@phdthesis{Fasemore2023,
  author    = {Fasemore, Akinyemi Mandela},
  title     = {Genomic and internet based analysis of \(Coxiella\) \(burnetii\)},
  doi       = {10.25972/OPUS-29663},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-296639},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {Coxiella burnetii, a Gram negative obligate intracellular bacterium, is the causative agent of Q fever. It has a world wide distribution and has been documented to be capable of causing infections in several domestic animals, livestock species, and human beings. Outbreaks of Q fever are still being observed in livestock across animal farms in Europe, and primary transmission to humans still oc- curs especially in animal handlers. Public health authorities in some countries like Germany are required by law to report human acute cases denoting the significance of the challenge posed by C. burnetii to public health. In this thesis, I have developed a platform alongside methods to address the challenges of genomic analyses of C. burnetii for typing purposes. Identification of C. burnetii isolates is an important task in the laboratory as well as in the clinics and genotyping is a reliable method to identify and characterize known and novel isolates. Therefore, I designed and implemented several methods to facilitate the genotyping analyses of C. burnetii genomes in silico via a web platform. As genotyping is a data intensive process, I also included additional features such as visualization methods and databases for interpretation and storage of obtained results. I also developed a method to profile the resistome of C. burnetii isolates using a machine learning approach. Data about antibiotic resistance in C. burnetii are scarce majorly due to its lifestyle and the difficulty of cultivation in laboratory media. Alternative methods that rely on homology identification of resistance genes are also inefficient in C. burnetii, hence, I opted for a novel approach that has been shown to be promising in other bacteria species. The applied method relied on an artificial neural network as well as amino acid composition of position specific scoring matrix profile for feature extraction. The resulting model achieved an accuracy of ≈ 0.96 on test data and the overall performance was significantly higher in comparison to existing models. Finally, I analyzed two new C. burnetii isolates obtained from an outbreak in Germany, I compared the genome to the RSA 493 reference isolate and found extensive deletions across the genome landscape. This work has provided a new digital infrastructure to analyze and character- ize C. burnetii genomes that was not in existence before and it has also made a significant contribution to the existing information about antibiotic resistance genes in C. burnetii.},
  language  = {en}
}
@phdthesis{Horn2017,
  author    = {Horn, Hannes},
  title     = {Analysis and interpretation of (meta-)genomic data from host-associated microorganisms},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-152035},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2017},
  abstract  = {Host-microbe interactions are the key to understand why and how microbes inhabit specific environments. With the scientific fields of microbial genomics and metagenomics, evolving on an unprecedented scale, one is able to gain insights in these interactions on a molecular and ecological level. The goal of this PhD thesis was to make (meta-)genomic data accessible, integrate it in a comparative manner and to gain comprehensive taxonomic and functional insights into bacterial strains and communities derived from two different environments: the phyllosphere of Arabidopsis thaliana and the mesohyl interior of marine sponges. This thesis focused first on the de novo assembly of bacterial genomes. A 5-step protocol was developed, each step including a quality control. The examination of different assembly software in a comparative way identified SPAdes as most suitable. The protocol enables the user to chose the best tailored assembly. Contamination issues were solved by an initial filtering of the data and methods normally used for the binning of metagenomic datasets. This step is missed in many published assembly pipelines. The described protocol offers assemblies of high quality ready for downstream analysis. Subsequently, assemblies generated with the developed protocol were annotated and explored in terms of their function. In a first study, the genome of a phyllosphere bacterium, Williamsia sp. ARP1, was analyzed, offering many adaptions to the leaf habitat: it can deal with temperature shifts, react to oxygen species, produces mycosporins as protection against UV-light, and is able to uptake photosynthates. Further, its taxonomic position within the Actinomycetales was infered from 16S rRNA and comparative genomics showing the close relation between the genera Williamsia and Gordonia. In a second study, six sponge-derived actinomycete genomes were investigated for secondary metabolism. By use of state-of-the-art software, these strains exhibited numerous gene clusters, mostly linked to polykethide synthases, non-ribosomal peptide synthesis, terpenes, fatty acids and saccharides. Subsequent predictions on these clusters offered a great variety of possible produced compounds with antibiotic, antifungal or anti-cancer activity. These analysis highlight the potential for the synthesis of natural products and the use of genomic data as screening toolkit. In a last study, three sponge-derived and one seawater metagenomes were functionally compared. Different signatures regarding the microbial composition and GC-distribution were observed between the two environments. With a focus on bacerial defense systems, the data indicates a pronounced repertoire of sponge associated bacteria for bacterial defense systems, in particular, Clustered Regularly Interspaced Short Palindromic Repeats, restriction modification system, DNA phosphorothioation and phage growth limitation. In addition, characterizing genes for secondary metabolite cluster differed between sponge and seawater microbiomes. Moreover, a variety of Type I polyketide synthases were only found within the sponge microbiomes. With that, metagenomics are shown to be a useful tool for the screening of secondary metabolite genes. Furthermore, enriched defense systems are highlighted as feature of sponge-associated microbes and marks them as a selective trait.},
  subject      = {Bakterien},
  language  = {en}
}