MP) from day 5 to day 11.

Fig. 2
figure 2

Evaluation of in vitro OBMA model for simulating the assembly of oral biofilm microbiota (OBM). A Time-series photographs show the development of OBMs using the OBMA model, reflected by changes in biomass. B Alpha diversity of OBMs assembled by the OBMA model over time. C Microbiota composition of OBM shown in robust principal component analysis (PCA) biplot, which divided the assembly process into four distinct phases. D The succession of top five genera and their associated main functions during OBM assembly. E The x-axis represents the metabolic pathways annotated in the OBM metagenome. Each column in the figure is labeled with the corresponding BioCyc ID of the specific metabolic pathway. The correlation coefficients are calculated by nonparametric Spearman’s correlation between core genera in OBM and functional pathways

During AP, oral bacteria started to colonize with the smallest biomass, which was hardly observed on the surface of the HA discs (Fig. 2A). We noticed that, even no significant difference of biomass in former 2 days, the Shannon index of the OBM on day 2 had a distinct increment compared to that in day 1 (Fig. 2B). This implies that a large and diverse group of bacteria were recruited before rapid development of OBM. In this case, we subdivided the AP phase into AP1 stage (day 0 ~ day 1) and AP2 stage (day 1 ~ day 2). During the period of AP (especially at AP1 stage), Streptococcus showed absolute abundance advantage in OBM (Fig. 2C and D; around 30 ~ 70%) and assumed more responsibilities for amino acid and nucleotide biosynthesis (Fig. 2E). After that, the assembly of OBM grew exponentially in GP (Fig. 2A), during which the richness was slightly increased (Fig. 2B). At this stage, the community structure was relatively stable (Fig. 2C) and was mainly characterized by the increment of Veillonella and Megasphaera (Fig. 2C and D). Once assembled into MP, Prevotella and Fusobacteria were further enriched (Fig. 2C and D) and the function of MP-OBM demonstrated more mature and invasive features such as peptidoglycan and secondary metabolite biosynthesis (Fig. 2E); we therefore concluded that the complete assembly of OBM was reproduced in vitro using the OBMA model.

The whole assembly process followed the trajectory of “adaptation-rapid proliferation-maturation,” during which the core genus succeed from Streptococcus, passing by Veillonella and Megasphaera, finally to Prevotella and Fusobacterium (Fig. 2C and D). The assembly process simulated by the OBMA model is consistent with the observed development of human oral plaque in previous studies [3], which reflects the reliability of the OBMA model in simulating the real assembly process and provides a reliable basis for studying the assembly rules and mechanisms of complex multispecies microbiota.

Diverse QS pathways were identified through mining OBM genomes

In this study, we created a QS reference database, containing 415 reference protein sequences linked to 26 types of QS pathways. This database was used as a subject data in a BLASTP search for homologues of QS proteins in OBM genomes. Using stringent criteria, 7531 putative protein sequences were identified from the non-redundant gene catalog of OBM genomes. After careful screening for correct functional CDs and annotations corresponding to the reference QS proteins, 2291 homologues were identified that were associated with 21 distinct QS signals. Among these, only 10 QS pathways had both signal-synthesizing and signal-sensing proteins (Table 1), while the remaining QS pathways were incomplete with only a signal-synthesizing or signal-sensing protein (Table S1). To ensure the necessity for signal delivery, only the 10 complete QS pathways were selected for further analysis. The roles of these pathways, summarized in Table 1, are mainly involved in regulating metabolisms such as bacteriocin production, biofilm formation, virulence, and more, through intraspecies, interspecies, and even interkingdom communications. Importantly, this study provides the first comprehensive picture of QS-based communicating in OBM.

Table 1 Proposed roles for the homologs of reference QS proteins found within the oral biofilm microbiota

The dominant bacteria play a key role as carriers of QS signaling within OBM

The QS pathways identified in this study were found to be sequentially enriched and converted during the AP-, GP-, and MP-assembling periods of OBM (Fig. S3). Specific QS pathways, such as those based on AIP_Agr-Fsr_Like, Lantibiotics, and Bacteriocin-II, were found to be AP-specific, with enrichment during AP but a sharp decrease once assembling into GP (Fig. 3A and B). The genus Streptococcus was the main participant involved in the AP-specific QS communications, utilizing these pathways to regulate adhesion [51], competence [49], biofilm formation [47], toxin production [57], and other related metabolisms involved in interference competition, preventing the invasion of other species, and establishing itself in a new community. The AP-QS communications enhanced the dominant colonization by Streptococcus to initiate the assembly of OBM. Similarly, MP-specific QS pathways, such as those based on AHK, AHL, and ethanolamine (Fig. 3A), were enriched during the MP period and mediated signal transmission among MP bacteria, especially Prevotella and Fusobacterium, and Bacteroides (Fig. 3B). MP-QS pathways regulate biofilm formation [72, 73], antibiotic resistance [75], virulence expression [74], and interspecies and even interkingdom cross-signaling [71, 73, 74], and stimulate the immune system of the host [76, 77], playing a crucial role in the formation of mature OBM.

