Shareef M. Dabdoub

Patient-specific Analysis of Periodontal and Peri-implant Microbiomes:

Periodontally involved teeth have been implicated as ‘microbial reservoirs’ in the etiology of peri-implant diseases. Therefore, the purpose of this investigation was to use a deep-sequencing approach to identify the degree of congruence between adjacent peri-implant and periodontal microbiomes in states of health and disease. Subgingival and peri-implant biofilm samples were collected from 81 partially edentulous individuals with periodontal and peri-implant health and disease. Bacterial DNA was isolated, and the 16S rRNA gene was amplified and sequenced by pyrotag sequencing. Chimera-depleted sequences were compared against a locally hosted curated database for bacterial identification. Statistical significance was determined by paired Student’s t tests between tooth-implant pairs. The 1.9 million sequences identified represented 523 species. Sixty percent of individuals shared less than 50% of all species between their periodontal and peri-implant biofilms, and 85% of individuals shared less than 8% of abundant species between tooth and implant. Additionally, the periodontal microbiome demonstrated significantly higher diversity than the implant, and distinct bacterial lineages were associated with health and disease in each ecosystem. Analysis of our data suggests that simple geographic proximity is not a sufficient determinant of colonization of topographically distinct niches, and that the peri-implant and periodontal microbiomes represent microbiologically distinct ecosystems.

The subgingival microbiome of clinically healthy current and never smokers.

Dysbiotic oral bacterial communities have a critical role in the etiology and progression of periodontal diseases. The goal of this study was to investigate the extent to which smoking increases risk for disease by influencing the composition of the subgingival microbiome in states of clinical health. Subgingival plaque samples were collected from 200 systemically and periodontally healthy smokers and nonsmokers. 16S pyrotag sequencing was preformed generating 1 623 713 classifiable sequences, which were compared with a curated version of the Greengenes database using the quantitative insights into microbial ecology pipeline. The subgingival microbial profiles of smokers and never-smokers were different at all taxonomic levels, and principal coordinate analysis revealed distinct clustering of the microbial communities based on smoking status. Smokers demonstrated a highly diverse, pathogen-rich, commensal-poor, anaerobic microbiome that is more closely aligned with a disease-associated community in clinically healthy individuals, suggesting that it creates an at-risk-for-harm environment that is primed for a future ecological catastrophe.

The Influence of Smoking on the Peri-Implant Microbiome

Smokers are at high risk for 2 bacterially driven oral diseases: peri-implant mucositis and peri-implantitis. Therefore, the purpose of this investigation was to use a deep-sequencing approach to identify the effect of smoking on the peri-implant microbiome in states of health and disease. Peri-implant biofilm samples were collected from 80 partially edentulous subjects with peri-implant health, peri-implant mucositis, and peri-implantitis. Bacterial DNA was isolated and 16S ribsomal RNA gene libraries sequenced using 454-pyrosequencing targeting the V1 to V3 and V7 to V9 regions. In total, 790,692 classifiable sequences were compared against the HOMD database for bacterial identification. Community-level comparisons were carried out using UniFrac and nonparametric tests. Microbial signatures of health in smokers exhibited lower diversity compared to nonsmokers, with significant enrichment for disease-associated species. Shifts from health to mucositis were accompanied by loss of several health-associated species, leading to a further decrease in diversity. Peri-implantitis did not differ significantly from mucositis in species richness or evenness. In nonsmokers, by contrast, the shift from health to mucositis resembled primary ecological succession, with acquisition of several species without replacement of pioneer organisms, thereby creating a significant increase in diversity. Again, few differences were detected between peri-implantitis and mucositis. Thus, our data suggest that smoking shapes the peri-implant microbiomes even in states of clinical health, by supporting a pathogen-rich community. In both smokers and nonsmokers, peri-implant mucositis appears to be a pivotal event in disease progression, creating high-at-risk-for-harm communities. However, ecological succession follows distinctly divergent pathways in smokers and nonsmokers, indicating a need for personalized therapeutics for control and prevention of disease in these 2 cohorts.

Aberrant community architecture and attenuated persistence of uropathogenic Escherichia coli in the absence of individual IHF subunits.

Uropathogenic Escherichia coli (UPEC) utilizes a complex community-based developmental pathway for growth within superficial epithelial cells of the bladder during cystitis. Extracellular DNA (eDNA) is a common matrix component of organized bacterial communities. Integration host factor (IHF) is a heterodimeric protein that binds to double-stranded DNA and produces a hairpin bend. IHF-dependent DNA architectural changes act both intrabacterially and extrabacterially to regulate gene expression and community stability, respectively. We demonstrate that both IHF subunits are required for efficient colonization of the bladder, but are dispensable for early colonization of the kidney. The community architecture of the intracellular bacterial communities (IBCs) is quantitatively different in the absence of either IhfA or IhfB in the murine model for human urinary tract infection (UTI). Restoration of Type 1 pili by ectopic production does not restore colonization in the absence of IhfA, but partially compensates in the absence of IhfB. Furthermore, we describe a binding site for IHF that is upstream of the operon that encodes for the P-pilus. Taken together, these data suggest that both IHF and its constituent subunits (independent of the heterodimer), are able to participate in multiple aspects of the UPEC pathogenic lifestyle, and may have utility as a target for treatment of bacterial cystitis.

