Model-based analysis of structure-function relationships in the human microbiome

Background/Question/Methods:  The human microbiome represents a vastly complex ecosystem that is tightly linked to our health. Multiple molecular assays now enable high-throughput profiling of this system, providing large-scale and comprehensive characterization of its ecology, functional capacity, and metabolic activity. To date, however, analyses of such multi-meta-omic data typically focus on statistical associations, often ignoring extensive prior knowledge of the mechanisms, dependencies, and regularities linking these various facets of the microbiome.

Results/Conclusions: In this talk, I will highlight the pressing need for the development of systems-level and model-based methods for integrating microbiome-derived multi-omic data. I will further introduce several novel computational frameworks for linking taxonomic, genomic, metagenomic, and metabolomic information about the microbiome. I will specifically discuss FishTaco, an analytical and computational framework that integrates taxonomic and functional comparative analyses to accurately quantify taxon-level contributions to disease-associated functional shifts. Applying FishTaco to several large-scale metagenomic cohorts, I will demonstrate that taxonomic drivers of functional imbalances in the microbiome are function-specific and disease-specific. I will also present MIMOSA, a metabolic model-based approach for integrating microbiome taxonomic and metabolomic profiles and for elucidating mechanistic links between ecological and metabolic variation. Finally, I will discuss the surprising discrepancy between taxonomic and functional variations and will show that revised metagenomic processing can uncover hidden and biologically meaningful functional variation in the human microbiome. Combined, such frameworks lead to an improved comprehensive, multi-scale, and mechanistic understanding of the microbiome in health and disease and of the structure-function relationship in this complex ecosystem. These methods can further inform efforts for personalized microbiome-based therapy and for pinpointing putative intervention targets for manipulating the microbiome’s functional capacity.