microTrait: A Toolset for a Trait-Based Representation of Microbial Genomes

Abstract

Remote sensing approaches have revolutionized the study of macroorganisms, allowing theories of population and community ecology to be tested across increasingly larger scales without much compromise in resolution of biological complexity. In microbial ecology, our remote window into the ecology of microorganisms is through the lens of genome sequencing. For microbial organisms, recent evidence from genomes recovered from metagenomic samples corroborate a highly complex view of their metabolic diversity and other associated traits which map into high physiological complexity. Regardless, during the first decades of this omics era, microbial ecological research has primarily focused on taxa and functional genes as ecological units, favoring breadth of coverage over resolution of biological complexity manifested as physiological diversity. Recently, the rate at which provisional draft genomes are generated has increased substantially, giving new insights into ecological processes and interactions. From a genotype perspective, the wide availability of genome-centric data requires new data synthesis approaches that place organismal genomes center stage in the study of environmental roles and functional performance. Extraction of ecologically relevant traits from microbial genomes will be essential to the future of microbial ecological research. Here, we present microTrait, a computational pipeline that infers and distills ecologically relevant traits from microbial genome sequences. microTrait maps a genome sequence into a trait space, including discrete and continuous traits, as well as simple and composite. Traits are inferred from genes and pathways representing energetic, resource acquisition, and stress tolerance mechanisms, while genome-wide signatures are used to infer composite, or life history, traits of microorganisms. This approach is extensible to any microbial habitat, although we provide initial examples of this approach with reference to soil microbiomes.

Ecological strategies of microbes: Thinking outside the triangle

“…Many microbial traits of interest cannot necessarily be measured directly, so we use other measurements as proxies (Martiny et al, 2015). For instance, we may characterize functional genes encoding a particular trait like investment in cell metabolic maintenance (Karaoz & Brodie, 2022; Romero‐Olivares et al, 2019, 2021). Nevertheless, just because a taxon has a given functional gene does not guarantee that the gene is expressed, or to what extent (Taylor et al, 2012).…”

Implications of microbial community assembly for ecosystem restoration: patterns, process, and potential

“…In turn, this is contingent on phylogenetic conservation of traits, or ecological coherence in which physiological diversity aligns with phylogenetic diversity [21,[99][100][101]. When determinism reigns, the coupling between effect and response traits varies depending on whether the environment selects for clades of organisms with optimal functional traits or acts on response traits that are unrelated to the ecosystem process of interest [82,[102][103][104][105]. When stochasticity reigns, community composition will largely depend on effect traits that facilitate or impede dispersal (e.g., spore formation), and thus response traits that dictate function may be decoupled from the environment.…”

“…Alpha-(within community), beta-(across local communities), and gamma-(across regional communities) diversity each serve as easy-to-measure assessments of ecosystem trajectories. At a finer level of resolution, trait-based approaches provide information on changes in response traits (e.g., changes in life history strategies) and effect traits (e.g., changes in carbon and nutrient cycling) through restoration [102,104,105]. For instance, fungal:bacterial ratios, ribosomal gene copy numbers, or shifts in bacterial and fungal composition are often leveraged to infer changes in r-vs. k-selection [14,22,71,[108][109][110].…”

Life history strategies and niches of soil bacteria emerge from interacting thermodynamic, biophysical, and metabolic traits

“…Trait-based approaches in microbial ecology are an attractive means to interpret and predict the mechanistic processes that underpin ecological patterns, focusing specifically on the functional traits of microorganisms more so than their phylogenetic origin. The current rate of generating isolate genomes, single-cell assembled genomes, and metagenome-assembled genomes provide an unprecedented resource to extract potential traits from genome sequences [1,2]. The idea that measurable genomic traits relate to microbial performance in the environment is an attractive foundation for developing predictive multi-scale computational models of ecosystem function.…”

“…Instead of relying on existing classifications of microbial life history strategies, traitbased models have the potential to represent trait variation that can be aggregated over hundreds or thousands of genomes [13]. Our recent work provides a computational pipeline and a toolset (microTrait [2]) to infer microbial traits from genomic data and establish links between each genome-derived trait and ecological strategy at different levels of trait granularity. The resulting information can be used to initialize and parameterize mechanistic trait-based models spanning a hierarchy of structural complexity to explore the drivers of variation in the distribution and co-occurrence of microbial traits [14].…”