Microbial signatures of oral dysbiosis, periodontitis and edentulism revealed by Gene Meter methodology

Persistence of salivary antibody responses after COVID-19 vaccination is associated with oral microbiome variation in both healthy and people living with HIV

Coevolution of microbiome and immunity at mucosal sites is essential for our health. Whether the oral microbiome, the second largest community after the gut, contributes to the immunogenicity of COVID-19 vaccines is not known. We investigated the baseline oral microbiome in individuals in the COVAXID clinical trial receiving the BNT162b2 mRNA vaccine. Participants (n=115) included healthy controls (HC; n=57) and people living with HIV (PLHIV; n=58) who met the study selection criteria. Vaccine-induced Spike antibodies in saliva and serum from 0 to 6 months were assessed and comparative analyses were performed against the individual salivary 16S ASV microbiome diversity. High- versus low vaccine responders were assessed on general, immunological, and oral microbiome features. Our analyses identified oral microbiome features enriched in high- vs. low-responders among healthy and PLHIV participants. In low-responders, an enrichment of Gram-negative, anaerobic species with proteolytic activity were found including Campylobacter, Butyrivibrio, Selenomonas, Lachnoanaerobaculum, Leptotrichia, Megasphaera, Prevotella and Stomatobaculum. In high-responders, enriched species were mainly Gram-positive and saccharolytic facultative anaerobes: Abiotrophia, Corynebacterium, Gemella, Granulicatella, Rothia, and Haemophilus. Combining identified microbial features in a classifier using the area under the receiver operating characteristic curve (ROC AUC) yielded scores of 0.879 (healthy controls) to 0.82 (PLHIV), supporting the oral microbiome contribution in the long-term vaccination outcome. The present study is the first to suggest that the oral microbiome has an impact on the durability of mucosal immunity after Covid-19 vaccination. Microbiome-targeted interventions to enhance long-term duration of mucosal vaccine immunity may be exploited.

Characterization of the Oral Microbiome Among Children With Type 1 Diabetes Compared With Healthy Children

Aim: Current microbiome profiling of type 1 diabetes mellitus (T1D) patients is mostly limited to gut microbiome. We characterized the oral microbiome associated with T1D in children after the onset of the disease and explored its relationship with oral physiological factors and dental status. Methods: This cohort study comprised 37 children aged 5-15 years with T1D and 29 healthy children matched in age and gender. Unstimulated whole saliva was collected from diabetic and non-diabetic children, in the morning after brushing their teeth and a fasting period of at least 1 h before sampling. 16S rRNA gene-based analysis was performed by Powersoil Pro kit by Qiagen and Phusion High-Fidelity PCR Master Mix. Oral physiological and dental parameters studied included decayed, missing, and filled teeth index, salivary flow rate, and salivary pH, glucose, calcium, phosphate, and urea levels. Results: Of the identified 105 different genera and 211 different species, the most abundant genera were Streptococcus, Prevotella, Veillonella, Haemophilus, and Neisseria. Streptococcus was more abundant in T1D children. The diabetes group had 22 taxa at the genus level and 33 taxa at the species level that were not present in the control group and the control group exhibited 6 taxa at the genus level and 9 taxa at the species level that did not exist in the diabetes group. In addition, Catonella, Fusobacterium, and Mogibacterium differed between healthy and T1D subjects. Eight species and eight subspecies were significantly more abundant among healthy children than in T1D children. Porphyromonas and Mogibacterium genera were significantly correlated with salivary parameters. We found similarities between taxa revealed in the present study and those found in gut microbiome in type 1 diabetes mellitus according to gutMDisorder database. Conclusions: Salivary microbiome analysis revealed unique microbial taxa that differed between T1D children and healthy subjects. Several genera found in the saliva of T1D children were associated with gut microbiome in T1D individuals.

Oral Microbiome in Relation to Periodontitis Severity and Systemic Inflammation

Systemic inflammation induced by periodontitis is suggested to be the link between periodontitis and cardiovascular disease. The aim of this work was to explore the oral microbiome in periodontitis in relation to disease severity and systemic inflammation. The saliva and subgingival microbiome from periodontal pocket samples of patients with severe (n = 12) and mild periodontitis (n = 13) were analyzed using metagenomic shotgun sequencing. The taxa and pathways abundances were quantified. The diversity was assessed and the abundances to phenotype associations were performed using ANCOM and linear regression. A panel of inflammatory markers was measured in blood and was associated with taxa abundance. The microbial diversity and species richness did not differ between severe and mild periodontitis in either saliva or periodontal pockets. However, there were significant differences in the microbial composition between severe and mild periodontitis in the subgingival microbiome (i.e., pocket samples) and, in a lower grade, in saliva, and this is positively associated with systemic inflammatory markers. The “red complex” and “cluster B” abundances in periodontal pockets were strongly associated with inflammatory markers interleukin-6 and the white blood cell count. Our data suggest that systemic inflammation in severe periodontitis may be driven by the oral microbiome and may support the indirect (inflammatory) mechanism for the association between periodontitis and cardiovascular disease.

