Construction of a chassis for hydrogen production: physiological and molecular characterization of a Synechocystis sp. PCC6803 mutant lacking a functional bidirectional hydrogenase

Abstract

Cyanobacteria are photosynthetic prokaryotes that are promising low-cost microbial cell factories due to their simple nutritional requirements, metabolic plasticity, and availability of tools for their genetic manipulation. The unicellular non-nitrogen fixing Synechocystis sp. PCC6803 is the best studied cyanobacterial strain and its genome was the first to be sequenced. The vast amount of physiological and molecular data available, together with a relative small genome, makes Synechocystis suitable for computational metabolic modeling and to be used as a photoautotrophic chassis in synthetic biology applications. To prepare it for the introduction of a synthetic hydrogen producing device, a Synechocystis sp. PCC6803 deletion mutant lacking an active bidirectional hydrogenase (ΔhoxYH) was produced and characterized at different levels: physiological, proteomics and transcriptional. The results showed that, in conditions favoring hydrogenase activity, from the 210 identified proteins 17 had significant differential fold changes comparing the mutant to the wild-type. Most of these proteins are related to the redox and energy state of the cell. Transcriptional studies revealed that only 6 genes encoding those proteins exhibited significant differences in transcript levels. Moreover, the mutant exhibits similar growth behavior compared to the wild-type, reflecting Synechocystis plasticity and metabolic adaptability. Overall, this study reveals that the Synechocystis ΔhoxYH mutant is robust and can be used as a photoautotrophic chassis for the integration of synthetic, i.e. molecular constructs assembled from well characterized biological and/or synthetic parts (e.g. promoters, regulators, coding regions, terminators) designed for a specific purpose.

Arnau Montagud

Researcher on Computational Systems Biology

My research interests include Boolean and multiscale modelling, data analyses and data integration.