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    A system of mechanical genetics with applications to pulmonary fibrosis

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    CHAMBERS-DISSERTATION-2018.pdf (28.25Mb)
    Date
    2018-07-19
    Author
    Chambers, Dwight McCoy
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    Abstract
    Over the past decade and a half, scientific research into pulmonary fibrosis has come full circle. The fibrotic matrix, a disorganized, stiff accumulation of extracellular proteins, has gone from being the unfortunate product of pathological fibroblast activation to being a key driver of its own accumulation through mechanical cues to the cells. Important mechanical regulation by the matrix occurs in distinct spatial domains throughout the cell (e.g. at the cell-ECM interface, in the mechanical machinery of the cytosol, and through the genome), which all contribute to the long-term disease phenotype. While the importance and therapeutic promise of understanding this pathologic mechanotransduction has been explored in the scientific literature, there are still no therapies to reverse pulmonary fibrosis, and life expectancy with this condition has been largely unchanged even after the advent of new medical management. The identification and demonstration of novel matrix-responsive therapies represents a new frontier in the medical approach to pulmonary fibrosis, which could directly address the disease feature (i.e. the matrix itself) responsible for the dyspnea and eventual asphyxiation that kills patients. This thesis focuses on the application of genetics and nucleic acids to directly treating and to identifying novel therapeutic targets that break this deadly feedback cycle. Each Chapter of this thesis is dedicated to applying genetics to a particular domain/step in pathologic mechanotransduction: Chapter 2 demonstrates the delivery of mRNA-based therapeutics to modulate cell-ECM interactions; Chapter 3 illustrates how the fibrotic matrix perverts the cell's innate mechanosensing machinery to potentiate pathologic TGFβ-driven transcription factor biology; and, Chapter 4 explores the structure and activation of a mechanically regulated transcription factor system, the MRTF/SRF axis, to allow for the rational exploitation of mechanical genetic. In Chapter 2, I have demonstrated the mRNA-based delivery and efficacy of two cell-tethered, conformationally sensitive fibronectin binding single chain antibody fragment variants, H5-CD8 and H5-GPI. I have shown that H5 expression alters the focal adhesion profile of fibroblasts in a linker dependent fashion and characterized the resultant mechanotransductive phenotypes of fibroblasts expressing these variants. Furthermore I have shown that these mRNA-based therapies can be translated in vivo in anticipation of efficacy testing in a bleomycin model of pulmonary fibrosis in mice. In Chapter 3, I have discovered a unique mechanism by which the fibrotic matrix sensitizes cells to TGFβ transcription through stiffness-dependent inhibition of a TGFβ inhibitor, LEMD3. I have found and characterized unique, cytosolic elements of LEMD3 biology and extended these finding into ex vivo human tissues. Finally, in Chapter 4, I have mapped the stiffness-based translocation of MRTF with and without TGFβ treatment in initial mechanosensing by fibroblasts. I have also characterized the genomic space that activated MRTF and its cofactor SRF act over by profiling the number and sequence characteristics of CArG elements in the murine genome. Finally, I have developed the tools necessary to complete a thermodynamic survey of this system in anticipation of the construction of models to predict the stiffness-dependent behavior of therapeutic CArG-based transgenes. These three particular, individual stories follow the path of mechanical information as it weaves from the matrix, into the cell and ultimately back into the matrix. Each Chapter suggests unique approaches to pathologic mechanosensing, and the integration of these insights and its implications for future work are discussed in the Chapter 5.
    URI
    http://hdl.handle.net/1853/61643
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    • Department of Biomedical Engineering Theses and Dissertations [575]
    • Georgia Tech Theses and Dissertations [23877]

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