In silico Comparison of 19 Porphyromonas gingivalis Strains in Genomics, Phylogenetics, Phylogenomics and Functional Genomics

Currently, genome sequences of a total of 19 Porphyromonas gingivalis strains are available, including eight completed genomes (strains W83, ATCC 33277, TDC60, HG66, A7436, AJW4, 381, and A7A1-28) and 11 high-coverage draft sequences (JCVI SC001, F0185, F0566, F0568, F0569, F0570, SJD2, W4087, W50, Ando, and MP4-504) that are assembled into fewer than 300 contigs. The objective was to compare these genomes at both nucleotide and protein sequence levels in order to understand their phylogenetic and functional relatedness. Four copies of 16S rRNA gene sequences were identified in each of the eight complete genomes and one in the other 11 unfinished genomes. These 43 16S rRNA sequences represent only 24 unique sequences and the derived phylogenetic tree suggests a possible evolutionary history for these strains. Phylogenomic comparison based on shared proteins and whole genome nucleotide sequences consistently showed two groups with closely related members: one consisted of ATCC 33277, 381, and HG66, another of W83, W50, and A7436. At least 1,037 core/shared proteins were identified in the 19 P. gingivalis genomes based on the most stringent detecting parameters. Comparative functional genomics based on genome-wide comparisons between NCBI and RAST annotations, as well as additional approaches, revealed functions that are unique or missing in individual P. gingivalis strains, or species-specific in all P. gingivalis strains, when compared to a neighboring species P. asaccharolytica. All the comparative results of this study are available online for download at ftp://www.homd.org/publication_data/20160425/

The Human Oral Microbiome Database: a web accessible resource for investigating oral microbe taxonomic and genomic information

The human oral microbiome is the most studied human microflora, but 53% of the species have not yet been validly named and 35% remain uncultivated. The uncultivated taxa are known primarily from 16S rRNA sequence information. Sequence information tied solely to obscure isolate or clone numbers, and usually lacking accurate phylogenetic placement, is a major impediment to working with human oral microbiome data. The goal of creating the Human Oral Microbiome Database (HOMD) is to provide the scientific community with a body site-specific comprehensive database for the more than 600 prokaryote species that are present in the human oral cavity based on a curated 16S rRNA gene-based provisional naming scheme. Currently, two primary types of information are provided in HOMD—taxonomic and genomic. Named oral species and taxa identified from 16S rRNA gene sequence analysis of oral isolates and cloning studies were placed into defined 16S rRNA phylotypes and each given unique Human Oral Taxon (HOT) number. The HOT interlinks phenotypic, phylogenetic, genomic, clinical and bibliographic information for each taxon. A BLAST search tool is provided to match user 16S rRNA gene sequences to a curated, full length, 16S rRNA gene reference data set. For genomic analysis, HOMD provides comprehensive set of analysis tools and maintains frequently updated annotations for all the human oral microbial genomes that have been sequenced and publicly released. Oral bacterial genome sequences, determined as part of the Human Microbiome Project, are being added to the HOMD as they become available. We provide HOMD as a conceptual model for the presentation of microbiome data for other human body sites

Strand-specific transcriptome profiling with directly labeled RNA on genomic tiling microarrays