PhyloToAST: Bioinformatics tools for species-level analysis and visualization of complex microbial datasets

The 16S rRNA gene is widely used for taxonomic profiling of microbial ecosystems; and recent advances in sequencing chemistry have allowed extremely large numbers of sequences to be generated from minimal amounts of biological samples. Analysis speed and resolution of data to species-level taxa are two important factors in large-scale explorations of complex microbiomes using 16S sequencing. We present here new software, Phylogenetic Tools for Analysis of Species-level Taxa (PhyloToAST), that completely integrates with the QIIME pipeline to improve analysis speed, reduce primer bias (requiring two sequencing primers), enhance species-level analysis, and add new visualization tools. The code is free and open source, and can be accessed at http://phylotoast.org.

A tale of two risks: smoking, diabetes and the subgingival microbiome

Although smoking and diabetes have been established as the only two risk factors for periodontitis, their individual and synergistic impacts on the periodontal microbiome are not well studied. The present investigation analyzed 2.7 million 16S sequences from 175 non-smoking normoglycemic individuals (controls), smokers, diabetics and diabetic smokers with periodontitis as well as periodontally healthy controls, smokers and diabetics to assess subgingival bacterial biodiversity and co-occurrence patterns. The microbial signatures of periodontally healthy smokers, but not diabetics, were highly aligned with the disease-associated microbiomes of their respective cohorts. Diabetics were dominated by species belonging to Fusobacterium, Parvimonas, Peptostreptococcus, Gemella, Streptococcus, Leptotrichia, Filifactor, Veillonella, TM7 and Terrahemophilus. These microbiomes exhibited significant clustering based on HbA1c levels (pre-diabetic (<6.5%), diabetic (6.5–9.9%), diabetics >10%). Smokers with periodontitis evidenced a robust core microbiome (species identified in at least 80% of individuals) dominated by anaerobes, with inter-individual differences attributable largely to the ‘rare biosphere’. Diabetics and diabetic smokers, on the other hand, were microbially heterogeneous and enriched for facultative species. In smokers, microbial co-occurrence networks were sparse and predominantly congeneric, while robust inter-generic networks were observed in diabetics and diabetic smokers. Smoking and hyperglycemia impact the subgingival microbiome in distinct ways, and when these perturbations intersect, their synergistic effect is greater than what would be expected from the sum of each effect separately. Thus, this study underscores the importance of early intervention strategies in maintaining health-compatible microbiomes in high-risk individuals, as well as the need to personalize these interventions based on the environmental perturbation.

FIND: A new software tool and development platform for enhanced multicolor flow analysis

BackgroundFlow Cytometry is a process by which cells, and other microscopic particles, can be identified, counted, and sorted mechanically through the use of hydrodynamic pressure and laser-activated fluorescence labeling. As immunostained cells pass individually through the flow chamber of the instrument, laser pulses cause fluorescence emissions that are recorded digitally for later analysis as multidimensional vectors. Current, widely adopted analysis software limits users to manual separation of events based on viewing two or three simultaneous dimensions. While this may be adequate for experiments using four or fewer colors, advances have lead to laser flow cytometers capable of recording 20 different colors simultaneously. In addition, mass-spectrometry based machines capable of recording at least 100 separate channels are being developed. Analysis of such high-dimensional data by visual exploration alone can be error-prone and susceptible to unnecessary bias. Fortunately, the field of Data Mining provides many tools for automated group classification of multi-dimensional data, and many algorithms have been adapted or created for flow cytometry. However, the majority of this research has not been made available to users through analysis software packages and, as such, are not in wide use.ResultsWe have developed a new software application for analysis of multi-color flow cytometry data. The main goals of this effort were to provide a user-friendly tool for automated gating (classification) of multi-color data as well as a platform for development and dissemination of new analysis tools. With this software, users can easily load single or multiple data sets, perform automated event classification, and graphically compare results within and between experiments. We also make available a simple plugin system that enables researchers to implement and share their data analysis and classification/population discovery algorithms.ConclusionsThe FIND (Flow Investigation using N-Dimensions) platform presented here provides a powerful, user-friendly environment for analysis of Flow Cytometry data as well as providing a common platform for implementation and distribution of new automated analysis techniques to users around the world.

Comparative metagenomics reveals taxonomically idiosyncratic yet functionally congruent communities in periodontitis.