Reassessing the Role of Entamoeba gingivalis in Periodontitis

The protozoan Entamoeba gingivalis resides in the oral cavity and is frequently observed in the periodontal pockets of humans and pets. This species of Entamoeba is closely related to the human pathogen Entamoeba histolytica, the agent of amoebiasis. Although E. gingivalis is highly enriched in people with periodontitis (a disease in which inflammation and bone loss correlate with changes in the microbial flora), the potential role of this protozoan in oral infectious diseases is not known. Periodontitis affects half the adult population in the world, eventually leads to edentulism, and has been linked to other pathologies, like diabetes and cardiovascular diseases. As aging is a risk factor for the disorder, it is considered an inevitable physiological process, even though it can be prevented and cured. However, the impact of periodontitis on the patient's health and quality of life, as well as its economic burden, are underestimated. Commonly accepted models explain the progression from health to gingivitis and then periodontitis by a gradual change in the identity and proportion of bacterial microorganisms in the gingival crevices. Though not pathognomonic, inflammation is always present in periodontitis. The recruitment of leukocytes to inflamed gums and their passage to the periodontal pocket lumen are speculated to fuel both tissue destruction and the development of the flora. The individual contribution to the disease of each bacterial species is difficult to establish and the eventual role of protozoa in the fate of this disease has been ignored. Following recent scientific findings, we discuss the relevance of these data and propose that the status of E. gingivalis be reconsidered as a potential pathogen contributing to periodontitis.

Initial stage of the biofilm formation on the NiTi and Ti6Al4V surface by the sulphur-oxidizing bacteria and sulphate-reducing bacteria

The susceptibility to the fouling of the NiTi and Ti6Al4V alloys due to the adhesion of microorganisms and the biofilm formation is very significant, especially in the context of an inflammatory state induced by implants contaminated by bacteria, and the implants corrosion stimulated by bacteria. The aim of this work was to examine the differences between the sulphur-oxidizing bacteria (SOB) and sulphate-reducing bacteria (SRB) strains in their affinity for NiTi and Ti6Al4V alloys. The biofilms formed on alloy surfaces by the cells of five bacterial strains (aerobic SOB Acidithiobacillus thiooxidans and Acidithiobacillus ferrooxidans, and anaerobic SRB Desulfovibrio desulfuricans-3 strains) were studied using scanning electron microscopy (SEM) and confocal laser scanning microscopy (CLSM). The protein concentrations in liquid media have also been analyzed. The results indicate that both alloys tested may be colonized by SOB and SRB strains. In the initial stage of the biofilm formation, the higher affinity of SRB to both the alloys has been documented. However, the SOB strains have indicated the higher (although differentiated) adaptability to changing environment as compared with SRB. Stimulation of the SRB growth on the alloys surface was observed during incubation in the liquid culture media supplemented with artificial saliva, especially of lower pH (imitated conditions under the inflammatory state, for example in the periodontitis course). The results point to the possible threat to the human health resulting from the contamination of the titanium implant alloys surface by the SOB (A. thiooxidans and A. ferrooxidans) and SRB (D. desulfuricans).

Gene Meter: Accurate abundance calculations of gene expression

We previously reported that thousands of transcripts in the mouse and zebrafish significantly increased in abundance in a time series spanning from life to several days after death. Transcript abundances were determined by: calibrating each microarray probe using a dilution series of pooled RNAs, fitting the probe-responses to adsorption models, and back-calculating abundances using the probe signal intensity of a sample and the best fitting model. The accuracy of the abundance measurements was not assessed in our previous study because individual transcript concentrations in the calibration pool were not known. Accurate transcript abundances are highly desired for modeling the dynamics of biological systems and investigating how systems respond to perturbations. In this study, we show that accurate transcript abundances can be determined by calibrating the probes using a calibration pool of transcripts with known concentrations. Instructions for determining accurate transcript abundances using the Gene Meter approach are provided.

