Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis.

TitleUnique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis.
Publication TypeJournal Article
Year of Publication2016
AuthorsBroddrick JT, Rubin BE, Welkie DG, Du N, Mih N, Diamond S, Lee JJ, Golden SS, Palsson BO
JournalProc Natl Acad Sci U S A
Volume113
Issue51
PaginationE8344-E8353
PubMed Date12/2016
ISSN1091-6490
Abstract

The model cyanobacterium, Synechococcus elongatus PCC 7942, is a genetically tractable obligate phototroph that is being developed for the bioproduction of high-value chemicals. Genome-scale models (GEMs) have been successfully used to assess and engineer cellular metabolism; however, GEMs of phototrophic metabolism have been limited by the lack of experimental datasets for model validation and the challenges of incorporating photon uptake. Here, we develop a GEM of metabolism in S. elongatus using random barcode transposon site sequencing (RB-TnSeq) essential gene and physiological data specific to photoautotrophic metabolism. The model explicitly describes photon absorption and accounts for shading, resulting in the characteristic linear growth curve of photoautotrophs. GEM predictions of gene essentiality were compared with data obtained from recent dense-transposon mutagenesis experiments. This dataset allowed major improvements to the accuracy of the model. Furthermore, discrepancies between GEM predictions and the in vivo dataset revealed biological characteristics, such as the importance of a truncated, linear TCA pathway, low flux toward amino acid synthesis from photorespiration, and knowledge gaps within nucleotide metabolism. Coupling of strong experimental support and photoautotrophic modeling methods thus resulted in a highly accurate model of S. elongatus metabolism that highlights previously unknown areas of S. elongatus biology.

Alternate JournalProc. Natl. Acad. Sci. U.S.A.
PubMed ID27911809
PubMed Central IDPMC5187688
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