Revealing microbial community characteristics in healthy human, cat and canine salivas and looking for species-specific microbes.

As two kinds of increasingly popular pets, the saliva of cat or canine is most likely to be left at the crime scene compared with the common types of body fluids in forensics. Accurately identifying the species of saliva samples found at the crime scene involving pets will help the investigators find available testing materials, reduce the consumption of reagents and save the investigative time of the case. Therefore, it is necessary to explore the characteristics and differences of saliva microbiomes of cat, canine and human. In this study, 16S rRNA gene amplicon sequencing technology was used to reveal microbial communities of saliva samples of healthy human, cat, and canine. Alpha diversity analyses indicated that canine saliva demonstrated the highest microbial diversity, followed by cat saliva, whereas human saliva microbial diversity was the lowest. The saliva samples of the three species all had their own unique microbial community compositions, and the dominant phyla of canine and cat salivas were Proteobacteria and Bacteroidete, while the dominant phyla of human saliva were Firmicutes and Proteobacteria. There was no significant statistical difference in the salivary microbiota obtained by the two collection methods (cotton swab and liquid saliva). The gender of cats and canines might have no effect on the salivary microbiota, but the different breeds had an impact on their saliva microbiomes. Principal coordinates analysis, non-metric multidimensional scaling analysis and random forest analysis all indicated significant differences in microbial community structures among the three species, allowing inference on the species sources of saliva samples by microbiome method. Differential microbial biomarkers for the salivas of three species were screened out using a variety of bioinformatics analyses, and the results demonstrated that Prevotella melaninogenica, Veillonella parvula, and Haemophilus parainfluenzae could be used as species-specific microbial biomarkers of human saliva. The detections of human species-specific microbes provide a potential method for determining human saliva.

The mediating roles of the oral microbiome in saliva and subgingival sites between e-cigarette smoking and gingival inflammation.

BACKGROUND: Electronic cigarettes (ECs) have been widely used by young individuals in the U.S. while being considered less harmful than conventional tobacco cigarettes. However, ECs have increasingly been regarded as a health risk, producing detrimental chemicals that may cause, combined with poor oral hygiene, substantial inflammation in gingival and subgingival sites. In this paper, we first report that EC smoking significantly increases the odds of gingival inflammation. Then, through mediation analysis, we seek to identify and explain the mechanism that underlies the relationship between EC smoking and gingival inflammation via the oral microbiome. METHODS: We collected saliva and subgingival samples from 75 EC users and 75 non-users between 18 and 34 years in age and profiled their microbial compositions via 16S rRNA amplicon sequencing. We conducted raw sequence data processing, denoising and taxonomic annotations using QIIME2 based on the expanded human oral microbiome database (eHOMD). We then created functional annotations (i.e., KEGG pathways) using PICRUSt2. RESULTS: We found significant increases in α-diversity for EC users and disparities in ß-diversity between EC users and non-users. We also found significant disparities between EC users and non-users in the relative abundance of 36 microbial taxa in the saliva site and 71 microbial taxa in the subgingival site. Finally, we found that 1 microbial taxon in the saliva site and 18 microbial taxa in the subgingival site significantly mediated the effects of EC smoking on gingival inflammation. The mediators on the genus level, for example, include Actinomyces, Rothia, Neisseria, and Enterococcus in the subgingival site. In addition, we report significant disparities between EC users and non-users in the relative abundance of 71 KEGG pathways in the subgingival site. CONCLUSIONS: These findings reveal that continued EC use can further increase microbial dysbiosis that may lead to periodontal disease. Our findings also suggest that continued surveillance for the effect of ECs on the oral microbiome and its transmission to oral diseases is needed.

Old age and other factors associated with salivary microbiome variation.

