(LCCS3) Is Caused by a Mutation in PIP5K1C, Which Encodes Pipkig of the Phophatidylinsitol Pathway
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PARSANA-DISSERTATION-2020.Pdf
DECIPHERING TRANSCRIPTIONAL PATTERNS OF GENE REGULATION: A COMPUTATIONAL APPROACH by Princy Parsana A dissertation submitted to The Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland July, 2020 © 2020 Princy Parsana All rights reserved Abstract With rapid advancements in sequencing technology, we now have the ability to sequence the entire human genome, and to quantify expression of tens of thousands of genes from hundreds of individuals. This provides an extraordinary opportunity to learn phenotype relevant genomic patterns that can improve our understanding of molecular and cellular processes underlying a trait. The high dimensional nature of genomic data presents a range of computational and statistical challenges. This dissertation presents a compilation of projects that were driven by the motivation to efficiently capture gene regulatory patterns in the human transcriptome, while addressing statistical and computational challenges that accompany this data. We attempt to address two major difficulties in this domain: a) artifacts and noise in transcriptomic data, andb) limited statistical power. First, we present our work on investigating the effect of artifactual variation in gene expression data and its impact on trans-eQTL discovery. Here we performed an in-depth analysis of diverse pre-recorded covariates and latent confounders to understand their contribution to heterogeneity in gene expression measurements. Next, we discovered 673 trans-eQTLs across 16 human tissues using v6 data from the Genotype Tissue Expression (GTEx) project. Finally, we characterized two trait-associated trans-eQTLs; one in Skeletal Muscle and another in Thyroid. Second, we present a principal component based residualization method to correct gene expression measurements prior to reconstruction of co-expression networks. -
Two Locus Inheritance of Non-Syndromic Midline Craniosynostosis Via Rare SMAD6 and 4 Common BMP2 Alleles 5 6 Andrew T
1 2 3 Two locus inheritance of non-syndromic midline craniosynostosis via rare SMAD6 and 4 common BMP2 alleles 5 6 Andrew T. Timberlake1-3, Jungmin Choi1,2, Samir Zaidi1,2, Qiongshi Lu4, Carol Nelson- 7 Williams1,2, Eric D. Brooks3, Kaya Bilguvar1,5, Irina Tikhonova5, Shrikant Mane1,5, Jenny F. 8 Yang3, Rajendra Sawh-Martinez3, Sarah Persing3, Elizabeth G. Zellner3, Erin Loring1,2,5, Carolyn 9 Chuang3, Amy Galm6, Peter W. Hashim3, Derek M. Steinbacher3, Michael L. DiLuna7, Charles 10 C. Duncan7, Kevin A. Pelphrey8, Hongyu Zhao4, John A. Persing3, Richard P. Lifton1,2,5,9 11 12 1Department of Genetics, Yale University School of Medicine, New Haven, CT, USA 13 2Howard Hughes Medical Institute, Yale University School of Medicine, New Haven, CT, USA 14 3Section of Plastic and Reconstructive Surgery, Department of Surgery, Yale University School of Medicine, New Haven, CT, USA 15 4Department of Biostatistics, Yale University School of Medicine, New Haven, CT, USA 16 5Yale Center for Genome Analysis, New Haven, CT, USA 17 6Craniosynostosis and Positional Plagiocephaly Support, New York, NY, USA 18 7Department of Neurosurgery, Yale University School of Medicine, New Haven, CT, USA 19 8Child Study Center, Yale University School of Medicine, New Haven, CT, USA 20 9The Rockefeller University, New York, NY, USA 21 22 ABSTRACT 23 Premature fusion of the cranial sutures (craniosynostosis), affecting 1 in 2,000 24 newborns, is treated surgically in infancy to prevent adverse neurologic outcomes. To 25 identify mutations contributing to common non-syndromic midline (sagittal and metopic) 26 craniosynostosis, we performed exome sequencing of 132 parent-offspring trios and 59 27 additional probands. -
Selection Signatures in Two Oldest Russian Native Cattle Breeds Revealed Using High- Density Single Nucleotide Polymorphism Analysis
