The mouth-to-gut microbial transmission dynamics
The human microbiome, a critical regulator of the host immune and metabolic homeostasis, inhabits diverse body surfaces with distinct microbial compositions and metabolic characteristics tailored to specific tissue environments. In the digestive tract, extrinsic factors such as pH, oxygen levels, nutrient profiles, and immune responses create independent microbial ecosystems despite the physical continuity through the oral cavity, small intestine, and colon. Disruptions in these compartmentalized microbiomes can lead to dysbiosis, which is associated with various gastrointestinal and metabolic disorders. Notably, the translocation and persistence of oral microbes in the gut have been implicated in disease progression. In this study, we investigated mouth-to-gut (MTG) microbial dynamics by collecting paired oral and fecal samples from individuals across five groups: healthy controls (HC), metabolic syndrome (MS), hypertension (HT), hyperlipidemia (HL), type 2 diabetes (T2D), gastric cancer (GC), and colorectal cancer (CRC). Using 16S rRNA gene sequencing, we identified gut-migrating microbes at the amplicon sequence variant level and developed an MTG index to quantify their proportion in the gut. The MTG index varied significantly across disease states, being elevated in gastrointestinal cancers and hypertension but reduced in T2D, with no significant changes observed in MS or HL. Additionally, the relative abundance of gut-migrating bacteria correlated with disease-specific patterns and host health indicators. A random forest model trained on transitional microbial profiles successfully classified disease states, including T2D, GC, and CRC, in independent datasets. Remarkably, the model accurately predicted CRC using only the oral microbiome data, underscoring the clinical utility of MTG-based diagnostics. This study highlights the potential of MTG microbial dynamics as a diagnostic tool and provides new insights into the interplay between oral microbes and gut health.