An ensemble approach to the structure-function problem in microbial communities

Abstract

The metabolic activity of microbial communities plays a primary role in the flow of essential nutrients throughout the biosphere. Molecular genetics has revealed the metabolic pathways that model organisms utilize to generate energy and biomass, but we understand little about how the metabolism of diverse, natural communities emerges from the collective action of its constituents. We propose that quantifying and mapping metabolic fluxes to sequencing measurements of genomic, taxonomic, or transcriptional variation across an ensemble of diverse communities, either in the laboratory or in the wild, can reveal low-dimensional descriptions of community structure that can explain or predict their emergent metabolic activity. We survey the types of communities for which this approach might be best suited, review the analytical techniques available for quantifying metabolite fluxes in communities, and discuss what types of data analysis approaches might be lucrative for learning the structure-function mapping in communities from these data.

Keywords: Biophysics; Microbial metabolism; Microbiology.

Graphical abstract

Figure 1

Sequence, structure and function in proteins and microbial communities We propose that there exist analogous solutions to the sequence-structure problem in protein folding and the structure-function problem in microbial communities. (A) The mapping from amino acid sequence to 3D protein structure can be accomplished either by a simulation approach (e.g., molecular dynamics) or by a statistical approach (e.g., direct coupling analysis). The former is a computationally intensive strategy to simulate 3D protein structure based on first-principles modeling of atomic interactions. The latter leverages information about residue coevolution from an ensemble of amino acid sequences to infer which residues are in contact, allowing for an elegant and interpretable statistical inference of 3D structure. (B) The mapping from genomic and metagenomic sequences to community metabolic activity can be achieved through community flux balance modeling or, as we propose, a statistical ensembles approach. The former requires genome-level metabolic models of each organism to be built, a labor-intensive iterative process that so far has been successful primarily in a handful of model organisms. The latter leverages the diversity and variation in an ensemble of communities to learn an effective mapping between community sequence content metabolic activity

Figure 2

Community structure and function in the wild (A) Algal blooms are microbial successional processes that follow from the input of exogenous nutrients to aquatic environments. Reduced carbon fixed from CO₂ by algae is consumed along with other nutrients by heterotrophic bacteria in reproducible successional dynamics. (B) Marine snow particles are aggregates of organic carbon that are formed near the ocean's surface and subsequently sink to the ocean floor. Microbial communities can degrade these particles, and the amount of carbon that is mineralized to CO₂ versus the amount that is sequestered on the ocean floor depends strongly on the structure of the microbial community. (C) Microbial mats are layered communities that occur at air-water interfaces, often in extreme thermal environments such as hot springs. The spatial structure of these communities follows from exchanges of nutrients governed by redox gradients. (D) Pink berries are microbial aggregates that cryptically (internally) cycle sulfur between photosynthetic purple sulfur bacteria and anaerobic sulfate reducing bacteria

Figure 3

Community structure and function under domestication (A) Kefir grains extracellular-polymeric aggregates host to microbial communities that inoculate milk for the production of kefir. These communities undergo a reproducible successional process that involves the production and consumption of fermentation byproducts, which ultimately give kefir its desired flavor. (B) Anaerobic bioreactors often use granulated microbial communities to remove waste products such as reduced carbon, nitrogen, and phosphorous from water. Improving the performance and efficiency of these systems through the ensembles-informed design of communities would increase their viability as alternatives to traditional wastewater treatment approaches that expend significant energy on aeration

Figure 4

Bringing wild communities into the lab (A) Winogradsky columns are laboratory-assembled communities with distinctive and reproducible spatially-stratified metabolite fluxes. These fluxes, and consequently community structure, arise from emergent redox gradients. (B) Materially closed ecosystems are communities grown in sealed vessels whose only energy input is light. Nutrient cycling in closed ecosystems arises from phototrophic organisms generating reduced carbon by fixing CO₂, which can then be consumed by heterotrophic organisms. Predators such as ciliates can consume whole cells, facilitating the recycling of macromolecular biomass. (C) Serially passaged communities enrich a complex environmentally-derived community on laboratory-controlled nutrient conditions (e.g., a fixed carbon source). The resulting communities are typically low-complexity, and demonstrate reproducible trophic roles and patterns of nutrient exchange

Figure 5

Learning the structure-function map from data (A) Hypothetical structure-function data from an ensemble of n communities. y denotes a metabolite measurement, either level or rate that could also be dynamic. Colored dots correspond to data points in (B and C). X denotes a matrix of n rows each denoting a single community in an ensemble. The columns denote the relative abundances (colored bars) of taxa, genes in the metagenome or transcripts in the metatranscriptome. (B) An unsupervised approach where dimensionality reduction is applied to X yielding a lower dimensional representation of community structure that is then associated with communities of differing function. (C) A supervised approach where the function $f\left(\overrightarrow{x}\right)$ is learned for mapping structural variation to functional variation. Regressors denote independent variables in a lower dimensional representation of X that provides good predictive power of y.