Investigating the Distribution of CRISPR Adaptive Immune Systems Among Prokaryotes

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dc.contributor.advisorJohnson, Philip L.F.en_US
dc.contributor.authorWeissman, Jakeen_US
dc.contributor.departmentBiologyen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2020-02-01T06:37:04Z
dc.date.available2020-02-01T06:37:04Z
dc.date.issued2019en_US
dc.description.abstractJust as larger organisms face the constant threat of infection by pathogens, so too do bacteria and archaea. In response, prokaryotes employ a diverse set of strategies to simultaneously cope with their viral and physical environments. Here I explore the ecology and evolution of the CRISPR adaptive immune system, a powerful form of protection against viruses that is the only known example of adaptive immunity in prokaryotes. CRISPR systems are widespread across diverse bacterial and archaeal lineages, suggesting that CRISPR effectively defends against viruses in a broad array of environments. Nevertheless, this defense system is nearly absent in many bacterial groups, and in many environments. I focus on understanding these patterns in CRISPR incidence and the ecological drivers behind them. First, I identify the ecological conditions that favor the adoption of a CRISPR-based defense strategy. I develop a phylogenetically-conscious machine learning approach to build a predictive model of CRISPR incidence using data on over 100 phenotypic traits across over 2600 species and discovered a strong but hitherto-unknown negative interaction between CRISPR and aerobicity. I then consider the multiplicity of CRISPR arrays on a genome, testing whether or not selection favors redundancy in immunity. I use a comparative genomics approach, looking across all prokaryotes to demonstrate that on average, organisms are under selection to maintain more than one CRISPR array. I then explain this surprising result with a theoretical model demonstrating that a trade-off between memory span and learning speed could select for paired “long-term memory” and “short-term memory” CRISPR arrays. Finally, I provide a theoretical examination of the phenomenon of immune loss, specifically in the context of CRISPR immunity. In doing so, I propose an additional mechanism to answer the perennial question: “How do bacteria and bacteriophage coexist stably over long time-spans?” I show that the regular loss of immunity by the bacterial host can produce host-phage coexistence more reliably than other mechanisms, pairing a general model of immunity with an experimental and theoretical case study of CRISPR-based immunity.en_US
dc.identifierhttps://doi.org/10.13016/7lgu-shlg
dc.identifier.urihttp://hdl.handle.net/1903/25415
dc.language.isoenen_US
dc.subject.pqcontrolledMicrobiologyen_US
dc.subject.pqcontrolledEcologyen_US
dc.subject.pqcontrolledEvolution & developmenten_US
dc.subject.pquncontrolledBacteriophageen_US
dc.subject.pquncontrolledCoevolutionen_US
dc.subject.pquncontrolledCRISPRen_US
dc.subject.pquncontrolledMicrobial ecologyen_US
dc.subject.pquncontrolledPhage-Host Interactionsen_US
dc.subject.pquncontrolledProkaryotic Immunityen_US
dc.titleInvestigating the Distribution of CRISPR Adaptive Immune Systems Among Prokaryotesen_US
dc.typeDissertationen_US

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