A Worldwide Map of Swine Short Tandem Repeats and Their
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Figure S1. Representative Report Generated by the Ion Torrent System Server for Each of the KCC71 Panel Analysis and Pcafusion Analysis
Figure S1. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. A Figure S1. Continued. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. B Figure S2. Comparative analysis of the variant frequency found by the KCC71 panel and calculated from publicly available cBioPortal datasets. For each of the 71 genes in the KCC71 panel, the frequency of variants was calculated as the variant number found in the examined cases. Datasets marked with different colors and sample numbers of prostate cancer are presented in the upper right. *Significantly high in the present study. Figure S3. Seven subnetworks extracted from each of seven public prostate cancer gene networks in TCNG (Table SVI). Blue dots represent genes that include initial seed genes (parent nodes), and parent‑child and child‑grandchild genes in the network. Graphical representation of node‑to‑node associations and subnetwork structures that differed among and were unique to each of the seven subnetworks. TCNG, The Cancer Network Galaxy. Figure S4. REVIGO tree map showing the predicted biological processes of prostate cancer in the Japanese. Each rectangle represents a biological function in terms of a Gene Ontology (GO) term, with the size adjusted to represent the P‑value of the GO term in the underlying GO term database. -
Downloaded from [266]
Patterns of DNA methylation on the human X chromosome and use in analyzing X-chromosome inactivation by Allison Marie Cotton B.Sc., The University of Guelph, 2005 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in The Faculty of Graduate Studies (Medical Genetics) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) January 2012 © Allison Marie Cotton, 2012 Abstract The process of X-chromosome inactivation achieves dosage compensation between mammalian males and females. In females one X chromosome is transcriptionally silenced through a variety of epigenetic modifications including DNA methylation. Most X-linked genes are subject to X-chromosome inactivation and only expressed from the active X chromosome. On the inactive X chromosome, the CpG island promoters of genes subject to X-chromosome inactivation are methylated in their promoter regions, while genes which escape from X- chromosome inactivation have unmethylated CpG island promoters on both the active and inactive X chromosomes. The first objective of this thesis was to determine if the DNA methylation of CpG island promoters could be used to accurately predict X chromosome inactivation status. The second objective was to use DNA methylation to predict X-chromosome inactivation status in a variety of tissues. A comparison of blood, muscle, kidney and neural tissues revealed tissue-specific X-chromosome inactivation, in which 12% of genes escaped from X-chromosome inactivation in some, but not all, tissues. X-linked DNA methylation analysis of placental tissues predicted four times higher escape from X-chromosome inactivation than in any other tissue. Despite the hypomethylation of repetitive elements on both the X chromosome and the autosomes, no changes were detected in the frequency or intensity of placental Cot-1 holes. -
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 -
A Database of Gene Regulatory Network Models Across Human Conditions
bioRxiv preprint doi: https://doi.org/10.1101/2021.06.18.448997; this version posted June 18, 2021. 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. GRAND: A database of gene regulatory network models across human conditions Marouen Ben Guebila1†, Camila M Lopes-Ramos1†, Deborah Weighill1,7, Abhijeet Rajendra Sonawane2, Rebekka Burkholz1, Behrouz Shamsaei3, John Platig4, Kimberly Glass1,4, Marieke L Kuijjer5,6, John Quackenbush1,4,*. 1Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA. 2Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA. 3Division of Biostatistics and Bioinformatics, Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA. 4Channing Division of Network Medicine, Department of Medicine, Harvard Medical School and Brigham and Women’s Hospital 5Center for Molecular Medicine Norway, Faculty of Medicine, University of Oslo, Norway 6Leiden University Medical Center, Leiden, the Netherlands 7Current address: UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA † Joint Authors. * To whom correspondence should be addressed. Email: [email protected]. 1 bioRxiv preprint doi: https://doi.org/10.1101/2021.06.18.448997; this version posted June 18, 2021. 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. -
