Matching Items (133)
Description
Glioblastoma multiforme (GBM) is an aggressive brain cancer without effectivetreatment options, leaving patient survival rates extremely low.
HDAC1 knockdown was found to initiate an invasive phenotype in vivo,
particularly within the BT145 human glioma stem cell (hGSC) line. Analysis through RNA
sequencing (RNA-seq) gene expression and regulatory networks found both CEBPβ, a
known transcription factor (TF) involved in cellular invasion, and the STAT3 pathway, a
notorious genetic component of GBM, were differentially expressed in BT145 hGSCs
after HDAC1 knockdown. Furthermore, overlap of genes regulated by CEBPβ and
STAT3 indicate the CEBPβ/STAT3 pathway may be involved in the observed BT145-
specific invasive phenotype.
The SYstems Genetics Network AnaLysis (SYGNAL) pipeline was applied to
construct sex-specific gene regulatory networks from The Cancer Genome Atlas (TCGA)
GBM patient expression data. Unique bicluster eigengenes were discovered separately
for all, female, and male patients. Through the application of these bicluster eigengenes
to a GBM cohort with multiparametric magnetic resonance imaging (mpMRI) localized
biopsies, sex-specific associations between bicluster expression, mpMRI readout, and
hallmarks of cancer were determined. Distinctive cancer functions were revealed
transcriptionally through bicluster expression, and connected to a unique mpMRI feature.
Specifically, SPGRC mpMRI indicated a strong signal for both immune hallmarks
(evading immune detection and tumor-promoting inflammation). At the same time, MD
mpMRI displayed a tendency toward sustained angiogenesis, possibly signaling the
formation of new blood vessels. Uncovering each mpMRI feature’s underlying biological
processes enables improved GBM diagnosis and treatment utilizing an individualized,
non-invasive approach.
ContributorsLewis, Erika (Author) / Plaisier, Christopher L (Thesis advisor) / Nikkhah, Medhi (Committee member) / Hu, Leland (Committee member) / Arizona State University (Publisher)
Created2021
Description
Over the past decades, the amount of data required to be processed and analyzed by computing systems has been increasing dramatically to exascale (10^18 bytes/s or ops). However, modern computing platforms' inability to deliver both energy-efficient and high-performance computing solutions leads to a gap between meets and needs, especially in resource-constraint Internet of Things (IoT) devices. Unfortunately, such a gap will keep widening mainly due to limitations in both devices and architectures. With this motivation, this dissertation's focus is on cross-layer (device/circuit/architecture/application) co-design of energy-efficient and high-performance Processing-in-Memory (PIM) platforms for implementing complex big data applications, i.e., deep learning, bioinformatics, graph processing tasks, and data encryption. The dissertation shows how to leverage innovations from device, circuit, and architecture to integrate memory and logic to break the existing memory and power walls and dramatically increase computing efficiency of today’s non-Von-Neumann computing systems.The proposed PIM platforms transform current volatile and non-volatile random access memory arrays to computational units capable of working as both memory and low-area-overhead, massively parallel, fast, reconfigurable in-memory logic. Instead of integrating complex logic units in cost-sensitive memory, the explored designs exploit hardware-friendly bit-line computing methods to implement complete Boolean logic functions between operands within a memory array in a reduced clock cycle, overcoming the multi-cycle logic issue in modern PIM platforms. Besides, new customized in-memory algorithms and mapping methods are developed to convert the crucial iteratively-used big data application's functions to bit-wise PIM-supported logic. To quantitatively analyze the performance of various PIM platforms running big data applications, a generic and comprehensive evaluation framework is presented. The overall system computing performance (throughput, latency, energy efficiency) for each application is explored through the developed framework. The device-to-algorithm co-simulation results on neural network acceleration demonstrate that the proposed platforms can obtain 36.8× higher energy-efficiency and 22× speed-up compared to state-of-the-art Graphics Processing Unit (GPU). In accelerating bioinformatics tasks such as biological sequence alignment, the presented PIM designs result in ~2×, 43.8×, 458× more throughput per Watt compared to state-of-the-art Application-Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), and GPU platforms, respectively.
ContributorsAngizi, Shaahin (Author) / Fan, Deliang (Thesis advisor) / Seo, Jae-Sun (Committee member) / Awad, Amro (Committee member) / Zhang, Wei (Committee member) / Arizona State University (Publisher)
Created2021
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
Pathways of Distinction Analysis of Liver Cancer Data: Genetic Differences Between Males and Females
Description
The Pathways of Distinction Analysis (PoDA) program calculates relationships between a given group of genes contained within a pathway, and a disease state. It was used here to investigate liver cancer, and to explore how genetic variability may contribute to the different rates of development of the disease in males and females. The goal of the study was to identify germline variation that differs by sex in hepatocellular carcinoma. Using the program, multiple pathways and genes were identified to have significant differences in their relationship to liver cancer in males and females. In animal studies, the genes which were identified using the PoDA analysis have been shown to impact liver cancer, often with different results for males and females. While these genes are often the focus in animal models, they are absent from current Genome Wide Association Studies (GWAS) catalogs for humans. By working to bridge the results of animal studies and human studies, the results help to identify the causes of liver cancer, and more specifically, the reason the disease affects males at much higher rates. The differences in pathways identified to be significant for the two sexes indicate the germline variance may play sex-specific roles in the development of hepatocellular carcinoma. Additionally, these results reinforce the capacity of the PoDA analysis to identify genes that may be missed by more traditional GWAS methods. This study lays the groundwork for further investigations into the identified genes and pathways, and how they behave differently within males and females.
