Deconstructingtaxa x taxa x environmentinteractions in the microbiota: A theoretical examination

Abstract

13 1. The animal gut is a complex ecosystem containing many interacting species. A major 14 objective of microbiota research is to identity the scale at which gut taxa shape hosts. 15However, most studies focus solely on pairwise interactions and ignore higher-order 16interactions involving three or more component taxa. Higher-order interactions 17 represent non-additive effects that cannot be predicted from first-order or pairwise 18interactions. 192. Possible reasons as to why studies of higher higher-order interactions have been 20 scarce is that many host-associated systems are experimentally intractable, gut 21 microbiota are prohibitively species rich, and the influence of any given taxon on 22hosts is often context-dependent. Furthermore, quantifying emergent effects that 23 represent higher-order interactions that are not simply the result of lower-order 24 interactions, present a combinatorial challenge for which there are few well-25 developed statistical approaches in host-microbiota studies. 263. In this perspective, our goal is to quantify the existence of emerging higher-order 27 effects and characterize their prevalence in the microbiota. To do so, we adapt a 28 method from evolutionary genetics used to quantify epistatic effects between 29 mutations and use it to quantify the effects of higher-order microbial interactions on 30 host infection risk. 31 4. We illustrate this approach by applying it to an in silico dataset generated to resemble 32 a population of hosts with gut-associated microbial communities. We assign each host 33 a pathogen load, and then determine how emergent interactions between gut taxa 34 influence this host trait. 35 5. We find that the effect of higher-order interactions generally increases in magnitude 36with the number of species in the gut community. Based on the average magnitude of 37 interaction for each order, we find that 9 th order interactions have the largest non-38 linear effect on determining host infection risk. 39 6. Our approach illustrates how incorporating the effects of higher-order interactions 40 among gut microbiota can be essential for understanding their effects on host 41 infection risk. We conclude that insofar as higher-order interactions between taxa may 42 profoundly shape important organismal phenotypes (such as susceptibility to 43 infection), that they deserve greater attention in microbiome studies. 44 1 45 46 Introduction 47 Animal guts contain complex microbial communities whose structure and function 48 depend upon the interactions among microbes and the host. Gut microbiota serve as 49 key actors in host health, impacting development, metabolism, and pathogen 50 susceptibility (Brugman et al., 2018). The development of microbe-free (also known as 51 germ-free) model hosts has made it possible to experimentally study how the 52 microbiota influences host susceptibility to infection (Goodman et al., 2011; Ridaura et 53 al., 2013). However, most studies rely on correlations between the relative abundances 54 of individual bacterial taxa an...

Metabolic rules of microbial community assembly

“…Interestingly, the main driver of taxonomic variability among replicates was the dominant member of the respirator group (a sub-dominant species). Amelioration of competition between two fermenter strains in the presence of one (but not the other) dominant respirator points to the subtle role that high-order interactions may play in community assembly (Billick and Case 1994;Mickalide and Kuehn 2019;Sanchez 2019;Sanchez-Gorostiaga et al 2019;Senay et al 2019;Levine et al 2017;Grilli et al 2017) . This is also in line with previous observations of the potential importance of sub-dominant bacteria in shaping the composition of microbial communities ) .…”

Artificially selecting bacterial communities using propagule strategies†

“…An important challenge of these synthetic approaches is the factorially growing number of interactions as the communities grow in size. This challenge is important, as these pairwise and highorder interactions can limit our ability to predict the function of even mid-size consortia (10 taxa or more) (Guo and Boedicker 2016;Gould et al 2018;Sanchez-Gorostiaga et al 2019;Senay et al 2019;Mickalide and Kuehn 2019;Sanchez 2019). A second important challenge was pointed out by Goldman and Brown: engineered functions in synthetic consortia can be affected both by ecological processes (e.g., invasions, species extinctions, and population dynamics) as well as by rapid evolution within the community (Goldman and Brown 2009;Escalante et al 2015).…”

Understanding the evolution of interspecies interactions in microbial communities

“…Finally, real communities consist of more than two species (or multiple strains of the same species, which should have similar consequences as long as these strains have sufficiently different growth characteristics and are unlikely to share the same cooperative gene (so that we can safely ignore kin selection [88,113])). This may spatially isolate interacting species [128,129], exert opposing selection pressures on a focal species [62] and result in higher-order interactions [24,130]. Predicting the joint effect of such forces is not straightforward.…”