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 -
Target Gene Gene Description Validation Diana Miranda
Supplemental Table S1. Mmu-miR-183-5p in silico predicted targets. TARGET GENE GENE DESCRIPTION VALIDATION DIANA MIRANDA MIRBRIDGE PICTAR PITA RNA22 TARGETSCAN TOTAL_HIT AP3M1 adaptor-related protein complex 3, mu 1 subunit V V V V V V V 7 BTG1 B-cell translocation gene 1, anti-proliferative V V V V V V V 7 CLCN3 chloride channel, voltage-sensitive 3 V V V V V V V 7 CTDSPL CTD (carboxy-terminal domain, RNA polymerase II, polypeptide A) small phosphatase-like V V V V V V V 7 DUSP10 dual specificity phosphatase 10 V V V V V V V 7 MAP3K4 mitogen-activated protein kinase kinase kinase 4 V V V V V V V 7 PDCD4 programmed cell death 4 (neoplastic transformation inhibitor) V V V V V V V 7 PPP2R5C protein phosphatase 2, regulatory subunit B', gamma V V V V V V V 7 PTPN4 protein tyrosine phosphatase, non-receptor type 4 (megakaryocyte) V V V V V V V 7 EZR ezrin V V V V V V 6 FOXO1 forkhead box O1 V V V V V V 6 ANKRD13C ankyrin repeat domain 13C V V V V V V 6 ARHGAP6 Rho GTPase activating protein 6 V V V V V V 6 BACH2 BTB and CNC homology 1, basic leucine zipper transcription factor 2 V V V V V V 6 BNIP3L BCL2/adenovirus E1B 19kDa interacting protein 3-like V V V V V V 6 BRMS1L breast cancer metastasis-suppressor 1-like V V V V V V 6 CDK5R1 cyclin-dependent kinase 5, regulatory subunit 1 (p35) V V V V V V 6 CTDSP1 CTD (carboxy-terminal domain, RNA polymerase II, polypeptide A) small phosphatase 1 V V V V V V 6 DCX doublecortin V V V V V V 6 ENAH enabled homolog (Drosophila) V V V V V V 6 EPHA4 EPH receptor A4 V V V V V V 6 FOXP1 forkhead box P1 V -
A High-Throughput Approach to Uncover Novel Roles of APOBEC2, a Functional Orphan of the AID/APOBEC Family
Rockefeller University Digital Commons @ RU Student Theses and Dissertations 2018 A High-Throughput Approach to Uncover Novel Roles of APOBEC2, a Functional Orphan of the AID/APOBEC Family Linda Molla Follow this and additional works at: https://digitalcommons.rockefeller.edu/ student_theses_and_dissertations Part of the Life Sciences Commons A HIGH-THROUGHPUT APPROACH TO UNCOVER NOVEL ROLES OF APOBEC2, A FUNCTIONAL ORPHAN OF THE AID/APOBEC FAMILY A Thesis Presented to the Faculty of The Rockefeller University in Partial Fulfillment of the Requirements for the degree of Doctor of Philosophy by Linda Molla June 2018 © Copyright by Linda Molla 2018 A HIGH-THROUGHPUT APPROACH TO UNCOVER NOVEL ROLES OF APOBEC2, A FUNCTIONAL ORPHAN OF THE AID/APOBEC FAMILY Linda Molla, Ph.D. The Rockefeller University 2018 APOBEC2 is a member of the AID/APOBEC cytidine deaminase family of proteins. Unlike most of AID/APOBEC, however, APOBEC2’s function remains elusive. Previous research has implicated APOBEC2 in diverse organisms and cellular processes such as muscle biology (in Mus musculus), regeneration (in Danio rerio), and development (in Xenopus laevis). APOBEC2 has also been implicated in cancer. However the enzymatic activity, substrate or physiological target(s) of APOBEC2 are unknown. For this thesis, I have combined Next Generation Sequencing (NGS) techniques with state-of-the-art molecular biology to determine the physiological targets of APOBEC2. Using a cell culture muscle differentiation system, and RNA sequencing (RNA-Seq) by polyA capture, I demonstrated that unlike the AID/APOBEC family member APOBEC1, APOBEC2 is not an RNA editor. Using the same system combined with enhanced Reduced Representation Bisulfite Sequencing (eRRBS) analyses I showed that, unlike the AID/APOBEC family member AID, APOBEC2 does not act as a 5-methyl-C deaminase. -
Genome Architecture and Transcription Data Reveal Allelic Bias During the Cell Cycle
