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

Area of Science:

• Microbiology
• Systems Biology
• Human Health

Background:

• Large-scale meta-omic projects are advancing human microbiome research.
• Identifying links between microbial dysbiosis and human diseases is a key focus.
• Current observational studies lack the power to establish causation due to microbiome complexity.

Purpose of the Study:

• To review existing experimental models for studying host-microbial community interactions.
• To highlight the need for robust models to test hypotheses derived from omic data.
• To discuss the pros and cons of various in vivo, in vitro, ex vivo, and in silico approaches.

Main Methods:

• Review of in vivo, in vitro, ex vivo, and in silico experimental models.
• Discussion of microfluidics-based in vitro co-culture systems for high-throughput experimentation.
• Analysis of approaches for systematic manipulation of variables to test hypotheses.

Main Results:

• Various experimental models exist for studying host-microbiota interactions, each with advantages and disadvantages.
• Microfluidics offer promising high-throughput capabilities for initial cause-and-effect studies.
• Combinatorial approaches using multiple model systems can enable systematic investigations.

Conclusions:

• Robust experimental models are crucial for deciphering the causal role of the microbiome in health and disease.
• Future models should facilitate high-throughput experimentation for efficient hypothesis testing.
• Systematic investigation of host-microbe interactions is essential for understanding human health and disease states.