Whole genome sequencing of a family with autosomal dominant features within the oculoauriculovertebral spectrum.

Background: Oculoauriculovertebral Spectrum (OAVS) encompasses abnormalities on derivatives from the first and second pharyngeal arches including macrostomia, hemifacial microsomia, micrognathia, preauricular tags, ocular and vertebral anomalies. We present genetic findings on a three-generation family affected with macrostomia, preauricular tags and uni- or bilateral ptosis following an autosomal dominant pattern. Methods: We generated whole genome sequencing data for the proband, affected parent and unaffected paternal grandparent followed by Sanger sequencing on 23 family members for the top 10 candidate genes: KCND2, PDGFRA, CASP9, NCOA3, WNT10A, SIX1, MTF1, KDR/VEGFR2, LRRK1, and TRIM2 We performed parent and sibling-based transmission disequilibrium tests and burden analysis via a penalized linear mixed model, for segregation and mutation burden respectively. Next, via bioinformatic tools we predicted protein function, mutation pathogenicity and pathway enrichment to investigate the biological relevance of mutations identified. Results: Rare missense mutations in SIX1, KDR/VEGFR2, and PDGFRA showed the best segregation with the OAV phenotypes in this family. When considering any of the 3 OAVS phenotypes as an outcome, SIX1 had the strongest associations in parent-TDTs and sib-TDTs (p=0.025, p=0.052) (unadjusted p-values). Burden analysis identified SIX1 (RC=0.87) and PDGFRA (RC=0.98) strongly associated with OAVS severity. Using phenotype-specific outcomes, sib-TDTs identified SIX1 with uni- or bilateral ptosis (p=0.049) and ear tags (p=0.01), and PDGFRA and KDR/VEGFR2 with ear tags (both p<0.01). Conclusion: SIX1, PDGFRA, and KDR/VEGFR2 are strongly associated to OAVS phenotypes. SIX1 has been previously associated with OAVS ear malformations and is co-expressed with EYA1 during ear development. Efforts to strengthen the genotype-phenotype co-relation underlying the OAVS are key to discover etiology, family counseling and prevention.

Sources of SARS-CoV-2 and Other Microorganisms in Dental Aerosols.

On March 16, 2020, 198,000 dentists in the United States closed their doors to patients, fueled by concerns that aerosols generated during dental procedures are potential vehicles for transmission of respiratory pathogens through saliva. Our knowledge of these aerosol constituents is sparse and gleaned from case reports and poorly controlled studies. Therefore, we tracked the origins of microbiota in aerosols generated during ultrasonic scaling, implant osteotomy, and restorative procedures by combining reverse transcriptase quantitative polymerase chain reaction (to identify and quantify SARS-CoV-2) and 16S sequencing (to characterize the entire microbiome) with fine-scale enumeration and source tracking. Linear discriminant analysis of Bray-Curtis dissimilarity distances revealed significant class separation between the salivary microbiome and aerosol microbiota deposited on the operator, patient, assistant, or the environment (P < 0.01, analysis of similarities). We also discovered that 78% of the microbiota in condensate could be traced to the dental irrigant, while saliva contributed to a median of 0% of aerosol microbiota. We also identified low copy numbers of SARS-CoV-2 virus in the saliva of several asymptomatic patients but none in aerosols generated from these patients. Together, the bacterial and viral data encourage us to conclude that when infection control measures are used, such as preoperative mouth rinses and intraoral high-volume evacuation, dental treatment is not a factor in increasing the risk for transmission of SARS-CoV-2 in asymptomatic patients and that standard infection control practices are sufficiently capable of protecting personnel and patients from exposure to potential pathogens. This information is of immediate urgency, not only for safe resumption of dental treatment during the ongoing COVID-19 pandemic, but also to inform evidence-based selection of personal protection equipment and infection control practices at a time when resources are stretched and personal protection equipment needs to be prioritized.

Subgingival Host-Microbial Interactions in Hyperglycemic Individuals.

Type 2 diabetes mellitus (T2DM) is an established risk factor for periodontitis, yet its contribution to creating host-bacterial disequilibrium in the subgingival crevice is poorly understood. The present investigation aimed to quantify the impact of hyperglycemia on host-bacterial interactions in established periodontitis and to map shifts in these dynamics following mechanical nonsurgical therapy. Seventeen T2DM and 17 non-T2DM subjects with generalized severe chronic periodontitis were recruited along with 20 periodontally healthy individuals. Subjects with periodontitis were treated with scaling and root planing (SRP). Samples of subgingival biofilm and gingival crevicular fluid were collected at baseline and at 1-, 3-, and 6 mo postoperatively. Correlations were generated between 13.7 million 16S ribosomal DNA sequences and 8 immune mediators. Intermicrobial and host-microbial interactions were modeled using differential network analysis. Periodontal health was characterized by a sparse interbacterial and highly connected cytokine-bacterial network, while both normoglycemics and T2DM subjects with periodontitis demonstrated robust congeneric and intergeneric hubs but significantly fewer cytokine-bacterial connections. Following SRP, the cytokine-bacterial edges demonstrated a 2-fold increase 1 mo postoperatively and a 10-fold increase at 6 mo in normoglycemics. In hyperglycemics, there was a doubling at 1 mo but no further changes thereafter. These shifts accompanied an increasingly sparse interbacterial network. In normoglycemics, the nodes anchored by interleukin (IL)-4, IL-6, and IL-10 demonstrated greatest rewiring, while in hyperglycemics, IL-1ß, IL-6, INF-γ, and IL-17 exhibited progressive rewiring. Thus, the present investigation points to a breakdown in host-bacterial mutualism in periodontitis, with interbacterial interactions rather than host-bacterial interactions primarily determining community assembly. Hyperglycemia further exacerbates this uncoupled mutualism. Our data also demonstrate that while nonsurgical therapy might not consistently alter microbial abundances or lower proinflammatory molecules, it "reboots" the interaction between the immunoinflammatory system and the newly colonizing microbiome, restoring a role for the immune system in determining bacterial colonization. However, this outcome is lower and delayed in hyperglycemics.

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.

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.