When you hear the word “microbe,” what do you think of? Chances are that your mind doesn’t go anywhere good, but to germs like salmonella and streptococcus that make us sick.
That reputation might not be completely fair, and in fact more knowledge about microbes could have substantial positive implications.
Unlike salmonella and streptococcus, many microbes in nature provide important ecological functions that are necessary to all life on earth. Photosynthesizers, for example, consume carbon dioxide and produce oxygen. These microbes occur in ecological communities, and understanding these communities is critical to using microbes to improve human health and mitigate the impacts of climate change.
Recent work from a team co-led by Northwestern Engineering’s Madhav Mani and University of Chicago professor Seppe Kuehn found there are simple ways of understanding the ecological functions that microbial communities are performing from their genes, which can now be easily measured via DNA sequencing.
Published January 26 in Cell, the paper “Genomic Structure Predicts Metabolite Dynamics in Microbial Communities” has implications for how scientists try to understand the activities of microbial communities, including their role in the nitrogen cycle and other important biogeochemical processes.
“Our study shows that simple mathematical models can relate the presence of key genes in individual organisms to the metabolic activity of a community, suggesting that in general the relationship between a community’s metabolic activity and its genomic parts list may be simpler than previously believed,” said Mani, assistant professor of engineering sciences and applied mathematics at the McCormick School of Engineering.
Microbial communities are complicated because the constituent organisms are continually sensing and responding to their environments, interacting, and co-evolving. This makes it challenging to understand how the overall metabolic activity of a community depends on its parts list: the genes and organisms present.
The study demonstrated a proof of concept that the metabolic dynamics of a microbial community can be statistically predicted simply from their gene content, without explicit knowledge or measurement of gene regulation or ecological processes. The researchers accomplished this by making controlled measurements of wild bacteria in the laboratory, and mathematically relating the dynamics of their metabolic activity to their gene content.
Through this, the team learned that metabolic genes can have conserved impacts on the quantitative metabolic traits of organisms, and, thereby, the metabolite dynamics of communities.
The study points to the possibility that microbial communities, long viewed as hopelessly complex, may be comprehensible in terms of a simple statistical language, and this language can be deciphered by making quantitative measurements of metabolic activity in wild communities and relating this information to the gene content that is readily available via sequencing.
“Our study opens the door to understanding how the metabolic activities of natural communities, which are responsible for the biogeochemical cycling of nutrients essential to all life on Earth, are responding to a changing climate by using sequence data alone,” Mani said.
The work by Mani and his team is not complete.
“There is still more work to do to see if these insights hold up in more complex natural environments. The experiments we performed were in well-mixed, highly controlled conditions, a far cry from the heterogeneous and fluctuating conditions of soils and aquatic environments,” Mani said. “Taking a similar statistical approach using complex, natural communities will be an important next step.”
Karna Gowda, the study’s first author, is a former applied mathematics graduate student at the McCormick School of Engineering. Gowda is currently a joint postdoctoral scholar working with Mani and Kuehn.