BACKGROUND: Many factors can contribute to the exact makeup of the salivary microbiome. Differences in the oral microbiome occur with old age, which may be due to oral conditions and diseases associated with old age, such as edentulism, as well as other unknown causes. METHODS: The salivary microbiome was sampled in patients from a large urban clinic. For all subjects age, gender, periodontal status, caries status, presence of edentulism, medications, and tobacco usage were recorded. Multifactor analysis was used to study variation in salivary microbiome profiles linked to these factors. RESULTS: In the population sampled, there were significantly higher numbers of edentulous subjects, and increased levels of polypharmacy found with aging. Large differences in alpha diversity and beta diversity of the salivary microbiome in the old age group were largely linked to edentulism. However, multivariable analysis revealed, even after adjusting for differences in edentulism, polypharmacy, tobacco usage, periodontal disease, caries level, and gender, that old age itself was associated with lower levels of taxa Porphyromonas endodontalis, Alloprevotella tannerae, Filifactor alocis, Treponema, Lautropia Mirabilis and Pseudopropionibacterium sp._HMT_194. Surprisingly, of these taxa, most were ones known to reside on or near tooth surfaces. CONCLUSIONS: Another factor or factors beyond edentulism, polypharmacy and periodontal disease play a role in the differences seen in oral microbiome with old age. The nature of this factor(s) is not known.

Microbial Communities and Functional Genes in Periodontitis and Healthy Controls.

BACKGROUND: Periodontitis is a chronic progressive disease and the leading cause of tooth loss in adults. Recent studies have shown the impact of oral microbial communities on systemic health and diseases such as cancer, atherosclerosis, rheumatoid arthritis, inflammatory bowel disease, diabetes, hypertension, and Alzheimer's disease. In previous case control studies investigatin the relationship between periodontal disease and the oral microbiota, little attention has been paid to the intersections of these domains. METHODS: Here, we used high-throughput 16S rRNA sequencing to analyse the differences in the microbial composition in saliva between a group of patients with chronic periodontitis (C; n = 51) and a healthy control group (H; n = 61) and predicted the functional gene composition by Phylogenetic Investigation of Communities by Reconstruction of Unobserved States. RESULTS: We found significant alterations in oral microbial diversity between C and H (P = 0.002). Sixteen genera were significantly different between C and H, and 15 of them were enriched in C linear discriminant analysis (LDA > 2). Fifty functional genes were significantly different between C and H, and 34 of them were enriched in C (P < .025). CONCLUSIONS: Periodontitis is associated with significant changes in the oral microbial community.

Host-microbiome associations in saliva predict COVID-19 severity.

Established evidence indicates that oral microbiota plays a crucial role in modulating host immune responses to viral infection. Following severe acute respiratory syndrome coronavirus 2, there are coordinated microbiome and inflammatory responses within the mucosal and systemic compartments that are unknown. The specific roles the oral microbiota and inflammatory cytokines play in the pathogenesis of coronavirus disease 2019 (COVID-19) are yet to be explored. Here, we evaluated the relationships between the salivary microbiome and host parameters in different groups of COVID-19 severity based on their oxygen requirement. Saliva and blood samples (n = 80) were collected from COVID-19 and from noninfected individuals. We characterized the oral microbiomes using 16S ribosomal RNA gene sequencing and evaluated saliva and serum cytokines and chemokines using multiplex analysis. Alpha diversity of the salivary microbial community was negatively associated with COVID-19 severity, while diversity increased with health. Integrated cytokine evaluations of saliva and serum showed that the oral host response was distinct from the systemic response. The hierarchical classification of COVID-19 status and respiratory severity using multiple modalities separately (i.e. microbiome, salivary cytokines, and systemic cytokines) and simultaneously (i.e. multimodal perturbation analyses) revealed that the microbiome perturbation analysis was the most informative for predicting COVID-19 status and severity, followed by the multimodal. Our findings suggest that oral microbiome and salivary cytokines may be predictive of COVID-19 status and severity, whereas atypical local mucosal immune suppression and systemic hyperinflammation provide new cues to understand the pathogenesis in immunologically compromised populations.

