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- Creators: Barrett, The Honors College
- Creators: Ye, Jieping
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 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
Description
Drosophila melanogaster, as an important model organism, is used to explore the mechanism which governs cell differentiation and embryonic development. Understanding the mechanism will help to reveal the effects of genes on other species or even human beings. Currently, digital camera techniques make high quality Drosophila gene expression imaging possible. On the other hand, due to the advances in biology, gene expression images which can reveal spatiotemporal patterns are generated in a high-throughput pace. Thus, an automated and efficient system that can analyze gene expression will become a necessary tool for investigating the gene functions, interactions and developmental processes. One investigation method is to compare the expression patterns of different developmental stages. Recently, however, the expression patterns are manually annotated with rough stage ranges. The work of annotation requires professional knowledge from experienced biologists. Hence, how to transfer the domain knowledge in biology into an automated system which can automatically annotate the patterns provides a challenging problem for computer scientists. In this thesis, the problem of stage annotation for Drosophila embryo is modeled in the machine learning framework. Three sparse learning algorithms and one ensemble algorithm are used to attack the problem. The sparse algorithms are Lasso, group Lasso and sparse group Lasso. The ensemble algorithm is based on a voting method. Besides that the proposed algorithms can annotate the patterns to stages instead of stage ranges with high accuracy; the decimal stage annotation algorithm presents a novel way to annotate the patterns to decimal stages. In addition, some analysis on the algorithm performance are made and corresponding explanations are given. Finally, with the proposed system, all the lateral view BDGP and FlyFish images are annotated and several interesting applications of decimal stage value are revealed.
ContributorsPan, Cheng (Author) / Ye, Jieping (Thesis advisor) / Li, Baoxin (Committee member) / Farin, Gerald (Committee member) / Arizona State University (Publisher)
Created2012
Description
A major goal of the Center for Biosignatures Discovery Automation (CBDA) is to design a diagnostic tool that detects novel cancer biosignatures at the single-cell level. We designed the Single-cell QUantitative In situ Reverse Transcription-Polymerase Chain Reaction (SQUIRT-PCR) system by combining a two-photon laser lysis (2PLL) system with a microfluidic reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) platform. It is important to identify early molecular changes from intact tissues as prognosis for premalignant conditions and develop new molecular targets for prevention of cancer progression and improved therapies. This project analyzes RNA expression at the single-cell level and presents itself with two major challenges: (1) detecting low levels of RNA and (2) minimizing RNA absorption in the polydimethylsiloxane (PDMS) microfluidic channel. The first challenge was overcome by successfully detecting picogram (pg) levels of RNA using the Fluidigm (FD) BioMark™ HD System (Fluidigm Corporation, South San Francisco, CA) for RT-qPCR analysis. This technology incorporates a highly precise integrated fluidic circuit (IFC) that allows for high-throughput genetic screening using microarrays. The second challenge entailed collecting data from RNA flow-through samples that were passed through microfluidic channels. One channel was treated with a coating of polyethylene glycol (PEG) and the other remained untreated. Various flow-through samples were subjected to RT-qPCR and analyzed using the FD FLEXsix™ Gene Expression IFC. As predicted, the results showed that the treated PDMS channel absorbed less RNA than the untreated PDMS channel. Once the optimization of the PDMS microfluidic platform is complete, it will be implemented into the 2PLL system. This novel technology will be able to identify cell populations in situ and could have a large impact on cancer diagnostics.
