Browsing by Author "Mooney, Sean D."

Now showing 1 - 7 of 7
  • Thumbnail Image
    Item
    Bioinformatics
    (Springer Nature, 2006) Altman, Russ B.; Mooney, Sean D.; Department of Medicine, Indiana University School of Medicine
    After reading this chapter, you should know the answers to these questions: Why is sequence, structure, and biological pathway information relevant to medicine? Where on the Internet should you look for a DNA sequence, a protein sequence, or a protein structure? What are two problems encountered in analyzing biological sequence, structure, and function? How has the age of genomics changed the landscape of bioinformatics? What two changes should we anticipate in the medical record as a result of these new information sources? What are two computational challenges in bioinformatics for the future?
  • Thumbnail Image
    Item
    Differential role of PI-3Kinase p85 (α & β) regulatory subunits in mast cell development
    (2011-08) Krishnan, Subha; Kapur, Reuben; Wek, Ronald C.; Quilliam, Lawrence; Mooney, Sean D.
    Stem cell factor (SCF) mediated c-Kit signaling, and downstream activation of Phosphatidylinositol-3 Kinase (PI-3K) is critical for multiple biological effects mediated by mast cells. Mast cells express multiple regulatory subunits of PI-3Kinase, including p85α, p85β, p50α and p55α. In the present study, we have examined the relationship between p85α and p85β subunit in mast cell development and show that loss of p85α in mast cell progenitors impairs their growth, maturation and survival whereas loss of p85β enhances this process. To further delineate the mechanism (s) by which p85α provides specificity to mast cell biology, we compared the amino acid sequences between p85α and p85β subunits. The two isoforms share significant structural homology in the two SH2 domains, but show significant differences in the N-terminal SH3 domain as well as the BCR homology domain. To determine whether the c-Kit induced reduction in growth of mast cells is contributed via the N-terminal SH3 or the BCR homology domain, we cloned and expressed the shorter splice variant p50α, and various truncated mutant versions of p85α in p85α deficient mast cells. We demonstrate both invitro and invivo that while the SH3 and the BH domains of p85 are dispensable for mast cell maturation; they are essential for normal growth and survival. In contrary to existing dogma on redundant functional role of PI-3K regulatory subunits, this study proves that p85α and p85β regulatory subunits of PI-3K have unique roles in mast cell development. We prove that p85α deficiency impairs the expression of multiple growth, survival and maturation related genes whereas p85β deficiency inhibits c-Kit receptor internalization and degradation. This novel finding on negative role of p85β in mast cell development has significant clinical implication, as this knowledge could be used to develop treatments for mast-cell-associated leukemia and mastocytosis.
  • Thumbnail Image
    Item
    Facilitating Pharmacogenetic Association Studies Using an Extensible Genotype Information Management System
    (2009-11-05T21:29:06Z) Fletcher, Rebecca; Mooney, Sean D.
    Large-scale genome data projects employing automated, high-throughput techniques have led to a deluge of genomic data that necessitate robust informatic solutions. COBRA-DB is an integrated web-based genome information management system that provides storage for pharmacogenomic information including genotypic, phenotypic and resequencing data. The system provides an integrated solution for the acquisition, organization, storage, retrieval and analysis of pharmacogenomic data and offers a platform for genome annotation and analysis. The system also includes an export utility to automate submission of data to other bioinformatic resources and public data repositories. A web interface provides flexible data import and export options and allows users to access and download data via simple query forms. The COBRA database is dedicated to the efficient management of pharmacogenomic data with the intent to facilitate genotype-phenotype association studies and catalyze pharmacogenomic research. COBRA-DB is an internal, proprietary application in use by the Division of Clinical Pharmacology at Indiana University School of Medicine.
  • Thumbnail Image
    Item
    Mining for Conserved Motifs and Significant Functions in S. Mansoni Cercarial Secretions
    Schmidbauer, Amy L.; Mooney, Sean D.
    Schistosomiasis is a disease caused by a parasitic flatworm of the genus Schistosoma. It infects an estimated 200 million people and 165 million head of livestock worldwide. There is medical interest in characterizing the parasitic proteins that interact with the human host for either the development of vaccines or the identification of drug targets. The cercarial secretome and adult tegument sub-proteome of S. mansoni have both been recently published (Knudsen, Medzihradszky et al. 2005) (van Balkom, van Gestel et al. 2005). As secretome proteins are secreted extracellularly, and tegument sub-proteome proteins are anchored in the cellular membrane, we hypothesize that both sets of proteins employ similar secretion machinery and mechanisms. Motivated by the discovery in the malarian parasite, Plasmodium falciparum, of conserved sequence motifs that are required for export downstream of N-terminal signal sequences (Hiller, Bhattacharjee et al. 2004), S. mansoni secretory and tegumental proteins were analyzed for conserved motifs using recursive iterations of MEME and MAST. To compliment the conserved motif analysis, an automated workflow to process InterProScan functional domain and GO annotation data, that employs statistical methods for determining significant functions, was developed. A conserved motif, enriched in the mechanically-induced vesicle secretion proteins, was elucidated and insight was gained into both the functions of proteins found to contain the motif, as well as the effects of different cercarial secretion induction methods. A hypothesis of the secretion model empoloyed by the invading parasite was generated.
