HIV Infection and Exposure Increases Cariogenic Taxa, Reduces Taxonomic Turnover, and Homogenizes Spatial Differentiation for the Supragingival Microbiome

HIV Infection and Exposure Increases Cariogenic Taxa, Reduces Taxonomic Turnover, and Homogenizes Spatial Differentiation for the Supragingival Microbiome | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article HIV Infection and Exposure Increases Cariogenic Taxa, Reduces Taxonomic Turnover, and Homogenizes Spatial Differentiation for the Supragingival Microbiome Allison E. Mann, Ciara Aumend, Suzanne Crull, Lauren M. O’Connell, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4720457/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Jun, 2025 Read the published version in Microbiome → Version 1 posted 4 You are reading this latest preprint version Abstract Background The oral microbiome comprises distinct microbial communities that colonize diverse ecological niches across the oral cavity, the composition of which are influenced by nutrient and substrate availability, host genetics, diet, behavior, age, and other diverse host and environmental factors. Unlike other densely populated human-associated microbial ecosystems (e.g., gut, urogenital), the oral microbiome is regularly and directly exposed to the external environment and is therefore likely less stable over time. Cross sectional studies of the oral microbiome capture a glimpse of this temporal dynamism, yet a full appreciation of the relative stability, robusticity, and spatial structure of the oral environment is necessary to understand the role of microbial communities in promoting health or disease. Results Here we investigate the spatial and temporal stability of the oral microbiome over three sampling time points in the context of HIV infection and exposure. Individual teeth were sampled from a cohort of 565 Nigerian children with varying levels of tooth decay severity (i.e., caries disease). We collected 1,960 supragingival plaque samples and characterized the oral microbiome using a metataxonomic approach targeting an approximately 478 bp region of the bacterial rpo C gene. We found that both infection and exposure to HIV have significant effects on the stability of the supragingival plaque microbiome at both the spatial and temporal scale. Specifically, we detect (1) significantly lower taxonomic turnover of the oral community among exposed and infected children compared to unexposed children, (2) we find that HIV infection homogenizes the oral community across the anterior and posterior dentition, and (3) that impaired immunity (i.e., low CD4 count) and low taxonomic turnover over time in children living with HIV is associated with higher frequency of cariogenic taxa including Streptococcus mutans . Conclusions Our results document substantial community fluctuations over time in children unexposed to HIV independent of oral health status. This suggests that the oral community, under typical conditions, rapidly adapts to environmental perturbations to maintain homeostasis and that long-term taxonomic rigidity is a signal of community dysfunction, potentially leading to a higher incidence of oral disease including caries. oral microbiome HIV longitudinal analysis caries disease CD4 count Figures Figure 1 Figure 2 Figure 3 Figure 4 BACKGROUND The human oral microbiome is a highly dynamic and personalized ecological system composed of heterogeneous microhabitats (e.g., saliva, cheeks, tongue, hard and soft palate, teeth, and gums) inhabited by diverse bacteria, archaea, microbial eukaryotes, and viruses. The most densely populated of these microhabitats are microbial biofilms that form on the teeth (i.e., dental plaques), which harbor a distinct community as compared to those formed on soft tissues. The supragingival plaque microbiome is further shaped by differences in substrate and oxygen availability, salivary flow, and the presence of other co-aggregating taxa [ 1 – 4 ]. Along with features of the oral environment that can be attributed to host genetics (e.g., morphology of the oral tissues, salivary production rate, composition, and pH, and host immune response), the composition and stability of the oral microbiome is heavily influenced by diet, behavior (e.g., oral hygiene practices, smoking), and age [ 5 – 8 ]. Dental plaques form on a tooth through a series of ordered colonization events, resulting in a highly structured, species rich microbial biofilm [ 9 , 10 ]. Community homeostasis of the plaque microbiome is essential for the maintenance of good oral health [ 9 ]. However, dental plaques are also in direct and regular contact with an individual’s external environment and are thus more susceptible to punctuated ecological shifts over time due to changing environmental conditions [ 8 , 11 , 12 ]. The relative resiliency of the oral environment to these shifts has important implications for the maintenance of good oral health and the prevention of microbial dysbiosis. Dysbiosis of the oral environment is associated with a variety of common chronic diseases, the most prevalent of which is tooth decay. If untreated, tooth decay results in the formation of carious lesions in the enamel surface, bacterial infection of the dentin, pulp cavity, and surrounding tissues, and eventually, tooth loss. Severe infection of a tooth can cause sepsis and even death. Untreated dental caries in permanent teeth is the most common chronic disease, globally affecting an estimated 2.5 billion people, despite it being a largely preventable condition [ 13 ]. The etiology of caries is initiated and exacerbated by ecological shifts in the plaque microbial community – driven by environmental changes in the oral cavity – and typically results in a reduction of overall community richness and complexity, though some studies have found an increase in diversity among teeth with active carious lesions [ 14 – 16 ]. While there are specific taxa that are directly implicated in the development or intensification of caries disease progression (primarily Streptococcus mutans and other aciduric and acidogenic taxa), it is generally accepted that caries is the result of a population imbalance in the oral microbial community [ 17 – 20 ], and that risk of caries disease is related to the structure, stability, and resilience of the community to ecological stressors [ 9 , 21 ]. Children living with HIV have a higher prevalence and more severe clinical presentation of dental caries in both primary and permanent dentition, the impact of which is associated with viral load and individual immune status [ 22 – 27 ]. Previous work by our group found a small but distinct effect of HIV status on the supragingival plaque microbiome which is exacerbated in the context of severe caries [ 28 ]. Moreover, there is growing evidence that perinatal exposure to HIV has systemic effects on the health, growth, and development of children [ 29 – 35 ] and previous work by our group found a distinct oral mycobiome [ 36 ], lower salivary flow and decreased salivary pH [ 37 ], increased developmental defects of the enamel surface [ 38 ], and increased incidence of stunting in both children exposed to and infected by HIV [Manuscript in preparation]. Given the relative susceptibility of the oral microbiome to perturbations as compared to other microbe-rich host-associated environments, cross-sectional studies of the oral microbiome only provide a brief snapshot of the ecological diversity and dynamics of the oral microbiome over time, and the impact of illness or stress on the long-term stability of the oral microbiome is poorly defined. In the current study, using three time points, we compare the temporal and spatial stability of the oral microbiome among children living with HIV (HI), children perinatally exposed to but uninfected by the virus (HEU), and unexposed and therefore uninfected (HUU) children. We find significantly lower taxonomic turnover in HEU and HI children as compared to HUU children over time. While this pattern in HI children is predominantly due to a higher prevalence of diseased teeth with low community complexity, we detect significantly lower taxonomic turnover for HEU children even when only healthy teeth are compared. In addition to temporal patterning of the plaque microbiome, we find that spatial patterning of the bacterial community across the dental anatomy is disrupted in children living with HIV as compared to HEU and HUU children wherein children exposed to or uninfected by HIV have a clear differentiation between the communities inhabiting the anterior and posterior teeth but there is no such separation among HI children. Finally, we document an initial increase in CD4 counts in HI children from visit one to visit two, followed by a slight decrease in visit three, possibly a result of ongoing anti-retroviral treatment and increasing age of the cohort. Importantly, increasing CD4 counts among HI children is associated with a reduction of cariogenic taxa. Our results document the importance of longitudinal and spatial sampling of the supragingival plaque microbiome for a more holistic appreciation of the role of oral bacteria in the development of caries in children living with HIV. METHODS Study design and sample collection. Samples included in this study were collected from 565 children between May, 2019 and February, 2021 during three separate clinical visits at the University of Benin Teaching Hospital in Benin City, Nigeria. In total, we collected 1,960 supragingival plaque samples as part of the Dental Caries and its Association with Oral Microbiome and HIV in Young Children – Nigeria (DOMHaIN) Study [ 25 ]. A total of 614 supragingival plaque samples were collected from uninfected and unexposed children (HUU), 611 from children exposed to HIV in utero but uninfected with HIV (HEU), and 735 from children living with HIV (HI). Supragingival plaque samples were collected from a single tooth and categorized into one of six progressive health categories. First, the sample was categorized according to the condition of the tooth of origin using the International Caries Detection and Assessment System [ 39 ] as either originating from (1) a tooth with no cavity (H; ICDAS score = 0), (2) a tooth with an active enamel lesion (E; ICDAS score = 1 to 3), or (3) a tooth with an active dentin carious lesion (D; ICDAS score ≥ 4). We next characterized individual plaque samples according to the observed caries experience of the child at the time of sampling as either (1) caries-free (CF), i.e., no clinical or reported evidence of caries (number of decayed, missing, and filled teeth [DMFT] = 0), (2) caries active with lesions present in the enamel of the tooth only (CE; DT = 0, MFT ≥ 0), or (3) caries active with at least two teeth presenting with active dentin lesions (CD; DT ≥ 2, MFT ≥ 0). Together, these two classification schemes were used to place each sample into one of six progressive disease states as follows: (1) plaque from a healthy tooth collected from a child with no caries (H-CF, n = 1,169), (2) plaque collected from a healthy tooth collected from a child with active enamel caries (H-CE, n = 119), (3) plaque collected from a healthy tooth collected from a child with active dentin caries (H-CD, n = 251), (4) plaque collected from a tooth with an active enamel cavity from a child with active enamel cavities (E-CE, n = 70), (5) plaque collected from a tooth with an active enamel cavity from a child with active dentin cavities (E-CD, n = 110), and (6) a plaque collected from a tooth with an active dentin cavity from a child with active dentin cavities (D-CD, n = 241). In brief, we collected each plaque using a sterile curette which was then stored in a sterile 2 mL cryogenic vial containing 500 µL of RNAlater. Post-collection, samples were placed immediately on ice and stored at -80°C within two hours of collection. Detailed sampling procedures are described in Coker et al. [ 25 ]. DNA extraction, library preparation, and sequencing We extracted DNA from each sample using the DNeasy PowerBiofilm kit (Qiagen, Valencia, CA, USA) following the manufacturer’s suggested protocol. We quantified the total DNA yield post-extraction for each sample using a Qubit fluorometer (Invitrogen, Carlsbad, CA). To track potential sources of contamination, an extraction blank using molecular-grade water was processed in parallel to all samples. To characterize the bacterial community, we amplified a fragment of the bacterial rpo C gene using custom primers (rpoCF: 5’ – MAYGARAARMGNATGYTNCARGA – 3’, rpoCR: 5’ – GMCATYTGRTCNCCRTCRAA – 3’) as described in Mann et al. [ 28 ]. Each PCR reaction consisted of the following: 0.5 µL each of the forward and reverse primers, 10 µL molecular grade water, 4 µL of template DNA, and 10 µL of the Platinum Hot Start PCR Master Mix (Invitrogen, Carlsbad, CA, USA). We processed PCR blanks (molecular grade water) in parallel to all samples to track sources of contamination. Each reaction was amplified using the following thermocycler conditions: 94°C for 3 minutes followed by 41 cycles of 94°C for 45 seconds, 39.5°C for 1 minute, and 72°C for 1 minute 30 seconds. A final elongation step was performed for 10 minutes at 72°C. Amplification of all samples was confirmed through both gel electrophoresis and Qubit fluorometer (Invitrogen, Carlsbad, CA, USA). Finally, we pooled each sample library at equimolar concentrations and sequenced the final pools on an Illumina MiSeq using V3 2x300 paired-end chemistry (Illumina, San Diego, CA, USA). Computational analyses We first removed primers and adapter sequences from our raw sequencing read files using Cutadapt (v 1.18) [ 40 ]. Next, we quality filtered, merged, generated Amplicon Sequence Variants (ASVs), and removed suspected chimeric sequences using DADA2 (v 1.22.0) [ 41 ] in an R (v 4.1.0) environment [ 42 ]. Quality-filtered paired-end reads that were shorter than 450 bp after merging were removed from downstream analysis. We next assigned a taxonomy to each ASV using Kraken2 (v 2.1.2) [ 43 ] and a custom rpo C database as reference [ 28 ]. ASVs that could not be assigned to the phylum level or below (i.e., kingdom only) were removed from analysis. We removed low-abundance ASVs with a prevalence threshold of 0.1% (n = ~ 2 samples) unless they had a total abundance of more than 1,000 reads, in which case the ASV was retained for downstream analysis. Finally, we removed samples with fewer than 5,000 reads post-filtering. Rarefaction curves for all samples post-quality filtering can be found in Figure S1 . We next performed diversity analyses for the filtered dataset using the R libraries phyloseq (v 1.38.0) [ 44 ], vegan (v 2.6-4) [ 45 ], compositions (v 2.0–6) [ 46 ], and microbiome (v 1.16.0) [ 47 ]. Significance in diversity metrics between groups was determined using PERMANOVA analysis (vegan::adonis2) performed on Center Log-Ratio (CLR) transformed data. We used coda4microbiome (v 0.1.4) [ 48 ] to identify microbial signatures that are predictive of sample metadata. This method calculates the minimum number of features (here, bacterial species) that have the maximum predictive power for a particular user-defined metadata category. A microbial signature, therefore, is defined by the relative abundance of two groups of taxa wherein the balance of taxa with positive and negative coefficients has the highest correlation with your chosen variable. We calculated the taxonomic turnover of the oral microbiome on a single tooth over time following the protocol detailed in Bastiaanssen et al. [ 49 ]. In brief, we defined taxonomic turnover as the absolute Euclidean distance between paired plaque samples from the same tooth and individual over two or more collection time points using an Aitchison distance matrix calculated from CLR transformed count data. Significance between groups was determined using a Wilcoxon rank signed test. Next, we wanted to identify conserved microbial associations over time (i.e., the co-occurrence of species pairs on distinct teeth/samples) within and between our HIV status and tooth health groups. To do this, we first generated microbial association networks with NetCoMi (v 1.1.0) [ 50 ] and Spiec-Easi (v 1.1.3) [ 51 ] using the neighborhood selection method (MB) [ 52 ]. Before network generation, we collapsed our ASV frequency table to the species level and only included species found in at least 1% of all samples with a minimum of 10 observations to minimize the effect of low frequency taxa. We generated microbial association networks for each HIV status (HI, HEU, HUU) and tooth health group (H, E, D) at each sampling time point (three networks per group) and used anuran (v 1.1.0) [ 53 ] to depict a single Core Association Network (CAN) for each HIV status or tooth health group across all time points (set size = 1.0). Anuran identifies conserved patterns (i.e., groupings) across networks. As such, CANs generated for this study represent the consensus of microbial associations over time within our groups. In addition, we generated a “global” CAN using all samples to act as a baseline comparison to our group-specific CANs (set size 0.6). Next, we used the greedy clustering algorithm implemented in igraph (v 1.6.0) [ 54 ] to cluster species and quantify cluster modularity within each CAN [ 55 ]. Only clusters with at least ten or more members were analyzed. Finally, we wanted to better understand the community ecology on individual teeth directly before or after the tooth has a high relative abundance of the cariogenic taxon S. mutans . While S. mutans is a dominant taxon in many supragingival plaque samples collected from late-stage cavitated teeth, it is often absent or at very low frequency at the early stages of tooth decay (i.e., enamel lesions or white spots) [ 28 , 56 , 57 ]. As such, it is unclear what role (if any) S. mutans or other bacterial groups play in the initiation of caries. Conversely, many late-stage cavitated teeth have very little or no S. mutans which may suggest that the proliferation of S. mutans during the intensification of tooth decay is self-limiting and the community collapses over time [ 28 ]. As such, it is important to know if there are predictable taxa that recolonize the tooth and if this recolonization recapitulates the original plaque community. To better understand the plaque environment before or after the proliferation of S. mutans , we first identified all individual teeth that were sampled from the same child at more than one time point. From this, we identified teeth that had a relatively low proportion of S. mutans ( = 10%). Using this subset of paired samples of the same tooth over time, we first performed a random forest classification analysis using ranger (v 0.16.0) [ 58 ] to determine if the microbial community can be used to predict later S. mutans colonization and proliferation (i.e., before S. mutans ) or if the community that recolonizes the tooth after S. mutans collapse is consistent and predictable (i.e., after S. mutans ). Finally, we used a post-hoc analysis of our random forest classification model to identify specific bacterial species that are explanatory of our two groups (either before or after S. mutans ) using FastShap (v 0.1.1) [ 59 ]. Conda environments and all scripts for data analysis can be found at https://github.com/aemann01/long_oral_microbiome and are archived at Zenodo (DOI: 10.5281/zenodo.11396311 ) for analytical reproducibility. RESULTS Sample demographic and taxonomic summary. After quality filtering and removal of samples with low read counts, we retained 14,111 unique ASVs assigned to a total of 1,960 individual supragingival plaque samples collected from 565 children. Our final sample demographic includes 900 samples collected from female participants and 1,060 from male participants. Of the plaque samples, 746 were collected at visit one, 596 at visit two, and 618 at visit three. The average age of participants at visit one was 6.9 years old (SD ± 1.9) and 7.2 years old (SD ± 2.0) at visit three. Of all plaque samples retained post-quality filtering, 38% originated from an HI participant, 31% from a HEU participant, and 31% from a HUU participant. Full sample metadata can be found in Table S1 . The top phyla found across all samples and all visits included Bacteroidetes (average proportion 31%), followed by Firmicutes (26%), Proteobacteria (21%), Actinobacteria (16%), and Fusobacteria (15%). Top genera include Streptococcus (66%), Ligilactobacillus (64%), Rothia (56%), Capnocytophaga (55%), and Prevotella (55%) (Table S2 ). HEU samples across all three visits had significantly higher alpha diversity as measured by the observed number of ASVs as compared to HI samples (p = 0.017) but not compared to HUU samples, and D-CD samples had significantly lower alpha diversity as measured by Shannon diversity as compared to E-CD and any healthy tooth independent of the overall oral health (p < 0.0001) (Figure S2 ). Children unexposed and uninfected with HIV have a higher rate of taxonomic turnover over time as compared to other children in this cohort. We defined taxonomic turnover on the same tooth over time as the absolute Euclidean distance between paired plaque samples using an Aitchison distance matrix. Teeth with high taxonomic turnover will be less similar over time while those with low taxonomic turnover will be more similar. A total of 184 teeth were sampled at both visit one and visit two with an average of 182 days between sampling periods, 255 individual teeth were sampled at both visit two and visit three with an average of 222 days separating sampling visits, and 203 individual teeth were sampled at both visit one and visit three with an average of 403 days between sampling visits. Among all teeth from all individuals, we detected a high degree of taxonomic turnover with no significant differences between the HIV status groups comparing visit one and visit two or visit two versus visit three. We did, however, detect a significant difference in taxonomic turnover between groups when comparing visit one and visit three. When comparing all teeth independent of individual tooth health status, we detected a significantly higher degree of taxonomic turnover among teeth sampled from HUU children as compared to teeth sampled from HEU children (p = 0.045) and a moderately