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Description
Telomerase ribonucleoprotein is a unique reverse transcriptase that adds telomeric DNA repeats to chromosome ends. Telomerase RNA (TER) is extremely divergent in size, sequence and has to date only been identified in vertebrate, yeast, ciliate and plant species. Herein, the identification and characterization of TERs from an evolutionarily distinct group, filamentous fungi, is presented. Based on phylogenetic analysis of 69 TER sequences and mutagenesis analysis of in vitro reconstituted Neurospora telomerase, we discovered a conserved functional core in filamentous fungal TERs sharing homologous structural features with vertebrate TERs. This core contains the template-pseudoknot and P6/P6.1 domains, essential for enzymatic activity, which retain function in trans. The in vitro reconstituted Neurospora telomerase is highly processive, synthesizing canonical TTAGGG repeats. Similar to Schizosaccharomycetes pombe, filamentous fungal TERs utilize the spliceosomal splicing machinery for 3' processing. Neurospora telomerase, while associating with the Est1 protein in vivo, does not bind homologous Ku or Sm proteins found in both budding and fission yeast telomerase holoenzyme, suggesting a unique biogenesis pathway. The development of Neurospora as a model organism to study telomeres and telomerase may shed light upon the evolution of the canonical TTAGGG telomeric repeat and telomerase processivity within fungal species.
ContributorsQi, Xiaodong (Author) / Chen, Julian (Thesis advisor) / Ghirlanda, Giovanna (Committee member) / Chaput, John (Committee member) / Arizona State University (Publisher)
Created2011
Description
The bleomycins are a family of glycopeptide-derived antibiotics isolated from various Streptomyces species and have been the subject of much attention from the scientific community as a consequence of their antitumor activity. Bleomycin clinically and is an integral part of a number of combination chemotherapy regimens. It has previously been shown that bleomycin has the ability to selectively target tumor cells over their non-malignant counterparts. Pyrimidoblamic acid, the N-terminal metal ion binding domain of bleomycin is known to be the moiety that is responsible for O2 activation and the subsequent chemistry leading to DNA strand scission and overall antitumor activity. Chapter 1 describes bleomycin and related DNA targeting antitumor agents as well as the specific structural domains of bleomycin. Various structural analogues of pyrimidoblamic acid were synthesized and subsequently incorporated into their corresponding full deglycoBLM A6 derivatives by utilizing a solid support. Their activity was measured using a pSP64 DNA plasmid relaxation assay and is summarized in Chapter 2. The specifics of bleomycin—DNA interaction and kinetics were studied via surface plasmon resonance and are presented in Chapter 3. By utilizing carefully selected 64-nucleotide DNA hairpins with variable 16-mer regions whose sequences showed strong binding in past selection studies, a kinetic profile was obtained for several BLMs for the first time since bleomycin was discovered in 1966. The disaccharide moiety of bleomycin has been previously shown to be a specific tumor cell targeting element comprised of L-gulose-D-mannose, especially between MCF-7 (breast cancer cells) and MCF-10A ("normal" breast cells). This phenomenon was further investigated via fluorescence microscopy using multiple cancerous cell lines with matched "normal" counterparts and is fully described in Chapter 4.
ContributorsBozeman, Trevor C (Author) / Hecht, Sidney M. (Thesis advisor) / Chaput, John (Committee member) / Gould, Ian (Committee member) / Arizona State University (Publisher)
Created2013
Description
Nucleic acids encode the information required to create life, and polymerases are the gatekeepers charged with maintaining the storage and flow of this genetic information. Synthetic biologists utilize this universal property to modify organisms and other systems to create unique traits or improve the function of others. One of the many realms in synthetic biology involves the study of biopolymers that do not exist naturally, which is known as xenobiology. Although life depends on two biopolymers for genetic storage, it may be possible that alternative molecules (xenonucleic acids – XNAs), could be used in their place in either a living or non-living system. However, implementation of an XNA based system requires the development of polymerases that can encode and decode information stored in these artificial polymers. A strategy called directed evolution is used to modify or alter the function of a protein of interest, but identifying mutations that can modify polymerase function is made problematic by their size and overall complexity. To reduce the amount of sequence space that needs to be samples when attempting to identify polymerase variants, we can try to make informed decisions about which amino acid residues may have functional roles in catalysis. An analysis of Family B polymerases has shown that residues which are involved in substrate specificity are often highly conserved both at the sequence and structure level. In order to validate the hypothesis that a strong correlation exists between structural conservation and catalytic activity, we have selected and mutated residues in the 9°N polymerase using a loss of function mutagenesis strategy based on a computational analysis of several homologues from a diverse range of taxa. Improvement of these models will hopefully lead to quicker identification of loci which are ideal engineering targets.