<p>Abstract</p> <p>Background</p> <p>With lower manufacturing cost, high spot density, and flexible probe design, genomic tiling microarrays are ideal for comprehensive transcriptome studies. Typically, transcriptome profiling using microarrays involves reverse transcription, which converts RNA to cDNA. The cDNA is then labeled and hybridized to the probes on the arrays, thus the RNA signals are detected indirectly. Reverse transcription is known to generate artifactual cDNA, in particular the synthesis of second-strand cDNA, leading to false discovery of antisense RNA. To address this issue, we have developed an effective method using RNA that is directly labeled, thus by-passing the cDNA generation. This paper describes this method and its application to the mapping of transcriptome profiles.</p> <p>Results</p> <p>RNA extracted from laboratory cultures of <it>Porphyromonas gingivalis </it>was fluorescently labeled with an alkylation reagent and hybridized directly to probes on genomic tiling microarrays specifically designed for this periodontal pathogen. The generated transcriptome profile was strand-specific and produced signals close to background level in most antisense regions of the genome. In contrast, high levels of signal were detected in the antisense regions when the hybridization was done with cDNA. Five antisense areas were tested with independent strand-specific RT-PCR and none to negligible amplification was detected, indicating that the strong antisense cDNA signals were experimental artifacts.</p> <p>Conclusions</p> <p>An efficient method was developed for mapping transcriptome profiles specific to both coding strands of a bacterial genome. This method chemically labels and uses extracted RNA directly in microarray hybridization. The generated transcriptome profile was free of cDNA artifactual signals. In addition, this method requires fewer processing steps and is potentially more sensitive in detecting small amount of RNA compared to conventional end-labeling methods due to the incorporation of more fluorescent molecules per RNA fragment.</p

The bioinformatics resource for oral pathogens

Complete genomic sequences of several oral pathogens have been deciphered and multiple sources of independently annotated data are available for the same genomes. Different gene identification schemes and functional annotation methods used in these databases present a challenge for cross-referencing and the efficient use of the data. The Bioinformatics Resource for Oral Pathogens (BROP) aims to integrate bioinformatics data from multiple sources for easy comparison, analysis and data-mining through specially designed software interfaces. Currently, databases and tools provided by BROP include: (i) a graphical genome viewer (Genome Viewer) that allows side-by-side visual comparison of independently annotated datasets for the same genome; (ii) a pipeline of automatic data-mining algorithms to keep the genome annotation always up-to-date; (iii) comparative genomic tools such as Genome-wide ORF Alignment (GOAL); and (iv) the Oral Pathogen Microarray Database. BROP can also handle unfinished genomic sequences and provides secure yet flexible control over data access. The concept of providing an integrated source of genomic data, as well as the data-mining model used in BROP can be applied to other organisms. BROP can be publicly accessed at

Hidden Gems in the Transcriptome Maps of Competent Streptococci

Natural transformation is regarded as an important mechanism in bacteria that allows for adaptation to different environmental stressors by ensuring genome plasticity. Since the discovery of this phenomenon in Streptococcus pneumoniae, remarkable progress has been made in the understanding of the molecular mechanisms and pathways coordinating this process. Recently, the advent of high-throughput sequencing allows the posing of questions that address the system at a larger scale but also allow for the creation of high-resolution maps of transcription. Thus, while much is already known about genetic competence in streptococci, recent studies continue to reveal intricate novel regulation pathways and components. In this perspective article, we highlight the use of transcriptional profiling and mapping as a valuable resource in the identification and characterization of “hidden gems” pertinent to the natural transformation system. Such strategies have recently been employed in a variety of different species. In S. mutans, for example, genome editing combined with the power of promoter mapping and RNA-Seq allowed for the identification of a link between the ComCDE and the ComRS systems, a ComR positive feedback loop mediated by SigX, and the XrpA peptide, encoded within sigX, which inhibits competence. In S. pneumoniae, a novel member of the competence regulon termed BriC was found to be directly under control of ComE and to promote biofilm formation and nasopharyngeal colonization but not competence. Together these new technologies enable us to discover new links and to revisit old pathways in the compelling study of natural genetic transformation

