In-Silico Guided Identification of Ciliogenesis Candidate Genes in a Non-Conventional Animal Model
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Genome-Wide Association Studies Identify Genetic Loci Associated With
SUPPLEMENTARY DATA Genome-wide Association Studies Identify Genetic Loci Associated with Albuminuria in Diabetes SUPPLEMENTAL MATERIALS This work is dedicated to the memory of our colleague Dr. Wen Hong Linda Kao, a wonderful person, brilliant scientist and central member of the CKDGen Consortium. 1 ©2016 American Diabetes Association. Published online at http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db15-1313/-/DC1 SUPPLEMENTARY DATA Table of Contents SUPPLEMENTARY FIGURE 1: QQ PLOTS FOR ALL GWAS META-ANALYSES ............................................. 3 SUPPLEMENTARY FIGURE 2: MANHATTAN PLOTS FOR ALL GWAS META-ANALYSES ............................. 4 SUPPLEMENTARY FIGURE 3: REGIONAL ASSOCIATION PLOTS............................................................... 6 SUPPLEMENTARY FIGURE 4: EVALUATION OF GLOMERULOSCLEROSIS IN RAB38 KO, CONGENIC AND TRANSGENIC RATS. ........................................................................................................................... 17 SUPPLEMENTARY TABLE 1: CHARACTERISTICS OF THE STUDY POPULATIONS ..................................... 18 SUPPLEMENTARY TABLE 2: INFORMATION ABOUT STUDY DESIGN AND UACR MEASUREMENT .......... 20 SUPPLEMENTARY TABLE 3: STUDY-SPECIFIC INFORMATION ABOUT GENOTYPING, IMPUTATION AND DATA MANAGEMENT AND ANALYSIS ................................................................................................ 31 SUPPLEMENTARY TABLE 4: SNPS ASSOCIATED WITH UACR AMONG ALL INDIVIDUALS WITH A P-VALUE OF <1E-05. ....................................................................................................................................... -
1 1 Alpha-Smooth Muscle Actin (ACTA2) Is Required for Metastatic Potential of Human 1 Lung Adenocarcinoma 2 3 Hye Won Lee*1,2,3
Author Manuscript Published OnlineFirst on August 30, 2013; DOI: 10.1158/1078-0432.CCR-13-1181 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. 1 1 Alpha-smooth muscle actin (ACTA2) is required for metastatic potential of human 2 lung adenocarcinoma 3 4 Hye Won Lee*1,2,3, Young Mi Park*3,4, Se Jeong Lee1,4,5, Hyun Jung Cho1,2, Duk-Hwan Kim6,7, 5 Jung-Il Lee2, Myung-Soo Kang3, Ho Jun Seol2, Young Mog Shim8, Do-Hyun Nam1,2,3, Hyeon 6 Ho Kim3,4, Kyeung Min Joo1,3,4,5 7 8 Authors’ Affiliations: 9 1Cancer Stem Cell Research Center, 2Department of Neurosurgery, 3Department of Health 10 Sciences and Technology, Samsung Advanced Institute for Health Sciences and 11 Technology (SAIHST), 4Samsung Biomedical Research Institute, 5Department of Anatomy 12 and Cell Biology, 6Department of Molecular Cell Biology, 7Center for Genome Research, 13 8Department of Thoracic Surgery, Samsung Medical Center, Sungkyunkwan University 14 School of Medicine, Seoul, Korea 15 16 *These authors contributed equally to this work. 17 18 Running title: ACTA2 confers metastatic potential on lung adenocarcinoma 19 Keywords: Non-small cell lung adenocarcinoma, alpha-smooth muscle actin, migration, 20 invasion, metastasis 1 Downloaded from clincancerres.aacrjournals.org on September 26, 2021. © 2013 American Association for Cancer Research. Author Manuscript Published OnlineFirst on August 30, 2013; DOI: 10.1158/1078-0432.CCR-13-1181 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. 2 1 Financial support: This study was supported by a grant from the Korea Healthcare 2 Technology R&D Project, Ministry for Health & Welfare Affairs, Republic of Korea (A092255), 3 and the Basic Science Research Program, National Research Foundation of Korea by the 4 Ministry of Education, Science, and Technology (2011-009329 to H. -
