Systematic Integrated Analyses of Methylomic And
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Supplement 1 Overview of Dystonia Genes
Supplement 1 Overview of genes that may cause dystonia in children and adolescents Gene (OMIM) Disease name/phenotype Mode of inheritance 1: (Formerly called) Primary dystonias (DYTs): TOR1A (605204) DYT1: Early-onset generalized AD primary torsion dystonia (PTD) TUBB4A (602662) DYT4: Whispering dystonia AD GCH1 (600225) DYT5: GTP-cyclohydrolase 1 AD deficiency THAP1 (609520) DYT6: Adolescent onset torsion AD dystonia, mixed type PNKD/MR1 (609023) DYT8: Paroxysmal non- AD kinesigenic dyskinesia SLC2A1 (138140) DYT9/18: Paroxysmal choreoathetosis with episodic AD ataxia and spasticity/GLUT1 deficiency syndrome-1 PRRT2 (614386) DYT10: Paroxysmal kinesigenic AD dyskinesia SGCE (604149) DYT11: Myoclonus-dystonia AD ATP1A3 (182350) DYT12: Rapid-onset dystonia AD parkinsonism PRKRA (603424) DYT16: Young-onset dystonia AR parkinsonism ANO3 (610110) DYT24: Primary focal dystonia AD GNAL (139312) DYT25: Primary torsion dystonia AD 2: Inborn errors of metabolism: GCDH (608801) Glutaric aciduria type 1 AR PCCA (232000) Propionic aciduria AR PCCB (232050) Propionic aciduria AR MUT (609058) Methylmalonic aciduria AR MMAA (607481) Cobalamin A deficiency AR MMAB (607568) Cobalamin B deficiency AR MMACHC (609831) Cobalamin C deficiency AR C2orf25 (611935) Cobalamin D deficiency AR MTRR (602568) Cobalamin E deficiency AR LMBRD1 (612625) Cobalamin F deficiency AR MTR (156570) Cobalamin G deficiency AR CBS (613381) Homocysteinuria AR PCBD (126090) Hyperphelaninemia variant D AR TH (191290) Tyrosine hydroxylase deficiency AR SPR (182125) Sepiaterine reductase -
Peripheral Neuropathy in Complex Inherited Diseases: an Approach To
PERIPHERAL NEUROPATHY IN COMPLEX INHERITED DISEASES: AN APPROACH TO DIAGNOSIS Rossor AM1*, Carr AS1*, Devine H1, Chandrashekar H2, Pelayo-Negro AL1, Pareyson D3, Shy ME4, Scherer SS5, Reilly MM1. 1. MRC Centre for Neuromuscular Diseases, UCL Institute of Neurology and National Hospital for Neurology and Neurosurgery, London, WC1N 3BG, UK. 2. Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, WC1N 3BG, UK. 3. Unit of Neurological Rare Diseases of Adulthood, Carlo Besta Neurological Institute IRCCS Foundation, Milan, Italy. 4. Department of Neurology, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA 5. Department of Neurology, University of Pennsylvania, Philadelphia, PA 19014, USA. * These authors contributed equally to this work Corresponding author: Mary M Reilly Address: MRC Centre for Neuromuscular Diseases, 8-11 Queen Square, London, WC1N 3BG, UK. Email: [email protected] Telephone: 0044 (0) 203 456 7890 Word count: 4825 ABSTRACT Peripheral neuropathy is a common finding in patients with complex inherited neurological diseases and may be subclinical or a major component of the phenotype. This review aims to provide a clinical approach to the diagnosis of this complex group of patients by addressing key questions including the predominant neurological syndrome associated with the neuropathy e.g. spasticity, the type of neuropathy, and the other neurological and non- neurological features of the syndrome. Priority is given to the diagnosis of treatable conditions. Using this approach, we associated neuropathy with one of three major syndromic categories - 1) ataxia, 2) spasticity, and 3) global neurodevelopmental impairment. Syndromes that do not fall easily into one of these three categories can be grouped according to the predominant system involved in addition to the neuropathy e.g. -
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). -
STRING V10: Protein–Protein Interaction Networks, Integrated
