Understanding how microbiomes influence the systems they inhabit

Abstract

Translating the ever-increasing wealth of information on microbiomes (environment, host or built environment) to advance our understanding of system-level processes is proving to be an exceptional research challenge. One reason for this challenge is that relationships between characteristics of microbiomes and the system-level processes that they influence are often evaluated in the absence of a robust conceptual framework and reported without elucidating the underlying causal mechanisms. The reliance on correlative approaches limits the potential to expand the inference of a single relationship to additional systems and advance the field. We propose that research focused on how microbiomes influence the systems they inhabit should work within a common framework and target known microbial processes that contribute to the system-level processes of interest. Here, we identify three distinct categories of microbiome characteristics (microbial processes, microbial community properties and microbial membership) and propose a framework to empirically link each of these categories to each other and the broader system-level processes that they affect. We posit that it is particularly important to distinguish microbial community properties that can be predicted using constituent taxa (community-aggregated traits) from those properties that cannot currently be predicted using constituent taxa (emergent properties). Existing methods in microbial ecology can be applied to more explicitly elucidate properties within each of these three categories of microbial characteristics and connect them with each other. We view this proposed framework, gleaned from a breadth of research on environmental microbiomes and ecosystem processes, as a promising pathway with the potential to advance discovery and understanding across a broad range of microbiome science.

Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change

“…In other cases, individual or population genomes can be assembled from culture-independent shotgun metagenomic datasets; this novel approach is gaining popularity as it facilitates physiological investigations of hitherto uncultured taxa (Hu et al, 2016). In addition, traits can be measured at the community level to integrate tradeoffs across populations and characterise trait impacts on ecosystem function (Geyer et al, 2016;Hall et al, 2018). Many phenotypic traits can also be measured directly, even if the underlying genetic mechanisms are complex and cannot be determined.…”

“…Approaches to modelling Y-A-S strategies to predict carbon fluxes Representing microbial diversity has been a big challenge for models projecting ecosystem responses to environmental change (Hall et al, 2018). This challenge introduces uncertainty that affects model predictions of future climatic change (Bradford et al, 2016;Sulman et al, 2018).…”

“…Likewise, a trait-based modelling framework based on microbial Y-A-S strategies holds promise for representing the immense diversity of microbial communities in simulations of system-level processes at various spatial scales (Hall et al, 2018). The cellular mechanisms underlying tradeoffs in key physiological traits can be incorporated into microbial functional models like MIMICS to reveal how these tradeoffs structure microbial communities and their resulting carbon cycle functions.…”

Evaluating the stoichiometric trait distributions of cultured bacterial populations and uncultured microbial communities

“…The advantage, or consequence, of this variability may lie in bet hedging or facilitating the development of microbial interactions within the population to allow for a division of labour (Ackermann, 2013;Nikolic et al, 2013;Ackermann, 2015;Rosenthal et al, 2018). Thus, the traits of the community or population may be community-aggregated traits, where the properties of the individual determine the properties of the community, or they may be emergent traits where the properties of the community or population are greater than the sum of their parts (Hall et al, 2018). Through the study of individuality of microbial physiology, we can begin to decipher which traits are community-aggregated or emergent traits and better assess the impact of the individuality on the ecosystem (Hall et al, 2018;Fontana et al, 2017).…”

Exploration of social spreading reveals behavior is prevalent amongPedobacterandP. fluorescensisolates, and shows variations in induction of phenotype

“…There exist important functions of microbial communities that cannot be attributed to any one taxa, and are, instead, a property of the collective. These emergent properties result from social interactions between different microbial species in a community context, and, importantly, cannot be predicted based on the study of the individual microbes in isolation (6,7). These community-intrinsic properties emerge from the interactions within a community, which can be as small as a pair of microbial species (8).…”