The phylogenetic characteristics of microbial communities associated with periodontitis have been well studied, however, little is known about the functional endowments of this ecosystem. The present study examined 73 microbial assemblages from 25 individuals with generalized chronic periodontitis and 25 periodontally healthy individuals using whole genome shotgun sequencing. Core metabolic networks were computed from taxa and genes identified in at least 80% of individuals in each group. 50% of genes and species identified in health formed part of the core microbiome, while the disease-associated core microbiome contained 33% of genes and only 1% of taxa. Clinically healthy sites in individuals with periodontitis were more aligned with sites with disease than with health. 68% of the health-associated metagenome was dedicated to energy utilization through oxidative pathways, while in disease; fermentation and methanogenesis were predominant energy transfer mechanisms. Expanded functionality was observed in periodontitis, with unique- or over-representation of genes encoding for fermentation, antibiotic resistance, detoxification stress, adhesion, invasion and intracellular resistance, proteolysis, quorum sensing, Type III/IV secretion systems, phages and toxins in the disease-associated core microbiome. However, different species or consortia contributed to these functions in each individual. Several genes, but not species, demonstrated robust discriminating power between health and disease.

New paradigms of urinary tract infections: Implications for patient management

Urinary tract infections (UTIs) represent one of the most commonly acquired diseases among the general population as well as hospital in-patients, yet remain difficult to effectively and consistently treat. High rates of recurrence, anatomic abnormalities, and functional disturbances of the urinary tract all contribute to the difficulty in management of these infections. However, recent advances reveal important molecular and genetic factors that contribute to bacterial invasion and persistence in the urinary tract, particularly for the most common causative agent, uropathogenic Escherichia coli. Recent studies using animal models of experimental UTIs have recently provided mechanistic insight into the clinical observations that question the effectiveness of antibiotic therapy in treatment. Ultimately, continuing research will be necessary to identify the best targets for effective treatment of this costly and widespread infectious disease.

The making of a miscreant: tobacco smoke and the creation of pathogen-rich biofilms

We have previously reported that oral biofilms in clinically healthy smokers are pathogen-rich, and that this enrichment occurs within 24 h of biofilm formation. The present investigation aimed to identify a mechanism by which smoking creates this altered community structure. By combining in vitro microbial–mucosal interface models of commensal (consisting of Streptococcus oralis, Streptococcus sanguis, Streptococcus mitis, Actinomyces naeslundii, Neisseria mucosa and Veillonella parvula) and pathogen-rich (comprising S.oralis, S.sanguis, S.mitis, A.naeslundii, N.mucosa and V.parvula, Fusobacterium nucleatum, Porphyromonas gingivalis, Filifactor alocis, Dialister pneumosintes, Selenonomas sputigena, Selenominas noxia, Catonella morbi, Parvimonas micra and Tannerella forsythia) communities with metatranscriptomics, targeted proteomics and fluorescent microscopy, we demonstrate that smoke exposure significantly downregulates essential metabolic functions within commensal biofilms, while significantly increasing expression of virulence genes, notably lipopolysaccharide (LPS), flagella and capsule synthesis. By contrast, in pathogen-rich biofilms several metabolic pathways were over-expressed in response to smoke exposure. Under smoke-rich conditions, epithelial cells mounted an early and amplified pro-inflammatory and oxidative stress response to these virulence-enhanced commensal biofilms, and a muted early response to pathogen-rich biofilms. Commensal biofilms also demonstrated early and widespread cell death. Similar results were observed when smoke-free epithelial cells were challenged with smoke-conditioned biofilms, but not vice versa. In conclusion, our data suggest that smoke-induced transcriptional shifts in commensal biofilms triggers a florid pro-inflammatory response, leading to early commensal death, which may preclude niche saturation by these beneficial organisms. The cytokine-rich, pro-oxidant, anaerobic environment sustains inflammophilic bacteria, and, in the absence of commensal antagonism, may promote the creation of pathogen-rich biofilms in smokers.Tobacco: How smoking promotes harmful biofilmsTobacco smoke inhibits the metabolism of beneficial bacteria in biofilms, while activating specific genes in pathogenic bacteria. This suggests a mechanism to explain how smoking quickly leads to the formation of damaging biofilms in the mouth and respiratory tract. Purnima Kumar and colleagues at Ohio State University, USA studied the effect of tobacco smoke on cultured biofilms used to model those that form on mucous membranes. They detected specific and varied changes in the activity of genes, proteins and metabolism that allowed pathogenic bacteria to displace beneficial “commensal” bacteria. The research suggests the transition toward pathogen-rich biofilms may contribute to the health effects of smoking by causing increased inflammation of mucous membranes and the production of damaging oxidant chemicals. Further research should investigate the chemical constituents of smoke responsible for these effects.