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The amount of energy that is stored in the body depends on the balance between energy intake and expenditure: when energy intake exceeds expenditure, excess energy is stored as fat, which leads to weight gain and eventually obesity. A number of factors are associated with the obesity epidemic that is spreading worldwide, such as individual genetic predisposition, diet and lifestyle. Recently, the community of microbes colonising the gastrointestinal tract, the gut microbiota, has been recognised as one such factor.1–3 Studies on mice maintained under germ-free conditions have shown that the gut microbiota promotes increased adiposity by enhancing energy extraction from food and modulating host genes that regulate fat storage.1
Culture independent analyses of the composition of the gut microbiota have revealed that obese individuals harboured a varied proportion of two prevailing phyla, Firmicutes and Bacteroidetes, in comparison to lean subjects.4 Furthermore, the obese metagenome, which is the set of all genes present in the genomes of gut microbes, was enriched in gene functions associated with carbohydrate and lipid metabolism.5 Both in animal and human studies, obesity has been linked to a different composition of the gut microbiota. However, results from different studies are not concordant as to the contribution of specific components of the gut microbiota to the pathogenesis of obesity6–8 and tend to suggest that variation in the composition of the gut microbiota at lower levels than phyla is involved in its development.9 10
Among many open questions, it is unclear how the gut microbiota is altered over time and whether an altered gut microbiota directly causes obesity or if it just reflects the disease. In their article, Murphy et al (see page 1635) address this problem using pyrosequencing of 16S rDNA in two different murine models of obesity.11 In particular, they address the impact of diet on the faecal gut microbiota composition and whether differences persist over time. Their findings demonstrated that both age and diet are important determinants for the composition of the gut microbiota, which may explain the heterogeneity in the results from clinical studies.7
Murphy et al found that Lepob/ob mice maintained on the same diet as lean controls had increased amounts of Firmicutes, which confirmed a previous study.12 However, this shift did not persist over time. Furthermore, Murphy et al observed that the mouse gut microbiota was not only dominated by Firmicutes and Bacteroidetes but also by Actinobacteria. Low abundant phyla such as Proteobacteria were also detected and a significant decrease in the numbers of microbes belonging to this phylum was measured as a result of obesity as well as high fat feeding.11 The latter findings underscore the importance of improving DNA extraction techniques and primer design for tracking the total diversity of the microbial flora by 16S rDNA sequencing. These two steps, in addition to bioinformatic analysis, are likely to be bottlenecks for 16S rDNA profiling of microbial communities, as next-generation sequencing technologies are capable of producing millions of DNA sequence reads in single runs.
Similar to Turnbaugh et al13 Murphy and co-workers observed reduced energy content and increased levels of short-chain fatty acids (SCFAs) in the caecum of young genetically obese mice. In contrast, these findings were not seen in animals kept on a high-fat diet or in older mice, which further emphasises the need for well-controlled study populations. Importantly, Murphy et al did not detect any correlation between the proportion of Firmicutes, Bacteroidetes and Actinobacteria and faecal energy content or acetate levels. Hence, this work further demonstrates that there is no unique, independently confirmed, microbial marker that can predict obesity or be used as a therapeutic target. The authors also conclude that the relationship between the composition of the gut microbiota and host's energy harvesting capacity is more complex than previously anticipated. To this end, it is worthwhile considering that the metabolic activities of gut microbes, which result in the production of SCFA and other metabolites, not only affect the amount of energy that can be extracted from food but also the host's ability to store energy and to respond to energy intake via the release of gut hormones such as peptide YY (PYY).14 15 It has also been suggested that the amount of SCFA produced by the gut microbes, more than the composition of the microbiota, could impact on the host's weight balance.8 In order to explore further the complexity of the relationship between the gut microbiota and energy harvesting it is now crucial to gain a better understanding of the functions and metabolic activities of the gut microbes in situ.
International sequencing projects, such as the MetaHIT (http://www.metahit.eu) and the Human Microbiome Project (HMP; http://www.hmpdacc.org) generate tremendous amounts of information about gut microbiota diversity and its potential metabolic function.16 However, the sequencing efforts need to be complemented with functional studies in which cross-feeding relationships and overall results of microbial fermentation are tested in situ. For instance, molecular tools such as stable-isotope probing could be used to identify which components of the obese gut microbiota are actively involved in the production of SCFAs and how different taxa interact over time or during specific feeding regimes. In vitro studies have successfully linked phylogenetics and metabolic activity of gut microbes during 13C-starch fermentation in a human colon model.17 This computer-controlled model, which reaches colonisation densities of 109–1010 /ml, allows control over parameters such as transit time and pH. Furthermore, samples can be harvested at any time for analysis of metabolites etc. Similar approaches will be useful to assess microbial activity in vivo and its contribution to obesity and other pathological conditions. Simplified models of the human gut microbiota, where gnobiotic mice are colonised by sequenced members of the gut community, could further exemplify the impact of the microbiota on host physiology. Recently, such a model system was used to study the involvement of members of Firmicutes and Bacteroidetes in the carbohydrate metabolism.18
The study by Murphy et al demonstrates the need of defining the effects of diet and age on gut microbiota composition and function and will be essential for analysing and interpreting the massive data sets generated in the different metagenomics projects worldwide. Furthermore, it emphasises the need of well-controlled human studies (age and diet) to delineate the role of the gut microbiota in health and disease.
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