Seeing the forest for the genes: using metagenomics to infer the aggregated traits of microbial communities

Abstract

Most environments harbor large numbers of microbial taxa with ecologies that remain poorly described and characterizing the functional capabilities of whole communities remains a key challenge in microbial ecology. Shotgun metagenomic analyses are increasingly recognized as a powerful tool to understand community-level attributes. However, much of this data is under-utilized due, in part, to a lack of conceptual strategies for linking the metagenomic data to the most relevant community-level characteristics. Microbial ecologists could benefit by borrowing the concept of community-aggregated traits (CATs) from plant ecologists to glean more insight from the ever-increasing amount of metagenomic data being generated. CATs can be used to quantify the mean and variance of functional traits found in a given community. A CAT-based strategy will often yield far more useful information for predicting the functional attributes of diverse microbial communities and changes in those attributes than the more commonly used analytical strategies. A more careful consideration of what CATs to measure and how they can be quantified from metagenomic data, will help build a more integrated understanding of complex microbial communities.

Keywords: community-aggregated traits; metagenomics; microbial diversity; microbial ecology; traits.

FIGURE 1

Conceptual distribution of a community-aggregated trait (CAT) as inferred from the frequency of sequence reads from five different metagenomes (represented as different colors). For an illustrative purpose, we can assume that the mean value of the trait increases along an environmental gradient or correlates with an ecosystem process of interest. Likewise, we can assume that the variance of this same trait is reduced at the extreme values of the gradient/process as a result of selective pressures. Trait distributions as conceptualized in this figure have been observed, for example, in the distribution of GC content across aquatic metagenomes (Barberan et al., 2012).