Gene overexpression screens to identify limitations on the productivity of cyanobacteria growth

Abstract: Cyanobacteria are a model organism for photosynthesis and the Calvin cycle, and a promising chassis for 4th generation biofuel production. There are many ongoing efforts to improve the performance of cyanobacteria, in terms of CO2 fixation and production rate of biofuels. One straightforward way to improve CO2 fixation could be to overexpress the genes of limiting enzymes. In this project, we used a high-throughput method to test the overexpression of thousands of genes in cyanobacteria and measure the effect on growth rate. We created barcoded overexpression libraries, consisting of gene fragments from different cyanobacteria strains and transformed them into a model cyanobacterium, Synechocystis PCC 6803. We then cultivated the transformed cyanobacteria libraries and screened for effects of increased gene copy number on both maximum growth rate and robustness of growth under stress conditions. The cell populations were cultivated in a turbidostat, resulting in competitive growth between transformants. The relative abundance of each mutant was estimated using deep sequencing. Fitness scores, for each gene show how expression of that gene affects growth rate. This method, competitive growth and tracking of mutant populations with deep sequencing, is a high throughput method for screening a large proportion of genes from several organism at once, allowing the identification of trans-species effects as well as the effect of single genes on the metabolism of the host cell.