Frontiers in Integrative Genomics and Translational Bioinformatics
BioMed Research International Frontiers in Integrative Genomics and Translational Bioinformatics Guest Editors: Zhongming Zhao, Victor X. Jin, Yufei Huang, Chittibabu Guda, and Jianhua Ruan Frontiers in Integrative Genomics and Translational Bioinformatics BioMed Research International Frontiers in Integrative Genomics and Translational Bioinformatics Guest Editors: Zhongming Zhao, Victor X. Jin, Yufei Huang, Chittibabu Guda, and Jianhua Ruan Copyright © òýÔ Hindawi Publishing Corporation. All rights reserved. is is a special issue published in “BioMed Research International.” All articles are open access articles distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Contents Frontiers in Integrative Genomics and Translational Bioinformatics, Zhongming Zhao, Victor X. Jin, Yufei Huang, Chittibabu Guda, and Jianhua Ruan Volume òýÔ , Article ID Þò ¥ÀÔ, ç pages Building Integrated Ontological Knowledge Structures with Ecient Approximation Algorithms, Yang Xiang and Sarath Chandra Janga Volume òýÔ , Article ID ýÔ ò, Ô¥ pages Predicting Drug-Target Interactions via Within-Score and Between-Score, Jian-Yu Shi, Zun Liu, Hui Yu, and Yong-Jun Li Volume òýÔ , Article ID ç ýÀç, À pages RNAseq by Total RNA Library Identies Additional RNAs Compared to Poly(A) RNA Library, Yan Guo, Shilin Zhao, Quanhu Sheng, Mingsheng Guo, Brian Lehmann, Jennifer Pietenpol, David C. Samuels, and Yu Shyr Volume òýÔ , Article ID âòÔçý, À pages Construction of Pancreatic Cancer Classier Based on SVM Optimized by Improved FOA, Huiyan Jiang, Di Zhao, Ruiping Zheng, and Xiaoqi Ma Volume òýÔ , Article ID ÞÔýòç, Ôò pages OperomeDB: A Database of Condition-Specic Transcription Units in Prokaryotic Genomes, Kashish Chetal and Sarath Chandra Janga Volume òýÔ , Article ID çÔòÔÞ, Ôý pages How to Choose In Vitro Systems to Predict In Vivo Drug Clearance: A System Pharmacology Perspective, Lei Wang, ChienWei Chiang, Hong Liang, Hengyi Wu, Weixing Feng, Sara K. -
Mathiomica: an Integrative Platform for Dynamic Omics George I
MathIOmica: An Integrative Platform for Dynamic Omics George I. Mias1,*, Tahir Yusufaly2, Raeuf Roushangar1, Lavida R. K. Brooks1, Vikas V. Singh1, and Christina Christou3 1Michigan State University, Biochemistry and Molecular Biology, East Lansing, MI 48824, USA 2University of Southern California, Department of Physics and Astronomy, Los Angeles, CA, 90089, USA 3Mercy Cancer Center, Department of Radiation Oncology, Mason City, IA 50401, USA *[email protected] SUPPLEMENTARY NOTE 1 1 1 MathIOmica: Omics Analysis Tutorial Loading the MathIOmica Package Metabolomic Data Data in MathIOmica Combined Data Clustering Transcriptome Data Visualization Proteomic Data Annotation and Enrichment MathIOmica is an omics analysis package designed to facilitate method development for the analysis of multiple omics in Mathematica, particularly for dynamics (time series/longitudinal data). This extensive tutorial follows the analysis of multiple dynamic omics data (transcriptomics, proteomics, and metabolomics from human samples). Various MathIOmica functions are introduced in the tutorial, including additional discussion of related functionality. We should note that the approach methods are simply an illustration of MathIOmica functionality, and should not be considered as a definitive appoach. Additionally, certain details are included to illustrate common complications (e.g. renaming samples, combining datasets, transforming accessions from one database