Published online 28 October 2014 Nucleic Acids Research, 2015, Vol. 43, Database issue D447–D452 doi: 10.1093/nar/gku1003 STRING v10: protein–protein interaction networks, integrated over the tree of life Damian Szklarczyk1, Andrea Franceschini1, Stefan Wyder1, Kristoffer Forslund2, Davide Heller1, Jaime Huerta-Cepas2, Milan Simonovic1, Alexander Roth1, Alberto Santos3, Kalliopi P. Tsafou3, Michael Kuhn4,5, Peer Bork2,*, Lars J. Jensen3,* and Christian von Mering1,* 1Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland, 2European Molecular Biology Laboratory, 69117 Heidelberg, Germany, 3Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark, 4Biotechnology Center, Technische Universitat¨ Dresden, 01062 Dresden, Germany and 5Max Planck Institute of Molecular Cell Biology and Downloaded from Genetics, 01062 Dresden, Germany Received September 15, 2014; Accepted October 07, 2014 ABSTRACT INTRODUCTION http://nar.oxfordjournals.org/ The many functional partnerships and interactions For a full description of a protein’s function, knowledge that occur between proteins are at the core of cel- about its specific interaction partners is an important pre- lular processing and their systematic characteriza- requisite. The concept of protein ‘function’ is somewhat hi- tion helps to provide context in molecular systems erarchical (1–4), and at all levels in this hierarchy, interac- biology. However, known and predicted interactions tions between proteins help to describe and narrow down a protein’s function: its three-dimensional structure may are scattered over multiple resources, and the avail- become meaningful only in the context of a larger pro- able data exhibit notable differences in terms of qual- tein assembly, its molecular actions may be regulated by at University of Aarhus on January 22, 2015 ity and completeness. -
Supplementary Material
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) J Neurol Neurosurg Psychiatry Page 1 / 45 SUPPLEMENTARY MATERIAL Appendix A1: Neuropsychological protocol. Appendix A2: Description of the four cases at the transitional stage. Table A1: Clinical status and center proportion in each batch. Table A2: Complete output from EdgeR. Table A3: List of the putative target genes. Table A4: Complete output from DIANA-miRPath v.3. Table A5: Comparison of studies investigating miRNAs from brain samples. Figure A1: Stratified nested cross-validation. Figure A2: Expression heatmap of miRNA signature. Figure A3: Bootstrapped ROC AUC scores. Figure A4: ROC AUC scores with 100 different fold splits. Figure A5: Presymptomatic subjects probability scores. Figure A6: Heatmap of the level of enrichment in KEGG pathways. Kmetzsch V, et al. J Neurol Neurosurg Psychiatry 2021; 92:485–493. doi: 10.1136/jnnp-2020-324647 BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) J Neurol Neurosurg Psychiatry Appendix A1. Neuropsychological protocol The PREV-DEMALS cognitive evaluation included standardized neuropsychological tests to investigate all cognitive domains, and in particular frontal lobe functions. The scores were provided previously (Bertrand et al., 2018). Briefly, global cognitive efficiency was evaluated by means of Mini-Mental State Examination (MMSE) and Mattis Dementia Rating Scale (MDRS). Frontal executive functions were assessed with Frontal Assessment Battery (FAB), forward and backward digit spans, Trail Making Test part A and B (TMT-A and TMT-B), Wisconsin Card Sorting Test (WCST), and Symbol-Digit Modalities test. -
Practice Parameter for the Diagnosis and Management of Primary Immunodeficiency