ContributorsBlatt, Amy Elissa (Author) / Meldrum, Deirdre R. (Thesis director) / Tran, Thai (Committee member) / Chao, Joseph (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2014-05
Description
The development of skeletal muscle during embryogenesis and repair in adults is dependent on the intricate balance between the proliferation of myogenic progenitor cells and the differentiation of those cells into functional muscle fibers. Recent studies demonstrate that the Drosophila melanogaster transcription factor CG9650 is expressed in muscle progenitor cells, where it maintains myoblast numbers. We are interested in the Mus musculus orthologs Bcl11a and Bcl11b (C2H2 zinc finger transcription factors), and understanding their role as molecular switches that control proliferation/differentiation decisions in muscle progenitor cells. Expression analysis revealed that Bcl11b, but not Bcl11a, is expressed in the region of the mouse embryo populated with myogenic progenitor cells; gene expression studies in muscle cell culture confirmed Bcl11b is also selectively transcribed in muscle. Furthermore, Bcl11b is down-regulated with differentiation, which is consistent with the belief that the gene plays a role in cell proliferation.
ContributorsDuong, Brittany Bach (Author) / Rawls, Alan (Thesis director) / Wilson-Rawls, Jeanne (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor) / School of Life Sciences (Contributor)
Created2014-05
Description
This research project investigated known and novel differential genetic variants and their associated molecular pathways involved in Type II diabetes mellitus for the purpose of improving diagnosis and treatment methods. The goal of this investigation was to 1) identify the genetic variants and SNPs in Type II diabetes to develop a gene regulatory pathway, and 2) utilize this pathway to determine suitable drug therapeutics for prevention and treatment. Using a Gene Set Enrichment Analysis (GSEA), a set of 1000 gene identifiers from a Mayo Clinic database was analyzed to determine the most significant genetic variants related to insulin signaling pathways involved in Type II Diabetes. The following genes were identified: NRAS, KRAS, PIK3CA, PDE3B, TSC1, AKT3, SOS1, NEU1, PRKAA2, AMPK, and ACC. In an extensive literature review and cross-analysis with Kegg and Reactome pathway databases, novel SNPs located on these gene variants were identified and used to determine suitable drug therapeutics for treatment. Overall, understanding how genetic mutations affect target gene function related to Type II Diabetes disease pathology is crucial to the development of effective diagnosis and treatment. This project provides new insight into the molecular basis of the Type II Diabetes, serving to help untangle the regulatory complexity of the disease and aid in the advancement of diagnosis and treatment. Keywords: Type II Diabetes mellitus, Gene Set Enrichment Analysis, genetic variants, KEGG Insulin Pathway, gene-regulatory pathway
ContributorsBucklin, Lindsay (Co-author) / Davis, Vanessa (Co-author) / Holechek, Susan (Thesis director) / Wang, Junwen (Committee member) / Nyarige, Verah (Committee member) / School of Human Evolution & Social Change (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Inhibitor of growth factor 4 (ING4) is a tumor suppressor of which low expression has been associated with poor patient survival and aggressive tumor progression in breast cancer. ING4 is characterized as a transcription regulator of inflammatory genes. Among the ING4-regulated genes is CXCL10, a chemokine secreted by endothelial cells during normal inflammation response, which induces chemotactic migration of immune cells to the site. High expression of CXCL10 has been implicated in aggressive breast cancer, but the mechanism is not well understood. A potential signaling molecule downstream of Cxcl10 is Janus Kinase 2 (Jak2), a kinase activated in normal immune response. Deregulation of Jak2 is associated with metastasis, immune evasion, and tumor progression in breast cancer. Thus, we hypothesized that the Ing4/Cxcl10/Jak2 axis plays a key role in breast cancer progression. We first investigated whether Cxcl10 affected breast cancer cell migration. We also investigated whether Cxcl10-mediated migration is dependent on ING4 expression levels. We utilized genetically engineered MDAmb231 breast cancer cells with a CRISPR/Cas9 ING4-knockout construct or a viral ING4 overexpression construct. We performed Western blot analysis to confirm Ing4 expression. Cell migration was assessed using Boyden Chamber assay with or without exogenous Cxcl10 treatment. The results showed that in the presence of Cxcl10, ING4-deficient cells had a two-fold increase in migration as compared to the vector controls, suggesting Ing4 inhibits Cxcl10-induced migration. These findings support our hypothesis that ING4-deficient tumor cells have increased migration when Cxcl10 signaling is present in breast cancer. These results implicate Ing4 is a key regulator of a chemokine-induced tumor migration. Our future plan includes evaluation of Jak2 as an intermediate signaling molecule in Cxcl10/Ing4 pathway. Therapeutic implications of these findings are targeting Cxcl10 and/or Jak2 may be effective in treating ING4-deficient aggressive breast cancer.