Fig. 3
figure 3

Time-series analysis of quorum sensing (QS) pathways retrieved from oral biofilm microbiota (OBM). A Sequential enrichment and classification of QS pathways into three types: Adapting Phase (AP)-, Growth Phase (GP)-, and Mature Phase (MP)-specific. The columns represent the samples that were collected daily throughout the OBM assembly process. Each column corresponds to one sample, and the specific collection day is indicated below each column. B The dominant genera that participate in QS signaling during AP, GP, and MP stages of OBM assembly

The other four QS pathways based on AI-2, AIP_RRNPP, DSF, and HAQ were classified as GP-specific and had relatively stable abundances throughout the assembling process (Fig. 3B). GP-QS pathways have been reported to mediate interspecies cross-talk [60, 61, 66,67,68,69], facilitating collaborative multispecies biofilm formation (Table 1). More importantly, the GP-QS pathways undertook longitudinal transmission of QS signals from initial colonizers to late colonizers, thereby promoting steady assembly of OBM from AP to MP period.

The longitudinal QS network shaped the direction of OBM assembly

By doing BLASTP, we identified key QS signal synthesis and signal reception proteins involved in QS pathways and annotated them to respective species (Fig. 3, Figure S5 and Table 1). Species that possess QS signal synthase have the ability to produce and release QS signals, which are then sensed by other species that possess receptors. We refer to this type of interaction as “cross-talk”. The sequentially dominant Streptococcus, Megasphaera and Veillonella, Prevotella, and Fusobacterium were found to be responsible for multiple QS signals synthesizing and sensing during the assembly of OBM by possessing the related signal synthases and receptors. That means the interspecies cross-talk will be intensive among these core QS generalists. Two types of cross-talk were identified among the QS hubs: forward and reverse. The forward cross-talk delivered QS signal from the OBM of the previous period to the later period, such as the transmission of AI-2 from Streptococcus to Megasphaera and Veillonella. Conversely, the reverse type conveyed QS signal from the OBM of the later period to the former period, such as the transmission of AI-2 from Fusobacterium to previously colonized Streptococcus (Fig. 4B). Both types of cross-talk were expected to notify responders in time to cooperate with their corresponding signal synthesizers. However, the difference between them is that the forward cross-talk was found to be conducive to the colonization and reproduction of later arrivals, while the reverse type inhibited the further proliferation of former colonists (Fig. 4A). Overall, bidirectional cross-talk among QS hubs was identified as a key factor in shaping the directional transformation of bacterial structure during OBM assembly.

Fig. 4
figure 4

The impact of longitudinal quorum sensing (QS) on oral biofilm microbiota (OBM) assembly. A Proliferation trends of the five QS hubs during OBM assembly, with arrows indicating periods of cross-talk. B Bidirectional cross-talk among the QS hubs shaping the directional conversion of community structure. Forward cross-talk promoted the colonization and reproduction of later arrivals, while reverse cross-talk inhibited the further proliferation of former colonists

AI-2 interfering experiment verified that the longitudinal QS network plays a crucial role in shaping OBM assembly

According to the longitudinal QS network, AI-2-based QS is the only one mediating both forward and reverse cross-talk across the whole assembling process (Fig. 4B). Therefore, AI-2 based subnetwork is more potentially involved in promoting OBM assembly than the others. If the delivery of AI-2 in OBM was blocked, according to our assumption, the proliferation of Megasphaera and Veillonella would be directly delayed, thereby delaying the conversion of downstream OBM; meanwhile, the reverse AI-2-based cross-talk would be deactivated, that is, AI-2 synthesized from Prevotella, Fusobacterium can no longer act backwards on initially colonized Streptococcus, resulting in a longer proliferation time and higher abundance of Streptococcus than those without AI-2 interference. This will also inhibit community conversion and normal assembly of OBM (Fig. S5).