PLOS ONE RESEARCH ARTICLE Selection signatures in two oldest Russian native cattle breeds revealed using high- density single nucleotide polymorphism analysis Natalia Anatolievna Zinovieva1*, Arsen Vladimirovich Dotsev1, Alexander Alexandrovich Sermyagin1, Tatiana Evgenievna Deniskova1, Alexandra 1 1 2 Sergeevna Abdelmanova , Veronika Ruslanovna KharzinovaID , Johann SoÈ lkner , a1111111111 Henry Reyer3, Klaus Wimmers3, Gottfried Brem1,4 a1111111111 a1111111111 1 L.K. Ernst Federal Science Center for Animal Husbandry, Federal Agency of Scientific Organizations, settl. Dubrovitzy, Podolsk Region, Moscow Province, Russia, 2 Division of Livestock Sciences, University of a1111111111 Natural Resources and Life Sciences, Vienna, Austria, 3 Institute of Genome Biology, Leibniz Institute for a1111111111 Farm Animal Biology [FBN], Dummerstorf, Germany, 4 Institute of Animal Breeding and Genetics, University of Veterinary Medicine [VMU], Vienna, Austria * [email protected] OPEN ACCESS Citation: Zinovieva NA, Dotsev AV, Sermyagin AA, Abstract Deniskova TE, Abdelmanova AS, Kharzinova VR, et al. (2020) Selection signatures in two oldest Native cattle breeds can carry specific signatures of selection reflecting their adaptation to Russian native cattle breeds revealed using high- the local environmental conditions and response to the breeding strategy used. In this density single nucleotide polymorphism analysis. study, we comprehensively analysed high-density single nucleotide polymorphism (SNP) PLoS ONE 15(11): e0242200. https://doi.org/ genotypes -
A Novel Resveratrol Analog: Its Cell Cycle Inhibitory, Pro-Apoptotic and Anti-Inflammatory Activities on Human Tumor Cells
A NOVEL RESVERATROL ANALOG : ITS CELL CYCLE INHIBITORY, PRO-APOPTOTIC AND ANTI-INFLAMMATORY ACTIVITIES ON HUMAN TUMOR CELLS A dissertation submitted to Kent State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy by Boren Lin May 2006 Dissertation written by Boren Lin B.S., Tunghai University, 1996 M.S., Kent State University, 2003 Ph. D., Kent State University, 2006 Approved by Dr. Chun-che Tsai , Chair, Doctoral Dissertation Committee Dr. Bryan R. G. Williams , Co-chair, Doctoral Dissertation Committee Dr. Johnnie W. Baker , Members, Doctoral Dissertation Committee Dr. James L. Blank , Dr. Bansidhar Datta , Dr. Gail C. Fraizer , Accepted by Dr. Robert V. Dorman , Director, School of Biomedical Sciences Dr. John R. Stalvey , Dean, College of Arts and Sciences ii TABLE OF CONTENTS LIST OF FIGURES……………………………………………………………….………v LIST OF TABLES……………………………………………………………………….vii ACKNOWLEDGEMENTS….………………………………………………………….viii I INTRODUCTION….………………………………………………….1 Background and Significance……………………………………………………..1 Specific Aims………………………………………………………………………12 II MATERIALS AND METHODS.…………………………………………….16 Cell Culture and Compounds…….……………….…………………………….….16 MTT Cell Viability Assay………………………………………………………….16 Trypan Blue Exclusive Assay……………………………………………………...18 Flow Cytometry for Cell Cycle Analysis……………..……………....……………19 DNA Fragmentation Assay……………………………………………...…………23 Caspase-3 Activity Assay………………………………...……….….…….………24 Annexin V-FITC Staining Assay…………………………………..…...….………28 NF-kappa B p65 Activity Assay……………………………………..………….…29 -
Supplementary Materials
Supplementary materials Supplementary Table S1: MGNC compound library Ingredien Molecule Caco- Mol ID MW AlogP OB (%) BBB DL FASA- HL t Name Name 2 shengdi MOL012254 campesterol 400.8 7.63 37.58 1.34 0.98 0.7 0.21 20.2 shengdi MOL000519 coniferin 314.4 3.16 31.11 0.42 -0.2 0.3 0.27 74.6 beta- shengdi MOL000359 414.8 8.08 36.91 1.32 0.99 0.8 0.23 20.2 sitosterol pachymic shengdi MOL000289 528.9 6.54 33.63 0.1 -0.6 0.8 0 9.27 acid Poricoic acid shengdi MOL000291 484.7 5.64 30.52 -0.08 -0.9 0.8 0 8.67 B Chrysanthem shengdi MOL004492 585 8.24 38.72 0.51 -1 0.6 0.3 17.5 axanthin 20- shengdi MOL011455 Hexadecano 418.6 1.91 32.7 -0.24 -0.4 0.7 0.29 104 ylingenol huanglian MOL001454 berberine 336.4 3.45 36.86 1.24 0.57 0.8 0.19 6.57 huanglian MOL013352 Obacunone 454.6 2.68 43.29 0.01 -0.4 0.8 0.31 -13 huanglian MOL002894 berberrubine 322.4 3.2 35.74 1.07 0.17 0.7 0.24 6.46 huanglian MOL002897 epiberberine 336.4 3.45 43.09 1.17 0.4 0.8 0.19 6.1 huanglian MOL002903 (R)-Canadine 339.4 3.4 55.37 1.04 0.57 0.8 0.2 6.41 huanglian MOL002904 Berlambine 351.4 2.49 36.68 0.97 0.17 0.8 0.28 7.33 Corchorosid huanglian MOL002907 404.6 1.34 105 -0.91 -1.3 0.8 0.29 6.68 e A_qt Magnogrand huanglian MOL000622 266.4 1.18 63.71 0.02 -0.2 0.2 0.3 3.17 iolide huanglian MOL000762 Palmidin A 510.5 4.52 35.36 -0.38 -1.5 0.7 0.39 33.2 huanglian MOL000785 palmatine 352.4 3.65 64.6 1.33 0.37 0.7 0.13 2.25 huanglian MOL000098 quercetin 302.3 1.5 46.43 0.05 -0.8 0.3 0.38 14.4 huanglian MOL001458 coptisine 320.3 3.25 30.67 1.21 0.32 0.9 0.26 9.33 huanglian MOL002668 Worenine -
