A schematic of the intestines showing different microbes. Image credit: DataBase Center for Life Science (DBCLS) (CC BY 4.0)
All the microbes that live in a specific environment, for example an organ, are collectively called the microbiota. In humans, the microbiota of the gut has been extensively studied by sequencing the DNA of the different microbes to identify them and determine the roles they play in health and disease. The DNA sequences of all the members of the microbiota is called the metagenome.
The chemical reactions that the gut microbiota perform to produce energy and make the biomolecules they need to survive are collectively referred to as the metabolism of these microbes. Studying the metabolism of the gut microbiota can help researchers understand the roles of the different microbes. However, the large variety of species in the gut microbiota and gaps in the information about them render these studies difficult, despite technology improving quickly.
To tackle this issue, Belcour, Frioux et al developed a new piece of software called Metage2Metabo (M2M) that simulates the metabolism of the gut microbiota and describes the metabolic relationships between the different microbes. Metage2Metabo analyses the roles of the metabolic genes of a large number of microbe species to establish how they complement each other metabolically. Then, it can calculate the minimum number of species needed to perform a metabolic role of interest within that microbiota, and which key species are associated with that role.
To test the new software, Belcour, Frioux et al. used Metage2Metabo to analyse genomes from the human gut microbiota and from the cow rumen (one of the cow’s stomachs). They showed that even if the metagenome was incomplete, the software was able to make stable predictions of key species involved in metabolic complementarity. Additionally, they also illustrated how the method can be used to study the gut microbiota of individuals.
This work presents a new method for determining the metabolic relationships between species within a microbiota. The software is highly flexible and could be adapted to identify key members within different communities. In the context of the gut microbiota, the predictions of Metage2Metabo could shed lights on the interactions between the host and the microbes and contribute to a better understanding of microbe environments.
