Matching Items (26)
Description
Rewired biological pathways and/or rewired microRNA (miRNA)-mRNA interactions might also influence the activity of biological pathways. Here, rewired biological pathways is defined as differential (rewiring) effect of genes on the topology of biological pathways between controls and cases. Similarly, rewired miRNA-mRNA interactions are defined as the differential (rewiring) effects of miRNAs on the topology of biological pathways between controls and cases. In the dissertation, it is discussed that how rewired biological pathways (Chapter 1) and/or rewired miRNA-mRNA interactions (Chapter 2) aberrantly influence the activity of biological pathways and their association with disease.
This dissertation proposes two PageRank-based analytical methods, Pathways of Topological Rank Analysis (PoTRA) and miR2Pathway, discussed in Chapter 1 and Chapter 2, respectively. PoTRA focuses on detecting pathways with an altered number of hub genes in corresponding pathways between two phenotypes. The basis for PoTRA is that the loss of connectivity is a common topological trait of cancer networks, as well as the prior knowledge that a normal biological network is a scale-free network whose degree distribution follows a power law where a small number of nodes are hubs and a large number of nodes are non-hubs. However, from normal to cancer, the process of the network losing connectivity might be the process of disrupting the scale-free structure of the network, namely, the number of hub genes might be altered in cancer compared to that in normal samples. Hence, it is hypothesized that if the number of hub genes is different in a pathway between normal and cancer, this pathway might be involved in cancer. MiR2Pathway focuses on quantifying the differential effects of miRNAs on the activity of a biological pathway when miRNA-mRNA connections are altered from normal to disease and rank disease risk of rewired miRNA-mediated biological pathways. This dissertation explores how rewired gene-gene interactions and rewired miRNA-mRNA interactions lead to aberrant activity of biological pathways, and rank pathways for their disease risk. The two methods proposed here can be used to complement existing genomics analysis methods to facilitate the study of biological mechanisms behind disease at the systems-level.
This dissertation proposes two PageRank-based analytical methods, Pathways of Topological Rank Analysis (PoTRA) and miR2Pathway, discussed in Chapter 1 and Chapter 2, respectively. PoTRA focuses on detecting pathways with an altered number of hub genes in corresponding pathways between two phenotypes. The basis for PoTRA is that the loss of connectivity is a common topological trait of cancer networks, as well as the prior knowledge that a normal biological network is a scale-free network whose degree distribution follows a power law where a small number of nodes are hubs and a large number of nodes are non-hubs. However, from normal to cancer, the process of the network losing connectivity might be the process of disrupting the scale-free structure of the network, namely, the number of hub genes might be altered in cancer compared to that in normal samples. Hence, it is hypothesized that if the number of hub genes is different in a pathway between normal and cancer, this pathway might be involved in cancer. MiR2Pathway focuses on quantifying the differential effects of miRNAs on the activity of a biological pathway when miRNA-mRNA connections are altered from normal to disease and rank disease risk of rewired miRNA-mediated biological pathways. This dissertation explores how rewired gene-gene interactions and rewired miRNA-mRNA interactions lead to aberrant activity of biological pathways, and rank pathways for their disease risk. The two methods proposed here can be used to complement existing genomics analysis methods to facilitate the study of biological mechanisms behind disease at the systems-level.
ContributorsLi, Chaoxing (Author) / Dinu, Valentin (Thesis advisor) / Kuang, Yang (Thesis advisor) / Liu, Li (Committee member) / Wang, Xiao (Committee member) / Arizona State University (Publisher)
Created2017
Description
Childhood Apraxia of Speech (CAS) is a severe motor speech disorder that is difficult to diagnose as there is currently no gold-standard measurement to differentiate between CAS and other speech disorders. In the present study, we investigate underlying biomarkers associated with CAS in addition to enhanced phenotyping through behavioral testing. Cortical electrophysiological measures were utilized to investigate differences in neural activation in response to native and non-native vowel contrasts between children with CAS and typically developing peers. Genetic analysis included full exome sequencing of a child with CAS and his unaffected parents in order to uncover underlying genetic variation that may be causal to the child’s severely impaired speech and language. Enhanced phenotyping was completed through extensive behavioral testing, including speech, language, reading, spelling, phonological awareness, gross/fine motor, and oral and hand motor tasks. Results from cortical electrophysiological measures are consistent with previous evidence of a heightened neural response to non-native sounds in CAS, potentially indicating over specified phonological representations in this population. Results of exome sequencing suggest multiple genetic variations contributing to the severely affected phenotype in the child and provide further evidence of heterogeneous genomic pathways associated with CAS. Finally, results of behavioral testing demonstrate significant impairments evident across tasks in CAS, suggesting underlying sequential processing deficits in multiple domains. Overall, these results have the potential to delineate functional pathways from genetic variations to the brain to observable behavioral phenotypes and motivate the development of preventative and targeted treatment approaches.
