Development and Genome-level Microevolution of Oral Microbiome during Surface Colonization

Development and Genome-level Microevolution of Oral Microbiome during Surface Colonization | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Development and Genome-level Microevolution of Oral Microbiome during Surface Colonization View ORCID Profile Yuchen Zhang , Yuguang Yang , Yibing Liu , View ORCID Profile Emily Ming-Chieh Lu , View ORCID Profile David Moyes , View ORCID Profile Sadia Ambreen Niazi , View ORCID Profile Qin Zhou doi: https://doi.org/10.1101/2025.11.07.685765 Yuchen Zhang 1 Centre for Oral Clinical & Translational Sciences, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London , London, UK 2 Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi’an Jiaotong University , Xi’an, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yuchen Zhang Yuguang Yang 3 Department of Civil and Environmental Engineering, Massachusetts Institute of Technology , Cambridge, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yibing Liu 2 Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi’an Jiaotong University , Xi’an, China 4 Department of Implant Dentistry, College of Stomatology, Xi’an Jiaotong University , Xi’an China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Emily Ming-Chieh Lu 5 Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Emily Ming-Chieh Lu David Moyes 5 Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for David Moyes Sadia Ambreen Niazi 1 Centre for Oral Clinical & Translational Sciences, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sadia Ambreen Niazi For correspondence: zhouqin{at}xjtu.edu.cn sadia.niazi{at}kcl.ac.uk Qin Zhou 2 Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi’an Jiaotong University , Xi’an, China 4 Department of Implant Dentistry, College of Stomatology, Xi’an Jiaotong University , Xi’an China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Qin Zhou For correspondence: zhouqin{at}xjtu.edu.cn sadia.niazi{at}kcl.ac.uk Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract The oral microbiome is essential to human health, yet its de novo ecological succession and microevolutionary dynamics remain poorly understood due to the absence of tractable in vivo models. Dental implants provide a unique opportunity to investigate these processes by establishing a defined time-zero for initial bacterial attachment. In this cohort study, we performed shotgun metagenomics on 95 subgingival plaque samples from 19 participants, including peri-implant sites at weeks 1-4 after crown placement and adjacent teeth as controls. Peri-implant and adjacent periodontal microbiomes exhibited distinct taxonomic and functional profiles. Even the same taxa had different functional potential across the two sites. These findings indicated that oral microbiome development is a niche-specific process with selective colonization rather than passive microbial translocation from adjacent sites. Longitudinal analysis identified three microbial community modules driving the succession of oral microbiome, including pioneer colonizers, constitutive species, and late commensals. Each module followed distinct temporal abundance patterns and played unique ecological roles throughout the succession process. Strain-level resolution revealed divergent microevolutionary trajectories: pioneer colonizers and late commensals exhibited higher cumulative mutation rates and greater strain heterogeneity overtime, with enrichment of nonsynonymous single nucleotide variants in genes related to virulence and metabolism, whereas constitutive species remained evolutionarily stable by contrast. Our study reveals that the development, succession, and microevolution of the oral microbiome is structured, niche-dependent, and modulated by inter-species facilitation and selective genomic adaptation. These findings advance the understanding of oral microbiome ecology and provide a conceptual foundation for manipulating microbial succession in health and disease. Introduction The oral cavity is a highly diverse, dynamic ecosystem comprised of multiple spatially distinct habitats [ 1 ]. These include the hard, non-shedding surfaces of natural teeth and prosthetic materials (e.g. dentures, crowns, implants), the soft epithelial surfaces of the oral mucosa (e.g. buccal and gingival mucosa), and additional niches such as tongue dorsum and gingival sulcus [ 2 ]. Each habitat exhibits unique histological and spatial features resulting in distinct physicochemical microenvironments (pH, oxygen tension, and nutrition availability) thereby shaping site specific microbial communities [ 3 , 4 ]. Oral microbial communities are complex systems regulated by dense microbe-microbe networks, constant host-microbe interactions, and recurrent external perturbations including dietary intake, salivary flow, and oral hygiene procedures. These factors repeatedly shift the local conditions, impacting the selective pressures experienced by individual microbes [ 5 , 6 ]. Together, these elements contributed to the uniqueness and complexity of the oral microbiome. Within this complexity, the microbial communities continually adjust themselves in response to environmental perturbations, host immune regulation, and changes in selective pressure [ 7 ]. These responses manifest as both compositional changes and functional alterations [ 3 ]. Genome-resolved longitudinal studies show rapid, selection-driven microevolution in vivo , altering gene content and functional potential via selective sweeps, single nucleotide variances (SNVs), and horizontal gene transfer, even under homeostatic conditions [ 8 – 10 ]. Currently, most literature has focused on established communities in the oral cavity, investigating how the oral microbiome maintains homeostasis and how dysbiosis emerges during diseases [ 11 , 12 ]. By contrast, fewer studies have evaluated de novo community assembly and ecological dynamics in oral microenvironments, including different ecological roles of oral taxa and their microevolution trajectories. Conventionally, the formation of new oral microbial communities has been regarded primarily as a passive consequence of bacterial migration from adjacent habitats. However, emerging evidence suggests that de novo community assembly should be far more complex, involving structured ecological succession, selective pressures imposed by local environment, and potential microevolution that shapes community composition and function [ 13 ]. Due to practical and ethical constraints, investigations of de novo community assembly on natural surfaces in the oral cavity are difficult to conduct. In this scenario, dental implants offer a feasible solution. Their supra-structures, including abutments and crowns, are fully