Discovery of a Novel Periodontal Disease-Associated Bacterium

Discovery of a Novel Periodontal Disease-Associated Bacterium One of the world’s most common infectious disease, periodontitis (PD), derives from largely uncharacterized communities of oral bacteria growing as biofilms (a.k.a. plaque) on teeth and gum surfaces in periodontal pockets. Bacteria associated with periodontal disease trigger inflammatory responses in immune cells, which in later stages of the disease cause loss of both soft and hard tissue structures supporting teeth. Thus far, only a handful of bacteria have been characterized as infectious agents of PD. Although deep sequencing technologies, such as whole community shotgun sequencing have the potential to capture a detailed picture of highly complex bacterial communities in any given environment, we still lack major reference genomes for the oral microbiome associated with PD and other diseases. In recent work, by using a combination of supervised machine learning and genome assembly, we identified a genome from a novel member of the Bacteroidetes phylum in periodontal samples. Here, by applying a comparative metagenomics read-classification approach, including 272 metagenomes from various human body sites, and our previously assembled draft genome of the uncultivated Candidatus Bacteroides periocalifornicus (CBP) bacterium, we show CBP’s ubiquitous distribution in dental plaque, as well as its strong association with the well-known pathogenic Bred complex^ that resides in deep periodontal pockets. . . . . Keywords Periodontitis Metagenomics Oral microbiome Bacteroidetes Candidate phyla Introduction increased our understanding of the diversity of oral bac- teria through two commonly used approaches: sequenc- Initial studies of periodontitis (PD) relied on culturing ing of conserved 16S ribosomal RNA genes and methods and traditional culture independent methods untargeted (Bshotgun^) sequencing of all (Bmeta^)mi- (i.e., DNA-DNA hybridization, cloning, and targeted crobial genomes (Bgenomics^). However, because we sequencing) [1, 2], neither of which allow microbial lack reference genome sequence data for large portions diversity to be fully understood. The advent of culture of the microbial tree of life, there remains a high po- independent high-throughput sequencing technology has tential for overlooking microbes that are truly present in any given environment. To fill in some of these knowl- edge gaps and bypass the need for sequence homology for taxonomic classification, studies have employed Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00248-018-1200-6) contains supplementary contig binning, i.e., short reads assembled into contigu- material, which is available to authorized users. ous sets of overlapping reads (contigs), which can be grouped into taxa based on sequence composition, sim- * Anna Edlund ilarity or read coverage [3]. One such approach includes aedlund@jcvi.org supervised binning, which assigns contigs into taxo- nomic classes using a model trained with available ref- Department of Biology, San Diego State University, San Diego, CA 92182, USA erence sequences [4]. In a previous study, we employed this methodology on metagenomics sequence data ob- Department of Periodontics, University of Washington School of Dentistry, Seattle, WA 98195, USA tained from microbial samples collected from 12 sub- jects with severe periodontal disease [5]. Briefly, all J. Craig Venter Institute, Genomic Medicine Group, La Jolla, CA 92037, USA quality-trimmed reads were de novo assembled using Torres P. J. et al. SPAdes v2.40[6, 7]. SPAdes was chosen as assembly systemic diseases, including atherosclerosis (ATH), car- algorithm, since this program has demonstrated excep- diovascular disease, type 2 diabetes, and rheumatoid ar- tionally high genome assembly quality as compared to thritis [9]. Despite decades of research, the substantial other available assemblers, both single-cell assemblers differences among periodontitis patients in disease inci- as well as assemblers for multi-cell data (e.g. Velvet and dence, progressivity, and response to treatment are poor- SoapDeNovo) [6, 7]. We conducted post-assembly pro- ly understood. cessing of contigs, which included taxonomic classifica- Subgingival microbiota of periodontally healthy sub- tion based on a machine learning algorithm using the jects has been shown to differ from that found in sub- MG Taxa tool as described earlier [4]. Several large jects with periodontal disease [10]. Studies also show contigs that were presented in a number of libraries had that there is a striking change in the composition of the same k-mer frequency and were originally classified the microbial profiles with greater disease severity. at a low score to uncultivated phylum OD1, indicating The shift is particularly marked for the known patho- they were distantly related to any previously sequenced gens in the so-called red complex, i.e., Porphyromonas genome [4]. These contigs, from a single sampling sub- gingivalis, Treponema denticola,and Tannerella ject, were then sorted into a bin and further inspected for forsythia [11], whose numbers increase with pocket k-mer frequency consistency and used for downstream depth [12]. Researchers have found little or no relation- genome analyses. The assembled draft genome is ship to pocket depth for the majority of other microbial 2.53 Mb and consists of 49 major contigs (sizes range species; however, most members of the so-called orange between 18,374 to 129,525 bp), with an overall GC content complex, which includes Fusobacterium nucleatum of 59.4% (GenBank accession number: LIIK00000000). among other species, and all species of the red complex Gene annotation using the Prokaryotic Genome strongly associate with deeper periodontal pockets and Automatic Annotation Pipeline (PGAAP) provided by disease severity [13]. In fact, the abundances of all the National Center for Biotechnology Information members of the red complex are highly correlated, and (NCBI) identified a total of 1875 genes, consisting of studies have shown that co-infection with multiple 1678 coding sequences, 39 tRNAs, and 1 rRNA oper- members cause more severe PD than individual infec- on (5S) [4]. Due to that metagenomic assembly meth- tions [11, 14]. The establishment of periodontal biofilms odologies cannot distinguish nearly identical sequences, on tooth surfaces is initiated by fast growing bacterial which may originate from different genomes within the community members of the yellow complex, such as sample, our draft genome may represent several closely Streptococcus mitis and S. oralis, while bridging species related bacterial strains. of the orange complex, i.e., Fusobacterium and late col- Uncultivated groups, such as Candidate bacterial onizers of the red complex require longer periods of phyla are prevalent in the oral cavity, including time to grow [13]. Co-culturing studies have shown that Saccharibacteria/TM7, Gracilibacteria (GN02), SR1, members of the orange complex, particularly and WPS-2 clades. Recently, strain TM7x, a member F. nucleatum, significantly enhance the growth of the of the elusive TM7 Candidate phylum, associated with more severe periodontal pathogens in the red complex severe PD and other inflammatory conditions, was iso- [15]. It is important to note that most studies require lated from a human saliva sample [8]. Its cultivation cultivation or rely on reference databases of known se- facilitated the sequencing of a complete genome and quences (16S rRNA gene and whole genomes); any bac- revealed its clearly symbiotic lifestyle as the genome teria that do not readily culture or are missing in the didnot containanyaminoacidbiosynthetic pathways. databases are ignored or missed. Aside from this rare example, the ecological and clin- Here, we were able to further characterize a recently ical role of uncultivated bacteria and archaea in PD discovered member of the Bacteriodetes phylum, CBP, still remains a challenge. by analyzing a total of 272 previously published PD, one of the world’s most common infectious dis- metagenomes from various human body sites representing eases, is a progressive polymicrobial infection that if both healthy adults and adults with PD. We found that untreated can progress to moderate and severe periodon- CBP is orally ubiquitous, existing