Downloaded At

bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1

Analysis of the human oral microbiome from modern and historical samples with SPARSE and EToKi Mark Achtman and Zhemin Zhou Warwick Medical School, University of Warwick, Coventry, UK Orcid ID: MA, 0000-0001-6815-0070; ZZ, 0000-0001-9783-0366

Subject Areas: methodology, metagenomics, population genomics, microbiology

Author for correspondence: Mark Achtman e-mail: [email protected] bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 2

Abstract Elsewhere we have provided details on a program for the probabilistic classification of sequence reads from metagenomes to microbial species (SPARSE) [1]. Similarly, we have also provided details on a stand-alone set of pipelines (EToKi) that are used to perform backend calculations for EnteroBase [2]. We have also provided examples of analyses of genomes reconstructed from metagenomes from ancient skeletons together with genomes from their modern relatives [3], which can also be visualized within EnteroBase. Finally, we have also described GrapeTree [4], a graphic visualizer of genetic distances between large numbers of genomes. Here we combine all of these approaches, and examine the microbial diversity within the human oral microbiome from hundreds of metagenomes of saliva, plaque and dental calculus, from modern as well as from historical samples. 1591 microbial species were detected by these methods, some of which differed dramatically in their frequency by source of the metagenomes. We had anticipated that the oral complexes of Socransky et al. [5] would predominate among such taxa. However, although some of those species did discriminate between different sources, we were unable to confirm the very existence of the oral complexes. As a further example of the functionality of these pipelines, we reconstructed multiple genomes in high coverage of Streptococcus mutans and Streptococcus sobrinus, two species that are associated with dental caries. Both were very rare in historical dental calculus but they were quite common in modern plaque, and even more common in saliva. The reconstructed genomes were compared with modern genomes from RefSeq, providing a detailed overview of the core genomic diversity of these two species. bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 3

1.Introduction Multiple research areas have undergone revolutionary changes in the last 10 years due to broad accessibility to high throughput DNA sequencing at reduced costs. These include the evolutionary biology of microbial pathogens based on metagenomic sequencing. Studies on Mycobacterium tuberculosis [6,7], Mycobacterium leprae [8,9], Yersinia pestis [10-14] and Salmonella enterica [3,15,16] have yielded important insights into the history of infectious diseases by combining modern and historical genomes. In most cases, interpretations of the newly deciphered historical genomes benefitted greatly because they could be slotted into an existing framework for the modern population genomic structure of the causative bacteria [17- 19]. However, interesting questions about other microbes are more difficult to address where a framework based on extensive analyses of modern organisms is still lacking. Exploring the genetic diversity of microbes directly from metagenomic sequences is usually performed by classifying the sequence reads into taxonomic units. Taxonomic assignments can be performed by the de novo assembly of the metagenomic reads into MAGs (metagenomic assembled genomes), or by assigning individual sequence reads to existing reference genomes. However most current metagenomic classifiers rely on the public genomes in NCBI, which are subject to an extreme sample bias and have a preponderance of genomes from pathogenic bacteria. Furthermore, shotgun metagenomes often include DNA from environmental sources, which include multiple micro-organisms that have never been cultivated, and may belong to unknown or poorly classified microbial taxa whose abundance is not reflected by existing databases. Recent evaluations have also demonstrated that current taxonomic classifiers either lack sufficient sensitivity for species-level assignments, or suffer from false positives, and that they overestimate the number of species in the metagenome [20-22]. Both tendencies are especially problematic for the identification of microbial species which are only present at low- abundance, e. g. detecting pathogens in ancient metagenomic samples. We designed SPARSE to provide accurate taxonomic assignments of metagenomic reads and published a technical description in 2018 [1]. SPARSE accounts for the existing bias in reference databases by creating a subset that represents the entire genetic diversity according to 99% average nucleotide identity (ANI99%) but assigns taxonomic designations based on its ANI95% superset, which is roughly equivalent to individual bacterial species [23,24]. To this end, it groups genomes of Bacteria, Archaea, Viruses and Protozoa from RefSeq into sequence similarity-based hierarchical clusters, and the resulting dataset includes only one reference genome per ANI99% cluster for fine-grained taxonomic assignments. SPARSE assigns metagenomic sequence reads to these clusters by using Minimap2 [25]. Unreliable alignments are excluded if the successful alignments were widely dispersed because wide dispersion reflects either ultra-conserved elements of uncertain specificity or a high probability of homoplasies due to horizontal gene transfer. The remaining metagenomic reads are then assigned to unique clusters on the basis of a probabilistic model, and labelled according to the taxonomic labels and pathogenic potential of the genomes within those clusters. The bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 4

probabilistic model specifically penalizes non-specific mappings of reads from unknown sources, and hence reduces false-positive assignments. Our methodological comparisons demonstrated that SPARSE has greater precision and sensitivity with simulated metagenomic data than 10 other taxonomic classifiers, and, unlike five other methods, consistently yielded correct identifications of pathogen reads within metagenomes of ancient DNA [1]. After SPARSE has assigned reads to taxa, it can also be used to extract reads corresponding to an ANI95% taxon of interest from the metagenomic data, which corresponds to a species [24]. The next step for genomic analyses is provided by EToKi [2], a stand-alone package of pipelines that we developed for EnteroBase to support manipulations with 100,000s of modern microbial genomes. The filter function in EToKi merges overlapping pair-ended reads within each data set, removes low quality bases and trims adapter sequences. It then conducts a fine-grained evaluation of the reads for greater sequence similarities to an in-group of genomes from the taxon of interest than to other genomes from a related but distinct out-group. EToKi then masks all nucleotides in an appropriate reference genome, and creates a pseudo-MAG equivalent by unmasking nucleotides with sufficient coverage among the reads that have passed the in-group/out-group comparisons. Finally, EToKi creates a SNP matrix from pseudo- MAGs plus additional draft genomes, and generates a Maximum-Likelihood phylogeny (RAxML 8.2 [26]). The ML tree can be imported together with its metadata for visual interrogation by graphic interfaces in EnteroBase such as GrapeTree [4] or Dendrogram, as recently illustrated for ancient metagenomes of Y. pestis [2].The availability of these tools is not restricted to ancient DNA genomes, and they can be readily used metagenomic analyses of modern samples. Here we illustrate how these tools can be used to investigate the taxonomic composition of metagenomes from the modern and ancient human oral flora, and also examine in greater detail the population genomic structures of Streptococcus mutans and Streptococcus sobrinus, which are associated with dental caries in some human populations [27-29].

2. Results (a) SPARSE analysis of oral metagenomes In its original incarnation in August 2017 [1], SPARSE used MASH [30] to assign 101,680 genomes from the NCBI RefSeq database to 28,732 ANI99% clusters of genomes [1]. By May 2018, 21,540 additional genomes had been added to NCBI RefSeq. These were merged into the existing database in the same manner as previously, either by creating a new cluster containing one genome if the ANI to all existing clusters was less than 99%, or else by merging that genome into an existing cluster. The resulting database contained 33,212 ANI99% clusters of microbial genomes from Eukaryotes, Prokaryotes and Viruses. One representative genome was chosen for each of the 32,378 ANI99% clusters containing Bacteria, Archaea or Viruses. The ANI99% representative database was supplemented by a human reference genome (Genome Reference Consortium Human Build 38) such that reads from human DNA could also be called. All the representative genomes were assigned to a superset of 20,054 ANI95% clusters, and this was used for species assignments and genomic extractions. We identified 17 public archives containing 1,016 sets of metagenomic sequences (Table 1) from 791 oral samples which had been obtained from modern human saliva, modern human dental plaque or historical dental calculus from a variety of global sources (Table S1). Individual sequence reads from those metagenomes were assigned to taxa with SPARSE, and then cleaned with EToKi filter. Seven metagenomes lacked bacterial reads from the oral microbiome (ancient dental calculus: 5; modern saliva: 2). These seven metagenomes were ignored for further analyses, leaving metagenomes from a total of 784 samples (Table 2). The Table S2 reports the percentage assignment of the reads in each sample to each of 1,592 taxa, except that assignments at a frequency of <0.0005% of the sequence reads are reported as 0%. That spreadsheet includes a column identifying potential pathogens, including assignments to the oral microbial complexes defined by Socransky et al. [5]. SPARSE also identified 158 samples containing Archaea from six species and 314 samples containing human viruses or bacteriophages (Table 3). (b) Comparisons of microbiomes from saliva, plaque and historical dental calculus We tested whether the quantitative levels of microbial taxa differed by sample source with multiple approaches. UMAP (Uniform Manifold Approximation and Projection), a recently described [31] high performance algorithm for dimensional reduction of diversity within large amounts of data by non-linear multidimensional clustering, was applied to the abundances of each taxon in each sample. Visual examination of a UMAP plot (Fig. 1A) shows three discrete clusters. The discrete nature of these clusters was confirmed by an independent application of machine learning via optimal k-mean clustering based on the first three components from the UMAP analysis (Supplemental Fig. S1A). Most members of each cluster were from a common source, i.e. one cluster from modern saliva, one from modern dental calculus, and one from ancient dental calculus (Fig. 1A). However, the correlation was imperfect, and each cluster also contained some metagenomes from alternative sources. Similar results were obtained with a bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 6