Hospital antimicrobial stewardship: profiling the oral microbiome after exposure to COVID-19 and antibiotics.

Introduction: During the COVID-19 Delta variant surge, the CLAIRE cross-sectional study sampled saliva from 120 hospitalized patients, 116 of whom had a positive COVID-19 PCR test. Patients received antibiotics upon admission due to possible secondary bacterial infections, with patients at risk of sepsis receiving broad-spectrum antibiotics (BSA). Methods: The saliva samples were analyzed with shotgun DNA metagenomics and respiratory RNA virome sequencing. Medical records for the period of hospitalization were obtained for all patients. Once hospitalization outcomes were known, patients were classified based on their COVID-19 disease severity and the antibiotics they received. Results: Our study reveals that BSA regimens differentially impacted the human salivary microbiome and disease progression. 12 patients died and all of them received BSA. Significant associations were found between the composition of the COVID-19 saliva microbiome and BSA use, between SARS-CoV-2 genome coverage and severity of disease. We also found significant associations between the non-bacterial microbiome and severity of disease, with Candida albicans detected most frequently in critical patients. For patients who did not receive BSA before saliva sampling, our study suggests Staphylococcus aureus as a potential risk factor for sepsis. Discussion: Our results indicate that the course of the infection may be explained by both monitoring antibiotic treatment and profiling a patient's salivary microbiome, establishing a compelling link between microbiome and the specific antibiotic type and timing of treatment. This approach can aid with emergency room triage and inpatient management but also requires a better understanding of and access to narrow-spectrum agents that target pathogenic bacteria.

Salivary microbiome profiles for different clinical phenotypes of pituitary adenomas by single-molecular long-read sequencing.

IMPORTANCE: The gut and salivary microbiomes have been widely reported to be significantly associated with a number of neurological disorders. The stability of the microbiome in the oral cavity makes it a potentially ideal sample that can be conveniently obtained for the investigation of microbiome-based pathogenesis in diseases. In the present study, we used a single-molecule long-read sequencing technique to study the distribution of the salivary microbiota in patients with pituitary adenoma (PA) and healthy individuals, as well as among four clinical phenotypes of PA. We found that the diversity of salivary microbes was more abundant in PA patients than in healthy individuals. We also observed some unique genera in different PA phenotypes. The bioinformatics-based functional predictions identified potential links between microbes and different clinical phenotypes of PA. This study improves the existing understanding of the pathogenesis of PA and may provide diagnostic and therapeutic targets for PA.

The uselessness of using salivary microbiota in forensic identification purposes of a person with recent antibiotic use.

The detection of microbial flora changes in saliva samples because of antibiotic use through advanced molecular genetic analysis is important for forensic and clinical applications. This study aims to reveal the variability in the microbial structure of human saliva after antibiotic use with metagenomic analysis techniques from a forensic point of view. Within the scope of the study, saliva samples were collected from patients who were under the effect of regional anesthesia to be administered a standardized course of antibiotic therapy that lasted for a week. The analysis was conducted on 56 saliva samples from 14 individuals over four different time intervals. Isolation of the 16S rRNA region and PCR analysis were performed prior to sequence analysis to determine the microbiome structure of the samples at phylum, genus, and species levels. As expected, changes were observed in bacterial species found in saliva samples after administration of antibiotics and this was linked to the specific type of antibiotics that were administered. This change was statistically significant for Firmicutes, Spirochetes, and Verrucomicrobiota. Furthermore, although the oral microbiome tends to return to its former state at the phylum and genus level within a 4-week period after the start of antibiotic use, it is observed that the change, especially in some bacterial species, still continues. The findings of this study show that because of the inability of stabilization at species-level in a period of 4 weeks from the start of antibiotic use, it is not suitable to assess saliva samples at species-level for forensic identification.

Unexpected lower level of oral periodontal pathogens in patients with high numbers of systemic diseases.

