@phdthesis{Somody2023,
  author    = {Somody, Joseph Christian Campbell},
  title     = {Leveraging deep learning for identification and structural determination of novel protein complexes from \(in\) \(situ\) electron cryotomography of \(Mycoplasma\) \(pneumoniae\)},
  doi       = {10.25972/OPUS-31344},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-313447},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {The holy grail of structural biology is to study a protein in situ, and this goal has been fast approaching since the resolution revolution and the achievement of atomic resolution. A cell's interior is not a dilute environment, and proteins have evolved to fold and function as needed in that environment; as such, an investigation of a cellular component should ideally include the full complexity of the cellular environment. Imaging whole cells in three dimensions using electron cryotomography is the best method to accomplish this goal, but it comes with a limitation on sample thickness and produces noisy data unamenable to direct analysis. This thesis establishes a novel workflow to systematically analyse whole-cell electron cryotomography data in three dimensions and to find and identify instances of protein complexes in the data to set up a determination of their structure and identity for success. Mycoplasma pneumoniae is a very small parasitic bacterium with fewer than 700 protein-coding genes, is thin enough and small enough to be imaged in large quantities by electron cryotomography, and can grow directly on the grids used for imaging, making it ideal for exploratory studies in structural proteomics. As part of the workflow, a methodology for training deep-learning-based particle-picking models is established. As a proof of principle, a dataset of whole-cell Mycoplasma pneumoniae tomograms is used with this workflow to characterize a novel membrane-associated complex observed in the data. Ultimately, 25431 such particles are picked from 353 tomograms and refined to a density map with a resolution of 11 {\AA}. Making good use of orthogonal datasets to filter search space and verify results, structures were predicted for candidate proteins and checked for suitable fit in the density map. In the end, with this approach, nine proteins were found to be part of the complex, which appears to be associated with chaperone activity and interact with translocon machinery. Visual proteomics refers to the ultimate potential of in situ electron cryotomography: the comprehensive interpretation of tomograms. The workflow presented here is demonstrated to help in reaching that potential.},
  subject      = {Kryoelektronenmikroskopie},
  language  = {en}
}
@phdthesis{Ferretti2022,
  author    = {Ferretti, Pamela},
  title     = {\(Clostridioides\) \(difficile\) beyond the disease-centred perspective: Beneficial properties in healthy infants and over-diagnosis in diseased adults identified by species- and SNV-based metagenomic analysis},
  doi       = {10.25972/OPUS-25417},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-254170},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {Clostridioides difficile is a bacterial species well known for its ability to cause C. difficile infection (also known as CDI). The investigation of the role of this species in the human gut has been so far dominated by a disease-centred perspective, focused on studying C. difficile in relation to its associated disease. In this context, the first aim of this thesis was to combine publicly available metagenomic data to analyse the microbial composition of stool samples from patients diagnosed with CDI, with a particular focus on identifying a CDI-specific microbial signature. However, similarly to many other bacterial species inhabiting the human gut, C. difficile association with disease is not valid in absolute terms, as C. difficile can be found also among healthy subjects. Further aims of this thesis were to 1) identify potential C. difficile reservoirs by screening a wide range of habitats, hosts, body sites and age groups, and characterize the biotic context associated with C. difficile presence, and 2) investigate C. difficile within-species diversity and its toxigenic potential across different age groups. The first part of the thesis starts with the description of the concepts and definitions used to identify bacterial species and within-species diversity, and then proceeds to provide an overview of the bacterial species at the centre of my investigation, C. difficile. The first Chapter includes a detailed description of the discovery, biology and physiology of this clinically relevant species, followed by an overview of the diagnostic protocols used in the clinical setting to diagnose CDI. The second part of the thesis describes the methodology used to investigate the questions mentioned above, while the third part presents the results of such investigative effort. I first show that C. difficile could be found in only a fraction of the CDI samples and that simultaneous colonization of multiple enteropathogenic species able to cause CDI-like clinical manifestations is more common than previously thought, raising concerns about CDI overdiagnosis. I then show that the CDIassociated gut microbiome is characterized by a specific microbial signature, distinguishable from the community composition associated with non-CDI diarrhea. Beyond the nosocomial and CDI context, I show that while rarely found in adults, C. difficile is a common member of the infant gut microbiome, where its presence is associated with multiple indicators typical of a desirable healthy microbiome development. In addition, I describe C. difficile extensive carriage among asymptomatic subjects, of all age groups and a potentially novel clade of C. difficile identified exclusively among infants. Finally, I discuss the limitations, challenges and future perspectives of my investigation.},
  language  = {en}
}