in both healthy and dis- titis. Overall, the disease refers to the inflammatory pro- eased individuals, but not present in gut or skin samples. cess that occurs in the tissues surrounding the teeth in CBP also increases with increased pocket depth, co-exists response to the growth of bacterial biofilms, or dental with both F. nucleatum, T. denticola,and P. gingivalis. Its plaque, along the gumline. Eventually, PD results in the abundance is strongly correlated with members of the red breakdown of the periodontal ligament and alveolar complex, but not healthy commensals, all of which sug- bone, and can lead to loss of teeth. PD affects the ma- gest that CBP is a novel candidate member of the symbi- jority of adults worldwide and may contribute to various otic and pathogenic red complex. Discovery of a Novel Periodontal Disease-Associated Bacterium Methods American Indian/Alaskan Native Study Dataset Candidatus Bacteriodes periocalifornicus Draft This dataset included metagenomes generated from 22 Genome Information subgingival samples from 12 different patients recruited from an American Indian/Alaskan Native population in Southern The draft genome LIIK00000000 was accessed via California [20]. See previous publication for details on sam- Bioproject Accession PRJNA289925, Biosample pling methods, disease classification, DNA extraction, and Accession SAMN03859889. Other relevant information study population [21]. The samples included ten sample pairs (e.g. annotations) associated with the LIIK00000000 ge- from the same patient, one obtained before, and one after nome can be found via NCBI Taxon ID 1702214, IMG standard periodontal treatment. Participants were classified Submission ID: 77482, GOLD ID in IMG Database Study to various degrees of periodontitis based on periodontal pock- ID: Gs0118016 Project ID: Gp0126827, GOLD Analysis et depth (PPD), clinical attachment loss (CAL), plaque score, Project Id: Ga0104344. and bleeding on probing (BOP). Individuals with PPD ≤ 4 mm, CAL ≤ 3, and BOP > 10% were classified as having gingivitis; individuals with PPD ≥5mm, CAL ≥ 4, and BOP Maximum Likelihood Tree ≥ 30% were classified as having mild-moderate periodontitis; and individuals with PPD ≥ 7 mm, CAL ≥ 6, and BOP ≥ 30% Seventy-eight genomes representing major lineages from the were classified as having severe periodontitis. Bacteroidetes phylum were downloaded from NCBI. Thirty- one taxa-specific marker genes, which were previously deter- University of Southern California Study Dataset mined as single copy genes and unique at the nucleotide level [16] were concatenated and analyzed for optimal tree topog- This data set includes metagenomes generated from 24 raphy under evolutionary criteria by using the Molecular subgingival samples from patients treated at the graduate peri- Evolutionary Genetics Analysis (MEGA) software, version odontology clinic at the Herman Ostrow School of Dentistry 6.0 [17]. Five thousand bootstrap iterations were performed. of the University of Southern California (USC). Information on molecular and clinical methods and study population can HMP Dataset be found in a previous study by Califf and colleagues [22]. Participants were recruited as part of a study investigating the The National Institutes of Health (NIH) Human Microbiome effectiveness of dilute sodium hypochlorite on periodontitis. Project (HMP) was established by the NIH Common Fund Each participant received a comprehensive clinical examina- (http://commonfund.nih.gov/hmp/) to provide a public tion, and was randomly assigned to a control or treatment resource to facilitate human microbiome research [18]. Two group [22, 23]. No scaling was performed before or during hundred and fourteen metagenomes were obtained from the the treatment. Each patient exhibited at least four separate HMP whole metagenomics shotgun sequencing website teeth with a pocket depth of ≥ 6 mm. Pocket depth categories (https://www.hmpdacc.org/HMIWGS/healthy/). This were as follows: class A = periodontal pocket depth up to included 18 gut, 14 left retroauricular crease a.k.a. skin, 6 6 mm, class b = 6–8 mm,andclass c>8mm. saliva, 16 subgingival, and 160 supragingival datasets (Table S1). Metagenomic Sequence Processing and Analysis Human Oral Microbiome Datasets Metagenome reads were trimmed using the Trimmomatic (v.0.36) [24] default settings (http://www.usadellab.org/ Published metagenomic libraries, representative of both cms/?page=trimmomatic). Metagenomes were then healthy and diseased subjects, were obtained from the subjected to stringent error filtering using PRINSEQ (v.0.20. Human Oral Microbiome Database (HOMD) under the sub- 4) [25] with the following parameters: minimum sequence mission number 20130522 (ftp://ftp.homd.org/publication_ length of 60 bp, minimum mean quality score of 25, data/20130522/) and from a study by Duran-Pinedo and col- sequences containing any BN’s^ were removed, and low- leagues [19], respectively. In all studies, healthy and periodon- complexity threshold of 50 (using entropy). Human DNA titis subjects were diagnosed by a clinician. The datasets in- was filtered out using the DeconSeq software (coverage > cluded subgingival samples from six healthy individuals and 90, identity > 90) (v.0.4.3) [26]. After DeconSeq, paired-end seven individuals diagnosed with periodontitis. One healthy files were rewritten to make sure all reads had a mate and individual (Metagenome_Healthy2) was excluded from the separated out singletons using FASTQ Pair, available at analysis due to its abnormally high level of CBP (0.63 versus https://github.com/linsalrob/EdwardsLab/. BBMerge (v.37. amean of 0.02 for allsamples). 36) [27] was used to merge overlapping pairs of reads using Torres P. J. et al. Maribacter sp. HTCC2170 Fig. 1 Phylogenetic relatedness Zobellia galactanivorans of CBP to bacterial lineages Cellulophaga algicola DSM 14237 within the phylum Bacteroidetes. 99 Cellulophaga lytica DSM 7489 Phylogenetic tree from Maximum Robiginitalea biformata HTCC2501 Likelihood estimation of 31 99 Muricauda ruestringensis DSM 13258 marker genes [16] representing 78 Lacinutrix sp. 5H-3-7-4 different lineages of bacteria Krokinobacter sp. 4H-3-7-5 98 Croceibacter atlanticus HTCC2559 within the Bacteroidetes phylum. Gramella forsetii KT0803 Five thousand bootstrap iterations 90 100 Zunongwangia profunda SM-A87 were performed. The analysis Flavobacterium psychrophilum JIP02 86 placed CBP (boldface in figure) Flavobacterium johnsoniae UW101 deeply within the Bacteroides,as 99 95 Flavobacterium branchiophilum sister-group to the genera Capnocytophaga ochracea DSM 7271 Odoribacter, Paludibacter, 100 Flavobacteriaceae bacterium 3519-10 Porphyromonas, 100 Riemerella anatipestifer DSM 15868 Parabacteroidetes,and Blattabacterium sp. (Periplaneta americana) str. BPLAN 100 Blattabacterium sp. (Blattella germanica) str. Bge Prevotella> Candidatus Bacteroides periocalifornicus Odoribacter splanchnicus DSM 20712 Candidatus Azobacteroides pseudotrichonymphae genomovar. CFP2 79 Paludibacter propionicigenes WB4 Porphyromonas gingivalis ATCC 33277 Porphyromonas gingivalis TDC60 Porphyromonas gingivalis W83 Parabacteroides distasonis ATCC 8503 Porphyromonas asaccharolytica DSM 20707 Prevotella ruminicola 23 Prevotella denticola F0289 Bacteroides vulgatus ATCC 8482 Bacteroides salanitronis DSM 18170 Bacteroides thetaiotaomicron VPI-5482 Bacteroides fragilis NCTC 9343 100 Bacteroides fragilis YCH46 Pedobacter heparinus DSM 2366 Pedobacter saltans DSM 12145 Sphingobacterium sp. 21 Marivirga tractuosa DSM 4126 Cyclobacterium marinum DSM 745 Leadbetterella byssophila DSM 17132 Spirosoma linguale DSM 74 Dyadobacter fermentans DSM 18053 93 Runella slithyformis DSM 19594 Chitinophaga pinensis DSM 2588 100 Haliscomenobacter hydrossis DSM 1100 Rhodothermus marinus SG0.5JP17-172 Salinibacter ruber DSM 13855 100 Salinibacter ruber M8 Chlorobium chlorochromatii CaD3 Pelodictyon phaeoclathratiforme BU-1 Chlorobium phaeobacteroides DSM 266 99 Chlorobium limicola DSM 245 Chlorobium phaeovibrioides DSM 265 Chlorobium tepidum TLS 100 Chlorobaculum parvum NCIB 8327 100 Chlorobium phaeobacteroides BS1 100 Prosthecochloris aestuarii DSM 271 Chloroherpeton thalassium ATCC 35110 Thermodesulfovibrio yellowstonii DSM 11347 Candidatus Nitrospira defluvii Thermodesulfatator indicus DSM 15286 100 Thermodesulfobacterium