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- All Subjects: Bioinformatics
- All Subjects: Developmental Biology
- All Subjects: RNA
- Creators: Kusumi, Kenro
- Creators: Scotch, Matthew
- Status: Published
Description
Well-established model systems exist in four out of the seven major classes of vertebrates. These include the mouse, chicken, frog and zebrafish. Noticeably missing from this list is a reptilian model organism for comparative studies between the vertebrates and for studies of biological processes unique to reptiles. To help fill in this gap the green anole lizard, Anolis carolinensis, is being adapted as a model organism. Despite the recent release of the complete genomic sequence of the A. carolinensis, the lizard lacks some resources to aid researchers in their studies. Particularly, the lack of transcriptomic resources for lizard has made it difficult to identify genes complete with alternative splice forms and untranslated regions (UTRs). As part of this work the genome annotation for A. carolinensis was improved through next generation sequencing and assembly of the transcriptomes from 14 different adult and embryonic tissues. This revised annotation of the lizard will improve comparative studies between vertebrates, as well as studies within A. carolinensis itself, by providing more accurate gene models, which provide the bases for molecular studies. To demonstrate the utility of the improved annotations and reptilian model organism, the developmental process of somitogenesis in the lizard was analyzed and compared with other vertebrates. This study identified several key features both divergent and convergent between the vertebrates, which was not previously known before analysis of a reptilian model organism. The improved genome annotations have also allowed for molecular studies of tail regeneration in the lizard. With the annotation of 3' UTR sequences and next generation sequencing, it is now possible to do expressional studies of miRNA and predict their mRNA target transcripts at genomic scale. Through next generation small RNA sequencing and subsequent analysis, several differentially expressed miRNAs were identified in the regenerating tail, suggesting miRNA may play a key role in regulating this process in lizards. Through miRNA target prediction several key biological pathways were identified as potentially under the regulation of miRNAs during tail regeneration. In total, this work has both helped advance A. carolinensis as model system and displayed the utility of a reptilian model system.
ContributorsEckalbar, Walter L (Author) / Kusumi, Kenro (Thesis advisor) / Huentelman, Matthew (Committee member) / Rawls, Jeffery (Committee member) / Wilson-Rawls, Norma (Committee member) / Arizona State University (Publisher)
Created2012
Description
Induced pluripotent stem cells (iPSCs) are an intriguing approach for neurological disease modeling, because neural lineage-specific cell types that retain the donors' complex genetics can be established in vitro. The statistical power of these iPSC-based models, however, is dependent on accurate diagnoses of the somatic cell donors; unfortunately, many neurodegenerative diseases are commonly misdiagnosed in live human subjects. Postmortem histopathological examination of a donor's brain, combined with premortem clinical criteria, is often the most robust approach to correctly classify an individual as a disease-specific case or unaffected control. We describe the establishment of primary dermal fibroblasts cells lines from 28 autopsy donors. These fibroblasts were used to examine the proliferative effects of establishment protocol, tissue amount, biopsy site, and donor age. As proof-of-principle, iPSCs were generated from fibroblasts from a 75-year-old male, whole body donor, defined as an unaffected neurological control by both clinical and histopathological criteria. To our knowledge, this is the first study describing autopsy donor-derived somatic cells being used for iPSC generation and subsequent neural differentiation. This unique approach also enables us to compare iPSC-derived cell cultures to endogenous tissues from the same donor. We utilized RNA sequencing (RNA-Seq) to evaluate the transcriptional progression of in vitro-differentiated neural cells (over a timecourse of 0, 35, 70, 105 and 140 days), and compared this with donor-identical temporal lobe tissue. We observed in vitro progression towards the reference brain tissue, supported by (i) a significant increasing monotonic correlation between the days of our timecourse and the number of actively transcribed protein-coding genes and long intergenic non-coding RNAs (lincRNAs) (P < 0.05), consistent with the transcriptional complexity of the brain, (ii) an increase in CpG methylation after neural differentiation that resembled the epigenomic signature of the endogenous tissue, and (iii) a significant decreasing monotonic correlation between the days of our timecourse and the percent of in vitro to brain-tissue differences (P < 0.05) for tissue-specific protein-coding genes and all putative lincRNAs. These studies support the utility of autopsy donors' somatic cells for iPSC-based neurological disease models, and provide evidence that in vitro neural differentiation can result in physiologically progression.