Practice parameter Practice parameter for the diagnosis and management of primary immunodeficiency Francisco A. Bonilla, MD, PhD, David A. Khan, MD, Zuhair K. Ballas, MD, Javier Chinen, MD, PhD, Michael M. Frank, MD, Joyce T. Hsu, MD, Michael Keller, MD, Lisa J. Kobrynski, MD, Hirsh D. Komarow, MD, Bruce Mazer, MD, Robert P. Nelson, Jr, MD, Jordan S. Orange, MD, PhD, John M. Routes, MD, William T. Shearer, MD, PhD, Ricardo U. Sorensen, MD, James W. Verbsky, MD, PhD, David I. Bernstein, MD, Joann Blessing-Moore, MD, David Lang, MD, Richard A. Nicklas, MD, John Oppenheimer, MD, Jay M. Portnoy, MD, Christopher R. Randolph, MD, Diane Schuller, MD, Sheldon L. Spector, MD, Stephen Tilles, MD, Dana Wallace, MD Chief Editor: Francisco A. Bonilla, MD, PhD Co-Editor: David A. Khan, MD Members of the Joint Task Force on Practice Parameters: David I. Bernstein, MD, Joann Blessing-Moore, MD, David Khan, MD, David Lang, MD, Richard A. Nicklas, MD, John Oppenheimer, MD, Jay M. Portnoy, MD, Christopher R. Randolph, MD, Diane Schuller, MD, Sheldon L. Spector, MD, Stephen Tilles, MD, Dana Wallace, MD Primary Immunodeficiency Workgroup: Chairman: Francisco A. Bonilla, MD, PhD Members: Zuhair K. Ballas, MD, Javier Chinen, MD, PhD, Michael M. Frank, MD, Joyce T. Hsu, MD, Michael Keller, MD, Lisa J. Kobrynski, MD, Hirsh D. Komarow, MD, Bruce Mazer, MD, Robert P. Nelson, Jr, MD, Jordan S. Orange, MD, PhD, John M. Routes, MD, William T. Shearer, MD, PhD, Ricardo U. Sorensen, MD, James W. Verbsky, MD, PhD GlaxoSmithKline, Merck, and Aerocrine; has received payment for lectures from Genentech/ These parameters were developed by the Joint Task Force on Practice Parameters, representing Novartis, GlaxoSmithKline, and Merck; and has received research support from Genentech/ the American Academy of Allergy, Asthma & Immunology; the American College of Novartis and Merck. -
Reactome Pathway Analysis
Fabregat et al. BMC Bioinformatics (2017) 18:142 DOI 10.1186/s12859-017-1559-2 SOFTWARE Open Access Reactome pathway analysis: a high- performance in-memory approach Antonio Fabregat1,2, Konstantinos Sidiropoulos1, Guilherme Viteri1, Oscar Forner1, Pablo Marin-Garcia3,4, Vicente Arnau5,6, Peter D’Eustachio7, Lincoln Stein8,9 and Henning Hermjakob1,10* Abstract Background: Reactome aims to provide bioinformatics tools for visualisation, interpretation and analysis of pathway knowledge to support basic research, genome analysis, modelling, systems biology and education. Pathway analysis methods have a broad range of applications in physiological and biomedical research; one of the main problems, from the analysis methods performance point of view, is the constantly increasing size of the data samples. Results: Here, we present a new high-performance in-memory implementation of the well-established over- representation analysis method. To achieve the target, the over-representation analysis method is divided in four different steps and, for each of them, specific data structures are used to improve performance and minimise the memory footprint. The first step, finding out whether an identifier in the user’s sample corresponds to an entity in Reactome, is addressed using a radix tree as a lookup table. The second step, modelling the proteins, chemicals, their orthologous in other species and their composition in complexes and sets, is addressed with a graph. The third and fourth steps, that aggregate the results and calculate the statistics, are solved with a double-linked tree. Conclusion: Through the use of highly optimised, in-memory data structures and algorithms, Reactome has achieved a stable, high performance pathway analysis service, enabling the analysis of genome-wide datasets within seconds, allowing interactive exploration and analysis of high throughput data. -
Human Induced Pluripotent Stem Cell–Derived Podocytes Mature Into Vascularized Glomeruli Upon Experimental Transplantation
