Need for speed - finding productive mutations using transcription factor-based biosensors, fluorescence-activated cell sorting and recombineering.

Abstract

The establishment of a bioeconomy requires technologies enabling the rapid development of microbial production strains and enzymes as highly efficient biocatalysts for the conversion of substrates derived from renewable carbon sources into a multitude of chemicals. For strain development, two major strategies are available. The classical approach uses several rounds of random mutagenesis and screening for the desired producer clones and targets the entire genome irrespective of knowledge on function. The rational approach of metabolic and genetic engineering, on the other hand, is based on current knowledge and was strongly enhanced by the development of ‘omics’ technologies and novel tools for genetic engineering, leading to the establishment of systems biology and synthetic biology (Keasling, 2010). Despite the huge success of metabolic engineering, many of the industrial production strains are still based on the classical approach alone or a mixture of both approaches because these strains outperform the ones based exclusively on rational engineering. This is simply due to the fact that our knowledge of even the best studied microorganisms such as Escherichia coli or Saccharomyces cerevisiae is still far from complete, as obviously shown by the fact that the functions of hundreds of genes in these species are still unknown (because of the lack of a universal strategy to identify these functions). Furthermore, despite the enormous advances in our understanding of enzymes, it is still difficult to predict which mutations in an enzyme will be favourable for a certain purpose. Consequently, random mutagenesis and screening still offer great chances to identify novel mutations that enhance production. Because of the possibility of cheap re-sequencing of clones obtained from screening, the productive mutations can in principle be identified, thus providing the basis for a molecular understanding of the effect of the mutation.