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- All Subjects: Bioinformatics
- Creators: Kusumi, Kenro
- Creators: Wang, Xiao
Description
Random peptide microarrays are a powerful tool for both the treatment and diagnostics of infectious diseases. On the treatment side, selected random peptides on the microarray have either binding or lytic potency against certain pathogens cells, thus they can be synthesized into new antimicrobial agents, denoted as synbodies (synthetic antibodies). On the diagnostic side, serum containing specific infection-related antibodies create unique and distinct "pathogen-immunosignatures" on the random peptide microarray distinct from the healthy control serum, and this different mode of binding can be used as a more precise measurement than traditional ELISA tests. My thesis project is separated into these two parts: the first part falls into the treatment side and the second one focuses on the diagnostic side. My first chapter shows that a substitution amino acid peptide library helps to improve the activity of a recently reported synthetic antimicrobial peptide selected by the random peptide microarray. By substituting one or two amino acids of the original lead peptide, the new substitutes show changed hemolytic effects against mouse red blood cells and changed potency against two pathogens: Staphylococcus aureus and Pseudomonas aeruginosa. Two new substitutes are then combined together to form the synbody, which shows a significantly antimicrobial potency against Staphylococcus aureus (<0.5uM). In the second chapter, I explore the possibility of using the 10K Ver.2 random peptide microarray to monitor the humoral immune response of dengue. Over 2.5 billion people (40% of the world's population) live in dengue transmitting areas. However, currently there is no efficient dengue treatment or vaccine. Here, with limited dengue patient serum samples, we show that the immunosignature has the potential to not only distinguish the dengue infection from non-infected people, but also the primary dengue infection from the secondary dengue infections, dengue infection from West Nile Virus (WNV) infection, and even between different dengue serotypes. By further bioinformatic analysis, we demonstrate that the significant peptides selected to distinguish dengue infected and normal samples may indicate the epitopes responsible for the immune response.
ContributorsWang, Xiao (Author) / Johnston, Stephen Albert (Thesis advisor) / Blattman, Joseph (Committee member) / Arntzen, Charles (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
Telomerase enzyme is a truly remarkable enzyme specialized for the addition of short, highly repetitive DNA sequences onto linear eukaryotic chromosome ends. The telomerase enzyme functions as a ribonucleoprotein, minimally composed of the highly conserved catalytic telomerase reverse transcriptase and essential telomerase RNA component containing an internalized short template region within the vastly larger non-coding RNA. Even among closely related groups of species, telomerase RNA is astonishingly divergent in sequence, length, and secondary structure. This massive disparity is highly prohibitive for telomerase RNA identification from previously unexplored groups of species, which is fundamental for secondary structure determination. Combined biochemical enrichment and computational screening methods were employed for the discovery of numerous telomerase RNAs from the poorly characterized echinoderm lineage. This resulted in the revelation that--while closely related to the vertebrate lineage and grossly resembling vertebrate telomerase RNA--the echinoderm telomerase RNA central domain varies extensively in structure and sequence, diverging even within echinoderms amongst sea urchins and brittle stars. Furthermore, the origins of telomerase RNA within the eukaryotic lineage have remained a persistent mystery. The ancient Trypanosoma telomerase RNA was previously identified, however, a functionally verified secondary structure remained elusive. Synthetic Trypanosoma telomerase was generated for molecular dissection of Trypanosoma telomerase RNA revealing two RNA domains functionally equivalent to those found in known telomerase RNAs, yet structurally distinct. This work demonstrates that telomerase RNA is uncommonly divergent in gross architecture, while retaining critical universal elements.