ContributorsHjelm, Brooke E (Author) / Craig, David W. (Thesis advisor) / Wilson-Rawls, Norma J. (Thesis advisor) / Huentelman, Matthew J. (Committee member) / Mason, Hugh S. (Committee member) / Kusumi, Kenro (Committee member) / Arizona State University (Publisher)
Created2013
Description
The development of the vertebrate musculoskeletal system is a highly dynamic process, requiring tight control of the specification and patterning of myogenic, chondrogenic and tenogenic cell types. Development of the diverse musculoskeletal lineages from a common embryonic origin in the paraxial mesoderm indicates the presence of a regulatory network of transcription factors that direct lineage decisions. The basic helix-loop-helix transcription factor, PARAXIS, is expressed in the paraxial mesoderm during vertebrate somitogenesis, where it has been shown to play a critical role in the mesenchymal-to-epithelial transition associated with somitogenesis, and the development of the hypaxial skeletal musculature and axial skeleton. In an effort to elucidate the underlying genetic mechanism by which PARAXIS regulates the musculoskeletal system, I performed a microarray-based, genome-wide analysis comparing transcription levels in the somites of Paraxis-/- and Paraxis+/+ embryos. This study revealed targets of PARAXIS involved in multiple aspects of mesenchymal-to-epithelial transition, including Fap and Dmrt2, which modulate cell-extracellular matrix adhesion. Additionally, in the epaxial dermomyotome, PARAXIS activates the expression of the integrin subunits a4 and a6, which bind fibronectin and laminin, respectively, and help organize the patterning of trunk skeletal muscle. Finally, PARAXIS activates the expression of genes required for the epithelial-to-mesenchymal transition and migration of hypaxial myoblasts into the limb, including Lbx1 and Met. Together, these data point to a role for PARAXIS in the morphogenetic control of musculoskeletal patterning.
ContributorsRowton, Megan (Author) / Rawls, Alan (Thesis advisor) / Wilson-Rawls, Jeanne (Committee member) / Kusumi, Kenro (Committee member) / Gadau, Juergen (Committee member) / Arizona State University (Publisher)
Created2013
Description
Vertebrate genomes demonstrate a remarkable range of sizes from 0.3 to 133 gigabase pairs. The proliferation of repeat elements are a major genomic expansion. In particular, long interspersed nuclear elements (LINES) are autonomous retrotransposons that have the ability to "cut and paste" themselves into a host genome through a mechanism called target-primed reverse transcription. LINES have been called "junk DNA," "viral DNA," and "selfish" DNA, and were once thought to be parasitic elements. However, LINES, which diversified before the emergence of many early vertebrates, has strongly shaped the evolution of eukaryotic genomes. This thesis will evaluate LINE abundance, diversity and activity in four anole lizards. An intrageneric analysis will be conducted using comparative phylogenetics and bioinformatics. Comparisons within the Anolis genus, which derives from a single lineage of an adaptive radiation, will be conducted to explore the relationship between LINE retrotransposon activity and causal changes in genomic size and composition.
