SI Appendix Overrepresented and Underrepresented Gene Ontology (GO) Terms
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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. -
Human and Mouse CD Marker Handbook Human and Mouse CD Marker Key Markers - Human Key Markers - Mouse
Welcome to More Choice CD Marker Handbook For more information, please visit: Human bdbiosciences.com/eu/go/humancdmarkers Mouse bdbiosciences.com/eu/go/mousecdmarkers Human and Mouse CD Marker Handbook Human and Mouse CD Marker Key Markers - Human Key Markers - Mouse CD3 CD3 CD (cluster of differentiation) molecules are cell surface markers T Cell CD4 CD4 useful for the identification and characterization of leukocytes. The CD CD8 CD8 nomenclature was developed and is maintained through the HLDA (Human Leukocyte Differentiation Antigens) workshop started in 1982. CD45R/B220 CD19 CD19 The goal is to provide standardization of monoclonal antibodies to B Cell CD20 CD22 (B cell activation marker) human antigens across laboratories. To characterize or “workshop” the antibodies, multiple laboratories carry out blind analyses of antibodies. These results independently validate antibody specificity. CD11c CD11c Dendritic Cell CD123 CD123 While the CD nomenclature has been developed for use with human antigens, it is applied to corresponding mouse antigens as well as antigens from other species. However, the mouse and other species NK Cell CD56 CD335 (NKp46) antibodies are not tested by HLDA. Human CD markers were reviewed by the HLDA. New CD markers Stem Cell/ CD34 CD34 were established at the HLDA9 meeting held in Barcelona in 2010. For Precursor hematopoetic stem cell only hematopoetic stem cell only additional information and CD markers please visit www.hcdm.org. Macrophage/ CD14 CD11b/ Mac-1 Monocyte CD33 Ly-71 (F4/80) CD66b Granulocyte CD66b Gr-1/Ly6G Ly6C CD41 CD41 CD61 (Integrin b3) CD61 Platelet CD9 CD62 CD62P (activated platelets) CD235a CD235a Erythrocyte Ter-119 CD146 MECA-32 CD106 CD146 Endothelial Cell CD31 CD62E (activated endothelial cells) Epithelial Cell CD236 CD326 (EPCAM1) For Research Use Only. -
Mechanical Forces Induce an Asthma Gene Signature in Healthy Airway Epithelial Cells Ayşe Kılıç1,10, Asher Ameli1,2,10, Jin-Ah Park3,10, Alvin T
www.nature.com/scientificreports OPEN Mechanical forces induce an asthma gene signature in healthy airway epithelial cells Ayşe Kılıç1,10, Asher Ameli1,2,10, Jin-Ah Park3,10, Alvin T. Kho4, Kelan Tantisira1, Marc Santolini 1,5, Feixiong Cheng6,7,8, Jennifer A. Mitchel3, Maureen McGill3, Michael J. O’Sullivan3, Margherita De Marzio1,3, Amitabh Sharma1, Scott H. Randell9, Jefrey M. Drazen3, Jefrey J. Fredberg3 & Scott T. Weiss1,3* Bronchospasm compresses the bronchial epithelium, and this compressive stress has been implicated in asthma pathogenesis. However, the molecular mechanisms by which this compressive stress alters pathways relevant to disease are not well understood. Using air-liquid interface cultures of primary human bronchial epithelial cells derived from non-asthmatic donors and asthmatic donors, we applied a compressive stress and then used a network approach to map resulting changes in the molecular interactome. In cells from non-asthmatic donors, compression by itself was sufcient to induce infammatory, late repair, and fbrotic pathways. Remarkably, this molecular profle of non-asthmatic cells after compression recapitulated the profle of asthmatic cells before compression. Together, these results show that even in the absence of any infammatory stimulus, mechanical compression alone is sufcient to induce an asthma-like molecular signature. Bronchial epithelial cells (BECs) form a physical barrier that protects pulmonary airways from inhaled irritants and invading pathogens1,2. Moreover, environmental stimuli such as allergens, pollutants and viruses can induce constriction of the airways3 and thereby expose the bronchial epithelium to compressive mechanical stress. In BECs, this compressive stress induces structural, biophysical, as well as molecular changes4,5, that interact with nearby mesenchyme6 to cause epithelial layer unjamming1, shedding of soluble factors, production of matrix proteins, and activation matrix modifying enzymes, which then act to coordinate infammatory and remodeling processes4,7–10. -
