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- Genre: Doctoral Dissertation
Description
The living world is replete with easily observed structural adaptations (e.g. teeth, claws, and stingers), but behavioral adaptations are no less impressive. Conspecific aggression can be defined as any harmful action directed by one animal at another of the same species. Because it is a potentially risky and costly behavior, aggression should be elicited only under optimal conditions. In honeybees, nestmate recognition is considered the driving factor determining whether colony guards will aggress against other honeybees attempting to gain entry to the colony. Models and empirical research support the conclusion that nestmate recognition should be favored over direct kin recognition. Thus, bees tend to use environmentally mediated cues associated with their colonies (e.g. colony odors) to recognize nestmates. The framework of nestmate recognition suggests that non-nestmates should always be aggressed against while nestmates should always be accepted. However, aggression towards nestmates and acceptance of non-nestmates are seen in a wide variety of eusocial insects, including honeybees. These are typically classified as rejection errors and acceptance errors, respectively. As such, they can be explained using signal detection theory and optimal acceptance threshold models, which postulate that recognition errors are inevitable if there is overlap in the cues used to distinguish “desirables” (fitness-enhancing) from “undesirables” (fitness-decrementing) conspecifics. In the context of social insects desirables are presumed to be nestmates and undesirables are presumed to be non-nestmates. I propose that honeybees may make more refined decisions concerning what conspecifics are desirable and undesirable, accounting for at least some of the phenomena previously reported as recognition errors. Some “errors” may be the result of guard bees responding to cues associated with threats and benefits beyond nestmate identity. I show that less threatening neighbors receive less aggression than highly threatening strangers. I show that well-fed colonies exhibit less aggression and that bees from well-fed colonies receive less aggression. I provide evidence that honeybees may decrease aggression towards nestmates and non-nestmate not involved in robbing while increasing aggression towards non-nestmate from a robber colony. Lastly, I show that pollen bearing foragers, regardless of nestmate identity, receive little to no aggression compared to non-pollen bearing foragers.
ContributorsJackson, Jonathan Cole (Author) / Pratt, Stephen (Thesis advisor) / Rutowski, Ronald (Committee member) / Fewell, Jennifer (Committee member) / Amazeen, Nia (Committee member) / Kaftanoglu, Osman (Committee member) / Arizona State University (Publisher)
Created2021
Description
Lipolysis or hydrolysis of triglyceride (TG) stored within intracellular lipid droplets (LD), is vital to maintaining metabolic homeostasis in mammals. Regulation of lipolysis and subsequent utilization of liberated fatty acids impacts cellular and organismal functions including body fat accumulation and thermogenesis. Adipose triglyceride lipase (ATGL) is the intracellular rate-limiting enzyme responsible for catalyzing hydrolysis of TG to diacylglycerol (DAG), the initial step of the lipolytic reaction. G0/G1 switch gene-2 (G0S2) and hypoxia-inducible gene-2 (HIG2) are selective inhibitors of ATGL. G0S2 facilitates accumulation of TG in the liver and adipose tissue, while HIG2 functions under hypoxic conditions. Sequence analysis and mutagenesis were used to confirm the presence of conserved domains between these proteins, and that these domains are required for efficient binding and inhibition of ATGL. Further analysis revealed a Positive sequence (Pos-Seq)-LD binding motif in G0S2 but not HIG2. The Pos-Seq mediated ATGL-independent localization to LD and was required for achieving maximal inhibition of ATGL activity by G0S2. Identification and mutational analysis of this motif revealed distinct mechanisms for HIG2 and G0S2 LD association. In addition to molecular characterization of known protein inhibitors of lipolysis, an intracellular member of the apolipoprotein L (ApoL) family, ApoL6, was also identified as a LD and mitochondria associated protein expressed in adipose tissue. Brown adipose tissue uses fatty acids as fuel for increasing its energy output as heat during acute responses to cold exposure. A Comprehensive Lab Animal Monitoring System was used to compare heat production at room temperature (RT) and 4oC in transgenic animals overexpressing ApoL6 in brown adipose tissue. Overexpression of ApoL6 delayed utilization of long-chain fatty acids (LCFAs) as a fuel source while promoting an enhanced thermogenic response during initial cold exposure. ApoL6 mediated inhibition of LCFA utilization results from binding of ApoL6 to Mitochondrial Trifunctional Protein (MTP/TFP), which catalyzes mitochondrial β-oxidation. Indirect calorimetry and fasting acute cold exposure experiments suggest the augmented thermogenic profile of ApoL6 transgenic animals is a result of enhanced utilization of medium-chain fatty acids (MCFAs), glucose, and amino acids as fuel sources. Cumulatively these results indicate multiple mechanisms for regulation lipolysis and fatty acid utilization.
ContributorsCampbell, Latoya E (Author) / Lake, Douglas (Thesis advisor) / Liu, Jun (Committee member) / Folmes, Clifford (Committee member) / Sweazea, Karen (Committee member) / Baluch, Debra (Committee member) / Arizona State University (Publisher)
Created2021
Description
Social insect groups, such as bees, termites, and ants, epitomize the emergence of group-level behaviors from the aggregated actions and interactions of individuals. Ants have the unique advantage that whole colonies can be observed in artificial, laboratory nests, and each individual's behavior can be continuously tracked using imaging software. In this dissertation, I study two group behaviors: (1) the spread of alarm signals from three agitated ants to a group of 61 quiescent nestmates, and (2) the reduction in per-capita energy use as colonies scale in size from tens of ants to thousands. For my first experiment, I track the motion of Pogonomyrmex californicus ants using an overhead camera, and I analyze how propagation of an initial alarm stimulus affects their walking speeds. I then build an agent-based model that simulates two-dimensional ant motion and the spread of the alarmed state. I find that implementing a simple set of rules for motion and alarm signal transmission reproduces the empirically observed speed dynamics. For the second experiment, I simulate social insect colony workers that collectively complete a set of tasks. By assuming that task switching is energetically costly, my model recovers a metabolic rate scaling pattern, known as hypometric metabolic scaling. This relationship, which predicts an organism's metabolic rate from its mass, is observed across a diverse set of social insect groups and animal species. The results suggest an explicit link between the degree of workers' task specialization and whole-colony energy use.
ContributorsLin, Michael Robert (Author) / Milner, Fabio A (Thesis advisor, Committee member) / Fewell, Jennifer H (Thesis advisor, Committee member) / Lampert, Adam (Committee member) / Arizona State University (Publisher)
Created2021
Description
The desire to start a family is something millions of people around the globe strive to achieve. However, many factors such as the societal changes in family planning due to increasing maternal age, use of birth control, and ever-changing lifestyles have increased the number of infertility cases seen in the United States each year. Infertility can manifest as a prolonged inability to conceive, or inability to carry a pregnancy full-term. Modern advancements in the field of reproductive medicine have begun to promote the use of Assisted Reproductive Technologies (ART) to circumvent reduced fertility in both men and women. Implementation of techniques such as In Vitro Fertilization, Intracytoplasmic Sperm Injection, and Pre-Implantation Genetic Testing have allowed many couples to conceive. There is continual effort being made towards developing more effective and personalized fertility treatments. This often begins in the form of animal research—a fundamental step in biomedical research. This dissertation examines infertility as a medical condition through the characterization of normal reproductive anatomy and physiology in the introductory overview of reproduction. Specific pathologies of male and female-factor infertility are described, which necessitates the use of ARTs. The various forms of ARTs currently utilized in a clinical setting are addressed including history, preparations, and protocols for each technology. To promote continual advancement of the field, both animal studies and human trials provide fundamental stepping-stones towards the execution of new techniques and protocols. Examples of research conducted for the betterment of human reproductive medicine are explored, including an animal study conducted in mice exploring the role of tyramine in ovulation. With the development and implementation of new technologies and protocols in the field, this also unearths ethical dilemmas that further complicate the addition of new technologies in the field. Combining an extensive review in assisted reproduction, research and clinical fieldwork, this study investigates the history and development of novel research conducted in reproductive medicine and explores the broader implications of new technologies in the field.
ContributorsPeck, Shelbi Marie (Author) / Baluch, Debra P (Thesis advisor) / Maienschein, Jane (Thesis advisor) / Sweazea, Karen (Committee member) / Ellison, Karin (Committee member) / Arizona State University (Publisher)
Created2021
Description
Next-generation sequencing is a powerful tool for detecting genetic variation. How-ever, it is also error-prone, with error rates that are much larger than mutation rates.
This can make mutation detection difficult; and while increasing sequencing depth
can often help, sequence-specific errors and other non-random biases cannot be de-
tected by increased depth. The problem of accurate genotyping is exacerbated when
there is not a reference genome or other auxiliary information available.
I explore several methods for sensitively detecting mutations in non-model or-
ganisms using an example Eucalyptus melliodora individual. I use the structure of
the tree to find bounds on its somatic mutation rate and evaluate several algorithms
for variant calling. I find that conventional methods are suitable if the genome of a
close relative can be adapted to the study organism. However, with structured data,
a likelihood framework that is aware of this structure is more accurate. I use the
techniques developed here to evaluate a reference-free variant calling algorithm.
I also use this data to evaluate a k-mer based base quality score recalibrator
(KBBQ), a tool I developed to recalibrate base quality scores attached to sequencing
data. Base quality scores can help detect errors in sequencing reads, but are often
inaccurate. The most popular method for correcting this issue requires a known
set of variant sites, which is unavailable in most cases. I simulate data and show
that errors in this set of variant sites can cause calibration errors. I then show that
KBBQ accurately recalibrates base quality scores while requiring no reference or other
information and performs as well as other methods.
Finally, I use the Eucalyptus data to investigate the impact of quality score calibra-
tion on the quality of output variant calls and show that improved base quality score
calibration increases the sensitivity and reduces the false positive rate of a variant
calling algorithm.
This can make mutation detection difficult; and while increasing sequencing depth
can often help, sequence-specific errors and other non-random biases cannot be de-
tected by increased depth. The problem of accurate genotyping is exacerbated when
there is not a reference genome or other auxiliary information available.
I explore several methods for sensitively detecting mutations in non-model or-
ganisms using an example Eucalyptus melliodora individual. I use the structure of
the tree to find bounds on its somatic mutation rate and evaluate several algorithms
for variant calling. I find that conventional methods are suitable if the genome of a
close relative can be adapted to the study organism. However, with structured data,
a likelihood framework that is aware of this structure is more accurate. I use the
techniques developed here to evaluate a reference-free variant calling algorithm.
I also use this data to evaluate a k-mer based base quality score recalibrator
(KBBQ), a tool I developed to recalibrate base quality scores attached to sequencing
data. Base quality scores can help detect errors in sequencing reads, but are often
inaccurate. The most popular method for correcting this issue requires a known
set of variant sites, which is unavailable in most cases. I simulate data and show
that errors in this set of variant sites can cause calibration errors. I then show that
KBBQ accurately recalibrates base quality scores while requiring no reference or other
information and performs as well as other methods.
Finally, I use the Eucalyptus data to investigate the impact of quality score calibra-
tion on the quality of output variant calls and show that improved base quality score
calibration increases the sensitivity and reduces the false positive rate of a variant
calling algorithm.
ContributorsOrr, Adam James (Author) / Cartwright, Reed (Thesis advisor) / Wilson, Melissa (Committee member) / Kusumi, Kenro (Committee member) / Taylor, Jesse (Committee member) / Pfeifer, Susanne (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
Radioactive cesium (137Cs), released from nuclear power plants and nuclear accidental releases, is a problem due to difficulties regarding its removal. Efforts have been focused on removing cesium and the remediation of the contaminated environment. Traditional treatment techniques include Prussian blue and nano zero-valent ion (nZVI) and nano-Fe/Cu particles to remove Cs from water; however, they are not efficient at removing Cs when present at low concentrations of about 10 parts-per-billion (ppb), typical of concentrations found in the radioactive contaminated sites.
The objective of this study was to develop an innovative and simple method to remove Cs+ present at low concentrations by engineering a proteoliposome transporter composed of an uptake protein reconstituted into a liposome vesicle. To achieve this, the uptake protein, Kup, from E. coli, was isolated through protein extraction and purification procedures. The new and simple extraction methodology developed in this study was highly efficient and resulted in purified Kup at ~1 mg/mL. A new method was also developed to insert purified Kup protein into the bilayers of liposome vesicles. Finally, removal of CsCl (10 and 100 ppb) was demonstrated by spiking the constructed proteoliposome in lab-fortified water, followed by incubation and ultracentrifugation, and measuring Cs+ with inductively coupled plasma mass spectrometry (ICP-MS).
The ICP-MS results from testing water contaminated with 100 ppb CsCl, revealed that adding 0.1 – 8 mL of Kup proteoliposome resulted in 0.29 – 12.7% Cs removal. Addition of 0.1 – 2 mL of proteoliposome to water contaminated with 10 ppb CsCl resulted in 0.65 – 3.43% Cs removal. These removal efficiencies were greater than the control, liposome with no protein.
A linear relationship was observed between the amount of proteoliposome added to the contaminated water and removal percentage. Consequently, by adding more volumes of proteoliposome, removal can be simply improved. This suggests that with ~ 60-70 mL of proteoliposome, removal of about 90% can be achieved. The novel technique developed herein is a contribution to emerging technologies in the water and wastewater treatment industry.
The objective of this study was to develop an innovative and simple method to remove Cs+ present at low concentrations by engineering a proteoliposome transporter composed of an uptake protein reconstituted into a liposome vesicle. To achieve this, the uptake protein, Kup, from E. coli, was isolated through protein extraction and purification procedures. The new and simple extraction methodology developed in this study was highly efficient and resulted in purified Kup at ~1 mg/mL. A new method was also developed to insert purified Kup protein into the bilayers of liposome vesicles. Finally, removal of CsCl (10 and 100 ppb) was demonstrated by spiking the constructed proteoliposome in lab-fortified water, followed by incubation and ultracentrifugation, and measuring Cs+ with inductively coupled plasma mass spectrometry (ICP-MS).
The ICP-MS results from testing water contaminated with 100 ppb CsCl, revealed that adding 0.1 – 8 mL of Kup proteoliposome resulted in 0.29 – 12.7% Cs removal. Addition of 0.1 – 2 mL of proteoliposome to water contaminated with 10 ppb CsCl resulted in 0.65 – 3.43% Cs removal. These removal efficiencies were greater than the control, liposome with no protein.
A linear relationship was observed between the amount of proteoliposome added to the contaminated water and removal percentage. Consequently, by adding more volumes of proteoliposome, removal can be simply improved. This suggests that with ~ 60-70 mL of proteoliposome, removal of about 90% can be achieved. The novel technique developed herein is a contribution to emerging technologies in the water and wastewater treatment industry.
ContributorsHakim Elahi, Sepideh (Author) / Conroy-Ben, Otakuye (Thesis advisor) / Abbaszadegan, Morteza (Committee member) / Fox, Peter (Committee member) / Arizona State University (Publisher)
Created2018
Description
Single-cell proteomics and transcriptomics analysis are crucial to gain insights of
healthy physiology and disease pathogenesis. The comprehensive profiling of biomolecules in individual cells of a heterogeneous system can provide deep insights into many important biological questions, such as the distinct cellular compositions or regulation of inter- and intracellular signaling pathways of healthy and diseased tissues. With multidimensional molecular imaging of many different biomarkers in patient biopsies, diseases can be accurately diagnosed to guide the selection of the ideal treatment.
As an urgent need to advance single-cell analysis, imaging-based technologies have been developed to detect and quantify multiple DNA, RNA and protein molecules in single cell in situ. Novel fluorescent probes have been designed and synthesized, which targets specifically either their nucleic acid counterpart or protein epitopes. These highly multiplexed imaging-based platforms have the potential to detect and quantify 100 different protein molecules and 1000 different nucleic acids in a single cell.
Using novel fluorescent probes, a large number of biomolecules have been detected and quantified in formalin-fixed paraffin-embedded (FFPE) brain tissue at single-cell resolution. By studying protein expression levels, neuronal heterogeneity has been revealed in distinct subregions of human hippocampus.
healthy physiology and disease pathogenesis. The comprehensive profiling of biomolecules in individual cells of a heterogeneous system can provide deep insights into many important biological questions, such as the distinct cellular compositions or regulation of inter- and intracellular signaling pathways of healthy and diseased tissues. With multidimensional molecular imaging of many different biomarkers in patient biopsies, diseases can be accurately diagnosed to guide the selection of the ideal treatment.
As an urgent need to advance single-cell analysis, imaging-based technologies have been developed to detect and quantify multiple DNA, RNA and protein molecules in single cell in situ. Novel fluorescent probes have been designed and synthesized, which targets specifically either their nucleic acid counterpart or protein epitopes. These highly multiplexed imaging-based platforms have the potential to detect and quantify 100 different protein molecules and 1000 different nucleic acids in a single cell.
Using novel fluorescent probes, a large number of biomolecules have been detected and quantified in formalin-fixed paraffin-embedded (FFPE) brain tissue at single-cell resolution. By studying protein expression levels, neuronal heterogeneity has been revealed in distinct subregions of human hippocampus.
ContributorsMondal, Manas (Author) / Guo, Jia (Thesis advisor) / Gould, Ian (Committee member) / Ros, Alexandra (Committee member) / Arizona State University (Publisher)
Created2018
Description
A key factor in the success of social animals is their organization of work. Mathematical models have been instrumental in unraveling how simple, individual-based rules can generate collective patterns via self-organization. However, existing models offer limited insights into how these patterns are shaped by behavioral differences within groups, in part because they focus on analyzing specific rules rather than general mechanisms that can explain behavior at the individual-level. My work argues for a more principled approach that focuses on the question of how individuals make decisions in costly environments.
In Chapters 2 and 3, I demonstrate how this approach provides novel insights into factors that shape the flexibility and robustness of task organization in harvester ant colonies (Pogonomyrmex barbatus). My results show that the degree to which colonies can respond to work in fluctuating environments depends on how individuals weigh the costs of activity and update their behavior in response to social information. In Chapter 4, I introduce a mathematical framework to study the emergence of collective organization in heterogenous groups. My approach, which is based on the theory of multi-agent systems, focuses on myopic agents whose behavior emerges out of an independent valuation of alternative choices in a given work environment. The product of this dynamic is an equilibrium organization in which agents perform different tasks (or abstain from work) with an analytically defined set of threshold probabilities. The framework is minimally developed, but can be extended to include other factors known to affect task decisions including individual experience and social facilitation. This research contributes a novel approach to developing (and analyzing) models of task organization that can be applied in a broader range of contexts where animals cooperate.
In Chapters 2 and 3, I demonstrate how this approach provides novel insights into factors that shape the flexibility and robustness of task organization in harvester ant colonies (Pogonomyrmex barbatus). My results show that the degree to which colonies can respond to work in fluctuating environments depends on how individuals weigh the costs of activity and update their behavior in response to social information. In Chapter 4, I introduce a mathematical framework to study the emergence of collective organization in heterogenous groups. My approach, which is based on the theory of multi-agent systems, focuses on myopic agents whose behavior emerges out of an independent valuation of alternative choices in a given work environment. The product of this dynamic is an equilibrium organization in which agents perform different tasks (or abstain from work) with an analytically defined set of threshold probabilities. The framework is minimally developed, but can be extended to include other factors known to affect task decisions including individual experience and social facilitation. This research contributes a novel approach to developing (and analyzing) models of task organization that can be applied in a broader range of contexts where animals cooperate.
ContributorsUdiani, Oyita (Author) / Kang, Yun (Thesis advisor) / Fewell, Jennifer H (Thesis advisor) / Janssen, Marcus A (Committee member) / Castillo-Chavez, Carlos (Committee member) / Arizona State University (Publisher)
Created2016