ContributorsPodlevsky, Joshua (Author) / Chen, Julian (Thesis advisor) / Mangone, Marco (Committee member) / Kusumi, Kenro (Committee member) / Wilson-Rawls, Norma (Committee member) / Arizona State University (Publisher)
Created2015
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
Damage to the central nervous system due to spinal cord or traumatic brain injury, as well as degenerative musculoskeletal disorders such as arthritis, drastically impact the quality of life. Regeneration of complex structures is quite limited in mammals, though other vertebrates possess this ability. Lizards are the most closely related organism to humans that can regenerate de novo skeletal muscle, hyaline cartilage, spinal cord, vasculature, and skin. Progress in studying the cellular and molecular mechanisms of lizard regeneration has previously been limited by a lack of genomic resources. Building on the release of the genome of the green anole, Anolis carolinensis, we developed a second generation, robust RNA-Seq-based genome annotation, and performed the first transcriptomic analysis of tail regeneration in this species. In order to investigate gene expression in regenerating tissue, we performed whole transcriptome and microRNA transcriptome analysis of regenerating tail tip and base and associated tissues, identifying key genetic targets in the regenerative process. These studies have identified components of a genetic program for regeneration in the lizard that includes both developmental and adult repair mechanisms shared with mammals, indicating value in the translation of these findings to future regenerative therapies.
ContributorsHutchins, Elizabeth (Author) / Kusumi, Kenro (Thesis advisor) / Rawls, Jeffrey A. (Committee member) / Denardo, Dale F. (Committee member) / Huentelman, Matthew J. (Committee member) / Arizona State University (Publisher)
Created2015
Description
In species with highly heteromorphic sex chromosomes, the degradation of one of the sex chromosomes can result in unequal gene expression between the sexes (e.g., between XX females and XY males) and between the sex chromosomes and the autosomes. Dosage compensation is a process whereby genes on the sex chromosomes achieve equal gene expression which prevents deleterious side effects from having too much or too little expression of genes on sex chromsomes. The green anole is part of a group of species that recently underwent an adaptive radiation. The green anole has XX/XY sex determination, but the content of the X chromosome and its evolution have not been described. Given its status as a model species, better understanding the green anole genome could reveal insights into other species. Genomic analyses are crucial for a comprehensive picture of sex chromosome differentiation and dosage compensation, in addition to understanding speciation.
In order to address this, multiple comparative genomics and bioinformatics analyses were conducted to elucidate patterns of evolution in the green anole and across multiple anole species. Comparative genomics analyses were used to infer additional X-linked loci in the green anole, RNAseq data from male and female samples were anayzed to quantify patterns of sex-biased gene expression across the genome, and the extent of dosage compensation on the anole X chromosome was characterized, providing evidence that the sex chromosomes in the green anole are dosage compensated.
In addition, X-linked genes have a lower ratio of nonsynonymous to synonymous substitution rates than the autosomes when compared to other Anolis species, and pairwise rates of evolution in genes across the anole genome were analyzed. To conduct this analysis a new pipeline was created for filtering alignments and performing batch calculations for whole genome coding sequences. This pipeline has been made publicly available.
In order to address this, multiple comparative genomics and bioinformatics analyses were conducted to elucidate patterns of evolution in the green anole and across multiple anole species. Comparative genomics analyses were used to infer additional X-linked loci in the green anole, RNAseq data from male and female samples were anayzed to quantify patterns of sex-biased gene expression across the genome, and the extent of dosage compensation on the anole X chromosome was characterized, providing evidence that the sex chromosomes in the green anole are dosage compensated.
In addition, X-linked genes have a lower ratio of nonsynonymous to synonymous substitution rates than the autosomes when compared to other Anolis species, and pairwise rates of evolution in genes across the anole genome were analyzed. To conduct this analysis a new pipeline was created for filtering alignments and performing batch calculations for whole genome coding sequences. This pipeline has been made publicly available.
ContributorsRupp, Shawn Michael (Author) / Wilson Sayres, Melissa A (Thesis advisor) / Kusumi, Kenro (Committee member) / DeNardo, Dale (Committee member) / Arizona State University (Publisher)
Created2016
Description
Agassiz’s desert tortoise (Gopherus agassizii) is a long-lived species native to the Mojave Desert and is listed as threatened under the US Endangered Species Act. To aid conservation efforts for preserving the genetic diversity of this species, we generated a whole genome reference sequence with an annotation based on deep transcriptome sequences of adult skeletal muscle, lung, brain, and blood. The draft genome assembly for G. agassizii has a scaffold N50 length of 252 kbp and a total length of 2.4 Gbp. Genome annotation reveals 20,172 protein-coding genes in the G. agassizii assembly, and that gene structure is more similar to chicken than other turtles. We provide a series of comparative analyses demonstrating (1) that turtles are among the slowest-evolving genome-enabled reptiles, (2) amino acid changes in genes controlling desert tortoise traits such as shell development, longevity and osmoregulation, and (3) fixed variants across the Gopherus species complex in genes related to desert adaptations, including circadian rhythm and innate immune response. This G. agassizii genome reference and annotation is the first such resource for any tortoise, and will serve as a foundation for future analysis of the genetic basis of adaptations to the desert environment, allow for investigation into genomic factors affecting tortoise health, disease and longevity, and serve as a valuable resource for additional studies in this species complex.