Regulation of Cdc42 and Its Effectors in Epithelial Morphogenesis Franck Pichaud1,2,*, Rhian F
© 2019. Published by The Company of Biologists Ltd | Journal of Cell Science (2019) 132, jcs217869. doi:10.1242/jcs.217869 REVIEW SUBJECT COLLECTION: ADHESION Regulation of Cdc42 and its effectors in epithelial morphogenesis Franck Pichaud1,2,*, Rhian F. Walther1 and Francisca Nunes de Almeida1 ABSTRACT An overview of Cdc42 Cdc42 – a member of the small Rho GTPase family – regulates cell Cdc42 was discovered in yeast and belongs to a large family of small – polarity across organisms from yeast to humans. It is an essential (20 30 kDa) GTP-binding proteins (Adams et al., 1990; Johnson regulator of polarized morphogenesis in epithelial cells, through and Pringle, 1990). It is part of the Ras-homologous Rho subfamily coordination of apical membrane morphogenesis, lumen formation and of GTPases, of which there are 20 members in humans, including junction maturation. In parallel, work in yeast and Caenorhabditis elegans the RhoA and Rac GTPases, (Hall, 2012). Rho, Rac and Cdc42 has provided important clues as to how this molecular switch can homologues are found in all eukaryotes, except for plants, which do generate and regulate polarity through localized activation or inhibition, not have a clear homologue for Cdc42. Together, the function of and cytoskeleton regulation. Recent studies have revealed how Rho GTPases influences most, if not all, cellular processes. important and complex these regulations can be during epithelial In the early 1990s, seminal work from Alan Hall and his morphogenesis. This complexity is mirrored by the fact that Cdc42 can collaborators identified Rho, Rac and Cdc42 as main regulators of exert its function through many effector proteins. -
The Utility of Genetic Risk Scores in Predicting the Onset of Stroke March 2021 6
DOT/FAA/AM-21/24 Office of Aerospace Medicine Washington, DC 20591 The Utility of Genetic Risk Scores in Predicting the Onset of Stroke Diana Judith Monroy Rios, M.D1 and Scott J. Nicholson, Ph.D.2 1. KR 30 # 45-03 University Campus, Building 471, 5th Floor, Office 510 Bogotá D.C. Colombia 2. FAA Civil Aerospace Medical Institute, 6500 S. MacArthur Blvd Rm. 354, Oklahoma City, OK 73125 March 2021 NOTICE This document is disseminated under the sponsorship of the U.S. Department of Transportation in the interest of information exchange. The United States Government assumes no liability for the contents thereof. _________________ This publication and all Office of Aerospace Medicine technical reports are available in full-text from the Civil Aerospace Medical Institute’s publications Web site: (www.faa.gov/go/oamtechreports) Technical Report Documentation Page 1. Report No. 2. Government Accession No. 3. Recipient's Catalog No. DOT/FAA/AM-21/24 4. Title and Subtitle 5. Report Date March 2021 The Utility of Genetic Risk Scores in Predicting the Onset of Stroke 6. Performing Organization Code 7. Author(s) 8. Performing Organization Report No. Diana Judith Monroy Rios M.D1, and Scott J. Nicholson, Ph.D.2 9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 1 KR 30 # 45-03 University Campus, Building 471, 5th Floor, Office 510, Bogotá D.C. Colombia 11. Contract or Grant No. 2 FAA Civil Aerospace Medical Institute, 6500 S. MacArthur Blvd Rm. 354, Oklahoma City, OK 73125 12. Sponsoring Agency name and Address 13. Type of Report and Period Covered Office of Aerospace Medicine Federal Aviation Administration 800 Independence Ave., S.W. -
Seq2pathway Vignette
seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway. -
Molecular Dissection of G-Protein Coupled Receptor Signaling and Oligomerization
