From meta-omics to causality: experimental models for human microbiome research

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.