ContributorsVose, Caitlin (Author) / Peter, Beate (Thesis advisor) / Liu, Li (Committee member) / Brewer, Gene (Committee member) / Arizona State University (Publisher)
Created2018
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 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.
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
Description
The prevalence of obesity and obesity-related disorders have increased world-wide. In the last decade, the intestinal microbiome has become a major indicator of metabolic and gastrointestinal health. Previous research has shown that high-fat diet (HFD) consumption can alter the microbial composition of the gut by increasing the abundance of gram-positive bacteria associated with the onset of obesity and type 2 diabetes. Although, the most common form of obesity and metabolic syndrome intervention is exercise and diet, these recommendations may not improve severe cases of obesity. Thus, an important relevance of my project was to investigate whether the intake of an organometallic complex (OMC) would prevent the onset of metabolic and gastrointestinal complications associated with high-fat diet intake. I hypothesized that the consumption of a HFD for 6 weeks would promote the development of metabolic and gastrointestinal disease risk factors. Next, it was hypothesized that OMC treatment would decrease metabolic risk factors by improving insulin sensitivity and decreasing weight gain. Finally, I hypothesized that HFD-intake would increase the abundance of gram-positive bacteria associated with gastrointestinal disease. My preliminary data investigated the effects of a 6-week HFD on the development of hepatic steatosis, intestinal permeability and inflammation in male Sprague Dawley rats. I found that a 6-week HFD increases hepatic triglyceride concentrations, plasma endotoxins and promotes the production of pro-inflammatory cytokines in the cecum wall. I then investigated whether OMC treatment could prevent metabolic risk factors in male Sprague-Dawley rats fed a HFD for 10 weeks and found that OMC can mitigate risk factors such hyperglycemia, liver disease, impaired endothelial function, and inflammation. Lastly, I investigated the effects of a 10-week HFD on the gastrointestinal system and found an increase in liver triglycerides and free glycerol and alterations of the distal gut microbiome. My results support the hypothesis that a HFD can promote metabolic risk factors, alter the gut microbiome and increase systemic inflammation and that OMC treatment may help mitigate some of these effects. Together, these studies are among the first to demonstrate the effects of a soil-derived compound on metabolic complications. Additionally, these conclusions also provide an essential basis for future gastrointestinal and microbiome studies of OMC treatment.
ContributorsCrawford, Meli'sa Shaunte (Author) / Sweazea, Karen L (Thesis advisor) / Deviche, Pierre (Thesis advisor) / Al-Nakkash, Layla (Committee member) / Whisner, Corrie (Committee member) / Hyatt, Jon-Philippe (Committee member) / Arizona State University (Publisher)
Created2019
Description
Introduction: Cystic fibrosis (CF) is the most common life-shortening autosomal recessive genetic disease affecting Caucasians. The disease is characterized by a dysfunctional cystic fibrosis transmembrane regulator (CFTR) protein and aberrant mucus accumulation that subsequently alters the physicochemical environment in numerous organ systems. These mucosal perturbations have been associated with inflammation and microbial dysbiosis, most notably in the lungs and gastrointestinal (GI) tract. Genistein, a soy isoflavone and dietary polyphenol, has been shown to modulate CFTR function in cell cultures and murine models, as well exert sex-dependent improvement of survival rates in a CF mouse model. However, it is unknown whether dietary genistein affects gut microbiome diversity and community structure in cystic fibrosis. This study sought to examine associations between dietary genistein treatment and gut microbiome diversity and community structure in a murine model of CF. Methods: Twenty-four male and female mice homozygous for the DF508 CFTR gene mutation were maintained on one of three diet regimens for a 45-day period (n=11, standard chow; n=7, Colyte-treated water and standard chow; n=6, 600 mg dietary genistein per kg body weight). One fecal pellet was collected per mouse post-treatment, and microbial genomic DNA was extracted from the fecal samples, quantified, amplified, and sequenced on the Illumina MiSeq platform. QIIME 2 was used to conduct alpha- and beta-diversity analyses on all samples. Results: Measures of alpha-diversity were significantly decreased in the dietary genistein group as compared to either standard chow or Colyte groups. Measures of beta-diversity showed that community structure differed significantly between dietary treatment groups; these differences were further illustrated by distinct clustering of taxa as shown by principal coordinates analysis plots. Conclusion: This 3-arm parallel experimental study showed that dietary genistein treatment was associated with decreased microbial diversity and differences in microbial community structure in DF508 mice.