To verify our hypothesis, D-ribose, a frequently used substrate competition inhibitor of AI-2, was supplied to deactivate AI-2 singling during OBM assembling (Fig. S2). We then analyzed changes in OBM biomass, growth rate of key QS hubs to evaluate the power of AI-2 subnetwork in shaping OBM assembly. The results turned out that OBM developed in the AI-2 interference group was more fragile compared to the control (Fig. 5A). Specifically, time needed for conversion of OBM from GP to MP was 1 day delayed when AI-2 signal was interfered (Fig. 5A), which was coincident with delayed proliferation of Veillonella (Fig. 5B). Moreover, Streptococcus proliferation was enhanced in AI-2 interference group (Fig. 5B). Its maximum biomass doubled, and its proliferation was prolonged compared with the control group (Fig. 5B, Fig. S6). In the meantime, growth of downstream QS hubs Prevotella and Fusobacterium were suppressed with significant lower biomass than the control group (Fig. 5B, Fig. S6). It can be seen that AI-2-based QS controlled the turnover of OBM community structure via timely regulation of the growth rate of key QS hubs, thereby shaping OBM assembly.

Fig. 5
figure 5

Validation of the driving role of the AI-2-based quorum sensing (QS) subnetwork in OBM assembly. A Biomass measurements of OBM in control and AI-2 interference experiments, as well as photographs at day 7 of growth. B Longitudinal differential analysis using MetaLonDA revealed temporal changes in normalized absolute abundances of core QS hubs between control and AI-2 interfering groups, with the gray shaded area indicating the significant time interval during which differences were observed


In this study, we have successfully developed an optimized in vitro oral biofilm model (OBMA) by incorporating human saliva and an optimized growth medium. This model not only demonstrates greater diversity but also provides growth conditions that closely resemble a real oral biofilm, thus yielding more realistic and reliable outcomes compared to previous in vitro models. By mixing saliva from multiple individuals, the aim is to create an environment conducive to studying the common characteristics of oral biofilm microbiota within hosts, while still accounting for individual specificity in the oral microbiome. Additionally, our findings from cohort studies provide supporting evidence for the reliability of reconstructing human oral biofilm using the OBMA model. Specifically, both the oral biofilms developed in the human oral cavity and in our OBMA model exhibited similar proliferation patterns, showing a rapid proliferation phase followed by reaching a stable state [78]. Secondly, a consistent pattern was observed during the transition of microbial community structures. Specifically, the initial population of human oral microbiome is facultative anaerobic Streptococcus and was ultimately replaced by gram-negative anaerobic Fusobacteria, Prevotella, and Porphyromonas along with the formation of oral biofilm [3, 78]. Additionally, the core genus Veillonella observed in the growing period in this study has been confirmed as a bridging species critical in guiding the development of multispecies biofilms in the human oral microenvironment [79, 80]. Above all, our constructed OBMA model is reliable to restore the maturation trajectory of human oral biofilm. The OBMA model developed in this study not only holds potential for exploring the regulatory mechanisms of oral biofilm assembly but also can be utilized in early-stage clinical research for screening and evaluating the effectiveness of drugs in controlling oral biofilms.

Our study explores the regulatory role of bacterial QS in the assembly of OBM regardless of host factors. Using the OBMA model, we can observe the entire assembly process while consuming fewer resources and manpower compared to cohort studies.

In contrast to previous strategies based on metabolic collaboration and competition, our study suggests that QS-based interactions regulate microbiota assembly ahead of the onset of other forms of metabolic interactions. QS signals are released and accumulated in the logarithmic growth phase of bacteria to mediate interspecific interactions before most enzymes and metabolites synthesized in stationary growth phase. These signals recruit bacteria clustering with kinship to facilitate microbiota assembly [81]. QS is also responsible for biofilm matrix formation, affecting the microbiota’s structure and function. QS is the fundamental underlying mechanism regulating microbiota assembly.

While it has been suggested QS can promote or inhibit biofilm formation of monocultures, it is challenging to predict how QS plays a role in the assembly of a complex microbial community with hundreds of species based solely on monoculture test results. Our study uncovers the successions of community structure and QS pathways during OBM assembly. We constructed a longitudinal QS network based on this and find it possessing more diverse and complex QS pathways than ever reported. These pathways sequentially enrich and play roles in initial colonizing, exponential proliferation, and maturation, respectively. QS signals in OBM transmit bidirectionally, allowing key QS hubs to regulate transformation of community structure to drive OBM assembly. The construction and regulatory effects of the QS network in the assembly of complex microbiota are unprecedented. Our study provides a new perspective for exploring the underlying mechanism of flora assembly.

