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Description
Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups

Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups or graphs. In this thesis, I first propose to solve a sparse learning model with a general group structure, where the predefined groups may overlap with each other. Then, I present three real world applications which can benefit from the group structured sparse learning technique. In the first application, I study the Alzheimer's Disease diagnosis problem using multi-modality neuroimaging data. In this dataset, not every subject has all data sources available, exhibiting an unique and challenging block-wise missing pattern. In the second application, I study the automatic annotation and retrieval of fruit-fly gene expression pattern images. Combined with the spatial information, sparse learning techniques can be used to construct effective representation of the expression images. In the third application, I present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores help us to illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.
ContributorsYuan, Lei (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Xue, Guoliang (Committee member) / Kumar, Sudhir (Committee member) / Arizona State University (Publisher)
Created2013
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Description
This thesis research focuses on developing a single-cell gene expression analysis method for marine diatom Thalassiosira pseudonana and constructing a chip level tool to realize the single cell RT-qPCR analysis. This chip will serve as a conceptual foundation for future deployable ocean monitoring systems. T. pseudonana, which is a common

This thesis research focuses on developing a single-cell gene expression analysis method for marine diatom Thalassiosira pseudonana and constructing a chip level tool to realize the single cell RT-qPCR analysis. This chip will serve as a conceptual foundation for future deployable ocean monitoring systems. T. pseudonana, which is a common surface water microorganism, was detected in the deep ocean as confirmed by phylogenetic and microbial community functional studies. Six-fold copy number differences between 23S rRNA and 23S rDNA were observed by RT-qPCR, demonstrating the moderate functional activity of detected photosynthetic microbes in the deep ocean including T. pseudonana. Because of the ubiquity of T. pseudonana, it is a good candidate for an early warning system for ocean environmental perturbation monitoring. This early warning system will depend on identifying outlier gene expression at the single-cell level. An early warning system based on single-cell analysis is expected to detect environmental perturbations earlier than population level analysis which can only be observed after a whole community has reacted. Preliminary work using tube-based, two-step RT-qPCR revealed for the first time, gene expression heterogeneity of T. pseudonana under different nutrient conditions. Heterogeneity was revealed by different gene expression activity for individual cells under the same conditions. This single cell analysis showed a skewed, lognormal distribution and helped to find outlier cells. The results indicate that the geometric average becomes more important and representative of the whole population than the arithmetic average. This is in contrast with population level analysis which is limited to arithmetic averages only and highlights the value of single cell analysis. In order to develop a deployable sensor in the ocean, a chip level device was constructed. The chip contains surface-adhering droplets, defined by hydrophilic patterning, that serve as real-time PCR reaction chambers when they are immersed in oil. The chip had demonstrated sensitivities at the single cell level for both DNA and RNA. The successful rate of these chip-based reactions was around 85%. The sensitivity of the chip was equivalent to published microfluidic devices with complicated designs and protocols, but the production process of the chip was simple and the materials were all easily accessible in conventional environmental and/or biology laboratories. On-chip tests provided heterogeneity information about the whole population and were validated by comparing with conventional tube based methods and by p-values analysis. The power of chip-based single-cell analyses were mainly between 65-90% which were acceptable and can be further increased by higher throughput devices. With this chip and single-cell analysis approaches, a new paradigm for robust early warning systems of ocean environmental perturbation is possible.
ContributorsShi, Xu (Author) / Meldrum, Deirdre R. (Thesis advisor) / Zhang, Weiwen (Committee member) / Chao, Shih-hui (Committee member) / Westerhoff, Paul (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Given the process of tumorigenesis, biological signaling pathways have become of interest in the field of oncology. Many of the regulatory mechanisms that are altered in cancer are directly related to signal transduction and cellular communication. Thus, identifying signaling pathways that have become deregulated may provide useful information

Given the process of tumorigenesis, biological signaling pathways have become of interest in the field of oncology. Many of the regulatory mechanisms that are altered in cancer are directly related to signal transduction and cellular communication. Thus, identifying signaling pathways that have become deregulated may provide useful information to better understanding altered regulatory mechanisms within cancer. Many methods that have been created to measure the distinct activity of signaling pathways have relied strictly upon transcription profiles. With advancements in comparative genomic hybridization techniques, copy number data has become extremely useful in providing valuable information pertaining to the genomic landscape of cancer. The purpose of this thesis is to develop a methodology that incorporates both gene expression and copy number data to identify signaling pathways that have become deregulated in cancer. The central idea is that copy number data may significantly assist in identifying signaling pathway deregulation by justifying the aberrant activity being measured in gene expression profiles. This method was then applied to four different subtypes of breast cancer resulting in the identification of signaling pathways associated with distinct functionalities for each of the breast cancer subtypes.
ContributorsTrevino, Robert (Author) / Kim, Seungchan (Thesis advisor) / Ringner, Markus (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2011
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Description
Plants are a promising upcoming platform for production of vaccine components and other desirable pharmaceutical proteins that can only, at present, be made in living systems. The unique soil microbe Agrobacterium tumefaciens can transfer DNA to plants very efficiently, essentially turning plants into factories capable of producing virtually any gene.

