Matching Items (96)
Description
Warning coloration deters predators from attacking prey that are defended, usually by being distasteful, toxic, or otherwise costly for predators to pursue and consume. Predators may have an innate response to warning colors or learn to associate them with a defense through trial and error. In general, predators should select for warning signals that are easy to learn and recognize. Previous research demonstrates long-wavelength colors (e.g. red and yellow) are effective because they are readily detected and learned. However, a number of defended animals display short-wavelength coloration (e.g. blue and violet), such as the pipevine swallowtail butterfly (Battus philenor). The role of blue coloration in warning signals had not previously been explicitly tested. My research showed in laboratory experiments that curve-billed thrashers (Toxostoma curvirostre) and Gambel's quail (Callipepla gambelii) can learn and recognize the iridescent blue of B. philenor as a warning signal and that it is innately avoided. I tested the attack rates of these colors in the field and blue was not as effective as orange. I concluded that blue colors may function as warning signals, but the effectiveness is likely dependent on the context and predator.
Blue colors are often iridescent in nature and the effect of iridescence on warning signal function was unknown. I reared B. philenor larvae under varied food deprivation treatments. Iridescent colors did not have more variation than pigment-based colors under these conditions; variation which could affect predator learning. Learning could also be affected by changes in appearance, as iridescent colors change in both hue and brightness as the angle of illuminating light and viewer change in relation to the color surface. Iridescent colors can also be much brighter than pigment-based colors and iridescent animals can statically display different hues. I tested these potential effects on warning signal learning by domestic chickens (Gallus gallus domesticus) and found that variation due to the directionality of iridescence and a brighter warning signal did not influence learning. However, blue-violet was learned more readily than blue-green. These experiments revealed that the directionality of iridescent coloration does not likely negatively affect its potential effectiveness as a warning signal.
Blue colors are often iridescent in nature and the effect of iridescence on warning signal function was unknown. I reared B. philenor larvae under varied food deprivation treatments. Iridescent colors did not have more variation than pigment-based colors under these conditions; variation which could affect predator learning. Learning could also be affected by changes in appearance, as iridescent colors change in both hue and brightness as the angle of illuminating light and viewer change in relation to the color surface. Iridescent colors can also be much brighter than pigment-based colors and iridescent animals can statically display different hues. I tested these potential effects on warning signal learning by domestic chickens (Gallus gallus domesticus) and found that variation due to the directionality of iridescence and a brighter warning signal did not influence learning. However, blue-violet was learned more readily than blue-green. These experiments revealed that the directionality of iridescent coloration does not likely negatively affect its potential effectiveness as a warning signal.
ContributorsPegram, Kimberly Vann (Author) / Rutowski, Ronald L (Thesis advisor) / Hoelldobler, Berthold (Committee member) / Liebig, Juergen (Committee member) / McGraw, Kevin (Committee member) / Smith, Brian H. (Committee member) / Arizona State University (Publisher)
Created2015
Description
Telomerase enzyme is a truly remarkable enzyme specialized for the addition of short, highly repetitive DNA sequences onto linear eukaryotic chromosome ends. The telomerase enzyme functions as a ribonucleoprotein, minimally composed of the highly conserved catalytic telomerase reverse transcriptase and essential telomerase RNA component containing an internalized short template region within the vastly larger non-coding RNA. Even among closely related groups of species, telomerase RNA is astonishingly divergent in sequence, length, and secondary structure. This massive disparity is highly prohibitive for telomerase RNA identification from previously unexplored groups of species, which is fundamental for secondary structure determination. Combined biochemical enrichment and computational screening methods were employed for the discovery of numerous telomerase RNAs from the poorly characterized echinoderm lineage. This resulted in the revelation that--while closely related to the vertebrate lineage and grossly resembling vertebrate telomerase RNA--the echinoderm telomerase RNA central domain varies extensively in structure and sequence, diverging even within echinoderms amongst sea urchins and brittle stars. Furthermore, the origins of telomerase RNA within the eukaryotic lineage have remained a persistent mystery. The ancient Trypanosoma telomerase RNA was previously identified, however, a functionally verified secondary structure remained elusive. Synthetic Trypanosoma telomerase was generated for molecular dissection of Trypanosoma telomerase RNA revealing two RNA domains functionally equivalent to those found in known telomerase RNAs, yet structurally distinct. This work demonstrates that telomerase RNA is uncommonly divergent in gross architecture, while retaining critical universal elements.