ContributorsOlson, Erik Jon (Author) / Buetow, Kenneth (Thesis advisor) / Wilson, Melissa (Committee member) / Cartwright, Reed (Committee member) / Arizona State University (Publisher)
Created2021
Description
This dissertation presents three novel algorithms with real-world applications to genomic oncology. While the methodologies presented here were all developed to overcome various challenges associated with the adoption of high throughput genomic data in clinical oncology, they can be used in other domains as well. First, a network informed feature ranking algorithm is presented, which shows a significant increase in ability to select true predictive features from simulated data sets when compared to other state of the art graphical feature ranking methods. The methodology also shows an increased ability to predict pathological complete response to preoperative chemotherapy from genomic sequencing data of breast cancer patients utilizing domain knowledge from protein-protein interaction networks.
Second, an algorithm that overcomes population biases inherent in the use of a human reference genome developed primarily from European populations is presented to classify microsatellite instability (MSI) status from next-generation-sequencing (NGS) data. The methodology significantly increases the accuracy of MSI status prediction in African and African American ancestries.
Finally, a single variable model is presented to capture the bimodality inherent in genomic data stemming from heterogeneous diseases. This model shows improvements over other parametric models in the measurements of receiver-operator characteristic (ROC) curves for bimodal data. The model is used to estimate ROC curves for heterogeneous biomarkers in a dataset containing breast cancer and cancer-free specimen.
ContributorsSaul, Michelle (Author) / Dinu, Valentin (Thesis advisor) / Liu, Li (Committee member) / Wang, Junwen (Committee member) / Arizona State University (Publisher)
Created2021
Description
There is intense interest in adopting computer-aided diagnosis (CAD) systems, particularly those developed based on deep learning algorithms, for applications in a number of medical specialties. However, success of these CAD systems relies heavily on large annotated datasets; otherwise, deep learning often results in algorithms that perform poorly and lack generalizability. Therefore, this dissertation seeks to address this critical problem: How to develop efficient and effective deep learning algorithms for medical applications where large annotated datasets are unavailable. In doing so, we have outlined three specific aims: (1) acquiring necessary annotations efficiently from human experts; (2) utilizing existing annotations effectively from advanced architecture; and (3) extracting generic knowledge directly from unannotated images. Our extensive experiments indicate that, with a small part of the dataset annotated, the developed deep learning methods can match, or even outperform those that require annotating the entire dataset. The last part of this dissertation presents the importance and application of imaging in healthcare, elaborating on how the developed techniques can impact several key facets of the CAD system for detecting pulmonary embolism. Further research is necessary to determine the feasibility of applying these advanced deep learning technologies in clinical practice, particularly when annotation is limited. Progress in this area has the potential to enable deep learning algorithms to generalize to real clinical data and eventually allow CAD systems to be employed in clinical medicine at the point of care.
ContributorsZhou, Zongwei (Author) / Liang, Jianming (Thesis advisor) / Shortliffe, Edward H (Committee member) / Greenes, Robert A (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2021
Description
Water is a vital resource, and its protection is a priority world-wide. One widespread threat to water quality is contamination by chlorinated solvents. These dry-cleaning and degreasing agents entered the watershed through spills and improper disposal and now are detected in 4% of U.S. aquifers and 4.5-18% of U.S. drinking water sources. The health effects of these contaminants can be severe, as they are associated with damage to the nervous, liver, kidney, and reproductive systems, developmental issues, and possibly cancer. Chlorinated solvents must be removed or transformed to improve water quality and protect human and environmental health. One remedy, bioaugmentation, the subsurface addition of microbial cultures able to transform contaminants, has been implemented successfully at hundreds of sites since the 1990s. Bioaugmentation uses the bacteria Dehalococcoides to transform chlorinated solvents with hydrogen, H2, as the electron donor. At advection limited sites, bioaugmentation can be combined with electrokinetics (EK-Bio) to enhance transport. However, challenges for successful bioremediation remain. In this work I addressed several knowledge gaps surrounding bioaugmentation and EK-Bio. I measured the H2 consuming capacity of soils, detailed the microbial metabolisms driving this demand, and evaluated how these finding relate to reductive dechlorination. I determined which reactions dominated at a contaminated site with mixed geochemistry treated with EK-Bio and compared it to traditional bioaugmentation. Lastly, I assessed the effect of EK-Bio on the microbial community at a field-scale site. Results showed the H2 consuming capacity of soils was greater than that predicted by initial measurements of inorganic electron acceptors and primarily driven by carbon-based microbial metabolisms. Other work demonstrated that, given the benefits of some carbon-based metabolisms to microbial reductive dechlorination, high levels of H2 consumption in soils are not necessarily indicative of hostile conditions for Dehalococcoides. Bench-scale experiments of EK-Bio under mixed geochemical conditions showed EK-Bio out-performed traditional bioaugmentation by facilitating biotic and abiotic transformations. Finally, results of microbial community analysis at a field-scale implementation of EK-Bio showed that while there were significant changes in alpha and beta diversity, the impact of EK-Bio on native microbial communities was minimal.
