| Reference : From meta-omics to causality: experimental models for human microbiome research |
| Scientific journals : Article | |||
| Human health sciences : Endocrinology, metabolism & nutrition Human health sciences : Immunology & infectious disease Human health sciences : Laboratory medicine & medical technology | |||
| http://hdl.handle.net/10993/2840 | |||
| From meta-omics to causality: experimental models for human microbiome research | |
| English | |
Fritz, Joëlle  [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >] | |
| Desai, Mahesh [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >] | |
| Shah, Pranjul [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >] | |
| Schneider, Jochen [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >] | |
| Wilmes, Paul [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >] | |
| 2013 | |
| Microbiome | |
| BioMed Central | |
| 1 | |
| 14 | |
| Yes (verified by ORBilu) | |
| International | |
| 2049-2618 | |
| London | |
| United Kingdom | |
| [en] Causality ; diet ; human microbiome ; hypothesis testing ; in vivo model ; in vitro model ; ex vivo model ; in silico model ; dysbiosis ; disease ; microfluidics ; host-microbe interactions | |
| [en] Large-scale ‘meta-omic’ projects are greatly advancing our knowledge of the human
microbiome and its specific role in governing health and disease states. A myriad of ongoing studies aim at identifying links between microbial community disequilibria (dysbiosis) and human diseases. However, due to the inherent complexity and heterogeneity of the human microbiome, cross-sectional, case–control and longitudinal studies may not have enough statistical power to allow causation to be deduced from patterns of association between variables in high-resolution omic datasets. Therefore, to move beyond reliance on the empirical method, experiments are critical. For these, robust experimental models are required that allow the systematic manipulation of variables to test the multitude of hypotheses, which arise from high-throughput molecular studies. Particularly promising in this respect are microfluidics-based in vitro co-culture systems, which allow high-throughput first-pass experiments aimed at proving cause-and-effect relationships prior to testing of hypotheses in animal models. This review focuses on widely used in vivo, in vitro, ex vivo and in silico approaches to study host-microbial community interactions. Such systems, either used in isolation or in a combinatory experimental approach, will allow systematic investigations of the impact of microbes on the health and disease of the human host. All the currently available models present pros and cons, which are described and discussed. Moreover, suggestions are made on how to develop future experimental models that not only allow the study of host-microbiota interactions but are also amenable to high-throughput experimentation. | |
| Luxembourg Centre for Systems Biomedicine (LCSB): Eco-Systems Biology (Wilmes Group) ; Luxembourg Centre for Systems Biomedicine (LCSB): Medical Translational Research (J. Schneider Group) | |
| http://hdl.handle.net/10993/2840 | |
| 10.1186/2049-2618-1-14 | |
| open access |
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