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. 2008 Feb 12;105(6):2117-22.
doi: 10.1073/pnas.0712038105. Epub 2008 Feb 5.

Symbiotic gut microbes modulate human metabolic phenotypes

Min Li  1 , Baohong Wang, Menghui Zhang, Mattias Rantalainen, Shengyue Wang, Haokui Zhou, Yan Zhang, Jian Shen, Xiaoyan Pang, Meiling Zhang, Hua Wei, Yu Chen, Haifeng Lu, Jian Zuo, Mingming Su, Yunping Qiu, Wei Jia, Chaoni Xiao, Leon M Smith, Shengli Yang, Elaine Holmes, Huiru Tang, Guoping Zhao, Jeremy K Nicholson, Lanjuan Li, Liping Zhao
Affiliations

Symbiotic gut microbes modulate human metabolic phenotypes

Min Li et al. Proc Natl Acad Sci U S A. .

Abstract

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 microbial-host 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.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Experimental procedure and structural comparison of gut microbiome between Chinese and American individuals. (A) Family tree diagram of the Chinese family. (B) Scheme of experimental procedure. (C) The division-level composition of gut microbiome of the Chinese family. (D) Species-level composition of gut microbiome of the Chinese family in comparison with reported American microbiome data (4, 5). The principal coordinate scores plot was generated by using UniFrac metrics. The percentages of variation described by the principal coordinates are shown in the parentheses.
Fig. 2.
Fig. 2.
Multivariate analysis for identifying associations between the gut microbiome structure and the urine metabolite profile. (A) DGGE gel for C. leptum subgroup. Mr, marker lane. (B) OPLS prediction of clostridia bands from the NMR urinary profile data. (C) Two-dimensional correlation map of NMR-derived metabolic profile variation in relation to DGGE fingerprints (only the aromatic region of urinary NMR spectra is shown); only points with absolute correlation level >0.7 are shown, red denotes positive correlation, blue denotes negative correlation. (D) OPLS prediction of aromatic region of the NMR spectrum from DGGE data. The color indicates the Q2 value.
Fig. 3.
Fig. 3.
Dendrogram of OTUs from DGGE bands, which are well predicted by metabolic variation, labeled as the nearest known neighbor with similarity value. Associations with specific urine metabolites are shown for each OTU with the direction of correlation indicated by red (positive) or green (negative) lines. Gender-related bands predicted by OPLS-DA are denoted by bold text.
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