sp. OPB45 Thermovibrio ammonificans HB-1 98 Desulfurobacterium thermolithotrophum DSM 11699 Sulfurihydrogenibium sp. YO3AOP1 100 Sulfurihydrogenibium azorense Az-Fu1 Persephonella marina EX-H1 100 Hydrogenobaculum sp. Y04AAS1 Aquifex aeolicus VF5 Hydrogenobacter thermophilus TK-6 100 Thermocrinis albus DSM 14484 0.1 Discovery of a Novel Periodontal Disease-Associated Bacterium default parameters. Forward reads showed very high quality multiple comparisons test when comparing three or more scores; therefore, those that did not merge were extracted from groups. the bbmerge unmerged output file (https://github.com/ pjtorres/xtract_forward) as to not discard useful data. Genome Mining of Virulence Factors and Biosynthetic Metagenomes were analyzed using Kraken [28]. Kraken is Gene Clusters able to assign taxonomic labels to short DNA sequences with high sensitivity and speed by utilizing exact alignment of short JGI IMG genome portal analysis pipeline (https://img.jgi.doe. subsequences of length k,called k-mers (default size k =31), gov/) was used to assess virulence properties of the CBP and a novel classification algorithm. Kraken first uses a refer- genome. The antiSMASH tool (the Bacterial version) [29] ence database and builds a new database by adding phyloge- was applied to search the genome for biosynthetic gene netic information to every k-mer in its database. Kraken then clusters. Default settings were applied. classifies reads by breaking each read into overlapping k-mers. Each k-mer is then mapped to the lowest common ancestor of Results and Discussion the genomes containing that k-mer in the precomputed data- base. For our study, we built a custom database containing all We used the metagenomic taxonomic classification tool complete bacterial reference genomes from the NCBI refseq database (ftp://ftp.ncbi.nlm.nih.gov/genomes/refseq/bacteria/) Kraken [28] to investigate the relationship of the uncultivated CBP to periodontal disease and to members of key periodontal and CBP. pathogens in the orange and red complex. CBP had previously been identified in a metagenome from a patient with severe Statistical Analysis PD in high relative abundance [5]. By performing Maximum Likelihood estimates using multiple marker genes from this Pearson’s product-moment correlation was performed when an- genome and 78 additional sequenced genomes, representing a alyzing CBP relative abundance over pocket depth using the broad diversity of bacteria within the Bacteroidetes phylum; RStudio statistical package (version 1.0.153). Kruskal-Wallis we found that CBP was deeply placed within the Bacteroides, nonparametric tests were used to determine whether the relative as sister-group to the genera Odoribacter, Paludibacter, abundance of CBP, P. gingivalis,or F. nucleatum differed be- Porphyromonas, Bacteroides, Parabacteroidetes,and tween two groups and this was followed by post-hoc Dunn’s Prevotella (Fig. 1). Its closest neighbor genome was 0.16 HMP HOMD USC American Indian/Alaskan Native 0.14 0.12 0.1 0.08 Fusobacterium nucleatum 0.06 Porphyromonas gingivalis 0.04 Candidatus Bacteroides periocalifornicus 0.02 Fig. 2 B.periocalifornicus (CBP) detected in oral sites but not the individuals (n = 5) and individuals diagnosed with periodontitis skin or gut. Stacked bar plot representing the relative abundance (%) (n = 7) from HOMD datasets [19]; oral disease class A (n =10), of CBP, P. gingivalis,and F. nucleatum from a combined total of disease class B (n = 4), and disease class C (n = 10) from USC 272 metagenomes generated from different body sites, including gut dataset [22]; oral pocket disease state ranging from normal (n = 3), (n = 18), skin (n = 14), saliva (n = 6), sub- (n = 16), and moderate (n = 10), and severe (n = 9) from the American Indian/ supragingival plaque (n = 160) from the HMP datasets; healthy Alaskan Native dataset [21] Gut Skin Saliva Subgingival plaque Supragingival plaque Healthy Pocket Class A Pocket Class B Pocket Class C Pocket Disease Normal Pocket Disease Moderate Pocket Disease Severe Percent Relative Abundance Torres P. J. et al. Alistipes putredinis with an average nucleotide identity of with periodontal disease (PD) (n = 53).Byperformingmultiple 68% [5]. comparative nonparametric statistical tests and correlation To deepen our understanding of the ecology and biogeo- analyses on the normalized relative abundance values obtained graphic distribution of CBP, we used the Kraken tool to deter- by Kraken, we found that CBP was unique to the oral cavity mine its general abundance in the oral cavity and whether it (Fig. 2), and that the relative abundance of CBP was signifi- was specific to the oral cavity or found commonly in other cantly higher in subgingival plaque compared to supragingival niches of the human microbiome. We analyzed a total of 272 plaque and saliva samples (Fig. S1 and Fig. 3a). These results previously published metagenomes from various human body strongly indicate that CBP is specifically adapted to the sites representing both healthy and non-healthy adults subgingival environment, where its relative abundance 0.024 (Table S1), including: gut (n =18), skin (n =14) saliva (n = (2.4%) is similar to that of P. gingivalis 0.002 (0.2%) and 6), sub- (n = 21) and supragingival plaque (n = 160), and adults F. nucleatum 0.01 (1.0%) (Fig. 3b, c). Comparisons of a b *** *** 0.03 0.002 0.02 0.001 0.01 0.00 0.000 Subging. plaque Supraging. plaque Subging. plaque Supraging. plaque c d N.S. *** 0.012 0.0004 0.0003 0.08 0.0002 0.04 0.0001 0.00 0.00 Subging. plaque Supraging. plaque Healthy Periodontitis Fig. 3 Relative abundance comparisons of B. periocalifornicus (CBP) in Institutes of Health Human Microbiome Project. Dataset for the relative subgingival plaque. Relative proportions of CBP in healthy individuals abundance of d CBP in subgingival samples from healthy individuals was compared to individuals diagnosed with periodontal disease (PD), as (n = 5) and individuals diagnosed with PD (n = 7) were obtained from well as proportions of P. gingivalis and F. nucleatum. CBP is highly the Human Oral Microbiome Database (HOMD) under the submission abundant in subgingival as compared to supragingival plaque. Bar plot number 20130522 (Table S1). (ftp://ftp.homd.org/publication_data/ (mean ± SEM) showing the relative abundance of a B. californicus, b P. 20130522/). Kruskal-Wallis nonparametric test was performed to com- gingivalis,and c F. nucleatum in sub- (n = 16) and supragingival plaque pare means among the two groups; *** p ≤ 0.001 (n = 160). Datasets were obtained from the publicly available National Candidatus Bacteroides periocalifornicus Porphyromonas gingivales Fusobacterium nucleatum Candidatus Bacteroides periocalifornicus Relative Abundance Relative Abundance Relative Abundance Relative Abundance Discovery of a Novel Periodontal Disease-Associated Bacterium sequence libraries between healthy subjects and subjects with abundance of CBP was greater in deeper pockets (Kruskal- PD found CBP to be enriched in patients with disease (Fig. 3d). Wallis, p =0.07; Fig. 4a), and a positive correlation between The association of CBP and the periodontal pocket milieu CBP relative abundance and pocket depth was observed was further explored by analyzing the presence of CBP in (Pearson’s; p = 0.02; Fig. 4b), as well as a trend for more sequence libraries obtained from subjects with different levels severely disease periodontal pockets and a higher relative of severe PD (detailed information on how PD was diagnosed abundances of CBP (Kruskal-Wallis, p =0.37; Fig. 4c). The and how samples were collected can be found in a previous latter correlation was not significant, which is likely due to the study by Califf and colleagues [22]). The mean relative high within group variability (a common feature of human ab 0.07 0.05 0.04 0.03 0.02 0.01 p = 0.02 r = 0.20 0.00 AB C 5.0 7.5 10.0 Pocket Class Pocket Depth de 0.05 0.06 0.06 0.04 0.04 0.04 0.03 0.02 0.02 0.02 0.01 0.00 0.00 0.00 Normal Moderate Severe Pre Post Improved Worsened Oral Pocket Disease State Periodontal Treatment Response To Treatment Fig. 4 Relative abundance of Candidatus B. periocalifornicus (CBP) in indicating the 95% confidence interval for the line of best fit. Bar plot patients with severe periodontal disease (PD). Relative abundance of (mean ± SEM) representing the relative