BASIC RESEARCH www.jasn.org Human Induced Pluripotent Stem Cell–Derived Podocytes Mature into Vascularized Glomeruli upon Experimental Transplantation † Sazia Sharmin,* Atsuhiro Taguchi,* Yusuke Kaku,* Yasuhiro Yoshimura,* Tomoko Ohmori,* ‡ † ‡ Tetsushi Sakuma, Masashi Mukoyama, Takashi Yamamoto, Hidetake Kurihara,§ and | Ryuichi Nishinakamura* *Department of Kidney Development, Institute of Molecular Embryology and Genetics, and †Department of Nephrology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan; ‡Department of Mathematical and Life Sciences, Graduate School of Science, Hiroshima University, Hiroshima, Japan; §Division of Anatomy, Juntendo University School of Medicine, Tokyo, Japan; and |Japan Science and Technology Agency, CREST, Kumamoto, Japan ABSTRACT Glomerular podocytes express proteins, such as nephrin, that constitute the slit diaphragm, thereby contributing to the filtration process in the kidney. Glomerular development has been analyzed mainly in mice, whereas analysis of human kidney development has been minimal because of limited access to embryonic kidneys. We previously reported the induction of three-dimensional primordial glomeruli from human induced pluripotent stem (iPS) cells. Here, using transcription activator–like effector nuclease-mediated homologous recombination, we generated human iPS cell lines that express green fluorescent protein (GFP) in the NPHS1 locus, which encodes nephrin, and we show that GFP expression facilitated accurate visualization of nephrin-positive podocyte formation in -
Network-Based Association of Hypoxia-Responsive Genes with Cardiovascular Diseases
Network-based association of hypoxia-responsive genes with cardiovascular diseases Rui-Sheng Wang, William M Oldham and Joseph Loscalzo Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA E-mail: [email protected] Received 19 May 2014, revised 21 July 2014 Accepted for publication 11 August 2014 Published 24 October 2014 New Journal of Physics 16 (2014) 105014 doi:10.1088/1367-2630/16/10/105014 Abstract Molecular oxygen is indispensable for cellular viability and function. Hypoxia is a stress condition in which oxygen demand exceeds supply. Low cellular oxygen content induces a number of molecular changes to activate regulatory pathways responsible for increasing the oxygen supply and optimizing cellular metabolism under limited oxygen conditions. Hypoxia plays critical roles in the pathobiol- ogy of many diseases, such as cancer, heart failure, myocardial ischemia, stroke, and chronic lung diseases. Although the complicated associations between hypoxia and cardiovascular (and cerebrovascular) diseases (CVD) have been recognized for some time, there are few studies that investigate their biological link from a systems biology perspective. In this study, we integrate hypoxia genes, CVD genes, and the human protein interactome in order to explore the relationship between hypoxia and cardiovascular diseases at a systems level. We show that hypoxia genes are much closer to CVD genes in the human protein interactome than that expected by chance. We also find that hypoxia genes play significant bridging roles in connecting different cardiovascular diseases. We construct a hypoxia-CVD bipartite network and find several interesting hypoxia- CVD modules with significant gene ontology similarity. Finally, we show that hypoxia genes tend to have more CVD interactors in the human interactome than in random networks of matching topology. -
CENTOGENE's Severe and Early Onset Disorder Gene List
CENTOGENE’s severe and early onset disorder gene list USED IN PRENATAL WES ANALYSIS AND IDENTIFICATION OF “PATHOGENIC” AND “LIKELY PATHOGENIC” CENTOMD® VARIANTS IN NGS PRODUCTS The following gene list shows all genes assessed in prenatal WES tests or analysed for P/LP CentoMD® variants in NGS products after April 1st, 2020. For searching a single gene coverage, just use the search on www.centoportal.com