ContributorsMay, Catherine (Author) / Kusumi, Kenro (Thesis advisor) / Gadau, Juergen (Committee member) / Rawls, Jeffery A (Committee member) / Arizona State University (Publisher)
Created2013
Description
Skeletal muscles arise from the myotome compartment of the somites that form during vertebrate embryonic development. Somites are transient structures serve as the anlagen for the axial skeleton, skeletal muscle, tendons, and dermis, as well as imposing the metameric patterning of the axial musculoskeletal system, peripheral nerves, and vasculature. Classic studies have described the role of Notch, Wnt, and FGF signaling pathways in controlling somite formation and muscle formation. However, little is known about the transformation of myotome compartments into identifiable post-natal muscle groups. Using a mouse model, I have undertaken an evaluation of morphological events, including hypertrophy and hyperplasia, related to the formation of several muscles positioned along the dorsal surface of the vertebrae and ribs. Lunatic fringe (Lfng) deficient embryos and neonates were also examined to further understand the role of the Notch pathway in these processes as it is a modulator of the Notch receptor and plays an important role in defining somite borders and anterior-posterior patterning in many vertebrates. Lunatic fringe deficient embryos showed defects in muscle fiber hyperplasia and hypertrophy in the iliocostalis and longissimus muscles of the erector spinae group. This novel data suggests an additional role for Lfng and the Notch signaling pathway in embryonic and fetal muscle development.
ContributorsDe Ruiter, Corinne (Author) / Rawls, J. Alan (Thesis advisor) / Wilson-Rawls, Jeanne (Committee member) / Kusumi, Kenro (Committee member) / Fisher, Rebecca E. (Committee member) / Arizona State University (Publisher)
Created2012
Description
Skeletal muscle can intrinsically repair itself in response to injury. This repair process has been shown to be mediated through signaling of the innate immune system. The immune response caused during repair helps to clear away debris in damage and promotes the activation and proliferation of muscle stem cells (MuSCs) that will repair the damage muscle. Dysregulation of this inflammation leads to fibrosis and decreased efficacy of the repair process. Despite the requirement of inflammatory signaling during muscle repair, muscle’s contribution during inflammation as only recently started to be explored. The objective of this dissertation is to assess the contribution of muscle in the early inflammatory response during repair as well attempting to modulate this inflammation during disease to ameliorate disease pathology in a model of Duchenne’s muscular dystrophy. I tested the hypotheses that 1) muscle is an active participant in the early inflammatory response, 2) the transcription factor Mohawk (Mkx) is a regulator of the early inflammatory response and, 3) If this inflammation can be modulated with a virally derived serine protease inhibitor in a model of muscle disrepair and chronic inflammation. I found that muscle is actively participating in the establishment early inflammation in repair through the production of chemokines used to promote infiltration of immune cells. As well as the identification of a new muscle subtype that produces more chemokines compared to the average MuSC and upregulated genes in the Interferon signaling pathway. I also discovered that presence of this muscle subtype is linked to the expression of Mkx. In Mkx null mice this population is not present, and these cells are deficient in chemokine expression compared to WT mice. I subsequently found that, using the myxomavirus derived serine protease inhibitor, Serp-1 I was able to modulate the chronic inflammation that is common in those affected with Duchenne’s muscular dystrophy (DMD) utilizing a high-fidelity mouse model of the disease. The result of this dissertation provides an expanded role for muscle in inflammation and gives a potential new class of therapeutics to be used in disease associated with chronic inflammation.
ContributorsAndre, Alex (Author) / Rawls, Alan (Thesis advisor) / Wilson-Rawls, Jeanne (Committee member) / Kusumi, Kenro (Committee member) / Lake, Doug (Committee member) / Chang, Yung (Committee member) / Arizona State University (Publisher)
Created2022
Evaluating the Heterogeneity of Logistic Regression Models to Predict Coronary Artery Disease Status
Description
Coronary artery disease (CAD) is one of the most diagnosed heart diseases globally, affecting about 5% of adults over the age of twenty[1]. Lifestyle changes can positively impact risk of developing CAD and are especially important for individuals with high genetic risk [1]. In this study, we sought to predict the likelihood of developing CAD using genetic, demographic, and clinical variables. Leveraging genetic and clinical data from the UK Biobank on over 500,000 individuals, we classified and separated 500 genetically similar individuals to a target individual from another 500 genetically dissimilar individuals. This process was repeated for 10 target individuals as a proof-of-concept. Then, CAD-related variables were used and these include age, relevant clinical factors, and polygenic risk score to train models for predicting CAD status for the 500 genetically similar and 500 genetically dissimilar groups, and determine which group predicts the likelihood of CAD more accurately. To compute genetic similarity to the target individuals we used the Mahalanobis distance. To reduce the heterogeneity between sexes and races, the studies were restricted to British male Caucasians. The models using the more similar individuals demonstrated better predictive performance. The area under the receiver operating characteristic curve (AUC) was found to be significantly higher for the ‘similar’ rather than the ’dissimilar’ groups, indicating better predictive capability (AUC=0.67 vs. 0.65, respectively; p-value<0.05). These findings support the potential of precision prevention strategies, since one should build predictive models of disease for any one target individual from more similar individuals to that target even within an otherwise homogenous group of individuals (e.g., British Caucasians). Although intuitive, such practices are not done routinely. Further validation and exploration of additional predictors are warranted to enhance the predictive accuracy and applicability of the model.