Plants are a promising upcoming platform for production of vaccine components and other desirable pharmaceutical proteins that can only, at present, be made in living systems. The unique soil microbe Agrobacterium tumefaciens can transfer DNA to plants very efficiently, essentially turning plants into factories capable of producing virtually any gene. While genetically modified bacteria have historically been used for producing useful biopharmaceuticals like human insulin, plants can assemble much more complicated proteins, like human antibodies, that bacterial systems cannot. As plants do not harbor human pathogens, they are also safer alternatives than animal cell cultures. Additionally, plants can be grown very cheaply, in massive quantities.

In my research, I have studied the genetic mechanisms that underlie gene expression, in order to improve plant-based biopharmaceutical production. To do this, inspiration was drawn from naturally-occurring gene regulatory mechanisms, especially those from plant viruses, which have evolved mechanisms to co-opt the plant cellular machinery to produce high levels of viral proteins. By testing, modifying, and combining genetic elements from diverse sources, an optimized expression system has been developed that allows very rapid production of vaccine components, monoclonal antibodies, and other biopharmaceuticals. To improve target gene expression while maintaining the health and function of the plants, I identified, studied, and modified 5’ untranslated regions, combined gene terminators, and a nuclear matrix attachment region. The replication mechanisms of a plant geminivirus were also studied, which lead to additional strategies to produce more toxic biopharmaceutical proteins. Finally, the mechanisms employed by a geminivirus to spread between cells were investigated. It was demonstrated that these movement mechanisms can be functionally transplanted into a separate genus of geminivirus, allowing modified virus-based gene expression vectors to be spread between neighboring plant cells. Additionally, my work helps shed light on the basic genetic mechanisms employed by all living organisms to control gene expression.
ContributorsDiamos, Andy (Author) / Mason, Hugh S (Thesis advisor) / Mor, Tsafrir (Committee member) / Hogue, Brenda (Committee member) / Stout, Valerie (Committee member) / Arizona State University (Publisher)
Created2017
Description
Circular RNAs (circRNAs) are a class of endogenous, non-coding RNAs that are formed when exons back-splice to each other and represent a new area of transcriptomics research. Numerous RNA sequencing (RNAseq) studies since 2012 have revealed that circRNAs are pervasively expressed in eukaryotes, especially in the mammalian brain. While their

Circular RNAs (circRNAs) are a class of endogenous, non-coding RNAs that are formed when exons back-splice to each other and represent a new area of transcriptomics research. Numerous RNA sequencing (RNAseq) studies since 2012 have revealed that circRNAs are pervasively expressed in eukaryotes, especially in the mammalian brain. While their functional role and impact remains to be clarified, circRNAs have been found to regulate micro-RNAs (miRNAs) as well as parental gene transcription and may thus have key roles in transcriptional regulation. Although circRNAs have continued to gain attention, our understanding of their expression in a cell-, tissue- , and brain region-specific context remains limited. Further, computational algorithms produce varied results in terms of what circRNAs are detected. This thesis aims to advance current knowledge of circRNA expression in a region specific context focusing on the human brain, as well as address computational challenges.

The overarching goal of my research unfolds over three aims: (i) evaluating circRNAs and their predicted impact on transcriptional regulatory networks in cell-specific RNAseq data; (ii) developing a novel solution for de novo detection of full length circRNAs as well as in silico validation of selected circRNA junctions using assembly; and (iii) application of these assembly based detection and validation workflows, and integrating existing tools, to systematically identify and characterize circRNAs in functionally distinct human brain regions. To this end, I have developed novel bioinformatics workflows that are applicable to non-polyA selected RNAseq datasets and can be used to characterize circRNA expression across various sample types and diseases. Further, I establish a reference dataset of circRNA expression profiles and regulatory networks in a brain region-specific manner. This resource along with existing databases such as circBase will be invaluable in advancing circRNA research as well as improving our understanding of their role in transcriptional regulation and various neurological conditions.
ContributorsSekar, Shobana (Author) / Liang, Winnie S (Thesis advisor) / Dinu, Valentin (Thesis advisor) / Craig, David (Committee member) / Liu, Li (Committee member) / Arizona State University (Publisher)
Created2018
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Description
Parkinson’s disease (PD) is a progressive neurodegenerative disorder, diagnosed late in

the disease by a series of motor deficits that manifest over years or decades. It is characterized by degeneration of mid-brain dopaminergic neurons with a high prevalence of dementia associated with the spread of pathology to cortical regions. Patients exhibiting

