Abstract
The animal gut is a complex ecosystem containing many interacting species. A major objective of microbiota research is to identity the scale at which gut taxa shape hosts. However, most studies focus solely on pairwise interactions and ignore higher-order interactions involving three or more component taxa. Higher-order interactions represent non-additive effects that cannot be predicted from first-order or pairwise interactions.
Possible reasons as to why studies of higher higher-order interactions have been scarce is that many host-associated systems are experimentally intractable, gut microbiota are prohibitively species rich, and the influence of any given taxon on hosts is often context-dependent. Furthermore, quantifying emergent effects that represent higher-order interactions that are not simply the result of lower-order interactions, present a combinatorial challenge for which there are few well-developed statistical approaches in host-microbiota studies.
In this perspective, our goal is to quantify the existence of emerging higher-order effects and characterize their prevalence in the microbiota. To do so, we adapt a method from evolutionary genetics used to quantify epistatic effects between mutations and use it to quantify the effects of higher-order microbial interactions on host infection risk.
We illustrate this approach by applying it to an in silico dataset generated to resemble a population of hosts with gut-associated microbial communities. We assign each host a pathogen load, and then determine how emergent interactions between gut taxa influence this host trait.
We find that the effect of higher-order interactions generally increases in magnitude with the number of species in the gut community. Based on the average magnitude of interaction for each order, we find that 9th order interactions have the largest non-linear effect on determining host infection risk.
Our approach illustrates how incorporating the effects of higher-order interactions among gut microbiota can be essential for understanding their effects on host infection risk. We conclude that insofar as higher-order interactions between taxa may profoundly shape important organismal phenotypes (such as susceptibility to infection), that they deserve greater attention in microbiome studies.
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