System-Level Modeling of Endothelial Permeability Pathway and High-throughput Data Analysis for Disease Biomarker Selection

Ph.DDOCTOR OF PHILOSOPH

Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics

This book is a collection of original research articles in the field of computer-aided drug design. It reports the use of current and validated computational approaches applied to drug discovery as well as the development of new computational tools to identify new and more potent drugs

Theoretical and computational modeling of rna-ligand interactions

Ribonucleic acid (RNA) is a polymeric nucleic acid that plays a variety of critical roles in gene expression and regulation at the level of transcription and translation. Recently, there has been an enormous interest in the development of therapeutic strategies that target RNA molecules. Instead of modifying the product of gene expression, i.e., proteins, RNAtargeted therapeutics aims to modulate the relevant key RNA elements in the disease-related cellular pathways. Such approaches have two significant advantages. First, diseases with related proteins that are difficult or unable to be drugged become druggable by targeting the corresponding messenger RNAs (mRNAs) that encode the amino acid sequences. Second, besides coding mRNAs, the vast majority of the human genome sequences are transcribed to noncoding RNAs (ncRNAs), which serve as enzymatic, structural, and regulatory elements in cellular pathways of most human diseases. Targeting noncoding RNAs would open up remarkable new opportunities for disease treatment. The first step in modeling the RNA-drug interaction is to understand the 3D structure of the given RNA target. With current theoretical models, accurate prediction of 3D structures for large RNAs from sequence remains computationally infeasible. One of the major challenges comes from the flexibility in the RNA molecule, especially in loop/junction regions, and the resulting rugged energy landscape. However, structure probing techniques, such as the “selective 20-hydroxyl acylation analyzed by primer extension” (SHAPE) experiment, enable the quantitative detection of the relative flexibility and hence structure information of RNA structural elements. Therefore, one may incorporate the SHAPE data into RNA 3D structure prediction. In the first project, we investigate the feasibility of using a machine-learning-based approach to predict the SHAPE reactivity from the 3D RNA structure and compare the machine-learning result to that of a physics-based model. In the second project, in order to provide a user-friendly tool for RNA biologists, we developed a fully automated web interface, “SHAPE predictoR” (SHAPER) for predicting SHAPE profile from any given 3D RNA structure. In a cellular environment, various factors, such as metal ions and small molecules, interact with an RNA molecule to modulate RNA cellular activity. RNA is a highly charged polymer with each backbone phosphate group carrying one unit of negative (electronic) charge. In order to fold into a compact functional tertiary structure, it requires metal ions to reduce Coulombic repulsive electrostatic forces by neutralizing the backbone charges. In particular, Mg2+ ion is essential for the folding and stability of RNA tertiary structures. In the third project, we introduce a machine-learning-based model, the “Magnesium convolutional neural network” (MgNet) model, to predict Mg2+ binding site for a given 3D RNA structure, and show the use of the model in investigating the important coordinating RNA atoms and identifying novel Mg2+ binding motifs. Besides Mg2+ ions, small molecules, such as drug molecules, can also bind to an RNA to modulate its activities. Motivated by the tremendous potential of RNA-targeted drug discovery, in the fourth project, we develop a novel approach to predicting RNA-small molecule binding. Specifically, we develop a statistical potential-based scoring/ranking method (SPRank) to identify the native binding mode of the small molecule from a pool of decoys and estimate the binding affinity for the given RNA-small molecule complex. The results tested on a widely used data set suggest that SPRank can achieve (moderately) better performance than the current state-of-art models

To Find and to Form: Cellular Strategies for Intracellular Target Search and Higher-Order Assembly