bioRxiv preprint doi: https://doi.org/10.1101/2020.03.15.992164; this version posted April 1, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Genome architecture and transcription data reveal allelic bias during the cell cycle Stephen Lindsly1, Wenlong Jia2, Haiming Chen1, Sijia Liu3, Scott Ronquist1, Can Chen4;7, Xingzhao Wen5, Gilbert Omenn1, Shuai Cheng Li2, Max Wicha1;6, Alnawaz Rehemtulla6, Lindsey Muir1, Indika Rajapakse1;7;∗ 1Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor 2Department of Computer Science, City University of Hong Kong 3MIT-IBM Watson AI Lab, IBM Research 4Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor 5Department of Bioengineering, University of California San Diego, La Jolla 6Department of Hematology/Oncology, University of Michigan, Ann Arbor 7Department of Mathematics, University of Michigan, Ann Arbor ∗To whom correspondence should be addressed; E-mail: [email protected]. bioRxiv preprint doi: https://doi.org/10.1101/2020.03.15.992164; this version posted April 1, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Highlights • Structural and functional differences between the maternal and paternal genomes, includ- ing similar allelic bias in related subsets of genes. • Coupling between the dynamics of gene expression and genome architecture for specific alleles illuminates an allele-specific 4D Nucleome. • Introduction of a novel allele-specific phasing algorithm for genome architecture and a quantitative framework for integration of gene expression and genome architecture. -
Engineered Type 1 Regulatory T Cells Designed for Clinical Use Kill Primary
ARTICLE Acute Myeloid Leukemia Engineered type 1 regulatory T cells designed Ferrata Storti Foundation for clinical use kill primary pediatric acute myeloid leukemia cells Brandon Cieniewicz,1* Molly Javier Uyeda,1,2* Ping (Pauline) Chen,1 Ece Canan Sayitoglu,1 Jeffrey Mao-Hwa Liu,1 Grazia Andolfi,3 Katharine Greenthal,1 Alice Bertaina,1,4 Silvia Gregori,3 Rosa Bacchetta,1,4 Norman James Lacayo,1 Alma-Martina Cepika1,4# and Maria Grazia Roncarolo1,2,4# Haematologica 2021 Volume 106(10):2588-2597 1Department of Pediatrics, Division of Stem Cell Transplantation and Regenerative Medicine, Stanford School of Medicine, Stanford, CA, USA; 2Stanford Institute for Stem Cell Biology and Regenerative Medicine, Stanford School of Medicine, Stanford, CA, USA; 3San Raffaele Telethon Institute for Gene Therapy, Milan, Italy and 4Center for Definitive and Curative Medicine, Stanford School of Medicine, Stanford, CA, USA *BC and MJU contributed equally as co-first authors #AMC and MGR contributed equally as co-senior authors ABSTRACT ype 1 regulatory (Tr1) T cells induced by enforced expression of interleukin-10 (LV-10) are being developed as a novel treatment for Tchemotherapy-resistant myeloid leukemias. In vivo, LV-10 cells do not cause graft-versus-host disease while mediating graft-versus-leukemia effect against adult acute myeloid leukemia (AML). Since pediatric AML (pAML) and adult AML are different on a genetic and epigenetic level, we investigate herein whether LV-10 cells also efficiently kill pAML cells. We show that the majority of primary pAML are killed by LV-10 cells, with different levels of sensitivity to killing. Transcriptionally, pAML sensitive to LV-10 killing expressed a myeloid maturation signature. -
Structure of Voltage-Modulated Sodium-Selective NALCN-FAM155A Channel Complex
bioRxiv preprint doi: https://doi.org/10.1101/2020.07.26.221747; this version posted July 26, 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. Structure of voltage-modulated sodium-selective NALCN-FAM155A channel complex Yunlu Kang 1, Jing-Xiang Wu 1,2,3, and Lei Chen 1,2,3* 1 State Key Laboratory of Membrane Biology, Institute of Molecular Medicine, Peking University, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Beijing 100871, China 2 Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China 3Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China *Correspondence: Lei Chen, [email protected] bioRxiv preprint doi: https://doi.org/10.1101/2020.07.26.221747; this version posted July 26, 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. Abstract Resting membrane potential determines the excitability of the cell and is essential for the cellular electrical activities. NALCN channel mediates sodium leak currents, which positively tune the resting membrane potential towards depolarization. NALCN channel is involved in many important neurological processes and is implicated in a spectrum of neurodevelopmental diseases. Despite its functional importance, the mechanisms of ion permeation and voltage-modulation for NALCN channel remain elusive. -