sterilized before being placed into the mouth, providing a defined time point for initial exposure to the microenvironment [ 14 ]. Biofilms then start to establish within hours to days, enabling longitudinal investigation on this process [ 15 ]. The surfaces of the implant components function as standardized, hard, non-shedding surfaces with well-defined topography. Together, these features make implants a tractable in vivo model for studying de novo community assembly and microevolution in the complex oral microenvironment. Here, we conducted a longitudinal cohort study to track peri-implant microbiome development from the day of final abutment and crown placement up to week four, using shotgun metagenomic sequencing. The adjacent periodontal microbiome served as the control. We aimed to determine whether community assembly of oral microbiome is consequential to the bacterial translocation from adjacent niches, and to characterize the development of the oral microbiome in complex microenvironments together with genome-level microevolution within its community. Methods Ethics approval This study was approved by the Ethics Committee of Xi’an Jiaotong University (No. 2023-XJKQIEC-025-002). The performance of this study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Written consent was obtained from each participant. Participant recruitment and sample collection This study recruited 19 patients with a single missing tooth that required implant restoration. The patients were free of other oral conditions. All implants used were commercial titanium bone-level implants. Detailed inclusion and exclusion criteria are described in Table S1. The surgical stages were performed by an experienced clinician following a standard protocol of implant placement with submucosal healing. After 3 to 6 months of healing, cone beam computed tomography (CBCT) scans were taken to confirm radiographic evidence of implant osseointegration. At the second-stage surgery, healing abutments were installed to shape the soft and hard tissue around the implant neck, followed by the placement of restoring abutments and the final crown. Subgingival plaque samples from the implant were collected at 1 week, 2 weeks, 3 weeks, and 4 weeks after final crown placement. As a control group, subgingival plaque samples from the adjacent teeth were also collected at 1 week ( Fig. 1 ). Prior to sample collection, participants were first asked to rinse their mouth with distilled water for 10 s. The selected sites were isolated using sterile cotton rolls to avoid contamination from the saliva. The supragingival plaque was gently removed with cotton pellets. The subgingival plaque samples were collected by inserting sterilized endodontic paper points (Dentsply Sirona, Bensheim, Germany) into the gingival sulcus for 30 seconds. This step was repeated a total of 6 times to ensure sufficient absorption of sulcus fluid and attachment of microbes. The collected paper points were placed in 1.5 mL microcentrifuge tubes (Eppendorf, Hamburg, Germany) containing phosphate buffered saline and frozen at -80 °C for further use. All samples underwent DNA extraction within 4 weeks of collection. Download figure Open in new tab Fig. 1. A flowchart of study design. DNA extraction and metagenomic sequencing Bacterial DNA was extracted from subgingival plaque samples using E.Z.N.A. Soil DNA kits (Omega Bio-tek, USA) following the instructions of the product manual. The extracted DNA was then quantified using Qubit dsDNA HS assay kits (Thermo Fisher Scientific, USA). Samples with sufficient DNA yield were then subjected to metagenomic library preparation and were pair-end sequenced using an Illumina NovaSeq 6000 PE150 (Illumina, CA, USA). Sequence quality control The raw sequences were quality-controlled and decontaminated using KneadData [ 16 ]. Briefly, the adapters and low-quality bases were trimmed using Trimmomatic with a sliding window cutoff of 4:20 and a minimum read length of 50 bp. The trimmed reads were then aligned against the human genome reference (hg39) using Bowtie2 under very-sensitive mode to remove host-derived contaminant sequences. Only reads that passed quality control on both directions were retained for downstream analyses. The quality of the cleaned reads was then checked with FastQC to ensure sufficient trimming and decontamination. Taxonomic and functional annotations of the microbiome reads MetaPhlAn4 was used to align the cleaned reads against ChocoPhlAn4 database to generate taxonomic annotations [ 17 ]. Per-sample relative abundances at the species level were extracted for downstream statistical analyses. HUMAnN3 was employed to detect the functional capacity of the microbiomes with ChocoPhlAn4 database and UniRef90 protein reference [ 18 ]. The identified pathways were regrouped into metabolic pathways based on the MetaCyc database and normalized to copies per million (CPM) unit for downstream analyses. Alpha and beta diversity analysis The alpha and beta diversity of each sample was calculated using vegan package in R (version 4.4.1). Alpha diversity was evaluated using Shannon and Simpson indices. Beta diversity was evaluated based on Bray-Curtis distance and was visualized using Principal Coordinate Analysis (PCoA). Identification of differentiating species and pathways To identify the bacterial species and gene pathways that had significantly higher abundance in the peri-implant microbiome compared to periodontal microbiome (namely differentiating species and pathways), MaAsLin3 was used to compare the prevalence (logistic models) and abundance (linear models) of each annotated species or pathway between peri-implant and periodontal microbiomes, adjusting for read depth, gender, age, and implant brand as fixed effects [ 19 ]. Bacterial relative abundance was normalized using total-sum scaling (TSS) and log-transformation prior to comparisons, while pathway abundance was compared in CPM units. Species abundance heatmap and hierarchical clustering The Z-scored relative abundance of the top 50 abundant species across the samples and time points were visualized as a heatmap using ComplexHeatmap R package. These species were then hierarchically clustered into three community modules based on Euclidean distance and complete linkage, as implemented in ComplexHeatmap . For each module-time stratum, the top 5 abundant pathways were also visualized. Computation of bacterial co-occurrence network To compute the bacterial co-occurrence networks, the correlations among bacterial species in peri-implant microbiome were tested using psych package in R based on Spearman correlation coefficients. The p-values were adjusted by controlling False Discovery Rate (FDR). Correlations with a |rho| > 0.4 and p-FDR < 0.05 were extracted