Boolean analysis reveals systematic interactions among low-abundance species in the human gut microbiome

The analysis of microbiome compositions in the human gut has gained increasing interest due to the broader availability of data and functional databases and substantial progress in data analysis methods, but also due to the high relevance of the microbiome in human health and disease. While most analyses infer interactions among highly abundant species, the large number of low-abundance species has received less attention. Here we present a novel analysis method based on Boolean operations applied to microbial co-occurrence patterns. We calibrate our approach with simulated data based on a dynamical Boolean network model from which we interpret the statistics of attractor states as a theoretical proxy for microbiome composition. We show that for given fractions of synergistic and competitive interactions in the model our Boolean abundance analysis can reliably detect these interactions. Analyzing a novel data set of 822 microbiome compositions of the human gut, we find a large number of highly significant synergistic interactions among these low-abundance species, forming a connected network, and a few isolated competitive interactions.Author summary: Over the last years the composition of microbial communities in the human gut, the gut microbiome, has gained prominence in clinical research. Providing an estimate of the microbial interaction network from compositional data is an important prerequisite for clinical interpretation and for a better theoretical understanding of such microbial communities. Many studies have focused on the dominant interactions of species that are highly abundant such as, on the phyla level, Bacteriodetes and Firmicutes. Using binarized abundance vectors (recording only the presence and absence of microbial species) we show that the low-abundance segment of the microbiome also contains a large number of systematic interactions. For low-abundant species, our inference method evaluates the transformation of pairs of such vectors ‘binary co-abundance’ under Boolean operations. First we calibrate our new method using simulated data. Then we apply it to novel microbiome data from a human population study. The method reveals a large number of significant positive interactions and several significant negative interactions among low-abundance microbial species. It can be argued that important inter-individual differences and adaptations to changes in environmental conditions rather occur on the level of the low-abundance species than in the few main highly abundant species. This hypothesis could explain the broad distribution of abundances in microbiome compositions.