Data Availability: All genomic and transcriptomic sequence files are available from the NIH-NCBI BioProject database (accession numbers PRJNA352725, PRJNA352726, and PRJNA281763). All genome assembly, transcriptome assembly, predicted protein, transcript, genome annotation, repeatmasker, phylogenetic trees, .vcf and GO enrichment files are available on Harvard Dataverse (doi:10.7910/DVN/EH2S9K).
ContributorsTollis, Marc (Author) / DeNardo, Dale F (Author) / Cornelius, John A (Author) / Dolby, Greer A (Author) / Edwards, Taylor (Author) / Henen, Brian T. (Author) / Karl, Alice E. (Author) / Murphy, Robert W. (Author) / Kusumi, Kenro (Author)
Created2017-05-31
Description
The past decade has seen a drastic increase in collaboration between Computer Science (CS) and Molecular Biology (MB). Current foci in CS such as deep learning require very large amounts of data, and MB research can often be rapidly advanced by analysis and models from CS. One of the places where CS could aid MB is during analysis of sequences to find binding sites, prediction of folding patterns of proteins. Maintenance and replication of stem-like cells is possible for long terms as well as differentiation of these cells into various tissue types. These behaviors are possible by controlling the expression of specific genes. These genes then cascade into a network effect by either promoting or repressing downstream gene expression. The expression level of all gene transcripts within a single cell can be analyzed using single cell RNA sequencing (scRNA-seq). A significant portion of noise in scRNA-seq data are results of extrinsic factors and could only be removed by customized scRNA-seq analysis pipeline. scRNA-seq experiments utilize next-gen sequencing to measure genome scale gene expression levels with single cell resolution.
Almost every step during analysis and quantification requires the use of an often empirically determined threshold, which makes quantification of noise less accurate. In addition, each research group often develops their own data analysis pipeline making it impossible to compare data from different groups. To remedy this problem a streamlined and standardized scRNA-seq data analysis and normalization protocol was designed and developed. After analyzing multiple experiments we identified the possible pipeline stages, and tools needed. Our pipeline is capable of handling data with adapters and barcodes, which was not the case with pipelines from some experiments. Our pipeline can be used to analyze single experiment scRNA-seq data and also to compare scRNA-seq data across experiments. Various processes like data gathering, file conversion, and data merging were automated in the pipeline. The main focus was to standardize and normalize single-cell RNA-seq data to minimize technical noise introduced by disparate platforms.
Almost every step during analysis and quantification requires the use of an often empirically determined threshold, which makes quantification of noise less accurate. In addition, each research group often develops their own data analysis pipeline making it impossible to compare data from different groups. To remedy this problem a streamlined and standardized scRNA-seq data analysis and normalization protocol was designed and developed. After analyzing multiple experiments we identified the possible pipeline stages, and tools needed. Our pipeline is capable of handling data with adapters and barcodes, which was not the case with pipelines from some experiments. Our pipeline can be used to analyze single experiment scRNA-seq data and also to compare scRNA-seq data across experiments. Various processes like data gathering, file conversion, and data merging were automated in the pipeline. The main focus was to standardize and normalize single-cell RNA-seq data to minimize technical noise introduced by disparate platforms.
