Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 130-153).

Ethylene is a plant growth regulator and plays a key role in leaf senescence. Its signaling, however, remains mostly unclear in sweet potato. Ethephon, an ethylene releasing compound, induced sweet potato detached leaf senescence and associated gene expression, and the effects were repressed by mitogen-activated protein kinase (MAPK) kinase inhibitor PD98059. These data suggest that MAPK cascade is likely involved in ethylene signaling leading to leaf senescence and associated gene expression. With gene-specific primers and RT-PCR methods, a full-length cDNA, SPMAPK, was isolated from ethephon-treated sweet potato leaves. SPMAPK contained 1098 nucleotides (365 amino acids) in the open reading frame. Sweet potato SPMAPK also exhibited high amino acid sequence identities (ca. 79.8% to 83.4%) with plant MAPKs, and was most close to Arabidopsis MPK3 and MPK6 in phylogenetic tree analysis. RT-PCR analysis showed that SPMAPK gene expression was detected in roots, stems, and leaves. The mature and partial yellowing leaves expressed higher amount. SPMAPK gene expression was also inducible and significantly enhanced by ethephon. Results from studies with inhibitors or effectors showed that ethephon treatment resulted in acceleration of leaf senescence in detached sweet potato leaves, promotion of leaf chlorophyll content reduction and decrease of photochemical Fv/Fm, and induction of associated gene expression. These ethephon-mediated effects were all delayed or repressed by pretreatment with ethylene receptor inhibitor 1-methylcyclopropene (1-MCP), MAPK kinase inhibitor PD98059, NADPH oxidase inhibitor diphenyleneiodonium (DPI), antioxidant reduced glutathione, calcium ion chelator EGTA, and de novo protein synthesis inhibitor cycloheximide, respectively. Based on these results we conclude that an ethylene-inducible mitogen-activated protein kinase SPMAPK was isolated from sweet potato leaves, and expressed higher amount in mature and partial yellowing leaves. Ethephon-induced sweet potato SPMAPK expression was significantly repressed by 1-MCP, PD98059, DPI, reduced glutathione, EGTA and cycloheximide. These data also suggest that the possible signal components in ethephon-mediated leaf senescence and associated gene expression in sweet potato leaves likely include ethylene receptor, MAPK cascade, elevated H2O2 , external calcium influx, and de novo synthesized proteins. A possible ethylene signaling model leading to sweet potato leaf senescence and associated gene expression was also proposed.

published_or_final_version / Medical Sciences / Master / Master of Medical Sciences

published_or_final_version / Medical Sciences / Master / Master of Medical Sciences

Ovarian cancer is the most lethal gynecological malignancy. Most of ovarian cancer patients relapse and subsequently die due to the development of resistance to chemotherapy. P73 belongs to the tumor suppressor p53 family. Like p53, the transcriptionally active TAp73 can bind specifically to p53 responsive elements and transactivates some of the p53 target genes, and finally leads to cell cycle arrest and apoptosis. TAp73 can be induced by DNA damage to enhance cellular sensitivity to anticancer agents in human cancer cells. However, the functions of TAp73 in ovarian cancer cells and the role in the regulation of cellular response to commonly used chemotherapeutic agents cisplatin are still poorly understood. The aims of this study were to examine the functions of TAp73 in ovarian cancer cells and its role in cellular response to cisplatin, as well as the relationship between TAp73 and p53 in ovarian cancer cells. Functional studies showed that over-expression of TAp73alpha (TAp73α) inhibited cell proliferation, colony formation ability and anchorage-independent growth of ovarian cancer cells, and this was irrespective of p53 expression status. In addition, TAp73α inhibited cell growth by arresting cell cycle at G2/M phase and up-regulating the expressions of G2/M regulators of p21, 14-3-3sigma and GADD45α. TAp73α enhanced the cellular sensitivity to cisplatin through the activation of JNK signaling pathway, at least partially, in ovarian cancer cells. TAp73α activated the JNK pathway through the up-regulation of its target gene GADD45α and subsequent activation of MKK4, the JNK up-stream kinase. Inhibition of JNK activity by a specific inhibitor (SP600125) or small interfering RNAs (siRNAs) significantly abrogated TAp73-mediated apoptosis induced by cisplatin. Moreover, the activations of MKK4, JNK and c-Jun were abolished when GADD45α was knocked down by siRNAs, and the JNK-dependent apoptosis was not observed. Collectively, these results supported that TAp73α was able to mediate apoptotic response to cisplatin through the GADD45α/MKK4/JNK signaling pathway, which was respective of p53 expression status. Further investigation on the relationship between TAp73α and p53 demonstrated that TAp73α increased p53 protein, but not mRNA expression by attenuating p53 protein degradation in wild-type p53 ovarian cancer cells. TAp73α could directly interact with p53 protein, which might interfere with the binding ability of MDM2 to p53, and consequently block the p53 protein degradation. In addition, TAp73α inactivated the Akt and ERK pathways and activated the p38 pathway in response to cisplatin in wild-type p53 OVCA433, but not in null-p53 SKOV3 cells, suggesting that the effect of TAp73α on these pathways might be p53-dependent. These results indicated that a functional cooperation of TAp73α and p53, to some extent, existed in ovarian cancer cells. In conclusion, this study demonstrated that TAp73α acted as a tumor suppressor in ovarian carcinogenesis. It promoted the cellular sensitivity to cisplatin via, at least partially, the activation of JNK signaling pathway. These TAp73α functions were irrespective of p53 expression. In addition, TAp73α was able to bind to p53 and increase p53 expression. / published_or_final_version / Obstetrics and Gynaecology / Doctoral / Doctor of Philosophy

Computational and mathematical models of cellular processes promise great benets in important elds such as molecular biology and medicine. Increasingly, researchers are incorporating the fundamentally discrete and stochastic nature of biochemical processes into the mathematical models that are intended to represent them. This has led to the formulation of models for genetic networks as continuous-time, discrete state, Markov processes, giving rise to the so-called Chemical Master Equation (CME), which is a discrete, partial dierential equation, that governs the evolution of the associated probability distribution function (PDF). While promising many insights, the CME is computationally challenging, especially as the dimension of the model grows. In this thesis, novel methods are developed for computing the PDF of the Master Equation. The problems associated with the high-dimensional nature of the Chemical Master Equation are addressed by adapting Krylov methods, in combination with Finite State Projection methods, to derive algorithms well-suited to the Master Equation. Variations of the approach that incorporate the Strang splitting and a stochastic analogue of the total quasi-steady-state approximation are also derived for chemical systems with disparate rates. Monte Carlo approaches, such as the Stochastic Simulation Algorithm, that simulate trajectories of the process governed by the CME have been a very popular approach and we compare these with the PDF approaches developed in this thesis. The thesis concludes with a discussion of various implementation issues along with numerical results for important applications in systems biology, including the gene toggle, the Goldbeter-Koshland switch and the Mitogen-Activated Protein Kinase Cascade.

Computational and mathematical models of cellular processes promise great benets in important elds such as molecular biology and medicine. Increasingly, researchers are incorporating the fundamentally discrete and stochastic nature of biochemical processes into the mathematical models that are intended to represent them. This has led to the formulation of models for genetic networks as continuous-time, discrete state, Markov processes, giving rise to the so-called Chemical Master Equation (CME), which is a discrete, partial dierential equation, that governs the evolution of the associated probability distribution function (PDF). While promising many insights, the CME is computationally challenging, especially as the dimension of the model grows. In this thesis, novel methods are developed for computing the PDF of the Master Equation. The problems associated with the high-dimensional nature of the Chemical Master Equation are addressed by adapting Krylov methods, in combination with Finite State Projection methods, to derive algorithms well-suited to the Master Equation. Variations of the approach that incorporate the Strang splitting and a stochastic analogue of the total quasi-steady-state approximation are also derived for chemical systems with disparate rates. Monte Carlo approaches, such as the Stochastic Simulation Algorithm, that simulate trajectories of the process governed by the CME have been a very popular approach and we compare these with the PDF approaches developed in this thesis. The thesis concludes with a discussion of various implementation issues along with numerical results for important applications in systems biology, including the gene toggle, the Goldbeter-Koshland switch and the Mitogen-Activated Protein Kinase Cascade.

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 137-154).

Diss. (sammanfattning) Stockholm : Karol. Inst., 2002. / Härtill 4 uppsatser.

Thesis (M. Med. Sc.)--University of Hong Kong, 2007.