Pathogen-Specific Adaptations to Conserved Signaling Pathways in Cryptococcus neoformans

Cryptococcus neoformans is an opportunistic fungal pathogen that causes significant disease worldwide. Even though this fungus has not evolved specifically to cause human disease, it has a remarkable ability to adapt to many different environments within its infected host. C. neoformans adapts by utilizing conserved eukaryotic and fungal-specific signaling pathways to sense and respond to stresses within the host. Upon infection, two of the most significant environmental changes this organism experiences are elevated temperature and high pH.

Conserved Rho and Ras family GTPases are central regulators of thermotolerance in C. neoformans. Many GTPases require prenylation to associate with cellular membranes and function properly. Using molecular genetic techniques, microscopy, and infection models, I demonstrated that the prenyltransferase, geranylgeranyl transferase I (GGTase I) is required for thermotolerance and pathogenesis. Using fluorescence microscopy, I found that only a subset of conserved GGTase I substrates requires this enzyme for membrane localization. Therefore, the C. neoformans GGTase I may recognize its substrate in a slightly different manner than other eukaryotic organisms.

The alkaline response transcription factor, Rim101, is a central regulator of stress-response genes important for adapting to the host environment. In particular, Rim101 regulates cell surface alterations involved in immune avoidance. In other fungi, Rim101 is activated by alkaline pH through a conserved signaling pathway, but this pathway had yet been characterized in C. neoformans. Using molecular genetic techniques, I identified and analyzed the conserved members of the Rim pathway. I found that it was only partially conserved in C. neoformans, missing the components that sense pH and initiate pathway activation. Using a genetic screen, I identified a novel Rim pathway component named Rra1. Structural prediction and genetic epistasis experiments suggest that Rra1 may serve as the Rim pathway pH sensor in C. neoformans and other related basidiomycete fungi.

To explore the relevance of Rim pathway signaling in the interaction of C neoformans with its host, I characterized the Rim101-regulated cell wall changes that prevent immune detection. Using HPLC, enzymatic degradation, and cell wall stains, I found that the rim101Δ mutation resulted in increased cell wall chitin exposure. In vitro co-culture assays demonstrated that increased chitin exposure is associated with enhanced activation of macrophages and dendritic cells. To further test this association, I demonstrated that other mutant strains with increased chitin exposure induce macrophage and dendritic cell responses similar to rim101Δ. We used primary macrophages from mutant mouse lines to demonstrate that members of both the Toll-like receptor and C-type lectin receptor families are involved in detecting strains with increased chitin exposure. Finally, in vivo immunological experiments demonstrated that the rim101Δ strain induced a global inflammatory immune response in infected mouse lungs, expanding upon our previous in vivo rim101Δ studies. These results demonstrate that cell wall organization largely determines how fungal cells are detected by the immune system.

Using Image Processing Methods to Improve the Detection of Buried Explosive Threats in GPR Data

Current state of the art techniques for landmine detection in ground penetrating radar (GPR) utilize statistical methods to identify characteristics of a landmine response. This research makes use of 2-D slices of data in which subsurface landmine responses have hyperbolic shapes. Various methods from the field of visual image processing are adapted to the 2-D GPR data, producing superior landmine detection results. This research goes on to develop a physics-based GPR augmentation method motivated by current advances in visual object detection. This GPR specific augmentation is used to mitigate issues caused by insufficient training sets. This work shows that augmentation improves detection performance under training conditions that are normally very difficult. Finally, this work introduces the use of convolutional neural networks as a method to learn feature extraction parameters. These learned convolutional features outperform hand-designed features in GPR detection tasks. This work presents a number of methods, both borrowed from and motivated by the substantial work in visual image processing. The methods developed and presented in this work show an improvement in overall detection performance and introduce a method to improve the robustness of statistical classification.