to construct the co-occurrence network for each time point. Simulation analysis of attachment probability To assess whether the attachment of late commensals was facilitated by the presence of constitutive species, we analysed their attachment probabilities through generalized Lotka–Volterra (gLV) model using MATLAB (r2024b). We assume a resident community composed of S species at a feasible equilibrium Then based on gLV model, the dynamics of the resident species are described by where X i ( t ) denotes the abundance of species i at time t , and A ij captures the effect of species j on species i . A feasible equilibrium satisfies When a new species (indexed as S + 1) is introduced at a low abundance, its per capita growth rate is given by If the right-hand side of this equation is positive, the species can grow when rare and is therefore considered able to attach. This condition is considered as the attachment criterion, similar to invasion criterion reported in other studies [ 20 , 21 ]. Within this framework, we constructed two dummy communities, one with pioneer colonizers only (P) and one with both pioneer colonizers and constitutive species (P + C). The interaction types among the bacterial species were inferred from the co-occurrence networks computed above, and the strengths of the interactions were sampled from a random distribution. We performed 1000 simulations for each late commensal species and denoted the proportion of simulations that met the attachment criterion ( N ) as attachment probability ( N /1000). Detection of strain-level mutations and strain heterogeneity StrainPhlAn was employed to detect the strain-level mutations of the top 50 most abundant species [ 22 ]. The consensus marker sequences of each species were extracted according to their species-level genome bins (SGBs) and were reconstructed. Sample sequences were aligned against the reconstructed marker sequences, allowing identification of temporal nucleotide variations within the same species. Samples were filtered with a minimum of 20 marker genes per sample and a 25% marker presence threshold prior to downstream analysis. Only markers present in at least 50% of primary samples were retained. If the marker genes were detected at more than two time points including week 1 in a given subject, the samples of that subject will be included for longitudinal comparisons for cumulative mutation rates. InStrain was further used to assess the strain-level single nucleotide variants (SNV) and strain heterogeneity across the time points in the selected species [ 23 ]. Briefly, the cleaned reads were mapped against a customized database comprising reference genomes (NCBI RefSeq) of the selected species. For each species, inStrain detected its SNVs (compared to reference genome) from week 1 to 4 and determined if these SNVs were synonymous, non-synonymous, or intergenic with Prodigal prediction [ 24 ]. Non-synonymous to synonymous SNV ratio (N/S ratio) was calculated for each species and were compared longitudinally overtime. Temporal strain heterogeneity was evaluated based on population average nucleotide identity (popANI) and shared genome coverage (SGC). A threshold of 99.9% popANI was used as an indicative level of potential strain shifts. For the non-synonymous SNVs detected above, we also examined the corresponding KEGG Orthology (KO) terms of the genes in which these SNVs were located. The KO terms of the references genome and sample sequences were annotated using KoFamScan. We then aggregated the number of non-synonymous SNVs per KO across samples and species. For each species at each time point, the top 5 KO terms that were most frequently affected by non-synonymous SNVs were extracted. Enrichment analyses based on Fisher’s exact tests were subsequently performed to check if these KOs were disproportionately impacted by non-synonymous SNVs in specific species. Through this, we were able to show potential directions of microbial functional shaping driven by microevolution. Statistical analysis Unless otherwise specified, the statistical comparisons across the groups, including weeks 1 to 4 of peri-implant microbiome and the periodontal microbiome from adjacent teeth (AT), were conducted using Friedman tests with Dunn’s post-hoc multiple comparisons in GraphPad Prism (version 10.4.2). Results Demographics of the cohort A total of 95 plaque samples were collected and analysed from 19 participants ( Fig. 1 ), comprising peri-implant plaque at weeks 1 to 4 (n = 19 per time point), and adjacent periodontal plaque at week 1 (n = 19). The demographics of the participants, including age, gender, and implant brands were summarized in Table S2 and Data S1. The mean age of the cohort was 37.32 ± 11.22 years old. There were 14 (73.68%) female participants and 5 (26.32%) male participants. The included implants were from 5 manufacturers including Bego (42.11%), Straumann (31.58%), Osstem (5.26%), Hiossen (15.79%), and Zimmer (5.26%). Distinct bacterial composition at adjacent oral microenvironments Following the metagenomic workflow, a total of 617 bacterial species were identified in these 95 samples. Among them, 299 species were shared by both peri-implant and periodontal ecological niches, whilst 41 and 51 species were exclusive to peri-implant and the adjacent periodontal microbiomes, respectively ( Fig. 2A ). Notably, within the peri-implant niche, weeks 1 to 4 each contained week-specific species in addition to a shared core, indicating ongoing community succession during biofilm assembly ( Fig. 2A ). Compositional differences between peri-implant and periodontal microbiomes were further confirmed by diversity analyses. Overall, peri-implant microbiome exhibited poorer microbial richness and evenness when compared to periodontal microbiome ( Fig. 2B ), with significantly lower Shannon index at weeks 1 and 2 (p < 0.05) and significantly lower inverse Simpson index at weeks 1 and 3 (p < 0.05). Principal coordinate analysis (PCoA) was used to visualize the beta diversity of the microbiomes based on Bray-Curtis distance ( Fig. 2C ). The microbial compositions of peri-implant microbiome were significantly different from adjacent periodontal microbiome (p < 0.0001, Kruskal-Wallis test on principle coordinate 1). Download figure Open in new tab Fig. 2. Distinct bacterial composition in peri-implant and periodontal microbiomes. (A) Demographic features of the cohort and an UpSet plot showing the number of bacterial species present in each group. (B) Comparison of alpha diversity based on Shannon index, inverse Simpson index, and the number of observed species. (C) Principal Coordinate Analysis (PCoA) of beta diversity based on Bray-Curtis distance. The ellipses represented the 90% confidence levels while the diamonds