Relative Contributions of Prenylation and Postprenylation Processing in Cryptococcus neoformans Pathogenesis.

Prenyltransferase enzymes promote the membrane localization of their target proteins by directing the attachment of a hydrophobic lipid group at a conserved C-terminal CAAX motif. Subsequently, the prenylated protein is further modified by postprenylation processing enzymes that cleave the terminal 3 amino acids and carboxymethylate the prenylated cysteine residue. Many prenylated proteins, including Ras1 and Ras-like proteins, require this multistep membrane localization process in order to function properly. In the human fungal pathogen Cryptococcus neoformans, previous studies have demonstrated that two distinct forms of protein prenylation, farnesylation and geranylgeranylation, are both required for cellular adaptation to stress, as well as full virulence in animal infection models. Here, we establish that the C. neoformans RAM1 gene encoding the farnesyltransferase β-subunit, though not strictly essential for growth under permissive in vitro conditions, is absolutely required for cryptococcal pathogenesis. We also identify and characterize postprenylation protease and carboxyl methyltransferase enzymes in C. neoformans. In contrast to the prenyltransferases, deletion of the genes encoding the Rce1 protease and Ste14 carboxyl methyltransferase results in subtle defects in stress response and only partial reductions in virulence. These postprenylation modifications, as well as the prenylation events themselves, do play important roles in mating and hyphal transitions, likely due to their regulation of peptide pheromones and other proteins involved in development. IMPORTANCE Cryptococcus neoformans is an important human fungal pathogen that causes disease and death in immunocompromised individuals. The growth and morphogenesis of this fungus are controlled by conserved Ras-like GTPases, which are also important for its pathogenicity. Many of these proteins require proper subcellular localization for full function, and they are directed to cellular membranes through a posttranslational modification process known as prenylation. These studies investigate the roles of one of the prenylation enzymes, farnesyltransferase, as well as the postprenylation processing enzymes in C. neoformans. We demonstrate that the postprenylation processing steps are dispensable for the localization of certain substrate proteins. However, both protein farnesylation and the subsequent postprenylation processing steps are required for full pathogenesis of this fungus.

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.