MOLECULAR DISSECTION OF G-PROTEIN COUPLED RECEPTOR SIGNALING AND OLIGOMERIZATION BY MICHAEL RIZZO A Dissertation Submitted to the Graduate Faculty of WAKE FOREST UNIVERSITY GRADUATE SCHOOL OF ARTS AND SCIENCES in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY Biology December, 2019 Winston-Salem, North Carolina Approved By: Erik C. Johnson, Ph.D. Advisor Wayne E. Pratt, Ph.D. Chair Pat C. Lord, Ph.D. Gloria K. Muday, Ph.D. Ke Zhang, Ph.D. ACKNOWLEDGEMENTS I would first like to thank my advisor, Dr. Erik Johnson, for his support, expertise, and leadership during my time in his lab. Without him, the work herein would not be possible. I would also like to thank the members of my committee, Dr. Gloria Muday, Dr. Ke Zhang, Dr. Wayne Pratt, and Dr. Pat Lord, for their guidance and advice that helped improve the quality of the research presented here. I would also like to thank members of the Johnson lab, both past and present, for being valuable colleagues and friends. I would especially like to thank Dr. Jason Braco, Dr. Jon Fisher, Dr. Jake Saunders, and Becky Perry, all of whom spent a great deal of time offering me advice, proofreading grants and manuscripts, and overall supporting me through the ups and downs of the research process. Finally, I would like to thank my family, both for instilling in me a passion for knowledge and education, and for their continued support. In particular, I would like to thank my wife Emerald – I am forever indebted to you for your support throughout this process, and I will never forget the sacrifices you made to help me get to where I am today. -
GABA Receptors
D Reviews • BIOTREND Reviews • BIOTREND Reviews • BIOTREND Reviews • BIOTREND Reviews Review No.7 / 1-2011 GABA receptors Wolfgang Froestl , CNS & Chemistry Expert, AC Immune SA, PSE Building B - EPFL, CH-1015 Lausanne, Phone: +41 21 693 91 43, FAX: +41 21 693 91 20, E-mail: [email protected] GABA Activation of the GABA A receptor leads to an influx of chloride GABA ( -aminobutyric acid; Figure 1) is the most important and ions and to a hyperpolarization of the membrane. 16 subunits with γ most abundant inhibitory neurotransmitter in the mammalian molecular weights between 50 and 65 kD have been identified brain 1,2 , where it was first discovered in 1950 3-5 . It is a small achiral so far, 6 subunits, 3 subunits, 3 subunits, and the , , α β γ δ ε θ molecule with molecular weight of 103 g/mol and high water solu - and subunits 8,9 . π bility. At 25°C one gram of water can dissolve 1.3 grams of GABA. 2 Such a hydrophilic molecule (log P = -2.13, PSA = 63.3 Å ) cannot In the meantime all GABA A receptor binding sites have been eluci - cross the blood brain barrier. It is produced in the brain by decarb- dated in great detail. The GABA site is located at the interface oxylation of L-glutamic acid by the enzyme glutamic acid decarb- between and subunits. Benzodiazepines interact with subunit α β oxylase (GAD, EC 4.1.1.15). It is a neutral amino acid with pK = combinations ( ) ( ) , which is the most abundant combi - 1 α1 2 β2 2 γ2 4.23 and pK = 10.43. -
A Gene-Level Methylome-Wide Association Analysis Identifies Novel
bioRxiv preprint doi: https://doi.org/10.1101/2020.07.13.201376; this version posted July 14, 2020. 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. 1 A gene-level methylome-wide association analysis identifies novel 2 Alzheimer’s disease genes 1 1 2 3 4 3 Chong Wu , Jonathan Bradley , Yanming Li , Lang Wu , and Hong-Wen Deng 1 4 Department of Statistics, Florida State University; 2 5 Department of Biostatistics & Data Science, University of Kansas Medical Center; 3 6 Population Sciences in the Pacific Program, University of Hawaii Cancer center; 4 7 Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, 8 Tulane University School of Medicine 9 Corresponding to: Chong Wu, Assistant Professor, Department of Statistics, Florida State 10 University, email: [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.07.13.201376; this version posted July 14, 2020. 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. 11 Abstract 12 Motivation: Transcriptome-wide association studies (TWAS) have successfully facilitated the dis- 13 covery of novel genetic risk loci for many complex traits, including late-onset Alzheimer’s disease 14 (AD). However, most existing TWAS methods rely only on gene expression and ignore epige- 15 netic modification (i.e., DNA methylation) and functional regulatory information (i.e., enhancer- 16 promoter interactions), both of which contribute significantly to the genetic basis ofAD. -