Background: Periodontal disease is associated with systemic conditions such as diabetes, arthritis, and cardiovascular disease, all diseases with large inflammatory components. Some, but not all, reports show periopathogens Porphyromonas gingivialis and Tannerella forsythia at higher levels orally in people with one of these chronic diseases and in people with more severe cases. These oral pathogens are thought to be positively associated with systemic inflammatory diseases through induction of oral inflammation that works to distort systemic inflammation or by directly inducing inflammation at distal sites in the body. This study aimed to determine if, among patients with severe periodontal disease, those with multi-morbidity (or many chronic diseases) showed higher levels of periodontal pathogens. Methods: A total of 201 adult subjects, including 84 with severe periodontal disease were recruited between 1/2017 and 6/2019 at a city dental clinic. Electronic charts supplied self-reported diseases and conditions which informed a morbidity index based on the number of chronic diseases and conditions present. Salivary composition was determined by 16S rRNA gene sequencing. Results: As expected, patients with severe periodontal disease showed higher levels of periodontal pathogens in their saliva. Also, those with severe periodontal disease showed higher levels of multiple chronic diseases (multimorbidity). An examination of the 84 patients with severe periodontal disease revealed some subjects despite being of advanced age were free or nearly free of systemic disease. Surprisingly, the salivary microbiota of the least healthy of these 84 subjects, defined here as those with maximal multimorbidity, showed significantly lower relative numbers of periodontal pathogens, including Porphyromonas gingivalis and Tannerella Forsythia, after controlling for active caries, tobacco usage, age, and gender. Analysis of a control group with none to moderate periodontal disease revealed no association of multimorbidity or numbers of medications used and specific oral bacteria, indicating the importance of severe periodontal disease as a variable of interest. Conclusion: The hypothesis that periodontal disease patients with higher levels of multimorbidity would have higher levels of oral periodontal pathogens is false. Multimorbidity is associated with a reduced relative number of periodontal pathogens Porphyromonas gingivalis and Tannerella forsythia.

Host-Microbiome Associations in Saliva Predict COVID-19 Severity.

Established evidence indicates that oral microbiota plays a crucial role in modulating host immune responses to viral infection. Following Severe Acute Respiratory Syndrome Coronavirus 2 - SARS-CoV-2 - there are coordinated microbiome and inflammatory responses within the mucosal and systemic compartments that are unknown. The specific roles that the oral microbiota and inflammatory cytokines play in the pathogenesis of COVID-19 are yet to be explored. We evaluated the relationships between the salivary microbiome and host parameters in different groups of COVID-19 severity based on their Oxygen requirement. Saliva and blood samples (n = 80) were collected from COVID-19 and from non-infected individuals. We characterized the oral microbiomes using 16S ribosomal RNA gene sequencing and evaluated saliva and serum cytokines using Luminex multiplex analysis. Alpha diversity of the salivary microbial community was negatively associated with COVID-19 severity. Integrated cytokine evaluations of saliva and serum showed that the oral host response was distinct from the systemic response. The hierarchical classification of COVID-19 status and respiratory severity using multiple modalities separately (i.e., microbiome, salivary cytokines, and systemic cytokines) and simultaneously (i.e., multi-modal perturbation analyses) revealed that the microbiome perturbation analysis was the most informative for predicting COVID-19 status and severity, followed by the multi-modal. Our findings suggest that oral microbiome and salivary cytokines may be predictive of COVID-19 status and severity, whereas atypical local mucosal immune suppression and systemic hyperinflammation provide new cues to understand the pathogenesis in immunologically naïve populations.

Oral Enrichment of Streptococcus and its Role in Systemic Inflammation Related to Monocyte Activation in Humans with Cocaine Use Disorder.