AAAS, AARS1, AARS2, ABAT, ABCA12, ABCA3, ABCB11, ABCB4, ABCB7, ABCC6, ABCC8, ABCC9, ABCD1, ABCD4, ABHD12, ABHD5, ACACA, ACAD9, ACADM, ACADS, ACADVL, ACAN, ACAT1, ACE, ACO2, ACOX1, ACP5, ACSL4, ACTA1, ACTA2, ACTB, ACTG1, ACTL6B, ACTN2, ACVR2B, ACVRL1, ACY1, ADA, ADAM17, ADAMTS2, ADAMTSL2, ADAR, ADARB1, ADAT3, ADCY5, ADGRG1, ADGRG6, ADGRV1, ADK, ADNP, ADPRHL2, ADSL, AFF2, AFG3L2, AGA, AGK, AGL, AGPAT2, AGPS, AGRN, AGT, AGTPBP1, AGTR1, AGXT, AHCY, AHDC1, AHI1, AIFM1, AIMP1, AIPL1, AIRE, AK2, AKR1D1, AKT1, AKT2, AKT3, ALAD, ALDH18A1, ALDH1A3, ALDH3A2, ALDH4A1, ALDH5A1, ALDH6A1, ALDH7A1, ALDOA, ALDOB, ALG1, ALG11, ALG12, ALG13, ALG14, ALG2, ALG3, ALG6, ALG8, ALG9, ALMS1, ALOX12B, ALPL, ALS2, ALX3, ALX4, AMACR, AMER1, AMN, AMPD1, AMPD2, AMT, ANK2, ANK3, ANKH, ANKRD11, ANKS6, ANO10, ANO5, ANOS1, ANTXR1, ANTXR2, AP1B1, AP1S1, AP1S2, AP3B1, AP3B2, AP4B1, AP4E1, AP4M1, AP4S1, APC2, APTX, AR, ARCN1, ARFGEF2, ARG1, ARHGAP31, ARHGDIA, ARHGEF9, ARID1A, ARID1B, ARID2, ARL13B, ARL3, ARL6, ARL6IP1, ARMC4, ARMC9, ARSA, ARSB, ARSL, ARV1, ARX, ASAH1, ASCC1, ASH1L, ASL, ASNS, ASPA, ASPH, ASPM, ASS1, ASXL1, ASXL2, ASXL3, ATAD3A, ATCAY, ATIC, ATL1, ATM, ATOH7, -
Biomolecule and Bioentity Interaction Databases in Systems Biology: a Comprehensive Review
biomolecules Review Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review Fotis A. Baltoumas 1,* , Sofia Zafeiropoulou 1, Evangelos Karatzas 1 , Mikaela Koutrouli 1,2, Foteini Thanati 1, Kleanthi Voutsadaki 1 , Maria Gkonta 1, Joana Hotova 1, Ioannis Kasionis 1, Pantelis Hatzis 1,3 and Georgios A. Pavlopoulos 1,3,* 1 Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; zafeiropoulou@fleming.gr (S.Z.); karatzas@fleming.gr (E.K.); [email protected] (M.K.); [email protected] (F.T.); voutsadaki@fleming.gr (K.V.); [email protected] (M.G.); hotova@fleming.gr (J.H.); [email protected] (I.K.); hatzis@fleming.gr (P.H.) 2 Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark 3 Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece * Correspondence: baltoumas@fleming.gr (F.A.B.); pavlopoulos@fleming.gr (G.A.P.); Tel.: +30-210-965-6310 (G.A.P.) Abstract: Technological advances in high-throughput techniques have resulted in tremendous growth Citation: Baltoumas, F.A.; of complex biological datasets providing evidence regarding various biomolecular interactions. Zafeiropoulou, S.; Karatzas, E.; To cope with this data flood, computational approaches, web services, and databases have been Koutrouli, M.; Thanati, F.; Voutsadaki, implemented to deal with issues such as data integration, visualization, exploration, organization, K.; Gkonta, M.; Hotova, J.; Kasionis, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more I.; Hatzis, P.; et al. Biomolecule and and more difficult for an end user to know what the scope and focus of each repository is and how Bioentity Interaction Databases in redundant the information between them is. -
Identification of Genomic Targets of Krüppel-Like Factor 9 in Mouse Hippocampal
Identification of Genomic Targets of Krüppel-like Factor 9 in Mouse Hippocampal Neurons: Evidence for a role in modulating peripheral circadian clocks by Joseph R. Knoedler A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Neuroscience) in the University of Michigan 2016 Doctoral Committee: Professor Robert J. Denver, Chair Professor Daniel Goldman Professor Diane Robins Professor Audrey Seasholtz Associate Professor Bing Ye ©Joseph R. Knoedler All Rights Reserved 2016 To my parents, who never once questioned my decision to become the other kind of doctor, And to Lucy, who has pushed me to be a better person from day one. ii Acknowledgements I have a huge number of people to thank for having made it to this point, so in no particular order: -I would like to thank my adviser, Dr. Robert J. Denver, for his guidance, encouragement, and patience over the last seven years; his mentorship has been indispensable for my growth as a scientist -I would also like to thank my committee members, Drs. Audrey Seasholtz, Dan Goldman, Diane Robins and Bing Ye, for their constructive feedback and their willingness to meet in a frequently cold, windowless room across campus from where they work -I am hugely indebted to Pia Bagamasbad and Yasuhiro Kyono for teaching me almost everything I know about molecular biology and bioinformatics, and to Arasakumar Subramani for his tireless work during the home stretch to my dissertation -I am grateful for the Neuroscience Program leadership and staff, in particular