Cytolethal distending toxins (CDTs) are tripartite protein exotoxins produced by a diverse group of pathogenic Gram-negative bacteria. Based on their ability to induce DNA damage, cell cycle arrest and apoptosis of cultured cells, CDTs are proposed to enhance virulence by blocking cellular division and/or directly killing epithelial and immune cells. Despite the widespread distribution of CDTs among several important human pathogens, our understanding of how these toxins interact with host cells is limited. This dissertation identifies and characterizes host factors that confer sensitivity to CDTs from Aggregatibacter actinomycetemcomitans, Haemophilus ducreyi, Escherichia coli, and Campylobacter jejuni. Host plasma membrane cholesterol supported intoxication and was found to be limiting for sensitivity to CDT in CHO-K1 cells. In contrast, a role for host glycans and the membrane protein TMEM181, which were previously implicated as receptors for binding of CDT to host cells, were found to be dispensable or play a negative role in sensitivity to CDT. Characterization of CDT-resistant mutants from two independent forward genetic screens identified a series of genes that play a role in CDT intoxication. One of these genes, Derlin-2 (Derl2), is a central component of endoplasmic reticulum associated degredation (ERAD) pathway, suggesting that CDT utilizes ERAD to escape from the lumen of the ER. Derl2 deficient cells are resistant to CDT due to decreased retrotranslocation of CDT from the lumen of the ER. Further, the mechanism of Derl2-dependent of escape of CDTs from the ER is distinct from previously described Derl2-dependent retrotranslocation of ERAD substrates. Specifically, two independent requirements for Derl2-mediated ERAD of misfolded proteins, a conserved WR motif and interaction with the AAA-ATPase p97, are dispensable for retrotranslocation of CDT and another retrograde trafficking toxin, ricin. This previously undescribed mechanism demonstrates a novel Derl2-dependent ERAD pathway exploited by retrograde trafficking toxins. In total, the findings presented here provide insight into the molecular and cellular basis of CDT-host interactions.
The development of gene expression profiling technology has enabled the high-throughput discovery of the genes and pathways that underlie disease pathophysiology and phenotype. This work analyzes microarray and RNA sequencing data to identify genes and functional pathways associated with human diseases. In the first part, gene expression profiles derived from pancreatic ductal adenocarcinoma tumors are correlated to patient disease free survival time in order to find genes that confer a protective advantage. Four genes found to be significantly correlated with disease free survival were validated in tissue using PCR. In the second part, publicly available gene expression profiles for 16 skin diseases were integrated to build a disease classifier as well as characterize genes, functions, and pathways associated with each condition. Since data was drawn from different laboratories and experiment batches, we used Frozen Robust MultiArray Average to normalize the data and identified disease specific gene signatures using a ranking algorithm. Finally, we integrated this skin database with public data on interferon-regulated gene programs to find a negative inverse correlation between Type I and Type II interferon. The final part of this work applies the principles of comparisons in multiple diseases to the problem of characterizing subtypes of one disease. mRNA-seq techniques were briefly explored to probe for genes which historically have been difficult to detect on microarray. We compared microarray gene expression profiles from four subtypes of leprosy--lepromatous leprosy (L-lep), tuberculoid leprosy (T-lep), reversal reaction, and erythema nodosum leprosum--to build a proportional median-random forest classifier and perform functional analyses, such as weighted gene correlation network analysis (WGCNA), to find genes and pathways associated with each leprosy subtype. Integrating our proportional median subtype signature for T-lep with the WGCNA module associated with T-lep, we identified MMP12 as a novel differentiator of T-lep from L-lep. This gene was verified in tissue sections of leprosy using immunohistochemistry. The use of high throughput gene expression profile analysis in these three projects demonstrates the versatility and utility of transcriptome analysis when applied to human disease systems.
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