Cocaine use is commonly associated with increased chronic systemic inflammation. However, the drivers for cocaine use-mediated systemic inflammation are not fully understood. In the current study, we recruited individuals with cocaine use disorder and healthy individuals who did not use cocaine and collected paired saliva and blood samples. The saliva samples were used to assess the oral microbiome, and the plasma samples were evaluated for 33 cytokines and chemokines. Cocaine users exhibited decreased saliva microbial diversities compared to non-users. Streptococcus was the only increased genus in the saliva from cocaine users, whereas several genera were decreased in cocaine users compared to non-users. Notably, cocaine users exhibited increased plasma levels of several monocyte activation markers, including monocyte chemoattractant protein (MCP)-4, macrophage inflammatory protein (MIP)-3α, macrophage-derived chemokine (MDC), and thymus and activation-regulated chemokine (TARC), all of which were correlated with increased saliva levels of three Streptococcus species. Furthermore, treatment with Streptococcus or its lipoteichoic acid preferentially activated primary human monocytes to produce proinflammatory cytokines and chemokines, such as MIP-3α and TARC, in vitro compared to controls. However, monocytes failed to produce these chemokines after exposure to cocaine or cocaine plus bacteria compared to medium or bacteria alone. This study revealed that chronic cocaine use-associated inflammation in the blood may result from increased oral Streptococcus and its effects on myeloid cell activation, but does not result from cocaine directly.

Prediction of Smoking Habits From Class-Imbalanced Saliva Microbiome Data Using Data Augmentation and Machine Learning.

Human microbiome research is moving from characterization and association studies to translational applications in medical research, clinical diagnostics, and others. One of these applications is the prediction of human traits, where machine learning (ML) methods are often employed, but face practical challenges. Class imbalance in available microbiome data is one of the major problems, which, if unaccounted for, leads to spurious prediction accuracies and limits the classifier's generalization. Here, we investigated the predictability of smoking habits from class-imbalanced saliva microbiome data by combining data augmentation techniques to account for class imbalance with ML methods for prediction. We collected publicly available saliva 16S rRNA gene sequencing data and smoking habit metadata demonstrating a serious class imbalance problem, i.e., 175 current vs. 1,070 non-current smokers. Three data augmentation techniques (synthetic minority over-sampling technique, adaptive synthetic, and tree-based associative data augmentation) were applied together with seven ML methods: logistic regression, k-nearest neighbors, support vector machine with linear and radial kernels, decision trees, random forest, and extreme gradient boosting. K-fold nested cross-validation was used with the different augmented data types and baseline non-augmented data to validate the prediction outcome. Combining data augmentation with ML generally outperformed baseline methods in our dataset. The final prediction model combined tree-based associative data augmentation and support vector machine with linear kernel, and achieved a classification performance expressed as Matthews correlation coefficient of 0.36 and AUC of 0.81. Our method successfully addresses the problem of class imbalance in microbiome data for reliable prediction of smoking habits.

Oral Microbiome Alterations and SARS-CoV-2 Saliva Viral Load in Patients with COVID-19.

Bacterial-viral interactions in saliva have been associated with morbidity and mortality for respiratory viruses such as influenza and SARS-CoV. However, such transkingdom relationships during SARS-CoV-2 infection are currently unknown. Here, we aimed to elucidate the relationship between saliva microbiota and SARS-CoV-2 in a cohort of newly hospitalized COVID-19 patients and controls. We used 16S rRNA sequencing to compare microbiome diversity and taxonomic composition between COVID-19 patients (n = 53) and controls (n = 59) and based on saliva SARS-CoV-2 viral load as measured using reverse transcription PCR (RT-PCR). The saliva microbiome did not differ markedly between COVID-19 patients and controls. However, we identified significant differential abundance of numerous taxa based on saliva SARS-CoV-2 viral load, including multiple species within Streptococcus and Prevotella. IMPORTANCE Alterations to the saliva microbiome based on SARS-CoV-2 viral load indicate potential biologically relevant bacterial-viral relationships which may affect clinical outcomes in COVID-19 disease.

Trick or Treating in Forensics-The Challenge of the Saliva Microbiome: A Narrative Review.