ContributorsBalachandran, Parithi (Author) / Wang, Xiao (Thesis advisor) / Brafman, David (Committee member) / Lockhart, Thurmon (Committee member) / Arizona State University (Publisher)
Created2017
Description
Rapid advancements in genomic technologies have increased our understanding of rare human disease. Generation of multiple types of biological data including genetic variation from genome or exome, expression from transcriptome, methylation patterns from epigenome, protein complexity from proteome and metabolite information from metabolome is feasible. "Omics" tools provide comprehensive view into biological mechanisms that impact disease trait and risk. In spite of available data types and ability to collect them simultaneously from patients, researchers still rely on their independent analysis. Combining information from multiple biological data can reduce missing information, increase confidence in single data findings, and provide a more complete view of genotype-phenotype correlations. Although rare disease genetics has been greatly improved by exome sequencing, a substantial portion of clinical patients remain undiagnosed. Multiple frameworks for integrative analysis of genomic and transcriptomic data are presented with focus on identifying functional genetic variations in patients with undiagnosed, rare childhood conditions. Direct quantitation of X inactivation ratio was developed from genomic and transcriptomic data using allele specific expression and segregation analysis to determine magnitude and inheritance mode of X inactivation. This approach was applied in two families revealing non-random X inactivation in female patients. Expression based analysis of X inactivation showed high correlation with standard clinical assay. These findings improved understanding of molecular mechanisms underlying X-linked disorders. In addition multivariate outlier analysis of gene and exon level data from RNA-seq using Mahalanobis distance, and its integration of distance scores with genomic data found genotype-phenotype correlations in variant prioritization process in 25 families. Mahalanobis distance scores revealed variants with large transcriptional impact in patients. In this dataset, frameshift variants were more likely result in outlier expression signatures than other types of functional variants. Integration of outlier estimates with genetic variants corroborated previously identified, presumed causal variants and highlighted new candidate in previously un-diagnosed case. Integrative genomic approaches in easily attainable tissue will facilitate the search for biomarkers that impact disease trait, uncover pharmacogenomics targets, provide novel insight into molecular underpinnings of un-characterized conditions, and help improve analytical approaches that use large datasets.
ContributorsSzelinger, Szabolcs (Author) / Craig, David W. (Thesis advisor) / Kusumi, Kenro (Thesis advisor) / Narayan, Vinodh (Committee member) / Rosenberg, Michael S. (Committee member) / Huentelman, Matthew J (Committee member) / Arizona State University (Publisher)
Created2015
Description
The WNT signaling pathway plays numerous roles in development and maintenance of adult homeostasis. In concordance with it’s numerous roles, dysfunction of WNT signaling leads to a variety of human diseases ranging from developmental disorders to cancer. WNT signaling is composed of a family of 19 WNT soluble secreted glycoproteins, which are evolutionarily conserved across all phyla of the animal kingdom. WNT ligands interact most commonly with a family of receptors known as frizzled (FZ) receptors, composed of 10 independent genes. Specific interactions between WNT proteins and FZ receptors are not well characterized and are known to be promiscuous, Traditionally canonical WNT signaling is described as a binary system in which WNT signaling is either off or on. In the ‘off’ state, in the absence of a WNT ligand, cytoplasmic β-catenin is continuously degraded by the action of the APC/Axin/GSK-3β destruction complex. In the ‘on’ state, when WNT binds to its Frizzled (Fz) receptor and LRP coreceptor, this protein destruction complex is disrupted, allowing β-catenin to translocate into the nucleus where it interacts with the DNA-bound T cell factor/lymphoid factor (TCF/LEF) family of proteins to regulate target gene expression. However in a variety of systems in development and disease canonical WNT signaling acts in a gradient fashion, suggesting more complex regulation of β-catenin transcriptional activity. As such, the traditional ‘binary’ view of WNT signaling does not clearly explain how this graded signal is transmitted intracellularly to control concentration-dependent changes in gene expression and cell identity. I have developed an in vitro human pluripotent stem cell (hPSC)-based model that recapitulates the same in vivo developmental effects of the WNT signaling gradient on the anterior-posterior (A/P) patterning of the neural tube observed during early development. Using RNA-seq and ChIP-seq I have characterized β-catenin binding at different levels of WNT signaling and identified different classes of β-catenin peaks that bind cis-regulatory elements to influence neural cell fate. This work expands the traditional binary view of canonical WNT signaling and illuminates WNT/β-catenin activity in other developmental and diseased contexts.
ContributorsCutts, Joshua Patrick (Author) / Brafman, David A (Thesis advisor) / Stabenfeldt, Sarah (Committee member) / Nikkhah, Mehdi (Committee member) / Wang, Xiao (Committee member) / Plaisier, Christopher (Committee member) / Arizona State University (Publisher)
Created2019