Coxiella burnetii, the etiologic agent of acute Q fever and chronic endocarditis, has a unique biphasic life cycle, which includes a metabolically active intracellular form that occupies a large lysosome-derived acidic vacuole. C. burnetii is the only bacterium known to thrive within such a hostile intracellular niche, and this ability is fundamental to its pathogenicity; however, very little is known about genes that facilitate Coxiella's intracellular growth. This lack of knowledge of Coxiella's basic biology and molecular pathogenesis is a critical barrier to developing more effective therapies.In this study, we aimed to understand both bacterial and host factors that have important roles during C. burnetii infections. Using an evolutionary genomics approach, we identified metabolic pathways that are critical to C. burnetii's ability to grow intracellularly. Among those found, the most promising are fatty acid, biotin, and heme biosyntheses pathways. Coxiella has horizontally acquired extra copies of genes that enhance these processes; when these genes were disrupted, Coxiella's growth was significantly inhibited. Also, by analyzing the host transcriptome, we identified human genes, including microRNA (miRNA) genes that are important during C. burnetii infections. Coxiella induces the expression of multiple anti-apoptotic miRNAs, which likely have a role in inhibiting apoptosis in order to sustain the intracellular replication of the pathogen. The biosynthetic pathways and miRNAs identified in this study are ideal targets for developing more effective therapeutic strategies against Q fever and its chronic and often fatal complications. ii ACKNOWLEDGEMENTS I would like to thank my research advisor Rahul Raghavan of the Biology Department at Portland State University. He has provided continual support but also generous freedom that has allowed me to design aspects and conduct much of this project independently. I would also like to acknowledge Rahul and Tina Schroyer for their assistance in writing my thesis. Their feedback has helped me become a better writer. I want to thank Abraham Moses and Fenil Kacharia for their assistance with conducting all the physical experiments and other lab duties. I also want to thank Todd Smith for taking the time to supplement my learning in bioinformatics. I would also like to thank my thesis committee: Susan Masta and Kenneth Stedman. I appreciate their time and effort reviewing my paper and supporting my research and education.