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dc.contributor.author
Fernbach, Jonas
dc.contributor.supervisor
Loessner, Martin J.
dc.contributor.supervisor
Kilcher, Samuel
dc.contributor.supervisor
Harms, Alexander
dc.date.accessioned
2023-04-14T10:31:48Z
dc.date.available
2023-04-11T13:55:30Z
dc.date.available
2023-04-14T09:02:22Z
dc.date.available
2023-04-14T10:31:48Z
dc.date.issued
2023
dc.identifier.uri
http://hdl.handle.net/20.500.11850/607236
dc.identifier.doi
10.3929/ethz-b-000607236
dc.description.abstract
The widespread, global availability and use of antibiotics has lead to a drastic increase in the number of antimicrobial resistant pathogens. The "golden age of antibiotics" from which humans have prospered for almost a century is in jeopardy. To avoid falling back into an era where even small infections can spread uncontrollably and lead to severe illness or death, there is an urgent need for alternative antimicrobials. Bacterial viruses, also termed bacteriophages, or simply phages, are vastly abundant in nature and pose an ideal alternative due to their ability to effectively infect and kill their bacterial hosts. Although naturally occuring phages have been successfully employed as therapeutics, genetically engineered phages have received increased attention in recent years, as many pitfalls associated with naturally occurring phages can be overcome, thereby increasing their potential for clinical use. In Manuscript I, we review state-of-the-art bacteriophage engineering methodologies, applications thereof, and discuss the outlook into the future of phage therapy, particularly in the context of machine learning-driven computational methods. Staphylococcus aureus is a major human pathogen and, due to emergence of numerous strains exhibiting antibiotic resistance, even to last-line treatment options, novel antimicrobials are urgently needed. Although most suitable for phage therapy, the genetic engineering of large, lytic S. aureus phages with broad host range has so far proved difficult using conventional engineering methods. In Manuscript II, we use an homologous recombination-based and CRISPR Cas9 counterselection-assisted approach to engineer the large, lytic, S. aureus-infecting Kayvirus K. We used this novel method to integrate a bioluminescent reporter payload into the phage genome, thereby facilitating rapid detection of a large range of S. aureus strains, including a selection of vancomycin resistant ii clinical isolates. Our engineered phage proved highly efficient both in vitro as well as in complex matrices such as human whole blood and bovine raw milk. As mentioned previously, one conventional method of genetically engineering phages is using a synthetic biology approach including assembly of synthetic phage genome fragments and subsequent reactivation in a suitable host organism. This can prove challenging, particularly for Gram-positive species such as S. aureus, where introduced exogenous DNA is met by wide array of intracellular defense mechanisms. In Manuscript III, we describe a detailed protocol for the development of cell-wall deficient L-form bacteria, which are capable of taking up large DNA molecules via simple chemical transformation. We demonstrate the conversion to the L-form state for three different Gram-positive species, Listeria monocytogenes, S. aureus and Staphylococcus xylosus, and demonstrate their ability to reactivate various different phage genomes, even across the genus barrier. Computational methods are already an essential part of modern science and likely will continue to be an integral component for driving forward advances in biological research, particularly in genetic engineering. In Manuscript IV we developed a machine learning-based method for predicting transcriptional promoters from primary phage sequence data. We used the same bioluminescent payload from Manuscript II, to experimentally validate our approach. We demonstrate that the accurate prediction of payload expression levels, based on the location of insertion sites behind promoters with various predicted expression strengths, is indeed possible. This has great potential for future applications to control the expression levels of effector payloads integrated into the phage backbone. One approach for enhancing phage efficacy for therapeutic applications is the genetic arming with antimicrobial effector payloads. In Manuscript V, we screened a large selection of payload gene candidates, which had previously been shown to exhibit bactericidal activity against S. aureus, for their suitability as effector payloads in K. Although we failed to obtain viable phages for the majority of payload genes, we succeeded in develiii oping two engineered phages containing the protein toxin MazF and the short-leaderless bacteriocin Lacticin Q as a genetic payload, respectively. Both phages showed significantly enhanced in vitro killing efficiency on a selection of S. aureus hosts when compared to the wildtype phage. With this thesis, I hope to have brought insight and advances into the field of phage engineering, specifically in the context of S. aureus-targeted applications. By demonstrating different approaches based on phage type and intended modification, we show that phage engineering has great potential for developing modified phages with enhanced properties directed towards future therapeutic applications. Overall, phage engineering and therapy shows great promise to mitigate the effects of the globally emerging antibiotic resistance crisis, and has the potential to be a major therapeutic in future times.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
ETH Zurich
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
Bacteriophage, Genetic Engineering, Synthetic Biology, Antimicrobial Resistance
en_US
dc.title
Synthetic Biology for Genetically Engineered Bacteriophages to Target Infectious Diseases
en_US
dc.type
Doctoral Thesis
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2023-04-14
ethz.size
228 p.
en_US
ethz.code.ddc
DDC - DDC::5 - Science::570 - Life sciences
en_US
ethz.identifier.diss
29062
en_US
ethz.publication.place
Zurich
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02070 - Dep. Gesundheitswiss. und Technologie / Dep. of Health Sciences and Technology::02701 - Inst.f. Lebensmittelwiss.,Ernährung,Ges. / Institute of Food, Nutrition, and Health::03651 - Loessner, Martin / Loessner, Martin
en_US
ethz.date.deposited
2023-04-11T13:55:30Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
ethz.date.embargoend
2024-04-14
ethz.rosetta.installDate
2023-04-14T10:31:50Z
ethz.rosetta.lastUpdated
2024-02-02T21:39:24Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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