Eukaryotic RNA-protein complexes have been widely reported to form membrane-less, higher-order assemblies inside cells under a range of conditions. How these structures contribute to the regulation of intracellular biochemistry remains poorly understood. Recent biophysical studies have revealed how phase-separation, a passive, thermodynamically driven process, can explain the assembly of such structures, referred to as condensates. This dissertation explores the relationship between macromolecular interactions that mediate the formation of dynamic condensates and the biochemical consequences of the resulting reorganization of the intracellular space. Organized into three parts, it implements and leverages new live-cell fluorescence microscopy approaches to visualize the formation of and localization of RNAs to condensates in real-time and at single-molecule resolution to address fundamental questions around intracellular biochemical regulation. First, the dissertation explores the RNA-sequence and protein translation-dependence of RNA localization to intracellular condensates called P-bodies. This work revealed that RNAs in P-bodies localize differently to the periphery or the core of these condensates depending on their translatability, and that stable RNA localization requires specific RNA-protein interactions. It next provides evidence for ubiquitous, proteome-wide, homomultimerization-driven phase-separation in response to osmotic volume fluctuations. These observations expand the molecular grammar of protein domains known to drive phase-separation, suggesting that a large fraction of the proteome may be poised to undergo rapid spatial reorganization upon small perturbations in intracellular molecular crowding. Additionally, these results provide possible explanations for previously reported features of osmotic stress response, by suggesting that hyperosmolarity-induced phase-separation of CPSF6 protein might provide a mechanistic basis for the widespread loss of premRNA cleavage activity under such conditions. These observations paint a new picture of the nature of the intracellular milieu, in which the organization of the intracellular space is inextricably linked with the macromolecular sequence of its constituents, where the concentration of individual molecular species can affect both its biochemical function and spatial organization. In the third part, the thesis discusses evidence that microRNA-induced silencing complexes may use a two-pronged strategy to search for mRNA targets inside the cell: on the one hand, transient binding and 3D search allow for rapid exploration; on the other hand, induced clustering of target mRNAs reduces the search space, such that these complexes can efficiently engage with their targets even when the concentration is limiting. Comparing the kinetics of individual microRNA-mRNA interactions in the cell across a range of mRNAs differing in the number of microRNA binding sites suggests that binding site number, a conserved feature of mRNAs, serves to both stabilize microRNA binding and promote AGO2-dependent clustering of mRNAs. This work refines an emerging paradigm in cell biology in which the intracellular space, far from being spatially homogeneous, is highly compartmentalized. Further, it demonstrates that such compartmentalization can be highly dynamic, and this dynamic organization is encoded by macromolecular sequence and biochemical activity. By applying single particle tracking to understand the assembly of intracellular condensate dynamics, this work opens up new ways for studying non-equilibrium phase separation and condensate formation in cells. Studying molecular association processes at single-molecule resolution in living cells represents a significant advance in quantitative cell biology by bridging single-molecule measurements in vitro and qualitative observations in vivo. This dissertation therefore advances the study of intracellular biochemistry by describing new methods and by applying them to uncover insights into the relationship between macromolecular sequence and subcellular organization.PHDCellular & Molecular BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168104/1/ameyapj_1.pd

Novel Algorithm Development for ‘NextGeneration’ Sequencing Data Analysis

In recent years, the decreasing cost of ‘Next generation’ sequencing has spawned numerous applications for interrogating whole genomes and transcriptomes in research, diagnostic and forensic settings. While the innovations in sequencing have been explosive, the development of scalable and robust bioinformatics software and algorithms for the analysis of new types of data generated by these technologies have struggled to keep up. As a result, large volumes of NGS data available in public repositories are severely underutilised, despite providing a rich resource for data mining applications. Indeed, the bottleneck in genome and transcriptome sequencing experiments has shifted from data generation to bioinformatics analysis and interpretation. This thesis focuses on development of novel bioinformatics software to bridge the gap between data availability and interpretation. The work is split between two core topics – computational prioritisation/identification of disease gene variants and identification of RNA N6 -adenosine Methylation from sequencing data. The first chapter briefly discusses the emergence and establishment of NGS technology as a core tool in biology and its current applications and perspectives. Chapter 2 introduces the problem of variant prioritisation in the context of Mendelian disease, where tens of thousands of potential candidates are generated by a typical sequencing experiment. Novel software developed for candidate gene prioritisation is described that utilises data mining of tissue-specific gene expression profiles (Chapter 3). The second part of chapter investigates an alternative approach to candidate variant prioritisation by leveraging functional and phenotypic descriptions of genes and diseases from multiple biomedical domain ontologies (Chapter 4). Chapter 5 discusses N6 AdenosineMethylation, a recently re-discovered posttranscriptional modification of RNA. The core of the chapter describes novel software developed for transcriptome-wide detection of this epitranscriptomic mark from sequencing data. Chapter 6 presents a case study application of the software, reporting the previously uncharacterised RNA methylome of Kaposi’s Sarcoma Herpes Virus. The chapter further discusses a putative novel N6-methyl-adenosine -RNA binding protein and its possible roles in the progression of viral infection

2015 Oklahoma Research Day Full Program

This document contains all abstracts from the 2015 Oklahoma Research Day held at Northeastern State University

Atherogenesis

This monograph will bring out the state-of-the-art advances in the dynamics of cholesterol transport and will address several important issues that pertain to oxidative stress and inflammation. The book is divided into three major sections. The book will offer insights into the roles of specific cytokines, inflammation, and oxidative stress in atherosclerosis and is intended for new researchers who are curious about atherosclerosis as well as for established senior researchers and clinicians who would be interested in novel findings that may link various aspects of the disease

Advances in the Diagnosis and Treatment of Thyroid Carcinoma

This reprint is related to the latest research in the field of thyroid surgery, including molecular and imaging diagnosis, surgical treatment, and the treatment of recurrent disease and advanced thyroid carcinoma