ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- All Subjects: Biology
Description
Division of labor, whereby different group members perform different functions, is a fundamental attribute of sociality. It appears across social systems, from simple cooperative groups to complex eusocial colonies. A core challenge in sociobiology is to explain how patterns of collective organization are generated. Theoretical models propose that division of labor self-organizes, or emerges, from interactions among group members and the environment; division of labor is also predicted to scale positively with group size. I empirically investigated the emergence and scaling of division of labor in evolutionarily incipient groups of sweat bees and in eusocial colonies of harvester ants. To test whether division of labor is an emergent property of group living during early social evolution, I created de novo communal groups of the normally solitary sweat bee Lasioglossum (Ctenonomia) NDA-1. A division of labor repeatedly arose between nest excavation and guarding tasks; results were consistent with hypothesized effects of spatial organization and intrinsic behavioral variability. Moreover, an experimental increase in group size spontaneously promoted higher task specialization and division of labor. Next, I examined the influence of colony size on division of labor in larger, more integrated colonies of the harvester ant Pogonomyrmex californicus. Division of labor scaled positively with colony size in two contexts: during early colony ontogeny, as colonies grew from tens to hundreds of workers, and among same-aged colonies that varied naturally in size. However, manipulation of colony size did not elicit a short-term response, suggesting that the scaling of division of labor in P. californicus colonies is a product of functional integration and underlying developmental processes, rather than a purely emergent epiphenomenon. This research provides novel insights into the organization of work in insect societies, and raises broader questions about the role of size in sociobiology.
ContributorsHolbrook, Carter Tate (Author) / Fewell, Jennifer H (Thesis advisor) / Gadau, Jürgen (Committee member) / Harrison, Jon F. (Committee member) / Hölldobler, Berthold (Committee member) / Johnson, Robert A. (Committee member) / Arizona State University (Publisher)
Created2011
Description
The most abundantly studied societies, with the exception of humans, are those of the eusocial insects, which include all ants. Eusocial insect societies are typically composed of many dozens to millions of individuals, referred to as nestmates, which require some form of communication to maintain colony cohesion and coordinate the activities within them. Nestmate recognition is the process of distinguishing between nestmates and non-nestmates, and embodies the first line of defense for social insect colonies. In ants, nestmate recognition is widely thought to occur through olfactory cues found on the exterior surfaces of individuals. These cues, called cuticular hydrocarbons (CHCs), comprise the overwhelming majority of ant nestmate profiles and help maintain colony identity. In this dissertation, I investigate how nestmate recognition is influenced by evolutionary, ontogenetic, and environmental factors. First, I contributed to the sequencing and description of three ant genomes including the red harvester ant, Pogonomyrmex barbatus, presented in detail here. Next, I studied how variation in nestmate cues may be shaped through evolution by comparatively studying a family of genes involved in fatty acid and hydrocarbon biosynthesis, i.e., the acyl-CoA desaturases, across seven ant species in comparison with other social and solitary insects. Then, I tested how genetic, developmental, and social factors influence CHC profile variation in P. barbatus, through a three-part study. (1) I conducted a descriptive, correlative study of desaturase gene expression and CHC variation in P. barbatus workers and queens; (2) I explored how larger-scale genetic variation in the P. barbatus species complex influences CHC variation across two genetically isolated lineages (J1/J2 genetic caste determining lineages); and (3) I experimentally examined how CHC development is influenced by an individual’s social environment. In the final part of my work, I resolved discrepancies between previous findings of nestmate recognition behavior in P. barbatus by studying how factors of territorial experience, i.e., spatiotemporal relationships, affect aggressive behaviors among red harvester ant colonies. Through this research, I was able to identify promising methodological approaches and candidate genes, which both broadens our understanding of P. barbatus nestmate recognition systems and supports future functional genetic studies of CHCs in ants.