ContributorsArnold, Emily (Author) / Kim, Suwon (Thesis director) / Blattman, Joseph (Thesis director) / Mason, Hugh (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Objective: Isoforms of insulin-like growth factor-1 (IGF-1) gene encodes different IGF-1 isoforms by alternative splicing, and which are known to play distinct roles in muscle growth and repair. These isoforms in humans exist as IGF-1Ea, IGF-1Eb and IGF-1Ec (the latter is also known as mechano-growth factor). We sought to determine whether mRNA expression of any of these isoforms is impaired in skeletal muscle of humans with obesity, and given that humans with obesity display reduced protein synthesis in muscle. Methods: We studied 10 subjects (3 females/7 males) with obesity (body mass index: 34 ± 1 kg/m2) and 14 subjects (6 females/8 males) that were lean (body mass index: 24 ± 1 kg/m2) and served as controls. The groups represented typical populations of individuals that differed (P < 0.05) in body fat (obese: 32 ± 2; lean: 22 ± 2) and insulin sensitivity (Matsuda insulin sensitivity index, obese: 5 ± 1; lean 11 ± 2). Total RNA was extracted from 20-30 mg of tissue from muscle biopsies performed after an overnight fast. Isolated RNA was used to perform cDNA synthesis. Real-time PCR was performed using predesigned TaqMan® gene expression assays (Thermo Fisher Scientific Inc) for IGF-1Ea (assay ID: Hs01547657_m1), IGF-1Eb (assay ID: Hs00153126_m1) and IGF-1Ec (assay ID: Hs03986524_m1), as well as ACTB (assay ID: Hs01060665_g1), which was used to adjust the IGF-1 isoform mRNA expression. Responses for mRNA expression were calculated using the comparative CT method (2-ΔΔCT). Results: IGF-1Eb mRNA expression was lower in the subjects with obesity compared to the lean controls (0.67 ± 0.09 vs 1.00 ± 0.13; P < 0.05) but that of IGF-1Ea (0.99 ± 0.16 vs 1.00 ± 0.33) or IGF-1Ec (0.83 ± 0.14 vs 1.00 ± 0.21) were not different between groups (P > 0.05). Conclusions: Among the IGF-1 mRNA isoforms, IGF-1Eb mRNA is uniquely decreased in humans with obesity. Lower IGF-1Eb mRNA expression in muscle of humans with obesity may explain the lower protein synthesis observed in these individuals. Furthermore, these findings may have direct implications for muscle growth and repair in humans with obesity.
ContributorsSon, John Lee (Author) / Katsanos, Christos (Thesis director) / Gu, Haiwei (Committee member) / College of Health Solutions (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Vitellogenin (vg) is a precursor protein of egg yolk in honeybees, but it is also known to have immunological functions. The purpose of this experiment was to determine the effect of vg on the viral load of deformed wing virus (DWV) in worker honey bees (Apis mellifera). I hypothesized that a reduction in vg expression would lead to an increase in the viral load. I collected 180 worker bees and split them into four groups: half the bees were subjected to a vg gene knockdown by injections of double stranded vg RNA, and the rest were injected with green fluorescent protein (gfp) double stranded RNA. Half of each group was thereafter injected with DWV, and half given a sham injection. The rate of mortality in all four groups was higher than expected, leaving only 17 bees total. I dissected these bees' fat bodies and extracted their RNA to test for vg and DWV. PCR results showed that, out of the small group of remaining bees, the levels of vg were not statistically different. Furthermore, both groups of virus-injected bees showed similar viral loads. Because of the high mortality rate bees and the lack of differing levels of vg transcript between experimental and control groups, I could not draw conclusions from these results. The high mortality could be caused by several factors: temperature-induced stress, repeated stress from the two injections, and stress from viral infection. In addition, it is possible that the vg dsRNA batch I used was faulty. This thesis exemplifies that information cannot safely be extracted when loss of sampling units result in a small datasets that do not represent the original sampling population.