  • Thumbnail Image
    Item
    MutDB: update on development of tools for the biochemical analysis of genetic variation
    (Oxford University Press, 2007-09-07) Singh, Arti; Olowoyeye, Adebayo; Baenziger, Peter H.; Dantzer, Jessica; Kann, Maricel G.; Radivojac, Predrag; Heiland, Randy; Mooney, Sean D.; Medical and Molecular Genetics, School of Medicine
    Understanding how genetic variation affects the molecular function of gene products is an emergent area of bioinformatic research. Here, we present updates to MutDB ( http://www.mutdb.org ), a tool aiming to aid bioinformatic studies by integrating publicly available databases of human genetic variation with molecular features and clinical phenotype data. MutDB, first developed in 2002, integrates annotated SNPs in dbSNP and amino acid substitutions in Swiss-Prot with protein structural information, links to scores that predict functional disruption and other useful annotations. Though these functional annotations are mainly focused on nonsynonymous SNPs, some information on other SNP types included in dbSNP is also provided. Additionally, we have developed a new functionality that facilitates KEGG pathway visualization of genes containing SNPs and a SNP query tool for visualizing and exporting sets of SNPs that share selected features based on certain filters.
  • Thumbnail Image
    Item
    Rationale and design of the Kidney Precision Medicine Project
    (Elsevier, 2021) de Boer, Ian H.; Alpers, Charles E.; Azeloglu, Evren U.; Balis, Ulysses G. J.; Barasch, Jonathan M.; Barisoni, Laura; Blank, Kristina N.; Bomback, Andrew S.; Brown, Keith; Dagher, Pierre C.; Dighe, Ashveena L.; Eadon, Michael T.; El-Achkar, Tarek M.; Gaut, Joseph P.; Hacohen, Nir; He, Yongqun; Hodgin, Jeffrey B.; Jain, Sanjay; Kellum, John A.; Kiryluk, Krzysztof; Knight, Richard; Laszik, Zoltan G.; Lienczewski, Chrysta; Mariani, Laura H.; McClelland, Robyn L.; Menez, Steven; Moledina, Dennis G.; Mooney, Sean D.; O'Toole, John F.; Palevsky, Paul M.; Parikh, Chirag R.; Poggio, Emilio D.; Rosas, Sylvia E.; Rosengart, Matthew R.; Sarwal, Minnie M.; Schaub, Jennifer A.; Sedor, John R.; Sharma, Kumar; Steck, Becky; Toto, Robert D.; Troyanskaya, Olga G.; Tuttle, Katherine R.; Vazquez, Miguel A.; Waikar, Sushrut S.; Williams, Kayleen; Wilson, Francis Perry; Zhang, Kun; Iyengar, Ravi; Kretzler, Matthias; Himmelfarb, Jonathan; Kidney Precision Medicine Project; Medicine, School of Medicine
    Chronic kidney disease (CKD) and acute kidney injury (AKI) are common, heterogeneous, and morbid diseases. Mechanistic characterization of CKD and AKI in patients may facilitate a precision-medicine approach to prevention, diagnosis, and treatment. The Kidney Precision Medicine Project aims to ethically and safely obtain kidney biopsies from participants with CKD or AKI, create a reference kidney atlas, and characterize disease subgroups to stratify patients based on molecular features of disease, clinical characteristics, and associated outcomes. An additional aim is to identify critical cells, pathways, and targets for novel therapies and preventive strategies. This project is a multicenter prospective cohort study of adults with CKD or AKI who undergo a protocol kidney biopsy for research purposes. This investigation focuses on kidney diseases that are most prevalent and therefore substantially burden the public health, including CKD attributed to diabetes or hypertension and AKI attributed to ischemic and toxic injuries. Reference kidney tissues (for example, living-donor kidney biopsies) will also be evaluated. Traditional and digital pathology will be combined with transcriptomic, proteomic, and metabolomic analysis of the kidney tissue as well as deep clinical phenotyping for supervised and unsupervised subgroup analysis and systems biology analysis. Participants will be followed prospectively for 10 years to ascertain clinical outcomes. Cell types, locations, and functions will be characterized in health and disease in an open, searchable, online kidney tissue atlas. All data from the Kidney Precision Medicine Project will be made readily available for broad use by scientists, clinicians, and patients.
  • Thumbnail Image
    Item
    Using RNase sequence specificity to refine the identification of RNA-protein binding regions
    (BioMed Central, 2008-03-20) Wang, Xin; Wang, Guohua; Shen, Changyu; Li, Lang; Wang, Xinguo; Mooney, Sean D.; Edenberg, Howard J.; Sanford, Jeremy R.; Liu, Yunlong; Medicine, School of Medicine
    Massively parallel pyrosequencing is a high-throughput technology that can sequence hundreds of thousands of DNA/RNA fragments in a single experiment. Combining it with immunoprecipitation-based biochemical assays, such as cross-linking immunoprecipitation (CLIP), provides a genome-wide method to detect the sites at which proteins bind DNA or RNA. In a CLIP-pyrosequencing experiment, the resolutions of the detected protein binding regions are partially determined by the length of the detected RNA fragments (CLIP amplicons) after trimming by RNase digestion. The lengths of these fragments usually range from 50-70 nucleotides. Many genomic regions are marked by multiple RNA fragments. In this paper, we report an empirical approach to refine the localization of protein binding regions by using the distribution pattern of the detected RNA fragments and the sequence specificity of RNase digestion. We present two regions to which multiple amplicons map as examples to demonstrate this approach.