The longitudinal QS network proposed in our study provides new possibilities for the application of QS regulation mechanisms in the manipulation of complex microbiota. Based on this network, we accurately predicted and verified the reverse effects of the forward and reverse AI-2 signaling and their respective target QS hubs. Our research results suggest that the exploration of QS regulation can be extended to other human microbiota research, such as the evolution and assembly of colorectal cancer microbiota. By targeting the core QS signals and QS hubs, we can regulate the proliferation of microbiota members, reshape the microbiota in a targeted manner, and ultimately develop new strategies to prevent or treat microbiota-related diseases.


Our results establish a longitudinal QS network scaffolded by dominant bacteria in shaping OBM assembly. This network demonstrates the predictability and controllability of QS interference in regulating the microbiota, thereby expanding our understanding of the mechanism governing microflora assembly, and boosting confidence in applying QS networks to modulate assembly and development of human microbiota.

Availability of data and materials

The sequencing data of metagenome used in this study have been deposited in the GenBank Short Read Archive with accession number PRJNA983519. The accession number can be found below: The scripts for the retrieval of QS proteins from OBM metagenomes are available in the following Git repository:



Quorum sensing


Oral biofilm microbiota


Oral biofilms microbiota assembling


Autoinducing peptide




Optimized growth medium




Quantitative PCR


Conserved domain database


Adapting phase


Growing phase


Mature phase


Diffusible signal factors






  1. Koenig JE, Spor A, Scalfone N, Fricker AD, Stombaugh J, Knight R, et al. Succession of microbial consortia in the developing infant gut microbiome. Proc Natl Acad Sci. 2011;108(Suppl 1):4578–85.

    Article  CAS  PubMed  Google Scholar 

  2. Guittar J, Shade A, Litchman E. Trait-based community assembly and succession of the infant gut microbiome. Nat Commun. 2019;10(1):512.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Wang J, Jia Z, Zhang B, Peng L, Zhao F. Tracing the accumulation of in vivo human oral microbiota elucidates microbial community dynamics at the gateway to the GI tract. Gut. 2020;69(7):1355–6.

    Article  PubMed  Google Scholar 

  4. Liu YY. Controlling the human microbiome. Cell Syst. 2023;14(2):135–59.

    Article  PubMed  Google Scholar 

  5. Venturelli OS, Wang HH, Estrela S, Huang KC, Ledesma-Amaro R, Fedorec AJ, et al. What is the key challenge in engineering microbiomes? Cell Syst. 2023;14:85–90.

    Article  CAS  PubMed  Google Scholar 

  6. Debray R, Herbert RA, Jaffe AL, Crits-Christoph A, Power ME, Koskella B. Priority effects in microbiome assembly. Nat Rev Microbiol. 2022;20(2):109–21.

  7. Blasche S, Kim Y, Mars RAT, Machado D, Maansson M, Kafkia E, et al. Metabolic cooperation and spatiotemporal niche partitioning in a kefir microbial community. Nat Microbiol. 2021;6(2):196–208.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Machado D, Maistrenko OM, Andrejev S, Kim Y, Bork P, Patil KR, et al. Polarization of microbial communities between competitive and cooperative metabolism. Nat Ecol Evol. 2021;5(2):195–203.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Friedman J, Higgins LM, Gore J. Community structure follows simple assembly rules in microbial microcosms. Nat Ecol Evol. 2017;1(5):109.

    Article  PubMed  Google Scholar 

  10. Ortiz A, Vega NM, Ratzke C, Gore J. Interspecies bacterial competition regulates community assembly in the C. elegans intestine. ISME J. 2021;15(7):2131–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Chen YJ, Leung PM, Cook PLM, Wong WW, Hutchinson T, Eate V, et al. Hydrodynamic disturbance controls microbial community assembly and biogeochemical processes in coastal sediments. ISME J. 2021;16:750–63.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Asahi Y, Noiri Y, Igarashi J, Asai H, Suga H, Ebisu S. Effects of N-acyl homoserine lactone analogues on Porphyromonas gingivalis biofilm formation. J Periodontal Res. 2010;45(2):255–61.

    Article  CAS  PubMed  Google Scholar 

  13. Rickard AH, Palmer RJ Jr, Blehert DS, Campagna SR, Semmelhack MF, Egland PG, et al. Autoinducer 2: a concentration-dependent signal for mutualistic bacterial biofilm growth. Mol Microbiol. 2006;60(6):1446–56.

    Article  CAS  PubMed  Google Scholar 

  14. Brown MM, Todd D, Cech N, Horswill A. Novel peptide from commensal Staphylococcus simulans blocks MRSA quorum sensing and protects host skin from damage. J Investig Dermatol. 2019;139(5):S90.