abundance of CBP from 22 CBP increases with worsening oral disease class, pocket class, pocket subjects based on c oral pocket disease state ranging from normal (n = depth and is more abundant in patients with severe oral pocket disease 3), moderate (n = 10), and severe (n = 9) (Kruskal-Wallis, p value = 0.37). state; decreased posttreatment. Bar plot (mean ± SEM) showing the d There were 11 subjects in the pre- and 11 in the posttreatment groups relative abundance of Candidatus Bacteroides periocalifornicus from [22] (Kruskal-Wallis, p value = 0.65). e Five subjects improved after 24 subjects grouped based on a Subjects were also grouped based on standard periodontal treatment and six worsened (Kruskal-Wallis, p value pocket class A (n = 18), pocket class B (n = 1) and pocket class C (n = = 0.47). Kruskal-Wallis nonparametric tests followed by post hoc Dunn’s 5) (Kruskal-Wallis, p value = 0.07). b Scatterplot and trend line showing multiple comparisons tests were used when comparing three groups. One- the relationship between pocket depth and ranked CBP relative way ANOVA on ranked data was used to compare the means between the abundance. Results of Pearson’s correlation (p value = 0.02) and two groups correlation coefficient are shown in box inset with gray shaded area Candidatus B. periocalifornicus Candidatus B. periocalifornicus Candidatus B. periocalifornicus Candidatus B. periocalifornicus Candidatus B. periocalifornicus Relative Abundance Relative Abundance Relative Abundance Rank Abundance Relative Abundance Torres P. J. et al. microbiome sequence data). In addition, by comparing se- virulence-associated genes. This analysis showed that the quence libraries from PD-patients who were subjected to a CBP genome encode the flagellar assembly proteins CheA, 0.25% sodium hypochlorite (diluted bleach) treatment, and CheB, CheR, CheW, and CheY, which are involved in che- whose conditions either improved or worsened after treatment motaxis (i.e., direct movement toward an attractant or away [22], a positive trend was observed, showing a higher abun- from a repellant), suggesting that CBP is motile and also har- dance of CBP in samples before treatment, and in samples that bors genes that are key in adhesion to a host and in host worsened after treatment (Kruskal-Wallis, p = 0.65; Fig. 4d, invasion [30]. The genome also includes multiple genes in- p = 0.47; Fig. 4e). The relative abundance of CBP was also volved in beta-lactam resistance, which is in line with numer- significantly correlated with all members of the red complex ous studies showing that Bacteroides species have the (Fig. 5a–c) as well as the orange complex member broadest spectrum of resistance to commonly used antimicro- F. nucleatum (Fig. 5d). Furthermore, no correlation was ob- bial agents, especially to beta-lactam compounds [31]. In ad- served between the abundance of CBP and S. mitis, a common dition, the genome harbors the rfbA, rfbB, rfbC,and rfBCD oral commensal bacterium (Fig. 5e). Intriguingly, our compar- genes, which encode enzymes that are involved in the biosyn- ative metagenomic read abundance analysis approach of the thesis of dTDP-rhamnose for the assembly of lipopolysaccha- CBP draft genome not only reveals an orally ubiquitous bac- ride (LPS), suggesting that CBP may have antagonist LPS terium, specifically adapted to the subgingival plaque environ- structures, similar to other Bacteroidetes,suchas P. gingivalis ment, but also a novel candidate member of the pathogenic red and Tannerella [32]. Two antioxidant enzymes were also iden- complex. tified (a peroxiredoxin, and a 1-Cys-peroxiredoxin), which are To further elucidate the functional capacity of CBP we known to control cytokine-induced peroxide levels and are employed the JGI IMG genome portal analysis pipeline avail- thereby mediating signal transduction in mammalian cells able at https://img.jgi.doe.gov/, and identified a number of [33]. Furthermore, by performing BLAST analysis of the b c -15 -10 -5 0 5 -15 -10 -5 0 5 -15 -10 -5 0 5 Candidatus Bacteriodes periocalifornicus de -15 -10 -5 0 5 -15 -10 -5 0 5 Candidatus Bacteriodes periocalifornicus Fig. 5 Candidatus B. periocalifornicus (CBP) relative abundance is Results of Pearson’s correlation (p value and correlation coefficient) are strongly correlated with all three members of the red complex. shown in the box inset with gray shaded area indication the 95% Scatterplot and trend line showing the relationship between CBP and a confidence interval for the line of best fit (n = 272). Relative abundance P. gingivalis, b T. denticola, c T. forsythia, d F. nucleatum, and e S. mitis. data was log 2 transformed (normalized) 0 0 -5 -5 -5 -10 -10 -10 p < 0.0001 p < 0.0001 -15 p < 0.0001 r = 0.32 r = 0.23 r = 0.26 -15 0 0 -5 -5 -10 p < 0.0001 -10 p = 0.34 r = 0.24 r = 0.004 Porphyromonas gingivalis Fusobacterium nucleatum Treponema denticola Streptococcus mitis Tannerella forsythia Discovery of a Novel Periodontal Disease-Associated Bacterium CBP genome against the well annotated P. gingivalis ATCC References 33277 genome, we identified the following shared virulence- associated genes: C25 domains encoding gingipains, which 1. Chen H, Jiang W (2014) Application of high-throughput sequenc- ing in understanding human oral microbiome related with health are well-known P. gingivalis proteases that target outer mem- and disease. Front. Microbiol. 5:508. https://doi.org/10.3389/fmicb. branes via the Bacteroidetes-specific type 9 secretion system, 2014.00508 ragA and ragB surface antigen genes, and hemolysin 2. Woyke T, Teeling H, Ivanova NN, Huntemann M, Richter M, encoding genes (Table S2). To further explore the capacity Gloeckner FO, Boffelli D, Anderson IJ, Barry KW, Shapiro HJ, Szeto E, Kyrpides NC, Mussmann M, Amann R, Bergin C, of CBP to produce bioactive small molecules, we applied Ruehland C, Rubin EM, Dubilier N (2006) Symbiosis insights the antiSMASH software [29]. This analysis predicted that through metagenomic analysis of a microbial consortium. Nature the genome harbors seven putative biosynthetic gene clusters 443:950–955. https://doi.org/10.1038/nature05192 (Table S3) of which one was associated with S-layer glycan 3. Mande SS, Mohammed MH, Ghosh TS (2012) Classification of metagenomic sequences: methods and challenges. Brief. biosynthesis, that supports glycosylation of proteins, and Bioinform. 13:669–681. https://doi.org/10.1093/bib/bbs054 gives the cell membrane fluidity, i.e., it is important for gliding 4. McLean JS, Lombardo MJ, Badger JH, Edlund A, Novotny M, motility. Another cluster encode an arylpolyene-like mole- Yee-Greenbaum J, Vyahhi N, Hall AP, Yang Y, Dupont CL, cule, which corresponds to flexirubin—a pigment associated Ziegler MG, Chitsaz H, Allen AE, Yooseph S, Tesler G, Pevzner PA, Friedman RM, Nealson KH, Venter JC, Lasken RS (2013) with all Bacteroidetes bacteria, and that is known to protect Candidate phylum TM6 genome recovered from a hospital sink the cell from oxidative stress [34]. An O-antigen biosynthetic biofilm provides genomic insights into this uncultivated phylum. gene cluster, encoding a group of molecules that is known for Proc. Natl. Acad. Sci. U. S. A. 110:E2390–E2399. https://doi.org/ being important in interactions with other bacterial cells and 10.1073/pnas.1219809110 5. McLean JS, Liu Q, Thompson J, Edlund A, Kelley S (2015) Draft with human host cells was also identified [35]. genome sequence of BCandidatus Bacteroides periocalifornicus,^ a Based on all the above findings, we suggest that the CBP new member of the Bacteriodetes phylum found within the oral genome harbors several genes and pathways similar to the other microbiome of periodontitis patients. Genome Announc 3: known oral pathogens belonging to the Bacteroidetes phylum, e01485–e01415. https://doi.org/10.1128/genomeA.01485-15 which are involved in PD-associated virulence, including host 6. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Nikolenko SI, Pham S, Prjibelski AD, cell modulation, which strengthens the evidence that this bac- Pyshkin AV, Sirotkin AV, Vyahhi N, Tesler G, Alekseyev MA, terium is a new member of the red complex. A goal is to further Pevzner PA (2012) SPAdes: a new genome assembly algorithm elucidate CBP’s role in oral health and disease by attempting and its applications to single-cell sequencing. J. Comput. Biol. 19: various cultivation approaches which, if successful, would al- 455–477. https://doi.org/10.1089/cmb.2012.0021 7. Nurk S, Bankevich A, Antipov D, Gurevich AA, Korobeynikov A, low us to study CBP in the research laboratory and to obtain a Lapidus A, Prjibelski AD, Pyshkin A, Sirotkin A, Sirotkin Y, complete genome sequence. Also, by applying fluorescent Stepanauskas R, Clingenpeel SR, Woyke T, McLean JS, Lasken staining techniques, targeting CBP and other oral bacteria, we R, Tesler G, Alekseyev MA, Pevzner PA (2013) Assembling could learn more about its spatial distribution and physical in- single-cell genomes and mini-metagenomes from chimeric MDA products. J. Comput. Biol. 20:714–737. https://doi.org/10.1089/ teractions with other biofilm community members. cmb.2013.0084 8. He X, McLean JS, Edlund A, Yooseph S, Hall AP, Liu SY, Acknowledgements We thank Rob Edwards for the use of the Anthill Dorrestein PC, Esquenazi E, Hunter RC, Cheng G, Nelson KE, computational cluster at San Diego State University. Lux R, Shi W (2015) Cultivation of a human-associated TM7 phy- lotype reveals a reduced genome and epibiotic parasitic lifestyle. Proc. Natl. Acad. Sci. U. S. A. 112:244–249. https://doi.org/10. Author Contributions All authors contributed extensively to the work 1073/pnas.1419038112 presented in this paper. 9. Kim J, Amar S (2006) Periodontal disease and systemic conditions: a bidirectional relationship. Odontology 94:10–21. https://doi.org/ Funding Information Part of this work was funded by the NIH grant 10.1007/s10266-006-0060-6 number U26IHS300292 to Dan Calac and Roberta Gottlieb. NIH 10. 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Periodontol. 79:670–676. https://doi.org/10.1902/jop. creativecommons.org/licenses/by/4.0/), which permits unrestricted use, 2008.070398 distribution, and reproduction in any medium, provided you give 13. Teles R, Teles F, Frias-Lopez J, Paster B, Haffajee A (2013) appropriate credit to the original author(s) and the source, provide a link Lessons learned and unlearned in periodontal microbiology. to the Creative Commons license, and indicate if changes were made. Periodontol. 62:95–162. https://doi.org/10.1111/prd.12010 Torres P. J. et al. 14. Hajishengallis G, Lamont RJ (2012) Beyond the red complex and 24. Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120. into more complexity: the polymicrobial synergy and dysbiosis (PSD) model of periodontal disease etiology. Mol Oral Microbiol https://doi.org/10.1093/bioinformatics/btu170 27:409–419. https://doi.org/10.1111/j.2041-1014.2012.00663.x 25. 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Discovery of a Novel Periodontal Disease-Associated Bacterium

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Life Sciences; Microbiology; Ecology; Microbial Ecology; Geoecology/Natural Processes; Nature Conservation; Water Quality/Water Pollution
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Abstract

One of the world’s most common infectious disease, periodontitis (PD), derives from largely uncharacterized communities of oral bacteria growing as biofilms (a.k.a. plaque) on teeth and gum surfaces in periodontal pockets. Bacteria associated with periodontal disease trigger inflammatory responses in immune cells, which in later stages of the disease cause loss of both soft and hard tissue structures supporting teeth. Thus far, only a handful of bacteria have been characterized as infectious agents of PD. Although deep sequencing technologies, such as whole community shotgun sequencing have the potential to capture a detailed picture of highly complex bacterial communities in any given environment, we still lack major reference genomes for the oral microbiome associated with PD and other diseases. In recent work, by using a combination of supervised machine learning and genome assembly, we identified a genome from a novel member of the Bacteroidetes phylum in periodontal samples. Here, by applying a comparative metagenomics read-classification approach, including 272 metagenomes from various human body sites, and our previously assembled draft genome of the uncultivated Candidatus Bacteroides periocalifornicus (CBP) bacterium, we show CBP’s ubiquitous distribution in dental plaque, as well as its strong association with the well-known pathogenic Bred complex^ that resides in deep periodontal pockets. . . . . Keywords Periodontitis Metagenomics Oral microbiome Bacteroidetes Candidate phyla Introduction increased our understanding of the diversity of oral bac- teria through two commonly used approaches: sequenc- Initial studies of periodontitis (PD) relied on culturing ing of conserved 16S ribosomal RNA genes and methods and traditional culture independent methods untargeted (Bshotgun^) sequencing of all (Bmeta^)mi- (i.e., DNA-DNA hybridization, cloning, and targeted crobial genomes (Bgenomics^). However, because we sequencing) [1, 2], neither of which allow microbial lack reference genome sequence data for large portions diversity to be fully understood. The advent of culture of the microbial tree of life, there remains a high po- independent high-throughput sequencing technology has tential for overlooking microbes that are truly present in any given environment. To fill in some of these knowl- edge gaps and bypass the need for sequence homology for taxonomic classification, studies have employed Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00248-018-1200-6) contains supplementary contig binning, i.e., short reads assembled into contigu- material, which is available to authorized users. ous sets of overlapping reads (contigs), which can be grouped into taxa based on sequence composition, sim- * Anna Edlund ilarity or read coverage [3]. One such approach includes aedlund@jcvi.org supervised binning, which assigns contigs into taxo- nomic classes using a model trained with available ref- Department of Biology, San Diego State University, San Diego, CA 92182, USA erence sequences [4]. In a previous study, we employed this methodology on metagenomics sequence data ob- Department of Periodontics, University of Washington School of Dentistry, Seattle, WA 98195, USA tained from microbial samples collected from 12 sub- jects with severe periodontal disease [5]. Briefly, all J. Craig Venter Institute, Genomic Medicine Group, La Jolla, CA 92037, USA quality-trimmed reads were de novo assembled using Torres P. J. et al. SPAdes v2.40[6, 7]. SPAdes was chosen as assembly systemic diseases, including atherosclerosis (ATH), car- algorithm, since this program has demonstrated excep- diovascular disease, type 2 diabetes, and rheumatoid ar- tionally high genome assembly quality as compared to thritis [9]. Despite decades of research, the substantial other available assemblers, both single-cell assemblers differences among periodontitis patients in disease inci- as well as assemblers for multi-cell data (e.g. Velvet and dence, progressivity, and response to treatment are poor- SoapDeNovo) [6, 7]. We conducted post-assembly pro- ly understood. cessing of contigs, which included taxonomic classifica- Subgingival microbiota of periodontally healthy sub- tion based on a machine learning algorithm using the jects has been shown to differ from that found in sub- MG Taxa tool as described earlier [4]. Several large jects with periodontal disease [10]. Studies also show contigs that were presented in a number of libraries had that there is a striking change in the composition of the same k-mer frequency and were originally classified the microbial profiles with greater disease severity. at a low score to uncultivated phylum OD1, indicating The shift is particularly marked for the known patho- they were distantly related to any previously sequenced gens in the so-called red complex, i.e., Porphyromonas genome [4]. These contigs, from a single sampling sub- gingivalis, Treponema denticola,and Tannerella ject, were then sorted into a bin and further inspected for forsythia [11], whose numbers increase with pocket k-mer frequency consistency and used for downstream depth [12]. Researchers have found little or no relation- genome analyses. The assembled draft genome is ship to pocket depth for the majority of other microbial 2.53 Mb and consists of 49 major contigs (sizes range species; however, most members of the so-called orange between 18,374 to 129,525 bp), with an overall