A behavioral model for mapping the genetic architecture of gut-microbiota networks

Abstract

The gut microbiota may play an important role in affecting human health. To explore the genetic mechanisms underlying microbiota-host relationships, many genome-wide association studies have begun to identify host genes that shape the microbial composition of the gut. It is becoming increasingly clear that the gut microbiota impacts host processes not only through the action of individual microbes but also their interaction networks. However, a systematic characterization of microbial interactions that occur in densely packed aggregates of the gut bacteria has proven to be extremely difficult. We develop a computational rule of thumb for addressing this issue by integrating ecological behavioral theory and genetic mapping theory. We introduce behavioral ecology theory to derive mathematical descriptors of how each microbe interacts with every other microbe through a web of cooperation and competition. We estimate the emergent properties of gut-microbiota networks reconstructed from these descriptors and map host-driven mutualism, antagonism, aggression, and altruism QTLs. We further integrate path analysis and mapping theory to detect and visualize how host genetic variants affect human diseases by perturbing the internal workings of the gut microbiota. As the proof of concept, we apply our model to analyze a published dataset of the gut microbiota, showing its usefulness and potential to gain new insight into how microbes are organized in human guts. The new model provides an analytical tool for revealing the “endophenotype” role of microbial networks in linking genotype to end-point phenotypes.

Network mapping of root-microbe interactions inArabidopsis thaliana

“…We developed a behavioral model to define the strengths of mutualism, antagonism, aggression, and altruism between each pair of microbes, quantitatively described by Z mu , Z an , Z ag , and Z al , respectively (see Experimental Procedures). These mathematical descriptors have been biologically validated (45, 46), allowing us to reconstruct mutualism, antagonism, aggression, and altruism networks for the root microbiota of the A. thaliana. To reduce the complexity of the networks, we chose the most abundant 100 OTUs in bacteria and fungi, respectively, for the reconstruction of four types of bacterial-fungal networks (OTU1-100 are listed as bacteria and OTU100-200 as fungi).…”

“…We calculated six centrality indices including connectivity (Con), closeness (C(u)), betweenness (B(u)), eccentricity (E(u)), eigencentrality (G(u)), and PageRank (P(u)) (Fig. 3) using the formulas given in Jiang et al (45). In the same network type, these indices exhibit differences among hosts and, also, the same index varies among network types.…”

“…In a well-designed GWAS study, J. Bergelson et al (33) found a few significant QTLs that are associated with root microbial species richness and community structure, which are involved in plant immunity, cell-wall integrity, root and root-hair development. In this study, we used a newly developed network mapping model (45) to reanalyze Bergelson et al’s (33) data, characterizing previously undetected QTLs that mediate microbial interactions. We found that most of the QTLs detected by the new model can be annotated to candidate genes with known biological functions including plant growth and development, resilience against pathogens, root development, and improved resistance against abiotic stress conditions (Table 1; Table S4).…”

“…However, traditional GWAS models can only detect the host QTLs responsible for the abundance of individual microbes, failing to disentangle the relationships of diverse microbial species and microbe-host interactions (23, 35, 44). In a previous study, we have integrated ecology theory and network science to derive a series of mathematical descriptors for measuring all possible types of microbe-microbe interactions, and quantify how each microbe interacts with every other through a web of cooperation and competition (45). These descriptors show their utility to to map the genetic architecture of the gut microbiota (45).…”

“…In a previous study, we have integrated ecology theory and network science to derive a series of mathematical descriptors for measuring all possible types of microbe-microbe interactions, and quantify how each microbe interacts with every other through a web of cooperation and competition (45). These descriptors show their utility to to map the genetic architecture of the gut microbiota (45).…”