Humans, animals and plants are covered in microbes. Such microbiomes have a major impact on the health of their hosts and have been linked to traits like growth promotion, stress resilience, and diseases. However, the mechanisms underlying microbiome-host interactions remain poorly understood. Recent studies have shown that microbiome-associated phenotypes are often mediated by specific molecules, a ‘chemical language’ that enables microbes to interact with each other and with the host. The biosynthesis of these molecules is encoded in metabolic gene clusters (MGCs) that are subject to frequent horizontal transfer and are therefore highly strain-specific.
Current computational methods for analysing microbiomes largely focus on comparative taxonomic analyses and generic metabolism, and overlook this complex "chemical dialog". Indeed, no adequate methods are available to systematically study the roles of MGCs in microbiomes. At the same time, metabolomics data from microbiomes are full of ‘dark matter’: unknown molecules that cannot be traced to their producers. Here, I propose to develop the first comprehensive computational framework to study the chemical language of the microbiome.
DECIPHER aims to develop new algorithms to link MGCs to their metabolic products and to predict their molecular and ecological functions in microbiomes. Subsequently, the new framework will be applied in systematic studies of microbiome- associated phenotypes in plants and humans. Together, the innovations proposed here will fill a key gap in the analysis of microbiome function and pave the way toward precision-engineering of microbiomes with specific metabolic capabilities for designer soils and microbiome-based therapies.
During its runtime, DECIPHER succesfully developed a range of new computaitonal tools for the analysis of functions of biosynthetic gene clusters and their metabolic products in microbiomes. Moreover, they were applied in a prototypical experimental setting, where culture-dependent and -independent approaches were successfully integrated to identify genes and BGCs responsible for a complex phenotype, disease suppression in rhizosphere microbiomes.
