Gut microorganisms, mammalian metabolism and personalized health care

Nicholson, Jeremy K.; Holmes, Elaine; Wilson, Ian D.

doi:10.1038/nrmicro1152

Cited by 828 publications

(728 citation statements)

References 78 publications

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“…Interestingly, not all important microbial functions were provided by dominant bacteria and Arumugam et al (2011) suggest that functional biomarkers are more robust than phylogenetic biomarkers for identifying enterotypes associated with host phenotype characteristics. The complementary metabolic pathways provided by the microbiome influence the phenotype of animals and humans and make them react differently to diet and drugs (Nicholson et al, 2005;Li et al, 2008;Claus et al, 2011).…”

Section: Linking Genomics and Metagenomics Data To Nutrition And Othementioning

confidence: 99%

Rumen microbial (meta)genomics and its application to ruminant production

Morgavi

Kelly

Janssen

et al. 2013

Animal

209

163

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Meat and milk produced by ruminants are important agricultural products and are major sources of protein for humans. Ruminant production is of considerable economic value and underpins food security in many regions of the world. However, the sector faces major challenges because of diminishing natural resources and ensuing increases in production costs, and also because of the increased awareness of the environmental impact of farming ruminants. The digestion of feed and the production of enteric methane are key functions that could be manipulated by having a thorough understanding of the rumen microbiome. Advances in DNA sequencing technologies and bioinformatics are transforming our understanding of complex microbial ecosystems, including the gastrointestinal tract of mammals. The application of these techniques to the rumen ecosystem has allowed the study of the microbial diversity under different dietary and production conditions. Furthermore, the sequencing of genomes from several cultured rumen bacterial and archaeal species is providing detailed information about their physiology. More recently, metagenomics, mainly aimed at understanding the enzymatic machinery involved in the degradation of plant structural polysaccharides, is starting to produce new insights by allowing access to the total community and sidestepping the limitations imposed by cultivation. These advances highlight the promise of these approaches for characterising the rumen microbial community structure and linking this with the functions of the rumen microbiota. Initial results using high-throughput culture-independent technologies have also shown that the rumen microbiome is far more complex and diverse than the human caecum. Therefore, cataloguing its genes will require a considerable sequencing and bioinformatic effort. Nevertheless, the construction of a rumen microbial gene catalogue through metagenomics and genomic sequencing of key populations is an attainable goal. A rumen microbial gene catalogue is necessary to understand the function of the microbiome and its interaction with the host animal and feeds, and it will provide a basis for integrative microbiome–host models and inform strategies promoting less-polluting, more robust and efficient ruminants.

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Section: Linking Genomics and Metagenomics Data To Nutrition And Othementioning

confidence: 99%

Rumen microbial (meta)genomics and its application to ruminant production

Morgavi

Kelly

Janssen

et al. 2013

Animal

209

163

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“…metagenomic (i.e., gut microbiota) effects and environmental factors. Indeed, both system-wide (i.e., whole organism) and organ-specific changes in biochemical processes have components driven by these factors (Martin et al 2009a, b;Nicholson et al 2005;Claus et al 2011;Mestdagh et al 2011;Merrifield et al 2011;Wikoff et al 2009).…”

Section: Electronic Supplementary Materialsmentioning

confidence: 99%

Current status on genome–metabolome-wide associations: an opportunity in nutrition research