The oral microbiome harbours microbial community signatures that differ among individuals, highlighting that it could be highly individualizing and potentially unique to each individual. Therefore, the oral microbial traces collected in crime scenes could produce investigative leads. This narrative review will describe the current state-of-the-art of how the salivary microbiome could be exploited as a genetic signature to make inferences in the forensic field. This review has been performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Guidelines. Even if further studies are needed to relate the variation in the oral microbiome to specific factors, in order to understand how the salivary microbiome is influenced by an individual's lifestyle, by reviewing the studies published so far, it is clear that the oral microbial analysis could become a useful forensic tool. Even if promising, caution is required in interpreting the results and an effort to direct research towards studies that fill the current knowledge gaps is certainly useful.

Health and saliva microbiomes of a semi-urbanized indigenous tribe in Peninsular Malaysia.

Background: The indigenous people of Peninsular Malaysia, also known as Orang Asli, have gradually been urbanized. A shift towards non-communicable diseases commonly associated with sedentary lifestyles have been reported in many tribes. This study engaged with a semi-urbanized Temiar tribe from Kampong Pos Piah, Perak, who are experiencing an epidemiological transition. Methods:  Weight, height, waist circumference, blood pressure, HbA1C and lipid levels were measured as indicators of cardio-metabolic health. DNA was extracted from saliva using salting-out method followed by PCR amplification of the V3-V4 region of the 16S rRNA gene and sequencing on Illumina MiSeq. Microbiome analysis was conducted on Qiime v1.9. Statistical analysis was conducted using Qiime v1.9 and R.   Results: The study revealed that 60.4% of the Temiar community were overweight/obese, with a higher prevalence among women. HbA1C levels showed that 45% of Temiar had pre-diabetes. Insulin resistance was identified in 21% of Temiar by using a surrogate marker, TG/HDL. In total, 56.5% of Temiar were pre-hypertensive, and the condition was prevalent across all age-groups. The saliva microbiome profiles of Temiar revealed significant differences by gender, BMI, abdominal obesity as well as smoking status. The relative abundance of Bifidobacterium, bacteria commonly found in dairy products, was increased in men. Prevotella, associated with consumption of plant-rich diets, was increased in women. Mogibacteriacea and Mycoplasma levels were significantly elevated in overweight individuals. Proteobacteria was significantly depleted in smokers. Conclusions: Temiar from Pos Piah had a high prevalence of cardio-metabolic risks, including general and abdominal obesity, pre-diabetes, prehypertension and hypertension. This phenomenon has not been previously reported in this tribe. The saliva microbiome profiles were significantly different for individuals of different gender, BMI scores and abdominal obesity and smoking status.

Enhanced microbial diversity in the saliva microbiome induced by short-term probiotic intake revealed by 16S rRNA sequencing on the IonTorrent PGM platform.

Microbial communities populating several human body habitats are important determinants of human health. Cultivation-free community-wide approaches like bacterial 16S rRNA sequencing recently revolutionized the study of such human-associated microbial diversity, and the continuously decreasing cost/throughput ratio of current sequencing platforms is further enhancing the availability and effectiveness of microbiome research. The IonTorrent PGM platform is among the latest available commercial high-throughput sequencing tools, but it is just starting to be used for 16S rRNA surveys with only episodic assessments of its performance. We present here the first IonTorrent profiling of the human saliva microbiome collected from 12 healthy individuals. In this cohort, a subset of the volunteers was asked to assume a probiotic product, in order to investigate its impact on the composition and the structure of the saliva microbiome. Analysis of the generated dataset suggests the suitability of the IonTorrent platform for 16S rRNA surveys, even though some platform-specific choices are required to optimize the consistency of the obtained bacterial profiles. Interestingly, we found a marked and statistically significant increase of the overall bacterial diversity in the saliva of individuals who received the probiotic product compared to the control group, suggesting a short-term effect of probiotic product administration on oral microbiome composition.