ContributorsArgo, Katy Bryana (Author) / Whisner, Corrie M (Thesis advisor) / Al-Nakkash, Layla (Committee member) / Sweazea, Karen L (Committee member) / Arizona State University (Publisher)
Created2019
Description
Morbid obesity is associated with cardiovascular and metabolic disorders. A major contributor to the pathogenesis of these diseases is impaired vasodilation resulting from elevated reactive oxygen species (ROS). This is because certain ROS such as superoxide are raised with obesity and scavenge the endogenous vasorelaxant nitric oxide, resulting in hypertension. The objective of this study was to measure the ability of genistein to quench superoxide in the vasculature of ob/ob mice, an animal model of obesity and type 2 diabetes. Genistein is an isoflavonic phytoestrogen naturally found in soy products. While genistein has documented antioxidant and anti-inflammatory properties, it is not known whether this protects the vasculature from oxidative stress. Genistein was hypothesized to reduce superoxide in arteries from female ob/ob mice. The superoxide indicator dihydroethidium (DHE) [2µL/mL HEPES buffer] was added to isolated aortae and mesenteric arteries from mice fed either a control (standard rodent chow containing 200-300 mg genistein/kg) or genistein-enriched (600mg genistein/kg rodent chow) diets for 4 weeks. Frozen tissues sections were collected onto glass microscope slides and examined using confocal microscopy. Contrary to the hypothesis, a diet containing twice the amount of genistein found in standard chow did not significantly reduce superoxide concentrations in aortae (p=0.287) or mesenteric arteries (p=0.352). Superoxide dismutase, an antioxidant enzyme that breaks down superoxide, was significantly upregulated in the genistein-enriched diet group (p=0.004), although this elevation did not promote the breakdown of superoxide. In addition, the inflammatory marker iNOS was not downregulated in the genistein-enriched diet group (p>0.05). The results indicate that high amounts of isoflavones, like genistein, may not exhibit the purported antioxidant effects in the vasculature of obese or diabetic subjects. Further studies examining arteries from ob/ob mice fed a genistein-free diet are needed to elucidate the true effects of genistein on oxidative stress.
ContributorsSimperova, Anna Marie (Co-author) / Al-Nakkash, Layla (Co-author) / Ricklefs, Kristin (Co-author) / Faust, James J. (Co-author) / Sweazea, Karen L. (Co-author) / Sweazea, Karen (Thesis director) / Gonzales, Rayna (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / T. Denny Sanford School of Social and Family Dynamics (Contributor)
Created2014-05
Description
Phylogenetic analyses that were conducted in the past didn't have the ability or functionality to inform and implement useful public health decisions while using clustering. Models can be constructed to conduct any further analyses for the result of meaningful data to be used in the future of public health informatics. A phylogenetic tree is considered one of the best ways for researchers to visualize and analyze the evolutionary history of a certain virus. The focus of this study was to research HIV phylodynamic and phylogenetic methods. This involved identifying the fast growing HIV transmission clusters and rates for certain risk groups in the US. In order to achieve these results an HIV database was required to retrieve real-time data for implementation, alignment software for multiple sequence alignment, Bayesian analysis software for the development and manipulation of models, and graphical tools for visualizing the output from the models created. This study began by conducting a literature review on HIV phylogeographies and phylodynamics. Sequence data was then obtained from a sequence database to be run in a multiple alignment software. The sequence that was obtained was unaligned which is why the alignment was required. Once the alignment was performed, the same file was loaded into a Bayesian analysis software for model creation of a phylogenetic tree. When the model was created, the tree was edited in a tree visualization software for the user to easily interpret. From this study the output of the tree resulted the way it did, due to a distant homology or the mixing of certain parameters. For a further continuation of this study, it would be interesting to use the same aligned sequence and use different model parameter selections for the initial creation of the model to see how the output changes. This is because one small change for the model parameter could greatly affect the output of the phylogenetic tree.
ContributorsNandan, Meghana (Author) / Scotch, Matthew (Thesis director) / Liu, Li (Committee member) / Biomedical Informatics Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Cancer is the second leading cause of death in the United States. Cancer is a serious, complex disease which causes cells to grow uncontrollably, causing millions of deaths per year [1]. Cancer is usually caused by a combination of environmental variables and biological pathways. The pathways have a very robust structure normally, but are altered because of cancer, resulting in a loss of connectivity between pathways. In order detect these pathways, a PageRank-based method called Pathways of Topological Rank Analysis (PoTRA) was created, which measures the relative rankings of the genes in each pathway. Applying this algorithm will allow us to figure out what pathways differed significantly in areas with cancer and areas without cancer. This would allow scientists to focus on specific pathways in order to learn more about the cancer and find more effective ways to treat it. So far, analysis using PoTRA has been successfully conducted on hepatocellular carcinoma (HCC) and its subtypes, resulting in all significant pathways found being cancer-associated. Now, using the TCGA data stored in Google Cloud's BigQuery, we created a pipeline to apply PoTRA to other cancer data sets and see how well it cross-applies to other cancers. The results show that even though some modification may need to be made to adapt to other datasets, many significant pathways were found for both HCC and breast cancer.
