Biology subjects

Devarajan, S.

Publications and source records attributed to Devarajan, S..

3 recordsLinked to original sources

bioRxiv2025

Comparative Machine Learning Analysis of Saliva and Plaque Microbiomes in Children with Type 1 Diabetes

BackgroundType 1 diabetes (T1D) is associated with microbial dysbiosis. While most research has focused on the gut microbiome, limited data address the role of the oral microbiome in T1D. The oral and gut microbiomes overlap substantially, and the oral cavity may influence the gut microbial composition. Saliva and dental plaque represent two distinct oral niches with unique microbial communities, but it remains unclear which better reflects systemic disease states such as T1D. This study compared the performance of salivary and plaque microbiomes in classifying pediatric T1D status. MethodsPaired saliva and plaque samples were collected from 46 children (23 with T1D and 23 healthy controls). Microbial DNA was extracted and sequenced to target the 16S rRNA gene. The data were processed via QIIME 2 for taxonomic classification and centered log-ratio transformation. Alpha diversity, microbial abundance, and clustering analyses were performed to compare the oral microbiome between the T1D and control groups. Random forest classifiers were used to evaluate and compare the predictive accuracy of saliva- and plaque-based models, both with and without clinical metadata. ResultsSaliva samples presented lower alpha diversity than plaque samples did but presented significantly greater bacterial loads and total microbial abundances. Saliva-based models outperformed plaque-based models, achieving a classification accuracy of 94.2% with or without clinical metadata, compared with 73.3% accuracy for plaque-based models. ROC curve analysis further supported this difference, with saliva models reaching an AUC of approximately 0.94 versus 0.75 for plaque, indicating superior discriminative performance. UMAP clustering revealed more distinct separation of the T1D and control groups in terms of the salivary profiles than in the plaque profiles. Feature importance analysis revealed both unique and shared taxa predictive of T1D in each niche. The incorporation of clinical and demographic metadata did not enhance model performance, underscoring the robustness and predictive strength of microbiome data alone. ConclusionThe salivary microbiome is a more effective biospecimen than dental plaque for detecting T1D-associated microbial profiles in children. It offers superior classification accuracy and greater sensitivity in distinguishing T1D status, supporting salivas potential as a noninvasive, scalable medium for future microbiome-based monitoring.

microbiology↗

bioRxiv2024

Sputum production and salivary microbiome in COVID-19 patients reveals oral-lung axis

ObjectiveSARS-CoV-2 is a severe respiratory disease that primarily targets the lungs and was the leading cause of death worldwide during the pandemic. Investigating the intricate interplay between the oral microbiome and inflammatory cytokines during the acute phase of infection is crucial for understanding host immune responses. This study aimed to explore the relationship between the oral microbiome and cytokines in COVID-19 patients, specifically examining those with and without sputum production. MethodsSaliva and blood samples from 50 COVID-19 patients were subjected to 16S ribosomal RNA gene sequencing to analyze the oral microbiome. Additionally, 65 saliva and serum cytokines were assessed using Luminex multiplex analysis. The Mann-Whitney test compared cytokine levels between individuals with and without sputum production. ResultsOur study revealed significant differences in the membership (Jaccard dissimilarity: p=0.016) and abundance (PhILR dissimilarity: p=0.048; metagenomeSeq) of salivary microbial communities between COVID-19 patients with and without sputum production. Seven bacterial genera, including Prevotella, Streptococcus, Actinomyces, Atopobium, Filifactor, Leptotrichia, and Selenomonas, were present in statistically higher proportions of patients with sputum production (p<0.05, Fishers exact test). Eight bacterial genera, including Prevotella, Megasphaera, Stomatobaculum, Leptotrichia, Veillonella, Actinomyces, Atopobium, and Corynebacteria were significantly more abundant in the sputum-producing group, while Lachnoacaerobaculum was notably more prevalent in the non-sputum-producing group (p<0.05, ANCOM-BC).We observed a significant positive correlation between salivary IFN-gamma (Interferon-gamma) and Eotaxin2/CCL24 (chemokine ligand 24) with sputum production. Conversely, negative correlations were noted in serum MCP3/CCL7 (monocyte-chemotactic protein 3/Chemokine ligand 7), MIG/CXCL9 (Monokine induced by gamma/Chemokine ligand 9), IL1 beta (interleukin 1 beta), and SCF (stem cell factor) with sputum production (p<0.05, Mann-Whitney test). ConclusionSubstantial distinctions in salivary microbial communities were evident between COVID-19 patients with and without sputum production, emphasizing the notable impact of sputum production on the oral microbiome and cytokine levels during the acute phase of infection.

microbiology↗

bioRxiv2023

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 naive populations. Significance StatementThe oral mucosa is one of the first sites encountered by bacterial and viral infections, including SARS-CoV-2. It consists of a primary barrier occupied by a commensal oral microbiome. The primary function of this barrier is to modulate immunity and provide protection against invading infection. The occupying commensal microbiome is an essential component that influences the immune systems function and homeostasis. The present study showed that the host oral immune response performs unique functions in response to SARS-CoV-2 when compared to systemic responses during the acute phase. We also demonstrated that there is a link between oral microbiome diversity and COVID-19 severity. Additionally, the salivary microbiome was predictive of not only disease status but also severity.

immunology↗