Parkinson’s disease (PD) is a progressive neurodegenerative disorder, diagnosed late in

the disease by a series of motor deficits that manifest over years or decades. It is characterized by degeneration of mid-brain dopaminergic neurons with a high prevalence of dementia associated with the spread of pathology to cortical regions. Patients exhibiting symptoms have already undergone significant neuronal loss without chance for recovery. Analysis of disease specific changes in gene expression directly from human patients can uncover invaluable clues about a still unknown etiology, the potential of which grows exponentially as additional gene regulatory measures are questioned. Epigenetic mechanisms are emerging as important components of neurodegeneration, including PD; the extent to which methylation changes correlate with disease progression has not yet been reported. This collection of work aims to define multiple layers of PD that will work toward developing biomarkers that not only could improve diagnostic accuracy, but also push the boundaries of the disease detection timeline. I examined changes in gene expression, alternative splicing of those gene products, and the regulatory mechanism of DNA methylation in the Parkinson’s disease system, as well as the pathologically related Alzheimer’s disease (AD). I first used RNA sequencing (RNAseq) to evaluate differential gene expression and alternative splicing in the posterior cingulate cortex of patients with PD and PD with dementia (PDD). Next, I performed a longitudinal genome-wide methylation study surveying ~850K CpG methylation sites in whole blood from 189 PD patients and 191 control individuals obtained at both a baseline and at a follow-up visit after 2 years. I also considered how symptom management medications could affect the regulatory mechanism of DNA methylation. In the last chapter of this work, I intersected RNAseq and DNA methylation array datasets from whole blood patient samples for integrated differential analyses of both PD and AD. Changes in gene expression and DNA methylation reveal clear patterns of pathway dysregulation that can be seen across brain and blood, from one study to the next. I present a thorough survey of molecular changes occurring within the idiopathic Parkinson’s disease patient and propose candidate targets for potential molecular biomarkers.
ContributorsHenderson, Adrienne Rose (Author) / Huentelman, Matthew J (Thesis advisor) / Newbern, Jason (Thesis advisor) / Dunckley, Travis L (Committee member) / Jensen, Kendall (Committee member) / Wilson, Melissa (Committee member) / Arizona State University (Publisher)
Created2019
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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

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.
ContributorsMondal, Manas (Author) / Guo, Jia (Thesis advisor) / Gould, Ian (Committee member) / Ros, Alexandra (Committee member) / Arizona State University (Publisher)
Created2018
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Description
One of the fundamental questions in molecular biology is how genes and the control of their expression give rise to so many diverse phenotypes in nature. The mRNA molecule plays a key role in this process as it directs the spatial and temporal expression of genetic information contained in the

One of the fundamental questions in molecular biology is how genes and the control of their expression give rise to so many diverse phenotypes in nature. The mRNA molecule plays a key role in this process as it directs the spatial and temporal expression of genetic information contained in the DNA molecule to precisely instruct biological processes in living organisms. The region located between the STOP codon and the poly(A)-tail of the mature mRNA, known as the 3′Untranslated Region (3′UTR), is a key modulator of these activities. It contains numerous sequence elements that are targeted by trans-acting factors that dose gene expression, including the repressive small non-coding RNAs, called microRNAs.

Recent transcriptome data from yeast, worm, plants, and humans has shown that alternative polyadenylation (APA), a mechanism that enables expression of multiple 3′UTR isoforms for the same gene, is widespread in eukaryotic organisms. It is still poorly understood why metazoans require multiple 3′UTRs for the same gene, but accumulating evidence suggests that APA is largely regulated at a tissue-specific level. APA may direct combinatorial variation between cis-elements and microRNAs, perhaps to regulate gene expression in a tissue-specific manner. Apart from a few single gene anecdotes, this idea has not been systematically explored.

