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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. -
Frequent Expression Loss of Inter-Alpha-Trypsin Inhibitor Heavy Chain (ITIH) Genes in Multiple Human Solid Tumors: a Systematic Expression Analysis
Hamm, A; Veeck, J; Bektas, N; Wild, P J; Hartmann, A; Heindrichs, U; Kristiansen, G; Werbowetski-Ogilvie, T; Del Maestro, R; Knuechel, R; Dahl, E (2008). Frequent expression loss of Inter-alpha-trypsin inhibitor heavy chain (ITIH) genes in multiple human solid tumors: a systematic expression analysis. BMC Cancer, 8:25. Postprint available at: http://www.zora.uzh.ch University of Zurich Posted at the Zurich Open Repository and Archive, University of Zurich. Zurich Open Repository and Archive http://www.zora.uzh.ch Originally published at: BMC Cancer 2008, 8:25. Winterthurerstr. 190 CH-8057 Zurich http://www.zora.uzh.ch Year: 2008 Frequent expression loss of Inter-alpha-trypsin inhibitor heavy chain (ITIH) genes in multiple human solid tumors: a systematic expression analysis Hamm, A; Veeck, J; Bektas, N; Wild, P J; Hartmann, A; Heindrichs, U; Kristiansen, G; Werbowetski-Ogilvie, T; Del Maestro, R; Knuechel, R; Dahl, E Hamm, A; Veeck, J; Bektas, N; Wild, P J; Hartmann, A; Heindrichs, U; Kristiansen, G; Werbowetski-Ogilvie, T; Del Maestro, R; Knuechel, R; Dahl, E (2008). Frequent expression loss of Inter-alpha-trypsin inhibitor heavy chain (ITIH) genes in multiple human solid tumors: a systematic expression analysis. BMC Cancer, 8:25. Postprint available at: http://www.zora.uzh.ch Posted at the Zurich Open Repository and Archive, University of Zurich. http://www.zora.uzh.ch Originally published at: BMC Cancer 2008, 8:25. Frequent expression loss of Inter-alpha-trypsin inhibitor heavy chain (ITIH) genes in multiple human solid tumors: a systematic expression analysis Abstract BACKGROUND: The inter-alpha-trypsin inhibitors (ITI) are a family of plasma protease inhibitors, assembled from a light chain - bikunin, encoded by AMBP - and five homologous heavy chains (encoded by ITIH1, ITIH2, ITIH3, ITIH4, and ITIH5), contributing to extracellular matrix stability by covalent linkage to hyaluronan. -
Supplementary Information Changes in the Plasma Proteome At
Supplementary Information Changes in the plasma proteome at asymptomatic and symptomatic stages of autosomal dominant Alzheimer’s disease Julia Muenchhoff1, Anne Poljak1,2,3, Anbupalam Thalamuthu1, Veer B. Gupta4,5, Pratishtha Chatterjee4,5,6, Mark Raftery2, Colin L. Masters7, John C. Morris8,9,10, Randall J. Bateman8,9, Anne M. Fagan8,9, Ralph N. Martins4,5,6, Perminder S. Sachdev1,11,* Supplementary Figure S1. Ratios of proteins differentially abundant in asymptomatic carriers of PSEN1 and APP Dutch mutations. Mean ratios and standard deviations of plasma proteins from asymptomatic PSEN1 mutation carriers (PSEN1) and APP Dutch mutation carriers (APP) relative to reference masterpool as quantified by iTRAQ. Ratios that significantly differed are marked with asterisks (* p < 0.05; ** p < 0.01). C4A, complement C4-A; AZGP1, zinc-α-2-glycoprotein; HPX, hemopexin; PGLYPR2, N-acetylmuramoyl-L-alanine amidase isoform 2; α2AP, α-2-antiplasmin; APOL1, apolipoprotein L1; C1 inhibitor, plasma protease C1 inhibitor; ITIH2, inter-α-trypsin inhibitor heavy chain H2. 2 A) ADAD)CSF) ADAD)plasma) B) ADAD)CSF) ADAD)plasma) (Ringman)et)al)2015)) (current)study)) (Ringman)et)al)2015)) (current)study)) ATRN↓,%%AHSG↑% 32028% 49% %%%%%%%%HC2↑,%%ApoM↓% 24367% 31% 10083%% %%%%TBG↑,%%LUM↑% 24256% ApoC1↓↑% 16565% %%AMBP↑% 11738%%% SERPINA3↓↑% 24373% C6↓↑% ITIH2% 10574%% %%%%%%%CPN2↓%% ↓↑% %%%%%TTR↑% 11977% 10970% %SERPINF2↓↑% CFH↓% C5↑% CP↓↑% 16566% 11412%% 10127%% %%ITIH4↓↑% SerpinG1↓% 11967% %%ORM1↓↑% SerpinC1↓% 10612% %%%A1BG↑%%% %%%%FN1↓% 11461% %%%%ITIH1↑% C3↓↑% 11027% 19325% 10395%% %%%%%%HPR↓↑% HRG↓% %%% 13814%% 10338%% %%% %ApoA1 % %%%%%%%%%GSN↑% ↓↑ %%%%%%%%%%%%ApoD↓% 11385% C4BPA↓↑% 18976%% %%%%%%%%%%%%%%%%%ApoJ↓↑% 23266%%%% %%%%%%%%%%%%%%%%%%%%%%ApoA2↓↑% %%%%%%%%%%%%%%%%%%%%%%%%%%%%A2M↓↑% IGHM↑,%%GC↓↑,%%ApoB↓↑% 13769% % FGA↓↑,%%FGB↓↑,%%FGG↓↑% AFM↓↑,%%CFB↓↑,%% 19143%% ApoH↓↑,%%C4BPA↓↑% ApoA4↓↑%%% LOAD/MCI)plasma) LOAD/MCI)plasma) LOAD/MCI)plasma) LOAD/MCI)plasma) (Song)et)al)2014)) (Muenchhoff)et)al)2015)) (Song)et)al)2014)) (Muenchhoff)et)al)2015)) Supplementary Figure S2. -