ContributorsPodlevsky, Joshua (Author) / Chen, Julian (Thesis advisor) / Mangone, Marco (Committee member) / Kusumi, Kenro (Committee member) / Wilson-Rawls, Norma (Committee member) / Arizona State University (Publisher)
Created2015
Description
The coordination of group behavior in the social insects is representative of a broader phenomenon in nature, emergent biological complexity. In such systems, it is believed that large-scale patterns result from the interaction of relatively simple subunits. This dissertation involved the study of one such system: the social foraging of the ant Temnothorax rugatulus. Physically tiny with small population sizes, these cavity-dwelling ants provide a good model system to explore the mechanisms and ultimate origins of collective behavior in insect societies. My studies showed that colonies robustly exploit sugar water. Given a choice between feeders unequal in quality, colonies allocate more foragers to the better feeder. If the feeders change in quality, colonies are able to reallocate their foragers to the new location of the better feeder. These qualities of flexibility and allocation could be explained by the nature of positive feedback (tandem run recruitment) that these ants use. By observing foraging colonies with paint-marked ants, I was able to determine the `rules' that individuals follow: foragers recruit more and give up less when they find a better food source. By altering the nutritional condition of colonies, I found that these rules are flexible - attuned to the colony state. In starved colonies, individual ants are more likely to explore and recruit to food sources than in well-fed colonies. Similar to honeybees, Temmnothorax foragers appear to modulate their exploitation and recruitment behavior in response to environmental and social cues. Finally, I explored the influence of ecology (resource distribution) on the foraging success of colonies. Larger colonies showed increased consistency and a greater rate of harvest than smaller colonies, but this advantage was mediated by the distribution of resources. While patchy or rare food sources exaggerated the relative success of large colonies, regularly (or easily found) distributions leveled the playing field for smaller colonies. Social foraging in ant societies can best be understood when we view the colony as a single organism and the phenotype - group size, communication, and individual behavior - as integrated components of a homeostatic unit.
ContributorsShaffer, Zachary (Author) / Pratt, Stephen C (Thesis advisor) / Hölldobler, Bert (Committee member) / Janssen, Marco (Committee member) / Fewell, Jennifer (Committee member) / Liebig, Juergen (Committee member) / Arizona State University (Publisher)
Created2014
Description
MicroRNAs (miRNAs) are short non-coding RNAs that play key roles during metazoan development, and are frequently misregulated in human disease. MiRNAs regulate gene output by targeting degenerate elements primarily in the 3´ untranslated regions of mRNAs. MiRNAs are often deeply conserved, but have undergone drastic expansions in higher metazoans, leading to families of miRNAs with highly similar sequences. The evolutionary advantage of maintaining multiple copies of duplicated miRNAs is not well understood, nor has the distinct functions of miRNA family members been systematically studied. Furthermore, the unbiased and high-throughput discovery of targets remains a major challenge, yet is required to understand the biological function of a given miRNA.
I hypothesize that duplication events grant miRNA families with enhanced regulatory capabilities, specifically through distinct targeting preferences by family members. This has relevance for our understanding of vertebrate evolution, as well disease detection and personalized medicine. To test this hypothesis, I apply a conjunction of bioinformatic and experimental approaches, and design a novel high-throughput screening platform to identify human miRNA targets. Combined with conventional approaches, this tool allows systematic testing for functional targets of human miRNAs, and the identification of novel target genes on an unprecedented scale.
In this dissertation, I explore evolutionary signatures of 62 deeply conserved metazoan miRNA families, as well as the targeting preferences for several human miRNAs. I find that constraints on miRNA processing impact sequence evolution, creating evolutionary hotspots within families that guide distinct target preferences. I apply our novel screening platform to two cancer-relevant miRNAs, and identify hundreds of previously undescribed targets. I also analyze critical features of functional miRNA target sites, finding that each miRNA recognizes surprisingly distinct features of targets. To further explore the functional distinction between family members, I analyze miRNA expression patterns in multiple contexts, including mouse embryogenesis, RNA-seq data from human tissues, and cancer cell lines. Together, my results inform a model that describes the evolution of metazoan miRNAs, and suggests that highly similar miRNA family members possess distinct functions. These findings broaden our understanding of miRNA function in vertebrate evolution and development, and how their misexpression contributes to human disease.
I hypothesize that duplication events grant miRNA families with enhanced regulatory capabilities, specifically through distinct targeting preferences by family members. This has relevance for our understanding of vertebrate evolution, as well disease detection and personalized medicine. To test this hypothesis, I apply a conjunction of bioinformatic and experimental approaches, and design a novel high-throughput screening platform to identify human miRNA targets. Combined with conventional approaches, this tool allows systematic testing for functional targets of human miRNAs, and the identification of novel target genes on an unprecedented scale.
In this dissertation, I explore evolutionary signatures of 62 deeply conserved metazoan miRNA families, as well as the targeting preferences for several human miRNAs. I find that constraints on miRNA processing impact sequence evolution, creating evolutionary hotspots within families that guide distinct target preferences. I apply our novel screening platform to two cancer-relevant miRNAs, and identify hundreds of previously undescribed targets. I also analyze critical features of functional miRNA target sites, finding that each miRNA recognizes surprisingly distinct features of targets. To further explore the functional distinction between family members, I analyze miRNA expression patterns in multiple contexts, including mouse embryogenesis, RNA-seq data from human tissues, and cancer cell lines. Together, my results inform a model that describes the evolution of metazoan miRNAs, and suggests that highly similar miRNA family members possess distinct functions. These findings broaden our understanding of miRNA function in vertebrate evolution and development, and how their misexpression contributes to human disease.
ContributorsWolter, Justin M (Author) / Mangone, Marco (Thesis advisor) / LaBaer, Joshua (Committee member) / Kusumi, Kenro (Committee member) / Anderson, Karen (Committee member) / Arizona State University (Publisher)
Created2016
Description
Extracellular vesicles (EVs) represent a heterogeneous population of small vesicles, consisting of a phospholipidic bilayer surrounding a soluble interior cargo. These vesicles play an important role in cellular communication by virtue of their protein, RNA, and lipid content, which can be transferred among cells. Peripheral blood is a rich source of circulating EVs. An analysis of EVs in peripheral blood could provide access to unparalleled amounts of biomarkers of great diagnostic, prognostic as well as therapeutic value. In the current study, a plasma EV enrichment method based on pluronic co-polymer was first established and characterized. Plasma EVs from breast cancer patients were then enriched, profiled and compared to non-cancer controls. Proteins signatures that contributed to the prediction of cancer samples from non-cancer controls were created by a random-forest based cross-validation approach. We found that a large portion of these signatures were related to breast cancer aggression. To verify such findings, KIAA0100, one of the features identified, was chosen for in vitro molecular and cellular studies in the breast cancer cell line MDA-MB-231. We found that KIAA0100 regulates cancer cell aggression in MDA-MB-231 in an anchorage-independent manner and is particularly associated with anoikis resistance through its interaction with HSPA1A. Lastly, plasma EVs contain not only individual proteins, but also numerous molecular complexes. In order to measure millions of proteins, isoforms, and complexes simultaneously, Adaptive Dynamic Artificial Poly-ligand Targeting (ADAPT) platform was applied. ADAPT employs an enriched library of single-stranded oligodeoxynucleotides to profile complex biological samples, thus achieving a deep coverage of system-wide, native biomolecules. Profiling of EVs from breast cancer patients was able to obtain a prediction AUC performance of 0.73 when compared biopsy-positive cancer patient to healthy controls and 0.64 compared to biopsy-negative controls and such performance was not associated with the physical breast condition indicated by BIRAD scores. Taken together, current research demonstrated the potential of profiling plasma EVs in searching for therapeutic targets as well as diagnostic signatures.
ContributorsZhong, Zhenyu (Author) / Spetzler, David (Thesis advisor) / Yan, Hao (Thesis advisor) / Lake, Douglas (Committee member) / Mangone, Marco (Committee member) / Arizona State University (Publisher)
Created2018
Description
Duchenne muscular dystrophy (DMD) is a lethal, X-linked disease characterized by progressive muscle degeneration. The condition is driven by out-of-frame mutations in the dystrophin gene, and the absence of a functional dystrophin protein ultimately leads to instability of the sarcolemma, skeletal muscle necrosis, and atrophy. While the structural changes that occur in dystrophic muscle are well characterized, resulting changes in muscle-specific gene expression that take place in dystrophin’s absence remain largely uncharacterized, as they are potentially obscured by the characteristic chronic inflammation in dystrophin deficient muscle.
The conservation of the dystrophin gene across metazoans suggests that both vertebrate and invertebrate model systems can provide valuable contributions to the understanding of DMD initiation and progression. Specifically, the invertebrate C. elegans possesses a dystrophin protein ortholog, dys-1, and a mild inflammatory response that is inactive in the muscle, allowing for the characterization of transcriptome rearrangements affecting disease progression independently of inflammation. Furthermore, C. elegans do not possess a satellite cell equivalent, meaning muscle regeneration does not occur. This makes C. elegans unique in that they allow for the study of dystrophin deficiencies without muscle regeneration that may obscure detection of subtle but consequential changes in gene expression.
I hypothesize that gaining a comprehensive definition of both the structural and signaling roles of dystrophin in C. elegans will improve the community’s understanding of the progression of DMD as a whole. To address this hypothesis, I have performed a phylogenetic analysis on the conservation of each member of the dystrophin associated protein complex (DAPC) across 10 species, established an in vivo system to identify muscle-specific changes in gene expression in the dystrophin-deficient C. elegans, and performed a functional analysis to test the biological significance of changes in gene expression identified in my sequencing results. The results from this study indicate that in C. elegans, dystrophin may have a signaling role early in development, and its absence may activate compensatory mechanisms that counteract disease progression. Furthermore, these findings allow for the identification of transcriptome changes that potentially serve as both independent drivers of disease and potential therapeutic targets for the treatment of DMD.
The conservation of the dystrophin gene across metazoans suggests that both vertebrate and invertebrate model systems can provide valuable contributions to the understanding of DMD initiation and progression. Specifically, the invertebrate C. elegans possesses a dystrophin protein ortholog, dys-1, and a mild inflammatory response that is inactive in the muscle, allowing for the characterization of transcriptome rearrangements affecting disease progression independently of inflammation. Furthermore, C. elegans do not possess a satellite cell equivalent, meaning muscle regeneration does not occur. This makes C. elegans unique in that they allow for the study of dystrophin deficiencies without muscle regeneration that may obscure detection of subtle but consequential changes in gene expression.
I hypothesize that gaining a comprehensive definition of both the structural and signaling roles of dystrophin in C. elegans will improve the community’s understanding of the progression of DMD as a whole. To address this hypothesis, I have performed a phylogenetic analysis on the conservation of each member of the dystrophin associated protein complex (DAPC) across 10 species, established an in vivo system to identify muscle-specific changes in gene expression in the dystrophin-deficient C. elegans, and performed a functional analysis to test the biological significance of changes in gene expression identified in my sequencing results. The results from this study indicate that in C. elegans, dystrophin may have a signaling role early in development, and its absence may activate compensatory mechanisms that counteract disease progression. Furthermore, these findings allow for the identification of transcriptome changes that potentially serve as both independent drivers of disease and potential therapeutic targets for the treatment of DMD.
