Computational and experimental analysis of mRNA degradationin Saccharomyces cerevisiae

Persistent Link:
http://hdl.handle.net/10150/280160
Title:
Computational and experimental analysis of mRNA degradationin Saccharomyces cerevisiae
Author:
Cao, Dan
Issue Date:
2002
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Because of its integration power, quantifying power, explanatory power and predictive power, mathematical and computational modeling is becoming an important tool to test and advance our understanding about cellular process in the post-genomic era. Iterative approach between modeling, making prediction and experimental testing might increase the rate of forming and testing hypotheses in Biology. mRNA decay is an ideal system to start knowledge based modeling. In the second chapter, I applied the computational modeling approach to test our understanding about normal mRNA turnover processes in yeast. The computational modeling reproduces experimental observations for the unstable MFA2 and stable PGK1 transcripts, suggesting we have a relatively robust understanding for the mRNA decay process in yeast. Subsequent analysis and a series of in silico experiments led to several important insights about this process, which are presented in the second chapter. In the last chapter, I extended this kind of computational analysis to nonsense mediated mRNA decay (NMD), which is a surveillance system all eukaryotic cells have to recognize and degrade mRNAs containing premature translation termination codons. Initial in silico analysis suggests the popular leaky surveillance model about NMD is inconsistent with previous observations. Further experimental analysis using PGK1 mRNA with a nonsense codon in four different positions revealed several new properties of NMD. First, regardless of the position of the nonsense codon, the entire observable population of transcripts is recognized as aberrant, which is different from the leaky surveillance model. Second, the rate of decapping is accelerated in a position dependent manner, although at all positions the dependence of decapping on deadenylation is removed. This provides a mechanistic explanation for the polarity in NMD wherein 5' nonsense codons exert larger effects than 3' nonsense codons. Third, NMD leads to enhanced deadenylation independent of the position of the nonsense codon. This multitude of changes in the metabolism of nonsense containing mRNAs suggests that these transcripts contain multiple alterations in mRNP structure and/or transcript localization. Based on these observations, I constructed a robust computational model that accurately describes the process of NMD and can serve as a predictive model for future work.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Biology, Molecular.; Biology, Cell.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Molecular and Cellular Biology
Degree Grantor:
University of Arizona
Advisor:
Parker, Roy

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleComputational and experimental analysis of mRNA degradationin Saccharomyces cerevisiaeen_US
dc.creatorCao, Danen_US
dc.contributor.authorCao, Danen_US
dc.date.issued2002en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractBecause of its integration power, quantifying power, explanatory power and predictive power, mathematical and computational modeling is becoming an important tool to test and advance our understanding about cellular process in the post-genomic era. Iterative approach between modeling, making prediction and experimental testing might increase the rate of forming and testing hypotheses in Biology. mRNA decay is an ideal system to start knowledge based modeling. In the second chapter, I applied the computational modeling approach to test our understanding about normal mRNA turnover processes in yeast. The computational modeling reproduces experimental observations for the unstable MFA2 and stable PGK1 transcripts, suggesting we have a relatively robust understanding for the mRNA decay process in yeast. Subsequent analysis and a series of in silico experiments led to several important insights about this process, which are presented in the second chapter. In the last chapter, I extended this kind of computational analysis to nonsense mediated mRNA decay (NMD), which is a surveillance system all eukaryotic cells have to recognize and degrade mRNAs containing premature translation termination codons. Initial in silico analysis suggests the popular leaky surveillance model about NMD is inconsistent with previous observations. Further experimental analysis using PGK1 mRNA with a nonsense codon in four different positions revealed several new properties of NMD. First, regardless of the position of the nonsense codon, the entire observable population of transcripts is recognized as aberrant, which is different from the leaky surveillance model. Second, the rate of decapping is accelerated in a position dependent manner, although at all positions the dependence of decapping on deadenylation is removed. This provides a mechanistic explanation for the polarity in NMD wherein 5' nonsense codons exert larger effects than 3' nonsense codons. Third, NMD leads to enhanced deadenylation independent of the position of the nonsense codon. This multitude of changes in the metabolism of nonsense containing mRNAs suggests that these transcripts contain multiple alterations in mRNP structure and/or transcript localization. Based on these observations, I constructed a robust computational model that accurately describes the process of NMD and can serve as a predictive model for future work.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectBiology, Molecular.en_US
dc.subjectBiology, Cell.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineMolecular and Cellular Biologyen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorParker, Royen_US
dc.identifier.proquest3073205en_US
dc.identifier.bibrecord.b43427753en_US
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