higher degree of taxonomic turnover when compared to HI children (p = 0.058) (Fig. 1 a, 1 b). Comparing only healthy teeth (H), however, we detected a significant increase in turnover among teeth collected from HUU children as compared to HEU children (p = 0.049) but no significant difference comparing HUU and HI children (p = 0.154) or HI to HEU children (p = 0.67). We next identified microbial signatures of high or low taxonomic turnover across any tooth with two or more sampling time points. The resulting microbial signature is defined by the relative abundance of two groups of taxa where taxa with negative coefficients are correlated with low taxonomic turnover and those with positive coefficients are correlated with high taxonomic turnover. The absolute value of the coefficient is reflective of the degree of impact of that taxon on the model. From this analysis, we found a positive linear association between the degree of taxonomic turnover and the resulting microbial signature prediction (R = 0.79, p < 2.2e-16) (Fig. 1 c). Among samples from children living with HIV, taxa associated with low taxonomic turnover include S. mutans (coeff: -0.42), Haemophilus paraphrohaemolyticus (coeff: -0.21), Mitsuokella sp. oral taxon 131 (coeff: -0.15), Prevotella multisaccharivorax (coeff: -0.14), and Neisseria cinerea (coeff: -0.08) (Fig. 1 d). Taxa associated with high taxonomic turnover in children living with HIV include Peptostreptococcaceae bacterium oral taxon 081 (coeff: 0.29) followed by Fusobacterium nucleatum subsp. vincentii (coeff: 0.24), Enterocloster bolteae (coeff: 0.21), Prevotella intermedia (coeff: 0.16), Leptotrichia buccalis (coeff: 0.08), and Treponema phagedenis (coeff: 0.02) (R = 0.8, p = 3.9e-13). Among HUU children, Prevotella denticola (coeff: -0.5), P. multisaccharivorax (coeff: -0.43), and H. paraphrohaemolyticus (coeff: -0.07) are associated with low taxonomic turnover while Catonella morbi (coeff: 0.6), Olsenella sp. oral taxon 807 (coeff: 0.15), an unknown species of Proteobacteria (coeff: 0.15), and Solobacterium moorei (coeff: 0.09) are associated with high taxonomic turnover (Fig. 1 d; Table S4). We detected no microbial signature of taxonomic turnover among HEU children. Despite differences in taxonomic turnover on individual teeth, across all samples there are groups of species that are consistently co-associated over time. We next used core microbial association networks (CANs) to identify clusters of species that are consistently co-associated with one another on teeth over all three clinical visits. First, we created a “global” CAN generated from all plaque samples across all three visits to act as a baseline comparison to group-specific CANs. In our global CAN we detected six distinct cluster communities, the largest of which were Cluster 2 (n = 35), Cluster 3 (n = 38), and Cluster 4 (n = 39) and the smallest, Cluster 6 (n = 10) (Fig. 2 a; Table S5). In general, co-associated species within clusters appear to have similar functional or clinical relevance. For example, commensal and structural plaque species (i.e., bacteria previously described as important in biofilm formation and structure) dominate Cluster 4 (e.g., Streptococcus sanguinis, Streptococcus gordonii, Neisseria mucosa, Haemophilus parainfluenzae, Streptococcus oralis, Streptococcus mitis , and Corynebacterium durum ) and Cluster 5 (e.g., Leptotrichia sp. oral taxon 215, Leptotrichia sp. oral taxon 212, Corynebacterium matruchotii, Streptococcus cristatus ) [ 60 – 62 ] while Cluster 1, Cluster 2, and Cluster 3 include a mixture of suspected commensal and potential pathogenic species. For example, Cluster 2 includes a variety of periodontal pathogens including members of the classic “red complex” in the etiology of periodontal disease (i.e., Treponema denticola, Tannerella forsythia , and Porphyromonas gingivalis ) as well as species that previously have been isolated from periodontal pockets or coaggregate with other periodontal pathogens including Eubacterium nodatum, Eubacterium saphenum, Filifactor alocis, Porphyromonas endodontalis , and Treponema medium [ 63 – 68 ]. The smallest cluster, Cluster 6, includes species almost exclusively associated with caries disease including S. mutans, Scardovia wiggsiae, Propionibacterium acidifaciens, P. multisaccharivorax, P. denticola , and Scardovia inopinata [ 15 , 57 , 69 – 75 ]. Next, to better understand how these core association networks differ across tooth health and HIV status groups, we calculated community modularity (Q) across all three visits within individual tooth health and HIV status groups. Modularity is a quantitative measure of network community structure wherein networks with high community modularity have more distinct (but potentially smaller) clusters that are themselves densely connected to other members of that cluster and at the same time are only loosely connected (or disconnected) from other clusters [ 76 ]. Conversely, low community modularity is reflective of fewer distinct, but potentially larger clusters of densely connected taxa. As our networks represent a consensus of co-associated taxa across all three sample visits, we expect that low community modularity (i.e., fewer distinct cluster groups) reflects higher core taxonomic stability over time. We find that community modularity among all healthy teeth (H) is relatively low (Q = 0.75) and increases as the disease progresses to enamel lesions (Q = 0.87) and eventually to dentin lesions (Q = 0.95) (Fig. 2 b). Within HIV groups, modularity of both our HUU and HI CAN networks were equivalent at Q = 0.80 while our HEU CAN network had slightly higher modularity at Q = 0.83 (Fig. 2 c). This suggests that while the bacterial community inhabiting individual teeth among HEU children changes little over time (i.e., low turnover), the community is less cohesive and more fragmented. Moreover, Cluster 6 is completely absent from the HEU CAN network and conversely is the only of the three HIV status groups to have a substantial cluster representative of global Cluster 2, potentially indicative of differences in susceptibility to caries vs periodontal disease. High S. mutans on an individual tooth is preceded by taxa typically associated with health and does not recapitulate the original community after S. mutans community collapse. Evidence from cross-sectional studies (e.g.,[ 28 , 57 ] suggest that caries disease progression is characterized by a rapid propagation of S. mutans and other acidogenic/aciduric bacteria during late-stage tooth decay, followed by a collapse of the community, and eventual recolonization. For our next analysis, we wanted to determine if this process is preceded or followed by predictable taxa or groups of taxa in the plaque community. To better understand the temporal dynamics of the oral microbiome before and after high levels of S. mutans , we performed a random forest classification and post-hoc explanatory analysis on individual teeth with low S. mutans ( = 10%). Our random forest model had high classification accuracy for teeth with high S. mutans (during high S. mutans : 83% correct) but had relatively low predictive accuracy for teeth designated as “before” or “after” high S. mutans . Accurate classification of teeth after high S. mutans was only 47% with most being misclassified as “during” and none as “before”. Teeth before high S. mutans were only classified correctly in 33% of cases with most being misidentified as during (50%) or after (17%). Taxa that were associated with teeth before high S. mutans include a variety of commensal species including S. sanguinis, S. cristatus, S. gordonii, Abiotrophia defectiva, Aggregatibacter aphrophilus , and L. buccalis as well as suspected opportunistic pathogens (e.g., Leptotrichia shahii, Cardiobacterium valvarum, Kingella dentrificans ) (Fig. 3 ). Interestingly, the community after high S. mutans is distinct from that found before high S. mutans with the top explanatory taxa including Cantonella morbi , Leptotrichia sp. oral taxon 215, and Bacteroidetes oral taxon 274 (Fig. 3 ). Importantly, the lack of S. sanguinis is indicative of the community after colonization of high abundance of S. mutans which suggests that the community does not recover to its previous state, at least not initially or within the period sampled here. More fine-grained longitudinal sampling is necessary to elucidate some of these patterns over time. HIV infection homogenizes the plaque microbiome across the posterior and anterior dentition. Next, we investigated the impact of HIV status on the spatial distribution of the microbial community across the dentition of adult teeth with no carious lesions (H-CF) from all three visits. We focused on healthy teeth only for this analysis to eliminate the effect of differences in oral health among the children. We detected conspicuous differentiation among the bacterial community colonizing the anterior dentition (i.e., central and lateral incisors, canines) as compared to the posterior dentition (i.e., premolars and molars) across all adult teeth with the posterior teeth exhibiting a higher relative abundance of Lachnoanaerobaculum saburreum, S. gordonii, and Porphyromonas sp. oral taxon 278 and a more minor contribution of species belonging to the genera Capnocytophaga, Campylobacter, Selenomonas, Leptotrichia, Streptococcus, Neisseria, Pseudoleptotrichia, Actinomyces, Actinobaculum, Aggregatibacter , and Fusobacterium . Anterior teeth, conversely, were strongly associated with C. durum followed by Prevotella sp. oral taxon 473, and S. sanguinis followed by species belonging to the genera Prevotella, Peptostreptococcus, Abiotrophia, Neisseria, Capnocytophaga, Granulicatella, Leptotrichia, Gemella, Parvimonas, Eubacterium , and Porphyromonas (Fig. 4 a, 4 b). Importantly, however, this differentiation is primarily driven by HEU and HUU samples where there is a clear distinction between the anterior and posterior community composition (pairwise PERMANOVA with Bonferroni adjusted p value, HUU: R 2 = 0.6, p = 0.001; HEU: R 2 = 0.06, p = 0.001). Conversely, while the oral community living on anterior and posterior teeth among children living with HIV show the same Capscale clustering pattern, the difference between communities is not significant (pairwise PERMANOVA with Bonferroni adjusted p value, R 2 = 0.4, p = 0.7) (Fig. 3 c). Additionally, differences between the anterior and posterior oral microbiome in HUU and HEU children are predicted by fewer taxa than in HI children. Among HUU children, posterior teeth are associated with a higher relative abundance of Neisseria weaveri, L. saburreum, Actinomyces sp. oral taxon 848, and Capnocytophaga granulosa using coda4microbiome balance analysis. In HEU children, posterior teeth are strongly associated with L. saburreum only. Anterior teeth in both HEU and HUU children are strongly associated with C. durum . In HI children, 25 taxa are needed to differentiate between the anterior and posterior teeth and while C. durum also is the highest predictive taxon for anterior teeth in HI children, L. saburreum is not associated with posterior teeth prediction. Depressed immune status is associated with a higher prevalence of cariogenic taxa. We next investigated the correlation of CD4 counts on the oral microbiome across all three visits. Across all samples, CD4 counts among HI children are significantly lower as compared to both HEU (p < 0.0001) and HUU children (p < 0.0001). Considering plaque samples collected at each visit, however, CD4 counts among children living with HIV (HI) significantly increased between visit one and visit two (p = 0.00016) and slightly decreased again between visit two and visit three (p = 0.014) (Figure S3). We detected no significant differences in CD4 count among HEU children between all three visits. Interestingly, HUU children had a slight but significant decrease in CD4 counts between visit one and visit three (p < 0.0001) and between visits two and three (p = 0.00016), a pattern also observed among HI children between visits two and three. Finally, we identified microbial signatures that were most predictive of CD4 counts in children living with HIV across our three sampling periods. We found that as CD4 counts increased over the three visits, the predictive power of microbial taxa decreased with visit one having the highest correlation coefficient (R = 0.6, p < 2.2e-16) followed by visit two (R = 0.53, p < 2.2e-16) and the lowest correlation coefficient at visit three (R = 0.38, p = 4.3e-09) (Figure S4). Taxa predictive of the lowest CD4 counts among children at visit one where the mean CD4 count is the lowest of our three sampling periods (775 ± 472) include a variety of taxa involved or associated with the progression of caries disease including S. mutans, Leptotrichia wadei, and L. saburreum [ 77 ]. Conversely, high CD4 counts are associated with a variety of taxa previously identified as potentially protective against caries development (e.g., Leptotrichia sp. oral taxon 212 [ 78 ]) but also taxa that have been identified with higher caries risk (e.g., Lachnospiraceae bacterium oral taxon 082) [ 77 ]. At visit two where the mean CD4 count increased to 961 (± 572) fewer taxa were identified to be predictive of CD4 count but included some overlap between taxa identified in visit one including L. wadei, Capnocytophaga sp. oral taxon 412, Lachnospiraceae oral taxon 107 str. F0167, C. sp. FDAARGOS 737, and Aggregatibacter sp. 2125159857. Additionally, T. phagedenis , a non-pathogenic spirochete that is not considered to be a resident oral taxon, but is closely related to the periodontal pathogen T. denticola [ 79 , 80 ], was found to be associated with low CD4 counts among children living with HIV in both visit one and visit two. While there are fewer pathogenic taxa contributing to the microbial signature of CD4 counts in visit two, Selenomonas sputigena , recently classified as a pathobiont capable of exacerbating the acidogenic activity of S. mutans in early childhood caries is highly weighted in the correlation coefficients driving lower CD4 counts [ 77 ]. Finally, while the mean CD4 count among children living with HIV drops at visit three (850 ± 521), the correlation coefficient between the relative balance of abundance between specific taxonomic groups is relatively weak (R = 0.38, p = 4.3e-09). DISCUSSION Our results suggest that the temporal and spatial dynamics of the supragingival plaque microbiome are altered by HIV infection and exposure. We hypothesize that these alterations are a consequence of a combination of factors including: (1) reduced salivary flow and increased salivary pH among children in this cohort, (2) differences in taxonomic plasticity and cohesion of the plaque community over time, and (3) immune-mediated alterations to the oral community. Moreover, our longitudinal approach bolsters previous observations [ 28 , 57 ] that while S. mutans is a dominant member of the supragingival plaque community during caries intensification, it is not involved or does not play a major role in the initiation of tooth decay. Instead, our results suggest that within-species strain-level functional diversity may be a catalyst for community dysbiosis and tooth decay inception. Further strain-level functional analyses are required to elucidate the role of the supragingival plaque community in the initiation of tooth decay. Reduced salivary flow and increased salivary pH are key contributors to alterations in the spatiotemporal structure of the supragingival plaque microbiome. Whole saliva is a key modulator of the oral microbiome both physically and biochemically. On average, a healthy individual produces 0.6 liters of saliva per day which continuously coats the oral tissues and helps to clear debris, desquamated epithelial cells, and microorganisms [ 81 , 82 ]. Saliva provides several key benefits essential for maintaining homeostasis of the oral cavity including lubrication to facilitate swallowing and removal of food particles from the oral tissues, digestive enzymes including amylase for the digestion of carbohydrates into sugars, and antimicrobial compounds including hydrogen peroxide, lactoferrin, lysozymes, and a variety of antimicrobial peptides that modulate the colonization of oral tissues by microbes [ 83 – 85 ]. Saliva also coats the teeth and other oral tissues with proteins and glycoproteins used by microbes to colonize the oral cavity, it transports nutrients and other metabolic substrates to members of the oral microbiome, and provides a moist environment with a stable pH (average ~ 6.7) that favors bacterial growth [ 82 , 86 ]. People living with HIV often experience chronic dry mouth (xerostomia), and children especially are affected by HIV-associated salivary gland disease (HIV-SGD) [ 87 ]. HIV-SGD affects the parotid, submandibular, and sublingual salivary glands and results in decreased saliva production and poorer quality saliva including decreased levels of sodium, calcium chloride, cystatin (essential for tooth remineralization), and lysozymes [ 87 , 88 ]. Comparisons of unstimulated salivary flow rates among children in this cohort found significant differences between HIV status groups. HUU children had the highest mean rate of salivary flow (0.33 ml/min) and HI children the lowest (0.22 ml/min) [ 37 ]. Importantly, the mean rate of salivary flow among HEU children was intermediate between HI and HUU at 0.27 ml/min [ 37 ], which may reflect long-lasting effects of the mother’s immune status on the oral biology of this growing cohort of children. Salivary gland hypofunction is associated with an increased risk of several oral diseases including caries disease and candidiasis [ 82 , 89 , 90 ]. Moreover, the mean pH of HI children in this cohort was significantly lower than HEU or HUU children which has important implications for the risk for tooth decay [ 37 ]. Given the biochemical properties of saliva and their importance in structuring the oral microbiome, we hypothesize that saliva is a primary contributor to differences in the biogeography of the supragingival plaque microbiome reported here. Location of individual teeth in the oral cavity, their proximity to major salivary ducts, morphological characteristics, and mechanical use (e.g., chewing vs tearing) influences their community composition. Previous research has found that the structure of the oral community follows an ecological gradient from the back to the front of the dentition so that the microbiome inhabiting the molars is distinct from that on the incisors [ 91 – 94 ]. This ecological pattern is clearly supported in the current study among HUU and HEU children. Conversely, individuals with impaired salivary flow due to injury or disease (e.g., Sjögren’s syndrome, see Proctor et al. [ 93 ]), experience a breakdown of this ecological gradient and a homogenization of the anterior and posterior oral communities, as is evident in our HI cohort. Interestingly, while the differences between the oral microbiome inhabiting anterior and posterior teeth in HEU children in the current study are significant, the distribution of these two communities is somewhat intermediate between HUU and HI children (see Fig. 4 c), which may be reflective of their intermediate status in terms of salivary flow [ 37 ]. Previous studies have documented that disruptions in the natural ecological barriers that separate the human oral-pharyngeal and gut microbiome are symptomatic of disease [ 95 , 96 ]. The results of this study suggest that these disruptions occur at much smaller spatial scales within the same host-associated habitat (i.e., the oral cavity) with possibly detrimental consequences towards overall oral and systemic health outcomes in HI children. Taxonomic turnover in the supragingival plaque microbiome may be adaptive response to changing host factors that is suppressed in disease. Using distance-based taxonomic turnover analyses we found that the community inhabiting a single tooth may experience substantial taxonomic turnover over time, particularly among HUU children. This contrasts with expectations from previous longitudinal analyses of human-associated microbial ecosystems (primarily the gut microbiome) wherein large-scale shifts in the bacterial community is typically associated with illness or stress [ 49 , 97 , 98 ]. Given that bacteria inhabiting the oral cavity are subject to more direct and regular exchange with the external environment, it is possible that taxonomic turnover in the supragingival plaque microbiome is a signal of community health. Specifically, that temporal flexibility is reflective of the community’s ability to rapidly adapt to environmental perturbations. Previous investigations into the temporal stability of the oral microbiome have suggested that it is more dynamic than other human-associated microbial habitats as well as more influenced by the individual’s behavior and environment than purely genetic determinants of the composition of the oral microbiome [ 6 , 8 , 11 , 12 ]. Other studies refute this, however, and argue that the oral microbiome is relatively stable over time [ 99 , 100 ]. The results presented here strongly suggest that community fluctuations over time are dependent on both the health status of the individual as well as the scale in which these observations are made. Importantly, however, our results document a strong association between low taxonomic turnover and cariogenic taxa, including S. mutans, P. multisaccharivorax , and P. denticola , which may indicate that diseased oral communities experience less change over time due to a lack of starting diversity driven by acidogenic/aciduric taxa. Despite substantial turnover on the individual tooth level, we do find clusters of taxa that are consistently co-associated over time within and across our HIV status groups. Thus, despite individualistic fluctuations in the overall community composition, a stable core of taxa is consistently present in the supragingival plaque microbiome, a pattern previously observed in longitudinal sampling of the oral cavity [ 8 ]. Clusters identified in the current study have similar functional expectations in that species that are associated with health or disease are consistently co-associated over time. For example, in our global core-association