GC content complex, which includes Fusobacterium nucleatum of 59.4% (GenBank accession number: LIIK00000000). among other species, and all species of the red complex Gene annotation using the Prokaryotic Genome strongly associate with deeper periodontal pockets and Automatic Annotation Pipeline (PGAAP) provided by disease severity [13]. In fact, the abundances of all the National Center for Biotechnology Information members of the red complex are highly correlated, and (NCBI) identified a total of 1875 genes, consisting of studies have shown that co-infection with multiple 1678 coding sequences, 39 tRNAs, and 1 rRNA oper- members cause more severe PD than individual infec- on (5S) [4]. Due to that metagenomic assembly meth- tions [11, 14]. The establishment of periodontal biofilms odologies cannot distinguish nearly identical sequences, on tooth surfaces is initiated by fast growing bacterial which may originate from different genomes within the community members of the yellow complex, such as sample, our draft genome may represent several closely Streptococcus mitis and S. oralis, while bridging species related bacterial strains. of the orange complex, i.e., Fusobacterium and late col- Uncultivated groups, such as Candidate bacterial onizers of the red complex require longer periods of phyla are prevalent in the oral cavity, including time to grow [13]. Co-culturing studies have shown that Saccharibacteria/TM7, Gracilibacteria (GN02), SR1, members of the orange complex, particularly and WPS-2 clades. Recently, strain TM7x, a member F. nucleatum, significantly enhance the growth of the of the elusive TM7 Candidate phylum, associated with more severe periodontal pathogens in the red complex severe PD and other inflammatory conditions, was iso- [15]. It is important to note that most studies require lated from a human saliva sample [8]. Its cultivation cultivation or rely on reference databases of known se- facilitated the sequencing of a complete genome and quences (16S rRNA gene and whole genomes); any bac- revealed its clearly symbiotic lifestyle as the genome teria that do not readily culture or are missing in the didnot containanyaminoacidbiosynthetic pathways. databases are ignored or missed. Aside from this rare example, the ecological and clin- Here, we were able to further characterize a recently ical role of uncultivated bacteria and archaea in PD discovered member of the Bacteriodetes phylum, CBP, still remains a challenge. by analyzing a total of 272 previously published PD, one of the world’s most common infectious dis- metagenomes from various human body sites representing eases, is a progressive polymicrobial infection that if both healthy adults and adults with PD. We found that untreated can progress to moderate and severe periodon- CBP is orally ubiquitous, existing in both healthy and dis- titis. Overall, the disease refers to the inflammatory pro- eased individuals, but not present in gut or skin samples. cess that occurs in the tissues surrounding the teeth in CBP also increases with increased pocket depth, co-exists response to the growth of bacterial biofilms, or dental with both F. nucleatum, T. denticola,and P. gingivalis. Its plaque, along the gumline. Eventually, PD results in the abundance is strongly correlated with members of the red breakdown of the periodontal ligament and alveolar complex, but not healthy commensals, all of which sug- bone, and can lead to loss of teeth. PD affects the ma- gest that CBP is a novel candidate member of the symbi- jority of adults worldwide and may contribute to various otic and pathogenic red complex. Discovery of a Novel Periodontal Disease-Associated Bacterium Methods American Indian/Alaskan Native Study Dataset Candidatus Bacteriodes periocalifornicus Draft This dataset included metagenomes generated from 22 Genome Information subgingival samples from 12 different patients recruited from an American Indian/Alaskan Native population in Southern The draft genome LIIK00000000 was accessed via California [20]. See previous publication for details on sam- Bioproject Accession PRJNA289925, Biosample pling methods, disease classification, DNA extraction, and Accession SAMN03859889. Other relevant information study population [21]. The samples included ten sample pairs (e.g. annotations) associated with the LIIK00000000 ge- from the same patient, one obtained before, and one after nome can be found via NCBI Taxon ID 1702214, IMG standard periodontal treatment. Participants were classified Submission ID: 77482, GOLD ID in IMG Database Study to various degrees of periodontitis based on periodontal pock- ID: Gs0118016 Project ID: Gp0126827, GOLD Analysis et depth (PPD), clinical attachment loss (CAL), plaque score, Project Id: Ga0104344. and bleeding on probing (BOP). Individuals with PPD ≤ 4 mm, CAL ≤ 3, and BOP > 10% were classified as having gingivitis; individuals with PPD ≥5mm, CAL ≥ 4, and BOP Maximum Likelihood Tree ≥ 30% were classified as having mild-moderate periodontitis; and individuals with PPD ≥ 7 mm, CAL ≥ 6, and BOP ≥ 30% Seventy-eight genomes representing major lineages from the were classified as having severe periodontitis. Bacteroidetes phylum were downloaded from NCBI. Thirty- one taxa-specific marker genes, which were previously deter- University of Southern California Study Dataset mined as single copy genes and unique at the nucleotide level [16] were concatenated and analyzed for optimal tree topog- This data set includes metagenomes generated from 24 raphy under evolutionary criteria by using the Molecular subgingival samples from patients treated at the graduate peri- Evolutionary Genetics Analysis (MEGA) software, version odontology clinic at the Herman Ostrow School of Dentistry 6.0 [17]. Five thousand bootstrap iterations were performed. of the University of Southern California (USC). Information on molecular and clinical methods and study population can HMP Dataset be found in a previous study by Califf and colleagues [22]. Participants were recruited as part of a study investigating the The National Institutes of Health (NIH) Human Microbiome effectiveness of dilute sodium hypochlorite on periodontitis. Project (HMP) was established by the NIH Common Fund Each participant received a comprehensive clinical examina- (http://commonfund.nih.gov/hmp/) to provide a public tion, and was randomly assigned to a control or treatment resource to facilitate human microbiome research [18]. Two group [22, 23]. No scaling was performed before or during hundred and fourteen metagenomes were obtained from the the treatment. Each patient exhibited at least four separate HMP whole metagenomics shotgun sequencing website teeth with a pocket depth of ≥ 6 mm. Pocket depth categories (https://www.hmpdacc.org/HMIWGS/healthy/). This were as follows: class A = periodontal pocket depth up to included 18 gut, 14 left retroauricular crease a.k.a. skin, 6 6 mm, class b = 6–8 mm,andclass c>8mm. saliva, 16 subgingival, and 160 supragingival datasets (Table S1). Metagenomic Sequence Processing and Analysis Human Oral Microbiome Datasets Metagenome reads were trimmed using the Trimmomatic (v.0.36) [24] default settings (http://www.usadellab.org/ Published metagenomic libraries, representative of both cms/?page=trimmomatic). Metagenomes were then healthy and diseased subjects, were obtained from the subjected to stringent error filtering using PRINSEQ (v.0.20. Human Oral Microbiome Database (HOMD) under the sub- 4) [25] with the following parameters: minimum sequence mission number 20130522 (ftp://ftp.homd.org/publication_ length of 60 bp, minimum mean quality score of 25, data/20130522/) and from a study by Duran-Pinedo and col- sequences containing any BN’s^ were removed, and low- leagues [19], respectively. In all studies, healthy and periodon- complexity threshold of 50 (using entropy). Human DNA titis subjects were diagnosed by a clinician. The datasets in- was filtered out using the DeconSeq software (coverage > cluded subgingival samples from six healthy individuals and 90, identity > 90) (v.0.4.3) [26]. After DeconSeq, paired-end seven individuals diagnosed with periodontitis. One healthy files were rewritten to make sure all reads had a mate and individual (Metagenome_Healthy2) was excluded from the separated out singletons using FASTQ Pair, available at analysis due to its abnormally high level of CBP (0.63 versus https://github.com/linsalrob/EdwardsLab/. BBMerge (v.37. amean of 0.02 for allsamples). 