Bayesian Hierarchical Modeling of High-Throughput Genomic Data with Applications to Cancer Bioinformatics and Stem Cell Differentiation
BAYESIAN HIERARCHICAL MODELING OF HIGH-THROUGHPUT GENOMIC DATA WITH APPLICATIONS TO CANCER BIOINFORMATICS AND STEM CELL DIFFERENTIATION by Keegan D. Korthauer A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Statistics) at the UNIVERSITY OF WISCONSIN–MADISON 2015 Date of final oral examination: 05/04/15 The dissertation is approved by the following members of the Final Oral Committee: Christina Kendziorski, Professor, Biostatistics and Medical Informatics Michael A. Newton, Professor, Statistics Sunduz Kele¸s,Professor, Biostatistics and Medical Informatics Sijian Wang, Associate Professor, Biostatistics and Medical Informatics Michael N. Gould, Professor, Oncology © Copyright by Keegan D. Korthauer 2015 All Rights Reserved i in memory of my grandparents Ma and Pa FL Grandma and John ii ACKNOWLEDGMENTS First and foremost, I am deeply grateful to my thesis advisor Christina Kendziorski for her invaluable advice, enthusiastic support, and unending patience throughout my time at UW-Madison. She has provided sound wisdom on everything from methodological principles to the intricacies of academic research. I especially appreciate that she has always encouraged me to eke out my own path and I attribute a great deal of credit to her for the successes I have achieved thus far. I also owe special thanks to my committee member Professor Michael Newton, who guided me through one of my first collaborative research experiences and has continued to provide key advice on my thesis research. I am also indebted to the other members of my thesis committee, Professor Sunduz Kele¸s,Professor Sijian Wang, and Professor Michael Gould, whose valuable comments, questions, and suggestions have greatly improved this dissertation. -
HOOK3 Is a Scaffold for the Opposite-Polarity Microtubule-Based
bioRxiv preprint doi: https://doi.org/10.1101/508887; this version posted December 31, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. HOOK3 is a scaffold for the opposite-polarity microtubule-based motors cytoplasmic dynein and KIF1C Agnieszka A. Kendrick1, William B. Redwine1,2†, Phuoc Tien Tran1‡, Laura Pontano Vaites2, Monika Dzieciatkowska4, J. Wade Harper2, and Samara L. Reck-Peterson1,3,5 1Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, 92093. 2 Department of Cell Biology, Harvard Medical School, Boston, MA 02115. 3Section of Cell and Developmental Biology, Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093. 4Department of Biochemistry and Molecular Genetics, University of Colorado Denver, Aurora, CO 80045. 5Howard Hughes Medical Institute †Present address: Stowers Institute for Medical Research, Kansas City, MO 64110 ‡Present address: Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138. *Correspondence to: Samara Reck-Peterson 9500 Gilman Drive, Leichtag 482 La Jolla CA, 92093 [email protected]; https://orcid.org/0000-0002-1553-465X 1 bioRxiv preprint doi: https://doi.org/10.1101/508887; this version posted December 31, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. -
Environmental Influences on Endothelial Gene Expression
