Humans have evolved intimate symbiotic relationships with a consortium of gut microbes (microbiome) and individual variations in the microbiome influence host health, may be implicated in disease etiology, and affect drug metabolism, toxicity, and efficacy. However, the molecular basis of these microbe-host interactions and the roles of individual bacterial species are obscure. We now demonstrate a''transgenomic'' approach to link gut microbiome and metabolic phenotype (metabotype) variation. We have used a combination of spectroscopic, microbiomic, and multivariate statistical tools to analyze fecal and urinary samples from seven Chinese individuals (sampled twice) and to model the microbialhost metabolic connectivities. At the species level, we found structural differences in the Chinese family gut microbiomes and those reported for American volunteers, which is consistent with population microbial cometabolic differences reported in epidemiological studies. We also introduce the concept of functional metagenomics, defined as ''the characterization of key functional members of the microbiome that most influence host metabolism and hence health.'' For example, Faecalibacterium prausnitzii population variation is associated with modulation of eight urinary metabolites of diverse structure, indicating that this species is a highly functionally active member of the microbiome, influencing numerous host pathways. Other species were identified showing different and varied metabolic interactions. Our approach for understanding the dynamic basis of host-microbiome symbiosis provides a foundation for the development of functional metagenomics as a probe of systemic effects of drugs and diet that are of relevance to personal and public health care solutions. covariation analysis ͉ gut microbiota ͉ metabonomics ͉ metabotype ͉ metagenomics
NMR-based metabonomics has been widely employed to understand the stressor-induced perturbations to mammalian metabolism. However, inter-sample chemical shift variations for metabolites remain an outstanding problem for effective data mining. In this work, we systematically investigated the effects of pH and ionic strength on the chemical shifts for a mixture of 9 urinary metabolites. We found that the chemical shifts were decreased with the rise of pH but increased with the increase of ionic strength, which probably resulted from the pH- and ionic strength-induced alteration to the ionization equilibrium for the function groups. We also found that the chemical shift variations for most metabolites were reduced to less than 0.004 ppm when the pH was 7.1-7.7 and the salt concentration was less than 0.15 M. Based on subsequent optimization to minimize chemical shift variation, sample dilution and maximize the signal-to-noise ratio, we proposed a new buffer system consisting of K(2)HPO(4) and NaH(2)PO(4) (pH 7.4, 1.5 M) with buffer-urine volume ratio of 1 : 10 for human urinary metabonomic studies; we suggest that the chemical shifts for the proton signals of citrate and aromatic signals of histidine be corrected prior to multivariate data analysis especially when high resolution data were employed. Based on these, an optimized sample preparation method has been developed for NMR-based urinary metabonomic studies.
Plant metabonomic analysis is essential for understanding plant systems responses to osmotic stresses. To understand the comprehensive metabolic responses of Salvia miltiorrhiza Bunge (SMB) to continuous and exhaustive water depletion, we characterized the SMB metabonomic variations induced by three different drying processes using the combined NMR and LC-DAD-MS method. NMR results showed that SMB extracts were dominated by 29 primary metabolites such as sugars, carboxylic acids and amino acids, which were comprehensively reported for the first time, and 8 secondary metabolites including polyphenolic acids and diterpenoids. LC-DAD-MS methods detected 44 secondary metabolites, among which 5 polyphenolic acids together with genipin, umbelliferone and tormentic acid were found for the first time in this plant. We found that aqueous methanol was efficient in extracting both primary metabolites and polyphenolic acids, whereas chloroform-methanol was effective in selectively extracting diterpenoids. We further found that air- and sun-drying markedly affected both primary and secondary metabolisms of SMB by enhancing tanshinone and glutamate-mediated proline biosynthesis and altering carbohydrate and amino acid metabolisms. The shikimate-mediated biosynthesis of polyphenolic acids was promoted by air-drying but suppressed by sun-drying. These findings fill the gap of our understandings to the metabolic responses of S. miltiorrhiza Bunge to water depletion and demonstrated effectiveness of the combined NMR and LC-DAD-MS methods in plant metabonomic analysis.
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