In silico screening for candidate chassis strains of free fatty acid-producing cyanobacteria
Artikel i vetenskaplig tidskrift, 2017

Background: Finding a source from which high-energy-density biofuels can be derived at an industrial scale has become an urgent challenge for renewable energy production. Some microorganisms can produce free fatty acids (FFA) as precursors towards such high-energy-density biofuels. In particular, photosynthetic cyanobacteria are capable of directly converting carbon dioxide into FFA. However, current engineered strains need several rounds of engineering to reach the level of production of FFA to be commercially viable thus new chassis strains that require less engineering are needed. Although more than 120 cyanobacterial genomes are sequenced, the natural potential of these strains for FFA production and excretion has not been systematically estimated. Results: Here we present the FFA SC (FFASC), an in silico screening method that evaluates the potential for FFA production and excretion of cyanobacterial strains based on their proteomes. A literature search allowed for the compilation of 64 proteins, most of which influence FFA production and a few of which affect FFA excretion. The proteins are classified into 49 orthologous groups (OGs) that helped create rules used in the scoring/ranking of algorithms developed to estimate the potential for FFA production and excretion of an organism. Among 125 cyanobacterial strains, FFASC identified 20 candidate chassis strains that rank in their FFA producing and excreting potential above the specifically engineered reference strain, Synechococcus sp. PCC 7002. We further show that the top ranked cyanobacterial strains are unicellular and primarily include Prochlorococcus (order Prochlorales) and marine Synechococcus (order Chroococcales) that cluster phylogenetically. Moreover, two principal categories of enzymes were shown to influence FFA production the most: those ensuring precursor availability for the biosynthesis of lipids, and those involved in handling the oxidative stress associated to FFA synthesis. Conclusion: To our knowledge FFASC is the first in silico method to screen cyanobacteria proteomes for their potential to produce and excrete FFA, as well as the first attempt to parameterize the criteria derived from genetic characteristics that are favorable/non-favorable for this purpose. Thus, FFASC helps focus experimental evaluation only on the most promising cyanobacteria.

Optimization

Cyanobacteria

Biofuel

Bioinformatics

Screening method

Computer science

Free fatty acids

Cell factories

Författare

Olaa Motwalli

King Abdullah University of Science and Technology (KAUST)

Magbubah Essack

King Abdullah University of Science and Technology (KAUST)

Boris R. Jankovic

King Abdullah University of Science and Technology (KAUST)

Boyang Ji

Chalmers, Biologi och bioteknik, Systembiologi

Xinyao Liu

SABIC-Corporate Research and Development (CRD) at KAUST

Hifzur Rahman Ansari

King Abdullah University of Science and Technology (KAUST)

Robert Hoehndorf

King Abdullah University of Science and Technology (KAUST)

Xin Gao

King Abdullah University of Science and Technology (KAUST)

Stefan T. Arold

King Abdullah University of Science and Technology (KAUST)

Katsuhiko Mineta

King Abdullah University of Science and Technology (KAUST)

J. Archer

King Abdullah University of Science and Technology (KAUST)

T. Gojobori

King Abdullah University of Science and Technology (KAUST)

Ivan Mijakovic

Chalmers, Biologi och bioteknik, Systembiologi

V. B. Bajic

King Abdullah University of Science and Technology (KAUST)

BMC Genomics

1471-2164 (ISSN)

Vol. 18 33

Ämneskategorier

Bioinformatik och systembiologi

DOI

10.1186/s12864-016-3389-4