network, Cluster 6 is made exclusively of known or suspected cariogenic taxa while Cluster 1, Cluster 2, Cluster 5, and Cluster 4 are composed primarily of oral symbionts and structural taxa. Importantly, while modularity (a measure of community cohesion over time) is roughly equivalent among HI, HEU, and HUU children, there are notable differences in the structure and composition of core-association networks among these three groups. The most conspicuous of these differences is the lack of Cluster 6 and prominence of Cluster 2 among HEU children. Given that Cluster 6 is composed of cariogenic taxa, its absence in part may explain why, despite experiencing poor health outcomes in other areas that are similar to HI children [ 29 – 35 ], HEU children do not have a higher caries burden as compared to HUU children [ 25 , 101 , 102 ]. Importantly, however, Cluster 2, which is prominent in HEU children as compared to HI and HUU children, contains taxa typically associated with periodontal disease even though no children in this cohort exhibited symptoms of periodontal disease. It is unclear if these results suggest future periodontal disease risk among HEU children. Immune status among children living with HIV is associated with the abundance of cariogenic taxa. Individual immune status among children living with HIV has previously been associated with the prevalence and severity of caries disease (see, for example [ 23 ]). In agreement with these findings, we find that low CD4 counts are strongly associated with the abundance of cariogenic taxa. Importantly, however, we also find that CD4 counts vary substantially among children living with HIV and that the mean CD4 count within this group fluctuates significantly over time. For example, we detect a significant increase in CD4 counts from visit one to visit two among children living with HIV (possibly the result of continued antiretroviral treatment), followed by a slight but significant decrease between visits two and three, a pattern also observed in HUU, but not HEU children. As CD4 counts generally decrease as children age [ 103 ], these patterns may be part of the normal aging process, however, given the impact of host immune status on the health of the oral cavity, we expect that continual monitoring of CD4 counts will be an important aspect of determining risk for tooth decay and other chronic health problems among HI children. Community dynamics before and after proliferation of S. mutans Finally, we used random forest classification and post-hoc directionality analysis to better understand the community dynamics on a single tooth before and after high levels of S. mutans on the same tooth at different time periods. While our random forest classification model was unable to precisely classify the oral community before and after high levels of S. mutans , we did detect specific taxa that were present before high S. mutans that were not retained after the recolonization of the tooth post S. mutans colonization. In particular, non-mutans streptococcal species including S. cristatus, S. sanguinis , and S. gordonii were indicative of the community before but not after S. mutans colonization and proliferation. Importantly, however, these patterns are driven by a small subset of samples which may be indicative of functional differences among strains of these non-mutans streptococci. For example, previous work by our group has found substantial functional diversity among members of the streptococci, including S. sanguinis [ 104 ]. Moreover, while S. sanguinis is typically associated with oral health, high resolution amplicon sequencing of S. sanguinis and other putatively commensal streptococci species has documented that some are strongly associated with later stages of caries disease [ 105 ]. Given that S. sanguinis is highly predictive of later S. mutans abundance among some of our samples, it may be the case that specific strains of S. sanguinis play a role in the initialization of caries while others are protective against caries [ 78 ]. Finally, our results suggest that the community inhabiting a tooth will not recover to its previous state after S. mutans is the dominant taxon. Monitoring these processes at smaller time scales may elucidate which taxa are predictive of the community before and after high levels of cariogenic taxa including S. mutans. CONCLUSIONS The results of this study highlight the importance of scale – both temporally and spatially – in understanding the impact of the bacterial supragingival plaque community in the development of caries in the context of HIV infection and exposure. We find that HIV infection and exposure has significant impact on the temporal and spatial structuring of the oral microbiome which may be the result of altered salivary flow and pH, individual host dynamics, and impaired immune status of individual children. Significantly, we find that healthy children within our cohort exhibit relatively high taxonomic turnover of the supragingival plaque microbiome while maintaining cohesive and consistent groups of taxa over time. Additionally, we find that low taxonomic turnover is associated with higher frequencies of cariogenic taxa including S. mutans . Moreover, we find that HIV infection homogenizes the oral microbiome across the anterior and posterior dentition, with altered salivary function likely a key factor. A further appreciation of the temporal and spatial dynamics of the oral microbiome in health and disease may be necessary to identify molecular mechanisms of oral microbiome dysbiosis and the initiation, progression, and ultimate consequences of tooth decay among children living with and exposed to HIV. Abbreviations HUU: Child unexposed and therefore uninfected with HIV HEU: Child exposed to but uninfected with HIV HI: Child living with HIV H-CF: A healthy tooth from a caries-free mouth H-CE: A healthy tooth from a mouth with active enamel lesions H-CD: Healthy tooth from a mouth with active dentin caries E-CE: Tooth with an enamel lesion from a mouth with active enamel caries E-CD: Tooth with an enamel lesion from a mouth with active dentin caries D-CD: Tooth with a dentin cavity from a mouth with active dentin cavities Declarations Ethics approval and consent to participate Informed consent was obtained from all parents, guardians, or caregivers, and children 8 years and older provided assent before joining the study - University of Maryland Baltimore (HP-00084081), Rutgers State University of New Jersey (Pro2019002047), and University of Benin Teaching Hospital, Benin City (ADM/E22/A/VOL. VII/14713)). Consent for publication Not applicable Availability of data and material The datasets generated and analyzed during the current study are available in the European Nucleotide Archive repository under accession number PRJEB76179. All preprocessing and analysis scripts are available at https://github.com/aemann01/long_oral_microbiome and are archived at Zenodo under the DOI: 10.5281/zenodo.11396312. Competing interests The authors declare that they have no competing interests Funding This research was supported by the National Institutes of Health National Institute of Dental and Craniofacial Research (R01DE028154 to V.P.R. and M.O.C.) Authors’ contributions AEM: Formal analysis, Investigation, Methodology, Software, Visualization, Writing-original draft, Writing-review & editing; CA: Formal analysis, Investigation, Writing-review & editing; SC: Formal analysis, Methodology, Writing-review & editing; LMO: Investigation, Writing-review & editing; EO: Resources, Writing-review & editing; PA: Resources, Writing-review & editing; OO: Resources, Writing-review & editing; AO: Resources, Writing-review & editing; MR: Investigation, Writing-review & editing; TB: Investigation, Writing-review & editing; AS: Investigation, Writing-review & editing; CK: Investigation, Writing-review & editing; MC: Funding acquisition, Resources, Supervision, Conceptualization, Writing-review & editing; VPR: Funding acquisition, Resources, Supervision, Conceptualization, Writing-review & editing Acknowledgements The authors thank the participating families of the DOMHaIN study for their commitment to this research. We also would like to thank members of The DOMHaIN Study Team, which comprises of the authors and the following team members: Oghenenero Igedegbe, Ruxton Adebiyi, Matron Christy Ndekwu, Uwagboe Odigie, Oyemwen Olaye, Ehioze Awanlemhen, Samuel Chukwumaeze, Matthew Imoe, Daniel Oakhu, and Susan Dare are acknowledged for the recruitment, sample, and data collection. Nosakhare Idemudia, Osasumwen Ehigie, Kelly Avenbuan, and Amara Godwins provided laboratory management and support with sample processing. Nneka Chukwumah, Stanley Iyorzor, Owen Omorogbe, and Chioma Ugorji and acknowledged for the clinical examination during study visits and for their flexibility with recruitment and scheduling. References Mager DL, Ximenez-Fyvie LA, Haffajee AD, Socransky SS. Distribution of selected bacterial species on intraoral surfaces. J Clin Periodontol. 2003;30:644–54. Mark Welch JL, Ramírez-Puebla ST, Borisy GG. Oral Microbiome Geography: Micron-Scale Habitat and Niche. Cell Host Microbe. 2020;28:160–8. Morillo-Lopez V, Sjaarda A, Islam I, Borisy GG, Mark Welch JL. Corncob structures in dental plaque reveal microhabitat taxon specificity. Microbiome. 2022;10:145. Ximénez-Fyvie LA, Haffajee AD, Socransky SS. Comparison of the microbiota of supra- and subgingival plaque in health and periodontitis. J Clin Periodontol. 2000;27:648–57. Demmitt BA, Corley RP, Huibregtse BM, Keller MC, Hewitt JK, McQueen MB, et al. Genetic influences on the human oral microbiome. BMC Genomics. 2017;18:659. Freire M, Moustafa A, Harkins DM, Torralba MG, Zhang Y, Leong P, et al. Longitudinal Study of Oral Microbiome Variation in Twins. Sci Rep. 2020;10:7954. Mason MR, Chambers S, Dabdoub SM, Thikkurissy S, Kumar PS. Characterizing oral microbial communities across dentition states and colonization niches. Microbiome. 2018;6:67. Zhou X, Shen X, Johnson JS, Spakowicz DJ, Agnello M, Zhou W, et al. Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease. Cell Host Microbe. 2024;32:506–526.e9. Marsh PD. Dental plaque as a biofilm and a microbial community – implications for health and disease. BMC Oral Health. 2006;6:S14. Teles FR, Teles RP, Uzel NG, Song XQ, Torresyap G, Socransky SS, et al. Early microbial succession in redeveloping dental biofilms in periodontal health and disease. J Periodontal Res. 2012;47:95–104. Ding T, Schloss PD. Dynamics and associations of microbial community types across the human body. Nature. 2014;509:357–60. Stahringer SS, Clemente JC, Corley RP, Hewitt J, Knights D, Walters WA, et al. Nurture trumps nature in a longitudinal survey of salivary bacterial communities in twins from early adolescence to early adulthood. Genome Res. 2012;22:2146–52. Global oral health status report: towards universal health coverage for oral health by 2030 [Internet]. 2022 [cited 2023 Jul 6]. Available from: https://www.who.int/publications-detail-redirect/9789240061484 Kalpana B, Prabhu P, Bhat AH, Senthilkumar A, Arun RP, Asokan S, et al. Bacterial diversity and functional analysis of severe early childhood caries and recurrence in India. Sci Rep. 2020;10:21248. Wolff D, Frese C, Schoilew K, Dalpke A, Wolff B, Boutin S. Amplicon-based microbiome study highlights the loss of diversity and the establishment of a set of species in patients with dentin caries. PLOS ONE. 2019;14:e0219714. Xiao C, Ran S, Huang Z, Liang J. Bacterial Diversity and Community Structure of Supragingival Plaques in Adults with Dental Health or Caries Revealed by 16S Pyrosequencing. Front Microbiol. 2016;7:1145. Giacaman R. Sugars and beyond. The role of sugars and the other nutrients and their potential impact on caries. Oral Dis. 2018;24:1185–97. Gross EL, Beall CJ, Kutsch SR, Firestone ND, Leys EJ, Griffen AL. Beyond Streptococcus mutans: Dental Caries Onset Linked to Multiple Species by 16S rRNA Community Analysis. PLOS ONE. 2012;7:e47722. van Houte J, Sansone C, Joshipura K, Kent R. Mutans streptococci and non-mutans streptococci acidogenic at low pH, and in vitro acidogenic potential of dental plaque in two different areas of the human dentition. J Dent Res. 1991;70:1503–7. van Ruyven FOJ, Lingström P, van Houte J, Kent R. Relationship among Mutans Streptococci, “Low-pH” Bacteria, and lodophilic Polysaccharide-producing Bacteria in Dental Plaque and Early Enamel Caries in Humans. J Dent Res. 2000;79:778–84. Marsh PD. Are dental diseases examples of ecological catastrophes? Microbiology. 2003;149:279–94. Akhigbe P, Chukwumah NM, Adebiyi R, Oluwatoyin Folayan M, Divaris K, Ozoemene O, et al. Age-specific associations with dental caries in HIV-infected, exposed but uninfected, and HIV-unexposed uninfected children in Nigeria. 2022; Coker M, El-Kamary S, Enwonwu C, Blattner W, Langenberg P, Mongodin E, et al. Perinatal HIV Infection and Exposure and their Association with Dental Caries in Nigerian Children. Pediatr Infect Dis J. 2018;37:59–65. Coker MO, Mongodin EF, El-Kamary SS, Akhigbe P, Obuekwe O, Omoigberale A, et al. Immune status, and not HIV infection or exposure, drives the development of the oral microbiota. Sci Rep. 2020;10:10830. Coker MO, Akhigbe P, Osagie E, Idemudia NL, Igedegbe O, Chukwumah N, et al. Dental caries and its association with the oral microbiomes and HIV in young children—Nigeria (DOMHaIN): a cohort study. BMC Oral Health. 2021;21:620. Oliveira CAGR, Tannure PN, de Souza IPR, Maia LC, Portela MB, Castro GFB de A. Is dental caries experience increased in HIV-infected children and adolescents? A meta-analysis. Acta Odontol Scand. 2015;73:481–7. Rajonson N, Meless D, Ba B, Faye M, Diby J-S, N’zore S, et al. High prevalence of dental caries among HIV-infected children in West Africa compared to uninfected siblings. J Public Health Dent. 2017;77:234–43. Mann AE, O’Connell LM, Osagie E, Akhigbe P, Obuekwe O, Omoigberale A, et al. Impact of HIV on the Oral Microbiome of Children Living in Sub-Saharan Africa, Determined by Using an rpoC Gene Fragment Metataxonomic Approach. Microbiol Spectr. 2023; Evans C, Jones CE, Prendergast AJ. HIV-exposed, uninfected infants: new global challenges in the era of paediatric HIV elimination. Lancet Infect Dis. 2016;16:e92–107. Jumare J, Datong P, Osawe S, Okolo F, Mohammed S, Inyang B, et al. Compromised Growth Among HIV-exposed Uninfected Compared With Unexposed Children in Nigeria. Pediatr Infect Dis J. 2019;38:280. McNally LM, Jeena PM, Gajee K, Thula SA, Sturm AW, Cassol S, et al. Effect of age, polymicrobial disease, and maternal HIV status on treatment response and cause of severe pneumonia in South African children: a prospective descriptive study. The Lancet. 2007;369:1440–51. Nozyce ML, Huo Y, Williams PL, Kapetanovic S, Hazra R, Nichols S, et al. Safety of In Utero and Neonatal ARV Exposure: Cognitive and Academic Outcomes in HIV-exposed, Uninfected Children Age 5–13 Years. Pediatr Infect Dis J. 2014;33:1128–33. Otieno RO, Ouma C, Ong’echa JM, Keller CC, Were T, Waindi EN, et al. Increased severe anemia in HIV-1-exposed and HIV-1-positive infants and children during acute malaria. AIDS. 2006;20:275. le Roux SM, Abrams EJ, Donald KA, Brittain K, Phillips TK, Zerbe A, et al. Infectious morbidity of breastfed, HIV-exposed uninfected infants under conditions of universal antiretroviral therapy in South Africa: a prospective cohort study. Lancet Child Adolesc Health. 2020;4:220–31. Shapiro RL, Lockman S, Kim S, Smeaton L, Rahkola JT, Thior I, et al. Infant Morbidity, Mortality, and Breast Milk Immunologic Profiles among Breast-Feeding HIV-Infected and HIV-Uninfected Women in Botswana. J Infect Dis. 2007;196:562–9. O’Connell LM, Mann AE, Osagie E, Akhigbe P, Blouin T, Soule A, et al. Supragingival mycobiome of HIV-exposed-but-uninfected children reflects a stronger correlation with caries-free associated taxa compared to HIV-infected or uninfected children. Microbiol Spectr [Internet]. 2023;11. Available from: https://doi.org/10.1128/spectrum.01491-23 Onyia NE, Osagie E, Akhigbe P, Idemudia NL, Ozoemene O, Omoigberale A, et al. Impact of Perinatal HIV Exposure and Infection on Salivary Properties Among Nigerian Children. Res Sq [Internet]. 2024 [cited 2024 Feb 14]; Available from: https://www.researchsquare.com Onyia NE, Akhigbe P, Osagie E, Obuekwe O, Omoigberale A, Richards VP, et al. Prevalence and associated factors of enamel developmental defects among Nigerian children with perinatal HIV exposure. J Clin Pediatr Dent. 2023;47:1–9. Gugnani N, Pandit I, Srivastava N, Gupta M, Sharma M. International Caries Detection and Assessment System (ICDAS): A New Concept. Int J Clin Pediatr Dent. 2011;4:93–100. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal. 2011;17:10–2. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3. R Core Team. R: A language and environment for statistical computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2017. Available from: https://www.R-project.org Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol. 2019;20:257. McMurdie PJ, Holmes S. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLOS ONE. 2013;8:e61217. Oksanen J, Blanchet FG, Friendly M, Kiindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package [Internet]. 2019. Available from: https://CRAN.R-project.org/package=vegan van den Boogaart KG, Tolosana-Delgado R, Bren M. compositions: Compositional Data Analysis [Internet]. 2023. Available from: https://CRAN.R-project.org/package=compositions Lahti L, Shetty S. microbiome R package. 2012; Available from: http://microbiome.github.io Calle ML, Pujolassos M, Susin A. coda4microbiome: compositional data analysis for microbiome cross-sectional and longitudinal studies. BMC Bioinformatics. 2023;24:82. Bastiaanssen TFS, Gururajan A, van de Wouw M, Moloney GM, Ritz NL, Long-Smith CM, et al. Volatility as a Concept to Understand the Impact of Stress on the Microbiome. Psychoneuroendocrinology. 2021;124:105047. Peschel S, Müller CL, von Mutius E, Boulesteix A-L, Depner M. NetCoMi: network construction and comparison for microbiome data in R. Brief Bioinform. 2021;22:bbaa290. Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. Sparse and Compositionally Robust Inference of Microbial Ecological Networks. PLOS Comput Biol. 2015;11:e1004226. Meinshausen N, Bühlmann P. High-dimensional graphs and variable selection with the Lasso. Ann Stat. 2006;34:1436–62. Röttjers L, Vandeputte D, Raes J, Faust K. Null-model-based network comparison reveals core associations. ISME Commun. 2021;1:1–8. Csardi G, Nepusz T. The igraph software package for complex network research. InterJournal [Internet]. 2006 [cited 2023 Dec 15];Complex Systems. Available from: https://zenodo.org/records/10369053 Clauset A, Newman MEJ, Moore C. Finding community structure in very large networks. Phys Rev E Stat Nonlin Soft Matter Phys. 2004;70:066111. Dinis M, Agnello M, Cen L, Shokeen B, He X, Shi W, et al. Oral Microbiome: Streptococcus mutans/Caries Concordant-Discordant Children. Front Microbiol. 2022;13:782825. Richards VP, Alvarez AJ, Luce AR, Bedenbaugh M, Mitchell ML, Burne RA, et al. Microbiomes of Site-Specific Dental Plaques from Children with Different Caries Status. Young VB, editor. Infect Immun [Internet]. 2017 [cited 2022 Feb 4];85. Available from: https://journals.asm.org/doi/10.1128/IAI.00106-17 Wright MN, Ziegler A. ranger: A Fast Implementation of Random Forests for High Dimensional Data in C + + and R. J Stat Softw. 2017;77:1–17. Jethani N, Sudarshan M, Covert I, Lee S-I, Ranganath R. FastSHAP: Real-Time Shapley Value Estimation [Internet]. arXiv; 2022 [cited 2024 Mar 22]. Available from: http://arxiv.org/abs/2107.07436 Morillo-Lopez V, Sjaarda A, Islam I, Borisy GG, Mark Welch JL. Corncob structures in dental plaque reveal microhabitat taxon specificity. Microbiome. 2022;10:145. Torres-Morales J, Mark Welch JL, Dewhirst FE, Borisy GG. Site-specialization of human oral Gemella species. J Oral Microbiol. 2023;15:2225261. Welch JLM, Rossetti BJ, Rieken CW, Dewhirst FE, Borisy GG. Biogeography of a human oral microbiome at the micron scale. Proc Natl Acad Sci. 2016;113:E791–800. Aja E, Mangar M, Fletcher HM, Mishra A. Filifactor alocis: Recent Insights and Advances. J Dent Res. 2021;100:790–7. Deng Z-L, Szafrański SP, Jarek M, Bhuju S, Wagner-Döbler I. Dysbiosis in chronic periodontitis: Key microbial players and interactions with the human host. Sci Rep. 2017;7:3703. Haffajee AD, Teles RP, Socransky SS. Association of Eubacterium nodatum and Treponema denticola with human periodontitis lesions. Oral Microbiol Immunol. 2006;21:269–82. Lombardo Bedran TB, Marcantonio RAC, Spin Neto R, Alves Mayer MP, Grenier D, Spolidorio LC, et al. Porphyromonas endodontalis in chronic periodontitis: a clinical and microbiological cross-sectional study. J Oral Microbiol. 2012;4: 10.3402/jom.v4i0.10123 . Umemoto T, Nakazawa F, Hoshino E, Okada K, Fukunaga M, Namikawa I. Treponema medium sp. nov., isolated from human subgingival dental plaque. Int J Syst Bacteriol. 1997;47:67–72. Wyss C, Choi BK, Schüpbach P, Guggenheim B, Göbel UB. Treponema maltophilum sp. nov., a small oral spirochete isolated from human periodontal lesions. Int J Syst Bacteriol. 1996;46:745–52. Isaac RD, Sanjeev K, Subbulakshmi CL, Amirtharaj LV, Sekar M. Identification of a novel bacterium Scardovia wiggsiae in high caries risk adolescence: A metagenomic and melt curve analysis. J Conserv Dent JCD. 2022;25:297–305. Kameda M, Abiko Y, Washio J, Tanner ACR, Kressirer CA, Mizoguchi I, et al. Sugar Metabolism of Scardovia wiggsiae, a Novel Caries-Associated Bacterium. Front Microbiol [Internet]. 2020 [cited 2022 Mar 30];11. Available from: https://www.frontiersin.org/article/ 10.3389/fmicb.2020.00479 Könönen E, Fteita D, Gursoy UK, Gursoy M. Prevotella species as oral residents and infectious agents with potential impact on systemic conditions. J Oral Microbiol. 2022;14:2079814. Modesto M, Biavati B, Mattarelli P. Occurrence of the Family Bifidobacteriaceae in Human Dental Caries and Plaque. Caries Res. 2006;40:271–6. Niu Y, Zhang C, Sun Y, Dong L, Si Y, Yang J, et al. Symbiotic relationship between Prevotella denticola and Streptococcus mutans enhances virulence of plaque biofilms. Arch Oral Biol. 2023;151:105714. Obata J, Fujishima K, Nagata E, Oho T. Pathogenic mechanisms of cariogenic Propionibacterium acidifaciens. Arch Oral Biol. 2019;105:46–51. Sakamoto M, Umeda M, Ishikawa I, Benno Y. Prevotella multisaccharivorax sp. nov., isolated from human subgingival plaque. Int J Syst Evol Microbiol. 