ContributorsMahesh, Sunny Nishant (Author) / Valentin, Dinu (Thesis director) / Liu, Li (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Random forest (RF) is a popular and powerful technique nowadays. It can be used for classification, regression and unsupervised clustering. In its original form introduced by Leo Breiman, RF is used as a predictive model to generate predictions for new observations. Recent researches have proposed several methods based on RF for feature selection and for generating prediction intervals. However, they are limited in their applicability and accuracy. In this dissertation, RF is applied to build a predictive model for a complex dataset, and used as the basis for two novel methods for biomarker discovery and generating prediction interval.
Firstly, a biodosimetry is developed using RF to determine absorbed radiation dose from gene expression measured from blood samples of potentially exposed individuals. To improve the prediction accuracy of the biodosimetry, day-specific models were built to deal with day interaction effect and a technique of nested modeling was proposed. The nested models can fit this complex data of large variability and non-linear relationships.
Secondly, a panel of biomarkers was selected using a data-driven feature selection method as well as handpick, considering prior knowledge and other constraints. To incorporate domain knowledge, a method called Know-GRRF was developed based on guided regularized RF. This method can incorporate domain knowledge as a penalized term to regulate selection of candidate features in RF. It adds more flexibility to data-driven feature selection and can improve the interpretability of models. Know-GRRF showed significant improvement in cross-species prediction when cross-species correlation was used to guide selection of biomarkers. The method can also compete with existing methods using intrinsic data characteristics as alternative of domain knowledge in simulated datasets.
Lastly, a novel non-parametric method, RFerr, was developed to generate prediction interval using RF regression. This method is widely applicable to any predictive models and was shown to have better coverage and precision than existing methods on the real-world radiation dataset, as well as benchmark and simulated datasets.
Firstly, a biodosimetry is developed using RF to determine absorbed radiation dose from gene expression measured from blood samples of potentially exposed individuals. To improve the prediction accuracy of the biodosimetry, day-specific models were built to deal with day interaction effect and a technique of nested modeling was proposed. The nested models can fit this complex data of large variability and non-linear relationships.
Secondly, a panel of biomarkers was selected using a data-driven feature selection method as well as handpick, considering prior knowledge and other constraints. To incorporate domain knowledge, a method called Know-GRRF was developed based on guided regularized RF. This method can incorporate domain knowledge as a penalized term to regulate selection of candidate features in RF. It adds more flexibility to data-driven feature selection and can improve the interpretability of models. Know-GRRF showed significant improvement in cross-species prediction when cross-species correlation was used to guide selection of biomarkers. The method can also compete with existing methods using intrinsic data characteristics as alternative of domain knowledge in simulated datasets.
Lastly, a novel non-parametric method, RFerr, was developed to generate prediction interval using RF regression. This method is widely applicable to any predictive models and was shown to have better coverage and precision than existing methods on the real-world radiation dataset, as well as benchmark and simulated datasets.
ContributorsGuan, Xin (Author) / Liu, Li (Thesis advisor) / Runger, George C. (Thesis advisor) / Dinu, Valentin (Committee member) / Arizona State University (Publisher)
Created2017
Description
Bridging social capital describes the diffusion of information across networks built between individuals of different social identities. This project aims to understand if the bridging ties of economic connectedness (EC), measured by data from Facebook friends and calculated as the average share of high socioeconomic status friends that an individual from a low socioeconomic status has, can be a predictor of variations in COVID-19 infection risk across Arizona ZIP code tabulation areas (ZCTAs). Economic connectedness values across Arizona ZCTAs was examined in addition to the correlation of EC to various social and demographic factors such as age, sex, race and ethnicity, educational background, income, and health insurance coverage. A multiple linear regression model was conducted to examine the association of EC to biweekly COVID-19 growth rate from October 2020 to November 2021, and to examine the longitudinal trends in the association between these two factors. The study found that the bridging ties of economic connectedness has a significant effect size comparable to that of other demographic features, and has implications in being used to identify vulnerabilities and health disparities in communities during the pandemic.
ContributorsBoby, Maria (Author) / Oh, Hyunsung (Thesis director) / Marsiglia, Flavio (Committee member) / Liu, Li (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / School of Human Evolution & Social Change (Contributor) / School of Social Work (Contributor)
Created2023-05