Impacts of copy number variation on gene regulatory networks and fitness in Candida

University Of Minnesota, Minneapolis MN

Investigators

Abstract

Project Summary/Abstract: The evolution of antimicrobial drug resistance is an urgent threat to public health. A better understanding of basic evolutionary principles in microorganisms can inform efforts to identify, prevent, and combat the emergence of drug resistance. Candida albicans, one of the most common fungal pathogens, can adapt to antifungal drugs via mutations that alter gene expression levels. Gene expression levels can be altered by changes in gene copy number and mutations that alter transcriptional regulatory networks. Basic science investigating the influence of gene copy number changes on fitness and the impact on the gene regulatory network across diverse genetic backgrounds is needed. These investigations will directly contribute to combating the emergence of antifungal drug resistance. The objective of the proposed research is to test the effects of copy number changes on fitness and gene regulatory networks, and relate these effects to the rate and dynamics of the evolution of antifungal drug resistance in C. albicans. In the long-term, this work can be extended to additional fungal pathogens and related to clinical sampling data, as well as inform general evolutionary biology principles that can be tested and applied across the tree of life. Aim 1 uses newly developed CRISPR-activation technology in C. albicans to test the fitness effects of gene overexpression in multiple genetic backgrounds and environments. These data will be used to describe the distribution of fitness effects of recurrently amplified genes. Aim 2 combines CRISPR- activation technology with single-cell RNA-sequencing to model transcriptional regulatory networks. These will be used to identify genome-wide transcriptional effects of gene amplification in diverse genetic backgrounds and environments. Aim 3 uses experimental evolution to test the hypothesis that a gene’s position within the regulatory network influences the rate and dynamics of adaptive gene expression change in resistance genes. The long-term career goals of the candidate are to establish a research program centered around genetic constraints and facilitations to evolutionary adaptation at a major biomedical research institution. The short-term training goals of this proposal are to further develop the candidate’s expertise in the computational representation of regulatory networks and in the application of single-cell RNA-sequencing technologies to pathogenic yeast. The University of Minnesota provides an ideal environment in which to pursue these training goals, with excellent resources for career development, extensive genomics and computing core facilities, and faculty with expertise in genomic networks, evolutionary biology, and microbiology that will serve as mentors and advisors to the candidate. The training and research proposed will launch the candidate into an independent and productive research career.