2005;55:1839–43. Newman MEJ. Modularity and community structure in networks. Proc Natl Acad Sci. 2006;103:8577–82. Cho H, Ren Z, Divaris K, Roach J, Lin BM, Liu C, et al. Selenomonas sputigena acts as a pathobiont mediating spatial structure and biofilm virulence in early childhood caries. Nat Commun. 2023;14:2919. Mann AE, Chakraborty B, O’Connell LM, Nascimento MM, Burne RA, Richards VP. Heterogeneous lineage-specific arginine deiminase expression within dental microbiome species. Microbiol Spectr [Internet]. 2024;12. Available from: https://doi.org/10.1128/spectrum.01445-23 You M, Mo S, Leung WK, Watt RM. Comparative analysis of oral treponemes associated with periodontal health and disease. BMC Infect Dis. 2013;13:174. Zeng H, Chan Y, Gao W, Leung WK, Watt RM. Diversity of Treponema denticola and other oral treponeme lineages in subjects with periodontitis and gingivitis. bioRxiv. 2021;2021.06.24.449858. Amerongen AN, Veerman E. Saliva – the defender of the oral cavity. Oral Dis. 2002;8:12–22. Lynge Pedersen AM, Belstrøm D. The role of natural salivary defences in maintaining a healthy oral microbiota. J Dent. 2019;80:S3–12. Gorr SU. Antimicrobial peptides of the oral cavity. Periodontol 2000. 2009;51:152–80. Mochon AB, Liu H. The Antimicrobial Peptide Histatin-5 Causes a Spatially Restricted Disruption on the Candida albicans Surface, Allowing Rapid Entry of the Peptide into the Cytoplasm. PLOS Pathog. 2008;4:e1000190. Vila T, Rizk AM, Sultan AS, Jabra-Rizk MA. The power of saliva: Antimicrobial and beyond. PLoS Pathog. 2019;15:e1008058. Takahashi N. Microbial ecosystem in the oral cavity: Metabolic diversity in an ecological niche and its relationship with oral diseases. Int Congr Ser. 2005;1284:103–12. Jeffers L, Webster-Cyriaque JY. Viruses and Salivary Gland Disease (SGD). Adv Dent Res. 2011;23:79–83. Lin AL, Johnson DA, Stephan KT, Yeh C-K. Alteration in Salivary Function in Early HIV Infection. J Dent Res. 2003;82:719–24. Bardow A, Nyvad B, Nauntofte B. Relationships between medication intake, complaints of dry mouth, salivary flow rate and composition, and the rate of tooth demineralization in situ. Arch Oral Biol. 2001;46:413–23. Jensen SB, Pedersen AML. Association Between Oral Infections and Salivary Gland Hypofunction. In: Lynge Pedersen AM, editor. Oral Infect Gen Health Mol Chairside [Internet]. Cham: Springer International Publishing; 2016 [cited 2023 Nov 15]. p. 79–94. Available from: https://doi.org/10.1007/978-3-319-25091-5_9 Esberg A, Eriksson L, Johansson I. Site- and Time-Dependent Compositional Shifts in Oral Microbiota Communities. Front Oral Health. 2022;3:826996. Gancz AS, Farrer AG, Nixon MP, Wright S, Arriola L, Adler C, et al. Ancient dental calculus reveals oral microbiome shifts associated with lifestyle and disease in Great Britain. Nat Microbiol. 2023;8:2315–25. Proctor DM, Fukuyama JA, Loomer PM, Armitage GC, Lee SA, Davis NM, et al. A spatial gradient of bacterial diversity in the human oral cavity shaped by salivary flow. Nat Commun. 2018;9:681. Simón-Soro Á, Tomás I, Cabrera-Rubio R, Catalan MD, Nyvad B, Mira A. Microbial Geography of the Oral Cavity. J Dent Res. 2013;92:616–21. Vonaesch P, Morien E, Andrianonimiadana L, Sanke H, Mbecko J-R, Huus KE, et al. Stunted childhood growth is associated with decompartmentalization of the gastrointestinal tract and overgrowth of oropharyngeal taxa. Proc Natl Acad Sci. 2018;115:E8489–98. Vonaesch P, Araújo JR, Gody J-C, Mbecko J-R, Sanke H, Andrianonimiadana L, et al. Stunted children display ectopic small intestinal colonization by oral bacteria, which cause lipid malabsorption in experimental models. Proc Natl Acad Sci. 2022;119:e2209589119. Adam G Clooney, Julia Eckenberger, Emilio Laserna-Mendieta, Kathryn A Sexton, Matthew T Bernstein, Kathy Vagianos, et al. Ranking microbiome variance in inflammatory bowel disease: a large longitudinal intercontinental study. Gut. 2021;70:499. Galloway-Peña JR, Smith DP, Sahasrabhojane P, Wadsworth WD, Fellman BM, Ajami NJ, et al. Characterization of oral and gut microbiome temporal variability in hospitalized cancer patients. Genome Med. 2017;9:21. Belstrøm D, Holmstrup P, Bardow A, Kokaras A, Fiehn N-E, Paster BJ. Temporal Stability of the Salivary Microbiota in Oral Health. PLOS ONE. 2016;11:e0147472. Cameron SJS, Huws SA, Hegarty MJ, Smith DPM, Mur LAJ. The human salivary microbiome exhibits temporal stability in bacterial diversity. FEMS Microbiol Ecol. 2015;91:fiv091. Birungi N, Fadnes LT, Engebretsen IMS, Lie SA, Tumwine JK, Åstrøm AN, et al. Caries experience and oral health related quality of life in a cohort of Ugandan HIV-1 exposed uninfected children compared with a matched cohort of HIV unexposed uninfected children. BMC Public Health. 2020;20:423. Moscicki A-B, Yao T-J, Ryder MI, Russell JS, Dominy SS, Patel K, et al. The Burden of Oral Disease among Perinatally HIV-Infected and HIV-Exposed Uninfected Youth. PLOS ONE. 2016;11:e0156459. WRIGHT S, PETOUMENOS K, BOYD M, CARR A, DOWNING S, O’CONNOR C, et al. Ageing & long-term CD4 cell count trends in HIV-positive patients with 5 years or more combination antiretroviral therapy experience. HIV Med. 2013;14:208–16. Velsko IM, Chakraborty B, Nascimento MM, Burne RA, Richards VP. Species Designations Belie Phenotypic and Genotypic Heterogeneity in Oral Streptococci. 2018;3:14. O’Connell LM, Blouin T, Soule A, Burne RA, Nascimento MM, Richards VP. Optimization and Evaluation of the 30S-S11 rRNA Gene for Taxonomic Profiling of Oral Streptococci. Appl Environ Microbiol. 2022;88:e0045322. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfigures.docx Supplementarytables.xlsx Cite Share Download PDF Status: Published Journal Publication published 16 Jun, 2025 Read the published version in Microbiome → Version 1 posted Editorial decision: Revision requested 18 Jul, 2024 Editor assigned by journal 18 Jul, 2024 Submission checks completed at journal 11 Jul, 2024 First submitted to journal 10 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4720457","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":328776569,"identity":"774e65d1-3da0-40e3-923b-fedf0d763c90","order_by":0,"name":"Allison E. Mann","email":"","orcid":"","institution":"University of Wyoming","correspondingAuthor":false,"prefix":"","firstName":"Allison","middleName":"E.","lastName":"Mann","suffix":""},{"id":328776570,"identity":"a2ab2399-f675-4200-bc06-bb21ee01fb0a","order_by":1,"name":"Ciara Aumend","email":"","orcid":"","institution":"Clemson University","correspondingAuthor":false,"prefix":"","firstName":"Ciara","middleName":"","lastName":"Aumend","suffix":""},{"id":328776571,"identity":"a2347659-dfa5-4d03-9e55-21f517a17af0","order_by":2,"name":"Suzanne Crull","email":"","orcid":"","institution":"Clemson University","correspondingAuthor":false,"prefix":"","firstName":"Suzanne","middleName":"","lastName":"Crull","suffix":""},{"id":328776572,"identity":"c1f2480c-d002-4ee9-9ac7-ec9227e448fd","order_by":3,"name":"Lauren M. O’Connell","email":"","orcid":"","institution":"Clemson University","correspondingAuthor":false,"prefix":"","firstName":"Lauren","middleName":"M.","lastName":"O’Connell","suffix":""},{"id":328776573,"identity":"7074027c-a69e-4254-89dd-7a3ba217c773","order_by":4,"name":"Esosa Osagie","email":"","orcid":"","institution":"Institute of Human Virology","correspondingAuthor":false,"prefix":"","firstName":"Esosa","middleName":"","lastName":"Osagie","suffix":""},{"id":328776574,"identity":"e66f3389-dceb-4f4d-833a-398a64ff7167","order_by":5,"name":"Paul Akhigbe","email":"","orcid":"","institution":"Institute of Human Virology","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"","lastName":"Akhigbe","suffix":""},{"id":328776575,"identity":"7a52978f-3bfa-4c8e-ba7e-4dc85847e70d","order_by":6,"name":"Ozoemene Obuekwe","email":"","orcid":"","institution":"University of Benin Teaching Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ozoemene","middleName":"","lastName":"Obuekwe","suffix":""},{"id":328776576,"identity":"40f5211a-68db-4747-ba7f-3bb3c61b50dd","order_by":7,"name":"Augustine Omoigberale","email":"","orcid":"","institution":"University of Benin Teaching Hospital","correspondingAuthor":false,"prefix":"","firstName":"Augustine","middleName":"","lastName":"Omoigberale","suffix":""},{"id":328776577,"identity":"0f2600d6-4f57-4247-92dc-5a48fbcf7299","order_by":8,"name":"Matthew Rowe","email":"","orcid":"","institution":"Clemson University","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Rowe","suffix":""},{"id":328776578,"identity":"4b44f2ee-a339-4bc0-b867-5469d50d9001","order_by":9,"name":"Thomas Blouin","email":"","orcid":"","institution":"Clemson University","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Blouin","suffix":""},{"id":328776579,"identity":"18947f24-4d78-4450-aa11-f929c2ab933a","order_by":10,"name":"Ashlyn Soule","email":"","orcid":"","institution":"Clemson University","correspondingAuthor":false,"prefix":"","firstName":"Ashlyn","middleName":"","lastName":"Soule","suffix":""},{"id":328776580,"identity":"05d8e83e-a65f-4b25-bebd-7cdbc4367261","order_by":11,"name":"Colton Kelly","email":"","orcid":"","institution":"Clemson University","correspondingAuthor":false,"prefix":"","firstName":"Colton","middleName":"","lastName":"Kelly","suffix":""},{"id":328776581,"identity":"024321dd-4347-4d3d-ba9a-b415959c1ed1","order_by":12,"name":"Robert A. Burne","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"A.","lastName":"Burne","suffix":""},{"id":328776582,"identity":"b483e9cb-8ad3-411a-9ca0-c17176dc7bd9","order_by":13,"name":"Modupe O. Coker","email":"","orcid":"","institution":"Rutgers University","correspondingAuthor":false,"prefix":"","firstName":"Modupe","middleName":"O.","lastName":"Coker","suffix":""},{"id":328776583,"identity":"fcc275e3-ec87-4670-9ca0-0eee103e010a","order_by":14,"name":"Vincent P. Richards","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYJCCA4wNDHIM7HB+AnFajBmYSdHCANSS2EC0FoPbvQ8P/Nxhl97fzHx044+KOwz87DkG+LXcOW5wsPdMcu6Mw2xpt3nOPGOQ7HmDX4vkjDSGw4xtzLkbmHnMbjO2HWYwuEHAFqiW+nQDZv5vN38CtdgT0sIvAdZyOMGAmYftBi/IFglCWmSOMRzsbTtuCPSLGdAvh3kkzjwrwKuFTbqN+cPPtmp5/vbmZzd/VByW429P3oBXC4MEGp8Hv3JsWkbBKBgFo2AUYAAAhUFHhyZ7BEcAAAAASUVORK5CYII=","orcid":"","institution":"Clemson University","correspondingAuthor":true,"prefix":"","firstName":"Vincent","middleName":"P.","lastName":"Richards","suffix":""}],"badges":[],"createdAt":"2024-07-10 20:10:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4720457/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4720457/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40168-025-02123-9","type":"published","date":"2025-06-16T15:57:28+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62189728,"identity":"817f6e93-a86b-4c66-8850-fab69c5ff982","added_by":"auto","created_at":"2024-08-10 12:23:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":251242,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTaxonomic turnover on individual teeth is higher in HUU as compared to HEU and HI children. \u003c/strong\u003e(a) Principal coordinate plot of individual teeth collected from visit one and visit three for each of the three HIV status groups. Lines connect plaque samples collected from the same tooth and same individual. Point shape indicates tooth health at the time of sampling. (b) Distribution of volatility scores for each of the three groups at visit one versus visit three. (c) Predictions plot of the microbial signature model generated using all teeth with more than one sampling point (visit one, two, and three). Correlation coefficient and p value calculated from all samples in the plot independent of HIV status. (d) The top three weighted taxa in the microbial signature model for high or low volatility for HI children (purple) and HUU children (blue). There were no significant taxa detected for HEU children.\u003c/p\u003e","description":"","filename":"Fig1volatility.png","url":"https://assets-eu.researchsquare.com/files/rs-4720457/v1/91328a5d89a1f13d4359b0fd.png"},{"id":62190786,"identity":"306eb5de-deac-4cb4-9bfe-1ea68a981256","added_by":"auto","created_at":"2024-08-10 12:31:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":99578,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHigh modularity of core association networks (CAN) associated with early and late-stage caries disease. \u003c/strong\u003e(a) Global CAN generated by comparing all samples across all three visits. We identified six clusters of co-associated taxa with variable predicted functional outcomes. Cluster six includes taxa commonly associated with caries disease (red). Clusters defined by this global CAN network are used to color code HIV status group and individual tooth health networks (b \u0026amp; c). (b) . CAN networks by individual tooth health. Healthy teeth (H) have a substantially more interconnected network with low modularity (Q). Both teeth with enamel lesions (E) and dentin cavities (D) have extremely sparse and poorly connected networks with high modularity. (c) CAN networks across all three visits grouped by HIV status. Modularity of each network denoted for each CAN network (Q). Polygons grouping clusters on each network are specific to that HIV status group CAN network. Colors represent cluster identity from global CAN (a). White nodes are nodes unique to that CAN.\u003c/p\u003e","description":"","filename":"Fig2CANnetworkv3.png","url":"https://assets-eu.researchsquare.com/files/rs-4720457/v1/55024c8b7db9b956185fd643.png"},{"id":62189729,"identity":"c71eb94c-150f-44a0-bea4-14913240a210","added_by":"auto","created_at":"2024-08-10 12:23:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":111314,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTaxa indicative of the community composition before and after high \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eS. mutans\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e relative abundance on a single tooth. \u003c/strong\u003eBee plots illustrate the importance of specific taxa in our random forest classification model to identify either (a) teeth before the proliferation of \u003cem\u003eS. \u003c/em\u003emutans or (b) teeth after the proliferation of \u003cem\u003eS. mutans\u003c/em\u003e. SHAP value (SHapley Additive exPlanations) indicates the degree of importance for each variable on the model. Positive SHAP values indicate that that bacterial species is important for identifying the sample as belonging to the group in question while negative SHAP values indicate that the species is more important for the opposite group (i.e., before or after \u003cem\u003eS. mutans\u003c/em\u003e). A SHAP value of zero indicates no impact on the model. For example, the red box in subpanel a is highlighting a subset of samples for which \u003cem\u003eStreptococcus sanguinis\u003c/em\u003e is highly predictive of the sample being before the proliferation of \u003cem\u003eS. mutans\u003c/em\u003e. Only the top ten taxa for either before or after high \u003cem\u003eS. mutans\u003c/em\u003e relative abundance are shown. Color of points indicate how the feature value for that individual compared to the average for the entire population in which high feature value is indicated by lighter colored points. Bolded species names indicate species found in both Shapley plots.\u003c/p\u003e","description":"","filename":"Fig3randomForestshapley.png","url":"https://assets-eu.researchsquare.com/files/rs-4720457/v1/c7478f826b0158ebc93e49ca.png"},{"id":62189726,"identity":"9990f784-6c24-47f1-8649-5db6c415c556","added_by":"auto","created_at":"2024-08-10 12:23:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":212508,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCommunity stratification of the anterior and posterior dentition is clear among HUU and HEU children but is dismantled in HI children. \u003c/strong\u003e(a) Microbial signature of the anterior and posterior teeth among all individuals independent of HIV status. The relative weight of each taxon is listed as its coefficient value (y axis). (b) Prediction box and density plots of the microbial signature model for anterior and posterior teeth driven by the balance of taxa listed in subplot a. (c) Capscale plot depicting the Aitchison distance of anterior and posterior teeth in HUU, HEU, and HI children. Significance between groups determined by PERMANOVA analysis and listed as Bonferroni adjusted p values. Only adult H-CF teeth were included in this analysis.\u003c/p\u003e","description":"","filename":"Fig4spatialstabilityv2.png","url":"https://assets-eu.researchsquare.com/files/rs-4720457/v1/c0bffac43a3fa68c54e14bae.png"},{"id":85231433,"identity":"12645dcb-dc61-4c67-a15b-934e231ac8f6","added_by":"auto","created_at":"2025-06-23 16:07:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1405797,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4720457/v1/150687b1-3f47-46da-a42e-db9579e9a0ef.pdf"},{"id":62189730,"identity":"897955f4-cdb7-48aa-a658-44b62822b5cd","added_by":"auto","created_at":"2024-08-10 12:23:32","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1732662,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-4720457/v1/5ab7831c87cbf53e0dcee6da.docx"},{"id":62189731,"identity":"e06294df-927e-4520-a029-9ced172aa415","added_by":"auto","created_at":"2024-08-10 12:23:33","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":82660628,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4720457/v1/7609bfa0f8dc3bf080189019.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"HIV Infection and Exposure Increases Cariogenic Taxa, Reduces Taxonomic Turnover, and Homogenizes Spatial Differentiation for the Supragingival Microbiome","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eThe human oral microbiome is a highly dynamic and personalized ecological system composed of heterogeneous microhabitats (e.g., saliva, cheeks, tongue, hard and soft palate, teeth, and gums) inhabited by diverse bacteria, archaea, microbial eukaryotes, and viruses. The most densely populated of these microhabitats are microbial biofilms that form on the teeth (i.e., dental plaques), which harbor a distinct community as compared to those formed on soft tissues. The supragingival plaque microbiome is further shaped by differences in substrate and oxygen availability, salivary flow, and the presence of other co-aggregating taxa [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Along with features of the oral environment that can be attributed to host genetics (e.g., morphology of the oral tissues, salivary production rate, composition, and pH, and host immune response), the composition and stability of the oral microbiome is heavily influenced by diet, behavior (e.g., oral hygiene practices, smoking), and age [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Dental plaques form on a tooth through a series of ordered colonization events, resulting in a highly structured, species rich microbial biofilm [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Community homeostasis of the plaque microbiome is essential for the maintenance of good oral health [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, dental plaques are also in direct and regular contact with an individual\u0026rsquo;s external environment and are thus more susceptible to punctuated ecological shifts over time due to changing environmental conditions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The relative resiliency of the oral environment to these shifts has important implications for the maintenance of good oral health and the prevention of microbial dysbiosis.\u003c/p\u003e \u003cp\u003eDysbiosis of the oral environment is associated with a variety of common chronic diseases, the most prevalent of which is tooth decay. If untreated, tooth decay results in the formation of carious lesions in the enamel surface, bacterial infection of the dentin, pulp cavity, and surrounding tissues, and eventually, tooth loss. Severe infection of a tooth can cause sepsis and even death. Untreated dental caries in permanent teeth is the most common chronic disease, globally affecting an estimated 2.5\u0026nbsp;billion people, despite it being a largely preventable condition [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The etiology of caries is initiated and exacerbated by ecological shifts in the plaque microbial community \u0026ndash; driven by environmental changes in the oral cavity \u0026ndash; and typically results in a reduction of overall community richness and complexity, though some studies have found an increase in diversity among teeth with active carious lesions [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. While there are specific taxa that are directly implicated in the development or intensification of caries disease progression (primarily \u003cem\u003eStreptococcus mutans\u003c/em\u003e and other aciduric and acidogenic taxa), it is generally accepted that caries is the result of a population imbalance in the oral microbial community [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and that risk of caries disease is related to the structure, stability, and resilience of the community to ecological stressors [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChildren living with HIV have a higher prevalence and more severe clinical presentation of dental caries in both primary and permanent dentition, the impact of which is associated with viral load and individual immune status [\u003cspan additionalcitationids=\"CR23 CR24 CR25 CR26\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Previous work by our group found a small but distinct effect of HIV status on the supragingival plaque microbiome which is exacerbated in the context of severe caries [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Moreover, there is growing evidence that perinatal exposure to HIV has systemic effects on the health, growth, and development of children [\u003cspan additionalcitationids=\"CR30 CR31 CR32 CR33 CR34\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and previous work by our group found a distinct oral mycobiome [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], lower salivary flow and decreased salivary pH [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], increased developmental defects of the enamel surface [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], and increased incidence of stunting in both children exposed to and infected by HIV [Manuscript in preparation].