represented their centroids. Projections on principle coordinate 1 and 2 were compared amongst groups using K-W tests. * p < 0.05, ** p < 0.01, **** p < 0.0001, AT: adjacent teeth. Differential taxa and functions at adjacent oral microenvironments To identify the bacterial species that were significantly different in either abundance or prevalence between peri-implant and periodontal microbiomes, MaAsLin3 was utilized based on linear and logistic regression models respectively, while adjusting for read depth, gender, age, and implant brands ( Fig. 3A ). The results showed that Streptococcus anginosus , Prevotella melaninogenica , Veillonella rogosae , Slackia exigua , Neisseria sicca , Rothia mucilaginosa , and Lancefieldella rimae were the most significantly enriched species in peri-implant microbiome compared to the adjacent periodontal microbiome (Fig. S1). The abundance and prevalence of these peri-implant-enriched species were not significantly associated with confounding factors, with the exception of N. sicca , which was positively associated with male gender and Bego brand implants ( Fig. 3A and Fig. S2). Among these 7 species, 6 of them demonstrated significantly higher relative abundance (p < 0.05, Friedman test with Dunn’s post-hoc) at one or more time points in peri-implant microbiome compared to the periodontal control ( Fig. 3B ). These species made up around 15-20% of the peri-implant community but less than 3% of the periodontal community ( Fig. 3C ), underlining niche-specific assembly of the oral microbiome. Download figure Open in new tab Fig. 3. Peri-implant-enriched species and functional pathways. (A) Linear and logistic regression models of bacterial abundance and prevalence in peri-implant microbiome compared to adjacent periodontal microbiome, adjusting for read depth, gender, age, and implant brands. Species highlighted in red were significantly more abundant in peri-implant microbiome (adjusted p-value < 0.05), namely peri-implant-enriched species. (B) Relative abundance of peri-implant-enriched species. (C) The proportion (summed relative abundance) of peri-implant-enriched species in peri-implant and adjacent periodontal microbiomes. (D) Linear and logistic regression models of pathway abundance and prevalence in peri-implant microbiome compared to adjacent periodontal microbiome, adjusting for read depth, gender, age, and implant brands. Pathways highlighted in red were significantly more enriched in peri-implant microbiome (adjusted p-value < 0.05). (E) Gene abundance (copies per million, CPM) of the peri-implant-enriched pathways in peri-implant and adjacent periodontal microbiomes. (F) The roles of anhydromuropeptides in both microbiome and host innate immune. * p < 0.05, ** p < 0.01, **** p < 0.0001. Following a similar protocol, the functional pathways that were significantly enriched in peri-implant microbiome compared to adjacent teeth were also visualized ( Fig. 3D & 3E ). Pathways related to anhydromuropeptides recycling (PWY-7883 and PWY0-1261) and tRNA processing (PWY0-1479) had significantly higher abundance in peri-implant microbiome after adjustment for read depth, gender, age, and implant brands (Fig. S3). Anhydromuropeptide has multiple roles in the microbial community in biofilm formation, nutrition complementation, and virulence regulation. It is also involved in inflammation by triggering innate immune responses through toll-like receptors (TLRs) and NOD-like receptors (NLRs) signaling [ 25 – 27 ] ( Fig. 3F ). The distinct bacterial compositions and functional capacities between peri-implant and its adjacent microbiomes indicated that the de novo assembly of oral microbiome is a niche-specific assembling process rather than passive translocation from neighboring sites. Niche-specific functional potential at species level Although species differ at the different ecological niches, it is possible that their functional potentials remain unchanged at different sites. To address this, we next compared the functional potentials of peri-implant-enriched species between the two ecological niches. These species exhibited specific pathway enrichment or depletion in peri-implant microbiome when compared to the same species in adjacent periodontal microbiome ( Fig. 4 ). Pathways that were differentially abundant between peri-implant and periodontal microbiomes were extracted and categorized into 5 main categories based on their functions: cell wall and extracellular matrix, energy metabolism, nucleotide metabolism, amino acid metabolism, and all others. In the peri-implant microbiome, P. melaninogenica exhibited pathway enrichment in the cell wall and extracellular matrix as well as amino acid metabolism categories, whilst V. rogosae showed enrichment in pathways related to nucleotide metabolism. R. mucilaginosa , S. anginosus , and N. sicca also demonstrated significant differences in gene abundances across several pathways between peri-implant and periodontal microbiomes (Data S2). These findings suggested niche-specific functional rewiring: even the same species can possess distinct functional potential across adjacent ecological niches. Download figure Open in new tab Fig. 4. Functional differences of the same species across adjacent ecological niches. Functional pathways in peri-implant-enriched species that had significantly different abundance in the peri-implant microbiome (bars) compared to adjacent periodontal microbiome (dashed lines). Oral microbiome succession is driven by distinct microbial community modules Having determined the species and functional differences between the two adjacent niches, we next investigated the succession dynamics of the peri-implant microbiome through longitudinal data series analysis. Firstly, the top 50 most abundant species were hierarchically categorized into three community modules according to the Euclidean distance of their Z-scored relative abundance across the time points ( Fig. 5A and Data S3). Based on the trends in their relative abundance from week 1 to 4, these community modules were named as pioneer colonizers (M1), constitutive species (M2), and late commensals (M3). The pioneer colonizers were more abundant at weeks 1 and 2 than weeks 3 and 4 ( Fig. 5A & 5B ). In contrast, the late commensals exhibited the inverse temporal pattern, being less abundant at weeks 1 and 2 but more abundant at weeks 3 and 4 ( Fig. 5A & 5B ). The abundance of constitutive species remained stable showing no significant changes across all time points ( Fig. 5A & 5B ). The changes in module abundance were confirmed using Friedman tests with Dunn’s post-hoc comparisons ( Fig. 5B ). Download figure Open in new tab Fig. 5. Oral microbiome succession driven by three microbial community modules. (A) Per-sample abundance (Z-score normalized) of the top 50 abundant bacterial species. Each column represents a sample while each row represented a bacterial species. (B) Module relative abundance across time points (Friedman test with Dunn’s post-hoc). (C-E) Top 5 abundant pathways in each module from week 1 to 4. Bars represent mean pathway abundance whilst circles represent relative contribution. (F) Members of each module and their corresponding phyla. (G-J) Co-occurrence networks of top 50 abundant species based on Spearman correlation coefficients. Each circle represents a bacterial species. Lager circles represent higher relative abundance. Blue and red lines represent positive and negative correlations, respectively. To investigate if species from different modules were also distinct in their functional potentials, the top 5 enriched pathways in each module were extracted for each time point ( Fig. 5C – 5E and Data S4). In pioneer colonizers, these pathways were associated with bacterial cell wall and nucleotides metabolism ( Fig. 5C ), suggesting their importance in biofilm formation and community growth. The constitutive species were also highly abundant in pathways related to cell wall and nucleotides, but in addition were also abundant in uridine monophosphate (UMP) and gondoate biosynthesis, which were potentially linked with nitrogen and carbon fluxes in the microbial community [ 28 ]. In contrast, the late commensals were characterised by greater abundance in pathways involved in dTDP-β-L-rhamnose and coenzyme A biosynthesis. Based on the relative abundance and the top-abundant gene pathways of each module, we hypothesized that the pioneer colonizers were highly competitive during the initial stage of peri-implant biofilm formation and can adapt to a microenvironment with limited resources, which made them more dominant during week 1 and 2. The constitutive species, although being less competitive than pioneer colonizers, were also able to survive the initial succession stage while having the potential to modulate the microenvironment to facilitate the attachment for late commensals. In contrast, the late commensals were less competitive during week 1 and 2, but their relative abundance gradually increased with peri-implant microbiome succession. To validate our hypothesis through microbial interactions, we next computed the bacterial co-occurrence networks for each time point based on Spearman coefficients to infer the ecological role of each community module ( Fig. 5G – 5J and Data S5). An obvious characteristic of the week 1 network was that there were numerous negative correlations between pioneer colonizers (M1) and the other modules ( Fig. 5G ), suggesting that their presence negatively affected the growth of other species, potentially by competing for the limited ecological niches and resources. At week 2 and 3 ( Fig. 5H & 5I ), the number of negative correlations decreased tremendously. There were more correlations both within and between constitutive species (M2) and late commensals (M3). These findings indicated that the relative abundances of these bacteria were either rising or declining in a coordinated manner, suggesting that the community was shifting from a strong competition towards a co-adaptation to the microenvironment. The week 4 network was characterized by the reemergence of negative correlations. In contrast to the week 1 network, most of the negative correlations by week 4 involved late commensals rather than pioneer colonizers ( Fig. 5J ). This might suggest that the late commensals had gained advantages over other modules, which explained their increased abundance at week 4. In addition, the number of correlations were more evenly distributed both across and within the three modules at week 4, indicating that the community was evolving towards a more balanced and mature status. Attachment of late commensals is facilitated by constitutive species Based on the correlations calculated above, we next constructed two dummy peri-implant microbial communities for each time point – one with only pioneer colonizers present (community P) and the other with both pioneer colonizers and constitutive species present (community P + C) – to simulate the attachment probability of late commensals in peri-implant microbiome using generalized Lotka-Volterra (gLV) models ( Fig. 6A & 6B ). In many cases, late commensals demonstrated higher attachment probability in community P + C compared to community P ( Fig. 6C – 6F ), suggesting a facilitative role of constitutive species towards the attachment of late commensals. To quantitatively evaluate this effect, we further calculated the odds ratio of attachment probability between the P and P + C communities for each late commensal from week 1 to 4 ( Fig. 6G ). Notably, some species, including Capnocytophaga SGB2480 (weeks 1-4), Corynebacterium matruchotii (week 2), Neisseria sp. oral taxon 014 (week 2), and Schaalia meyeri (week 4), displayed significantly increased attachment probabilities (log 10 OR > 0.5, p < 0.05) with the presence of constitutive species (Data S6). Download figure Open in new tab Fig. 6. Attachment of late commensals is facilitated by constitutive species. (A) Generalized Lotka–Volterra (gLV) model used to predict attachment probability of the late commensals. Attachment of a newly introduced species is considered successful only when growth rate is positive. The attachment probability of this species is defined as the proportion of successful attachments out of 1000 simulations. (B) Attachment simulation on two dummy communities, one comprising only pioneer colonizers (community P) whilst the other consisting of both pioneer colonizers and constitutive species (community P + C). (C-F) Attachment probabilities of late commensals across weeks 1 to 4. Blue bars represent pioneer-only community (P) while red bars represent community with both pioneer colonizers and constitutive species (P + C). (G) Log 10 -transformed odds ratio of attachment probability between communities P + C and P. Positive values indicate higher attachment probability of late commensals in the presence of constitutive species, whereas negative values suggest potential inhibition. * p < 0.05. Strain-level heterogeneity and temporal microevolution in each community module In order to see if different community modules also exhibited different extents of strain heterogeneity and microevolution, we compared longitudinally the consensus marker sequences of each species from week 1 to 4 using StrainPhlAn , and calculated their cumulative mutation rates at weeks 2 to 4 against week 1 ( Fig. 7A ). Overall, constitutive species exhibited lower cumulative mutations rates in their marker genes when compared to pioneer colonizers and late commensals ( Fig. 7a and