Resolvin E1 Reverses Experimental Periodontitis and Dysbiosis

Periodontitis is a biofilm-induced inflammatory disease characterized by dysbiosis of the commensal periodontal microbiota. It is unclear how natural regulation of inflammation affects the periodontal biofilm. Promoters of active resolution of inflammation including Resolvin E1 (RvE1) effectively treat inflammatory periodontitis in animal models. The goals of this study were 1) to compare periodontal tissue gene expression in different clinical conditions, 2) to determine the impact of local inflammation on the composition of subgingival bacteria, and 3) to understand how inflammation impacts these changes. Two clinically-relevant experiments were performed in rats: prevention and treatment of ligature-induced periodontitis with RvE1 topical treatment. The gingival transcriptome was evaluated by RNA-seq sequencing of mRNA. The composition of the subgingival microbiota was characterized by 16S rDNA sequencing. Periodontitis was assessed by bone morphometric measurements and histomorphometry of block sections. H&E and, tartrate resistant acid phosphatase staining were used to characterize and quantify inflammatory changes. RvE1 treatment prevented bone loss in ligature induced periodontitis. Osteoclast density and inflammatory cell infiltration in the RvE1 groups were lower than those in the placebo group. RvE1 treatment reduced expression of inflammation-related genes returning the expression profile to one more similar to health. Treatment of established periodontitis with RvE1 reversed bone loss, reversed inflammatory gene expression and reduced osteoclast density. Assessment of the rat subgingival microbiota after RvE1 treatment revealed marked changes in both prevention and treatment experiments. The data suggest that modulation of local inflammation has a major role in shaping the composition of the subgingival microbiota

Bacteriome analysis of Aggregatibacter actinomycetemcomitans-JP2 genotype-associated Grade C periodontitis in Moroccan adolescents

BackgroundGrade C (previously aggressive) periodontitis (GCP) in adolescents is prevalent in certain parts of Africa where it is associated with JP2 genotype, a highly virulent strain of Aggregatibacter actinomycetemcomitans. The aim of this study was to characterize the subgingival bacteriome in Moroccan subjects with GCP positive to A. actinomycetemcomitans JP2 genotype.MethodsSubgingival plaque samples were collected from shallow and deep pockets of 8 subjects with GCP (17.2 ± 1.5 years) and from gingival sulci of 13 controls with no periodontitis (14.6 ± 1.1 years). Identification and genotyping of A. actinomycetemcomitans was performed using PCR analysis of the ltx operon, while bacteriome profiling was done by 16S rRNA gene sequencing (V1–V3 region). Groups were compared in terms of microbial diversity, abundances, and dysbiosis.ResultsThe shallow and deep pocket sites from GCP cases had a significantly altered microbial composition compared to controls. Species associated with health included Haemophilus parainfluenzae, Lautropia mirabilis, Streptococcus spp., Gemella spp., and Rothia spp. While known periodontal pathogens, including Porphyromonas gingivalis, Tannerella forsythia, Treponema spp. and Fretibacterium spp., were significantly enriched in GCP, non-conventional taxa, including Pseudomonas oral taxon C61 and Enterobacter cloacae were more abundant and showed stronger association with the disease. Less significant differences in abundances of individual taxa were observed between shallow and deep pockets. Overall dysbiosis measured in terms of Subgingival Microbial Dysbiosis Index (SMDI) differentiated between GCP and no-periodontitis with 95% accuracy.ConclusionsThe results suggest that several periodontal pathogens involved in the adult-type periodontitis also play a role in JP2 genotype-associated GCP. The potential role of non-conventional taxa in the pathogenesis of GCP warrants further investigation

Salivary Oral Microbiome of Children With Juvenile Idiopathic Arthritis: A Norwegian Cross-Sectional Study

Background: The oral microbiota has been connected to the pathogenesis of rheumatoid arthritis through activation of mucosal immunity. The objective of this study was to characterize the salivary oral microbiome associated with juvenile idiopathic arthritis (JIA), and correlate it with the disease activity including gingival inflammation. Methods: Fifty-nine patients with JIA (mean age, 12.6 ± 2.7 years) and 34 healthy controls (HC; mean age 12.3 ± 3.0 years) were consecutively recruited in this Norwegian cross-sectional study. Information about demographics, disease activity, medication history, frequency of tooth brushing and a modified version of the gingival bleeding index (GBI) and the simplified oral hygiene index (OHI-S) was obtained. Microbiome profiling of saliva samples was performed by sequencing of the V1-V3 region of the 16S rRNA gene, coupled with a species-level taxonomy assignment algorithm; QIIME, LEfSe and R-package for Spearman correlation matrix were used for downstream analysis. Results: There were no significant differences between JIA and HC in alpha- and beta-diversity. However, differential abundance analysis revealed several taxa to be associated with JIA: TM7-G1, Solobacterium and Mogibacterium at the genus level; and Leptotrichia oral taxon 417, TM7-G1 oral taxon 352 and Capnocytophaga oral taxon 864 among others, at the species level. Haemophilus species, Leptotrichia oral taxon 223, and Bacillus subtilis, were associated with healthy controls. Gemella morbillorum, Leptotrichia sp. oral taxon 498 and Alloprevotella oral taxon 914 correlated positively with the composite juvenile arthritis 10-joint disease activity score (JADAS10), while Campylobacter oral taxon 44 among others, correlated with the number of active joints. Of all microbial markers identified, only Bacillus subtilis and Campylobacter oral taxon 44 maintained false discovery rate (FDR) Conclusions: In this exploratory study of salivary oral microbiome we found similar alpha- and beta-diversity among children with JIA and healthy. Several taxa associated with chronic inflammation were found to be associated with JIA and disease activity, which warrants further investigation

