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Pertussis is a highly contagious acute respiratory disease of humans which is mainly caused by the gram-negative obligate human pathogen Bordetella pertussis. Despite the availability and extensive use of vaccines, the disease persists and has shown periodic re-emergence resulting in an estimated 640,000 deaths worldwide in 2014. The pathogen expresses various virulence factors that enable it to modulate the host immune response, allowing it to colonise the ciliated airway mucosa. Many of these factors also directly interfere with host signal transduction systems, causing damage to the ciliated airway mucosa and increase mucous production. Of the many virulence factors of B. pertussis, only the tracheal cytotoxin (TCT) is able to recapitulate the pathophysiology of ciliated cell extrusion and blebbing in animal models and in human nasal biopsies. Furthermore, due to the lack of appropriate human models and donor materials, the role of bacterial virulence factors has been extrapolated from studies using animal models infected with either B. pertussis or with the closely related species B. bronchiseptica which naturally causes respiratory infections in these animals and produces many similar virulence factors. Thus, in the present work, in vitro airway mucosa models developed by co-culturing human airway epithelia cells and fibroblasts from the conduction zone of the respiratory tract on a decellularized porcine small intestine submucosa scaffold (SISser®) were used, since these models have a high correlation to native human conducting zone respiratory epithelia. The major aim was to use the engineered airway mucosa models to elucidate the contribution of B. pertussis TCT in the pathophysiology of the disease as well as the virulence mechanism of B. pertussis in general. TCT and lipopolysaccharide (LPS) either alone or in combination were observed to induce epithelial cell blebbing and necrosis in the in vitro airway mucosa model. Additionally, the toxins induced viscous hyper-mucous secretion and significantly disrupted barrier properties of the in vitro airway mucosa models. This work also sought to assess the invasion and intracellular survival of B. pertussis in the polarised epithelia, which has been critically discussed for many years in the literature. Infection of the models with B. pertussis showed that the bacteria can adhere to the models and invade the epithelial cells as early as 6 hours post inoculation. Invasion and intracellular survival assays indicated the bacteria could invade and persist intracellularly in the epithelial cells for up to 3 days. Due to the novelty of the in vitro airway mucosa models, this work also intended to establish a method for isolating individual cells for scRNA-seq after infection with B. pertussis. Cold dissociation with Bacillus licheniformis subtilisin A was found to be capable of dissociating the cells without inducing a strong fragmentation, a problem which occurs when collagenase and trypsin/EDTA are used. In summary, the present work showed that TCT acts possibly in conjunction with LPS to disrupt the human airway mucosa much like previously shown in the hamster tracheal ring models and thus appears to play an important role during the natural B. pertussis infection. Furthermore, we established a method for infecting and isolating infected cells from the airway mucosa models in order to further investigate the effect of B. pertussis infection on the different cell populations in the airway by single cell analytics in the future.
Interactions between host and pathogen determine the development, progression and outcomes
of disease. Medicine benefits from better descriptions of these interactions through increased
precision of prevention, diagnosis and treatment of diseases. Single-cell genomics is a
disruptive technology revolutionizing science by increasing the resolution with which we study
diseases. Cell type specific changes in abundance or gene expression are now routinely investigated
in diseases. Meanwhile, detecting cellular phenotypes across diseases can connect
scientific fields and fuel discovery. Insights acquired through systematic analysis of high resolution
data will soon be translated into clinical practice and improve decision making. Therefore,
the continued use of single-cell technologies and their application towards clinical samples will
improve molecular interpretation, patient stratification, and the prediction of outcomes.
In the past years, I was fortunate to participate in interdisciplinary research groups bridging
biology, clinical research and data science. I was able to contribute to diverse projects through
computational analysis and biological interpretation of sequencing data. Together, we were
able to discover cellular phenotypes that influence disease progression and outcomes as well
as the response to treatment. Here, I will present four studies that I have conducted in my PhD.
First, we performed a case study of relapse from cell-based immunotherapy in Multiple Myeloma.
We identified genomic deletion of the epitope as mechanism of immune escape and implicate
heterozygosity or monosomy of the genomic locus at baseline as a potential risk factor. Second,
we investigated the pathomechanisms of severe COVID-19 at the earliest stage of the COVID-
19 pandemic in Germany in March 2020. We discovered that profibrotic macrophages and
lung fibrosis can be caused by SARS-CoV-2 infection. Third, we used a mouse model of chronic
infection with Staphylococcus aureus that causes Osteomyelitis similar to the human disease.
We were able to identify dysregulated immunometabolism associated with the generation of
myeloid-derived suppressor cells (MDSC). Fourth, we investigated Salmonella infection of the
human small intestine in an in vitro model and describe features of pathogen invasion and host
response.
Overall, I have been able to successfully employ single-cell sequencing to discover important
aspects of diseases ranging from development to treatment and outcome. I analyzed samples
from the clinics, human donors, mouse models and organoid models to investigate different
aspects of diseases and managed to integrate data across sample types, technologies and
diseases. Based on successful studies, we increased our efforts to combine data from multiple
sources to build comprehensive references for the integration of large collections of clinical
samples. Our findings exemplify how single-cell sequencing can improve clinical research and
highlights the potential of mechanistic discoveries to drive precision medicine.