Evolutionary dynamics and phylogenetic inference of CRISPR systems in prokaryotic populations

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The revolutionary CRISPR/Cas technology to alter genomes has its origin in the natural CRISPR systems in bacteria and archaea.Many of these systems can act as an adaptive defense system targeting specific viral sequences.Beyond this defensive function, CRISPR systems can influence the evolution of bacterial populations without viruses being involved.The overarching goal of this project is to develop and apply new state of the art models for the evolution of CRISPR in prokaryotes. Many studies have focused on the coevolutionary aspects between CRISPR possessing bacteria and the corresponding viruses. In contrast, we will consider the intrinsic evolution of CRISPR Cas systems in bacteria and archaea. In particular, we aim to answer the following questions:a) To identify specific targets the CRISPR system contains an array of spacers that align with the targets. This spacer array provides a unique insight into the evolutionary history since spacers in this array are arranged in chronological order. Can we explain the observed pattern in spacer arrays and identify spacers under selection?Can we reconstruct ancestral spacer insertion times and link them to their ancestral history?b) Accidental self-targeting usually leads to death such that potential self-targets in the bacterial genome are presumably under strong selection.Can we model the selective effect of self-targeting CRISPR systems from the genome sequences of bacteria possessing CRISPR?c) CRISPR systems that target closely related species are capable to block recombination between bacterial strains and can modulate the frequency of genes in the population. Moreover, bacteria can use such systems as an offensive weapon targeting closely related competitors.How does CRISPR influence the frequency of genes in bacteria?Can we understand within population dynamics of CRISPR arrays targeting related species? Are CRISPR systems targeting subpopulations a promising new anti-microbial treatment?We will address these questions with a mixture of mathematical and computational approaches.