ContributorsHaeberle, Tyler Matthew (Author) / Chaput, John (Thesis director) / Chen, Julian (Committee member) / Larsen, Andrew (Committee member) / Barrett, The Honors College (Contributor) / Department of Chemistry and Biochemistry (Contributor) / School of Life Sciences (Contributor)
Created2015-05
Description
Bacteria have been shown to possess a large array of regulatory mechanisms to not just respond to a diverse array of environmental stresses, but to injurious artificial proteins as well. A previous investigation introduced DX, a man-made ATP sequestering protein into Escherichia coli (E. coli) which resulted in the formation of novel endoliposome structures and induced a viable but non-culturable state (VBNC) that was not easily reversed. It was hypothesized that the broadly conserved bacterial stringent response pathway may have been responsible for the observed phenotypic changes. With the goal of unveiling the molecular mechanism behind this novel response, changes in cellular morphology and physiology upon DX expression were assessed in a population of E. coli encoding a dysfunctional relA gene, one of the two genes controlling the induction of the stringent response. It was ultimately shown that RelA directly contributed to cellular filamentation, endoliposome structure formation, and the induction of a VBNC state. While the stringent response has been extensively shown to induce a VBNC state, to our knowledge, relA has not yet been shown to induce filamentation or coordinate the formation of endoliposome structures in bacteria. As the stringent response has been shown to be increasingly involved in antibiotic tolerance, this study provided an exciting opportunity to further characterize this adaptive response pathway to aid in the future development of novel therapeutics. In addition to this, this study continued to highlight that the DX protein may serve one of the first tools to allow for the direct selection of bacteria in a VBNC state by morphologically distinguishing non-culturable cells through cellular filamentation.
ContributorsFrost, Fredrick Charles (Author) / Chaput, John (Thesis director) / Wachter, Rebekka (Committee member) / Korch, Shaleen (Committee member) / Barrett, The Honors College (Contributor) / Department of Chemistry and Biochemistry (Contributor)
Created2014-12
Description
Identifying disease biomarkers may aid in the early detection of breast cancer and improve patient outcomes. Recent evidence suggests that tumors are immunogenic and therefore patients may launch an autoantibody response to tumor associated antigens. Single-chain variable fragments of autoantibodies derived from regional lymph node B cells of breast cancer patients were used to discover these tumor associated biomarkers on protein microarrays. Six candidate biomarkers were discovered from 22 heavy chain-only variable region antibody fragments screened. Validation tests are necessary to confirm the tumorgenicity of these antigens. However, the use of single-chain variable autoantibody fragments presents a novel platform for diagnostics and cancer therapeutics.
ContributorsSharman, M. Camila (Author) / Magee, Dewey (Mitch) (Thesis director) / Wallstrom, Garrick (Committee member) / Petritis, Brianne (Committee member) / Barrett, The Honors College (Contributor) / College of Liberal Arts and Sciences (Contributor) / Virginia G. Piper Center for Personalized Diagnostics (Contributor) / Biodesign Institute (Contributor)
Created2012-12
Description
Cell-free protein synthesis (CFPS) is becoming an increasingly popular method of in vitro protein expression for biotechnology applications. However, there is still no comprehensive resource that outlines the most effective lysate and template combinations for efficient eukaryotic CFPS. To address this issue, expression vectors were constructed and assayed in order to determine their activity within three commercial eukaryotic CFPS systems: Wheat Germ Extract (WGE), Rabbit Reticulocyte Lysate (RRL), and HeLa Cell Lysate (HCL). Previously in the Chaput lab, a luciferase reporter vector was expressed in each lysate system, testing different template variables impacting protein expression, including the 5' UTR sequence, presence of poly(A) tail, and DNA type. It was found that plasmid DNA templates generally yielded ~500-fold greater amount of protein than linear DNA templates and the inclusion of a poly(A) tail did not significantly increase protein expression in the plasmid systems. Additionally, the incorporation of a viral translation enhancing sequence into the 5' UTR increased translation in a lysate-specific manner. The HCL system had a strong preference for the EMCV sequence, WGE had a preference for the sequences from AMV and TMV, and RRL showed no specific preference. Overall, the HCL-EMCV system generated the greatest amount of protein per volume, producing 10-fold more protein than the second best template-lysate combination tested. Here, four human genes fused with a c-Myc tag were expressed in each lysate using the EMCV 5' UTR sequence in order to test the generality of the previous results. Protein synthesis was assayed using a luciferase construct with a c-Myc tag to recapitulate the previous luminometer data and western blotting of the human proteins. These analyses showed the same EMCV expression trends across all systems, with the HCL system synthesizing the greatest amount of each protein. In the future, when choosing commercial eukaryotic CFPS systems for gene expression, these template variables should be considered when performing cost analysis for cell-free protein production.
ContributorsHartsough, Emily Mae (Author) / Chaput, John (Thesis director) / Chandler, Douglas (Committee member) / Larsen, Andrew (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / School of Arts, Media and Engineering (Contributor)
Created2015-05
Description
Recent studies suggest a role for the microbiota in autism spectrum disorders (ASD), potentially arising from their role in modulating the immune system and gastrointestinal (GI) function or from gut–brain interactions dependent or independent from the immune system. GI problems such as chronic constipation and/or diarrhea are common in children with ASD, and significantly worsen their behavior and their quality of life. Here we first summarize previously published data supporting that GI dysfunction is common in individuals with ASD and the role of the microbiota in ASD. Second, by comparing with other publically available microbiome datasets, we provide some evidence that the shifted microbiota can be a result of westernization and that this shift could also be framing an altered immune system. Third, we explore the possibility that gut–brain interactions could also be a direct result of microbially produced metabolites.
ContributorsKrajmalnik-Brown, Rosa (Author) / Lozupone, Catherine (Author) / Kang, Dae Wook (Author) / Adams, James (Author) / Biodesign Institute (Contributor)
Created2015-03-12
Description
Background
Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
Results
We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach.
Conclusion
In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.
Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
Results
We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach.
Conclusion
In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.
ContributorsSun, Qian (Author) / Muckatira, Sherin (Author) / Yuan, Lei (Author) / Ji, Shuiwang (Author) / Newfeld, Stuart (Author) / Kumar, Sudhir (Author) / Ye, Jieping (Author) / Biodesign Institute (Contributor) / Center for Evolution and Medicine (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor) / Ira A. Fulton Schools of Engineering (Contributor)
Created2013-12-03
Description
Background
“Stoichioproteomics” relates the elemental composition of proteins and proteomes to variation in the physiological and ecological environment. To help harness and explore the wealth of hypotheses made possible under this framework, we introduce GRASP (http://www.graspdb.net), a public bioinformatic knowledgebase containing information on the frequencies of 20 amino acids and atomic composition of their side chains. GRASP integrates comparative protein composition data with annotation data from multiple public databases. Currently, GRASP includes information on proteins of 12 sequenced Drosophila (fruit fly) proteomes, which will be expanded to include increasingly diverse organisms over time. In this paper we illustrate the potential of GRASP for testing stoichioproteomic hypotheses by conducting an exploratory investigation into the composition of 12 Drosophila proteomes, testing the prediction that protein atomic content is associated with species ecology and with protein expression levels.
Results
Elements varied predictably along multivariate axes. Species were broadly similar, with the D. willistoni proteome a clear outlier. As expected, individual protein atomic content within proteomes was influenced by protein function and amino acid biochemistry. Evolution in elemental composition across the phylogeny followed less predictable patterns, but was associated with broad ecological variation in diet. Using expression data available for D. melanogaster, we found evidence consistent with selection for efficient usage of elements within the proteome: as expected, nitrogen content was reduced in highly expressed proteins in most tissues, most strongly in the gut, where nutrients are assimilated, and least strongly in the germline.
Conclusions
The patterns identified here using GRASP provide a foundation on which to base future research into the evolution of atomic composition in Drosophila and other taxa.
“Stoichioproteomics” relates the elemental composition of proteins and proteomes to variation in the physiological and ecological environment. To help harness and explore the wealth of hypotheses made possible under this framework, we introduce GRASP (http://www.graspdb.net), a public bioinformatic knowledgebase containing information on the frequencies of 20 amino acids and atomic composition of their side chains. GRASP integrates comparative protein composition data with annotation data from multiple public databases. Currently, GRASP includes information on proteins of 12 sequenced Drosophila (fruit fly) proteomes, which will be expanded to include increasingly diverse organisms over time. In this paper we illustrate the potential of GRASP for testing stoichioproteomic hypotheses by conducting an exploratory investigation into the composition of 12 Drosophila proteomes, testing the prediction that protein atomic content is associated with species ecology and with protein expression levels.
Results
Elements varied predictably along multivariate axes. Species were broadly similar, with the D. willistoni proteome a clear outlier. As expected, individual protein atomic content within proteomes was influenced by protein function and amino acid biochemistry. Evolution in elemental composition across the phylogeny followed less predictable patterns, but was associated with broad ecological variation in diet. Using expression data available for D. melanogaster, we found evidence consistent with selection for efficient usage of elements within the proteome: as expected, nitrogen content was reduced in highly expressed proteins in most tissues, most strongly in the gut, where nutrients are assimilated, and least strongly in the germline.
Conclusions
The patterns identified here using GRASP provide a foundation on which to base future research into the evolution of atomic composition in Drosophila and other taxa.
ContributorsGilbert, James D. J. (Author) / Acquisti, Claudia (Author) / Martinson, Holly M. (Author) / Elser, James (Author) / Kumar, Sudhir (Author) / Fagan, William F. (Author) / Biodesign Institute (Contributor) / Center for Evolution and Medicine (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor)
Created2013-09-04
Description
Background
Improvements in sequencing technology now allow easy acquisition of large datasets; however, analyzing these data for phylogenetics can be challenging. We have developed a novel method to rapidly obtain homologous genomic data for phylogenetics directly from next-generation sequencing reads without the use of a reference genome. This software, called SISRS, avoids the time consuming steps of de novo whole genome assembly, multiple genome alignment, and annotation.
Results
For simulations SISRS is able to identify large numbers of loci containing variable sites with phylogenetic signal. For genomic data from apes, SISRS identified thousands of variable sites, from which we produced an accurate phylogeny. Finally, we used SISRS to identify phylogenetic markers that we used to estimate the phylogeny of placental mammals. We recovered eight phylogenies that resolved the basal relationships among mammals using datasets with different levels of missing data. The three alternate resolutions of the basal relationships are consistent with the major hypotheses for the relationships among mammals, all of which have been supported previously by different molecular datasets.
Conclusions
SISRS has the potential to transform phylogenetic research. This method eliminates the need for expensive marker development in many studies by using whole genome shotgun sequence data directly. SISRS is open source and freely available at https://github.com/rachelss/SISRS/releases.
Improvements in sequencing technology now allow easy acquisition of large datasets; however, analyzing these data for phylogenetics can be challenging. We have developed a novel method to rapidly obtain homologous genomic data for phylogenetics directly from next-generation sequencing reads without the use of a reference genome. This software, called SISRS, avoids the time consuming steps of de novo whole genome assembly, multiple genome alignment, and annotation.
Results
For simulations SISRS is able to identify large numbers of loci containing variable sites with phylogenetic signal. For genomic data from apes, SISRS identified thousands of variable sites, from which we produced an accurate phylogeny. Finally, we used SISRS to identify phylogenetic markers that we used to estimate the phylogeny of placental mammals. We recovered eight phylogenies that resolved the basal relationships among mammals using datasets with different levels of missing data. The three alternate resolutions of the basal relationships are consistent with the major hypotheses for the relationships among mammals, all of which have been supported previously by different molecular datasets.
Conclusions
SISRS has the potential to transform phylogenetic research. This method eliminates the need for expensive marker development in many studies by using whole genome shotgun sequence data directly. SISRS is open source and freely available at https://github.com/rachelss/SISRS/releases.
ContributorsSchwartz, Rachel (Author) / Harkins, Kelly (Author) / Stone, Anne (Author) / Cartwright, Reed (Author) / Biodesign Institute (Contributor) / Center for Evolution and Medicine (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Human Evolution and Social Change (Contributor) / School of Life Sciences (Contributor)
Created2015-06-11