ContributorsPandari, Sadhana (Author) / Ghassamzadeh, Hassan (Thesis director) / Scotch, Matthew (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor)
Created2024-05
Description
A genome wide association study (GWAS) of treatment outcomes for citalopram and escitalopram, two frontline SSRI treatments for Major Depressive Disorder, was conducted with 529 subjects on an imputed dataset. While no variants of genome-wide significance were identified, various potentially interesting variants were identified that warrant further exploration. These findings have the potential to elucidate novel mechanisms underlying drug response for SSRIs. This work will be continued further, with machine learning and deep learning analyses to perform non-linear analyses and employing a biologist or geneticist to provide more specialized knowledge for interpretation of results.
ContributorsLeiter-Weintraub, Ethan (Author) / Dinu, Valentin (Thesis director) / Scotch, Matthew (Committee member) / Barrett, The Honors College (Contributor) / Dean, W.P. Carey School of Business (Contributor) / College of Health Solutions (Contributor) / School of Life Sciences (Contributor)
Created2024-05
Description
No two cancers are alike. Cancer is a dynamic and heterogeneous disease, such heterogeneity arise among patients with the same cancer type, among cancer cells within the same individual’s tumor and even among cells within the same sub-clone over time. The recent application of next-generation sequencing and precision medicine techniques is the driving force to uncover the complexity of cancer and the best clinical practice. The core concept of precision medicine is to move away from crowd-based, best-for-most treatment and take individual variability into account when optimizing the prevention and treatment strategies. Next-generation sequencing is the method to sift through the entire 3 billion letters of each patient’s DNA genetic code in a massively parallel fashion.
The deluge of next-generation sequencing data nowadays has shifted the bottleneck of cancer research from multiple “-omics” data collection to integrative analysis and data interpretation. In this dissertation, I attempt to address two distinct, but dependent, challenges. The first is to design specific computational algorithms and tools that can process and extract useful information from the raw data in an efficient, robust, and reproducible manner. The second challenge is to develop high-level computational methods and data frameworks for integrating and interpreting these data. Specifically, Chapter 2 presents a tool called Snipea (SNv Integration, Prioritization, Ensemble, and Annotation) to further identify, prioritize and annotate somatic SNVs (Single Nucleotide Variant) called from multiple variant callers. Chapter 3 describes a novel alignment-based algorithm to accurately and losslessly classify sequencing reads from xenograft models. Chapter 4 describes a direct and biologically motivated framework and associated methods for identification of putative aberrations causing survival difference in GBM patients by integrating whole-genome sequencing, exome sequencing, RNA-Sequencing, methylation array and clinical data. Lastly, chapter 5 explores longitudinal and intratumor heterogeneity studies to reveal the temporal and spatial context of tumor evolution. The long-term goal is to help patients with cancer, particularly those who are in front of us today. Genome-based analysis of the patient tumor can identify genomic alterations unique to each patient’s tumor that are candidate therapeutic targets to decrease therapy resistance and improve clinical outcome.