    Article  Google Scholar 

  15. Singh PK, Schaefer AL, Parsek MR, Moninger TO, Welsh MJ, Greenberg E. Quorum-sensing signals indicate that cystic fibrosis lungs are infected with bacterial biofilms. Nature. 2000;407(6805):762–4.

    Article  CAS  PubMed  Google Scholar 

  16. Thompson JA, Oliveira RA, Djukovic A, Ubeda C, Xavier KB. Manipulation of the quorum sensing signal AI-2 affects the antibiotic-treated gut microbiota. Cell Rep. 2015;10(11):1861–71.

    Article  CAS  PubMed  Google Scholar 

  17. Thompson JA, Oliveira RA, Xavier KB. Chemical conversations in the gut microbiota. Gut Microbes. 2016;7(2):163–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Mark Welch JL, Rossetti BJ, Rieken CW, Dewhirst FE, Borisy GG. Biogeography of a human oral microbiome at the micron scale. Proc Natl Acad Sci. 2016;113(6):E791-800.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Mark Welch JL, Ramirez-Puebla ST, Borisy GG. Oral microbiome geography: micron-scale habitat and niche. Cell Host Microbe. 2020;28(2):160–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Flemming HC, Wingender J, Szewzyk U, Steinberg P, Rice SA, Kjelleberg S. Biofilms: an emergent form of bacterial life. Nat Rev Microbiol. 2016;14(9):563–75.

    Article  CAS  PubMed  Google Scholar 

  21. Lebeaux D, Ghigo JM, Beloin C. Biofilm-related infections: bridging the gap between clinical management and fundamental aspects of recalcitrance toward antibiotics. Microbiol Mol Biol Rev. 2014;78(3):510–43.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Kim MK, Ingremeau F, Zhao A, Bassler BL, Stone HA. Local and global consequences of flow on bacterial quorum sensing. Nat Microbiol. 2016;1(1):1–5.

    Article  Google Scholar 

  23. Jakubovics NS, Yassin SA, Rickard AH. Community interactions of oral streptococci. Adv Appl Microbiol. 2014;87:43–110.

    Article  CAS  PubMed  Google Scholar 

  24. Verbeke F, De Craemer S, Debunne N, Janssens Y, Wynendaele E, Van de Wiele C, et al. Peptides as quorum sensing molecules: measurement techniques and obtained levels in vitro and in vivo. Front Neurosci. 2017;11:183.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Frias J, Olle E, Alsina M. Periodontal pathogens produce quorum sensing signal molecules. Infect Immun. 2001;69(5):3431–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Jang Y-J, Choi Y-J, Lee S-H, Jun H-K, Choi B-K. Autoinducer 2 of Fusobacterium nucleatum as a target molecule to inhibit biofilm formation of periodontopathogens. Arch Oral Biol. 2013;58(1):17–27.

    Article  CAS  PubMed  Google Scholar 

  27. Muras A, Mayer C, Otero-Casal P, Exterkate RA, Brandt BW, Crielaard W, et al. Short-chain N-acylhomoserine lactone quorum-sensing molecules promote periodontal pathogens in in vitro oral biofilms. Appl Environ Microbiol. 2020;86(3):e01941-19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Muras A, Otero-Casal P, Blanc V, Otero A. Acyl homoserine lactone-mediated quorum sensing in the oral cavity: a paradigm revisited. Sci Rep. 2020;10(1):9800.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Wu S, Feng J, Liu C, Wu H, Qiu Z, Ge J, et al. Machine learning aided construction of the quorum sensing communication network for human gut microbiota. Nat Commun. 2022;13(1):3079.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Sanchez MC, Llama-Palacios A, Blanc V, Leon R, Herrera D, Sanz M. Structure, viability and bacterial kinetics of an in vitro biofilm model using six bacteria from the subgingival microbiota. J Periodontal Res. 2011;46(2):252–60.

    Article  CAS  PubMed  Google Scholar 

  31. Guggenheim M, Shapiro S, Gmur R, Guggenheim B. Spatial arrangements and associative behavior of species in an in vitro oral biofilm model. Appl Environ Microbiol. 2001;67(3):1343–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Fierer N, Jackson JA, Vilgalys R, Jackson RB. Assessment of soil microbial community structure by use of taxon-specific quantitative PCR assays. Appl Environ Microbiol. 2005;71(7):4117–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv preprint arXiv. 2013.

  34. Rajput A, Kaur K, Kumar M. SigMol: repertoire of quorum sensing signaling molecules in prokaryotes. Nucleic Acids Res. 2016;44(D1):D634–9.