This dissertation research employs a systems biology approach to study the somatic tissue dynamics of APA and its impact on microRNA targeting networks in the small nematode C. elegans. In the first aim, tools were developed and applied to isolate and sequence mRNA from worm intestine and muscle tissues, which revealed pervasive tissue-specific APA correlated with microRNA regulation. The second aim provides genetic evidence that two worm genes use APA to escape repression by microRNAs in the body muscle. Finally, in aim three, mRNA from five additional somatic worm tissues was sequenced and their 3′ends mapped, allowing for an integrative study of APA and microRNA targeting dynamics in worms. Together, this work provides evidence that APA is a pervasive mechanism operating in somatic tissues of C. elegans with the potential to significantly rearrange their microRNA regulatory networks and precisely dose their gene expression.
ContributorsBlazie, Stephen M (Author) / Mangone, Marco (Thesis advisor) / LaBaer, Josh (Committee member) / Lake, Doug (Committee member) / Newfeld, Stuart (Committee member) / Arizona State University (Publisher)
Created2016
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Description
A central task for historians and philosophers of science is to characterize and analyze the epistemic practices in a given science. The epistemic practice of a science includes its explanatory goals as well as the methods used to achieve these goals. This dissertation addresses the epistemic practices in gene expression

A central task for historians and philosophers of science is to characterize and analyze the epistemic practices in a given science. The epistemic practice of a science includes its explanatory goals as well as the methods used to achieve these goals. This dissertation addresses the epistemic practices in gene expression research spanning the mid-twentieth century to the twenty-first century. The critical evaluation of the standard historical narratives of the molecular life sciences clarifies certain philosophical problems with respect to reduction, emergence, and representation, and offers new ways with which to think about the development of scientific research and the nature of scientific change.

The first chapter revisits some of the key experiments that contributed to the development of the repression model of genetic regulation in the lac operon and concludes that the early research on gene expression and genetic regulation depict an iterative and integrative process, which was neither reductionist nor holist. In doing so, it challenges a common application of a conceptual framework in the history of biology and offers an alternative framework. The second chapter argues that the concept of emergence in the history and philosophy of biology is too ambiguous to account for the current research in post-genomic molecular biology and it is often erroneously used to argue against some reductionist theses. The third chapter investigates the use of network representations of gene expression in developmental evolution research and takes up some of the conceptual and methodological problems it has generated. The concluding comments present potential avenues for future research arising from each substantial chapter.

In sum, this dissertation argues that the epistemic practices of gene expression research are an iterative and integrative process, which produces theoretical representations of the complex interactions in gene expression as networks. Moreover, conceptualizing these interactions as networks constrains empirical research strategies by the limited number of ways in which gene expression can be controlled through general rules of network interactions. Making these strategies explicit helps to clarify how they can explain the dynamic and adaptive features of genomes.
ContributorsRacine, Valerie (Author) / Maienschein, Jane (Thesis advisor) / Laubichler, Manfred D (Thesis advisor) / Creath, Richard (Committee member) / Newfeld, Stuart (Committee member) / Morange, Michel (Committee member) / Arizona State University (Publisher)
Created2016
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Description
In species with highly heteromorphic sex chromosomes, the degradation of one of the sex chromosomes can result in unequal gene expression between the sexes (e.g., between XX females and XY males) and between the sex chromosomes and the autosomes. Dosage compensation is a process whereby genes on the sex chromosomes

In species with highly heteromorphic sex chromosomes, the degradation of one of the sex chromosomes can result in unequal gene expression between the sexes (e.g., between XX females and XY males) and between the sex chromosomes and the autosomes. Dosage compensation is a process whereby genes on the sex chromosomes achieve equal gene expression which prevents deleterious side effects from having too much or too little expression of genes on sex chromsomes. The green anole is part of a group of species that recently underwent an adaptive radiation. The green anole has XX/XY sex determination, but the content of the X chromosome and its evolution have not been described. Given its status as a model species, better understanding the green anole genome could reveal insights into other species. Genomic analyses are crucial for a comprehensive picture of sex chromosome differentiation and dosage compensation, in addition to understanding speciation.

In order to address this, multiple comparative genomics and bioinformatics analyses were conducted to elucidate patterns of evolution in the green anole and across multiple anole species. Comparative genomics analyses were used to infer additional X-linked loci in the green anole, RNAseq data from male and female samples were anayzed to quantify patterns of sex-biased gene expression across the genome, and the extent of dosage compensation on the anole X chromosome was characterized, providing evidence that the sex chromosomes in the green anole are dosage compensated.

In addition, X-linked genes have a lower ratio of nonsynonymous to synonymous substitution rates than the autosomes when compared to other Anolis species, and pairwise rates of evolution in genes across the anole genome were analyzed. To conduct this analysis a new pipeline was created for filtering alignments and performing batch calculations for whole genome coding sequences. This pipeline has been made publicly available.
ContributorsRupp, Shawn Michael (Author) / Wilson Sayres, Melissa A (Thesis advisor) / Kusumi, Kenro (Committee member) / DeNardo, Dale (Committee member) / Arizona State University (Publisher)
Created2016