Format

Send to

Choose Destination
Proc Natl Acad Sci U S A. 2016 Dec 20;113(51):E8344-E8353. doi: 10.1073/pnas.1613446113. Epub 2016 Dec 1.

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

Author information

1
Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093.
2
Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093.
3
Center for Circadian Biology, University of California, San Diego, La Jolla, CA 92093.
4
Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA 92093.
5
Microbial and Environmental Genomics, J. Craig Venter Institute, La Jolla, CA 92037.
6
Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093.
7
Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093; sgolden@ucsd.edu.

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.

KEYWORDS:

Synechococcus elongatus; TCA cycle; constraint-based modeling; cyanobacteria; photosynthesis

PMID:
27911809
PMCID:
PMC5187688
DOI:
10.1073/pnas.1613446113
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for HighWire Icon for PubMed Central
Loading ...
Support Center