Abstract
The study of intracellular metabolic fluxes and inter-species metabolite exchange for microbial communities is of crucial importance to understand and predict their behaviour. The most authoritative method of measuring intracellular fluxes, 13C Metabolic Flux Analysis (13C MFA), uses the labeling pattern obtained from metabolites (typically amino acids) during 13C labeling experiments to derive intracellular fluxes. However, these metabolite labeling patterns cannot easily be obtained for each of the members of the community. Here we propose a new type of 13C MFA that infers fluxes based on peptide labeling, instead of amino acid labeling. The advantage of this method resides in the fact that the peptide sequence can be used to identify the microbial species it originates from and, simultaneously, the peptide labeling can be used to infer intracellular metabolic fluxes. Peptide identity and labeling patterns can be obtained in a high-throughput manner from modern proteomics techniques. We show that, using this method, it is theoretically possible to recover intracellular metabolic fluxes in the same way as through the standard amino acid based 13C MFA, and quantify the amount of information lost as a consequence of using peptides instead of amino acids. We show that by using a relatively small number of peptides we can counter this information loss. We computationally tested this method with a well-characterized simple microbial community consisting of two species.
Highlights
Microbial communities have radically altered Earth’s chemical composition and are largely responsible for the biogeochemical cycling of energy and carbon on its surface [1]
Amino acid-based 13C Metabolic Flux Analysis (13C MFA) 13C MFA uses the result of 13C labeling experiments to determine intracellular metabolic fluxes for a variety of organisms. 13C labeling experiments consist of feeding a culture of the organism (Escherichia coli in this case) a labeled carbon source
The 13C MFA algorithm requires the following as inputs: a model of metabolism which includes carbon transition information, measured values for extracellular fluxes, and the labeling pattern of each of the metabolites measured after the labeling experiment, typically through gas chromatography-mass spectrometry (GC-MS) [37], liquid chromatography-mass spectrometry (LC-MS) [38], or nuclear magnetic resonance (NMR) spectroscopy [39]
Summary
Microbial communities have radically altered Earth’s chemical composition and are largely responsible for the biogeochemical cycling of energy and carbon on its surface [1] Their activities underpin a variety of important biochemical processes ranging from lignocellulose degradation in termite guts [2] to gigantic underground cave formation [3]. While the recent advent of metagenomics [7], metatranscriptomics [8] and metaproteomics [9] has revolutionized our understanding of microbial communities, these techniques provide a knowledge that is descriptive in nature, rather than predictive Questions such as: ‘‘which species will become dominant if pH is altered?’’, or ‘‘how will the community’s metabolic activity affect the acetate levels of its environment’’ are, as of today, not answerable from just the knowledge of the genomes, transcripts, proteins and metabolites present in a microbial community. FBA has been used to PLOS Computational Biology | www.ploscompbiol.org
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