to another, dealing with replicates and Missing data, etc.). After a brief discussion of data in MathIOmica, each example data (transcriptome, proteome and metabolome) are imported and preprocessed. Next a simulation is carried out to obtain datasets for each omics used to assess statistical significance cutoffs. The datasets are combined, and classified for time series patterns, followed by clustering. The clusters are visualized, and biological annotation of Gene Ontology (GO) and pathway analysis (KEGG: Kyoto Encyclopedia of Genes and Genomes) are finally considered. -
File Download
The genetic dissection of Myo7a gene expression in the retinas of BXD mice Ye Lu, Zhejiang University Diana Zhou, University of Tennessee Rebeccca King, Emory University Shuang Zhu, University of Texas Medical Branch Claire L. Simpson, University of Tennessee Byron C. Jones, University of Tennessee Wenbo Zhang, University of Texas Medical Branch Eldon Geisert Jr, Emory University Lu Lu, University of Tennessee Journal Title: Molecular Vision Volume: Volume 24 Publisher: Molecular Vision | 2018-02-03, Pages 115-126 Type of Work: Article | Final Publisher PDF Permanent URL: https://pid.emory.edu/ark:/25593/s87np Final published version: http://www.molvis.org/molvis/ Copyright information: © 2018 Molecular Vision. This is an Open Access work distributed under the terms of the Creative Commons Attribution-NonCommerical-NoDerivs 3.0 Unported License (http://creativecommons.org/licenses/by-nc-nd/3.0/). Accessed September 30, 2021 2:32 AM EDT Molecular Vision 2018; 24:115-126 <http://www.molvis.org/molvis/v24/115> © 2018 Molecular Vision Received 7 June 2017 | Accepted 1 February 2018 | Published 3 February 2018 The genetic dissection of Myo7a gene expression in the retinas of BXD mice Ye Lu,1 Diana Zhou,2 Rebecca King,3 Shuang Zhu,4 Claire L. Simpson,2 Byron C. Jones,2 Wenbo Zhang,4 Eldon E. Geisert,3 Lu Lu2 (The first two authors contributed equally to this work.) 1Department of Ophthalmology, The First Affiliated Hospital, Zhejiang University College of Medicine, Hangzhou, China; 2Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN; 3Department of Ophthalmology and Emory Eye Center, Emory University, Atlanta, GA; 4Department of Ophthalmology & Visual Sciences, University of Texas Medical Branch, Galveston, TX Purpose: Usher syndrome (US) is characterized by a loss of vision due to retinitis pigmentosa (RP) and deafness. -
The Molecular Karyotype of 25 Clinical-Grade Human Embryonic Stem Cell Lines Received: 07 August 2015 1 1 2 3,4 Accepted: 27 October 2015 Maurice A
www.nature.com/scientificreports OPEN The Molecular Karyotype of 25 Clinical-Grade Human Embryonic Stem Cell Lines Received: 07 August 2015 1 1 2 3,4 Accepted: 27 October 2015 Maurice A. Canham , Amy Van Deusen , Daniel R. Brison , Paul A. De Sousa , 3 5 6 5 7 Published: 26 November 2015 Janet Downie , Liani Devito , Zoe A. Hewitt , Dusko Ilic , Susan J. Kimber , Harry D. Moore6, Helen Murray3 & Tilo Kunath1 The application of human embryonic stem cell (hESC) derivatives to regenerative medicine is now becoming a reality. Although the vast majority of hESC lines have been derived for research purposes only, about 50 lines have been established under Good Manufacturing Practice (GMP) conditions. Cell types differentiated from these designated lines may be used as a cell therapy to treat macular degeneration, Parkinson’s, Huntington’s, diabetes, osteoarthritis and other degenerative conditions. It is essential to know the genetic stability of the hESC lines before progressing to clinical trials. We evaluated the molecular karyotype of 25 clinical-grade hESC lines by whole-genome