et al. 2012

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Genome-wide association studies (GWASs) have become a very important tool to address the genetic origin of phenotypic variability, in particular associated with diseases. Nevertheless, these types of studies provide limited information about disease etiology and the molecular mechanisms involved. Recently, the incorporation of metabolomics into the analysis has offered novel opportunities for a better understanding of disease-related metabolic deregulation. The pattern emerging from this work is that gene-driven changes in metabolism are prevalent and that common genetic variations can have a profound impact on the homeostatic concentrations of specific metabolites. A particularly interesting aspect of this work takes into account interactions of environment and lifestyle with the genome and how this interaction translates into changes in the metabolome. For instance, the role of PY-ROXD2 in trimethylamine metabolism points to an interaction between host and microbiome genomes (host/ microbiota). Often, these findings reveal metabolic deregulations, which could eventually be tuned with a nutritional intervention. Here we review the development of gene-metabolism association studies from a single-gene/ single-metabolite to a genome-wide/metabolome-wide approach and highlight the conceptual changes associated with this ongoing transition. Moreover, we report some of our recent GWAS results on a cohort of 265 individuals from an ethnically diverse population that validate and refine previous findings on gene-urine metabolism interactions. Specifically, our results confirm the effect of PYROXD2 polymorphisms on trimethylamine metabolism and suggest that a previously reported association of N-acetylated compounds with the ALMS1/NAT8 locus is driven by SNPs in the ALMS1 gene.

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“…(In plants and yeast, most metabolites measured by gas chromatography-mass spectrometry are presently of uncertain identity.) The measured metabolome is greater than that encoded by the genome, as it will include molecules acquired exogenously as drugs, foods or food additives, and will also include molecules derived from the microflora of the host [58].…”

Section: Sizes Of the Human And Other Metabolomesmentioning

confidence: 99%

Systems biology, metabolic modelling and metabolomics in drug discovery and development

Kell

2006

Drug Discovery Today

266

163

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Unlike signalling pathways, metabolic networks are subject to strict stoichiometric constraints. Metabolomics amplifies changes in the proteome, and represents more closely the phenotype of an organism. Recent advances enable the production (and computer-readable encoding as SBML) of metabolic network models reconstructed from genome sequences, as well as experimental measurements of much of the metabolome. There is increasing convergence between the number of human metabolites estimated via genomics ( 3000) and the number measured experimentally. It is thus both timely, and now possible, to bring these two approaches together as an integrated (if distributed) whole to help understand the genesis of metabolic biomarkers, the progress of disease, and the modes of action, efficacy, off-target effects and toxicity of pharmaceutical drugs. Systems biology and metabolic modelling in the 21st CenturyAlthough there are many individual definitions, most commentators (including this one [1,2]) take it that systems biology involves an iterative interplay between more or less high-throughput and high-content 'wet' experiments, technology development, theory and computational modelling, and that it is the involvement of computational modelling, in particular, in the process that sets systems biology apart from the more traditional and more reductionist molecular biology. Metabolomics illustrates this amply. There is also the view that the perceived decrease in the effectiveness of the target-based drug discovery process [3][4][5], including the still-high levels of attrition [6], means that we must move towards understanding organisms at something more akin to a whole-system level [7][8][9][10][11].The question then arises as to what part of a system one might first beneficially model? Although there has, unsurprisingly, been considerable interest in modelling the major signalling pathways (e.g. Refs [12,13]), there are several reasons why it is timely to turn our attention to the level of small molecule metabolism, which is the focus of this review. Metabolism is more discriminatingIt has long been known, and proven through the formalism of metabolic control analysis [7,10,[14][15][16], that whereas small changes in the concentrations of enzymes (and the transcripts that encode them) have only small effects on the fluxes through metabolic pathways, they have substantial effects on the concentrations of metabolic intermediates. Because the metabolome (nominally the concentrations of 'all' the metabolites measured in a system of interest [17]) is downstream of the proteome, it is thereby 'amplified' both in theory [18] and in practice [19,20] and represents a more sensitive level of organisation than do the macromolecular 'omes for understanding a complex biological system, and the changes in it that might be occasioned by disease or pharmaceutical intervention [21,22]. Metabolic reconstruction is now mature and timelyAn attractive feature for the purposes of modelling is that metabolism, in contrast to signalling pathways, ...

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Gut microorganisms, mammalian metabolism and personalized health care

Cited by 828 publications

References 78 publications

Rumen microbial (meta)genomics and its application to ruminant production

Rumen microbial (meta)genomics and its application to ruminant production

Current status on genome–metabolome-wide associations: an opportunity in nutrition research

Systems biology, metabolic modelling and metabolomics in drug discovery and development

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