ContributorsAltizer, Megan Leigh (Author) / Torres, César I (Thesis advisor) / Krajmalnik-Brown, Rosa (Thesis advisor) / Rittmann, Bruce E (Committee member) / Kavazanjian, Edward (Committee member) / Delgado, Anca G (Committee member) / Arizona State University (Publisher)
Created2020
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
Callithrix penicillata, also known as the Black-tufted marmoset primarily lives in the Brazilian highlands and has had little research conducted on it. For this project I performed a genome curation on the newly assembled genome of this species. The scaffolds obtained by the Dovetail Genomics reads were organized and labeled into chromosomes using the 2014 Callithrix jacchus genome as a reference. Then, using that same genome as a reference, 13 of the chromosomes were reverse complimented to be continuous with the 2014 Callithrix jacchus genome. The N50 statistics of the assembly were calculated and found to be 124 Mb. Quality scores were run for the final genome using referee and visualized with a bar plot, with 99% of sites scoring above 0. Heterozygosity was also calculated and found to be 0.3%. Finally, the final version of the genome was visually compared to the 2017 Callithrix jacchus genome and the GRCh38 human genome. This genome was submitted to the NCBIs database to await further approval.
ContributorsJohnson, Joelle Genevieve (Author) / Cartwright, Reed (Thesis director) / Stone, Anne (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
Description
Cancer is a disease in which abnormal cells divide uncontrollably and destroy body tissue, and currently plagues today’s world. Carcinomas are cancers derived from epithelial cells and include breast and prostate cancer. Breast cancer is a type of carcinoma that forms in breast tissue cells. The tumor cells can be further categorized after testing the cells for the presence of certain molecules. Hormone receptor positive breast cancer includes the tumor cells with receptors that respond to the steroid hormones, estrogen and progesterone, or the peptide hormone, HER2. These forms of cancer respond well to chemotherapy and endocrine therapy. On the other hand, triple negative breast cancer (TNBC) is characterized by the lack of hormone receptor expression and tends to have a worse prognosis in women. Prostate cancer forms in the cells of the prostate gland and has been attributed to mutations in androgen receptor ligand specificity. In a subset of triple negative breast cancer, genetic expression profiling has found a luminal androgen receptor that is dependent on androgen signaling. TNBC has also been found to respond well to enzalutamide, a an androgen receptor inhibitor. As the gene of the androgen receptor, AR, is located on the X chromosome and expressed in a variety of tissues, the responsiveness of TNBC to androgen receptor inhibition could be due to the differential usage of isoforms - different gene mRNA transcripts that produce different proteins. Thus, this study analyzed differential gene expression and differential isoform usage between TNBC cancers – that do and do not express the androgen receptor – and prostate cancer in order to better understand the underlying mechanism behind the effectiveness of androgen receptor inhibition in TNBC. Through the analysis of differential gene expression between the TNBC AR+ and AR- conditions, it was found that seven genes are significantly differentially expressed between the two types of tissues. Genes of significance are AR and EN1, which was found to be a potential prognostic marker in a subtype of TNBC. While some genes are differentially expressed between the TNBC AR+ and AR- tissues, the differences in isoform expression between the two tissues do not reflect the difference in gene expression. We discovered 11 genes that exhibited significant isoform switching between AR+ and AR- TNBC and have been found to contribute to cancer characteristics. The genes CLIC1 and RGS5 have been found to help the rapid, uncontrolled growth of cancer cells. HSD11B2, IRAK1, and COL1Al have been found to contribute to general cancer characteristics and metastasis in breast cancer. PSMA7 has been found to play a role in androgen receptor activation. Finally, SIDT1 and GLYATL1 are both associated with breast and prostate cancers. Overall, through the analysis of differential isoform usage between AR+ and AR- samples, we uncovered differences that were not detected by a gene level differential expression analysis. Thus, future work will focus on analyzing differential gene and isoform expression across all types of breast cancer and prostate cancer to better understand the responsiveness of TNBC to androgen receptor inhibition.
ContributorsDeshpande, Anagha J (Author) / Wilson-Sayres, Melissa (Thesis director) / Buetow, Kenneth (Committee member) / Natri, Heini (Committee member) / School of Human Evolution & Social Change (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05