ContributorsCash, Elizabeth I (Author) / Gadau, Jürgen (Thesis advisor) / Liebig, Jürgen (Thesis advisor) / Fewell, Jennifer (Committee member) / Hölldobler, Berthold (Committee member) / Kusumi, Kenro (Committee member) / Arizona State University (Publisher)
Created2016
Description
Mycobacterium leprae, the causative agent of Hansen’s disease (leprosy), has plagued humans and other animal species for millennia and remains of concern to public health throughout the world today. Recent research into the expanded use of medical tissues preserved as formalin-fixed, paraffin-embedded samples (FFPE), opened the door for the study of M. leprae DNA from preserved skin samples. However, problems persist with damage to the DNA including fragmentation and cross linkage. This study evaluated two methods commonly used for the recovery of host DNA from FFPE samples for their efficacy in extracting pathogen DNA (hot alkaline lysis protocol and QIAGEN QIAamp FFPE DNA kit). Twenty FFPE skin samples collected from 1995-2015 from human subjects in the Pacific Islands suffering from M. leprae infection, each exhibiting a range of bacillary loads, were analyzed to determine which extraction method was most successful in terms of ability to consistently yield reliable, robust traces of M. leprae infection. This study further examined these samples to understand the phylogeny of leprosy in the region, where gaps in the evolutionary history of M. leprae persist. DNA recovery from paired samples was similar using either method. However, by extending the incubation time of post-paraffin removal sample lysis, both protocols were more likely to yield positive traces of M. leprae, with this enhancement being especially evident in paucibacillary samples with low bacterial presence. The qPCR assay findings suggest that the hot alkaline procedure is most likely to yield positive identification of infection in these traditionally challenging samples.
ContributorsKing, Felicia Clarice (Author) / Stone, Anne (Thesis advisor) / Wilson, Melissa (Committee member) / Buetow, Ken (Committee member) / Arizona State University (Publisher)
Created2023
Description
While only the sixth most common cancer globally, liver cancer is the third most deadly. Despite the importance of accurate diagnosis and effective treatment, standard diagnostic tests for most solid organ neoplasms are not required for the most common type of liver cancer, Hepatocellular Carcinoma (HCC). In addition, major discrepancies in the practices currently in place limits the ability to develop more precise oncological treatment and prognosis. This study aimed to identify biomarkers, with potential to more accurately diagnose how far cancer has advanced within a patient and determine prognosis. It is the hope that pathways provided by this study form the basis for future research into more standardized practices and potential treatment based on specific affected biological processes. The PathOlogist tool was utilized to calculate activity metrics for 1,324 biological pathways in 374 The Cancer Genome Atlas (TCGA) hepatocellular carcinoma donors. Further statistical analysis was done on two datasets, formed to identify grade or stage at time of diagnosis for the activity levels calculated by PathOlogist. The datasets were evaluated individually. Based on the variance and normality of each pathway’s activity levels in the respective data sets analysis of variance, Tukey-Kramer, Kruskal-Wallis, and Mann-Whitney-Wilcox tests were performed, when appropriate, to determine any statistically significant differences in pathway activity levels. Pathways were identified in both stage and grade data analyses that show significant differences in activity levels across designation. While some overlap is seen, there was a significant number of pathways unique to either stage or grade. These pathways are known to affect the cell cycle, cellular transport, disease, immune system, and metabolism regulation.
The biological pathways named by this research depict prospective biomarkers for progression of hepatocellular carcinoma per subdivision within both stage and grade. These findings may be instrumental to new methods of early and more accurate diagnosis. The distinct differences in identified pathways in grade and stage illustrate the need for these new methods to not only look at stage but also grade when determining prognosis. Furthermore, the pathways identified herein have potential to aid in the development of targeted treatment based on the affected biological processes.
ContributorsGarrison, Alyssa Cameron (Author) / Buetow, Kenneth (Thesis advisor) / Hinde, Katie (Committee member) / Wilson, Melissa (Committee member) / Arizona State University (Publisher)
Created2022
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
Hepatocellular carcinoma (HCC) is the third leading cause of cancer death worldwide and exhibits a male-bias in occurrence and mortality. Previous studies have provided insight into the role of inherited genetic regulation of transcription in modulating sex-differences in HCC etiology and mortality. This study uses pathway analysis to add insight into the biological processes that drive sex-differences in HCC etiology as well as a provide additional framework for future studies on sex-biased cancers. Gene expression data from normal, tumor adjacent, and HCC liver tissue were used to calculate pathway scores using a tool called PathOlogist that not only takes into consideration the molecules in a biological pathway, but also the interaction type and directionality of the signaling pathways. Analysis of the pathway scores uncovered etiologically relevant pathways differentiating male and female HCC. In normal and tumor adjacent liver tissue, males showed higher activity of pathways related to translation factors and signaling. Females did not show higher activity of any pathways compared to males in normal and tumor adjacent liver tissue. Work suggest biologic processes that underlie sex-biases in HCC occurrence and mortality. Both males and females differed in the activation of pathways related apoptosis, cell cycle, signaling, and metabolism in HCC. These results identify clinically relevant pathways for future research and therapeutic targeting.
ContributorsRehling, Thomas E (Author) / Buetow, Kenneth (Thesis advisor) / Wilson, Melissa (Committee member) / Maley, Carlo (Committee member) / Arizona State University (Publisher)
Created2021