single nucleotide polymorphism (SNP) array analysis. A total of 15 unique copy number variations (CNVs) greater than 100 kb were detected, most of which were found to be naturally occurring in the human population and none were associated with culture adaptation. In addition, three copy-neutral loss of heterozygosity (CN-LOH) regions greater than 1 Mb were observed and all were relatively small and interstitial suggesting they did not arise in culture. The large number of available clinical-grade hESC lines with defined molecular karyotypes provides a substantial starting platform from which the development of pre-clinical and clinical trials in regenerative medicine can be realised. -
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. -
Systematic Detection of Divergent Brain Proteins in Human Evolution and Their Roles in Cognition
bioRxiv preprint doi: https://doi.org/10.1101/658658; this version posted June 3, 2019. 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 4.0 International license. Systematic detection of divergent brain proteins in human evolution and their roles in cognition Guillaume Dumas1,*, Simon Malesys1 and Thomas Bourgeron1 Affiliations: 1 Human Genetics and Cognitive Functions, Institut Pasteur, UMR3571 CNRS, Université de Paris, Paris, (75015) France * Corresponding author: [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/658658; this version posted June 3, 2019. 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 4.0 International license. Abstract The human brain differs from that of other primates, but the underlying genetic mechanisms remain unclear. Here we measured the evolutionary pressures acting on all human protein- coding genes (N=17,808) based on their divergence from early hominins such as Neanderthal, and non-human primates. We confirm that genes encoding brain-related proteins are among the most conserved of the human proteome. Conversely, several of the most divergent proteins in humans compared to other primates are associated with brain-associated diseases such as micro/macrocephaly, dyslexia, and autism. We identified specific eXpression profiles of a set of divergent genes in ciliated cells of the cerebellum, that might have contributed to the emergence of fine motor skills and social cognition in humans. -
Wnt/Β-Catenin Signaling Regulates Regeneration in Diverse Tissues of the Zebrafish
Wnt/β-catenin Signaling Regulates Regeneration in Diverse Tissues of the Zebrafish Nicholas Stockton Strand A dissertation Submitted in partial fulfillment of the Requirements for the degree of Doctor of Philosophy University of Washington 2016 Reading Committee: Randall Moon, Chair Neil Nathanson Ronald Kwon Program Authorized to Offer Degree: Pharmacology ©Copyright 2016 Nicholas Stockton Strand University of Washington Abstract Wnt/β-catenin Signaling Regulates Regeneration in Diverse Tissues of the Zebrafish Nicholas Stockton Strand Chair of the Supervisory Committee: Professor Randall T Moon Department of Pharmacology The ability to regenerate tissue after injury is limited by species, tissue type, and age of the organism. Understanding the mechanisms of endogenous regeneration provides greater insight into this remarkable biological process while also offering up potential therapeutic targets for promoting regeneration in humans. The Wnt/β-catenin signaling pathway has been implicated in zebrafish regeneration, including the fin and nervous system. The body of work presented here expands upon the role of Wnt/β-catenin signaling in regeneration, characterizing roles for Wnt/β-catenin signaling in multiple tissues. We show that cholinergic signaling is required for blastema