classical principal component analysis (PCA), except that the clusters were not as clearly distinguished and each cluster contained a higher proportion of exceptions (Supplemental Fig. S1B). The assignments of source affiliations to cluster were largely consistent between UMAP and PCA, with occasional exceptions (Supplemental Fig. S1C). We also compared the correlation between clusters and source by hierarchical clustering. To this end, we calculated the Euclidean p- distances between each pair of samples, and subjected them to hierarchical clustering by the neighbor-joining algorithm with the results shown in Fig. 1B. This approach also largely separated the samples by source, with only few exceptions. Samples from modern saliva formed one large cluster. Samples from modern dental plaque formed two related but discrete sub-clusters, one of which included a sub-sub cluster of samples from historical dental calculus. These clusters also largely corresponded to the clusters found by k-mean clustering of UMAP data (Supplemental Fig. S1A). Thus, three independent methods each found three primary and distinct clusters of the quantitative numbers of reads in microbial taxa, and these clusters largely corresponded to modern saliva, modern plaque and historical dental calculus. This finding indicates that there are source-specific taxa in the metabiomes from these sources. (c) Source-specific taxa We used the SVM machine learning approach to identify the most important bacterial taxa for the observed clustering of microbial taxon composition by sample source. The results for the 40 most discriminating ANI95% taxa with the most optimal of 300 variants of the SVM model are presented in descending order of their SVM weights in Fig. 2. Fig. 2 also includes mini-histograms for each of the 40 taxa that summarize their relative abundance of sequence reads by source. Multiple taxa were dramatically more prominent in samples from one source than from either of the two other sources. However, the most prominent sample source varied with the taxon. Eleven of the 40 discriminatory taxa belonged to the oral complexes that are associated with periodontitis according to Socransky et al. [5]. In general agreement with this observation, seven species from oral complexes (Veillonella parvula, Fusobacterium nucleatum, Capnocytophaga gingivalis, Streptococcus gordonii, Actinomyces naeslundii, Actinomyces viscosus, and Capnocytophaga sputigena) were most abundant in modern plaque and two other species (Streptococcus sanguinis, Tannerella forsythia) were most abundant in historical dental calculus. The yellow complex includes Streptococcus mitis, which SPARSE subdivided into two distinct ANI95% clusters designated S. mitis A and S. mitis B in accordance with a recent publication [32]. Each of the two taxa was most frequent in saliva than in dental plaque or dental calculus. We were somewhat surprised that 17 other taxa that were assigned to an oral complex by Socransky et al. [5] were not included in the 40 most discriminatory taxa. We therefore examined the relative abundances of all 28 taxa from oral complexes in greater detail (Fig. 3). Three of the four taxa in the Blue and Purple Complexes are very abundant in oral bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 7

metagenomes, and all four are preferentially found in modern plaque. However, the other oral complexes are not uniform in their patterns of relative abundances. For example, within the Red complex, both T. forsythia and Treponema denticola were most frequently found in historical dental calculus but Porphyromonas gingivalis is is most frequent in modern plaque, and is generally much less abundant. Similar intra-complex discrepancies were found for the Orange, Yellow, and Green Complexes. The inconsistent frequencies of members of an oral complex raises questions about whether the compositions of those complexes are consistent in individual samples. Careful reading of Socransky et al. [5] revealed that the oral complexes were considered as a hypothesis, whereas they have now attained the status of conventional wisdom, and even play a prominent role in routine laboratory investigations of periodontitis. The data in that publication were based on 28 cultivated bacterial species, whose presence or absence was determined by DNA hybridization against a small number of probes. This technology is now outdated; the number of oral taxa has increased dramatically; and the data presented here are for relative abundance rather than presence or absence. We have therefore examined the strengths of association into the oral complexes from the current data presented here according to similar criteria and similar methods as those used in Socransky et al. publication. The original composition of the oral complexes depended strongly on results from hierarchical clustering based on a concordance between pairs of species for presence or absence in individual samples. The tree in Fig. 4 shows neighbour-joining clustering of the common microbial taxa detected by SPARSE, some of which have been cultivated whereas others have not. The taxa were clustered by the similarities of their abundances over all samples. This tree provides no support for the original composition of the oral complexes because the four areas of the tree where oral complex taxa are clustered each contain representatives from multiple complexes, and none of those clusters corresponds to the original compositions proposed by Socransky et al. [5]. It seemed possible that the discrepancies between Fig. 4 and the original compositions of the oral complexes might reflect the fact that this study identified many additional taxa. We therefore performed cluster analyses with our current data for the original set of 31 cultivatable bacterial species examined by Socransky et al. We compared the Neighbor-joining algorithm used here with the less powerful, agglomerative clustering method (UPGMA, Unweighted Pair Group Method with Arithmetic Mean) used by Socransky et al. We also compared the abundances across all samples with abundances in plaque, which was the primary source for bacteria tested by Socransky et al. The results (Fig. S3) show dramatic inconsistencies between independent trees in regard to the clustering of the oral complex bacteria. For example, T. forsythia, T. dentocola and P. gingivalis of the Red Complex cluster together in Fig. S3A,C,F,G. However, T. denticola and T. forsythia are separated from P. gingivalis in the four other parts of Fig. S3. And do not even cluster together with each other Fig. S3E. Similar or even greater discrepancies are visible for the other oral complexes in Fig. S3. bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 8

These inconsistencies in clustering patterns across minor differences in sampling and clustering algorithms raise severe doubts about the very existence of the oral complexes as defined by Socransky et al. [5]. (d) Numbers of taxa per source The rarefaction curves in Fig. 5A provide a breakdown of taxa by sample source as additional samples are tested. SPARSE detected 1591 microbial taxa over all 784 metagenomic samples: 1,389 from modern saliva; 842 from modern plaque and 696 from historical calculus. These estimates will increase as additional samples are added, but at increasingly slower rates because the rarefaction curves seem to be reaching a plateau, except possibly for historical dental calculus where the fewest samples have been evaluated until now. The median numbers of taxa per sample were much more moderate than the total numbers, ranging from 177 (historical dental calculus) to 288 (modern saliva). These median values reflect a bimodal distribution for numbers of taxa per sample (Fig. 5B), wherein a few samples had jackpots of large numbers of taxa but all other samples had only few. The analyses described above focused on differences in taxon composition by source. However, the Venn diagram in Fig. 5C shows that 447 taxa were common to all three sources, even if their relative abundance varied. Modern plaque yielded only 34 taxa which were not found in either historical dental calculus or modern saliva. More source-specific taxa were found in historical dental calculus, which may possibly reflect some contamination with environmental material. Alternatively, some taxa may be absent in modern dental plaque because historical lineages have become extinct [3]. Saliva yielded 504 unique taxa, some of which might be transient, and do not persist long enough to be incorporated into plaque. (e) Population genomics of organisms associated with dental caries The causes of dental caries remain controversial [28,33-35], other than that the disease is usually preceded by dental plaque, and that modern dental plaque contains high concentrations of Streptococcus mutans and Streptococcus sobrinus. Our data confirms that reads belonging to these two taxa are abundant in modern dental plaque, and even more abundant in modern saliva (Fig. 6A,C). Our data also shows that the abundance of both S. mutans and S. sobrinus was extremely low in historical dental calculus, supporting prior conclusions that S. mutans first became common in the last 200 years after industrialization introduced high levels of sugar to human diets [36]. S. sobrinus was undetectable in the historical samples (<10 reads per metagenome) but up to 0.01% of all reads in some historical samples were assigned to S. mutans. These reads showed an increase of deamination in their 5’-ends (Fig. S4), confirming that they were truly from ancient DNA, and that S. mutans has been part of the human oral microbiome for millennia [36]. We exploited the high frequency of sequence reads from these two Streptococcus species in modern dental plaque and saliva to illustrate how SPARSE and EToKi can be used to extract MAGs from metagenomic sequence reads, and combine them with genomes sequenced from bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 9

cultivated bacteria. To this end, we extracted all sequence reads specific to the ANI95% clusters for S. mutans and S. sobrinus from metagenomes in which there was high coverage (Fig. 6E,F), and cleaned them with the EToKi prepare module. The reads were filtered with EToKi assemble by specifying a reference genome as well as an ingroup and an outgroup of additional genomes. Sequence reads were excluded which had higher alignment scores to the outgroup than to the ingroup genomes. EToKi creates a pseudo-genome from each reference genome (S. mutans: UA159; S. sobrinus: NCTC12279) in which all nucleotides are masked in order to ensure that only nucleotides supported by metagenomic reads will be used for phylogenetic analysis. Sites in the pseudo-genome were unmasked which were covered by ≥3 reads. These procedures resulted in a total of 31 MAGs for S. mutans and 15 MAGs for S. sobrinus in which over 70% of the reference genome had been unmasked, most of which were from Chinese samples [37]. The MAGs were combined with genomes from cultivated bacteria of the same species from Brazil, the U.S. and the UK as well as other countries (Table 2) and EToKi align was used to extract non-repetitive SNP matrices, which were subjected to calculation of Maximum Likelihood (ML) phylogenies (Fig. 7) using EToKi phylo. The ML phylogenies of the two species showed interesting differences. Geographic clustering was apparent within the ML tree of S. sobrinus. All Chinese MAGs clustered together, separate from the bacterial genomes from Brazil. In contrast, S. mutans MAGs from Chinese isolates did not show obvious phylogeographic specificities, and were inter-dispersed among bacterial genomes from multiple geographic locations. Similar conclusions about a lack of phylogeographic specificity have been reached with a subset of 57 genomes in previous studies [38].

3. Discussion Several years ago, we accidentally became interested in comparing genomes assembled from metagenomes from historical sources with draft genomes from cultivated bacteria. Our initial efforts involved the deployment of individual bioinformatic tools, comparisons of multiple publicly available algorithms, and compilation of draft genomes from publicly available sequence read archives of short read sequences [7]. In parallel we were also involved in developing EnteroBase, a compendium of 100,000s of draft genomes assembled from multiple genera that can cause enteric disease in humans. Including Salmonella [2,19]. These two directions overlapped when we had the opportunity to examine the evolutionary history of Salmonella enterica based on metagenomics sequences from 800 year old bones, teeth and dental calculus [3]. In that case, reads from S. enterica were found in teeth and bone, but nor in dental calculus. However, we were motivated to examine further samples of dental calculus, and quickly realized that life was too short for manual analyses. Optimised pipelines were needed, but none of the existing tools proved to be adequately reliable and sufficiently sensitive for assigning sequence reads from historical metagenomes to the tree of microbial life. We therefore took a step back, and developed SPARSE to satisfy our requirements. In parallel, we developed EToKi to be able to efficiently handle manipulations with sequence reads bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 10

and genomic assemblies for the back end operations in EnteroBase. Finally, we developed GrapeTree [4], which supports the graphic visualization and manipulation of phylogenetic trees representing large numbers of genomes. Here we have demonstrated how to combine these tools to obtain an overview of the microbial flora in samples from human oral saliva, modern dental plaque and historical dental calculus. We also demonstrate how these tools can be used to reconstruct genomes of taxa present at moderate concentrations within metagenomics sequences, and compare them with conventional draft genomes. The experimental procedures for processing 791 metagenomes consisted of running SPARSE in the background for 2 months (~100,000 CPU hours). The pipelines described here permitted all other procedures and evaluations described here to be completed in less than two weeks. All data produced here are freely accessible, including genomes, taxon abundances per sample, and examples of the commands used https://github.com/zheminzhou/OralMicrobiome. The programs and source code are also publicly available. As novices to the area of oral biology, we were prepared to believe in the existence of oral complexes of bacteria [5]. However, we were not able to reliably reconstruct the original patterns which led to their definition (Fig. 4, Fig. S3), and suggest that their existence may have depended on currently outdated technology and a focus on limited samples of microbial taxa. Given our inability to corroborate this concept, we suggest that the existence and composition of the oral complexes should be re-examined by others, with other datasets and other methods. We were also curious about organisms associated with dental caries and late stages of dental plaque formation, S. mutans and S. sobrinus, especially because there were sufficient reads to assemble partial genomes (MAGs) from multiple samples as well as multiple draft genomes from cultivated bacteria. We were also intrigued by the claim that S. mutans was rare in historical plaque [36]. Our data support that claim, and we found only very low frequencies of reads of either organism in the historical calculus samples that we examined. Our data also support prior conclusions of a lack of phylogeographic differentiation within S. mutans [38]. However, although the data are still somewhat limited, there seems to be a clear distinction between S. sobrinus from China and Brazil, which might reflect phylogeographical signals. Unfortunately, the two sets of genomes are also different by source because the Chinese genomes were MAGs reconstructed from metagenomes and the Brazil genomes were from cultivated bacteria. Other scientists with an interest in this organism might want to obtain additional genomes of S. sobrinus from other geographical areas to determine whether the phylogeographical trends stand up to further investigation. Such efforts could also be facilitated by creating an EnteroBase for Streptococcus, which would be relatively easy to do if there were any interested curators and sufficient interest in the Streptococcus community. In summary, we illustrate the use of a variety of reliable, high throughput tools for determining the microbial diversity and extracting microbial genomes from metagenomic data. We illustrate bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 11

these tools with metagenomes from modern and historical samples, and release all the data and methods for further use by others.

4. Methods (a) Dimension reduction of frequencies of reads. The SPARSE results were submitted to two forms of dimensional reduction of diversity. UMAP analysis was performed with its Python implementation [31], using the parameters min_neighbors=5 and min_dist=0.0. PCA was performed using the decomposition.PCA module of the scikit-learn Python library [39]. Optimal k-mean clusters of the first three components from the UMAP analysis were calculated with the sklearn.cluster module of the scikit-learn Python library. (b) Ranking of microbial species by their SVM weights A supervised Support Vector Machine [40] classification of samples was performed using the SVM module of the scikit-learn Python library on the raw SPARSE results (Dataset S3). The SVM classification was performed 300 times on a randomly chosen training set consisting of 60% of all samples with varying penalty hyper-parameter C, and scored using 5-fold cross-validation. The model was then tested with the optimal hyper-parameter on the remaining 40% of samples, and correctly inferred the oral source for >96% of the test samples. The optimal SVM coefficients for each individual species were estimated by training that model once again on all the oral samples. The order of the species in Fig. 2 consists of the SVM weights (squares of the coefficients; [41]) in descending order. (c) Genome reconstructions for Streptococcus mutans and Streptococcus sobrinus We set a minimum criterion for reconstruction of MAGs at least two million nucleotides covered by sequence reads in a metagenome. SPARSE identified 66 and 28 samples which met these criteria for S. mutans (ANI95% cluster s5) or S. sobrinus (s3465), respectively (Figs. 4B,D). The species specific reads from these samples were extracted from the metagenomes and subjected to reference-guided assemblies using ‘EToKi assemble’ as described elsewhere [2]. These assemblies were performed against reference genomes UA159 (S. mutans, GCF_000007465) and NCTC12279 (S. sobrinus, GCF_900475395). All other genomes deposited in RefSeq from the same species (S. mutans: 194, S. sobrinus: 49) were used as an ingroup and 262 genomes from other species in the Streptococcus Mutans group were used as an outgroup (Table S3). Individuals SNPs were unmasked which were supported by at least three reads, and whose consensus base was supported by ≥70% of the mapped reads. The successfully reconstructed genomes for S. mutans and S. sobrinus can be downloaded at https://github.com/zheminzhou/OralMicrobiome. An alignment (EToKi align) of the 31 S. mutans MAGs plus all 195 S. mutans genomes plus the only S. troglodytae genome in RefSeq (Table S3) included 1.73 MB that were shared by ≥ 95% of the genomes, and 181,321 core SNPs. Similarly, a S. sobrinus alignment of 15 MAGs as well as 50 draft or complete genomes of S. sobrinus plus 6 genomes of Streptococcus downei from RefSeq spanned 1.16 MB and contained 160,863 core SNPs. These alignments were subjected to Maximum Likelihood phylogeny reconstruction using EToKi phylo. Both ML trees were then visualised with GrapeTree [4]. bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Table 1. Sources of metagenomic reads.

Archive Accession Sets of Number Source Institute Citation short of. reads samples 1 PRJNA445215 62 48 calculus Max Planck Institute for the Science of Human History [42] 2 PRJEB30331, PRJNA454196 45 44 calculus University of Oxford [43] 3 PRJNA216965 9 2 calculus University of Oklahoma [44] 4 PRJNA383868 87 87 plaque J. Craig Venter Institute [45] 5 PRJNA255922 48 48 plaque University of California, Los Angeles [46] 6 PRJNA78025 7 4 plaque University of Maryland [47] 7 PRJNA289925 1 1 plaque University of Washington [48] PRJEB6997 298 298 plaque & BGI [37] 8 saliva PRJNA230363 12 12 plaque & Chinese Academy of Sciences [49] 9 saliva PRJEB24090 61 61 saliva University of California San Diego [50] 10 11 PRJNA380727 56 55 saliva Peking University School of Stomatology 12 PRJNA396840 30 30 saliva University of Copenhagen [51] 13 PRJEB14383 28 28 saliva University College London [52] 14 PRJDB4115 26 26 saliva University of Tokyo [53] 15 PRJNA217052 217 18 saliva Broad Institute [54] 16 PRJNA188481 8 8 saliva Broad Institute [55] http://dx.doi.org/10.4225/5 21 21 calculus OAGR, University of Adelaide [56] 17 5/584775546a409

Note: Calculus refers to ancient dental calculus from historical samples. Plaque and saliva refer to modern dental plaque and saliva.

All Sets were downloaded from GenBank except for Archive 17, which was downloaded from the Online Ancient Genome Repository.

Table 2. Sources and properties of single-colony isolated bacterial strains and metagenomic samples from which genomes were used for analyses.

Category Sub-category Number Bacterial genomes 262 S. mutans 195 S. sobrinus 50 others 17

Metagenome source 791 Ancient dental calculus 114 Modern plaque 288 Modern saliva 389 Sample size (nucleotides) 0-2GB 348 2-4GB 130 4-6GB 162 6-8GB 93 8-10GB 45 >10GB 13 Countries Asia 423 China 365 Japan 26 Philippines 28 Others 4

North America 122 U.S.A. 120 Guadeloupe 2

Europe 127 Denmark 30 Ireland 36 Germany 44 Others 17

Oceania 110 Australia 92 Fiji 18

Africa 7 South Africa 4 Sudan 2 Sierra Leone 1 bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 15

Table 3. Detailed summary of Archaea and Viruses in all 786 samples.

No. ancient % reads No. plaque % reads No. saliva Taxonomy samples (109) (relative) (288) (relative) (387) % reads (relative) Host (Human) 109 0.31 244 2E-4 335 7.05 (100) Archaea (6)# 81 1.78 (100) 28 2E-4 49 1E-4 (100) (100) Methanobrevibacter oralis 79 1.78 (100) 28 47 1E-4 (98.0) Methanobrevibacter smithii 1 3E-5 (2E-3) 1 8E-7 (0.77) Candidatus Nitrosoarchaeum 1 1E-5 (7E-4) 0 koreensis *Thermoplasmatales 1 7E-6 (4E-4) 0 archaeon BRNA1 Methanobrevibacter smithii 1 1E-6 (8E-5) 4E-4 1 1E-6 (1.32) (100) Virus (18)# 4 1E-5 (100) 54 9E-6 256 4E-3 (100) (2.49) Human betaherpesvirus 7 9 3E-4 150 6E-4 (16.3) (88.1) Human gammaherpesvirus 4 19 5E-6 86 3E-3 (75.3) (1.32) Human alphaherpesvirus 1 1 9 8E-5 (2.13) Human betaherpesvirus 6B 3E-5 7 2E-5 (0.62) (8.06) Bacterophages (12) 4 1E-5 (100) 30 128 2E-4 (5.62)

Data accessibility. Python scripts and source material for mathematical modelling and dimension reduction of SPARSE results, as well as all 46 MAGs are deposited at https://github.com/zheminzhou/OralMicrobiome as Datasets S1 to S3.

