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Schroth, R. J.

Publications and source records attributed to Schroth, R. J..

4 recordsLinked to original sources

Exploratory Network Analysis of Oral Bacteria Taste Signaling Autophagy Crosstalk in Oral Squamous Cell Carcinoma and Multi-Target Ligand Design for the MAPK1 STAT3 mTOR Axis

G protein-coupled receptor (GPCR) signaling represents a critical interface between oral bacteria and host cellular regulation in oral squamous cell carcinoma (OSCC). Here, we integrated systems biology, exploratory machine learning, and structure-based drug design to characterize potential associations between bacteria-related signaling and autophagy and to identify candidate therapeutic targets. Taste-associated signaling genes belonging to the GPCR superfamily were curated from KEGG, while OSCC- and autophagy-associated proteins were obtained from STRING, Reactome, UniProt, KEGG, and HMDB. Ten bacteria-associated host-interaction datasets were integrated using NetworkAnalyst to construct protein- protein interaction networks, and key hub nodes were identified through degree and betweenness centrality. Feature matrices derived from network topology were analyzed using exploratory dimensionality reduction (PCA), hierarchical clustering, and supervised models (SVM and Gradient Boosting) to assess whether network-derived features showed separability according to literature-informed bacterial reference categories; a Dysbiosis Index was additionally calculated. Results suggested that bacterial sensing through taste-associated GPCR signaling may converge on a MAPK1-centered axis linking calcium signaling, autophagy, and oncogenic pathways. Pathobiont-associated networks showed greater representation of inflammatory and terminal-autophagy-related signaling through MAPK1-STAT3, whereas commensal-associated networks were more closely aligned with cytoprotective autophagy through balanced MAPK1-TP53/PTEN networks. Exploratory machine learning analyses highlighted MDM2 and AKT3 as high-contribution, network-associated candidate features linked to group separability within the current dataset. A dual-target MTDL (SG101) was designed to target downstream nodes (MDM2 and JAK2), showing favorable predicted docking interactions and computationally predicted ADMET properties. In conclusion, bacteria-associated host taste signaling may be linked to differing autophagy-related network states in OSCC, and targeting downstream regulatory hubs with multi-target ligands represents a hypothesis-generating strategy that warrants experimental validation for pathway-oriented therapy.

cancer biology↗

Hierarchical Machine Learning Uncovers Topological Signatures of Autophagy Regulation by Oral Bacteria in Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma (OSCC) progression has been increasingly linked to dysbiosis of the oral microbiome. We hypothesized that pathogenic versus commensal bacteria differentially rewire host autophagy networks to either promote or inhibit OSCC progression. To test this, we constructed host-bacterium autophagy interactomes from KEGG, STRING, and curated databases, identifying key network hubs (e.g., MAPK1, STAT3) via graph-theoretic metrics. We then applied a hierarchical unsupervised machine learning pipeline, combining two-stage principal component analysis with permutation testing and linear discriminant analysis (LDA), to interrogate differences in network topology. This multi-layer approach revealed a clear separation between pro-cancer (pathogenic) and anti-cancer (commensal) bacterial network signatures, with Fusobacterium nucleatum and Streptococcus mitis emerging as dominant global outliers. Pathogenic taxa activated inflammatory-metabolic autophagy signatures (e.g., NFKB1, MYC, ACACA), whereas commensals stabilized kinase-homeostasis signaling (EGFR, PTEN, HSP90AA1). Permutation testing confirmed that these network differences were highly significant and non-random (p < 0.001). We also derived a Dysbiosis Index that robustly distinguished the pro- versus anti-cancer bacterial cohorts with high predictive power. Collectively, our findings highlight oral microbiota-autophagy network topologies as potential biomarkers of OSCC dysbiosis and as novel therapeutic targets. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=189 SRC="FIGDIR/small/696881v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@13eb27forg.highwire.dtl.DTLVardef@138bccborg.highwire.dtl.DTLVardef@1f2e651org.highwire.dtl.DTLVardef@1eee140_HPS_FORMAT_FIGEXP M_FIG C_FIG Lay summaryHealthy mouth bacteria help cells stay balanced and protected. When harmful bacteria take over, they disrupt cell recycling (autophagy), increase inflammation, and causing cells to become more aggressive, which can promote oral cancer development.

cancer biology↗

Integrative analysis of taste genetics and the dental plaque microbiome in early childhood caries

Early childhood caries (ECC) is a multifactorial disease mainly caused by the oral microbiome; however, it is also influenced by host genetics and environmental factors. The combinatorial analysis of these multiple factors influencing ECC susceptibility requires further research. This study investigated the interplay between genetic variants in taste-related genes and the microbiome in ECC, targeting taste genes because of their role in taste preference and potential interactions with oral fungi and bacteria. Using a case-control design involving 538 children, we obtained dental plaque microbiome profiles and genetic variants across 55 candidate genes through next-generation sequencing. Our association analysis for taste genetics and ECC outcome used the socioeconomic factor index (SEFI) and rural-urban status as confounders. We observed a few taste gene variants associated with ECC and microbial diversity. However, no specific association was observed between the variants and cariogenic species. Furthermore, our analysis indicated that Streptococcus mutans is a partial mediator between these genetic variants and ECC outcomes. Machine learning models integrating microbiome, genetics, and covariates achieved robust ECC vs. caries-free classification (AUROC = 0.96), with Streptococcus mutans, rural-urban status, a bitter taste receptor variant, Candida dubliniensis, and SEFI as the top ECC-associated factors. Our findings highlight the association between host genetics and the oral microbiome, underscoring the need for multiomics approaches in ECC risk assessment. HighlightsO_LIS. mutans, C. dubliniensis, and taste genetic variants are associated with ECC. C_LIO_LIRural-urban status and SEFI score are among the top social markers for ECC prediction. C_LIO_LITaste-related genetic factors modulate the composition of dental plaque microbiome. C_LI

genomics↗

Role of socioeconomic factors and interkingdom crosstalk in the dental plaque microbiome in early childhood caries

Early childhood caries (ECC) is influenced by microbial and host factors, including social, behavioral, and oral health. In this cross-sectional study, we analyzed interkingdom dynamics in the dental plaque microbiome and its association with host variables. The samples collected from the preschool children underwent 16S rRNA and ITS1 rRNA gene sequencing. The questionnaire data were analyzed for social determinants of oral health. The results indicated a significant enrichment of Streptococcus mutans and Candida dubliniensis in ECC samples, in contrast to Neisseria oralis in caries-free children. Our interkingdom correlation analysis revealed that Candida dubliniensis was strongly correlated with both Neisseria bacilliformis and Prevotella veroralis in ECC. Additionally, ECC showed significant associations with host variables, including oral health status, age, place of residence, and mode of childbirth. This study provides empirical evidence associating the oral microbiome with socioeconomic and behavioral factors in relation to ECC, offering insights for developing targeted prevention strategies. HIGHLIGHTSO_LICharacterized interkingdom association between cariogenic species of genus Neisseria and Candida C_LIO_LIBoth bacterial and fungal species are important for caries status prediction using artificial intelligence C_LIO_LISocioeconomic index is associated with caries status and caries-associated microbial markers C_LI

microbiology↗