13C Metabolic flux analysis of co-culture systems: a novel approach

Nikodimos A. Gebreselassie, Maciek R. Antoniewicz
Department of Chemical and Biomolecular Engineering, University of Delaware
150 Academy Street, Colburn Laboratory, Newark, DE 19716

Abstract

Microbial communities play an important role in biofuel production, biomedical research, food
production, and waste water treatment. The capabilities of multi-microorganism systems are often enhanced by synergistic interactions at different levels. Co-culture systems particularly have unique advantages over mono-culture systems in optimizing product yield as a result of synergistic interaction, communication, division of labor, and separate and diverse metabolic pathways of its components. Among the many omics tools used to gain insight into the physiology of microbial systems, fluxomics is the most direct and relevant method to study the actual in vivo metabolic state.

13C-Metabolic flux analysis (13C-MFA) is the most widely used model-based experimental approach to study flux distributions within metabolic pathways. In the past, 13C-MFA for co- culture systems has required physical separation of proteins and/or cells to resolve individual populations in a co-culture. In this work, we demonstrate a novel co-culture 13C-MFA framework that does not require any physical separation of cells or proteins. Rather, fluxes for

individual populations are computationally deconvoluted from the overall co-culture 13C-labeling data. We show that the overall 13C-labeling data has abundant information not only to estimate
the fluxes in the two populations, but also to determine the fraction of each cell population in the co-culture. We also demonstrate how optimal 13C-tracers should be selected to maximize the resolution of fluxes in co-culture systems. Significantly, we show that commonly used tracers such as [1-13C]glucose and [1-13C]glucose/[U-13C]glucose are poor tracer choices for co-culture systems. Instead, the less commonly used [1,2-13C]glucose tracer provides optimal resolution of fluxes in co-cultures. We demonstrate our methodology experimentally using a co-culture system of two E. coli knockout strains, â??zwf (knockout of the first step in the pentose phosphate
pathway) and â??pgi (knockout of the first step in glycolysis pathway). We also present results from currently on-going work on yeast/E. coli co-culture and thermophilic co-culture systems.
The new flux analysis methodology that we have developed for analyzing co-culture systems adds a new dimension to the field of 13C-MFA and provides an enormous resource to the metabolic engineering and biotechnology communities.