Name
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, 13 C Metabolic Flux Analysis (C-13 MFA), uses the labeling pattern obtained from metabolites (typically amino acids) during 13 C 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 C-13 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 C-13 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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1371/journal.pcbi.1003827 | PLOS COMPUTATIONAL BIOLOGY |
Keywords | Field | DocType |
bioinformatics,biomedical research | Methanococcus,Biology,Proteomics,Biochemistry,Amino acid,Peptide,Metabolic flux analysis,Intracellular,Bioinformatics,Metabolite,Peptide sequence | Journal |
Volume | Issue | ISSN |
10 | 9 | 1553-7358 |
Citations | PageRank | References |
1 | 0.35 | 2 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amit Ghosh | 1 | 6 | 1.90 |
| Jerome Nilmeier | 2 | 1 | 0.35 |
| Daniel Weaver | 3 | 5 | 1.55 |
| Paul D. Adams | 4 | 11 | 2.85 |
| Jay D Keasling | 5 | 4 | 1.55 |
| Aindrila Mukhopadhyay | 6 | 4 | 0.87 |
| Christopher J Petzold | 7 | 1 | 0.35 |
| Héctor García Martín | 8 | 4 | 0.87 |