ContributorsHrach, Heather (Author) / Mangone, Marco (Thesis advisor) / LaBaer, Joshua (Committee member) / Newbern, Jason (Committee member) / Rawls, Jeffery (Committee member) / Arizona State University (Publisher)
Created2020
Description
Schizophrenia, a debilitating neuropsychiatric disorder, affects 1% of the population. This multifaceted disorder is comprised of positive (hallucinations/psychosis), negative (social withdrawal/anhedonia) and cognitive symptoms. While treatments for schizophrenia have advanced over the past few years, high economic burdens are still conferred to society, totaling more than $34 billion in direct annual costs to the United States of America. Thus, a critical need exists to identify the factors that contribute towards the etiology of schizophrenia. This research aimed to determine the interactions between environmental factors and genetics in the etiology of schizophrenia. Specifically, this research shows that the immediate early gene, early growth response 3 (EGR3), which is upregulated in response to neuronal activity, resides at the center of a biological pathway to confer risk for schizophrenia. While schizophrenia-risk proteins including neuregulin 1 (NRG1) and N-methyl-D-aspartate receptors (NMDAR’s) have been identified upstream of EGR3, the downstream targets of EGR3 remain relatively unknown. This research demonstrates that early growth response 3 regulates the expression of the serotonin 2A-receptor (5HT2AR) in the frontal cortex following the physiologic stimulus, sleep deprivation. This effect is translated to the level of protein as 8 hours of sleep-deprivation results in the upregulation of 5HT2ARs, a target of antipsychotic medications. Additional downstream targets were identified following maximal upregulation of EGR3 through electroconvulsive stimulation (ECS). Both brain-derived neurotrophic factor (BDNF) and its epigenetic regulator, growth arrest DNA-damage-inducible 45 beta (GADD45B) are upregulated one-hour following ECS in the hippocampus and require the presence of EGR3. These proteins play important roles in both cellular proliferation and dendritic structural changes. Next, the effects of ECS on downstream neurobiological processes, hippocampal cellular proliferation and dendritic structural changes were examined. Following ECS, hippocampal cellular proliferationwas increased, and dendritic structural changes were observed in both wild-type and early growth response 3 knock-out (Egr3-/-) mice. Effects in the number of dendritic spines and dendritic complexity following ECS were not found to require EGR3. Collectively, these results demonstrate that neuronal activity leads to the regulation of schizophrenia risk proteins by EGR3 and point to a possible molecular mechanism contributing risk for schizophrenia.
ContributorsMeyers, Kimberly (Author) / Gallitano, Amelia L (Thesis advisor) / Newbern, Jason (Thesis advisor) / Mangone, Marco (Committee member) / Nikulina, Ella (Committee member) / Qiu, Shenfeng (Committee member) / Ferguson, Deveroux (Committee member) / Arizona State University (Publisher)
Created2020
Description
A complex social system, whether artificial or natural, can possess its macroscopic properties as a collective, which may change in real time as a result of local behavioral interactions among a number of agents in it. If a reliable indicator is available to abstract the macrolevel states, decision makers could use it to take a proactive action, whenever needed, in order for the entire system to avoid unacceptable states or con-verge to desired ones. In realistic scenarios, however, there can be many challenges in learning a model of dynamic global states from interactions of agents, such as 1) high complexity of the system itself, 2) absence of holistic perception, 3) variability of group size, 4) biased observations on state space, and 5) identification of salient behavioral cues. In this dissertation, I introduce useful applications of macrostate estimation in complex multi-agent systems and explore effective deep learning frameworks to ad-dress the inherited challenges. First of all, Remote Teammate Localization (ReTLo)is developed in multi-robot teams, in which an individual robot can use its local interactions with a nearby robot as an information channel to estimate the holistic view of the group. Within the problem, I will show (a) learning a model of a modular team can generalize to all others to gain the global awareness of the team of variable sizes, and (b) active interactions are necessary to diversify training data and speed up the overall learning process. The complexity of the next focal system escalates to a colony of over 50 individual ants undergoing 18-day social stabilization since a chaotic event. I will utilize this natural platform to demonstrate, in contrast to (b), (c)monotonic samples only from “before chaos” can be sufficient to model the panicked society, and (d) the model can also be used to discover salient behaviors to precisely predict macrostates.