Fig. S4-S9). The differences at week 3 between constitutive species and both pioneer colonizers and late commensals, and at week 4 between constitutive species and late commensals were statistically significant (p < 0.05). Download figure Open in new tab Fig. 7. Assessment of temporal microevolution in each community module. (A) Cumulative mutation rates in consensus marker sequences at weeks 2 to 4 compared to week 1. * p < 0.05, ** p < 0.01. (B-D) Temporal alterations in the N/S ratios of the selected representative species from each community module. The dashed lines represent the week 1 N/S ratio level for each species. (E-G) Temporal strain heterogeneity in selected species assessed by popANI and SGC. Colour gradients reflect popANI values while square sizes reflect normalized SGC. Squares highlighted in black boarders denote popANI < 0.999, suggesting potential strain shifts. (h) Top KO terms with the highest counts of non-synonymous SNVs for each representative species and time point. Circles indicate statistically significant enrichment of non-synonymous SNVs (PFDR < 0.05), while diamonds indicate non-significant enrichment. To assess the evolutionary pressure of different modules, we further looked into representative species of each community module (the peri-implant-enriched species identified above. P. melaninogenica , S. anginosus , and R. mucilaginosa for pioneer colonizers, N. sicca for constitutive species, and S. exigua and L. rimae for late commensals) and investigated their strain-level single nucleotide variants (SNVs) using inStrain . We calculated the non-synonymous to synonymous SNV ratios (N/S ratio) in each community module across the time points (Data S7). The pioneer colonizers and late commensals demonstrated an increasing trend in their N/S ratios ( Fig. 7B & 7D ) whereas constitutive species showed a slight decreasing trend in N/S ratio ( Fig. 7C ), suggesting module-specific evolutionary pressures. We then compared the temporal strain heterogeneity within these species by assessing the population average nucleotide identity (popANI) and shared genome coverage (SGC) at weeks 2 to 4 compared to week 1. The pioneer colonizers ( Fig. 7E ) and late commensals ( Fig. 7G ) exhibited greater temporal strain heterogeneity when compared to constitutive species ( Fig. 7F ), indicating different extents of microevolution towards strain divergence in different modules. For the non-synonymous SNVs within each species, we also mapped the genes in which they were located to the KEGG Orthology (KO) terms. KO terms with the highest counts of non-synonymous SNVs were visualized and statistically tested to assess whether non-synonymous SNVs were overrepresented in these KO terms when compared to background expectations ( Fig. 7H ). Except for P. melaninogenica , non-synonymous SNVs across all representative species, irrespective of their community module, exhibited significant enrichment in K20386: cylB (ATP-binding cassette, subfamily B, bacterial CylB) and K15558: ophH (phthalate transport system ATP-binding protein). For P. melaninogenica , enrichment was observed in K21572: susD, K21573: susC, and K02014: TC.FEV.OM. In addition to K20386: cylB and K15558: ophH, enrichment in K27305: aprG, curF and K24122: ctmAB was observed in R. mucilaginosa and S. exigua , respectively. Discussion The oral microbiome is involved in various oral diseases and is closely associated with human health. However, investigating the de novo assembly of oral microbial communities remains challenging, largely due to the difficulty in defining an accurate starting point for bacterial colonization, given the physiological and anatomical complexity of the oral cavity. Dental implants offer a unique opportunity in this regard. The placement of prosthetic components provides a feasible, well-defined starting point for microbial exposure. In this study, we applied longitudinal shotgun metagenomic analysis to subgingival plaque samples from peri-implant and adjacent periodontal sites. This approach enabled us to characterize the de novo assembly, temporal succession and microevolution of oral microbiome within a complex microenvironment, while also comparing against an adjacent, established community. Our results showed that oral microbiome development is driven by three distinct microbial community modules, each playing a different role in the succession process, with different microevolution trajectories ( Fig. 8 ). Download figure Open in new tab Fig. 8. Succession pattern of the oral microbiome in complex microenvironment. Conventionally, adjacent ecological niches have been considered reservoirs contributing to the formation of the oral microbiome at specific sites [ 29 ]. For example, a higher proportion of periodontal pathogens were also detected in peri-implant lesions [ 30 ], which suggests a link between peri-implant microbiome and the microbiome from natural teeth. Building on this, our longitudinal cohort revealed distinct taxonomical and functional profiles in peri-implant microbiome compared to the microbiome from adjacent teeth. The two adjacent niches demonstrated significantly different alpha and beta diversity ( Fig. 2B & 2C ), each harboured a substantial number of exclusive species that were not shared between the two communities ( Fig. 2A ). These findings suggest that the development of the oral microbiome is not merely a passive consequence of bacterial translocation from adjacent microenvironments but rather involves selective colonization and niche-specific microbial assembly. The establishment of the microbial community in oral biofilms follows a commonly recognized pattern [ 31 ]. It begins with the initial attachment of the pioneer species, followed by the sequential adhesion of secondary and late colonizers [ 32 , 33 ]. Each stage of colonization modifies the microenvironment, paving the way for the community succession towards maturation of the biofilm. Similarly in our study, distinct abundance trajectories of the microbial species were observed in the peri-implant microbiome ( Fig. 5 ). Based on this, the community was categorized into three modules, namely pioneer colonizers (M1), constitutive species (M2), and late commensals (M3), each comprised of different bacterial species with different functional potentials ( Fig. 5 ). Previous studies have identified Streptococcus species as classical pioneer colonizers in the oral cavity [ 34 – 36 ]. Consistent with these findings, our results also identified multiple Streptococcus species in the pioneer module. In addition, we also identified other microbes such as Prevotella , Haemophilus , and Rothia species as potential pioneer colonizers that were essential in the early-stage formation of the peri-implant biofilm. These