ContributorsCrable, Emma Lewis (Author) / Amdam, Gro (Thesis director) / Wang, Ying (Committee member) / Dahan, Romain (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
It is well documented that menopause and the related decline in circulatory steroid hormones estrogen and progesterone are associated with memory alterations. Rodent models of surgical menopause can be used to study these effects, including ovariectomy (Ovx), or the surgical removal of the ovaries. This thesis aimed to characterize the effects of surgical menopause on spatial working and reference memory in rats and examine profiles of uterine gene expression alterations that may serve as indications of mechanisms underlying this association. Eighteen female rats were randomly assigned to one of two surgical treatment groups, either Ovx (the surgical menopause group) or sham (the control group). All subjects underwent testing on the water version of the radial arm maze (WRAM) which allows for the assessment of reference memory errors and two types of working memory errors. After behavioral testing, rat uterine tissues were dissected and RNA sequenced. The results showed that Ovx impaired spatial reference memory performance during a maze learning phase, with Ovx rats making reference memory failures earlier in the day, even before working memory load increased, as compared to control rats. There were no surgical menopause effects on spatial working memory, which may be due to the low working memory load and the young age of the rats. Post-hoc analyses showed that reference memory performance was correlated with nerve growth factor (NGF) and acetylcholinesterase (AChE) gene expression in uterine tissues. These findings add to the literature on the impact of estrogen and female cyclicity on memory and cognition. The results suggest that Ovx impairment of the ability to learn long-term spatial memory information relates to uterine gene expression underlying cellular functioning and that NGF and AChE genes are involved in pathways that give way to underlying cellular functioning that impacts cognition. Future studies should continue to evaluate the effects of menopause on memory function and the effectiveness of hormone therapy.
ContributorsOyen, Emma (Author) / Bimonte-Nelson, Heather (Thesis director) / Corbin, William (Committee member) / Wilson, Melissa (Committee member) / Lizik, Camryn (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor)
Created2024-05
Description
Understanding the complexity of temporal and spatial characteristics of gene expression over brain development is one of the crucial research topics in neuroscience. An accurate description of the locations and expression status of relative genes requires extensive experiment resources. The Allen Developing Mouse Brain Atlas provides a large number of in situ hybridization (ISH) images of gene expression over seven different mouse brain developmental stages. Studying mouse brain models helps us understand the gene expressions in human brains. This atlas collects about thousands of genes and now they are manually annotated by biologists. Due to the high labor cost of manual annotation, investigating an efficient approach to perform automated gene expression annotation on mouse brain images becomes necessary. In this thesis, a novel efficient approach based on machine learning framework is proposed. Features are extracted from raw brain images, and both binary classification and multi-class classification models are built with some supervised learning methods. To generate features, one of the most adopted methods in current research effort is to apply the bag-of-words (BoW) algorithm. However, both the efficiency and the accuracy of BoW are not outstanding when dealing with large-scale data. Thus, an augmented sparse coding method, which is called Stochastic Coordinate Coding, is adopted to generate high-level features in this thesis. In addition, a new multi-label classification model is proposed in this thesis. Label hierarchy is built based on the given brain ontology structure. Experiments have been conducted on the atlas and the results show that this approach is efficient and classifies the images with a relatively higher accuracy.
ContributorsZhao, Xinlin (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Thesis advisor) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2016