Authors’ contributions. Z.Z. analysed data and prepared the figures. M.A. and Z.Z. interpreted the results and wrote the manuscript.

Competing interests. We have no competing interests. Funding. This project was supported by the Wellcome Trust (202792/Z/16/Z) and EnteroBase development was funded by the BBSRC (BB/L020319/1).

References

1. Zhou, Z., Luhmann, N., Alikhan, N.-F., Quince, C., and Achtman, M. 2018 Accurate reconstruction

of microbial strains from metagenomic sequencing using representative reference

genomes. In RECOMB 2018, pp. 225-240: Springer, Cham.

2. Zhou, Z., Alikhan, N.-F., Mohamed, K., The Agama Study Group, and Achtman, M. 2019 The user's

guide to comparative genomics with EnteroBase. Three case studies: micro-clades within

Salmonella enterica, serovar Agama, ancient and modern populations of Yersinia pestis,

and core genomic diversity of all Escherichia. BioRxiv, 613554. (doi:doi:10.1101/613554v1)

3. Zhou, Z. et al.. 2018 Pan-genome analysis of ancient and modern Salmonella enterica

demonstrates genomic stability of the invasive Para C Lineage for millennia. Curr Biol 28,

2420-2428. (doi:https://doi.org/10.1016/j.cub.2018.05.058)

4. Zhou, Z., Alikhan, N.-F., Sergeant, M. J., Luhmann, N., Vaz, C., Francisco, A. P., Carrico, J. A., and

Achtman, M. 2018 GrapeTree: Visualization of core genomic relationships among 100,000

bacterial pathogens. Genome Res 28, 1395-1404. (doi:DOI: 10.1101/gr.232397.117)

5. Socransky, S. S., Haffajee, A. D., Cugini, M. A., Smith, C., and Kent, R. L., Jr. 1998 Microbial

complexes in subgingival plaque. J Clin Periodontol 25, 134-144.

6. Bos, K. I. et al.. 2014 Pre-Columbian mycobacterial genomes reveal seals as a source of New World

human tuberculosis. Nature 514, 494-497. (doi:nature13591 [pii];10.1038/nature13591

[doi]) bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 18

7. Kay, G. L. et al.. 2015 Eighteenth-century genomes show that mixed infections were common at

time of peak tuberculosis in Europe. Nat Commun 6, 6717. (doi:ncomms7717

[pii];10.1038/ncomms7717 [doi])

8. Schilling, A. K. et al.. 2019 British red squirrels remain the only known wild rodent host for leprosy

bacilli. Front Vet Sci 6, 8. (doi:10.3389/fvets.2019.00008 [doi])

9. Schuenemann, V. J. et al.. 2018 Ancient genomes reveal a high diversity of Mycobacterium leprae

in medieval Europe. PLoS Pathog 14, e1006997. (doi:10.1371/journal.ppat.1006997

[doi];PPATHOGENS-D-17-02430 [pii])

10. Bos, K. I. et al.. 2011 A draft genome of Yersinia pestis from victims of the Black Death. Nature 478,

506-510.

11. Rasmussen, S. et al.. 2015 Early divergent strains of Yersinia pestis in Eurasia 5,000 years ago. Cell

163, 571-582. (doi:S0092-8674(15)01322-7 [pii];10.1016/j.cell.2015.10.009 [doi])

12. Damgaard, P. B. et al.. 2018 137 ancient human genomes from across the Eurasian steppes.

Nature 557, 369-374. (doi:10.1038/s41586-018-0094-2 [doi];10.1038/s41586-018-0094-2

[pii])

13. Keller, M. et al.. 2019 Ancient Yersinia pestis genomes from across Western Europe reveal early

diversification during the First Pandemic (541-750). Proc Natl Acad Sci U S A 116, 12363-

12372. (doi:1820447116 [pii];10.1073/pnas.1820447116 [doi]) bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 19

14. Spyrou, M. A. et al.. 2019 Phylogeography of the second plague pandemic revealed through

analysis of historical Yersinia pestis genomes. Nat Commun 10, 4470.

(doi:10.1038/s41467-019-12154-0 [doi];10.1038/s41467-019-12154-0 [pii])

15. Vågene, Å. J. et al.. 2018 Salmonella enterica genomes from victims of a major sixteenth-century

epidemic in Mexico. Nature Ecology & Evolution 2, 520-528.

16. Key, F. M. et al. 2020 Evolution towards human-specific Salmonella enterica accompanied the

Western Eurasian Neolithization process. In (Anon.).

17. Coll, F., McNerney, R., Guerra-Assuncao, J. A., Glynn, J. R., Perdigao, J., Viveiros, M., Portugal, I.,

Pain, A., Martin, N., and Clark, T. G. 2014 A robust SNP barcode for typing Mycobacterium

tuberculosis complex strains. Nat Commun 5, 4812. (doi:ncomms5812

[pii];10.1038/ncomms5812 [doi])

18. Achtman, M. 2016 How old are bacterial pathogens? Proc Biol Sci 283, 1836.

19. Alikhan, N.-F., Zhou, Z., Sergeant, M. J., and Achtman, M. 2018 A genomic overview of the

population structure of Salmonella. PLoS Genet 14, e1007261.

(doi:10.1371/journal.pgen.1007261 [doi];PGENETICS-D-18-00122 [pii])

20. McIntyre, A. B. R. et al.. 2017 Comprehensive benchmarking and ensemble approaches for

metagenomic classifiers. Genome Biol 18, 182. (doi:10.1186/s13059-017-1299-7

[doi];10.1186/s13059-017-1299-7 [pii]) bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 20

21. Sczyrba, A. et al.. 2017 Critical assessment of metagenome interpretation - a benchmark of

computational metagenomics software. BioRxiv. (doi:10.1101/099127)

22. Velsko, I. M., Frantz, L. A. F., Herbig, A., Larson, G., and Warinner, C. 2018 Selection of appropriate

metagenome taxonomic classifiers for ancient microbiome research. mSystems 3.

(doi:10.1128/mSystems.00080-18 [doi];mSystems00080-18 [pii])

23. Konstantinidis, K. T. and Tiedje, J. M. 2005 Genomic insights that advance the species definition

for prokaryotes. Proc Natl Acad Sci USA 102, 2567-2572.

24. Jain, C., Rodriguez, R., Phillippy, A. M., Konstantinidis, K. T., and Aluru, S. 2018 High throughput

ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun 9,

5114. (doi:10.1038/s41467-018-07641-9 [doi];10.1038/s41467-018-07641-9 [pii])

25. Li, H. 2018 Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094-3100.

(doi:4994778 [pii];10.1093/bioinformatics/bty191 [doi])

26. Stamatakis, A. 2014 RAxML version 8: a tool for phylogenetic analysis and post-analysis of large

phylogenies. Bioinformatics 30, 1312-1313. (doi:btu033

[pii];10.1093/bioinformatics/btu033 [doi])

27. Abranches, J., Zeng, L., Kajfasz, J. K., Palmer, S. R., Chakraborty, B., Wen, Z. T., Richards, V. P.,

Brady, L. J., and Lemos, J. A. 2018 Biology of oral streptococci. Microbiol Spectr 6.

(doi:10.1128/microbiolspec.GPP3-0042-2018 [doi]) bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 21

28. Johansson, I., Witkowska, E., Kaveh, B., Lif, H. P., and Tanner, A. C. 2016 The microbiome in

populations with a low and high prevalence of caries. J Dent Res 95, 80-86.

(doi:0022034515609554 [pii];10.1177/0022034515609554 [doi])

29. Oda, Y., Hayashi, F., and Okada, M. 2015 Longitudinal study of dental caries incidence associated

with Streptococcus mutans and Streptococcus sobrinus in patients with intellectual

disabilities. BMC Oral Health 15, 102. (doi:10.1186/s12903-015-0087-6

[doi];10.1186/s12903-015-0087-6 [pii])

30. Ondov, B. D., Treangen, T. J., Melsted, P., Mallonee, A. B., Bergman, N. H., Koren, S., and Phillippy,

A. M. 2016 Mash: fast genome and metagenome distance estimation using MinHash.

Genome Biol 17, 132. (doi:10.1186/s13059-016-0997-x [doi];10.1186/s13059-016-0997-x

[pii])

31. Becht, E., McInnes, L., Healy, J., Dutertre, C. A., Kwok, I. W. H., Ng, L. G., Ginhoux, F., and Newell, E.

W. 2019 Dimensionality reduction for visualizing single-cell data using UMAP. Nat

Biotechnol 37, 38-44. (doi:nbt.4314 [pii];10.1038/nbt.4314 [doi])

32. Velsko, I. M., Perez, M. S., and Richards, V. P. 2019 Resolving phylogenetic relationships for

Streptococcus mitis and Streptococcus oralis through core- and pan-genome analyses.

Genome Biol Evol 11, 1077-1087. (doi:5371073 [pii];10.1093/gbe/evz049 [doi])

33. Simón-Soro, A. and Mira, A. 2015 Solving the etiology of dental caries. Trends Microbiol 23, 76-82.

(doi:S0966-842X(14)00225-X [pii];10.1016/j.tim.2014.10.010 [doi]) bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 22

34. Richards, V. P., Alvarez, A. J., Luce, A. R., Bedenbaugh, M., Mitchell, M. L., Burne, R. A., and

Nascimento, M. M. 2017 Microbiomes of site-specific dental plaques from children with

different caries status. Infect Immun 85. (doi:IAI.00106-17 [pii];10.1128/IAI.00106-17

[doi])

35. Bowen, W. H., Burne, R. A., Wu, H., and Koo, H. 2018 Oral biofilms: Pathogens, matrix, and

polymicrobial interactions in microenvironments. Trends Microbiol 26, 229-242.

