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From mystery to mechanism: can proteomics build systems-level understanding of our gut microbes?

Research over the past decade has firmly established gut microbiota as a critical component of human health. This immensely complex ecosystem is characterized by dynamic interactions between the host, diet, and the gut microbiota, and contributes to metabolic disorders, autoimmunity, and cancer [1,2]. Driven by large 16S ribosomal RNA (16S) and metagenomic sequencing efforts, foundational studies have described stereotypical changes in microbiota composition which reflect numerous states of health and pathology [3]. However, the mechanisms shaping how these fluctuations occur, and how these changes influence host physiology are poorly understood. Multi-omic, systems-level analyses stand to describe host–microbiota interactions with even greater resolution, and therefore promise to more rapidly unlock the gut microbiota’s therapeutic potential [4]*. A handful of recent studies have demonstrated the powerful, yet underexploited capability mass spectometry-based proteomics has for building quantitative models of the gut environment. Such ‘metaproteomic’ approaches represent an important way to add functional annotations to the extremely deep microbiome sequencing data generated across the globe. However, methodological and computational limitations in metaproteomic workflows will need to be overcome in order to build truly mechanistic understanding of host–microbe relationships.