CrEdit: CRISPR mediated multi-loci gene integration in Saccharomyces cerevisiae.

BACKGROUND: One of the bottlenecks in production of biochemicals and pharmaceuticals in Saccharomyces cerevisiae is stable and homogeneous expression of pathway genes. Integration of genes into the genome of the production organism is often a preferred option when compared to expression from episomal vectors. Existing approaches for achieving stable simultaneous genome integrations of multiple DNA fragments often result in relatively low integration efficiencies and furthermore rely on the use of selection markers. RESULTS: Here, we have developed a novel method, CrEdit (CRISPR/Cas9 mediated genome Editing), which utilizes targeted double strand breaks caused by CRISPR/Cas9 to significantly increase the efficiency of homologous integration in order to edit and manipulate genomic DNA. Using CrEdit, the efficiency and locus specificity of targeted genome integrations reach close to 100% for single gene integration using short homology arms down to 60 base pairs both with and without selection. This enables direct and cost efficient inclusion of homology arms in PCR primers. As a proof of concept, a non-native ß-carotene pathway was reconstructed in S. cerevisiae by simultaneous integration of three pathway genes into individual intergenic genomic sites. Using longer homology arms, we demonstrate highly efficient and locus-specific genome integration even without selection with up to 84% correct clones for simultaneous integration of three gene expression cassettes. CONCLUSIONS: The CrEdit approach enables fast and cost effective genome integration for engineering of S. cerevisiae. Since the choice of the targeting sites is flexible, CrEdit is a powerful tool for diverse genome engineering applications.

An automated workflow for multi-omics screening of microbial model organisms.

Multi-omics datasets are becoming of key importance to drive discovery in fundamental research as much as generating knowledge for applied biotechnology. However, the construction of such large datasets is usually time-consuming and expensive. Automation might enable to overcome these issues by streamlining workflows from sample generation to data analysis. Here, we describe the construction of a complex workflow for the generation of high-throughput microbial multi-omics datasets. The workflow comprises a custom-built platform for automated cultivation and sampling of microbes, sample preparation protocols, analytical methods for sample analysis and automated scripts for raw data processing. We demonstrate possibilities and limitations of such workflow in generating data for three biotechnologically relevant model organisms, namely Escherichia coli, Saccharomyces cerevisiae, and Pseudomonas putida.

High-Resolution Scanning of Optimal Biosensor Reporter Promoters in Yeast.

Small-molecule binding allosteric transcription factors (aTFs) derived from bacteria enable real-time monitoring of metabolite abundances, high-throughput screening of genetic designs, and dynamic control of metabolism. Yet, engineering of reporter promoter designs of prokaryotic aTF biosensors in eukaryotic cells is complex. Here we investigate the impact of aTF binding site positions at single-nucleotide resolution in >300 reporter promoter designs in Saccharomyces cerevisiae. From this we identify biosensor output landscapes with transient and distinct aTF binding site position effects for aTF repressors and activators, respectively. Next, we present positions for tunable reporter promoter outputs enabling metabolite-responsive designs for a total of four repressor-type and three activator-type aTF biosensors with dynamic output ranges up to 8- and 26-fold, respectively. This study highlights aTF binding site positions in reporter promoters as key for successful biosensor engineering and that repressor-type aTF biosensors allows for more flexibility in terms of choice of binding site positioning compared to activator-type aTF biosensors.

Genome-wide systematic identification of methyltransferase recognition and modification patterns.

Genome-wide analysis of DNA methylation patterns using single molecule real-time DNA sequencing has boosted the number of publicly available methylomes. However, there is a lack of tools coupling methylation patterns and the corresponding methyltransferase genes. Here we demonstrate a high-throughput method for coupling methyltransferases with their respective motifs, using automated cloning and analysing the methyltransferases in vectors carrying a strain-specific cassette containing all potential target sites. To validate the method, we analyse the genomes of the thermophile Moorella thermoacetica and the mesophile Acetobacterium woodii, two acetogenic bacteria having substantially modified genomes with 12 methylation motifs and a total of 23 methyltransferase genes. Using our method, we characterize the 23 methyltransferases, assign motifs to the respective enzymes and verify activity for 11 of the 12 motifs.