A neural network model for dose–response of foodborne pathogens

Inferring microbial interaction network from microbiome data using RMN algorithm

Background

Microbial interactions are ubiquitous in nature. Recently, many similarity-based approaches have been developed to study the interaction in microbial ecosystems. These approaches can only explain the non-directional interactions yet a more complete view on how microbes regulate each other remains elusive. In addition, the strength of microbial interactions is difficult to be quantified by only using correlation analysis.

Results

In this study, a rule-based microbial network (RMN) algorithm, which integrates regulatory OTU-triplet model with parametric weighting function, is being developed to construct microbial regulatory networks. The RMN algorithm not only can extrapolate the cooperative and competitive relationships between microbes, but also can infer the direction of such interactions. In addition, RMN algorithm can theoretically characterize the regulatory relationship composed of microbial pairs with low correlation coefficient in microbial networks. Our results suggested that Bifidobacterium, Streptococcus, Clostridium XI, and Bacteroides are essential for causing abundance changes of Veillonella in gut microbiome. Furthermore, we inferred some possible microbial interactions, including the competitive relationship between Veillonella and Bacteroides, and the cooperative relationship between Veillonella and Clostridium XI.

Conclusions

The RMN algorithm provides the reconstruction of gut microbe networks, and can shed light on the dynamical interactions of microbes in the infant intestinal tract.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0199-2) contains supplementary material, which is available to authorized users.