ENDOTHELIAL CELL GENE EXPRESSION John Matthew Jeff Herbert Supervisors: Prof. Roy Bicknell and Dr. Victoria Heath PhD thesis University of Birmingham August 2012 University of Birmingham Research Archive e-theses repository This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder. ABSTRACT Tumour angiogenesis is a vital process in the pathology of tumour development and metastasis. Targeting markers of tumour endothelium provide a means of targeted destruction of a tumours oxygen and nutrient supply via destruction of tumour vasculature, which in turn ultimately leads to beneficial consequences to patients. Although current anti -angiogenic and vascular targeting strategies help patients, more potently in combination with chemo therapy, there is still a need for more tumour endothelial marker discoveries as current treatments have cardiovascular and other side effects. For the first time, the analyses of in-vivo biotinylation of an embryonic system is performed to obtain putative vascular targets. Also for the first time, deep sequencing is applied to freshly isolated tumour and normal endothelial cells from lung, colon and bladder tissues for the identification of pan-vascular-targets. Integration of the proteomic, deep sequencing, public cDNA libraries and microarrays, delivers 5,892 putative vascular targets to the science community. -
Supplemental Figures Tables Importance of the MHC (SLA) in Swine Health and Biomedical Research
Supplemental Material: Annu. Rev. Anim. Biosci. 2020. 8:171-198 https://doi.org/10.1146/annurev-animal-020518-115014 Importance of the Major Histocompatibility Complex (Swine Leukocyte Antigen) in Swine Health and Biomedical Research Hammer, Ho, Ando, Rogel-Gaillard, Charles, Tector, Tector, and Lunney Supplemental Figures Tables Importance of the MHC (SLA) in swine health and biomedical research Annual Review Animal Biosciences AV08 Lunney Supplemental Figure 1. Chromosomal mapping of the human (HLA complex) and swine MHC (SLA complex) a b HSA 6p21 SSC 7p11-7q11 MOG MOG Class I Class III Class I Class II Class III RING1 Centromere Class II RING1 HLA complex SLA complex Human Leucocyte Antigen Swine Leucocyte Antigen Supplemental Figure 1. Chromosomal mapping of the human (HLA complex) and swine MHC (SLA complex). A. Schematic representation of the chromosome mapping and orientation of the HLA complex on HSA 6p21 and of the pig SLA complex on both sides of the centromere on swine chromosome 7 (SSC7). B. Fluorescent in situ hybridization (FISH) map demonstrating SLA location on SSC7 p11 using a YAC clone containing SLA class Ia genes (adapted from Velten F, Rogel-Gaillard C, Renard C, Pontarotti P, Tazi-Ahnini R, et al. 1998. A first map of the porcine major histocompatibility complex class I region. Tissue Antigens 51:183–94). Supplemental Figure 2. Detailed Physical Map of SLA genes Position Name Position Name Position Name Position Name Position Name 7 22 595 564 22 606 449+ MOG 23 190 757 23 207 872- DHX16 23 705 030 23 706 737- LTB -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
1 Metabolic Dysfunction Is Restricted to the Sciatic Nerve in Experimental
Page 1 of 255 Diabetes Metabolic dysfunction is restricted to the sciatic nerve in experimental diabetic neuropathy Oliver J. Freeman1,2, Richard D. Unwin2,3, Andrew W. Dowsey2,3, Paul Begley2,3, Sumia Ali1, Katherine A. Hollywood2,3, Nitin Rustogi2,3, Rasmus S. Petersen1, Warwick B. Dunn2,3†, Garth J.S. Cooper2,3,4,5* & Natalie J. Gardiner1* 1 Faculty of Life Sciences, University of Manchester, UK 2 Centre for Advanced Discovery and Experimental Therapeutics (CADET), Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, UK 3 Centre for Endocrinology and Diabetes, Institute of Human Development, Faculty of Medical and Human Sciences, University of Manchester, UK 4 School of Biological Sciences, University of Auckland, New Zealand 5 Department of Pharmacology, Medical Sciences