Algorithms for sequence-based reverse metabolic engineering

Abstract

Microorganisms are employed on a large scale in a variety of industrial production processes, such as production of food and ingredients, beverages, pharmaceutical compo- nents and fine- and bulk chemicals. Improving the behaviour of these microorganisms is of crucial importance in the design of efficient production processes. In this thesis we focus use directed evolution, which is a method to improve the phe- notype of a microorganism without knowing what to change in the DNA to obtain the desired effect. Improved strains are obtained through the natural evolution processes of variation and selection by enforcing a strong selective pressure in the laboratory. The mutations that occur during such short evolution can be determined by sequencing the DNA of the improved strains and comparing it to the starting strain. Finally, relating the relevant mutations to the enforced phenotype is called reverse engineering. We describe the reverse engineering of mutations in ADY2 enabling lactate transport across the cell wall and mutations in ACE2, which in combination with aneuploidy, could be related to the origin of multi-cellular growth in yeast. Next-generation sequencing was employed to reconstruct genomes and infer mutations. This technology has made tremendous progress since its introduction in 2008 and has become affordable for individual laboratories. Sequencing poses many challenges to the field of bioinformatics, since genomes are long and repetitive (e.g. yeasts have millions of base pairs and humans billions), but current sequencing technology can only read up to 100 to 1000 base pairs in sequence. The computational methods developed in this thesis enable the use of sequencing technology in industrial microbiology research. A starting strain in this field is the yeast Saccharomyces cerevisiae CEN.PK113-7D. To determine its genome sequence we devel- oped an algorithm that uses a Tabu-search that exploits previously sequenced genomes and heterogeneous sequencing data types. Additionally, two methods were developed to infer mutations between genomes in absence of a high-quality closely related reference genome. First, we describe an algorithm that estimates the number of times a DNA subsequence exists in the full genome using a Poisson mixture model. We used this method to infer copy number differences between aneuploid lager brewing yeasts. Second, we describe an algorithm that uses graph decomposition to find variation in metagenomes, which are a combination of many bacterial genomes within one sample and play an important role in industrial fermentation, such as in production of cheese and yoghurt.