Populationsdynamik und Phylogenie von CRISPR Systemen in prokaryotischen Populationen

01/01/2021 to 17/07/2022

The revolutionary CRISPR/Cas technology for precise modification of genomes has its origins in the natural CRISPR systems in bacteria and archaea. Many of these systems act as an adaptive defence system that can respond to specific viral gene sequences. However, in addition, CRISPR systems can also directly influence the evolution of bacterial populations without the involvement of viruses. The aim of this project is to develop and apply new models for CRISPR evolution in prokaryotes. Many scientific papers have already addressed the coevolutionary aspects between bacteria with CRISPR and the corresponding viruses. In contrast, we consider the intrinsic evolution of CRISPR-Cas systems in bacteria and archaea. In particular, we would like to answer the following questions: a) In order to identify specific gene sequences, the CRISPR system contains a spacer array in which so-called "spacers" store information from the target sequences. Because these spacers are arranged in chronological order, they provide a unique insight into the evolutionary history of bacteria. Can we explain the observed patterns within the spacer arrays and identify spacers under selection? Can we reconstruct when a spacer was included in the past to better understand the evolution of CRISPR-Cas in bacteria? b) If one's own gene sequences are accidentally used as a target sequence for CRISPR, this usually leads to the death of the bacterium. Therefore, potential targets in the bacterial genome are likely to be under strong selection pressure. Can we model the selective effect of self-targeting CRISPR systems from the genome sequences of bacteria that have CRISPR? c) CRISPR systems whose spacers target alleles within the population are able to block recombination between bacterial strains and can influence the distribution of genes within the population. In addition, such systems can be used by bacteria as an offensive weapon against conspecific competitors. Can we understand the population dynamics of CRISPR arrays that modify the abundance of bacterial genes? Are artificial CRISPR systems a promising anti-bacterial treatment? We will investigate these questions using mathematical methods and approaches from bioinformatics.