Gene Ontology Functional Annotations and Pleiotropy
Network based analysis of genetic disease associations Sarah Gilman Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy under the Executive Committee of the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2014 © 2013 Sarah Gilman All Rights Reserved ABSTRACT Network based analysis of genetic disease associations Sarah Gilman Despite extensive efforts and many promising early findings, genome-wide association studies have explained only a small fraction of the genetic factors contributing to common human diseases. There are many theories about where this “missing heritability” might lie, but increasingly the prevailing view is that common variants, the target of GWAS, are not solely responsible for susceptibility to common diseases and a substantial portion of human disease risk will be found among rare variants. Relatively new, such variants have not been subject to purifying selection, and therefore may be particularly pertinent for neuropsychiatric disorders and other diseases with greatly reduced fecundity. Recently, several researchers have made great progress towards uncovering the genetics behind autism and schizophrenia. By sequencing families, they have found hundreds of de novo variants occurring only in affected individuals, both large structural copy number variants and single nucleotide variants. Despite studying large cohorts there has been little recurrence among the genes implicated suggesting that many hundreds of genes may underlie these complex phenotypes. The question -
Open Data for Differential Network Analysis in Glioma
International Journal of Molecular Sciences Article Open Data for Differential Network Analysis in Glioma , Claire Jean-Quartier * y , Fleur Jeanquartier y and Andreas Holzinger Holzinger Group HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036 Graz, Austria; [email protected] (F.J.); [email protected] (A.H.) * Correspondence: [email protected] These authors contributed equally to this work. y Received: 27 October 2019; Accepted: 3 January 2020; Published: 15 January 2020 Abstract: The complexity of cancer diseases demands bioinformatic techniques and translational research based on big data and personalized medicine. Open data enables researchers to accelerate cancer studies, save resources and foster collaboration. Several tools and programming approaches are available for analyzing data, including annotation, clustering, comparison and extrapolation, merging, enrichment, functional association and statistics. We exploit openly available data via cancer gene expression analysis, we apply refinement as well as enrichment analysis via gene ontology and conclude with graph-based visualization of involved protein interaction networks as a basis for signaling. The different databases allowed for the construction of huge networks or specified ones consisting of high-confidence interactions only. Several genes associated to glioma were isolated via a network analysis from top hub nodes as well as from an outlier analysis. The latter approach highlights a mitogen-activated protein kinase next to a member of histondeacetylases and a protein phosphatase as genes uncommonly associated with glioma. Cluster analysis from top hub nodes lists several identified glioma-associated gene products to function within protein complexes, including epidermal growth factors as well as cell cycle proteins or RAS proto-oncogenes. -
PTK6 Inhibition Promotes Apoptosis of Lapatinib-Resistant Her2+ Breast
