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Description
Three populations of experimentally evolved Drosophila melanogaster populations made up of high temperature (H, constant 25 ᵒC), low temperature (C, constant 16 ᵒC) and temporal homogeneity (T, environment changes between 16 ᵒC and 25 ᵒC) were prepared and assayed to determine difference in citrate synthase activity. Between the three groups, the results were inconclusive: the resulting reaction rates in units of nmol min-1mgfly-1 were 81.8 + 20.6, 101 + 15.6, and 96.9 + 25.2 for the hot (H), cold (C), and temporally homogeneous (T) groups, respectively. We conclude that the high associated variability was due to a lack of control regarding the collection time of the experimentally evolved Drosophila.
ContributorsBelohlavek, David (Author) / Angilletta, Michael (Thesis director) / Francisco, Wilson (Committee member) / Barrett, The Honors College (Contributor) / Department of Chemistry and Biochemistry (Contributor)
Created2015-05
Description
The splicing of precursor messenger RNAs (pre-mRNAs) plays an essential role in dictating the mature mRNA profiles of eukaryotic cells. Mis-regulation of splicing, due to mutations in pre-mRNAs or in components of the splicing machinery, is associated with many diseases. Therefore, knowledge of pre-mRNA splicing mechanisms is required to understand gene expression regulation during states of homeostasis and disease, and for the development of therapeutic interventions.Splicing is catalyzed by the spliceosome, a dynamic and protein-rich ribozyme composed of five small nuclear ribonucleoproteins (snRNPs) and ~170 auxiliary factors. Early interactions that occur in prespliceosomal complexes formed by the 5′- and 3′-splice-site bound U1 and U2 snRNPs are responsible for committing introns for removal. However, the mechanisms underlying these early interactions remain to be fully characterized for understanding the influence of alternative splicing factors and the impact of recurrent disease-associated mutations in prespliceosomal proteins. The goal of my dissertation research was to delineate the role of the U1 small nuclear RNA (snRNA) during prespliceosome assembly. By applying a cellular minigene reporter assay and a variety of in vitro techniques including cell-free protein expression, UV-crosslinking, electrophoretic mobility shift assays, surface plasmon resonance, and RNA affinity purification, my work establishes critical roles for the U1 snRNA stem-loops 3 (SL3) and 4 (SL4) in formation of intron definition interactions during prespliceosome assembly. Previously, the SL4 of the U1 snRNA was shown to form a molecular bridge across introns by contacting the U2-specific splicing factor 3A1 (SF3A1). I identified the Ubiquitin-like domain of SF3A1 as a non-canonical RNA binding domain responsible for U1-SL4 binding. I also determined a role for the SL3 region of the U1 snRNA in splicing and characterized the spliceosomal RNA helicase UAP56 as an SL3 interacting protein. By knocking-down the SL3- and SL4-interacting proteins, I confirmed that U1 splicing activity in vivo relies on UAP56 and SF3A1 and that their functions are interdependent. These findings, in addition to the observations made using in vitro splicing assays, support a model whereby UAP56, through its interaction with U1-SL3, enhances the cross-intron interaction between U1-SL4 and SF3A1 to promote prespliceosome formation.
ContributorsMartelly, William (Author) / Sharma, Shalini (Thesis advisor) / Mangone, Marco (Thesis advisor) / Gustin, Kurt (Committee member) / Chen, Julian (Committee member) / Arizona State University (Publisher)
Created2021
Description
Efforts to treat prostate cancer have seen an uptick, as the world’s most commoncancer in men continues to have increasing global incidence. Clinically, metastatic
prostate cancer is most commonly treated with hormonal therapy. The idea behind
hormonal therapy is to reduce androgen production, which prostate cancer cells
require for growth. Recently, the exploration of the synergistic effects of the drugs
used in hormonal therapy has begun. The aim was to build off of these recent
advancements and further refine the synergistic drug model. The advancements I
implement come by addressing biological shortcomings and improving the model’s
internal mechanistic structure. The drug families being modeled, anti-androgens,
and gonadotropin-releasing hormone analogs, interact with androgen production in a
way that is not completely understood in the scientific community. Thus the models
representing the drugs show progress through their ability to capture their effect
on serum androgen. Prostate-specific antigen is the primary biomarker for prostate
cancer and is generally how population models on the subject are validated. Fitting
the model to clinical data and comparing it to other clinical models through the
ability to fit and forecast prostate-specific antigen and serum androgen is how this
improved model achieves validation. The improved model results further suggest that
the drugs’ dynamics should be considered in adaptive therapy for prostate cancer.
prostate cancer is most commonly treated with hormonal therapy. The idea behind
hormonal therapy is to reduce androgen production, which prostate cancer cells
require for growth. Recently, the exploration of the synergistic effects of the drugs
used in hormonal therapy has begun. The aim was to build off of these recent
advancements and further refine the synergistic drug model. The advancements I
implement come by addressing biological shortcomings and improving the model’s
internal mechanistic structure. The drug families being modeled, anti-androgens,
and gonadotropin-releasing hormone analogs, interact with androgen production in a
way that is not completely understood in the scientific community. Thus the models
representing the drugs show progress through their ability to capture their effect
on serum androgen. Prostate-specific antigen is the primary biomarker for prostate
cancer and is generally how population models on the subject are validated. Fitting
the model to clinical data and comparing it to other clinical models through the
ability to fit and forecast prostate-specific antigen and serum androgen is how this
improved model achieves validation. The improved model results further suggest that
the drugs’ dynamics should be considered in adaptive therapy for prostate cancer.
ContributorsReckell, Trevor (Author) / Kostelich, Eric (Thesis advisor) / Kuang, Yang (Committee member) / Mahalov, Alex (Committee member) / Arizona State University (Publisher)
Created2020