Longitudinal changes in subgingival biofilm composition following periodontal treatment

Background: Current periodontal treatment involves instrumentation using hand and/or ultrasonic instruments, which are used either alone or in combination based on patient and clinician preference, with comparable clinical outcomes. This study sought to investigate early and later changes in the subgingival biofilm following periodontal treatment; to identify whether these changes were associated with treatment outcomes; and to investigate whether the biofilm responded differently to hand compared with ultrasonic instruments. Methods: This was a secondary-outcome analysis of a randomised controlled trial. Thirty-eight periodontitis patients received full-mouth subgingival instrumentation using hand (n = 20) or ultrasonic instrumentation (n = 18). Subgingival plaque was sampled at baseline and 1, 7 and 90 days following treatment. Bacterial DNA was analysed using 16S rRNA sequencing. Periodontal clinical parameters were evaluated before and after treatment. Results: Biofilm composition was comparable in both (hand and ultrasonics) treatment groups at all timepoints (all genus and species; p[adjusted]&gt;0.05). Large-scale changes were observed within-groups across timepoints. At days 1 and 7, taxonomic diversity and dysbiosis were reduced, with an increase in health-associated genera including Streptococcus and Rothia equating to 30-40% of the relative abundance. When reassessed at day 90 a subset of samples reformed a microbiome more comparable with baseline, which was independent of instrumentation choice and residual disease. Conclusions: Hand and ultrasonic instruments induced comparable impacts on the subgingival plaque microbiome. There were marked early changes in the subgingival biofilm composition, although there was limited evidence that community shifts associated with treatment outcomes

Dynamic probe selection for studying microbial transcriptome with high-density genomic tiling microarrays

<p>Abstract</p> <p>Background</p> <p>Current commercial high-density oligonucleotide microarrays can hold millions of probe spots on a single microscopic glass slide and are ideal for studying the transcriptome of microbial genomes using a tiling probe design. This paper describes a comprehensive computational pipeline implemented specifically for designing tiling probe sets to study microbial transcriptome profiles.</p> <p>Results</p> <p>The pipeline identifies every possible probe sequence from both forward and reverse-complement strands of all DNA sequences in the target genome including circular or linear chromosomes and plasmids. Final probe sequence lengths are adjusted based on the maximal oligonucleotide synthesis cycles and best isothermality allowed. Optimal probes are then selected in two stages - sequential and gap-filling. In the sequential stage, probes are selected from sequence windows tiled alongside the genome. In the gap-filling stage, additional probes are selected from the largest gaps between adjacent probes that have already been selected, until a predefined number of probes is reached. Selection of the highest quality probe within each window and gap is based on five criteria: sequence uniqueness, probe self-annealing, melting temperature, oligonucleotide length, and probe position.</p> <p>Conclusions</p> <p>The probe selection pipeline evaluates global and local probe sequence properties and selects a set of probes dynamically and evenly distributed along the target genome. Unique to other similar methods, an exact number of non-redundant probes can be designed to utilize all the available probe spots on any chosen microarray platform. The pipeline can be applied to microbial genomes when designing high-density tiling arrays for comparative genomics, ChIP chip, gene expression and comprehensive transcriptome studies.</p