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. -
Identify Distinct Prognostic Impact of ALDH1 Family Members by TCGA Database in Acute Myeloid Leukemia
Open Access Annals of Hematology & Oncology Research Article Identify Distinct Prognostic Impact of ALDH1 Family Members by TCGA Database in Acute Myeloid Leukemia Yi H, Deng R, Fan F, Sun H, He G, Lai S and Su Y* Department of Hematology, General Hospital of Chengdu Abstract Military Region, China Background: Acute myeloid leukemia is a heterogeneous disease. Identify *Corresponding author: Su Y, Department of the prognostic biomarker is important to guide stratification and therapeutic Hematology, General Hospital of Chengdu Military strategies. Region, Chengdu, 610083, China Method: We detected the expression level and the prognostic impact of Received: November 25, 2017; Accepted: January 18, each ALDH1 family members in AML by The Cancer Genome Atlas (TCGA) 2018; Published: February 06, 2018 database. Results: Upon 168 patients whose expression level of ALDH1 family members were available. We found that the level of ALDH1A1correlated to the prognosis of AML by the National Comprehensive Cancer Network (NCCN) stratification but not in other ALDH1 members. Moreover, we got survival data from 160 AML patients in TCGA database. We found that high ALDH1A1 expression correlated to poor Overall Survival (OS), mostly in Fms-like Tyrosine Kinase-3 (FLT3) mutated group. HighALDH1A2 expression significantly correlated to poor OS in FLT3 wild type population but not in FLT3 mutated group. High ALDH1A3 expression significantly correlated to poor OS in FLT3 mutated group but not in FLT3 wild type group. There was no relationship between the OS of AML with the level of ALDH1B1, ALDH1L1 and ALDH1L2. Conclusion: The prognostic impacts were different in each ALDH1 family members, which needs further investigation. -
Celsr1-3 Cadherins in PCP and Brain Development
CHAPTER SEVEN Celsr1–3 Cadherins in PCP and Brain Development Camille Boutin, André M. Goffinet1, Fadel Tissir1 Institute of Neuroscience, Developmental Neurobiology, Universite´ Catholique de Louvain, Brussels, Belgium 1Corresponding authors: Equal contribution. e-mail address: [email protected]; andre. [email protected] Contents 1. Celsr1–3 Expression Patterns 164 2. Celsr1: A Major Player in Vertebrate PCP 165 3. Celsr2 and 3 in Ciliogenesis 169 4. Celsr1–3 in Neuronal Migration 171 5. Celsr2 and Celsr3 in Brain Wiring 174 5.1 Motifs of Celsr important for their functions 176 References 179 Abstract Cadherin EGF LAG seven-pass G-type receptors 1, 2, and 3 (Celsr1–3) form a family of three atypical cadherins with multiple functions in epithelia and in the nervous system. During the past decade, evidence has accumulated for important and distinct roles of Celsr1–3 in planar cell polarity (PCP) and brain development and maintenance. Although the role of Celsr in PCP is conserved from flies to mammals, other functions may be more distantly related, with Celsr working only with one or a subset of the classical PCP partners. Here, we review the literature on Celsr in PCP and neural devel- opment, point to several remaining questions, and consider future challenges and possible research trends. Celsr1–3 genes encode atypical cadherins of more than 3000 amino acids ( Fig. 7.1). Their large ectodomain is composed of nine N-terminal cadherin repeats (typical cadherins have five repeats), six epidermal growth factor (EGF)-like domains, two laminin G repeats, one hormone receptor motif (HRM), and a G-protein-coupled receptor proteolytic site (GPS).