    Article  CAS  PubMed  Google Scholar 

  35. Wynendaele E, Bronselaer A, Nielandt J, D’Hondt M, Stalmans S, Bracke N, et al. Quorumpeps database: chemical space, microbial origin and functionality of quorum sensing peptides. Nucleic Acids Res. 2013;41(D1):D655–9.

    Article  CAS  PubMed  Google Scholar 

  36. Bernard C, Lannes R, Li Y, Bapteste E, Lopez P. Rich repertoire of quorum sensing protein coding sequences in CPR and DPANN associated with interspecies and interkingdom communication. mSystems. 2020;5(5):10–128.

    Article  Google Scholar 

  37. Marchler-Bauer A, Lu S, Anderson JB, Chitsaz F, Derbyshire MK, DeWeese-Scott C, et al. CDD: a Conserved Domain Database for the functional annotation of proteins. Nucleic Acids Res. 2010;39(suppl_1):D225–9.

    PubMed  PubMed Central  Google Scholar 

  38. Beszteri B, Temperton B, Frickenhaus S, Giovannoni S. Average genome size: a potential source of bias in comparative metagenomics. ISME J. 2010;4(8):1075–7.

    Article  PubMed  Google Scholar 

  39. Nayfach S, Pollard KS. Toward accurate and quantitative comparative metagenomics. Cell. 2016;166(5):1103–16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Liu L, Wu R, Zhang J, Shang N, Li P. D-Ribose interferes with quorum sensing to inhibit biofilm formation of Lactobacillus paraplantarum L-ZS9. Front Microbiol. 2017;8:1860.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Cho YJ, Song HY, Ben Amara H, Choi BK, Eunju R, Cho YA, et al. In vivo inhibition of porphyromonas gingivalis growth and prevention of periodontitis with Quorum-Sensing inhibitors. J Periodontol. 2016;87(9):1075–82.

    Article  CAS  PubMed  Google Scholar 

  42. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci U S A. 2011;108(supplement_1):4516–22.

    Article  CAS  PubMed  Google Scholar 

  43. Kuczynski J, Stombaugh J, Walters WA, González A, Caporaso JG, Knight R. Using QIIME to analyze 16S rRNA gene sequences from microbial communities. Curr Protoc Microbiol. 2012;27(1):1E. 5.1-1E. 5.20.

    Article  Google Scholar 

  44. Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14(6):927–30.

    Article  Google Scholar 

  45. Xu S, Zhan L, Tang W, Wang Q, Dai Z, Zhou L, et al. MicrobiotaProcess: a comprehensive R package for deep mining microbiome. Innovation (Camb). 2023;4(2):100388.

    CAS  PubMed  Google Scholar 

  46. Luo D, Ziebell S, An L. An informative approach on differential abundance analysis for time-course metagenomic sequencing data. Bioinformatics. 2017;33(9):1286–92.

    Article  CAS  PubMed  Google Scholar 

  47. Qin X, Singh KV, Weinstock GM, Murray BE. Effects of Enterococcus faecalis fsr genes on production of gelatinase and a serine protease and virulence. Infect Immun. 2000;68(5):2579–86.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Hancock LE, Perego M. The Enterococcus faecalis fsr two-component system controls biofilm development through production of gelatinase. J Bacteriol. 2004;186(17):5629–39.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Mashburn-Warren L, Morrison DA, Federle MJ. A novel double-tryptophan peptide pheromone is conserved in mutans and pyogenic streptococci and controls competence in streptococcus mutans via an rgg regulator. Mol Microbiol. 2010;78(3):589.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Cvitkovitch DG, Li YH, Ellen RP. Quorum sensing and biofilm formation in Streptococcal infections. J Clin Investig. 2003;112(11):1626–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Sturme MH, Nakayama J, Molenaar D, Murakami Y, Kunugi R, Fujii T, et al. An agr-like two-component regulatory system in Lactobacillus plantarum is involved in production of a novel cyclic peptide and regulation of adherence. J Bacteriol. 2005;187(15):5224–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Diep DB, Håvarstein L, Nissen-Meyer J, Nes I. The gene encoding plantaricin A, a bacteriocin from Lactobacillus plantarum C11, is located on the same transcription unit as an agr-like regulatory system. Appl Environ Microbiol. 1994;60(1):160–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Heilbronner S, Holden MT, van Tonder A, Geoghegan JA, Foster TJ, Parkhill J, et al. Genome sequence of Staphylococcus lugdunensis N920143 allows identification of putative colonization and virulence factors. FEMS Microbiol Lett. 2011;322(1):60–7.