\u003c/p\u003e \u003cp\u003e Given the relative susceptibility of the oral microbiome to perturbations as compared to other microbe-rich host-associated environments, cross-sectional studies of the oral microbiome only provide a brief snapshot of the ecological diversity and dynamics of the oral microbiome over time, and the impact of illness or stress on the long-term stability of the oral microbiome is poorly defined. In the current study, using three time points, we compare the temporal and spatial stability of the oral microbiome among children living with HIV (HI), children perinatally exposed to but uninfected by the virus (HEU), and unexposed and therefore uninfected (HUU) children. We find significantly lower taxonomic turnover in HEU and HI children as compared to HUU children over time. While this pattern in HI children is predominantly due to a higher prevalence of diseased teeth with low community complexity, we detect significantly lower taxonomic turnover for HEU children even when only healthy teeth are compared.\u003c/p\u003e \u003cp\u003eIn addition to temporal patterning of the plaque microbiome, we find that spatial patterning of the bacterial community across the dental anatomy is disrupted in children living with HIV as compared to HEU and HUU children wherein children exposed to or uninfected by HIV have a clear differentiation between the communities inhabiting the anterior and posterior teeth but there is no such separation among HI children. Finally, we document an initial increase in CD4 counts in HI children from visit one to visit two, followed by a slight decrease in visit three, possibly a result of ongoing anti-retroviral treatment and increasing age of the cohort. Importantly, increasing CD4 counts among HI children is associated with a reduction of cariogenic taxa. Our results document the importance of longitudinal and spatial sampling of the supragingival plaque microbiome for a more holistic appreciation of the role of oral bacteria in the development of caries in children living with HIV.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e \u003cem\u003eStudy design and sample collection.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eSamples included in this study were collected from 565 children between May, 2019 and February, 2021 during three separate clinical visits at the University of Benin Teaching Hospital in Benin City, Nigeria. In total, we collected 1,960 supragingival plaque samples as part of the Dental Caries and its Association with Oral Microbiome and HIV in Young Children \u0026ndash; Nigeria (DOMHaIN) Study [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. A total of 614 supragingival plaque samples were collected from uninfected and unexposed children (HUU), 611 from children exposed to HIV in utero but uninfected with HIV (HEU), and 735 from children living with HIV (HI).\u003c/p\u003e \u003cp\u003eSupragingival plaque samples were collected from a single tooth and categorized into one of six progressive health categories. First, the sample was categorized according to the condition of the tooth of origin using the International Caries Detection and Assessment System [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] as either originating from (1) a tooth with no cavity (H; ICDAS score\u0026thinsp;=\u0026thinsp;0), (2) a tooth with an active enamel lesion (E; ICDAS score\u0026thinsp;=\u0026thinsp;1 to 3), or (3) a tooth with an active dentin carious lesion (D; ICDAS score\u0026thinsp;\u0026ge;\u0026thinsp;4). We next characterized individual plaque samples according to the observed caries experience of the child at the time of sampling as either (1) caries-free (CF), i.e., no clinical or reported evidence of caries (number of decayed, missing, and filled teeth [DMFT]\u0026thinsp;=\u0026thinsp;0), (2) caries active with lesions present in the enamel of the tooth only (CE; DT\u0026thinsp;=\u0026thinsp;0, MFT\u0026thinsp;\u0026ge;\u0026thinsp;0), or (3) caries active with at least two teeth presenting with active dentin lesions (CD; DT\u0026thinsp;\u0026ge;\u0026thinsp;2, MFT\u0026thinsp;\u0026ge;\u0026thinsp;0). Together, these two classification schemes were used to place each sample into one of six progressive disease states as follows: (1) plaque from a healthy tooth collected from a child with no caries (H-CF, n\u0026thinsp;=\u0026thinsp;1,169), (2) plaque collected from a healthy tooth collected from a child with active enamel caries (H-CE, n\u0026thinsp;=\u0026thinsp;119), (3) plaque collected from a healthy tooth collected from a child with active dentin caries (H-CD, n\u0026thinsp;=\u0026thinsp;251), (4) plaque collected from a tooth with an active enamel cavity from a child with active enamel cavities (E-CE, n\u0026thinsp;=\u0026thinsp;70), (5) plaque collected from a tooth with an active enamel cavity from a child with active dentin cavities (E-CD, n\u0026thinsp;=\u0026thinsp;110), and (6) a plaque collected from a tooth with an active dentin cavity from a child with active dentin cavities (D-CD, n\u0026thinsp;=\u0026thinsp;241). In brief, we collected each plaque using a sterile curette which was then stored in a sterile 2 mL cryogenic vial containing 500 \u0026micro;L of RNAlater. Post-collection, samples were placed immediately on ice and stored at -80\u0026deg;C within two hours of collection. Detailed sampling procedures are described in Coker et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction, library preparation, and sequencing\u003c/h2\u003e \u003cp\u003eWe extracted DNA from each sample using the DNeasy PowerBiofilm kit (Qiagen, Valencia, CA, USA) following the manufacturer\u0026rsquo;s suggested protocol. We quantified the total DNA yield post-extraction for each sample using a Qubit fluorometer (Invitrogen, Carlsbad, CA). To track potential sources of contamination, an extraction blank using molecular-grade water was processed in parallel to all samples. To characterize the bacterial community, we amplified a fragment of the bacterial \u003cem\u003erpo\u003c/em\u003eC gene using custom primers (rpoCF: 5\u0026rsquo; \u0026ndash; MAYGARAARMGNATGYTNCARGA \u0026ndash; 3\u0026rsquo;, rpoCR: 5\u0026rsquo; \u0026ndash; GMCATYTGRTCNCCRTCRAA \u0026ndash; 3\u0026rsquo;) as described in Mann et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Each PCR reaction consisted of the following: 0.5 \u0026micro;L each of the forward and reverse primers, 10 \u0026micro;L molecular grade water, 4 \u0026micro;L of template DNA, and 10 \u0026micro;L of the Platinum Hot Start PCR Master Mix (Invitrogen, Carlsbad, CA, USA). We processed PCR blanks (molecular grade water) in parallel to all samples to track sources of contamination. Each reaction was amplified using the following thermocycler conditions: 94\u0026deg;C for 3 minutes followed by 41 cycles of 94\u0026deg;C for 45 seconds, 39.5\u0026deg;C for 1 minute, and 72\u0026deg;C for 1 minute 30 seconds. A final elongation step was performed for 10 minutes at 72\u0026deg;C. Amplification of all samples was confirmed through both gel electrophoresis and Qubit fluorometer (Invitrogen, Carlsbad, CA, USA). Finally, we pooled each sample library at equimolar concentrations and sequenced the final pools on an Illumina MiSeq using V3 2x300 paired-end chemistry (Illumina, San Diego, CA, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eComputational analyses\u003c/h2\u003e \u003cp\u003eWe first removed primers and adapter sequences from our raw sequencing read files using Cutadapt (v 1.18) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Next, we quality filtered, merged, generated Amplicon Sequence Variants (ASVs), and removed suspected chimeric sequences using DADA2 (v 1.22.0) [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] in an R (v 4.1.0) environment [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Quality-filtered paired-end reads that were shorter than 450 bp after merging were removed from downstream analysis. We next assigned a taxonomy to each ASV using Kraken2 (v 2.1.2) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] and a custom \u003cem\u003erpo\u003c/em\u003eC database as reference [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. ASVs that could not be assigned to the phylum level or below (i.e., kingdom only) were removed from analysis. We removed low-abundance ASVs with a prevalence threshold of 0.1% (n\u0026thinsp;=\u0026thinsp;~\u0026thinsp;2 samples) unless they had a total abundance of more than 1,000 reads, in which case the ASV was retained for downstream analysis. Finally, we removed samples with fewer than 5,000 reads post-filtering. Rarefaction curves for all samples post-quality filtering can be found in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eWe next performed diversity analyses for the filtered dataset using the R libraries phyloseq (v 1.38.0) [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], vegan (v 2.6-4) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], compositions (v 2.0\u0026ndash;6) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], and microbiome (v 1.16.0) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Significance in diversity metrics between groups was determined using PERMANOVA analysis (vegan::adonis2) performed on Center Log-Ratio (CLR) transformed data. We used coda4microbiome (v 0.1.4) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] to identify microbial signatures that are predictive of sample metadata. This method calculates the minimum number of features (here, bacterial species) that have the maximum predictive power for a particular user-defined metadata category. A microbial signature, therefore, is defined by the relative abundance of two groups of taxa wherein the balance of taxa with positive and negative coefficients has the highest correlation with your chosen variable.\u003c/p\u003e \u003cp\u003e We calculated the taxonomic turnover of the oral microbiome on a single tooth over time following the protocol detailed in Bastiaanssen et al. [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In brief, we defined taxonomic turnover as the absolute Euclidean distance between paired plaque samples from the same tooth and individual over two or more collection time points using an Aitchison distance matrix calculated from CLR transformed count data. Significance between groups was determined using a Wilcoxon rank signed test.\u003c/p\u003e \u003cp\u003eNext, we wanted to identify conserved microbial associations over time (i.e., the co-occurrence of species pairs on distinct teeth/samples) within and between our HIV status and tooth health groups. To do this, we first generated microbial association networks with NetCoMi (v 1.1.0) [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] and Spiec-Easi (v 1.1.3) [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] using the neighborhood selection method (MB) [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Before network generation, we collapsed our ASV frequency table to the species level and only included species found in at least 1% of all samples with a minimum of 10 observations to minimize the effect of low frequency taxa. We generated microbial association networks for each HIV status (HI, HEU, HUU) and tooth health group (H, E, D) at each sampling time point (three networks per group) and used anuran (v 1.1.0) [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] to depict a single Core Association Network (CAN) for each HIV status or tooth health group across all time points (set size\u0026thinsp;=\u0026thinsp;1.0). Anuran identifies conserved patterns (i.e., groupings) across networks. As such, CANs generated for this study represent the consensus of microbial associations over time within our groups. In addition, we generated a \u0026ldquo;global\u0026rdquo; CAN using all samples to act as a baseline comparison to our group-specific CANs (set size 0.6). Next, we used the greedy clustering algorithm implemented in igraph (v 1.6.0) [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] to cluster species and quantify cluster modularity within each CAN [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Only clusters with at least ten or more members were analyzed.\u003c/p\u003e \u003cp\u003eFinally, we wanted to better understand the community ecology on individual teeth directly before or after the tooth has a high relative abundance of the cariogenic taxon \u003cem\u003eS. mutans\u003c/em\u003e. While \u003cem\u003eS. mutans\u003c/em\u003e is a dominant taxon in many supragingival plaque samples collected from late-stage cavitated teeth, it is often absent or at very low frequency at the early stages of tooth decay (i.e., enamel lesions or white spots) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. As such, it is unclear what role (if any) \u003cem\u003eS. mutans\u003c/em\u003e or other bacterial groups play in the initiation of caries. Conversely, many late-stage cavitated teeth have very little or no \u003cem\u003eS. mutans\u003c/em\u003e which may suggest that the proliferation of \u003cem\u003eS. mutans\u003c/em\u003e during the intensification of tooth decay is self-limiting and the community collapses over time [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. As such, it is important to know if there are predictable taxa that recolonize the tooth and if this recolonization recapitulates the original plaque community.\u003c/p\u003e \u003cp\u003eTo better understand the plaque environment before or after the proliferation of \u003cem\u003eS. mutans\u003c/em\u003e, we first identified all individual teeth that were sampled from the same child at more than one time point. From this, we identified teeth that had a relatively low proportion of \u003cem\u003eS. mutans\u003c/em\u003e (\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;5%) either before or after the same tooth had a relatively high proportion of \u003cem\u003eS. mutans\u003c/em\u003e (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;10%). Using this subset of paired samples of the same tooth over time, we first performed a random forest classification analysis using ranger (v 0.16.0) [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] to determine if the microbial community can be used to predict later \u003cem\u003eS. mutans\u003c/em\u003e colonization and proliferation (i.e., before \u003cem\u003eS. mutans\u003c/em\u003e) or if the community that recolonizes the tooth after \u003cem\u003eS. mutans\u003c/em\u003e collapse is consistent and predictable (i.e., after \u003cem\u003eS. mutans\u003c/em\u003e). Finally, we used a post-hoc analysis of our random forest classification model to identify specific bacterial species that are explanatory of our two groups (either before or after \u003cem\u003eS. mutans\u003c/em\u003e) using FastShap (v 0.1.1) [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConda environments and all scripts for data analysis can be found at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/aemann01/long_oral_microbiome\u003c/span\u003e\u003cspan address=\"https://github.com/aemann01/long_oral_microbiome\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and are archived at Zenodo (DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/zenodo.11396311\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.11396311\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for analytical reproducibility.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eSample demographic and taxonomic summary.\u003c/span\u003e \u003c/p\u003e \u003cp\u003eAfter quality filtering and removal of samples with low read counts, we retained 14,111 unique ASVs assigned to a total of 1,960 individual supragingival plaque samples collected from 565 children. Our final sample demographic includes 900 samples collected from female participants and 1,060 from male participants. Of the plaque samples, 746 were collected at visit one, 596 at visit two, and 618 at visit three. The average age of participants at visit one was 6.9 years old (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9) and 7.2 years old (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0) at visit three. Of all plaque samples retained post-quality filtering, 38% originated from an HI participant, 31% from a HEU participant, and 31% from a HUU participant. Full sample metadata can be found in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The top phyla found across all samples and all visits included \u003cem\u003eBacteroidetes\u003c/em\u003e (average proportion 31%), followed by \u003cem\u003eFirmicutes\u003c/em\u003e (26%), \u003cem\u003eProteobacteria\u003c/em\u003e (21%), \u003cem\u003eActinobacteria\u003c/em\u003e (16%), and \u003cem\u003eFusobacteria\u003c/em\u003e (15%). Top genera include \u003cem\u003eStreptococcus\u003c/em\u003e (66%), \u003cem\u003eLigilactobacillus\u003c/em\u003e (64%), \u003cem\u003eRothia\u003c/em\u003e (56%), \u003cem\u003eCapnocytophaga\u003c/em\u003e (55%), and \u003cem\u003ePrevotella\u003c/em\u003e (55%) (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). HEU samples across all three visits had significantly higher alpha diversity as measured by the observed number of ASVs as compared to HI samples (p\u0026thinsp;=\u0026thinsp;0.017) but not compared to HUU samples, and D-CD samples had significantly lower alpha diversity as measured by Shannon diversity as compared to E-CD and any healthy tooth independent of the overall oral health (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eChildren unexposed and uninfected with HIV have a higher rate of taxonomic turnover over time as compared to other children in this cohort.\u003c/span\u003e \u003c/p\u003e \u003cp\u003eWe defined taxonomic turnover on the same tooth over time as the absolute Euclidean distance between paired plaque samples using an Aitchison distance matrix. Teeth with high taxonomic turnover will be less similar over time while those with low taxonomic turnover will be more similar. A total of 184 teeth were sampled at both visit one and visit two with an average of 182 days between sampling periods, 255 individual teeth were sampled at both visit two and visit three with an average of 222 days separating sampling visits, and 203 individual teeth were sampled at both visit one and visit three with an average of 403 days between sampling visits. Among all teeth from all individuals, we detected a high degree of taxonomic turnover with no significant differences between the HIV status groups comparing visit one and visit two or visit two versus visit three. We did, however, detect a significant difference in taxonomic turnover between groups when comparing visit one and visit three. When comparing all teeth independent of individual tooth health status, we detected a significantly higher degree of taxonomic turnover among teeth sampled from HUU children as compared to teeth sampled from HEU children (p\u0026thinsp;=\u0026thinsp;0.045) and a moderately higher degree of taxonomic turnover when compared to HI children (p\u0026thinsp;=\u0026thinsp;0.058) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Comparing only healthy teeth (H), however, we detected a significant increase in turnover among teeth collected from HUU children as compared to HEU children (p\u0026thinsp;=\u0026thinsp;0.049) but no significant difference comparing HUU and HI children (p\u0026thinsp;=\u0026thinsp;0.154) or HI to HEU children (p\u0026thinsp;=\u0026thinsp;0.67).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe next identified microbial signatures of high or low taxonomic turnover across any tooth with two or more sampling time points. The resulting microbial signature is defined by the relative abundance of two groups of taxa where taxa with negative coefficients are correlated with low taxonomic turnover and those with positive coefficients are correlated with high taxonomic turnover. The absolute value of the coefficient is reflective of the degree of impact of that taxon on the model. From this analysis, we found a positive linear association between the degree of taxonomic turnover and the resulting microbial signature prediction (R\u0026thinsp;=\u0026thinsp;0.79, p\u0026thinsp;\u0026lt;\u0026thinsp;2.2e-16) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Among samples from children living with HIV, taxa associated with low taxonomic turnover include \u003cem\u003eS. mutans\u003c/em\u003e (coeff: -0.42), \u003cem\u003eHaemophilus paraphrohaemolyticus\u003c/em\u003e (coeff: -0.21), \u003cem\u003eMitsuokella\u003c/em\u003e sp. oral taxon 131 (coeff: -0.15), \u003cem\u003ePrevotella multisaccharivorax\u003c/em\u003e (coeff: -0.14), and \u003cem\u003eNeisseria cinerea\u003c/em\u003e (coeff: -0.08) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Taxa associated with high taxonomic turnover in children living with HIV include Peptostreptococcaceae bacterium oral taxon 081 (coeff: 0.29) followed by \u003cem\u003eFusobacterium nucleatum\u003c/em\u003e subsp. vincentii (coeff: 0.24), \u003cem\u003eEnterocloster bolteae\u003c/em\u003e (coeff: 0.21), \u003cem\u003ePrevotella intermedia\u003c/em\u003e (coeff: 0.16), \u003cem\u003eLeptotrichia buccalis\u003c/em\u003e (coeff: 0.08), and \u003cem\u003eTreponema phagedenis\u003c/em\u003e (coeff: 0.02) (R\u0026thinsp;=\u0026thinsp;0.8, p\u0026thinsp;=\u0026thinsp;3.9e-13). Among HUU children, \u003cem\u003ePrevotella denticola\u003c/em\u003e (coeff: -0.5), \u003cem\u003eP. multisaccharivorax\u003c/em\u003e (coeff: -0.43), and \u003cem\u003eH. paraphrohaemolyticus\u003c/em\u003e (coeff: -0.07) are associated with low taxonomic turnover while \u003cem\u003eCatonella morbi\u003c/em\u003e (coeff: 0.6), \u003cem\u003eOlsenella\u003c/em\u003e sp. oral taxon 807 (coeff: 0.15), an unknown species of Proteobacteria (coeff: 0.15), and \u003cem\u003eSolobacterium moorei\u003c/em\u003e (coeff: 0.09) are associated with high taxonomic turnover (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed; Table S4). We detected no microbial signature of taxonomic turnover among HEU children.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eDespite differences in taxonomic turnover on individual teeth, across all samples there are groups of species that are consistently co-associated over time.\u003c/span\u003e \u003c/p\u003e \u003cp\u003eWe next used core microbial association networks (CANs) to identify clusters of species that are consistently co-associated with one another on teeth over all three clinical visits. First, we created a \u0026ldquo;global\u0026rdquo; CAN generated from all plaque samples across all three visits to act as a baseline comparison to group-specific CANs. In our global CAN we detected six distinct cluster communities, the largest of which were Cluster 2 (n\u0026thinsp;=\u0026thinsp;35), Cluster 3 (n\u0026thinsp;=\u0026thinsp;38), and Cluster 4 (n\u0026thinsp;=\u0026thinsp;39) and the smallest, Cluster 6 (n\u0026thinsp;=\u0026thinsp;10) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea; Table S5). In general, co-associated species within clusters appear to have similar functional or clinical relevance. For example, commensal\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eand structural plaque species (i.e., bacteria previously described as important in biofilm formation and structure) dominate Cluster 4 (e.g., \u003cem\u003eStreptococcus sanguinis, Streptococcus gordonii, Neisseria mucosa, Haemophilus parainfluenzae, Streptococcus oralis, Streptococcus mitis\u003c/em\u003e, and \u003cem\u003eCorynebacterium durum\u003c/em\u003e) and Cluster 5 (e.g., \u003cem\u003eLeptotrichia\u003c/em\u003e sp. oral taxon 215, \u003cem\u003eLeptotrichia\u003c/em\u003e sp. oral taxon 212, \u003cem\u003eCorynebacterium matruchotii, Streptococcus cristatus\u003c/em\u003e) [\u003cspan additionalcitationids=\"CR61\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] while Cluster 1, Cluster 2, and Cluster 3 include a mixture of suspected commensal and potential pathogenic species. For example, Cluster 2 includes a variety of periodontal pathogens including members of the classic \u0026ldquo;red complex\u0026rdquo; in the etiology of periodontal disease (i.e., \u003cem\u003eTreponema denticola, Tannerella forsythia\u003c/em\u003e, and \u003cem\u003ePorphyromonas gingivalis\u003c/em\u003e) as well as species that previously have been isolated from periodontal pockets or coaggregate with other periodontal pathogens including \u003cem\u003eEubacterium nodatum, Eubacterium saphenum, Filifactor alocis, Porphyromonas endodontalis\u003c/em\u003e, and \u003cem\u003eTreponema medium\u003c/em\u003e [\u003cspan additionalcitationids=\"CR64 CR65 CR66 CR67\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. The smallest cluster, Cluster 6, includes species almost exclusively associated with caries disease including \u003cem\u003eS. mutans, Scardovia wiggsiae, Propionibacterium acidifaciens, P. multisaccharivorax, P. denticola\u003c/em\u003e, and \u003cem\u003eScardovia inopinata\u003c/em\u003e [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan additionalcitationids=\"CR70 CR71 CR72 CR73 CR74\" citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNext, to better understand how these core association networks differ across tooth health and HIV status groups, we calculated community modularity (Q) across all three visits within individual tooth health and HIV status groups. Modularity is a quantitative measure of network community structure wherein networks with high community modularity have more distinct (but potentially smaller) clusters that are themselves densely connected to other members of that cluster and at the same time are only loosely connected (or disconnected) from other clusters [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Conversely, low community modularity is reflective of fewer distinct, but potentially larger clusters of densely connected taxa. As our networks represent a consensus of co-associated taxa across all three sample visits, we expect that low community modularity (i.e., fewer distinct cluster groups) reflects higher core taxonomic stability over time.\u003c/p\u003e \u003cp\u003eWe find that community modularity among all healthy teeth (H) is relatively low (Q\u0026thinsp;=\u0026thinsp;0.75) and increases as the disease progresses to enamel lesions (Q\u0026thinsp;=\u0026thinsp;0.87) and eventually to dentin lesions (Q\u0026thinsp;=\u0026thinsp;0.95) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Within HIV groups, modularity of both our HUU and HI CAN networks were equivalent at Q\u0026thinsp;=\u0026thinsp;0.80 while our HEU CAN network had slightly higher modularity at Q\u0026thinsp;=\u0026thinsp;0.83 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). This suggests that while the bacterial community inhabiting individual teeth among HEU children changes little over time (i.e., low turnover), the community is less cohesive and more fragmented. Moreover, Cluster 6 is completely absent from the HEU CAN network and conversely is the only of the three HIV status groups to have a substantial cluster representative of global Cluster 2, potentially indicative of differences in susceptibility to caries vs periodontal disease.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eHigh S. mutans on an individual tooth is preceded by taxa typically associated with health and does not recapitulate the original community after S. mutans community collapse.\u003c/span\u003e \u003c/p\u003e \u003cp\u003eEvidence from cross-sectional studies (e.g.,[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] suggest that caries disease progression is characterized by a rapid propagation of \u003cem\u003eS. mutans\u003c/em\u003e and other acidogenic/aciduric bacteria during late-stage tooth decay, followed by a collapse of the community, and eventual recolonization. For our next analysis, we wanted to determine if this process is preceded or followed by predictable taxa or groups of taxa in the plaque community. To better understand the temporal dynamics of the oral microbiome before and after high levels of \u003cem\u003eS. mutans\u003c/em\u003e, we performed a random forest classification and post-hoc explanatory analysis on individual teeth with low \u003cem\u003eS. mutans\u003c/em\u003e (\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;5%) either before or after the community on the same tooth had a high level of \u003cem\u003eS. mutans\u003c/em\u003e (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;10%). Our random forest model had high classification accuracy for teeth with high \u003cem\u003eS. mutans\u003c/em\u003e (during high \u003cem\u003eS. mutans\u003c/em\u003e: 83% correct) but had relatively low predictive accuracy for teeth designated as \u0026ldquo;before\u0026rdquo; or \u0026ldquo;after\u0026rdquo; high \u003cem\u003eS. mutans\u003c/em\u003e. Accurate classification of teeth after high \u003cem\u003eS. mutans\u003c/em\u003e was only 47% with most being misclassified as \u0026ldquo;during\u0026rdquo; and none as \u0026ldquo;before\u0026rdquo;. Teeth before high \u003cem\u003eS. mutans\u003c/em\u003e were only classified correctly in 33% of cases with most being misidentified as during (50%) or after (17%). Taxa that were associated with teeth before high \u003cem\u003eS. mutans\u003c/em\u003e include a variety of commensal species including \u003cem\u003eS. sanguinis, S. cristatus, S. gordonii, Abiotrophia defectiva, Aggregatibacter aphrophilus\u003c/em\u003e, and \u003cem\u003eL. buccalis\u003c/em\u003e as well as suspected opportunistic pathogens (e.g., \u003cem\u003eLeptotrichia shahii, Cardiobacterium valvarum, Kingella dentrificans\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Interestingly, the community after high \u003cem\u003eS. mutans\u003c/em\u003e is distinct from that found before high \u003cem\u003eS. mutans\u003c/em\u003e with the top explanatory taxa including \u003cem\u003eCantonella morbi\u003c/em\u003e, \u003cem\u003eLeptotrichia\u003c/em\u003e sp. oral taxon 215, and \u003cem\u003eBacteroidetes\u003c/em\u003e oral taxon 274 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Importantly, the lack of \u003cem\u003eS. sanguinis\u003c/em\u003e is indicative of the community after colonization of high abundance of \u003cem\u003eS. mutans\u003c/em\u003e which suggests that the community does not recover to its previous state, at least not initially or within the period sampled here. More fine-grained longitudinal sampling is necessary to elucidate some of these patterns over time.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eHIV infection homogenizes the plaque microbiome across the posterior and anterior dentition.\u003c/span\u003e \u003c/p\u003e \u003cp\u003eNext, we investigated the impact of HIV status on the spatial distribution of the microbial community across the dentition of adult teeth with no carious lesions (H-CF) from all three visits. We focused on healthy teeth only for this analysis to eliminate the effect of differences in oral health among the children. We detected conspicuous differentiation among the bacterial community colonizing the anterior dentition (i.e., central and lateral incisors, canines) as compared to the posterior dentition (i.e., premolars and molars) across all adult teeth with the posterior teeth exhibiting a higher relative abundance of \u003cem\u003eLachnoanaerobaculum saburreum, S. gordonii, and Porphyromonas\u003c/em\u003e sp. oral taxon 278 and a more minor contribution of species belonging to the genera \u003cem\u003eCapnocytophaga, Campylobacter, Selenomonas, Leptotrichia, Streptococcus, Neisseria, Pseudoleptotrichia, Actinomyces, Actinobaculum, Aggregatibacter\u003c/em\u003e, and \u003cem\u003eFusobacterium\u003c/em\u003e. Anterior teeth, conversely, were strongly associated with \u003cem\u003eC. durum\u003c/em\u003e followed by \u003cem\u003ePrevotella\u003c/em\u003e sp. oral taxon 473, and \u003cem\u003eS. sanguinis\u003c/em\u003e followed by species belonging to the genera \u003cem\u003ePrevotella, Peptostreptococcus, Abiotrophia, Neisseria, Capnocytophaga, Granulicatella, Leptotrichia, Gemella, Parvimonas, Eubacterium\u003c/em\u003e, and \u003cem\u003ePorphyromonas\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Importantly, however, this differentiation is primarily driven by HEU and HUU samples where there is a clear distinction between the anterior and posterior community composition (pairwise PERMANOVA with Bonferroni adjusted p value, HUU: R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.6, p\u0026thinsp;=\u0026thinsp;0.001; HEU: R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.06, p\u0026thinsp;=\u0026thinsp;0.001). Conversely, while the oral community living on anterior and posterior teeth among children living with HIV show the same Capscale clustering pattern, the difference between communities is not significant (pairwise PERMANOVA with Bonferroni adjusted p value, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.4, p\u0026thinsp;=\u0026thinsp;0.7) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Additionally, differences between the anterior and posterior oral microbiome in HUU and HEU children are predicted by fewer taxa than in HI children. Among HUU children, posterior teeth are associated with a higher relative abundance of \u003cem\u003eNeisseria weaveri, L. saburreum, Actinomyces\u003c/em\u003e sp. oral taxon 848, and \u003cem\u003eCapnocytophaga granulosa\u003c/em\u003e using coda4microbiome balance analysis. In HEU children, posterior teeth are strongly associated with \u003cem\u003eL. saburreum\u003c/em\u003e only. Anterior teeth in both HEU and HUU children are strongly associated with \u003cem\u003eC. durum\u003c/em\u003e. In HI children, 25 taxa are needed to differentiate between the anterior and posterior teeth and while \u003cem\u003eC. durum\u003c/em\u003e also is the highest predictive taxon for anterior teeth in HI children, \u003cem\u003eL. saburreum\u003c/em\u003e is not associated with posterior teeth prediction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eDepressed immune status is associated with a higher prevalence of cariogenic taxa.\u003c/span\u003e \u003c/p\u003e \u003cp\u003eWe next investigated the correlation of CD4 counts on the oral microbiome across all three visits. Across all samples, CD4 counts among HI children are significantly lower as compared to both HEU (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and HUU children (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Considering plaque samples collected at each visit, however, CD4 counts among children living with HIV (HI) significantly increased between visit one and visit two (p\u0026thinsp;=\u0026thinsp;0.00016) and slightly decreased again between visit two and visit three (p\u0026thinsp;=\u0026thinsp;0.014) (Figure S3). We detected no significant differences in CD4 count among HEU children between all three visits. Interestingly, HUU children had a slight but significant decrease in CD4 counts between visit one and visit three (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and between visits two and three (p\u0026thinsp;=\u0026thinsp;0.00016), a pattern also observed among HI children between visits two and three.\u003c/p\u003e \u003cp\u003eFinally, we identified microbial signatures that were most predictive of CD4 counts in children living with HIV across our three sampling periods. We found that as CD4 counts increased over the three visits, the predictive power of microbial taxa decreased with visit one having the highest correlation coefficient (R\u0026thinsp;=\u0026thinsp;0.6, p\u0026thinsp;\u0026lt;\u0026thinsp;2.2e-16) followed by visit two (R\u0026thinsp;=\u0026thinsp;0.53, p\u0026thinsp;\u0026lt;\u0026thinsp;2.2e-16) and the lowest correlation coefficient at visit three (R\u0026thinsp;=\u0026thinsp;0.38, p\u0026thinsp;=\u0026thinsp;4.3e-09) (Figure S4). Taxa predictive of the lowest CD4 counts among children at visit one where the mean CD4 count is the lowest of our three sampling periods (775\u0026thinsp;\u0026plusmn;\u0026thinsp;472) include a variety of taxa involved or associated with the progression of caries disease including \u003cem\u003eS. mutans, Leptotrichia wadei, and L. saburreum\u003c/em\u003e [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Conversely, high CD4 counts are associated with a variety of taxa previously identified as potentially protective against caries development (e.g., \u003cem\u003eLeptotrichia\u003c/em\u003e sp. oral taxon 212 [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]) but also taxa that have been identified with higher caries risk (e.g., Lachnospiraceae bacterium oral taxon 082) [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. At visit two where the mean CD4 count increased to 961 (\u0026plusmn;\u0026thinsp;572) fewer taxa were identified to be predictive of CD4 count but included some overlap between taxa identified in visit one including \u003cem\u003eL. wadei, Capnocytophaga\u003c/em\u003e sp. oral taxon 412, Lachnospiraceae oral taxon 107 str. F0167, \u003cem\u003eC.\u003c/em\u003e sp. FDAARGOS 737, and \u003cem\u003eAggregatibacter\u003c/em\u003e sp. 2125159857. Additionally, \u003cem\u003eT. phagedenis\u003c/em\u003e, a non-pathogenic spirochete that is not considered to be a resident oral taxon, but is closely related to the periodontal pathogen \u003cem\u003eT. denticola\u003c/em\u003e [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e], was found to be associated with low CD4 counts among children living with HIV in both visit one and visit two. While there are fewer pathogenic taxa contributing to the microbial signature of CD4 counts in visit two, \u003cem\u003eSelenomonas sputigena\u003c/em\u003e, recently classified as a pathobiont capable of exacerbating the acidogenic activity of \u003cem\u003eS. mutans\u003c/em\u003e in early childhood caries is highly weighted in the correlation coefficients driving lower CD4 counts [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Finally, while the mean CD4 count among children living with HIV drops at visit three (850\u0026thinsp;\u0026plusmn;\u0026thinsp;521), the correlation coefficient between the relative balance of abundance between specific taxonomic groups is relatively weak (R\u0026thinsp;=\u0026thinsp;0.38, p\u0026thinsp;=\u0026thinsp;4.3e-09).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur results suggest that the temporal and spatial dynamics of the supragingival plaque microbiome are altered by HIV infection and exposure. We hypothesize that these alterations are a consequence of a combination of factors including: (1) reduced salivary flow and increased salivary pH among children in this cohort, (2) differences in taxonomic plasticity and cohesion of the plaque community over time, and (3) immune-mediated alterations to the oral community. Moreover, our longitudinal approach bolsters previous observations [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] that while \u003cem\u003eS. mutans\u003c/em\u003e is a dominant member of the supragingival plaque community during caries intensification, it is not involved or does not play a major role in the initiation of tooth decay. Instead, our results suggest that within-species strain-level functional diversity may be a catalyst for community dysbiosis and tooth decay inception. Further strain-level functional analyses are required to elucidate the role of the supragingival plaque community in the initiation of tooth decay.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eReduced salivary flow and increased salivary pH are key contributors to alterations in the spatiotemporal structure of the supragingival plaque microbiome.\u003c/span\u003e \u003c/p\u003e \u003cp\u003e Whole saliva is a key modulator of the oral microbiome both physically and biochemically. On average, a healthy individual produces 0.6 liters of saliva per day which continuously coats the oral tissues and helps to clear debris, desquamated epithelial cells, and microorganisms [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. Saliva provides several key benefits essential for maintaining homeostasis of the oral cavity including lubrication to facilitate swallowing and removal of food particles from the oral tissues, digestive enzymes including amylase for the digestion of carbohydrates into sugars, and antimicrobial compounds including hydrogen peroxide, lactoferrin, lysozymes, and a variety of antimicrobial peptides that modulate the colonization of oral tissues by microbes [\u003cspan additionalcitationids=\"CR84\" citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. Saliva also coats the teeth and other oral tissues with proteins and glycoproteins used by microbes to colonize the oral cavity, it transports nutrients and other metabolic substrates to members of the oral microbiome, and provides a moist environment with a stable pH (average\u0026thinsp;~\u0026thinsp;6.7) that favors bacterial growth [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePeople living with HIV often experience chronic dry mouth (xerostomia), and children especially are affected by HIV-associated salivary gland disease (HIV-SGD) [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. HIV-SGD affects the parotid, submandibular, and sublingual salivary glands and results in decreased saliva production and poorer quality saliva including decreased levels of sodium, calcium chloride, cystatin (essential for tooth remineralization), and lysozymes [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. Comparisons of unstimulated salivary flow rates among children in this cohort found significant differences between HIV status groups. HUU children had the highest mean rate of salivary flow (0.33 ml/min) and HI children the lowest (0.22 ml/min) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Importantly, the mean rate of salivary flow among HEU children was intermediate between HI and HUU at 0.27 ml/min [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], which may reflect long-lasting effects of the mother\u0026rsquo;s immune status on the oral biology of this growing cohort of children. Salivary gland hypofunction is associated with an increased risk of several oral diseases including caries disease and candidiasis [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. Moreover, the mean pH of HI children in this cohort was significantly lower than HEU or HUU children which has important implications for the risk for tooth decay [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e Given the biochemical properties of saliva and their importance in structuring the oral microbiome, we hypothesize that saliva is a primary contributor to differences in the biogeography of the supragingival plaque microbiome reported here. Location of individual teeth in the oral cavity, their proximity to major salivary ducts, morphological characteristics, and mechanical use (e.g., chewing vs tearing) influences their community composition. Previous research has found that the structure of the oral community follows an ecological gradient from the back to the front of the dentition so that the microbiome inhabiting the molars is distinct from that on the incisors [\u003cspan additionalcitationids=\"CR92 CR93\" citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. This ecological pattern is clearly supported in the current study among HUU and HEU children. Conversely, individuals with impaired salivary flow due to injury or disease (e.g., Sj\u0026ouml;gren\u0026rsquo;s syndrome, see Proctor et al. [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]), experience a breakdown of this ecological gradient and a homogenization of the anterior and posterior oral communities, as is evident in our HI cohort. Interestingly, while the differences between the oral microbiome inhabiting anterior and posterior teeth in HEU children in the current study are significant, the distribution of these two communities is somewhat intermediate between HUU and HI children (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec), which may be reflective of their intermediate status in terms of salivary flow [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Previous studies have documented that disruptions in the natural ecological barriers that separate the human oral-pharyngeal and gut microbiome are symptomatic of disease [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]. The results of this study suggest that these disruptions occur at much smaller spatial scales within the same host-associated habitat (i.e., the oral cavity) with possibly detrimental consequences towards overall oral and systemic health outcomes in HI children.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eTaxonomic turnover in the supragingival plaque microbiome may be adaptive response to changing host factors that is suppressed in disease.\u003c/span\u003e \u003c/p\u003e \u003cp\u003eUsing distance-based taxonomic turnover analyses we found that the community inhabiting a single tooth may experience substantial taxonomic turnover over time, particularly among HUU children. This contrasts with expectations from previous longitudinal analyses of human-associated microbial ecosystems (primarily the gut microbiome) wherein large-scale shifts in the bacterial community is typically associated with illness or stress [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e]. Given that bacteria inhabiting the oral cavity are subject to more direct and regular exchange with the external environment, it is possible that taxonomic turnover in the supragingival plaque microbiome is a signal of community health. Specifically, that temporal flexibility is reflective of the community\u0026rsquo;s ability to rapidly adapt to environmental perturbations. Previous investigations into the temporal stability of the oral microbiome have suggested that it is more dynamic than other human-associated microbial habitats as well as more influenced by the individual\u0026rsquo;s behavior and environment than purely genetic determinants of the composition of the oral microbiome [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Other studies refute this, however, and argue that the oral microbiome is relatively stable over time [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]. The results presented here strongly suggest that community fluctuations over time are dependent on both the health status of the individual as well as the scale in which these observations are made. Importantly, however, our results document a strong association between low taxonomic turnover and cariogenic taxa, including \u003cem\u003eS. mutans, P. multisaccharivorax\u003c/em\u003e, and \u003cem\u003eP. denticola\u003c/em\u003e, which may indicate that diseased oral communities experience less change over time due to a lack of starting diversity driven by acidogenic/aciduric taxa.\u003c/p\u003e \u003cp\u003eDespite substantial turnover on the individual tooth level, we do find clusters of taxa that are consistently co-associated over time within and across our HIV status groups. Thus, despite individualistic fluctuations in the overall community composition, a stable core of taxa is consistently present in the supragingival plaque microbiome, a pattern previously observed in longitudinal sampling of the oral cavity [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Clusters identified in the current study have similar functional expectations in that species that are associated with health or disease are consistently co-associated over time. For example, in our global core-association network, Cluster 6 is made exclusively of known or suspected cariogenic taxa while Cluster 1, Cluster 2, Cluster 5, and Cluster 4 are composed primarily of oral symbionts and structural taxa. Importantly, while modularity (a measure of community cohesion over time) is roughly equivalent among HI, HEU, and HUU children, there are notable differences in the structure and composition of core-association networks among these three groups. The most conspicuous of these differences is the lack of Cluster 6 and prominence of Cluster 2 among HEU children. Given that Cluster 6 is composed of cariogenic taxa, its absence in part may explain why, despite experiencing poor health outcomes in other areas that are similar to HI children [\u003cspan additionalcitationids=\"CR30 CR31 CR32 CR33 CR34\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], HEU children do not have a higher caries burden as compared to HUU children [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e]. Importantly, however, Cluster 2, which is prominent in HEU children as compared to HI and HUU children, contains taxa typically associated with periodontal disease even though no children in this cohort exhibited symptoms of periodontal disease. It is unclear if these results suggest future periodontal disease risk among HEU children.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eImmune status among children living with HIV is associated with the abundance of cariogenic taxa.\u003c/span\u003e \u003c/p\u003e \u003cp\u003eIndividual immune status among children living with HIV has previously been associated with the prevalence and severity of caries disease (see, for example [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]). In agreement with these findings, we find that low CD4 counts are strongly associated with the abundance of cariogenic taxa. Importantly, however, we also find that CD4 counts vary substantially among children living with HIV and that the mean CD4 count within this group fluctuates significantly over time. For example, we detect a significant increase in CD4 counts from visit one to visit two among children living with HIV (possibly the result of continued antiretroviral treatment), followed by a slight but significant decrease between visits two and three, a pattern also observed in HUU, but not HEU children. As CD4 counts generally decrease as children age [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e], these patterns may be part of the normal aging process, however, given the impact of host immune status on the health of the oral cavity, we expect that continual monitoring of CD4 counts will be an important aspect of determining risk for tooth decay and other chronic health problems among HI children.\u003c/p\u003e\n\u003ch3\u003eCommunity dynamics before and after proliferation of S. mutans\u003c/h3\u003e\n\u003cp\u003eFinally, we used random forest classification and post-hoc directionality analysis to better understand the community dynamics on a single tooth before and after high levels of \u003cem\u003eS. mutans\u003c/em\u003e on the same tooth at different time periods. While our random forest classification model was unable to precisely classify the oral community before and after high levels of \u003cem\u003eS. mutans\u003c/em\u003e, we did detect specific taxa that were present before high \u003cem\u003eS. mutans\u003c/em\u003e that were not retained after the recolonization of the tooth post \u003cem\u003eS. mutans\u003c/em\u003e colonization. In particular, non-mutans streptococcal species including \u003cem\u003eS. cristatus, S. sanguinis\u003c/em\u003e, and \u003cem\u003eS. gordonii\u003c/em\u003e were indicative of the community before but not after \u003cem\u003eS. mutans\u003c/em\u003e colonization and proliferation. Importantly, however, these patterns are driven by a small subset of samples which may be indicative of functional differences among strains of these non-mutans streptococci. For example, previous work by our group has found substantial functional diversity among members of the streptococci, including \u003cem\u003eS. sanguinis\u003c/em\u003e [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e]. Moreover, while \u003cem\u003eS. sanguinis\u003c/em\u003e is typically associated with oral health, high resolution amplicon sequencing of \u003cem\u003eS. sanguinis\u003c/em\u003e and other putatively commensal streptococci species has documented that some are strongly associated with later stages of caries disease [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e]. Given that \u003cem\u003eS. sanguinis\u003c/em\u003e is highly predictive of later \u003cem\u003eS. mutans\u003c/em\u003e abundance among some of our samples, it may be the case that specific strains of \u003cem\u003eS. sanguinis\u003c/em\u003e play a role in the initialization of caries while others are protective against caries [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Finally, our results suggest that the community inhabiting a tooth will not recover to its previous state after \u003cem\u003eS. mutans\u003c/em\u003e is the dominant taxon. Monitoring these processes at smaller time scales may elucidate which taxa are predictive of the community before and after high levels of cariogenic taxa including \u003cem\u003eS. mutans.\u003c/em\u003e\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThe results of this study highlight the importance of scale \u0026ndash; both temporally and spatially \u0026ndash; in understanding the impact of the bacterial supragingival plaque community in the development of caries in the context of HIV infection and exposure. We find that HIV infection and exposure has significant impact on the temporal and spatial structuring of the oral microbiome which may be the result of altered salivary flow and pH, individual host dynamics, and impaired immune status of individual children. Significantly, we find that healthy children within our cohort exhibit relatively high taxonomic turnover of the supragingival plaque microbiome while maintaining cohesive and consistent groups of taxa over time. Additionally, we find that low taxonomic turnover is associated with higher frequencies of cariogenic taxa including \u003cem\u003eS. mutans\u003c/em\u003e. Moreover, we find that HIV infection homogenizes the oral microbiome across the anterior and posterior dentition, with altered salivary function likely a key factor. A further appreciation of the temporal and spatial dynamics of the oral microbiome in health and disease may be necessary to identify molecular mechanisms of oral microbiome dysbiosis and the initiation, progression, and ultimate consequences of tooth decay among children living with and exposed to HIV.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cul\u003e\n \u003cli\u003eHUU: Child unexposed and therefore uninfected with HIV\u003c/li\u003e\n \u003cli\u003eHEU: Child exposed to but uninfected with HIV\u003c/li\u003e\n \u003cli\u003eHI: Child living with HIV\u003c/li\u003e\n \u003cli\u003eH-CF: A healthy tooth from a caries-free mouth\u003c/li\u003e\n \u003cli\u003eH-CE: A healthy tooth from a mouth with active enamel lesions\u003c/li\u003e\n \u003cli\u003eH-CD: Healthy tooth from a mouth with active dentin caries\u003c/li\u003e\n \u003cli\u003eE-CE: Tooth with an enamel lesion from a mouth with active enamel caries\u003c/li\u003e\n \u003cli\u003eE-CD: Tooth with an enamel lesion from a mouth with active dentin caries\u003c/li\u003e\n \u003cli\u003eD-CD: Tooth with a dentin cavity from a mouth with active dentin cavities\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all parents, guardians, or caregivers, and children 8 years and older provided assent before joining the study -\u0026nbsp;University of Maryland Baltimore (HP-00084081), Rutgers State University of New Jersey (Pro2019002047), and University of Benin Teaching Hospital, Benin City (ADM/E22/A/VOL. VII/14713)).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and material\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available in the European Nucleotide Archive repository under accession number PRJEB76179. All preprocessing and analysis scripts are available at https://github.com/aemann01/long_oral_microbiome and are archived at Zenodo under the DOI: 10.5281/zenodo.11396312.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Institutes of Health National Institute of Dental and Craniofacial Research (R01DE028154 to V.P.R. and M.O.C.)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026rsquo; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAEM: Formal analysis, Investigation, Methodology, Software, Visualization, Writing-original draft, Writing-review \u0026amp; editing; CA: Formal analysis, Investigation, Writing-review \u0026amp; editing; SC: Formal analysis, Methodology, Writing-review \u0026amp; editing; LMO: Investigation, Writing-review \u0026amp; editing; EO: Resources, Writing-review \u0026amp; editing; PA: Resources, Writing-review \u0026amp; editing; OO: Resources, Writing-review \u0026amp; editing; AO: Resources, Writing-review \u0026amp; editing; MR: Investigation, Writing-review \u0026amp; editing; TB: Investigation, Writing-review \u0026amp; editing; AS: Investigation, Writing-review \u0026amp; editing; CK: Investigation, Writing-review \u0026amp; editing; MC: Funding acquisition, Resources, Supervision, Conceptualization, Writing-review \u0026amp; editing; VPR: Funding acquisition, Resources, Supervision, Conceptualization, Writing-review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the participating families of the DOMHaIN study for their commitment to this research. We also would like to thank members of The DOMHaIN Study Team, which comprises of the authors and the following team members: Oghenenero Igedegbe, Ruxton Adebiyi, Matron Christy Ndekwu, Uwagboe Odigie, Oyemwen Olaye, Ehioze Awanlemhen, Samuel Chukwumaeze, Matthew Imoe, Daniel Oakhu, and Susan Dare are acknowledged for the recruitment, sample, and data collection. Nosakhare Idemudia, Osasumwen Ehigie, Kelly Avenbuan, and Amara Godwins provided laboratory management and support with sample processing. Nneka Chukwumah, Stanley Iyorzor, Owen Omorogbe, and Chioma Ugorji and acknowledged for the clinical examination during study visits and for their flexibility with recruitment and scheduling.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMager DL, Ximenez-Fyvie LA, Haffajee AD, Socransky SS. Distribution of selected bacterial species on intraoral surfaces. J Clin Periodontol. 2003;30:644\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMark Welch JL, Ram\u0026iacute;rez-Puebla ST, Borisy GG. Oral Microbiome Geography: Micron-Scale Habitat and Niche. Cell Host Microbe. 2020;28:160\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorillo-Lopez V, Sjaarda A, Islam I, Borisy GG, Mark Welch JL. Corncob structures in dental plaque reveal microhabitat taxon specificity. Microbiome. 2022;10:145.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXim\u0026eacute;nez-Fyvie LA, Haffajee AD, Socransky SS. Comparison of the microbiota of supra- and subgingival plaque in health and periodontitis. J Clin Periodontol. 2000;27:648\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDemmitt BA, Corley RP, Huibregtse BM, Keller MC, Hewitt JK, McQueen MB, et al. Genetic influences on the human oral microbiome. BMC Genomics. 2017;18:659.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFreire M, Moustafa A, Harkins DM, Torralba MG, Zhang Y, Leong P, et al. Longitudinal Study of Oral Microbiome Variation in Twins. Sci Rep. 2020;10:7954.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMason MR, Chambers S, Dabdoub SM, Thikkurissy S, Kumar PS. Characterizing oral microbial communities across dentition states and colonization niches. Microbiome. 2018;6:67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou X, Shen X, Johnson JS, Spakowicz DJ, Agnello M, Zhou W, et al. Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease. Cell Host Microbe. 2024;32:506\u0026ndash;526.e9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarsh PD. Dental plaque as a biofilm and a microbial community \u0026ndash; implications for health and disease. BMC Oral Health. 2006;6:S14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeles FR, Teles RP, Uzel NG, Song XQ, Torresyap G, Socransky SS, et al. Early microbial succession in redeveloping dental biofilms in periodontal health and disease. J Periodontal Res. 2012;47:95\u0026ndash;104.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing T, Schloss PD. Dynamics and associations of microbial community types across the human body. Nature. 2014;509:357\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStahringer SS, Clemente JC, Corley RP, Hewitt J, Knights D, Walters WA, et al. Nurture trumps nature in a longitudinal survey of salivary bacterial communities in twins from early adolescence to early adulthood. Genome Res. 2012;22:2146\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal oral health status report: towards universal health coverage for oral health by 2030 [Internet]. 2022 [cited 2023 Jul 6]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications-detail-redirect/9789240061484\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications-detail-redirect/9789240061484\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalpana B, Prabhu P, Bhat AH, Senthilkumar A, Arun RP, Asokan S, et al. Bacterial diversity and functional analysis of severe early childhood caries and recurrence in India. Sci Rep. 2020;10:21248.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolff D, Frese C, Schoilew K, Dalpke A, Wolff B, Boutin S. Amplicon-based microbiome study highlights the loss of diversity and the establishment of a set of species in patients with dentin caries. PLOS ONE. 2019;14:e0219714.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao C, Ran S, Huang Z, Liang J. Bacterial Diversity and Community Structure of Supragingival Plaques in Adults with Dental Health or Caries Revealed by 16S Pyrosequencing. Front Microbiol. 2016;7:1145.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiacaman R. Sugars and beyond. The role of sugars and the other nutrients and their potential impact on caries. Oral Dis. 2018;24:1185\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGross EL, Beall CJ, Kutsch SR, Firestone ND, Leys EJ, Griffen AL. Beyond Streptococcus mutans: Dental Caries Onset Linked to Multiple Species by 16S rRNA Community Analysis. PLOS ONE. 2012;7:e47722.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Houte J, Sansone C, Joshipura K, Kent R. Mutans streptococci and non-mutans streptococci acidogenic at low pH, and in vitro acidogenic potential of dental plaque in two different areas of the human dentition. J Dent Res. 1991;70:1503\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Ruyven FOJ, Lingstr\u0026ouml;m P, van Houte J, Kent R. Relationship among Mutans Streptococci, \u0026ldquo;Low-pH\u0026rdquo; Bacteria, and lodophilic Polysaccharide-producing Bacteria in Dental Plaque and Early Enamel Caries in Humans. J Dent Res. 2000;79:778\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarsh PD. Are dental diseases examples of ecological catastrophes? Microbiology. 2003;149:279\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkhigbe P, Chukwumah NM, Adebiyi R, Oluwatoyin Folayan M, Divaris K, Ozoemene O, et al. Age-specific associations with dental caries in HIV-infected, exposed but uninfected, and HIV-unexposed uninfected children in Nigeria. 2022;\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoker M, El-Kamary S, Enwonwu C, Blattner W, Langenberg P, Mongodin E, et al. Perinatal HIV Infection and Exposure and their Association with Dental Caries in Nigerian Children. Pediatr Infect Dis J. 2018;37:59\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoker MO, Mongodin EF, El-Kamary SS, Akhigbe P, Obuekwe O, Omoigberale A, et al. Immune status, and not HIV infection or exposure, drives the development of the oral microbiota. Sci Rep. 2020;10:10830.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoker MO, Akhigbe P, Osagie E, Idemudia NL, Igedegbe O, Chukwumah N, et al. Dental caries and its association with the oral microbiomes and HIV in young children\u0026mdash;Nigeria (DOMHaIN): a cohort study. BMC Oral Health. 2021;21:620.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOliveira CAGR, Tannure PN, de Souza IPR, Maia LC, Portela MB, Castro GFB de A. Is dental caries experience increased in HIV-infected children and adolescents? A meta-analysis. Acta Odontol Scand. 2015;73:481\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajonson N, Meless D, Ba B, Faye M, Diby J-S, N\u0026rsquo;zore S, et al. High prevalence of dental caries among HIV-infected children in West Africa compared to uninfected siblings. J Public Health Dent. 2017;77:234\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMann AE, O\u0026rsquo;Connell LM, Osagie E, Akhigbe P, Obuekwe O, Omoigberale A, et al. Impact of HIV on the Oral Microbiome of Children Living in Sub-Saharan Africa, Determined by Using an rpoC Gene Fragment Metataxonomic Approach. Microbiol Spectr. 2023;\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEvans C, Jones CE, Prendergast AJ. HIV-exposed, uninfected infants: new global challenges in the era of paediatric HIV elimination. Lancet Infect Dis. 2016;16:e92\u0026ndash;107.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJumare J, Datong P, Osawe S, Okolo F, Mohammed S, Inyang B, et al. Compromised Growth Among HIV-exposed Uninfected Compared With Unexposed Children in Nigeria. Pediatr Infect Dis J. 2019;38:280.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcNally LM, Jeena PM, Gajee K, Thula SA, Sturm AW, Cassol S, et al. Effect of age, polymicrobial disease, and maternal HIV status on treatment response and cause of severe pneumonia in South African children: a prospective descriptive study. The Lancet. 2007;369:1440\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNozyce ML, Huo Y, Williams PL, Kapetanovic S, Hazra R, Nichols S, et al. Safety of In Utero and Neonatal ARV Exposure: Cognitive and Academic Outcomes in HIV-exposed, Uninfected Children Age 5\u0026ndash;13 Years. Pediatr Infect Dis J. 2014;33:1128\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOtieno RO, Ouma C, Ong\u0026rsquo;echa JM, Keller CC, Were T, Waindi EN, et al. Increased severe anemia in HIV-1-exposed and HIV-1-positive infants and children during acute malaria. AIDS. 2006;20:275.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ele Roux SM, Abrams EJ, Donald KA, Brittain K, Phillips TK, Zerbe A, et al. Infectious morbidity of breastfed, HIV-exposed uninfected infants under conditions of universal antiretroviral therapy in South Africa: a prospective cohort study. Lancet Child Adolesc Health. 2020;4:220\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShapiro RL, Lockman S, Kim S, Smeaton L, Rahkola JT, Thior I, et al. Infant Morbidity, Mortality, and Breast Milk Immunologic Profiles among Breast-Feeding HIV-Infected and HIV-Uninfected Women in Botswana. J Infect Dis. 2007;196:562\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO\u0026rsquo;Connell LM, Mann AE, Osagie E, Akhigbe P, Blouin T, Soule A, et al. Supragingival mycobiome of HIV-exposed-but-uninfected children reflects a stronger correlation with caries-free associated taxa compared to HIV-infected or uninfected children. Microbiol Spectr [Internet]. 2023;11. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/spectrum.01491-23\u003c/span\u003e\u003cspan address=\"10.1128/spectrum.01491-23\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOnyia NE, Osagie E, Akhigbe P, Idemudia NL, Ozoemene O, Omoigberale A, et al. Impact of Perinatal HIV Exposure and Infection on Salivary Properties Among Nigerian Children. Res Sq [Internet]. 2024 [cited 2024 Feb 14]; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.researchsquare.com\u003c/span\u003e\u003cspan address=\"https://www.researchsquare.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOnyia NE, Akhigbe P, Osagie E, Obuekwe O, Omoigberale A, Richards VP, et al. Prevalence and associated factors of enamel developmental defects among Nigerian children with perinatal HIV exposure. J Clin Pediatr Dent. 2023;47:1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGugnani N, Pandit I, Srivastava N, Gupta M, Sharma M. International Caries Detection and Assessment System (ICDAS): A New Concept. Int J Clin Pediatr Dent. 2011;4:93\u0026ndash;100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal. 2011;17:10\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCallahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team. R: A language and environment for statistical computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2017. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org\u003c/span\u003e\u003cspan address=\"https://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol. 2019;20:257.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcMurdie PJ, Holmes S. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLOS ONE. 2013;8:e61217.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOksanen J, Blanchet FG, Friendly M, Kiindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package [Internet]. 2019. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=vegan\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=vegan\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan den Boogaart KG, Tolosana-Delgado R, Bren M. compositions: Compositional Data Analysis [Internet]. 2023. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=compositions\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=compositions\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLahti L, Shetty S. microbiome R package. 2012; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://microbiome.github.io\u003c/span\u003e\u003cspan address=\"http://microbiome.github.io\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalle ML, Pujolassos M, Susin A. coda4microbiome: compositional data analysis for microbiome cross-sectional and longitudinal studies. BMC Bioinformatics. 2023;24:82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBastiaanssen TFS, Gururajan A, van de Wouw M, Moloney GM, Ritz NL, Long-Smith CM, et al. Volatility as a Concept to Understand the Impact of Stress on the Microbiome. Psychoneuroendocrinology. 2021;124:105047.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeschel S, M\u0026uuml;ller CL, von Mutius E, Boulesteix A-L, Depner M. NetCoMi: network construction and comparison for microbiome data in R. Brief Bioinform. 2021;22:bbaa290.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKurtz ZD, M\u0026uuml;ller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. Sparse and Compositionally Robust Inference of Microbial Ecological Networks. PLOS Comput Biol. 2015;11:e1004226.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeinshausen N, B\u0026uuml;hlmann P. High-dimensional graphs and variable selection with the Lasso. Ann Stat. 2006;34:1436\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR\u0026ouml;ttjers L, Vandeputte D, Raes J, Faust K. Null-model-based network comparison reveals core associations. ISME Commun. 2021;1:1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCsardi G, Nepusz T. The igraph software package for complex network research. InterJournal [Internet]. 2006 [cited 2023 Dec 15];Complex Systems. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://zenodo.org/records/10369053\u003c/span\u003e\u003cspan address=\"https://zenodo.org/records/10369053\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClauset A, Newman MEJ, Moore C. Finding community structure in very large networks. Phys Rev E Stat Nonlin Soft Matter Phys. 2004;70:066111.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDinis M, Agnello M, Cen L, Shokeen B, He X, Shi W, et al. Oral Microbiome: Streptococcus mutans/Caries Concordant-Discordant Children. Front Microbiol. 2022;13:782825.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichards VP, Alvarez AJ, Luce AR, Bedenbaugh M, Mitchell ML, Burne RA, et al. Microbiomes of Site-Specific Dental Plaques from Children with Different Caries Status. Young VB, editor. Infect Immun [Internet]. 2017 [cited 2022 Feb 4];85. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://journals.asm.org/doi/10.1128/IAI.00106-17\u003c/span\u003e\u003cspan address=\"https://journals.asm.doi/10.1128/IAI.00106-17\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWright MN, Ziegler A. ranger: A Fast Implementation of Random Forests for High Dimensional Data in C\u0026thinsp;+\u0026thinsp;+\u0026thinsp;and R. J Stat Softw. 2017;77:1\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJethani N, Sudarshan M, Covert I, Lee S-I, Ranganath R. FastSHAP: Real-Time Shapley Value Estimation [Internet]. arXiv; 2022 [cited 2024 Mar 22]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://arxiv.org/abs/2107.07436\u003c/span\u003e\u003cspan address=\"http://arxiv.org/abs/2107.07436\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorillo-Lopez V, Sjaarda A, Islam I, Borisy GG, Mark Welch JL. Corncob structures in dental plaque reveal microhabitat taxon specificity. Microbiome. 2022;10:145.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorres-Morales J, Mark Welch JL, Dewhirst FE, Borisy GG. Site-specialization of human oral Gemella species. J Oral Microbiol. 2023;15:2225261.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWelch JLM, Rossetti BJ, Rieken CW, Dewhirst FE, Borisy GG. Biogeography of a human oral microbiome at the micron scale. Proc Natl Acad Sci. 2016;113:E791\u0026ndash;800.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAja E, Mangar M, Fletcher HM, Mishra A. Filifactor alocis: Recent Insights and Advances. J Dent Res. 2021;100:790\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng Z-L, Szafrański SP, Jarek M, Bhuju S, Wagner-D\u0026ouml;bler I. Dysbiosis in chronic periodontitis: Key microbial players and interactions with the human host. Sci Rep. 2017;7:3703.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaffajee AD, Teles RP, Socransky SS. Association of Eubacterium nodatum and Treponema denticola with human periodontitis lesions. Oral Microbiol Immunol. 2006;21:269\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLombardo Bedran TB, Marcantonio RAC, Spin Neto R, Alves Mayer MP, Grenier D, Spolidorio LC, et al. Porphyromonas endodontalis in chronic periodontitis: a clinical and microbiological cross-sectional study. J Oral Microbiol. 2012;4:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3402/jom.v4i0.10123\u003c/span\u003e\u003cspan address=\"10.3402/jom.v4i0.10123\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUmemoto T, Nakazawa F, Hoshino E, Okada K, Fukunaga M, Namikawa I. Treponema medium sp. nov., isolated from human subgingival dental plaque. Int J Syst Bacteriol. 1997;47:67\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWyss C, Choi BK, Sch\u0026uuml;pbach P, Guggenheim B, G\u0026ouml;bel UB. Treponema maltophilum sp. nov., a small oral spirochete isolated from human periodontal lesions. Int J Syst Bacteriol. 1996;46:745\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIsaac RD, Sanjeev K, Subbulakshmi CL, Amirtharaj LV, Sekar M. Identification of a novel bacterium Scardovia wiggsiae in high caries risk adolescence: A metagenomic and melt curve analysis. J Conserv Dent JCD. 2022;25:297\u0026ndash;305.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKameda M, Abiko Y, Washio J, Tanner ACR, Kressirer CA, Mizoguchi I, et al. Sugar Metabolism of Scardovia wiggsiae, a Novel Caries-Associated Bacterium. Front Microbiol [Internet]. 2020 [cited 2022 Mar 30];11. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.frontiersin.org/article/\u003c/span\u003e\u003cspan address=\"https://www.frontiersin.org/article/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmicb.2020.00479\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2020.00479\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK\u0026ouml;n\u0026ouml;nen E, Fteita D, Gursoy UK, Gursoy M. \u003cem\u003ePrevotella\u003c/em\u003e species as oral residents and infectious agents with potential impact on systemic conditions. J Oral Microbiol. 2022;14:2079814.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eModesto M, Biavati B, Mattarelli P. Occurrence of the Family Bifidobacteriaceae in Human Dental Caries and Plaque. Caries Res. 2006;40:271\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiu Y, Zhang C, Sun Y, Dong L, Si Y, Yang J, et al. Symbiotic relationship between \u003cem\u003ePrevotella denticola\u003c/em\u003e and \u003cem\u003eStreptococcus mutans\u003c/em\u003e enhances virulence of plaque biofilms. Arch Oral Biol. 2023;151:105714.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eObata J, Fujishima K, Nagata E, Oho T. Pathogenic mechanisms of cariogenic Propionibacterium acidifaciens. Arch Oral Biol. 2019;105:46\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSakamoto M, Umeda M, Ishikawa I, Benno Y. Prevotella multisaccharivorax sp. nov., isolated from human subgingival plaque. Int J Syst Evol Microbiol. 2005;55:1839\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNewman MEJ. Modularity and community structure in networks. Proc Natl Acad Sci. 2006;103:8577\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCho H, Ren Z, Divaris K, Roach J, Lin BM, Liu C, et al. Selenomonas sputigena acts as a pathobiont mediating spatial structure and biofilm virulence in early childhood caries. Nat Commun. 2023;14:2919.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMann AE, Chakraborty B, O\u0026rsquo;Connell LM, Nascimento MM, Burne RA, Richards VP. Heterogeneous lineage-specific arginine deiminase expression within dental microbiome species. Microbiol Spectr [Internet]. 2024;12. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/spectrum.01445-23\u003c/span\u003e\u003cspan address=\"10.1128/spectrum.01445-23\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYou M, Mo S, Leung WK, Watt RM. Comparative analysis of oral treponemes associated with periodontal health and disease. BMC Infect Dis. 2013;13:174.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng H, Chan Y, Gao W, Leung WK, Watt RM. Diversity of Treponema denticola and other oral treponeme lineages in subjects with periodontitis and gingivitis. bioRxiv. 2021;2021.06.24.449858.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmerongen AN, Veerman E. Saliva \u0026ndash; the defender of the oral cavity. Oral Dis. 2002;8:12\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLynge Pedersen AM, Belstr\u0026oslash;m D. The role of natural salivary defences in maintaining a healthy oral microbiota. J Dent. 2019;80:S3\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGorr SU. Antimicrobial peptides of the oral cavity. Periodontol 2000. 2009;51:152\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMochon AB, Liu H. The Antimicrobial Peptide Histatin-5 Causes a Spatially Restricted Disruption on the Candida albicans Surface, Allowing Rapid Entry of the Peptide into the Cytoplasm. PLOS Pathog. 2008;4:e1000190.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVila T, Rizk AM, Sultan AS, Jabra-Rizk MA. The power of saliva: Antimicrobial and beyond. PLoS Pathog. 2019;15:e1008058.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakahashi N. Microbial ecosystem in the oral cavity: Metabolic diversity in an ecological niche and its relationship with oral diseases. Int Congr Ser. 2005;1284:103\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJeffers L, Webster-Cyriaque JY. Viruses and Salivary Gland Disease (SGD). Adv Dent Res. 2011;23:79\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin AL, Johnson DA, Stephan KT, Yeh C-K. Alteration in Salivary Function in Early HIV Infection. J Dent Res. 2003;82:719\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBardow A, Nyvad B, Nauntofte B. Relationships between medication intake, complaints of dry mouth, salivary flow rate and composition, and the rate of tooth demineralization in situ. Arch Oral Biol. 2001;46:413\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJensen SB, Pedersen AML. Association Between Oral Infections and Salivary Gland Hypofunction. In: Lynge Pedersen AM, editor. Oral Infect Gen Health Mol Chairside [Internet]. Cham: Springer International Publishing; 2016 [cited 2023 Nov 15]. p. 79\u0026ndash;94. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-319-25091-5_9\u003c/span\u003e\u003cspan address=\"10.1007/978-3-319-25091-5_9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEsberg A, Eriksson L, Johansson I. Site- and Time-Dependent Compositional Shifts in Oral Microbiota Communities. Front Oral Health. 2022;3:826996.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGancz AS, Farrer AG, Nixon MP, Wright S, Arriola L, Adler C, et al. Ancient dental calculus reveals oral microbiome shifts associated with lifestyle and disease in Great Britain. Nat Microbiol. 2023;8:2315\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eProctor DM, Fukuyama JA, Loomer PM, Armitage GC, Lee SA, Davis NM, et al. A spatial gradient of bacterial diversity in the human oral cavity shaped by salivary flow. Nat Commun. 2018;9:681.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSim\u0026oacute;n-Soro \u0026Aacute;, Tom\u0026aacute;s I, Cabrera-Rubio R, Catalan MD, Nyvad B, Mira A. Microbial Geography of the Oral Cavity. J Dent Res. 2013;92:616\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVonaesch P, Morien E, Andrianonimiadana L, Sanke H, Mbecko J-R, Huus KE, et al. Stunted childhood growth is associated with decompartmentalization of the gastrointestinal tract and overgrowth of oropharyngeal taxa. Proc Natl Acad Sci. 2018;115:E8489\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVonaesch P, Ara\u0026uacute;jo JR, Gody J-C, Mbecko J-R, Sanke H, Andrianonimiadana L, et al. Stunted children display ectopic small intestinal colonization by oral bacteria, which cause lipid malabsorption in experimental models. Proc Natl Acad Sci. 2022;119:e2209589119.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdam G Clooney, Julia Eckenberger, Emilio Laserna-Mendieta, Kathryn A Sexton, Matthew T Bernstein, Kathy Vagianos, et al. Ranking microbiome variance in inflammatory bowel disease: a large longitudinal intercontinental study. Gut. 2021;70:499.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalloway-Pe\u0026ntilde;a JR, Smith DP, Sahasrabhojane P, Wadsworth WD, Fellman BM, Ajami NJ, et al. Characterization of oral and gut microbiome temporal variability in hospitalized cancer patients. Genome Med. 2017;9:21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelstr\u0026oslash;m D, Holmstrup P, Bardow A, Kokaras A, Fiehn N-E, Paster BJ. Temporal Stability of the Salivary Microbiota in Oral Health. PLOS ONE. 2016;11:e0147472.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCameron SJS, Huws SA, Hegarty MJ, Smith DPM, Mur LAJ. The human salivary microbiome exhibits temporal stability in bacterial diversity. FEMS Microbiol Ecol. 2015;91:fiv091.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBirungi N, Fadnes LT, Engebretsen IMS, Lie SA, Tumwine JK, \u0026Aring;str\u0026oslash;m AN, et al. Caries experience and oral health related quality of life in a cohort of Ugandan HIV-1 exposed uninfected children compared with a matched cohort of HIV unexposed uninfected children. BMC Public Health. 2020;20:423.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoscicki A-B, Yao T-J, Ryder MI, Russell JS, Dominy SS, Patel K, et al. The Burden of Oral Disease among Perinatally HIV-Infected and HIV-Exposed Uninfected Youth. PLOS ONE. 2016;11:e0156459.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWRIGHT S, PETOUMENOS K, BOYD M, CARR A, DOWNING S, O\u0026rsquo;CONNOR C, et al. Ageing \u0026amp; long-term CD4 cell count trends in HIV-positive patients with 5 years or more combination antiretroviral therapy experience. HIV Med. 2013;14:208\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVelsko IM, Chakraborty B, Nascimento MM, Burne RA, Richards VP. Species Designations Belie Phenotypic and Genotypic Heterogeneity in Oral Streptococci. 2018;3:14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO\u0026rsquo;Connell LM, Blouin T, Soule A, Burne RA, Nascimento MM, Richards VP. Optimization and Evaluation of the 30S-S11 rRNA Gene for Taxonomic Profiling of Oral Streptococci. Appl Environ Microbiol. 2022;88:e0045322.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mbio","sideBox":"Learn more about [Microbiome](http://microbiomejournal.biomedcentral.com/)","snPcode":"40168","submissionUrl":"https://submission.nature.com/new-submission/40168/3","title":"Microbiome","twitterHandle":"@MicrobiomeJ","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"oral microbiome, HIV, longitudinal analysis, caries disease, CD4 count","lastPublishedDoi":"10.21203/rs.3.rs-4720457/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4720457/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003e The oral microbiome comprises distinct microbial communities that colonize diverse ecological niches across the oral cavity, the composition of which are influenced by nutrient and substrate availability, host genetics, diet, behavior, age, and other diverse host and environmental factors. Unlike other densely populated human-associated microbial ecosystems (e.g., gut, urogenital), the oral microbiome is regularly and directly exposed to the external environment and is therefore likely less stable over time. Cross sectional studies of the oral microbiome capture a glimpse of this temporal dynamism, yet a full appreciation of the relative stability, robusticity, and spatial structure of the oral environment is necessary to understand the role of microbial communities in promoting health or disease.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHere we investigate the spatial and temporal stability of the oral microbiome over three sampling time points in the context of HIV infection and exposure. Individual teeth were sampled from a cohort of 565 Nigerian children with varying levels of tooth decay severity (i.e., caries disease). We collected 1,960 supragingival plaque samples and characterized the oral microbiome using a metataxonomic approach targeting an approximately 478 bp region of the bacterial \u003cem\u003erpo\u003c/em\u003eC gene. We found that both infection and exposure to HIV have significant effects on the stability of the supragingival plaque microbiome at both the spatial and temporal scale. Specifically, we detect (1) significantly lower taxonomic turnover of the oral community among exposed and infected children compared to unexposed children, (2) we find that HIV infection homogenizes the oral community across the anterior and posterior dentition, and (3) that impaired immunity (i.e., low CD4 count) and low taxonomic turnover over time in children living with HIV is associated with higher frequency of cariogenic taxa including \u003cem\u003eStreptococcus mutans\u003c/em\u003e.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur results document substantial community fluctuations over time in children unexposed to HIV independent of oral health status. This suggests that the oral community, under typical conditions, rapidly adapts to environmental perturbations to maintain homeostasis and that long-term taxonomic rigidity is a signal of community dysfunction, potentially leading to a higher incidence of oral disease including caries.\u003c/p\u003e","manuscriptTitle":"HIV Infection and Exposure Increases Cariogenic Taxa, Reduces Taxonomic Turnover, and Homogenizes Spatial Differentiation for the Supragingival Microbiome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-10 12:23:27","doi":"10.21203/rs.3.rs-4720457/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-18T15:01:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-18T15:00:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-12T02:56:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Microbiome","date":"2024-07-10T20:09:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mbio","sideBox":"Learn more about [Microbiome](http://microbiomejournal.biomedcentral.com/)","snPcode":"40168","submissionUrl":"https://submission.nature.com/new-submission/40168/3","title":"Microbiome","twitterHandle":"@MicrobiomeJ","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fcb813b3-de1e-4ce7-8371-6c4cce032b8d","owner":[],"postedDate":"August 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-23T16:03:55+00:00","versionOfRecord":{"articleIdentity":"rs-4720457","link":"https://doi.org/10.1186/s40168-025-02123-9","journal":{"identity":"microbiome","isVorOnly":false,"title":"Microbiome"},"publishedOn":"2025-06-16 15:57:28","publishedOnDateReadable":"June 16th, 2025"},"versionCreatedAt":"2024-08-10 12:23:27","video":"","vorDoi":"10.1186/s40168-025-02123-9","vorDoiUrl":"https://doi.org/10.1186/s40168-025-02123-9","workflowStages":[]},"version":"v1","identity":"rs-4720457","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4720457","identity":"rs-4720457","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}