ContributorsChoi, Taeyeong (Author) / Pavlic, Theodore (Thesis advisor) / Richa, Andrea (Committee member) / Ben Amor, Heni (Committee member) / Yang, Yezhou (Committee member) / Liebig, Juergen (Committee member) / Arizona State University (Publisher)
Created2020
Description
The flexibility and robustness of social insect colonies, when they cope with challenges as integrated units, raise many questions, such as how hundreds and thousands of individual local responses are coordinated without a central controlling process. Answering such questions requires: 1. Quantifiable collective responses of colonies under specific scenarios; 2. Decomposability of the collective colony-level response into individual responses; and 3. Mechanisms to integrate the colony- and individual-level responses. In the first part of my dissertation, I explore coordinated collective responses of colonies in during the alarm response to an alarmed nestmate (chapter 2&3). I develop a machine-learning approach to quantitatively estimate the collective and individual alarm response (chapter 2). Using this methodology, I demonstrate that colony alarm responses to the introduction of alarmed nestmates can be decomposed into immediately cascading, followed by variable dampening processes. Each of those processes are found to be modulated by variation in individual alarm responsiveness, as measured by alarm response threshold and persistence of alarm behavior. This variation is modulated in turn by environmental context, in particular with task-related social context (chapter 3). In the second part of my dissertation, I examine the mechanisms responsible for colonial changes in metabolic rate during ontogeny. Prior studies have found that larger ant colonies (as for larger organisms) have lower mass-specific metabolic rates, but the mechanisms remain unclear. In a 3.5-year study on 25 colonies, metabolic rates of colonies and colony components were measured during ontogeny (chapter 4). The scaling of metabolic rate during ontogeny was fit better by segmented regression or quadratic regression models than simple linear regression models, showing that colonies do not follow a universal power-law of metabolism during the ontogenetic development. Furthermore, I showed that the scaling of colonial metabolic rates can be primarily explained by changes in the ratio of brood to adult workers, which nonlinearly affects colonial metabolic rates. At high ratios of brood to workers, colony metabolic rates are low because the metabolic rate of larvae and pupae are much lower than adult workers. However, the high colony metabolic rates were observed in colonies with moderate brood: adult ratios, because higher ratios cause adult workers to be more active and have higher metabolic rates, presumably due to the extra work required to feed more brood.
ContributorsGuo, Xiaohui (Author) / Fewell, Jennifer H (Thesis advisor) / Kang, Yun (Thesis advisor) / Harrison, Jon F (Committee member) / Liebig, Juergen (Committee member) / Pratt, Stephen C (Committee member) / Pavlic, Theodore P (Committee member) / Arizona State University (Publisher)
Created2021
Description
MicroRNAs (miRNAs) are short non-coding RNAs that regulate gene output at the post-transcriptional level by targeting degenerate elements primarily in 3′untranslated regions (3′UTRs) of mRNAs. Individual miRNAs can regulate networks of hundreds of genes, yet for the majority of miRNAs few, if any, targets are known. Misexpression of miRNAs is also a major contributor to cancer progression, thus there is a critical need to validate miRNA targets in high-throughput to understand miRNAs' contribution to tumorigenesis. Here we introduce a novel high-throughput assay to detect miRNA targets in 3′UTRs, called Luminescent Identification of Functional Elements in 3′UTRs (3′LIFE). We demonstrate the feasibility of 3′LIFE using a data set of 275 human 3′UTRs and two cancer-relevant miRNAs, let-7c and miR-10b, and compare our results to alternative methods to detect miRNA targets throughout the genome. We identify a large number of novel gene targets for these miRNAs, with only 32% of hits being bioinformatically predicted and 27% directed by non-canonical interactions. Functional analysis of target genes reveals consistent roles for each miRNA as either a tumor suppressor (let-7c) or oncogenic miRNA (miR-10b), and preferentially target multiple genes within regulatory networks, suggesting 3′LIFE is a rapid and sensitive method to detect miRNA targets in high-throughput.
ContributorsWolter, Justin (Author) / Kotagama, Kasuen (Author) / Pierre-Bez, Alexandra C. (Author) / Firago, Mari (Author) / Mangone, Marco (Author) / College of Liberal Arts and Sciences (Contributor)
Created2014-09-29