36) [27] was used to merge overlapping pairs of reads using Torres P. J. et al. Maribacter sp. HTCC2170 Fig. 1 Phylogenetic relatedness Zobellia galactanivorans of CBP to bacterial lineages Cellulophaga algicola DSM 14237 within the phylum Bacteroidetes. 99 Cellulophaga lytica DSM 7489 Phylogenetic tree from Maximum Robiginitalea biformata HTCC2501 Likelihood estimation of 31 99 Muricauda ruestringensis DSM 13258 marker genes [16] representing 78 Lacinutrix sp. 5H-3-7-4 different lineages of bacteria Krokinobacter sp. 4H-3-7-5 98 Croceibacter atlanticus HTCC2559 within the Bacteroidetes phylum. Gramella forsetii KT0803 Five thousand bootstrap iterations 90 100 Zunongwangia profunda SM-A87 were performed. The analysis Flavobacterium psychrophilum JIP02 86 placed CBP (boldface in figure) Flavobacterium johnsoniae UW101 deeply within the Bacteroides,as 99 95 Flavobacterium branchiophilum sister-group to the genera Capnocytophaga ochracea DSM 7271 Odoribacter, Paludibacter, 100 Flavobacteriaceae bacterium 3519-10 Porphyromonas, 100 Riemerella anatipestifer DSM 15868 Parabacteroidetes,and Blattabacterium sp. (Periplaneta americana) str. BPLAN 100 Blattabacterium sp. (Blattella germanica) str. Bge Prevotella> Candidatus Bacteroides periocalifornicus Odoribacter splanchnicus DSM 20712 Candidatus Azobacteroides pseudotrichonymphae genomovar. CFP2 79 Paludibacter propionicigenes WB4 Porphyromonas gingivalis ATCC 33277 Porphyromonas gingivalis TDC60 Porphyromonas gingivalis W83 Parabacteroides distasonis ATCC 8503 Porphyromonas asaccharolytica DSM 20707 Prevotella ruminicola 23 Prevotella denticola F0289 Bacteroides vulgatus ATCC 8482 Bacteroides salanitronis DSM 18170 Bacteroides thetaiotaomicron VPI-5482 Bacteroides fragilis NCTC 9343 100 Bacteroides fragilis YCH46 Pedobacter heparinus DSM 2366 Pedobacter saltans DSM 12145 Sphingobacterium sp. 21 Marivirga tractuosa DSM 4126 Cyclobacterium marinum DSM 745 Leadbetterella byssophila DSM 17132 Spirosoma linguale DSM 74 Dyadobacter fermentans DSM 18053 93 Runella slithyformis DSM 19594 Chitinophaga pinensis DSM 2588 100 Haliscomenobacter hydrossis DSM 1100 Rhodothermus marinus SG0.5JP17-172 Salinibacter ruber DSM 13855 100 Salinibacter ruber M8 Chlorobium chlorochromatii CaD3 Pelodictyon phaeoclathratiforme BU-1 Chlorobium phaeobacteroides DSM 266 99 Chlorobium limicola DSM 245 Chlorobium phaeovibrioides DSM 265 Chlorobium tepidum TLS 100 Chlorobaculum parvum NCIB 8327 100 Chlorobium phaeobacteroides BS1 100 Prosthecochloris aestuarii DSM 271 Chloroherpeton thalassium ATCC 35110 Thermodesulfovibrio yellowstonii DSM 11347 Candidatus Nitrospira defluvii Thermodesulfatator indicus DSM 15286 100 Thermodesulfobacterium sp. OPB45 Thermovibrio ammonificans HB-1 98 Desulfurobacterium thermolithotrophum DSM 11699 Sulfurihydrogenibium sp. YO3AOP1 100 Sulfurihydrogenibium azorense Az-Fu1 Persephonella marina EX-H1 100 Hydrogenobaculum sp. Y04AAS1 Aquifex aeolicus VF5 Hydrogenobacter thermophilus TK-6 100 Thermocrinis albus DSM 14484 0.1 Discovery of a Novel Periodontal Disease-Associated Bacterium default parameters. Forward reads showed very high quality multiple comparisons test when comparing three or more scores; therefore, those that did not merge were extracted from groups. the bbmerge unmerged output file (https://github.com/ pjtorres/xtract_forward) as to not discard useful data. Genome Mining of Virulence Factors and Biosynthetic Metagenomes were analyzed using Kraken [28]. Kraken is Gene Clusters able to assign taxonomic labels to short DNA sequences with high sensitivity and speed by utilizing exact alignment of short JGI IMG genome portal analysis pipeline (https://img.jgi.doe. subsequences of length k,called k-mers (default size k =31), gov/) was used to assess virulence properties of the CBP and a novel classification algorithm. Kraken first uses a refer- genome. The antiSMASH tool (the Bacterial version) [29] ence database and builds a new database by adding phyloge- was applied to search the genome for biosynthetic gene netic information to every k-mer in its database. Kraken then clusters. Default settings were applied. classifies reads by breaking each read into overlapping k-mers. Each k-mer is then mapped to the lowest common ancestor of Results and Discussion the genomes containing that k-mer in the precomputed data- base. For our study, we built a custom database containing all We used the metagenomic taxonomic classification tool complete bacterial reference genomes from the NCBI refseq database (ftp://ftp.ncbi.nlm.nih.gov/genomes/refseq/bacteria/) Kraken [28] to investigate the relationship of the uncultivated CBP to periodontal disease and to members of key periodontal and CBP. pathogens in the orange and red complex. CBP had previously been identified in a metagenome from a patient with severe Statistical Analysis PD in high relative abundance [5]. By performing Maximum Likelihood estimates using multiple marker genes from this Pearson’s product-moment correlation was performed when an- genome and 78 additional sequenced genomes, representing a alyzing CBP relative abundance over pocket depth using the broad diversity of bacteria within the Bacteroidetes phylum; RStudio statistical package (version 1.0.153). Kruskal-Wallis we found that CBP was deeply placed within the Bacteroides, nonparametric tests were used to determine whether the relative as sister-group to the genera Odoribacter, Paludibacter, abundance of CBP, P. gingivalis,or F. nucleatum differed be- Porphyromonas, Bacteroides, Parabacteroidetes,and tween two groups and this was followed by post-hoc Dunn’s Prevotella (Fig. 1). Its closest neighbor genome was 0.16 HMP HOMD USC American Indian/Alaskan Native 0.14 0.12 0.1 0.08 Fusobacterium nucleatum 0.06 Porphyromonas gingivalis 0.04 Candidatus Bacteroides periocalifornicus 0.02 Fig. 2 B.periocalifornicus (CBP) detected in oral sites but not the individuals (n = 5) and individuals diagnosed with periodontitis skin or gut. Stacked bar plot representing the relative abundance (%) (n = 7) from HOMD datasets [19]; oral disease class A (n =10), of CBP, P. gingivalis,and F. nucleatum from a combined total of disease class B (n = 4), and disease class C (n = 10) from USC 272 metagenomes generated from different body sites, including gut dataset [22]; oral pocket disease state ranging from normal (n = 3), (n = 18), skin (n = 14), saliva (n = 6), sub- (n = 16), and moderate (n = 10), and severe (n = 9) from the American Indian/ supragingival plaque (n = 160) from the HMP datasets; healthy Alaskan Native dataset [21] Gut Skin Saliva Subgingival plaque Supragingival plaque Healthy Pocket Class A Pocket Class B Pocket Class C Pocket Disease Normal Pocket Disease Moderate Pocket Disease Severe Percent Relative Abundance Torres P. J. et al. Alistipes putredinis with an average nucleotide identity of with periodontal disease (PD) (n = 53).Byperformingmultiple 68% [5]. comparative nonparametric statistical tests and correlation To deepen our understanding of the ecology and biogeo- analyses on the normalized relative abundance values obtained graphic distribution of CBP, we used the Kraken tool to deter- by Kraken, we found that CBP was unique to the oral cavity mine its general abundance in the oral cavity and whether it (Fig. 2), and that the relative abundance of CBP was signifi- was specific to the oral cavity or found commonly in other cantly higher in subgingival plaque compared to supragingival niches of the human microbiome. We analyzed a total of 272 plaque and saliva samples (Fig. S1 and Fig. 3a). These results previously published metagenomes from various human body strongly indicate that CBP is specifically adapted to the sites representing both healthy and non-healthy adults subgingival environment, where its relative abundance 0.024 (Table S1), including: gut (n =18), skin (n =14) saliva (n = (2.4%) is similar to that of P. gingivalis 0.002 (0.2%) and 6), sub- (n = 21) and supragingival plaque (n = 160), and adults F. nucleatum 0.01 (1.0%) (Fig. 3b, c). Comparisons of a b *** *** 0.03 0.002 0.02 0.001 0.01 0.00 0.000 Subging. plaque Supraging. plaque Subging. plaque Supraging. plaque c d N.S. *** 0.012 0.0004 0.0003 0.08 0.0002 0.04 0.0001 0.00 0.00 Subging. plaque