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A peptide-based method for 13C Metabolic Flux Analysis in microbial communities.
Ghosh, Amit; Nilmeier, Jerome; Weaver, Daniel; Adams, Paul D; Keasling, Jay D; Mukhopadhyay, Aindrila; Petzold, Christopher J; Martín, Héctor García.
Afiliação
  • Ghosh A; Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America; Joint BioEnergy Institute, Emeryville, California, United States of America.
  • Nilmeier J; Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America; Joint BioEnergy Institute, Emeryville, California, United States of America.
  • Weaver D; Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America; Joint BioEnergy Institute, Emeryville, California, United States of America.
  • Adams PD; Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America; Joint BioEnergy Institute, Emeryville, California, United States of America; Department of Bioengineering, University of California, Berkeley, Berkeley, California, United States of
  • Keasling JD; Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America; Joint BioEnergy Institute, Emeryville, California, United States of America; Department of Bioengineering, University of California, Berkeley, Berkeley, California, United States of
  • Mukhopadhyay A; Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America; Joint BioEnergy Institute, Emeryville, California, United States of America.
  • Petzold CJ; Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America; Joint BioEnergy Institute, Emeryville, California, United States of America.
  • Martín HG; Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America; Joint BioEnergy Institute, Emeryville, California, United States of America.
PLoS Comput Biol ; 10(9): e1003827, 2014 Sep.
Article em En | MEDLINE | ID: mdl-25188426
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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Peptídeos / Isótopos de Carbono / Análise do Fluxo Metabólico / Modelos Biológicos Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2014 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Peptídeos / Isótopos de Carbono / Análise do Fluxo Metabólico / Modelos Biológicos Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2014 Tipo de documento: Article