Gene expression in the twilight of death: The increase of thousands of transcripts has implications to transplantation, cancer, and forensic research

After a vertebrate dies, many of its organ systems, tissues, and cells remain functional while its body no longer works as a whole. We define this state as the "twilight of death" - the transition from a living body to a decomposed corpse. We claim that the study of the twilight of death is important to ethical, legal and medical science. We examined gene expression at the twilight of death in the zebrafish and mouse reaching the conclusion that apparently thousands of transcripts significantly increase in abundance from life to several hours/days postmortem relative to live controls. Transcript dynamics of different genes provided "proof-of-principle" that models accurately predict an individual's elapsed-time-of-death (i.e. postmortem interval). While many transcripts were associated with survival and stress compensation, others were associated with epigenetic factors, developmental control, and cancer. Future studies are needed to determine whether the high incidence of cancer in transplant recipients is due to the postmortem processes in donor organs.

Accurate predictions of postmortem interval using linear regression analyses of gene meter expression data

In criminal and civil investigations, postmortem interval is used as evidence to help sort out circumstances at the time of human death. Many biological, chemical, and physical indicators can be used to determine the postmortem interval - but most are not accurate. Here, we sought to validate an experimental design to accurately predict the time of death by analyzing the expression of hundreds of upregulated genes in two model organisms, the zebrafish and mouse. In a previous study, the death of healthy adults was conducted under strictly controlled conditions to minimize the effects of confounding factors such as lifestyle and temperature. A total of 74,179 microarray probes were calibrated using the Gene Meter approach and the transcriptional profiles of 1063 genes that significantly increased in abundance were assembled into a time series spanning from life to 48 or 96h postmortem. In this study, the experimental design involved splitting the transcription profiles into training and testing datasets, randomly selecting groups of profiles, determining the modeling parameters of the genes to postmortem time using over- and/or perfectly-defined linear regression analyses, and calculating the fit (R²) and slope of predicted versus actual postmortem times. This design was repeated several thousand to million times to find the top predictive groups of gene transcription profiles. A group of eleven zebrafish genes yielded R² of 1 and a slope of 0.99, while a group of seven mouse liver genes yielded a R² of 0.98 and a slope of 0.97, and seven mouse brain genes yielded a R² of 0.95 and a slope of 0.87. In all cases, groups of gene transcripts yielded better postmortem time predictions than individual gene transcripts. The significance of this study is two-fold: selected groups of gene transcripts provide accurate prediction of postmortem time, and the successfully validated experimental design can now be used to accurately predict postmortem time in cadavers.

Tracing the dynamics of gene transcripts after organismal death

In life, genetic and epigenetic networks precisely coordinate the expression of genes-but in death, it is not known if gene expression diminishes gradually or abruptly stops or if specific genes and pathways are involved. We studied this by identifying mRNA transcripts that apparently increase in relative abundance after death, assessing their functions, and comparing their abundance profiles through postmortem time in two species, mouse and zebrafish. We found mRNA transcript profiles of 1063 genes became significantly more abundant after death of healthy adult animals in a time series spanning up to 96 h postmortem. Ordination plots revealed non-random patterns in the profiles by time. While most of these transcript levels increased within 0.5 h postmortem, some increased only at 24 and 48 h postmortem. Functional characterization of the most abundant transcripts revealed the following categories: stress, immunity, inflammation, apoptosis, transport, development, epigenetic regulation and cancer. The data suggest a step-wise shutdown occurs in organismal death that is manifested by the apparent increase of certain transcripts with various abundance maxima and durations.

Datasets used to discover the microbial signatures of oral dysbiosis, periodontitis and edentulism in humans

This article provides supporting data for the research article ‘Microbial Signatures of Oral Dysbiosis, Periodontitis and Edentulism Revealed by Gene Meter Methodology’ (M.C. Hunter, A.E. Pozhitkov, P.A. Noble, 2016) [1]. In that article, we determined the microbial abundance signatures for patient with periodontics, edentulism, or health using Gene Meter Technology. Here we provide the data used to make the DNA microarray and the resulting microbial abundance data that was determined using the calibrated probes and the 16S rRNA genes harvested from patients. The first data matrix contains two columns: one is the GenInfo Identifier (GI) numbers of the 16S rRNA gene sequences and the other is the corresponding oral bacterial taxonomy. The probes were then screened for redundancy and if they were found to be unique, they were synthesized onto the surface of the DNA microarrays. The second data matrix consists of the abundances of the 576 16S rRNA genes that was determined using the median value of all individual calibrated probes targeting each gene. The data matrix consists of 16 columns and 576 rows, with the columns representing the 16 patients and the rows representing 576 different oral microorganisms. The third data matrix consists of the abundances of 567 16S rRNA genes determined using the calibrated abundance of all aggregated probes targeting the same 16S rRNA gene. The data matrix of the aggregated probes consists of 16 samples and 567 rows.