    Article  CAS  PubMed  Google Scholar 

  54. Dufour P, Jarraud S, Vandenesch F, Greenland T, Novick RP, Bes M, et al. High genetic variability of the agr locus in Staphylococcus species. J Bacteriol. 2002;184:1180–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Chatterjee C, Paul M, Xie L, Van Der Donk WA. Biosynthesis and mode of action of lantibiotics. Chem Rev. 2005;105(2):633–84.

    Article  CAS  PubMed  Google Scholar 

  56. Kleerebezem M. Quorum sensing control of lantibiotic production; nisin and subtilin autoregulate their own biosynthesis. Peptides. 2004;25(9):1405–14.

    Article  CAS  PubMed  Google Scholar 

  57. Santagati M, Scillato M, Patane F, Aiello C, Stefani S. Bacteriocin-producing oral streptococci and inhibition of respiratory pathogens. FEMS Immunol Med Microbiol. 2012;65(1):23–31.

    Article  CAS  PubMed  Google Scholar 

  58. Wescombe PA, Heng NC, Burton JP, Chilcott CN, Tagg JR. Streptococcal bacteriocins and the case for Streptococcus salivarius as model oral probiotics. Future Microbiol. 2009;4(7):819–35.

    Article  CAS  PubMed  Google Scholar 

  59. Guglielmetti S, Taverniti V, Minuzzo M, Arioli S, Stuknyte M, Karp M, et al. Oral bacteria as potential probiotics for the pharyngeal mucosa. Appl Environ Microbiol. 2010;76(12):3948–58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Miller MB, Bassler BL. Quorum sensing in bacteria. Annu Rev Microbiol. 2001;55(1):165–99.

    Article  CAS  PubMed  Google Scholar 

  61. Xavier KB, Bassler BL. LuxS quorum sensing: more than just a numbers game. Curr Opin Microbiol. 2003;6(2):191–7.

    Article  CAS  PubMed  Google Scholar 

  62. Pereira CS, Thompson JA, Xavier KB. AI-2-mediated signalling in bacteria. FEMS Microbiol Rev. 2013;37(2):156–81.

    Article  CAS  PubMed  Google Scholar 

  63. Cuadra-Saenz G, Rao DL, Underwood AJ, Belapure SA, Campagna SR, Sun Z, et al. Autoinducer-2 influences interactions amongst pioneer colonizing streptococci in oral biofilms. Microbiology. 2012;158(Pt 7):1783.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Rocha-Estrada J, Aceves-Diez AE, Guarneros G, de la Torre M. The RNPP family of quorum-sensing proteins in Gram-positive bacteria. Appl Microbiol Biotechnol. 2010;87(3):913–23.

    Article  CAS  PubMed  Google Scholar 

  65. Sulavik M, Tardif G, Clewell D. Identification of a gene, rgg, which regulates expression of glucosyltransferase and influences the Spp phenotype of Streptococcus gordonii Challis. J Bacteriol. 1992;174(11):3577–86.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Cook LC, LaSarre B, Federle M. Interspecies communication among commensal and pathogenic streptococci. MBio. 2013;4(4):e00382-e313.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Fleuchot B, Guillot A, Mezange C, Besset C, Chambellon E, Monnet V, et al. Rgg-associated SHP signaling peptides mediate cross-talk in Streptococci. PLoS One. 2013;8(6):e66042.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Zhou L, Zhang L-H, Cámara M, He Y-W. The DSF family of quorum sensing signals: diversity, biosynthesis, and turnover. Trends Microbiol. 2017;25(4):293–303.

    Article  CAS  PubMed  Google Scholar 

  69. Déziel E, Lépine F, Milot S, He J, Mindrinos MN, Tompkins RG, et al. Analysis of Pseudomonas aeruginosa 4-hydroxy-2-alkylquinolines (HAQs) reveals a role for 4-hydroxy-2-heptylquinoline in cell-to-cell communication. Proc Natl Acad Sci. 2004;101(5):1339–44.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Kim K, Kim YU, Koh BH, Hwang SS, Kim SH, Lépine F, et al. HHQ and PQS, two Pseudomonas aeruginosa quorum-sensing molecules, down-regulate the innate immune responses through the nuclear factor-κB pathway. Immunology. 2010;129(4):578–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. González JF, Venturi V. A novel widespread interkingdom signaling circuit. Trends Plant Sci. 2013;18(3):167–74.

    Article  PubMed  Google Scholar 

  72. Wang J, Liu Q, Li X, Ma S, Hu H, Wu B, et al. In-situ monitoring AHL-mediated quorum-sensing regulation of the initial phase of wastewater biofilm formation. Environ Int. 2020;135:105326.