species were dominant in the initial microenvironment and occupied the ecological niches via exclusive competition. However, they gradually lost their dominance as the microbial community evolved. The high cumulative mutation rates observed in their marker genes may reflect ongoing micro-evolution in concert with the temporal alterations in the microenvironment. Among the pioneer colonizers, P. melaninogenica was a representative being significantly more abundant in peri-implant microbiome compared to periodontal microbiome. P. melaninogenica is frequently present in the microbiome of the oral cavity and upper respiratory tract [ 37 ], and is potentially associated with periodontitis due to the action of its type □ and □ secretion systems [ 38 , 39 ]. Interestingly, in our study, we found that P. melaninogenica might have different roles to play in periodontal and peri-implant microbiomes, as the same species exhibited significantly higher gene abundance in pathways related to cell wall and extracellular matrix metabolism in peri-implant microbiome than periodontal microbiome, suggesting their importance in biofilm formation ( Fig. 4 ). Similar phenomena were observed in other peri-implant-enriched species including V. rogosae , R. mucilaginosa , and S. anginosus , suggesting that bacterial species may exhibit niche-specific functional adaptations across different ecological sites in the oral cavity. According to our functional annotations, the pioneer and constitutive species exhibited enrichment in peptidoglycan biosynthesis pathways, the major component of the bacterial cell wall. Peptidoglycan and its fragments generated during the degradation and recycling of the cell wall (e.g. anhydromuropeptides) are multifunctional in the microbial community [ 27 ]. Besides maintaining cell wall homeostasis [ 40 ], they also act as a community signal to promote biofilm formation [ 41 ], and are also responsible for virulence regulation and nutrition complementation in certain bacterial species [ 41 , 42 ]. Peptidoglycan and its fragments are also major pathogen-associated molecular pattern molecules (PAMPs) and can trigger innate immune responses. These PAMPs can be recognized by TLRs and NLRs thus activating nuclear factor kappa B (NFκB) and mitogen-activated protein kinase (MAPK) intracellular signalling pathways, resulting in the elevation of pro-inflammatory cytokines and promotion of inflammation onset ( Fig. 3F ). Enrichment in peptidoglycan biosynthesis pathways implies a role for pioneer and constitutive species in not only biofilm formation, but also suggested integration and regulation of the entire microbial community, and more generally, in host-microbiome interactions. A well-known example of secondary and late colonizers in the oral biofilm is the orange and red complexes discovered by Sorcransky et al . [ 43 ]. The attachment of orange complex species such as Fusobacterium nucleatum increases the complexity of the biofilm and modulates the microenvironment to facilitate the subsequent recruitment of the late colonizers such as Porphyromonas gingivalis [ 44 ]. This trajectory is typically associated with the transition towards dysbiosis and clinical diseases. Here, we showed that a similar trajectory also exists in the assembly of healthy biofilm, albeit with different microbes ( Fig. 5 ). Represented by N. sicca , the constitutive species demonstrated potentials to modulate the microenvironment through pathways like UMP and gondoate biosynthesis ( Fig. 5C ). UMP biosynthesis is closely linked to cellular carbon–nitrogen metabolic coupling [ 28 ] and therefore influences carbon and nitrogen fluxes in the microbial community. The production of gondoate, a monounsaturated fatty acid synthesized independently of oxygen, by constitutive species may enhance membrane fluidity and surface compatibility [ 45 ], thereby facilitating the adhesion of late commensals. This was confirmed by our computational simulations based on gLV models ( Fig. 6 ), where the attachment probabilities of late commensal species tended to increase with the presence of constitutive species. Unlike the other two modules, the late commensals did not demonstrate enrichment in pathways related to cell wall metabolism at week 1 ( Fig. 5C ). This suggested that they contributed less during the initial biofilm formation. Instead, pathways related to pentapeptide and peptidoglycan biosynthesis began to emerge at week 2 and onwards, possibly marking the proliferation of the late commensals, which is in line with our finding that late commensals were more abundant during weeks 3 and 4 compared to the initial week. Notably the consistently high abundance of coenzyme A biosynthesis across the time points suggested that these species might possess greater metabolic activity compared to other community members, given the central position of coenzyme A in multiple metabolic pathways [ 46 , 47 ]. Our strain-resolution analyses revealed distinct trajectories of microevolution among the three community modules. Constitutive species demonstrated the most stable evolutionary trajectory, as evidenced by fewer cumulative mutations ( Fig. 7A ), consistently lower N/S SNVs ratio ( Fig. 7C ) over time, and robust strain similarity across the time points ( Fig. 7F ). These findings suggested that constitutive species faced less selective pressure from the peri-implant microenvironment [ 48 ] and might play as the “fundamental” components of the peri-implant microbiome. In contrast, pioneer colonizers and late commensals exhibited notable microevolutions with higher cumulative mutation rates ( Fig. 7A ), increased N/S SNVs ratios over time ( Fig. 7B & 7D ), and greater temporal strain heterogeneity ( Fig. 7E & 7G ). These two modules represented divergent responses to the environmental selective process. The pioneer colonizers were dominant during the initial weeks but gradually declined with the succession of peri-implant microbiome, likely due to reduced competitive fitness or niche compatibility, thereby vacating ecological niches. In contrast, late commensals gained dominance over time, potentially facilitated by both environmental modulation and a series of adaptive evolutions. These results are in agreement with previous study showing that in complex microbial communities, initially competitive species can end up in extremely low abundance with the succession process, and that the composition of a stable community was independent of its initial species proportions [ 49 ]. The representative species for each community module, except for P. melaninogenica , exhibited non-synonymous SNV enrichment in KO terms K20386: cylB (ATP-binding cassette, subfamily B, bacterial CylB) and K15558: ophH (phthalate transport system ATP-binding