Humans have evolved close symbiotic relationships with gut microbes, and individual variations in the microbiome influence host health, disease etiology, and drug metabolism. This study presents a "transgenomic" approach to link gut microbiome and metabolic phenotype (metabotype) variation. Using spectroscopic, microbiomic, and multivariate statistical tools, the researchers analyzed fecal and urinary samples from seven Chinese individuals to model microbial-host metabolic connectivities. They found structural differences in the Chinese family's gut microbiomes compared to American volunteers, consistent with population microbial cometabolic differences. The concept of functional metagenomics is introduced, defined as the characterization of key functional members of the microbiome that most influence host metabolism. For example, Faecalibacterium prausnitzii population variation is associated with modulation of eight urinary metabolites, indicating its high functional activity. Other species were identified showing different metabolic interactions. The approach provides a foundation for functional metagenomics as a probe of systemic effects of drugs and diet relevant to personal and public health. The study highlights the importance of gut microbiota in host metabolism, as genetically homogeneous animals can have diverse metabolic phenotypes with structurally different gut microbiota. The unique combination of gut bacteria in each animal may have an important role in their host's metabolism. The researchers used a multivariate strategy to model covariation between gut microbiomic structural patterns and host metabotype. This approach allows visualization and mapping of microbiome-host transgenomic interactions through noninvasive metabolite profiles and fecal microbiota composition. The study found that the gut microbiome composition of the Chinese family was similar to that of lean American individuals, but the only marginally overweight family member had the lowest Bacteroidetes-to-Firmicutes ratio. The study also found that the Chinese family's gut microbiome was different at the species level compared to American individuals. The researchers used DGGE profiling to analyze inter- and intraindividual variation in gut microbiota and found sex-related differences in bacterial abundance. Metabolic profiling using 1H NMR spectroscopy revealed gender-related differences in urine metabolites. Covariation analysis between DGGE microbiotal profiling and NMR-based metabolic profiling showed correlations between specific gut bacteria and host metabolites. The study identified key OTUs associated with specific metabolites, such as Bacteroides thetaiotaomicron and Faecalibacterium prausnitzii, which are important for host metabolism. The study concludes that gut microbiota composition is influenced by host genotype, diet, age, and sex, and that understanding the relationships between gut microbiota and host metabolism is essential for personalized health care. The study provides a proof-of-principle for the development of a transgenic methodology for exploring microbial host metabolic relationships. The findings suggest that there are profound host-microbiota symbiotic connections that influence global metabolism, and that gut microbiota dynamics play a significant role in disease progression and drug metabolism.Humans have evolved close symbiotic relationships with gut microbes, and individual variations in the microbiome influence host health, disease etiology, and drug metabolism. This study presents a "transgenomic" approach to link gut microbiome and metabolic phenotype (metabotype) variation. Using spectroscopic, microbiomic, and multivariate statistical tools, the researchers analyzed fecal and urinary samples from seven Chinese individuals to model microbial-host metabolic connectivities. They found structural differences in the Chinese family's gut microbiomes compared to American volunteers, consistent with population microbial cometabolic differences. The concept of functional metagenomics is introduced, defined as the characterization of key functional members of the microbiome that most influence host metabolism. For example, Faecalibacterium prausnitzii population variation is associated with modulation of eight urinary metabolites, indicating its high functional activity. Other species were identified showing different metabolic interactions. The approach provides a foundation for functional metagenomics as a probe of systemic effects of drugs and diet relevant to personal and public health. The study highlights the importance of gut microbiota in host metabolism, as genetically homogeneous animals can have diverse metabolic phenotypes with structurally different gut microbiota. The unique combination of gut bacteria in each animal may have an important role in their host's metabolism. The researchers used a multivariate strategy to model covariation between gut microbiomic structural patterns and host metabotype. This approach allows visualization and mapping of microbiome-host transgenomic interactions through noninvasive metabolite profiles and fecal microbiota composition. The study found that the gut microbiome composition of the Chinese family was similar to that of lean American individuals, but the only marginally overweight family member had the lowest Bacteroidetes-to-Firmicutes ratio. The study also found that the Chinese family's gut microbiome was different at the species level compared to American individuals. The researchers used DGGE profiling to analyze inter- and intraindividual variation in gut microbiota and found sex-related differences in bacterial abundance. Metabolic profiling using 1H NMR spectroscopy revealed gender-related differences in urine metabolites. Covariation analysis between DGGE microbiotal profiling and NMR-based metabolic profiling showed correlations between specific gut bacteria and host metabolites. The study identified key OTUs associated with specific metabolites, such as Bacteroides thetaiotaomicron and Faecalibacterium prausnitzii, which are important for host metabolism. The study concludes that gut microbiota composition is influenced by host genotype, diet, age, and sex, and that understanding the relationships between gut microbiota and host metabolism is essential for personalized health care. The study provides a proof-of-principle for the development of a transgenic methodology for exploring microbial host metabolic relationships. The findings suggest that there are profound host-microbiota symbiotic connections that influence global metabolism, and that gut microbiota dynamics play a significant role in disease progression and drug metabolism.