Division, University of Oxford, UK † Present address: School of Biosciences, University of Birmingham, UK *Joint corresponding authors: Natalie J. Gardiner and Garth J.S. Cooper Email: [email protected]; [email protected] Address: University of Manchester, AV Hill Building, Oxford Road, Manchester, M13 9PT, United Kingdom Telephone: +44 161 275 5768; +44 161 701 0240 Word count: 4,490 Number of tables: 1, Number of figures: 6 Running title: Metabolic dysfunction in diabetic neuropathy 1 Diabetes Publish Ahead of Print, published online October 15, 2015 Diabetes Page 2 of 255 Abstract High glucose levels in the peripheral nervous system (PNS) have been implicated in the pathogenesis of diabetic neuropathy (DN). However our understanding of the molecular mechanisms which cause the marked distal pathology is incomplete. Here we performed a comprehensive, system-wide analysis of the PNS of a rodent model of DN. -
4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4). -
Research Article Characterization of the Equine Skeletal Muscle
McGivney et al. BMC Genomics 2010, 11:398 http://www.biomedcentral.com/1471-2164/11/398 RESEARCH ARTICLE Open Access CharacterizationResearch article of the equine skeletal muscle transcriptome identifies novel functional responses to exercise training Beatrice A McGivney1, Paul A McGettigan1, John A Browne1, Alexander CO Evans1,3, Rita G Fonseca2, Brendan J Loftus3, Amanda Lohan3, David E MacHugh1,3, Barbara A Murphy1, Lisa M Katz2 and Emmeline W Hill*1 Abstract Background: Digital gene expression profiling was used to characterize the assembly of genes expressed in equine skeletal muscle and to identify the subset of genes that were differentially expressed following a ten-month period of exercise training. The study cohort comprised seven Thoroughbred racehorses from a single training yard. Skeletal muscle biopsies were collected at rest from the gluteus medius at two time points: T1 - untrained, (9 ± 0.5 months old) and T2 - trained (20 ± 0.7 months old). Results: The most abundant mRNA transcripts in the muscle transcriptome were those involved in muscle contraction, aerobic respiration and mitochondrial function. A previously unreported over-representation of genes related to RNA processing, the stress response and proteolysis was observed. Following training 92 tags were differentially expressed of which 74 were annotated. Sixteen genes showed increased expression, including the mitochondrial genes ACADVL, MRPS21 and SLC25A29 encoded by the nuclear genome. Among the 58 genes with decreased expression, MSTN, a negative regulator of muscle growth, had the greatest decrease. Functional analysis of all expressed genes using FatiScan revealed an asymmetric distribution of 482 Gene Ontology (GO) groups and 18 KEGG pathways. -
Proquest Dissertations
INFORMATION TO USERS This manuscript has been reproduced from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand comer and continuing from left to right in equal sections with small overlaps. Each original is also photographed in one exposure and is included in reduced form at the back of the book. Photographs included in the original manuscript have been reproduced xerographically in this copy. Higher quality 6” x 9” black and white photographic prints are available for any photographs or illustrations appearing in this copy for an additional charge. Contact UMI directly to order. UMI Bell & Howell Information and Learning 300 North Zeeb Road, Ann Arbor, Ml 48106-1346 USA 800-521-0600 NOTE TO USERS Page(s) missing in number only; text follows. Microfilmed as received. 222-229 This reproduction is the best copy available. UMI MOLECULAR GENETIC AND BIOCHEMICAL STUDIES OF THE HUMAN AND MOUSE MHC COMPLEMENT GENE CLUSTERS DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Zhenyu Yang, M.S.