protein). CylB is part of an ATP-binding cassette (ABC) transporter that is responsible for producing and transporting cytolysin, a bacterial virulence factor that disrupts cell membrane and triggers cell lysis [ 50 ]. Non-synonymous SNVs in CylB can modulate cytolysin secretion, thereby changing the virulence of the microbes. Altered virulence, whether attenuated or enhanced, would reshape host-microbe crosstalk via regulation in immune activation. This indicates how microbes, through genome-level microevolution, dynamically adapted to host-derived pressure, thereby determining which taxa persist and expand as succession proceeds. In parallel, ophH is a transporter for phthalates. Under normal conditions, phthalates are unlikely to be abundant in the oral cavity. However, they are used in various dental materials as plasticizers, from resin composites to adhesive cement [ 51 ]. After crown cementation, even trace amounts of excess cement can serve as a potential source of leachable phthalates. The enrichment of non-synonymous SNVs in ophH further reflected rapid adaptation of the oral microbes to specific chemical compounds present in the oral environment. Caution should be taken when further interpreting the results of this study, however. Firstly, the oral microbiome is highly individualized [ 52 ]. Even the same species can exhibit different phenotypes and functional potentials in different subjects, complicating efforts to assign fixed roles to any single taxon. Secondly, oral ecological niches differ in many aspects such as pH, oxygen tension, nutrient availability, host factors, and surface properties. Consequently, generalizing the succession pattern observed in peri-implant microbiome should be done with care. Future studies investigating evolution at multiple oral sites and explicitly measuring local physicochemical parameters will be important to conclude a universal pattern for oral microbiome development. It should also be noted that the succession of the oral microbiome is a continuous process. However, due to practical constraints, the samples were collected at four discrete time points to infer the community dynamics in this study. Although our findings provide a relatively detailed description of the succession patterns of the peri-implant microbiome, the intervals of sample collection may not fully capture the complexity within this process. Therefore, longer investigations with denser review points are needed for future studies. Conclusion Through this longitudinal cohort study, we showed that oral microbiome development in complex microenvironment is not a passive consequence of bacterial translocation from adjacent niches but a process of selective colonization and niche-specific microbial assembly. Such process follows a structured and dynamic succession pattern driven by three microbial modules, including pioneer colonizers, constitutive species, and late commensals. Each microbial module plays a distinct ecological role and exhibits different trajectories of microevolution. These findings provide a generalizable framework for understanding how oral biofilms originate and evolve in the complex microenvironments of the oral cavity. Data availability Sequences generated during shotgun metagenomics and related metadata were deposited in NCBI Short Reads Archive with Bioproject ID: PRJNA1148759 ( https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1148759 ). Computational codes used for analyses and visualization are available on GitHub ( https://github.com/Yuchen-D-Z/Oral_microbiome_development ). Other data generated during downstream analyses are provided in the Supplementary Materials. Ethics approval and consent to participate This study was approved by the Ethics Committee of Xi’an Jiaotong University (No. 2023-XJKQIEC-025-002). The performance of this study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Written consent was obtained from each participant. Competing interests The authors declare that they have no competing interests. Funding This study was partly supported by Key Research and Development Program of Shaanxi Province, China (2023-YBSF-162), the Royal College of Surgeons of England - FDS Pump Priming Grant 2022, and King’s-China Scholarship Council Scholarship. Authors’ contributions Y.Z. designed the study; performed the metagenomic analyses including the taxonomical and functional annotations, the cross-niche and longitudinal comparisons, the bacterial co-occurrence networks, and the genome-level mutation tracking; visualized and interpreted the data; and wrote the manuscript. Y.Y. performed the mathematical work of the gLV model used to predict attachment probability; performed the computational simulation; and contributed to the initial draft. Y.L. collected and stored the clinical samples and curated the raw sequence data as well as subject metadata. E.M.L., D.M., and S.A.N. advised the methodology of the cohort and genome-level mutations, provided the access to computational resources, and revised the manuscript. S.A.N. and Q.Z. supervised the study and raised the fundings. Q.Z. designed the study, conceived and administrated the project. All the authors reviewed, revised, and approved the final manuscript. Acknowledgements We thank the clinicians and nurses at Department of Implant Dentistry, Hospital of Stomatology, Xi’an Jiaotong University for their help in sample collection and storage. 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Share Development and Genome-level Microevolution of Oral Microbiome during Surface Colonization Yuchen Zhang , Yuguang Yang , Yibing Liu , Emily Ming-Chieh Lu , David Moyes , Sadia Ambreen Niazi , Qin Zhou bioRxiv 2025.11.07.685765; doi: https://doi.org/10.1101/2025.11.07.685765 Share This Article: Copy Citation Tools Development and Genome-level Microevolution of Oral Microbiome during Surface Colonization Yuchen Zhang , Yuguang Yang , Yibing Liu , Emily Ming-Chieh Lu , David Moyes , Sadia Ambreen Niazi , Qin Zhou bioRxiv 2025.11.07.685765; doi: https://doi.org/10.1101/2025.11.07.685765 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Microbiology Subject Areas All Articles Animal Behavior and Cognition (7635) Biochemistry (17697) Bioengineering (13894) Bioinformatics (41951) Biophysics (21455) Cancer Biology (18593) Cell Biology (25509) Clinical Trials (138) Developmental Biology (13380) Ecology (19903) Epidemiology (2067) Evolutionary Biology (24322) Genetics (15611) Genomics (22509) Immunology (17737) Microbiology (40398) Molecular Biology (17183) Neuroscience (88619) Paleontology (667) Pathology (2833) Pharmacology and Toxicology (4825) Physiology (7644) Plant Biology (15158) Scientific Communication and Education (2046) Synthetic Biology (4296) Systems Biology (9825) Zoology (2271)