Prediction of source nutrients for microorganisms using metabolic networks

TL;DR

This chapter provides an overview of metabolic networks and modelling and how they can be used to predict the nutrient requirements of a microorganism, followed by a sample protocol using a toy metabolic network, which is then expanded to a genome-scale metabolic network application.

Abstract

Metagenomics has lowered the barrier to microbial discovery--enabling the identification of novel microbes without isolation--but cultures remain imperative for the deep study of microbes. Cultivation and isolation of non-model microbes remains a major challenge, despite advances in high-throughput culturomic methods. The quantity of simultaneous experimental variables is constrained by time and resources, but the list can be reduced using computational biology. Given an annotated genome, metabolic modelling can be used to predict source nutrients required for the growth of a microbe, which acts as an initial screen to inform culture and isolation experiments. This chapter provides an overview of metabolic networks and modelling and how they can be used to predict the nutrient requirements of a microorganism, followed by a sample protocol using a toy metabolic network, which is then expanded to a genome-scale metabolic network application. These methods can be applied to any metabolic network of interest--which in turn can be created from any genome of interest--and are a starting point for experimental validation of source nutrients required for microorganisms that remain uncultivated to date.

Figures (13)

• Figure 1: Overview of metabolic network-based nutrient inference from genomes for uncultivated microbes. A genome-scale metabolic network needs to be reconstructed from the genome of interest, or obtained from existing knowledge bases. The structure of the network can be used to identify external metabolites (graph analysis), or simulations with several modelling frameworks can be applied to predict source nutrients (seeds). Predicted seeds can then guide experimentation under lab conditions.
• Figure 2: Depiction of a toy metabolic network, with metabolites as blue circles and reactions as arrows. The directionality of a reaction is represented by the arrowheads. This network includes two compartments: intracellular (grey) and extracellular (yellow). Exchange reactions illustrating the boundaries of the modelled system are shown in yellow; the biomass reaction is in red.
• Figure 3: Network expansion demonstrated on the toy network, with S1 and S2 as initial seeds (purple). Each iteration expands the reached metabolites (scope) by including metabolites newly reachable from those in the previous step (blue). Metabolites that cannot be reached from these seeds remain grey.
• Figure 4: Flux balance analysis demonstrated on the toy network. After optimization of the objective function, reaction fluxes are represented by arrows with widths proportional to their flux values. Reactions carrying zero flux are not shown. The objective function (biomass reaction) is shown in red.
• Figure 5: Metabolic modelling as a A) direct problem vs. B) an inverse problem (seed inference). Classical metabolic modelling uses known seeds (available nutrients, purple) and a metabolic network (grey) to predict which metabolites can be reached (green), while seed inference does the reverse by applying a known metabolic objective (red) to a network to predict seeds.