The deluge of next-generation sequencing data nowadays has shifted the bottleneck of cancer research from multiple “-omics” data collection to integrative analysis and data interpretation. In this dissertation, I attempt to address two distinct, but dependent, challenges. The first is to design specific computational algorithms and tools that can process and extract useful information from the raw data in an efficient, robust, and reproducible manner. The second challenge is to develop high-level computational methods and data frameworks for integrating and interpreting these data. Specifically, Chapter 2 presents a tool called Snipea (SNv Integration, Prioritization, Ensemble, and Annotation) to further identify, prioritize and annotate somatic SNVs (Single Nucleotide Variant) called from multiple variant callers. Chapter 3 describes a novel alignment-based algorithm to accurately and losslessly classify sequencing reads from xenograft models. Chapter 4 describes a direct and biologically motivated framework and associated methods for identification of putative aberrations causing survival difference in GBM patients by integrating whole-genome sequencing, exome sequencing, RNA-Sequencing, methylation array and clinical data. Lastly, chapter 5 explores longitudinal and intratumor heterogeneity studies to reveal the temporal and spatial context of tumor evolution. The long-term goal is to help patients with cancer, particularly those who are in front of us today. Genome-based analysis of the patient tumor can identify genomic alterations unique to each patient’s tumor that are candidate therapeutic targets to decrease therapy resistance and improve clinical outcome.
ContributorsPeng, Sen (Author) / Dinu, Valentin (Thesis advisor) / Scotch, Matthew (Committee member) / Wallstrom, Garrick (Committee member) / Arizona State University (Publisher)
Created2015
Description
Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks such as pharmacovigilance via the use of Natural Language Processing (NLP) techniques. One of the critical steps in information extraction pipelines is Named Entity Recognition (NER), where the mentions of entities such as diseases are located in text and their entity type are identified. However, the language in social media is highly informal, and user-expressed health-related concepts are often non-technical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and advanced machine learning-based NLP techniques have been underutilized. This work explores the effectiveness of different machine learning techniques, and particularly deep learning, to address the challenges associated with extraction of health-related concepts from social media. Deep learning has recently attracted a lot of attention in machine learning research and has shown remarkable success in several applications particularly imaging and speech recognition. However, thus far, deep learning techniques are relatively unexplored for biomedical text mining and, in particular, this is the first attempt in applying deep learning for health information extraction from social media.
This work presents ADRMine that uses a Conditional Random Field (CRF) sequence tagger for extraction of complex health-related concepts. It utilizes a large volume of unlabeled user posts for automatic learning of embedding cluster features, a novel application of deep learning in modeling the similarity between the tokens. ADRMine significantly improved the medical NER performance compared to the baseline systems.
This work also presents DeepHealthMiner, a deep learning pipeline for health-related concept extraction. Most of the machine learning methods require sophisticated task-specific manual feature design which is a challenging step in processing the informal and noisy content of social media. DeepHealthMiner automatically learns classification features using neural networks and utilizing a large volume of unlabeled user posts. Using a relatively small labeled training set, DeepHealthMiner could accurately identify most of the concepts, including the consumer expressions that were not observed in the training data or in the standard medical lexicons outperforming the state-of-the-art baseline techniques.
This work presents ADRMine that uses a Conditional Random Field (CRF) sequence tagger for extraction of complex health-related concepts. It utilizes a large volume of unlabeled user posts for automatic learning of embedding cluster features, a novel application of deep learning in modeling the similarity between the tokens. ADRMine significantly improved the medical NER performance compared to the baseline systems.
This work also presents DeepHealthMiner, a deep learning pipeline for health-related concept extraction. Most of the machine learning methods require sophisticated task-specific manual feature design which is a challenging step in processing the informal and noisy content of social media. DeepHealthMiner automatically learns classification features using neural networks and utilizing a large volume of unlabeled user posts. Using a relatively small labeled training set, DeepHealthMiner could accurately identify most of the concepts, including the consumer expressions that were not observed in the training data or in the standard medical lexicons outperforming the state-of-the-art baseline techniques.
ContributorsNikfarjam, Azadeh (Author) / Gonzalez, Graciela (Thesis advisor) / Greenes, Robert (Committee member) / Scotch, Matthew (Committee member) / Arizona State University (Publisher)
Created2016