Supraging. plaque Healthy Periodontitis Fig. 3 Relative abundance comparisons of B. periocalifornicus (CBP) in Institutes of Health Human Microbiome Project. Dataset for the relative subgingival plaque. Relative proportions of CBP in healthy individuals abundance of d CBP in subgingival samples from healthy individuals was compared to individuals diagnosed with periodontal disease (PD), as (n = 5) and individuals diagnosed with PD (n = 7) were obtained from well as proportions of P. gingivalis and F. nucleatum. CBP is highly the Human Oral Microbiome Database (HOMD) under the submission abundant in subgingival as compared to supragingival plaque. Bar plot number 20130522 (Table S1). (ftp://ftp.homd.org/publication_data/ (mean ± SEM) showing the relative abundance of a B. californicus, b P. 20130522/). Kruskal-Wallis nonparametric test was performed to com- gingivalis,and c F. nucleatum in sub- (n = 16) and supragingival plaque pare means among the two groups; *** p ≤ 0.001 (n = 160). Datasets were obtained from the publicly available National Candidatus Bacteroides periocalifornicus Porphyromonas gingivales Fusobacterium nucleatum Candidatus Bacteroides periocalifornicus Relative Abundance Relative Abundance Relative Abundance Relative Abundance Discovery of a Novel Periodontal Disease-Associated Bacterium sequence libraries between healthy subjects and subjects with abundance of CBP was greater in deeper pockets (Kruskal- PD found CBP to be enriched in patients with disease (Fig. 3d). Wallis, p =0.07; Fig. 4a), and a positive correlation between The association of CBP and the periodontal pocket milieu CBP relative abundance and pocket depth was observed was further explored by analyzing the presence of CBP in (Pearson’s; p = 0.02; Fig. 4b), as well as a trend for more sequence libraries obtained from subjects with different levels severely disease periodontal pockets and a higher relative of severe PD (detailed information on how PD was diagnosed abundances of CBP (Kruskal-Wallis, p =0.37; Fig. 4c). The and how samples were collected can be found in a previous latter correlation was not significant, which is likely due to the study by Califf and colleagues [22]). The mean relative high within group variability (a common feature of human ab 0.07 0.05 0.04 0.03 0.02 0.01 p = 0.02 r = 0.20 0.00 AB C 5.0 7.5 10.0 Pocket Class Pocket Depth de 0.05 0.06 0.06 0.04 0.04 0.04 0.03 0.02 0.02 0.02 0.01 0.00 0.00 0.00 Normal Moderate Severe Pre Post Improved Worsened Oral Pocket Disease State Periodontal Treatment Response To Treatment Fig. 4 Relative abundance of Candidatus B. periocalifornicus (CBP) in indicating the 95% confidence interval for the line of best fit. Bar plot patients with severe periodontal disease (PD). Relative abundance of (mean ± SEM) representing the relative abundance of CBP from 22 CBP increases with worsening oral disease class, pocket class, pocket subjects based on c oral pocket disease state ranging from normal (n = depth and is more abundant in patients with severe oral pocket disease 3), moderate (n = 10), and severe (n = 9) (Kruskal-Wallis, p value = 0.37). state; decreased posttreatment. Bar plot (mean ± SEM) showing the d There were 11 subjects in the pre- and 11 in the posttreatment groups relative abundance of Candidatus Bacteroides periocalifornicus from [22] (Kruskal-Wallis, p value = 0.65). e Five subjects improved after 24 subjects grouped based on a Subjects were also grouped based on standard periodontal treatment and six worsened (Kruskal-Wallis, p value pocket class A (n = 18), pocket class B (n = 1) and pocket class C (n = = 0.47). Kruskal-Wallis nonparametric tests followed by post hoc Dunn’s 5) (Kruskal-Wallis, p value = 0.07). b Scatterplot and trend line showing multiple comparisons tests were used when comparing three groups. One- the relationship between pocket depth and ranked CBP relative way ANOVA on ranked data was used to compare the means between the abundance. Results of Pearson’s correlation (p value = 0.02) and two groups correlation coefficient are shown in box inset with gray shaded area Candidatus B. periocalifornicus Candidatus B. periocalifornicus Candidatus B. periocalifornicus Candidatus B. periocalifornicus Candidatus B. periocalifornicus Relative Abundance Relative Abundance Relative Abundance Rank Abundance Relative Abundance Torres P. J. et al. microbiome sequence data). In addition, by comparing se- virulence-associated genes. This analysis showed that the quence libraries from PD-patients who were subjected to a CBP genome encode the flagellar assembly proteins CheA, 0.25% sodium hypochlorite (diluted bleach) treatment, and CheB, CheR, CheW, and CheY, which are involved in che- whose conditions either improved or worsened after treatment motaxis (i.e., direct movement toward an attractant or away [22], a positive trend was observed, showing a higher abun- from a repellant), suggesting that CBP is motile and also har- dance of CBP in samples before treatment, and in samples that bors genes that are key in adhesion to a host and in host worsened after treatment (Kruskal-Wallis, p = 0.65; Fig. 4d, invasion [30]. The genome also includes multiple genes in- p = 0.47; Fig. 4e). The relative abundance of CBP was also volved in beta-lactam resistance, which is in line with numer- significantly correlated with all members of the red complex ous studies showing that Bacteroides species have the (Fig. 5a–c) as well as the orange complex member broadest spectrum of resistance to commonly used antimicro- F. nucleatum (Fig. 5d). Furthermore, no correlation was ob- bial agents, especially to beta-lactam compounds [31]. In ad- served between the abundance of CBP and S. mitis, a common dition, the genome harbors the rfbA, rfbB, rfbC,and rfBCD oral commensal bacterium (Fig. 5e). Intriguingly, our compar- genes, which encode enzymes that are involved in the biosyn- ative metagenomic read abundance analysis approach of the thesis of dTDP-rhamnose for the assembly of lipopolysaccha- CBP draft genome not only reveals an orally ubiquitous bac- ride (LPS), suggesting that CBP may have antagonist LPS terium, specifically adapted to the subgingival plaque environ- structures, similar to other Bacteroidetes,suchas P. gingivalis ment, but also a novel candidate member of the pathogenic red and Tannerella [32]. Two antioxidant enzymes were also iden- complex. tified (a peroxiredoxin, and a 1-Cys-peroxiredoxin), which are To further elucidate the functional capacity of CBP we known to control cytokine-induced peroxide levels and are employed the JGI IMG genome portal analysis pipeline avail- thereby mediating signal transduction in mammalian cells able at https://img.jgi.doe.gov/, and identified a number of [33]. Furthermore, by performing BLAST analysis of the b c -15 -10 -5 0 5 -15 -10 -5 0 5 -15 -10 -5 0 5 Candidatus Bacteriodes periocalifornicus de -15 -10 -5 0 5 -15 -10 -5 0 5 Candidatus Bacteriodes periocalifornicus Fig. 5 Candidatus B. periocalifornicus (CBP) relative abundance is Results of Pearson’s correlation (p value and correlation coefficient) are strongly correlated with all three members of the red complex. shown in the box inset with gray shaded area indication the 95% Scatterplot and trend line showing the relationship between CBP and a confidence interval for the line of best fit (n = 272). Relative abundance P. gingivalis, b T. denticola, c T. forsythia, d F. nucleatum, and e S. mitis. data was log 2 transformed (normalized) 0 0 -5 -5 -5 -10 -10 -10 p < 0.0001 p < 0.0001 -15 p < 0.0001 r = 0.32 r = 0.23 r = 0.26 -15 0 0 -5 -5 -10 p < 0.0001 -10 p = 0.34 r = 0.24 r = 0.004 Porphyromonas gingivalis Fusobacterium nucleatum Treponema denticola Streptococcus mitis Tannerella forsythia Discovery of a Novel Periodontal Disease-Associated Bacterium CBP genome against the well annotated P. gingivalis ATCC References 33277 genome, we identified the following shared virulence- associated genes: C25 domains encoding gingipains, which 1. Chen H, Jiang W (2014) Application of high-throughput sequenc- ing in understanding human oral microbiome related with health are well-known P. gingivalis proteases that target outer mem- and disease. Front. 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Microbial EcologySpringer Journals

Published: Jun 2, 2018

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