Park et al. Breast Cancer Research (2015) 17:86 DOI 10.1186/s13058-015-0594-z RESEARCH ARTICLE Open Access PTK6 inhibition promotes apoptosis of Lapatinib-resistant Her2+ breast cancer cells by inducing Bim Sun Hee Park1, Koichi Ito1, William Olcott1, Igor Katsyv1, Gwyneth Halstead-Nussloch1 and Hanna Y. Irie1,2* Abstract Introduction: Protein tyrosine kinase 6 (PTK6) is a non-receptor tyrosine kinase that is highly expressed in Human Epidermal Growth Factor 2+ (Her2+) breast cancers. Overexpression of PTK6 enhances anchorage-independent survival, proliferation, and migration of breast cancer cells. We hypothesized that PTK6 inhibition is an effective strategy to inhibit growth and survival of Her2+ breast cancer cells, including those that are relatively resistant to Lapatinib, a targeted therapy for Her2+ breast cancer, either intrinsically or acquired after continuous drug exposure. Methods: To determine the effects of PTK6 inhibition on Lapatinib-resistant Her2+ breast cancer cell lines (UACC893R1 and MDA-MB-453), we used short hairpin ribonucleic acid (shRNA) vectors to downregulate PTK6 expression. We determined the effects of PTK6 downregulation on growth and survival in vitro and in vivo, as well as the mechanisms responsible for these effects. Results: Lapatinib treatment of “sensitive” Her2+ cells induces apoptotic cell death and enhances transcript and protein levels of Bim, a pro-apoptotic Bcl2 family member. In contrast, treatment of relatively “resistant” Her2+ cells fails to induce Bim or enhance levels of cleaved, poly-ADP ribose polymerase (PARP). Downregulation of PTK6 expression in these “resistant” cells enhances Bim expression, resulting in apoptotic cell death. PTK6 downregulation impairs growth of these cells in in vitro 3-D MatrigelTM cultures, and also inhibits growth of Her2+ primary tumor xenografts. -
Identification of Protein Partners for Nibp, a Novel Nik-And Ikkb-Binding Protein Through Experimental, Computational and Bioinformatics Techniques
IDENTIFICATION OF PROTEIN PARTNERS FOR NIBP, A NOVEL NIK-AND IKKB-BINDING PROTEIN THROUGH EXPERIMENTAL, COMPUTATIONAL AND BIOINFORMATICS TECHNIQUES A Thesis Submitted to the Temple University Graduate Board In Partial Fulfillment of the Requirements for the Degree MASTER OF SCIENCE by Sombudha Adhikari August 2013 Thesis Approvals: Mohammad. F. Kiani, Ph.D., Thesis Advisor, College of Engineering Wenhui Hu, M.D., Ph.D., School of Medicine Roland Dunbrack, Ph.D, Fox Chase Cancer Center Peter Lelkes, Ph.D., College of Engineering ABSTRACT Identification of protein partners for NIBP, a novel NIK- and IKK β-binding protein through experimental, computational and bioinformatics techniques NIBP is a prototype member of a novel protein family. It forms a novel subcomplex of NIK-NIBP-IKK β and enhances cytokine-induced IKK β-mediated NF κB activation. It is also named TRAPPC9 as a key member of trafficking particle protein (TRAPP) complex II, which is essential in trans -Golgi networking (TGN). The signaling pathways and molecular mechanisms for NIBP actions remain largely unknown. The aim of this research is to identify potential proteins interacting with NIBP, resulting in the regulation of NF κB signaling pathways and other unknown signaling pathways. At Dr. Wenhui Hu’s lab in the Department of Neuroscience, Temple University, sixteen partner proteins were experimentally identified that potentially bind to NIBP. NIBP is a novel protein with no entry in the Protein Data Bank. From a computational and bioinformatics standpoint, we use prediction of secondary structure and protein disorder as well as homology-based structural modeling approaches to create a hypothesis on protein-protein interaction between NIBP and the partner proteins. -
The Human Gene Connectome As a Map of Short Cuts for Morbid Allele Discovery
The human gene connectome as a map of short cuts for morbid allele discovery Yuval Itana,1, Shen-Ying Zhanga,b, Guillaume Vogta,b, Avinash Abhyankara, Melina Hermana, Patrick Nitschkec, Dror Friedd, Lluis Quintana-Murcie, Laurent Abela,b, and Jean-Laurent Casanovaa,b,f aSt. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY 10065; bLaboratory of Human Genetics of Infectious Diseases, Necker Branch, Paris Descartes University, Institut National de la Santé et de la Recherche Médicale U980, Necker Medical School, 75015 Paris, France; cPlateforme Bioinformatique, Université Paris Descartes, 75116 Paris, France; dDepartment of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel; eUnit of Human Evolutionary Genetics, Centre National de la Recherche Scientifique, Unité de Recherche Associée 3012, Institut Pasteur, F-75015 Paris, France; and fPediatric Immunology-Hematology Unit, Necker Hospital for Sick Children, 75015 Paris, France Edited* by Bruce Beutler, University of Texas Southwestern Medical Center, Dallas, TX, and approved February 15, 2013 (received for review October 19, 2012) High-throughput genomic data reveal thousands of gene variants to detect a single mutated gene, with the other polymorphic genes per patient, and it is often difficult to determine which of these being of less interest. This goes some way to explaining why, variants underlies disease in a given individual. However, at the despite the abundance of NGS data, the discovery of disease- population level, there may be some degree of phenotypic homo- causing alleles from such data remains somewhat limited. geneity, with alterations of specific physiological pathways under- We developed the human gene connectome (HGC) to over- come this problem. -
Assessment of Network Module Identification Across Complex Diseases
ANALYSIS https://doi.org/10.1038/s41592-019-0509-5 Assessment of network module identification across complex diseases Sarvenaz Choobdar1,2,20, Mehmet E. Ahsen3,117, Jake Crawford4,117, Mattia Tomasoni 1,2, Tao Fang5, David Lamparter1,2,6, Junyuan Lin7, Benjamin Hescott8, Xiaozhe Hu7, Johnathan Mercer9,10, Ted Natoli11, Rajiv Narayan11, The DREAM Module Identification Challenge Consortium12, Aravind Subramanian11, Jitao D. Zhang 5, Gustavo Stolovitzky 3,13, Zoltán Kutalik2,14, Kasper Lage 9,10,15, Donna K. Slonim 4,16, Julio Saez-Rodriguez 17,18, Lenore J. Cowen4,7, Sven Bergmann 1,2,19,21* and Daniel Marbach 1,2,5,21* Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the ‘Disease Module Identification DREAM Challenge’, an open competition to comprehensively assess module identification methods across diverse protein–protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait- associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often com- prise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology. omplex diseases involve many genes and molecules that inter- assessed on in silico generated benchmark graphs11. -
Datasheet: AHP633 Product Details
Datasheet: AHP633 Description: GOAT ANTI HUMAN BKS (C-TERMINAL) Specificity: BKS (C-TERMINAL) Format: Purified Product Type: Polyclonal Antibody Isotype: Polyclonal IgG Quantity: 0.1 mg Product Details Applications This product has been reported to work in the following applications. This information is derived from testing within our laboratories, peer-reviewed publications or personal communications from the originators. Please refer to references indicated for further information. For general protocol recommendations, please visit www.bio-rad-antibodies.com/protocols. Yes No Not Determined Suggested Dilution Flow Cytometry Immunohistology - Frozen Immunohistology - Paraffin ELISA Immunoprecipitation Western Blotting 1ug/ml - 3ug/ml Where this antibody has not been tested for use in a particular technique this does not necessarily exclude its use in such procedures. It is recommended that the user titrates the antibody for use in their own system using appropriate negative/positive controls. Target Species Human Product Form Purified IgG - liquid Antiserum Preparation Antisera to human BKS were raised by repeated immunisations of goats with highly purified antigen. Purified IgG was prepared from whole serum by affinity chromatography. Buffer Solution TRIS buffered saline Preservative 0.02% Sodium Azide Stabilisers 0.5% Bovine Serum Albumin Approx. Protein IgG concentration 0.5 mg/ml Concentrations Immunogen Peptide sequence ELQKKLEKRRALEH corresponding to the C-terminal region of human BKS. External Database Links UniProt: Q9UGK3 Related reagents Entrez Gene: Page 1 of 2 55620 STAP2 Related reagents Synonyms BKS Specificity Goat anti Human BKS antibody recognizes human BKS, an SH2 domain containing adaptor-like protein, which is a substrate for non-receptor tyrosine kinase BRK, BKS protein is expressed in most adult human tissues.