A Temporally Controlled Sequence of X-Chromosome Inactivation and Reactivation Defines Female Mouse in Vitro Germ Cells with Meiotic Potential
bioRxiv preprint doi: https://doi.org/10.1101/2021.08.11.455976; this version posted August 11, 2021. 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 4.0 International license. A temporally controlled sequence of X-chromosome inactivation and reactivation defines female mouse in vitro germ cells with meiotic potential Jacqueline Severino1†, Moritz Bauer1,9†, Tom Mattimoe1, Niccolò Arecco1, Luca Cozzuto1, Patricia Lorden2, Norio Hamada3, Yoshiaki Nosaka4,5,6, So Nagaoka4,5,6, Holger Heyn2, Katsuhiko Hayashi7, Mitinori Saitou4,5,6 and Bernhard Payer1,8* Abstract The early mammalian germ cell lineage is characterized by extensive epigenetic reprogramming, which is required for the maturation into functional eggs and sperm. In particular, the epigenome needs to be reset before parental marks can be established and then transmitted to the next generation. In the female germ line, reactivation of the inactive X- chromosome is one of the most prominent epigenetic reprogramming events, and despite its scale involving an entire chromosome affecting hundreds of genes, very little is known about its kinetics and biological function. Here we investigate X-chromosome inactivation and reactivation dynamics by employing a tailor-made in vitro system to visualize the X-status during differentiation of primordial germ cell-like cells (PGCLCs) from female mouse embryonic stem cells (ESCs). We find that the degree of X-inactivation in PGCLCs is moderate when compared to somatic cells and characterized by a large number of genes escaping full inactivation. -
Structure of Voltage-Modulated Sodium-Selective NALCN-FAM155A Channel Complex ✉ Yunlu Kang 1, Jing-Xiang Wu1,2,3 & Lei Chen 1,2,3
ARTICLE https://doi.org/10.1038/s41467-020-20002-9 OPEN Structure of voltage-modulated sodium-selective NALCN-FAM155A channel complex ✉ Yunlu Kang 1, Jing-Xiang Wu1,2,3 & Lei Chen 1,2,3 Resting membrane potential determines the excitability of the cell and is essential for the cellular electrical activities. The NALCN channel mediates sodium leak currents, which positively adjust resting membrane potential towards depolarization. The NALCN channel is 1234567890():,; involved in several neurological processes and has been implicated in a spectrum of neu- rodevelopmental diseases. Here, we report the cryo-EM structure of rat NALCN and mouse FAM155A complex to 2.7 Å resolution. The structure reveals detailed interactions between NALCN and the extracellular cysteine-rich domain of FAM155A. We find that the non- canonical architecture of NALCN selectivity filter dictates its sodium selectivity and calcium block, and that the asymmetric arrangement of two functional voltage sensors confers the modulation by membrane potential. Moreover, mutations associated with human diseases map to the domain-domain interfaces or the pore domain of NALCN, intuitively suggesting their pathological mechanisms. 1 State Key Laboratory of Membrane Biology, College of Future Technology, Institute of Molecular Medicine, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Peking University, 100871 Beijing, China. 2 Peking-Tsinghua Center for Life Sciences, Peking University, 100871 Beijing, China. ✉ 3 Academy for Advanced Interdisciplinary Studies, Peking University, 100871 Beijing, China. email: [email protected] NATURE COMMUNICATIONS | (2020) 11:6199 | https://doi.org/10.1038/s41467-020-20002-9 | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-20002-9 ALCN channel is a voltage-modulated sodium-selective Therefore, we started with the cryo-EM studies of the NALCN– ion channel1.