(doi:S0966-842X(17)30213-5 [pii];10.1016/j.tim.2017.09.008 [doi])

36. Adler, C. J. et al.. 2013 Sequencing ancient calcified dental plaque shows changes in oral

microbiota with dietary shifts of the Neolithic and Industrial revolutions. Nature Genet.

37. Zhang, X. et al.. 2015 The oral and gut microbiomes are perturbed in rheumatoid arthritis and

partly normalized after treatment. Nat Med 21, 895-905. (doi:nm.3914

[pii];10.1038/nm.3914 [doi])

38. Cornejo, O. E. et al.. 2013 Evolutionary and population genomics of the cavity causing bacteria

Streptococcus mutans. Mol Biol Evol 30, 881-893.

39. Pedregosa, F. et al.. 2011 Scikit-learn: Machine Learning in Python. Journal of Machine Learning

Research 12, 2825-2830.

40. Platt, J. C. 2019 Probabilistic outputs for Support Vector Machines and comparisons to regularized

likelihood methods. Advances in Large Margin Classifiers. bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 23

41. Guyon, I., Weston, J., Barnhill, S., and Vapnik, V. 2002 Gene selection for cancer classification using

Support Vector Machines. Machine Learning 46, 389-422.

(doi:10.1023/A:1012487302797)

42. Mann, A. E. et al.. 2018 Differential preservation of endogenous human and microbial DNA in

dental calculus and dentin. Sci Rep 8, 9822. (doi:10.1038/s41598-018-28091-9

[doi];10.1038/s41598-018-28091-9 [pii])

43. Velsko, I. M. et al.. 2019 Microbial differences between dental plaque and historic dental calculus

are related to oral biofilm maturation stage. Microbiome 7, 102. (doi:10.1186/s40168-019-

0717-3 [doi];10.1186/s40168-019-0717-3 [pii])

44. Warinner, C. et al.. 2014 Pathogens and host immunity in the ancient human oral cavity. Nature

Genet 46, 336-344. (doi:ng.2906 [pii];10.1038/ng.2906 [doi])

45. Espinoza, J. L. et al.. 2018 Supragingival plaque microbiome ecology and functional potential in the

context of health and disease. MBio 9. (doi:mBio.01631-18 [pii];10.1128/mBio.01631-18

[doi])

46. Shi, B., Chang, M., Martin, J., Mitreva, M., Lux, R., Klokkevold, P., Sodergren, E., Weinstock, G. M.,

Haake, S. K., and Li, H. 2015 Dynamic changes in the subgingival microbiome and their

potential for diagnosis and prognosis of periodontitis. MBio 6, e01926-14.

(doi:mBio.01926-14 [pii];10.1128/mBio.01926-14 [doi]) bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 24

47. Liu, B. et al.. 2012 Deep sequencing of the oral microbiome reveals signatures of periodontal

disease. PLoS ONE 7, e37919. (doi:10.1371/journal.pone.0037919 [doi];PONE-D-11-24763

[pii])

48. McLean, J. S., Liu, Q., Thompson, J., Edlund, A., and Kelley, S. 2015 Draft genome sequence of

"Candidatus Bacteroides periocalifornicus," a new member of the Bacteriodetes phylum

found within the oral microbiome of periodontitis patients. Genome Announc 3.

(doi:3/6/e01485-15 [pii];10.1128/genomeA.01485-15 [doi])

49. Wang, J., Jia, Z., Zhang, B., Peng, L., and Zhao, F. 2019 Tracing the accumulation of in vivo human

oral microbiota elucidates microbial community dynamics at the gateway to the GI tract.

Gut. (doi:gutjnl-2019-318977 [pii];10.1136/gutjnl-2019-318977 [doi])

50. Marotz, C. A., Sanders, J. G., Zuniga, C., Zaramela, L. S., Knight, R., and Zengler, K. 2018 Improving

saliva shotgun metagenomics by chemical host DNA depletion. Microbiome 6, 42.

(doi:10.1186/s40168-018-0426-3 [doi];10.1186/s40168-018-0426-3 [pii])

51. Belstrom, D., Constancias, F., Liu, Y., Yang, L., Drautz-Moses, D. I., Schuster, S. C., Kohli, G. S.,

Jakobsen, T. H., Holmstrup, P., and Givskov, M. 2017 Metagenomic and

metatranscriptomic analysis of saliva reveals disease-associated microbiota in patients

with periodontitis and dental caries. NPJ Biofilms Microbiomes 3, 23. (doi:10.1038/s41522-

017-0031-4 [doi];31 [pii])

52. Lassalle, F., Spagnoletti, M., Fumagalli, M., Shaw, L., Dyble, M., Walker, C., Thomas, M. G.,

Bamberg, M. A., and Balloux, F. 2018 Oral microbiomes from hunter-gatherers and bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 25

traditional farmers reveal shifts in commensal balance and pathogen load linked to diet.

Mol Ecol 27, 182-195. (doi:10.1111/mec.14435 [doi])

53. Takayasu, L. et al.. 2017 Circadian oscillations of microbial and functional composition in the

human salivary microbiome. DNA Res 24, 261-270. (doi:3052236

[pii];10.1093/dnares/dsx001 [doi])

54. Brito, I. L. et al.. 2019 Transmission of human-associated microbiota along family and social

networks. Nat Microbiol 4, 964-971. (doi:10.1038/s41564-019-0409-6

[doi];10.1038/s41564-019-0409-6 [pii])

55. Franzosa, E. A. et al.. 2014 Relating the metatranscriptome and metagenome of the human gut.

Proc Natl Acad Sci U S A 111, E2329-E2338. (doi:1319284111

[pii];10.1073/pnas.1319284111 [doi])

56. Weyrich, L. S. et al.. 2017 Neanderthal behaviour, diet, and disease inferred from ancient DNA in

dental calculus. Nature 544, 357-361. (doi:nature21674 [pii];10.1038/nature21674 [doi])

57. Lefort, V., Desper, R., and Gascuel, O. 2015 FastME 2.0: A Comprehensive, Accurate, and Fast

Distance-Based Phylogeny Inference Program. Mol Biol Evol 32, 2798-2800. (doi:msv150

[pii];10.1093/molbev/msv150 [doi])

Figure 1. Source specificity of the percentage of species composition in 784 oral microbiomes according to SPARSE. (A) X-Y plot of the first two components from a UMAP (Uniform Manifold Approximation and Projection) [31] dimensional reduction of taxon abundances. (B) Neighbour- joining (FastMe2; [57]) hierarchical clustering based on the Euclidean distances between pairs of microbiomes. Euclidean p- distances were calculated between each pair as the square root of the sum of the squared pairwise differences in the percentage of reads assigned by SPARSE to each microbial taxon. Nodes whose cluster location was inconsistent with the UMAP clustering in part A are highlighted with black perimeters. Tree visualization: GrapeTree [4]. bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 27

Figure 2. Average percentage abundance (left axis) of bacterial species by source for the 40 most influential species according to Support Vector Machine analysis. The relative abundances per source are indicated by the three bars for each species in mini-histograms. Species are ordered from left to right according to decreasing SVM weight (right axis). SVM weight was calculated as the squared coefficients in the optimal SVM model that separated samples according to oral source. Species belonging to oral complexes are indicated by filled circles of the corresponding colours. Legend: Source colours and symbol for SVM weight bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 28

Figure 3. Average percentage abundances of 28 species from six oral complexes [5] in 784 metagenomes by oral source. The percentage abundances per source are indicated by the three bars for each species in mini-histograms. Species are ordered from left to right by oral complex (colours). Within each oral complex, the order is by decreasing total abundance. bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 29

Figure 4. Neighbour-joining (FastMe2; [57]) hierarchical clustering based on the Euclidean distances between pairs of 245 microbial species whose percentage abundance was >2% in at least one microbiome. Species names are indicated for members of the six oral complexes [5], and four apparent clusters of oral complexes are highlighted in gray. An expanded version of the same tree including all species labels is available in Fig. S2. bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 30

Figure 5. Numbers of microbial taxa by source. A). Rarefaction curves of numbers of species with 95% confidence estimates (shadow) by source. Inset data indicates median numbers of species per source as well as the total numbers for all samples. Rarefactions were performed with the program script called SPARSE_curve.py using 1000 randomized permutations of the order of samples. B). Binned histogram of number of species by percentage of samples containing those numbers. The data for this plot was also calculated with SPARSE_curve. C) Venn diagram of overlapping presence of taxa (>0.0005% abundance) for the three oral sources. bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 31

Figure 6. Reconstruction of S. mutans and S. sobrinus MAGs from oral metagenomes. (A, C) Numbers of oral samples by source internally binned by the percentage of reads specific to S. mutans (A) and S. sobrinus (C). (B, D) Numbers of oral samples by source internally binned by the predicted read coverage of a reference genome of S. mutans (B) and S. sobrinus (D). (E, F) Read coverage (left) and percentage of the reference genome that was unmasked (≥3 reads; ≥ 70% consistency) (right) in S. mutans (E) and S. sobrinus (F). Ordered by decreasing coverage. bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 32

Figure 7. ML phylogenies of S. mutans and S. sobrinus genomes. (A) A RaxML tree [26] of 195 genomes from RefSeq and 31 MAGs (metagenomic assembled genome) of S. mutans plus one genome of S. troglodytae as an outgroup. The tree was based on 181,321 non-repetitive SNPs in 1.73 Mb. (B) A RaxML tree of 50 genomes from RefSeq and 16 S. sobrinus MAGs plus 6 S. downei genomes as an outgroup. This tree was based on 160,863 non-repetitive SNPs in 1.13 Mb. MAGs are highlighted by thick black perimeters. Visualisation with GrapeTree [4]. Branches with a genetic distance of >0.1 were shortened for clarity, and shown as dashed lines. Legend: Numbers of strains by country of origin for both trees. bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

B. GrapeTree A. UMAP 6

Oral sources 0 Historical calculus

UMAP2 Modern plaque Modern saliva -2

-4

-6 -8 -4 0 4 8 UMAP1

0.07

Figure 1. Source specificity of the percentage of species composition in 784 oral microbiomes according to SPARSE. (A) X-Y plot of the first two components from a UMAP (Uniform Manifold Approximation and Projection) [31] dimensional reduction of taxon abundances. (B) Neighbour-joining (FastMe2; [57]) hierarchical clustering based on the Euclidean distances between pairs of microbiomes. Euclidean p- distances were calculated between each pair as the square root of the sum of the squared pairwise differences in the percentage of reads assigned by SPARSE to each microbial taxon. Nodes whose cluster location was inconsistent with the UMAP clustering in part A are highlighted with black perimeters. Tree visualization: GrapeTree [25]. bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

25 1 Historical calculus 20 Modern plaque 0.8 Modern saliva 15 SVM weight 0.6

10 0.4 SVM Weight 5 0.2 Percentage abundance Percentage

0 i 0 ti s ava s B ti s A ﬂ gena ti D04 ti gena uenzaeti ti ﬂ lla forsythia

nomyces dentalis nomyces viscosus Neisseria subVeillonellabacter Lautropiaparvula aphrophilus mirabilis ti Neisseria elongatati ti Neisseria lactamica nomyces naeslundii Tannere Prevotella loescheii Streptococcus mi Ac ti Ac StreptococcusStreptococcus infan mi Ac Streptococcus gordonii *Desulfobulbus sp. ORNL Streptococcus sanguinis Selenomonas spu Streptococcus salivarius *Streptococcus sp. D owia sp. Marseille-P4747 *Rothia sp. HMSC068E02 teroidetesFusobacterium oral taxon nucleatum Fusobacterium274 hwasookii tt Methanobrevibacter oralis Capnocytophaganomyces sp. gingivalisoral taxon 849 PrevotellaCapnocytophaga melaninogenica spu *O Aggrega Corynebacterium*Abiotrophia matruchoHaemophilus sp. HMSC24B09 parain *Olsenella sp. oral taxon 807 ti nobaculum sp. oral taxon 183 Streptococcus parasanguinis *Bac ti *Ac *Leptotrichia sp. oral taxon 212 *Ac *Porphyromonas sp. oral taxon 279 Pseudopropionibacterium propionicum

*Anaerolineaceae bacterium oral taxon 439

Figure 2. Percentage abundance (left axis) of bacterial species by source for the 40 most influential species according to Support Vector Machine analysis. The relative abundances per source are indicated by the three bars for each species in mini-histograms. Species are ordered from left to right according to decreasing SVM weight (right axis). SVM weight was calculated as the squared coefficients in the optimal SVM model that separated samples according to oral source. Species belonging to oral complexes are indicated by filled circles of the corresponding colours. Legend: Source colours and symbol for SVM weight. bioRxiv preprint not certifiedbypeerreview)istheauthor/funder,whohasgrantedbioRxivalicensetodisplaypreprintinperpetuity.Itmadeavailable

Tannerella forsythia by decreasing total abundance. tograms. Speciesare ordered to from rightby left oral complex (colours). Within eachoral complex, theorder is oral source. The percentage abundances persource are indicated by thethree barsfor eachspeciesinmini-his- Figure 3.Average percentage abundances of28speciesfrom sixoral complexes [30]in784 metagenomesby Treponema den Porphyromonas gingivalis

Percentage abundance doi: 0 1 2 3 4 5 https://doi.org/10.1101/842542 Fusobacterium nucleatumti cola

Fusobacterium periodon Red

Prevotella intermedia

Prevotella nigrescens ti Campylobacter showaecum

Campylobacter rectus

Parvimonas micra

Eubacterium nodatum Orange Streptococcus constellatus under a ;

Campylobacter gracilis this versionpostedNovember15,2019. CC-BY-NC-ND 4.0Internationallicense

Streptococcus oralis Streptococcus sanguinis

Streptococcus mi Streptococcus gordonii Streptococcus intermedius Yellow ti Capnocytop s

Capnocytophaga gingivalis haga spu Capnocytophaga ochracea

ti gena

Eikenella corrodens Campylobacter concisus The copyrightholderforthispreprint(whichwas Green ac . ti nomycetemcomitans Aggrega

Ac ti ti nomyces naeslundiibacter Ac ti nomyces viscosus

Ac ti Veillonella parvula Blue nomyces odontoly Purple ti cus bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

T. forsythia A. odontolyticus T. denticola Capnocytophaga sp. V. parvula P. gingivalis C. rectus F. nucleatum A. viscosus Eubacterium sp. Treponema. sp C. matruchotii Streptococcus sp. Prevotella sp. A. dentalis Leptotrichia sp. Rothia sp. 0.1

Figure 4. Neighbour-joining (FastMe2; [57]) hierarchical clustering based on the Euclidean distances between pairs of 245 microbial species whose percentage abundance was >2% in at least one microbiome. Species names are indicated for members of the six oral complexes [30], and four apparent clusters of oral complexes are highlighted in gray. An expanded version of the same tree including all species labels is available in Fig. S2. bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

A B 1600 1200

1400 1000

1200

800 1000

800 600 # Species # Species

600 400

400 Median All samples All sources 251 1591 200 156 200 Historical calculus 177 696 Modern plaque 242 842 94 Modern saliva 288 1398 58 0 0 0 100 200 300 400 500 600 700 800 0- 10- 20- 30- 40- 50- 60- 70- 80- 90- # Samples 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% % Samples

C Modern plaque Figure 5. Numbers of microbial taxa by source. A). Rarefaction curves of numbers of species with 95% con dence estimates (shadow) by source. Inset data indicates median numbers of 34 species per source as well as the total numbers for all samples. Rarefactions were performed with the program script called SPARSE_curve.py using 1000 randomized permutations of the 5 356 order of samples. B). Binned histogram of number of species by percentage of samples containing those numbers. The data for this 447 plot was also calculated with SPARSE_curve. C) Venn diagram of overlapping presence of taxa (>0.0005% abundance) for the three oral sources. 154 504 91

Historical calculus Modern saliva bioRxivA. S. preprint mutans doi: https://doi.org/10.1101/842542B. S. mutans ; this version posted NovemberC. S. sobrinus 15, 2019. The copyright holderD. S. sobrinus for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available 250 Percentage of reads 60 Coverage 250 Percentage of reads 60 Coverage ≥1 ≥100under aCC-BY-NC-ND 4.0 International≥1 license. ≥100 [0.1, 1) [10, 100) [0.1, 1) [10, 100) 200 [0.01, 0.1) [1, 10) 200 [0.01, 0.1) [1, 10) [0.001, 0.01) [0.001, 0.01) 40 1 40 [0.0001, 0.001) [0.0001, 0.001) 150 150 17 100 100 20 20 1 No. of samples No. 50 27 50 2 21 10 11 4 0 0 0 0 Historical Modern Modern Historical Modern Modern Historical Modern Modern Historical Modern Modern calculus plaque saliva calculus plaque saliva calculus plaque saliva calculus plaque saliva

E. 31 reconstructed S. mutans genomes F. 15 reconstructed S. sobrinus genomes 400 Saliva Saliva 100 Modern plaque Modern plaque

100 80

10 40 Coverage

20 % unmasking

1 0 1 5 10 15 20 25 30 1 5 10 15

Figure 6. Reconstruction of S. mutans and S. sobrinus MAGs from oral metagenomes. (A, C) Numbers of oral samples by source internally binned by the percentage of reads speci c to S. mutans (A) and S. sobrinus (C). (B, D) Numbers of oral samples by source internally binned by the predicted read coverage of a reference genome of S. mutans (B) and S. sobrinus (D). (E, F) Read coverage (left) and percentage of the reference genome that was unmasked (≥3 reads; ≥70% consistency) (right) in S. mutans (E) and S. sobrinus (F). Ordered by decreasing coverage. bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was A. not certified by peer review) is the author/funder, who has granted bioRxiv a license to displayB. the preprint in perpetuity.S. sobrinus It is made available S. mutans under aCC-BY-NC-ND 4.0 International license.

No. strains Country S.mutans S.sobrinus Brazil [91] [43] China [32] [14] United States [39] [1] United Kingdom [25] [3] Australia [4] [1] South Korea [1] [1] Japan [6] Iceland [5] Denmark [3] Germany [3] Turkey [3] South Africa [2] Italy [1] Papua New Guinea [1] Philippines [1] Sweden [1]

MAG 0.002 0.007 RefSeq genome

S. downei

S. troglodytae

Figure 7. ML phylogenies of S. mutans and S. sobrinus genomes. (A) A RaxML tree [24] of 195 genomes from RefSeq and 31 MAGs (metagenomic assembled genome) of S. mutans plus one genome of S. troglodytae as an outgroup. The tree was based on 181,321 non-repetitive SNPs in 1.73 Mb. (B) A RaxML tree of 50 genomes from RefSeq and 16 S. sobrinus MAGs plus 6 S. downei genomes as an outgroup. This tree was based on 160,863 non-repetitive SNPs in 1.13 Mb. MAGs are highlighted by thick black perimeters. Visualisation with GrapeTree [25]. Branches with a genetic distance of >0.1 were shortened for clarity, and shown as dashed lines. Legend: Numbers of strains by country of origin for both trees. bioRxiv preprint not certifiedbypeerreview)istheauthor/funder,whohasgrantedbioRxivalicensetodisplaypreprintinperpetuity.Itmadeavailable 02-. . . . . 0.5 0.4 0.3 0.2 0.1 0 -0.1 -0.2 A. UMAP-K-meanclusters ed by K-mean clusters in(A). components analysis. ofthePCA (C) The sameplotas(B)withnodescolor-cod- the three clusters intheplaneofUMAP components. (B)Plot ofthe rsttwo the UMAP analyses andtheirvisualizationplots. inPCA (A) The visualization of Supplemental Figure S1. The K-mean clusters ofthe rstthree components of C. PCA-K-meanclusters 8- 0 -4 -8 doi: https://doi.org/10.1101/842542 K-mean clusters K-mean clusters C_2 C_1 C_0 C_2 C_1 C_0 4 under a ; 8 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 this versionpostedNovember15,2019. -6 -4 -2 0 2 4 6 CC-BY-NC-ND 4.0Internationallicense

-0.3 -0.2 -0.1 PC2 - 0.11 0.1 0.2 0.3 0.4 B. PCA-Oralsources 0 02-. . . . . 0.5 0.4 0.3 0.2 0.1 0 -0.1 -0.2 PC1 -0.15 The copyrightholderforthispreprint(whichwas . Oral sources Modern saliva Modern plaque Historical calculus Gemella_morbillorum Streptococcus_cristatus Granulicatella_sp._HMSC30F09 bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxivRothia_sp._HMSC08A08 a license to display the preprint in perpetuity. It is made available Fusobacterium_hwasookii under aCC-BY-NC-NDAggregatibacter_aphrophilus 4.0 International license. Streptococcus_sp._NPS_308 Abiotrophia_sp._HMSC24B09 Haemophilus_parainfluenzae Neisseria_sicca Kingella_denitrificans Lautropia_mirabilis Aggregatibacter_sp._oral_taxon_458 Aggregatibacter_segnis Neisseria_lactamica Cardiobacterium_hominis Neisseria_elongata Actinomyces_sp._oral_taxon_171 Corynebacterium_durum Streptococcus_pneumoniae Gemella_haemolysans Granulicatella_elegans Streptococcus_sp._UMB1385 Streptococcus_sp._M334 Haemophilus_parahaemolyticus Haemophilus_haemolyticus Haemophilus_paraphrohaemolyticus Actinomyces_sp._oral_taxon_170 Actinomyces_meyeri Rothia_dentocariosa Actinomyces_sp._oral_taxon_877 Selenomonas_noxia Actinobaculum_sp._oral_taxon_183 Actinomyces_massiliensis Porphyromonas_catoniae Actinomyces_sp._oral_taxon_849 Actinomyces_gerencseriae Kingella_oralis Neisseria_sp._oral_taxon_014 Prevotella_maculosa Prevotella_oulorum Tannerella_sp._oral_taxon_HOT-286 Leptotrichia_sp._oral_taxon_498 Lachnoanaerobaculum_saburreum Leptotrichia_wadei Leptotrichia_buccalis Capnocytophaga_granulosa Leptotrichia_hofstadii Corynebacterium_matruchotii Lachnoanaerobaculum_sp._ICM7 Leptotrichia_sp._oral_taxon_212 Leptotrichia_sp._Marseille-P3007 Capnocytophaga_leadbetteri Capnocytophaga_sp._ChDC_OS43 Capnocytophaga_sp._oral_taxon_332 Prevotella_micans Leptotrichia_shahii Treponema_socranskii

Treponema_maltophilum Peptostreptococcaceae_bacterium_oral_taxon_113 Desulfomicrobium_orale Anaerolineaceae_bacterium_oral_taxon_439 Desulfobulbus_sp._ORNL Olsenella_sp._oral_taxon_807 Actinomyces_dentalis Bacteroidetes_oral_taxon_274 Pseudopropionibacterium_propionicum Parvimonas_sp._oral_taxon_393 Streptococcus_sp._DD04 Peptostreptococcaceae_bacterium_AS15 Johnsonella_ignava Peptostreptococcus_stomatis Slackia_sp._CM382 Prevotella_sp._F0091 Treponema_sp._OMZ_838 Selenomonas_sp._oral_taxon_892 Catonella_morbi Prevotella_saccharolytica [Eubacterium]_yurii Prevotella_loescheii Ottowia_sp._Marseille-P4747 Neisseria_bacilliformis Selenomonas_sp._oral_taxon_920 Selenomonas_sp._FOBRC6 Actinomyces_sp._oral_taxon_897 Actinomyces_israelii Treponema_lecithinolyticum Prevotella_pleuritidis Prevotella_dentalis Porphyromonas_endodontalis [Eubacterium]_saphenum Mogibacterium_sp._CM50 Filifactor_alocis Parvimonas_micra [Eubacterium]_brachy Selenomonas_sp._oral_taxon_126 Selenomonas_artemidis Selenomonas_infelix Oribacterium_sp._oral_taxon_078 Porphyromonas_sp._oral_taxon_278 Prevotella_oris Alloprevotella_tannerae Prevotella_conceptionensis Veillonella_infantium Veillonella_atypica Megasphaera_micronuciformis Prevotella_sp._oral_taxon_306 Prevotella_sp._C561 Prevotella_histicola Prevotella_salivae Prevotella_pallens Prevotella_melaninogenica Streptococcus_sp._HPH0090 Veillonella_sp._T11011-6 Mogibacterium_diversum [Eubacterium]_sulci Atopobium_parvulum Actinomyces_sp._Marseille-P2985 Sanguibacter_keddieii Actinomyces_graevenitzii Actinomyces_sp._HMSC035G02 Trueperella_pyogenes Rothia_mucilaginosa Actinomyces_sp._ICM47 Streptococcus_sp._263_SSPC Streptococcus_infantis Actinomyces_sp._ICM39 Streptococcus_parasanguinis Streptococcus_salivarius Streptococcus_sp._A12 Gemella_sanguinis Streptococcus_sp._HMSC074B11 Streptococcus_pseudopneumoniae Xylanimonas_cellulosilytica Rothia_sp._HMSC068E02 Porphyromonas_sp._oral_taxon_279 Neisseria_subflava Leptotrichia_sp._oral_taxon_215 Veillonella_rogosae Haemophilus_sp._HMSC066D02 Prevotella_nanceiensis Haemophilus_sputorum Oribacterium_sinus Prevotella_shahii Prevotellaceae_bacterium_Marseille-P2826 Veillonella_tobetsuensis Prevotella_aurantiaca Streptococcus_timonensis Streptococcus_sp._I-G2 Lactococcus_lactis Actinomyces_sp._ICM58 Selenomonas_sp._oral_taxon_478 Prevotella_denticola Prevotella_veroralis Stomatobaculum_longum Lachnospiraceae_bacterium_oral_taxon_096 Actinomyces_sp._HPA0247 Selenomonas_massiliensis Centipeda_periodontii Selenomonas_flueggei Anaeroglobus_geminatus Solobacterium_moorei Selenomonas_sputigena Olsenella_sp._Marseille-P2936 Pseudoramibacter_alactolyticus Olsenella_uli Atopobium_rimae [Eubacterium]_infirmum Dialister_invisus Actinomyces_sp._oral_taxon_180 Streptococcus_vestibularis Streptococcus_anginosus Supplemental FIgure S2. Neighbor-joining Prevotella_multiformis Actinomyces_sp._oral_taxon_448 clustering of microbial species based on Scardovia_wiggsiae Actinomycetaceae_bacterium_BA112 Neisseria_sp._KEM232 Actinomyces_sp._Marseille-P3109 the similarities of their abundances in all Pyramidobacter_piscolens Phocaeicola_abscessus Propionibacterium_sp._oral_taxon_192 samples as in Figure 4. Actinomyces_sp._Marseille-P3257 Synergistes_jonesii Actinomyces_cardiffensis Methanobrevibacter_oralis Olsenella_profusa Actinomyces_sp._Marseille-P2825 Neisseria_cinerea Prevotella_sp._oral_taxon_376 Treponema_vincentii Cardiobacterium_valvarum Corynebacterium_argentoratense Bifidobacterium_dentium Lactobacillus_salivarius Mitsuokella_sp._oral_taxon_131 Propionibacterium_acidifaciens Pseudomonas_aeruginosa Sphingomonas_melonis Ralstonia_insidiosa Bulleidia_extructa Lactobacillus_plantarum Actinomyces_timonensis Agrobacterium_genomosp._5 Bradyrhizobium_sp._MOS004 Pseudomonas_fluorescens Cutibacterium_acnes Acidovorax_temperans Ralstonia_pickettii Aeromonas_taiwanensis Citrobacter_freundii Achromobacter_arsenitoxydans Enterobacter_cloacae Peptoniphilus_sp._ChDC_B134 Kocuria_palustris Brachybacterium_paraconglomeratum Moraxella_catarrhalis Acinetobacter_sp._WCHA29 Staphylococcus_nepalensis Mogibacterium_pumilum bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

A. All sources, abundances, NJ B. All sources, presences, NJ C. Plaque, abundances, NJ D. Plaque, presences, NJ Capnocytophaga_gingivalis Streptococcus_intermedius Actinomyces_odontolyticus Veillonella_parvula Capnocytophaga_sputigena Prevotella_nigrescens Actinomyces_viscosus Streptococcus_oralis Fusobacterium_nucleatum_subsp._polymorphum Actinomyces_odontolyticus Veillonella_parvula Actinomyces_viscosus Capnocytophaga_ochracea Fusobacterium_nucleatum_subsp._vincentii Streptococcus_gordonii Actinomyces_naeslundii Eikenella_corrodens Campylobacter_concisus Streptococcus_mitis Capnocytophaga_ochracea Campylobacter_showae Fusobacterium_periodonticum Streptococcus_oralis Fusobacterium_nucleatum_subsp._polymorphum Streptococcus_mitis Streptococcus_mitis Streptococcus_sanguinis Selenomonas_noxia Streptococcus_oralis Veillonella_parvula Capnocytophaga_gingivalis Streptococcus_mitis Streptococcus_sanguinis Streptococcus_sanguinis Capnocytophaga_sputigena Streptococcus_sanguinis Campylobacter_concisus Streptococcus_oralis Fusobacterium_nucleatum_subsp._polymorphum Eikenella_corrodens Fusobacterium_periodonticum Actinomyces_viscosus Fusobacterium_periodonticum Capnocytophaga_sputigena Actinomyces_odontolyticus Streptococcus_gordonii Actinomyces_naeslundii Capnocytophaga_gingivalis Actinomyces_viscosus Actinomyces_naeslundii Selenomonas_noxia Actinomyces_odontolyticus Veillonella_parvula Capnocytophaga_gingivalis Campylobacter_concisus Fusobacterium_periodonticum Streptococcus_gordonii Capnocytophaga_sputigena Capnocytophaga_ochracea Streptococcus_gordonii Actinomyces_naeslundii Fusobacterium_nucleatum_subsp._polymorphum Eikenella_corrodens Campylobacter_concisus Selenomonas_noxia Capnocytophaga_ochracea Campylobacter_showae Campylobacter_showae Streptococcus_intermedius Selenomonas_noxia Streptococcus_intermedius Prevotella_nigrescens Fusobacterium_nucleatum_subsp._vincentii Eikenella_corrodens Fusobacterium_nucleatum_subsp._vincentii Fusobacterium_nucleatum_subsp._vincentii Prevotella_nigrescens Campylobacter_showae Prevotella_nigrescens Streptococcus_intermedius Campylobacter_rectus Aggregatibacter_actinomycetemcomitans Streptococcus_constellatus Fusobacterium_nucleatum_subsp._nucleatum Tannerella_forsythia Campylobacter_gracilis Parvimonas_micra Aggregatibacter_actinomycetemcomitans Porphyromonas_gingivalis Eubacterium_nodatum Campylobacter_rectus Campylobacter_gracilis Treponema_denticola Porphyromonas_gingivalis Porphyromonas_gingivalis Porphyromonas_gingivalis Prevotella_intermedia Streptococcus_constellatus Treponema_denticola Eubacterium_nodatum Parvimonas_micra Campylobacter_rectus Tannerella_forsythia Streptococcus_constellatus Fusobacterium_nucleatum_subsp._nucleatum Treponema_denticola Prevotella_intermedia Treponema_denticola Streptococcus_constellatus Tannerella_forsythia Fusobacterium_nucleatum_subsp._nucleatum Tannerella_forsythia Eubacterium_nodatum Parvimonas_micra Eubacterium_nodatum Campylobacter_rectus Aggregatibacter_actinomycetemcomitans Prevotella_intermedia Aggregatibacter_actinomycetemcomitans Prevotella_intermedia Campylobacter_gracilis Fusobacterium_nucleatum_subsp._nucleatum Campylobacter_gracilis Parvimonas_micra

9.0E-4 50.0 0.003 20.0 E. All sources, abundances, UPGMA F. All sources, presences, UPGMA G. Plaque, abundances, UPGMA H. Plaque, presences, UPGMA Streptococcus_mitis Streptococcus_sanguinis Campylobacter_concisus Veillonella_parvula Streptococcus_oralis Streptococcus_oralis Complex Capnocytophaga_ochracea Streptococcus_oralis red_complex Streptococcus_sanguinis Actinomyces_viscosus Orange_complex_core Fusobacterium_nucleatum_subsp._polymorphum Actinomyces_viscosus Veillonella_parvula Veillonella_parvula Orange_complex Campylobacter_showae Actinomyces_naeslundii Yellow_complex Capnocytophaga_gingivalis Streptococcus_mitis Green_complex Selenomonas_noxia Capnocytophaga_ochracea Capnocytophaga_sputigena Campylobacter_concisus Blue_complex Actinomyces_naeslundii Fusobacterium_nucleatum_subsp._polymorphum Purple_complex Capnocytophaga_ochracea Fusobacterium_periodonticum Fusobacterium_periodonticum Selenomonas_noxia non Fusobacterium_nucleatum_subsp._polymorphum Capnocytophaga_ochracea Veillonella_parvula Streptococcus_mitis Campylobacter_showae Fusobacterium_nucleatum_subsp._polymorphum Streptococcus_oralis Streptococcus_sanguinis Campylobacter_concisus Actinomyces_naeslundii Streptococcus_sanguinis Capnocytophaga_gingivalis Fusobacterium_periodonticum Capnocytophaga_gingivalis Streptococcus_mitis Capnocytophaga_sputigena Fusobacterium_nucleatum_subsp._vincentii Capnocytophaga_sputigena Capnocytophaga_gingivalis Eikenella_corrodens Streptococcus_constellatus Streptococcus_gordonii Capnocytophaga_sputigena Campylobacter_concisus Parvimonas_micra Selenomonas_noxia Actinomyces_viscosus Fusobacterium_periodonticum Fusobacterium_nucleatum_subsp._nucleatum Prevotella_nigrescens Streptococcus_gordonii Actinomyces_odontolyticus Treponema_denticola Fusobacterium_nucleatum_subsp._vincentii Actinomyces_odontolyticus Streptococcus_gordonii Eubacterium_nodatum Actinomyces_odontolyticus Eikenella_corrodens Campylobacter_showae Prevotella_intermedia Campylobacter_rectus Parvimonas_micra Fusobacterium_nucleatum_subsp._vincentii Actinomyces_odontolyticus Treponema_denticola Fusobacterium_nucleatum_subsp._vincentii Fusobacterium_nucleatum_subsp._nucleatum Actinomyces_viscosus Tannerella_forsythia Streptococcus_constellatus Prevotella_nigrescens Streptococcus_gordonii Porphyromonas_gingivalis Prevotella_nigrescens Treponema_denticola Selenomonas_noxia Streptococcus_constellatus Eubacterium_nodatum Tannerella_forsythia Actinomyces_naeslundii Parvimonas_micra Fusobacterium_nucleatum_subsp._nucleatum Campylobacter_rectus Prevotella_nigrescens Prevotella_intermedia Prevotella_intermedia Prevotella_intermedia Eikenella_corrodens Fusobacterium_nucleatum_subsp._nucleatum Campylobacter_rectus Parvimonas_micra Streptococcus_intermedius Eikenella_corrodens Treponema_denticola Eubacterium_nodatum Campylobacter_rectus Campylobacter_showae Porphyromonas_gingivalis Porphyromonas_gingivalis Tannerella_forsythia Streptococcus_intermedius Tannerella_forsythia Streptococcus_constellatus Aggregatibacter_actinomycetemcomitans Aggregatibacter_actinomycetemcomitans Streptococcus_intermedius Aggregatibacter_actinomycetemcomitans Porphyromonas_gingivalis Campylobacter_gracilis Campylobacter_gracilis Campylobacter_gracilis Campylobacter_gracilis Eubacterium_nodatum Aggregatibacter_actinomycetemcomitans Streptococcus_intermedius

7.0E-4 30.0 0.002 9.0 Figure S3. Neighbor-joining and UPGMA clustering of the 28 species described in Socransky et al. based on their abundances or presences in the oral samples. bioRxiv preprint doi: https://doi.org/10.1101/842542; this version posted November 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

A CS15 1770-1855 United Kingdom:Oxford B AfrPP1 740+/-40 BP South Africa:Near Cape Town 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.1 Frequency Frequency 0.0 0.0 0.0 0.0 1 3 5 7 9 1 3 5 7 9 11 13 15 17 19 21 23 25 11 13 15 17 19 21 23 25 −9 −7 −5 −3 −1 −9 −7 −5 −3 −1 −25 −23 −21 −19 −17 −15 −13 −11 −25 −23 −21 −19 −17 −15 −13 −11 C KT08calc 600–1300 CE Ireland D S40calc 400–650 Nepal 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.1 Frequency Frequency 0.0 0.0 0.0 0.0 1 3 5 7 9 1 3 5 7 9 11 13 15 17 19 21 23 25 11 13 15 17 19 21 23 25 −9 −7 −5 −3 −1 −9 −7 −5 −3 −1 −25 −23 −21 −19 −17 −15 −13 −11 −25 −23 −21 −19 −17 −15 −13 −11 DNA damages E SouthAfr2 3835+/-50 BP South Africa:Near Cape Town F 1.0 0.3 0.3 0.8 S40calc 0.2 0.2 AfrPP1 0.6 SouthAfr2 0.4 0.1 0.1 KT08calc

Frequency 0.2 CS15 0.0 0.0 0.0 1 3 5 7 9 11 13 15 17 19 21 23 25 −9 −7 −5 −3 −1 -2500 -2000 -1500 -1000 -500 0 500 1000 1500 2000 −25 −23 −21 −19 −17 −15 −13 −11 Figure S4. Increased deamination frequencies at the 5’-end of sequencing reads from ancient metagenomes. (A-E) The results of MapDamage2 on selected read sets from historical samples. (F) A summary of mapDamage estimates (∆s) of S. mutans reads within the historical metagenomes. MapDamage2 was run on a BAM alignment of species-speci c reads against the reference genome of S. mutans UA159 using default parameters.