    Article  PubMed  Google Scholar 

  73. Tiaden A, Hilbi H. α-Hydroxyketone synthesis and sensing by Legionella and Vibrio. Sensors. 2012;12(3):2899–919.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Kendall MM, Gruber CC, Parker CT, Sperandio V. Ethanolamine controls expression of genes encoding components involved in interkingdom signaling and virulence in enterohemorrhagic Escherichia coli O157: H7. MBio. 2012;3(3):e00050-e12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Kim J, Park W. Indole: a signaling molecule or a mere metabolic byproduct that alters bacterial physiology at a high concentration? J Microbiol. 2015;53(7):421–8.

    Article  CAS  PubMed  Google Scholar 

  76. Darveau RP. Periodontitis: a polymicrobial disruption of host homeostasis. Nat Rev Microbiol. 2010;8(7):481–90.

    Article  CAS  PubMed  Google Scholar 

  77. Curtis MA, Diaz PI, Van Dyke TE. The role of the microbiota in periodontal disease. Periodontol 2000. 2020;83(1):14–25.

    Article  PubMed  Google Scholar 

  78. Wake N, Asahi Y, Noiri Y, Hayashi M, Motooka D, Nakamura S, et al. Temporal dynamics of bacterial microbiota in the human oral cavity determined using an in situ model of dental biofilms. NPJ Biofilms Microbiomes. 2016;2:16018.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Coyte KZ, Rao C, Rakoff-Nahoum S, Foster KR. Ecological rules for the assembly of microbiome communities. PLoS Biol. 2021;19(2):e3001116.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Periasamy S, Kolenbrander PE. Aggregatibacter actinomycetemcomitans builds mutualistic biofilm communities with Fusobacterium nucleatum and Veillonella species in saliva. Infect Immun. 2009;77(9):3542–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Schluter J, Schoech AP, Foster KR, Mitri S. The evolution of quorum sensing as a mechanism to infer kinship. PLoS Comput Biol. 2016;12(4):e1004848.

    Article  PubMed  PubMed Central  Google Scholar 

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We thank Cancan Fan of Hospital of Stomatology, Sun Yat-Sen University, for the assistance with sample collection. We thank the volunteers who participated in this study for providing samples.


This project was supported by the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2022A1515011912 and 2021A1515110810 to Y.S.), the China Postdoctoral Science Foundation (Grant No. 2021M703758 to Y.S.), the National Natural Science Foundation of China (Grant number 81971906 to T.D.), and the Guangdong Key Area Research and Development Program (Grant number 2019B020228001 to T.D.).

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Authors and Affiliations



Y.S. and T.D. created the study concept and designed the experiments. Y.S., M.X., L.X., R.C., and Y.C. finished the sample collection, OBMA model development, and experiment performance. Y.S., Y.C., and J.Z. analyzed the data. Y.S. and Y.C generated research materials. Y.S. and T.D. generated figures and legends from analyzed data and interpreted the experiments. Y.L. and T.D. wrote and revised the manuscript. T.D. conceived, designed, and supervised the overall study. All authors reviewed the manuscript.

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Correspondence to Tao Ding.

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The study was approved by the ethical committee of Zhongshan School of Medicine, Sun Yat-Sen University (ZSSOM-SYSU IRB protocol 2021–028). Informed written consent was obtained from all participants.

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Supplementary Information

Additional file 1: Fig. S1.

Workflow for retrieval of QS proteins from OBM metagen. Fig. S2. The study design of QS-interfering experiment. Fig. S3. Profiles of QS pathways of OBM shown in robust principal component analysis (PCA) biplot, whose transition correlated with the turn over of the assembly phase of OBM. Fig. S4. The dominant genera that participate in QS signal receptions during AP, GP, and MP stages of OBM assembly. Fig. S5. Diagram of dynamical responses of key QS hubs in the AI-2 interfering experiment. A) Expected proliferation trends of the five QS hubs during OBM assembly when AI-2 signaling gets interfered. B) Diagram of changes in the bidirectional cross talk among the QS hubs when AI-2 signaling gets interfered. Fig. S6. Validation of driving role by AI-2 based QS subnetwork in the assembly of OBM. Longitudinal differential analysis using MetaLonDA revealed temporal changes in absolute abundances of core QS hubs between control and AI-2 interfering groups, with the gray shaded area indicating the significant time interval during which differences were observed.

Additional file 2: Table S1.

Proposed roles for the homologs of reference QS proteins found within the oral biofilm microbiota.

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Su, Y., Xu, My., Cui, Y. et al. Bacterial quorum sensing orchestrates longitudinal interactions to shape microbiota assembly. Microbiome 11, 241 (2023).

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