Microbes perform chemistry in the soils, oceans, and even our bodies. They accomplish this collectively in communities composed of many distinct members, each with their genetically-encoded capabilities, all participating in an intricate metabolic trade network. The health and robustness of organisms and ecosystems alike depend on the metabolism of microbes. A deep understanding of the principles that predict the collective metabolism of microbial communities would grant us the ability to control and engineer these communities for the betterment of human health, and the health of our environment. DNA sequencing is one of our most powerful tools for characterizing microbes. Unfortunately, it remains challenging to relate the DNA sequences to the metabolic capabilities of a microbial community. This problem is challenging given the complexity of cellular physiology, and the many complex interactions between different species. Using natural bacterial isolates that perform denitrification, a process that makes up an important part of the nitrogen cycle, mathematical modeling, and machine learning, the researchers will show that it is possible to predict community metabolism from genomic sequences. The work is groundbreaking because it means that by sequencing the DNA of complex communities one can now "readout" their metabolic function. The insight will enable the design of microbial communities with predefined metabolic function. Other broader impacts of this research include training of undergraduate researchers in biochemical, statistical, and computational research and outreach to parents and children regarding microbiomes and denitrification.
The collective metabolic function of microbial communities emerges through a hierarchy of genomically-encoded processes, from sub-cellular information processing and gene expression to interactions mediated by extracellular metabolites, abiotic factors, and collective phenomena. Understanding how this collective metabolic function is encoded in the genomic structure of the consortium is a core challenge for microbial ecology. This research proposed leverages denitrification, a cascade of reactions that reduce oxidized nitrogen compounds via anaerobic respiration, as a model metabolic function to build a bridge between genomic structure and metabolic function in microbial communities. By employing metabolite measurements on a library of sequenced denitrifying bacterial isolates, the researchers will determine the predictive quantitative relationship between the denitrification genes that each strain possesses and metabolic dynamics. Community assembly experiments will then be used to predict community metabolic dynamics from the dynamics of individual populations. In cases where community dynamics are not predictable from the dynamics of individual populations alone, the researchers will investigate genomic predictors and molecular mechanisms underlying the behavior. Once the bridge between genomic structure and metabolic function is built in one experimental system, the researchers will attempt to do the same in a more complex system that better approximates a natural context. The result will be a generalizable mapping from genomic structure to metabolic function that enables the rational design of microbial communities and provides a new quantitative interpretation of functional gene content.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