formation and Wnt/β-catenin signaling initiation in the caudal fin, and that overexpression of Wnt/β-catenin ligand is sufficient to rescue blastema formation in fins lacking cholinergic activity. Next, we characterized the glial response to Wnt/β-catenin signaling after spinal cord injury, demonstrating that Wnt/β-catenin signaling is necessary for recovery of motor function and the formation of bipolar glia after spinal cord injury. Lastly, we defined a role for Wnt/β-catenin signaling in heart regeneration, showing that cardiomyocyte proliferation is regulated by Wnt/β-catenin signaling. -
Cellular and Molecular Signatures in the Disease Tissue of Early
Cellular and Molecular Signatures in the Disease Tissue of Early Rheumatoid Arthritis Stratify Clinical Response to csDMARD-Therapy and Predict Radiographic Progression Frances Humby1,* Myles Lewis1,* Nandhini Ramamoorthi2, Jason Hackney3, Michael Barnes1, Michele Bombardieri1, Francesca Setiadi2, Stephen Kelly1, Fabiola Bene1, Maria di Cicco1, Sudeh Riahi1, Vidalba Rocher-Ros1, Nora Ng1, Ilias Lazorou1, Rebecca E. Hands1, Desiree van der Heijde4, Robert Landewé5, Annette van der Helm-van Mil4, Alberto Cauli6, Iain B. McInnes7, Christopher D. Buckley8, Ernest Choy9, Peter Taylor10, Michael J. Townsend2 & Costantino Pitzalis1 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Departments of 2Biomarker Discovery OMNI, 3Bioinformatics and Computational Biology, Genentech Research and Early Development, South San Francisco, California 94080 USA 4Department of Rheumatology, Leiden University Medical Center, The Netherlands 5Department of Clinical Immunology & Rheumatology, Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands 6Rheumatology Unit, Department of Medical Sciences, Policlinico of the University of Cagliari, Cagliari, Italy 7Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, UK 8Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham B15 2WB, UK 9Institute of -
Table SI. Primer List of Genes Used for Reverse Transcription‑Quantitative PCR Validation
Table SI. Primer list of genes used for reverse transcription‑quantitative PCR validation. Genes Forward (5'‑3') Reverse (5'‑3') Length COL1A1 AGTGGTTTGGATGGTGCCAA GCACCATCATTTCCACGAGC 170 COL6A1 CCCCTCCCCACTCATCACTA CGAATCAGGTTGGTCGGGAA 65 COL2A1 GGTCCTGCAGGTGAACCC CTCTGTCTCCTTGCTTGCCA 181 DCT CTACGAAACCAGGATGACCGT ACCATCATTGGTTTGCCTTTCA 192 PDE4D ATTGCCCACGATAGCTGCTC GCAGATGTGCCATTGTCCAC 181 RP11‑428C19.4 ACGCTAGAAACAGTGGTGCG AATCCCCGGAAAGATCCAGC 179 GPC‑AS2 TCTCAACTCCCCTCCTTCGAG TTACATTTCCCGGCCCATCTC 151 XLOC_110310 AGTGGTAGGGCAAGTCCTCT CGTGGTGGGATTCAAAGGGA 187 COL1A1, collagen type I alpha 1; COL6A1, collagen type VI, alpha 1; COL2A1, collagen type II alpha 1; DCT, dopachrome tautomerase; PDE4D, phosphodiesterase 4D cAMP‑specific. Table SII. The differentially expressed mRNAs in the ParoAF_Control group. Gene ID logFC P‑Value Symbol Description ENSG00000165480 ‑6.4838 8.32E‑12 SKA3 Spindle and kinetochore associated complex subunit 3 ENSG00000165424 ‑6.43924 0.002056 ZCCHC24 Zinc finger, CCHC domain containing 24 ENSG00000182836 ‑6.20215 0.000817 PLCXD3 Phosphatidylinositol‑specific phospholipase C, X domain containing 3 ENSG00000174358 ‑5.79775 0.029093 SLC6A19 Solute carrier family 6 (neutral amino acid transporter), member 19 ENSG00000168916 ‑5.761 0.004046 ZNF608 Zinc finger protein 608 ENSG00000134343 ‑5.56371 0.01356 ANO3 Anoctamin 3 ENSG00000110400 ‑5.48194 0.004123 PVRL1 Poliovirus receptor‑related 1 (herpesvirus entry mediator C) ENSG00000124882 ‑5.45849 0.022164 EREG Epiregulin ENSG00000113448 ‑5.41752 0.000577 PDE4D Phosphodiesterase -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase