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Preclinical Research 2020 Sections
Sackler Faculty of Medicine Preclinical Research 2020 Sections Cancer and Molecular Therapies 8 Dental Health and Medicine 52 Diabetes, Metabolic and Endocrine Diseases 62 Genomics & Personalized Medicine 78 Hearing, Language & Speech Sciences and Disorders 98 Infectious Diseases 117 Inflammatory and Autoimmune Diseases 137 Medical Education and Ethics 147 Nervous System and Brain Disorders 154 Nursing, Occupational and Physical Therapy 200 Public Health 235 Reproduction, Development and Evolution 263 Stem Cells and Regenerative Medicine 280 Cover images (from bottom left, clockwise): Image 1: Human embryonic stem cell derived cardiomyocytes stained with fluorescent antibodies. The cardiac marker alpha-actinin (green), calcium channel modulator, Ahnak1 (red) – Shimrit Oz, Nathan Dascal. Image 2: Islet of Langerhans containing insulin-producing beta-cells (green) and glucagon- producing alpha-cells (red) – Daria Baer, Limor Landsman. Image 3: β-catenin in C. elegans vulva – Michal Caspi, Limor Broday, Rina Rosin-Arbesfeld. Image 4: Stereocilia of a sensory outer hair cell from a mouse inner ear – Shaked Shivatzki, Karen Avraham. Image 5: Electron scanning micrograph of middle ear ossicles from a mouse ear stained with pseudo colors – Shaked Shivatski, Karen Avraham. Image 6: Resistin-like molecule alpha (red), eosinophil major basic protein (green) and DAPI (blue) staining of asthmatic mice – Danielle Karo-Atar, Ariel Munitz. © All rights reserved Editor: Prof. Karen Avraham Graphic design: Michal Semo Kovetz, TAU Graphic Design Studio February 2020 Sackler Faculty of Medicine Research 2020 2 The Sackler Faculty of Medicine The Sackler Faculty of Medicine is Israel’s largest diabetes, neurodegenerative diseases, infectious medical research and training complex. The Sackler diseases and genetic diseases, including but not Faculty of Medicine of Tel Aviv University (TAU) was imited to Alzheimer’s disease, Parkinson’s disease founded in 1964 following the generous contributions and HIV/AIDS. -
Topologically Associating Domain Boundaries Are Commonly
bioRxiv preprint doi: https://doi.org/10.1101/2021.05.06.443037; this version posted May 7, 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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. Topologically Associating Domain Boundaries are Commonly Required for Normal Genome Function Sudha Rajderkar1, Iros Barozzi1,2,3, Yiwen Zhu1, Rong Hu4, Yanxiao Zhang4, Bin Li4, Yoko Fukuda- Yuzawa1,5, Guy Kelman1,6, Adyam Akeza1, Matthew J. Blow15, Quan Pham1, Anne N. Harrington1, Janeth Godoy1, Eman M. Meky1, Kianna von Maydell1, Catherine S. Novak1, Ingrid Plajzer-Frick1, Veena Afzal1, Stella Tran1, Michael E. Talkowski7,8,9, K. C. Kent Lloyd10,11, Bing Ren4,12,13,14, Diane E. Dickel1,*, Axel Visel1,15,16,*, Len A. Pennacchio1,15, 17,* 1 Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA. 2 Institute of Cancer Research Medical University of Vienna, Borschkegasse 8a 1090, Vienna, Austria. 3 Department of Surgery and Cancer, Imperial College London, London, UK. 4 Ludwig Institute for Cancer Research, La Jolla, CA, USA. 5 Institute of Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan. 6 The Jerusalem Center for Personalized Computational Medicine, Hebrew University of Jerusalem, Jerusalem, Israel. 7 Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA. 8 Program in Medical and Population Genetics and Stanley Center for Psychiatric Disorders, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA 02142, USA. -
The Mutational Landscape of Myeloid Leukaemia in Down Syndrome
cancers Review The Mutational Landscape of Myeloid Leukaemia in Down Syndrome Carini Picardi Morais de Castro 1, Maria Cadefau 1,2 and Sergi Cuartero 1,2,* 1 Josep Carreras Leukaemia Research Institute (IJC), Campus Can Ruti, 08916 Badalona, Spain; [email protected] (C.P.M.d.C); [email protected] (M.C.) 2 Germans Trias i Pujol Research Institute (IGTP), Campus Can Ruti, 08916 Badalona, Spain * Correspondence: [email protected] Simple Summary: Leukaemia occurs when specific mutations promote aberrant transcriptional and proliferation programs, which drive uncontrolled cell division and inhibit the cell’s capacity to differentiate. In this review, we summarize the most frequent genetic lesions found in myeloid leukaemia of Down syndrome, a rare paediatric leukaemia specific to individuals with trisomy 21. The evolution of this disease follows a well-defined sequence of events and represents a unique model to understand how the ordered acquisition of mutations drives malignancy. Abstract: Children with Down syndrome (DS) are particularly prone to haematopoietic disorders. Paediatric myeloid malignancies in DS occur at an unusually high frequency and generally follow a well-defined stepwise clinical evolution. First, the acquisition of mutations in the GATA1 transcription factor gives rise to a transient myeloproliferative disorder (TMD) in DS newborns. While this condition spontaneously resolves in most cases, some clones can acquire additional mutations, which trigger myeloid leukaemia of Down syndrome (ML-DS). These secondary mutations are predominantly found in chromatin and epigenetic regulators—such as cohesin, CTCF or EZH2—and Citation: de Castro, C.P.M.; Cadefau, in signalling mediators of the JAK/STAT and RAS pathways. -
'Next- Generation' Sequencing Data Analysis
Novel Algorithm Development for ‘Next- Generation’ Sequencing Data Analysis Agne Antanaviciute Submitted in accordance with the requirements for the degree of Doctor of Philosophy University of Leeds School of Medicine Leeds Institute of Biomedical and Clinical Sciences 12/2017 ii The candidate confirms that the work submitted is her own, except where work which has formed part of jointly-authored publications has been included. The contribution of the candidate and the other authors to this work has been explicitly given within the thesis where reference has been made to the work of others. This copy has been supplied on the understanding that it is copyright material and that no quotation from the thesis may be published without proper acknowledgement ©2017 The University of Leeds and Agne Antanaviciute The right of Agne Antanaviciute to be identified as Author of this work has been asserted by her in accordance with the Copyright, Designs and Patents Act 1988. Acknowledgements I would like to thank all the people who have contributed to this work. First and foremost, my supervisors Dr Ian Carr, Professor David Bonthron and Dr Christopher Watson, who have provided guidance, support and motivation. I could not have asked for a better supervisory team. I would also like to thank my collaborators Dr Belinda Baquero and Professor Adrian Whitehouse for opening new, interesting research avenues. A special thanks to Dr Belinda Baquero for all the hard wet lab work without which at least half of this thesis would not exist. Thanks to everyone at the NGS Facility – Carolina Lascelles, Catherine Daley, Sally Harrison, Ummey Hany and Laura Crinnion – for the generation of NGS data used in this work and creating a supportive and stimulating work environment. -
Whole Exome Sequencing in Families at High Risk for Hodgkin Lymphoma: Identification of a Predisposing Mutation in the KDR Gene
Hodgkin Lymphoma SUPPLEMENTARY APPENDIX Whole exome sequencing in families at high risk for Hodgkin lymphoma: identification of a predisposing mutation in the KDR gene Melissa Rotunno, 1 Mary L. McMaster, 1 Joseph Boland, 2 Sara Bass, 2 Xijun Zhang, 2 Laurie Burdett, 2 Belynda Hicks, 2 Sarangan Ravichandran, 3 Brian T. Luke, 3 Meredith Yeager, 2 Laura Fontaine, 4 Paula L. Hyland, 1 Alisa M. Goldstein, 1 NCI DCEG Cancer Sequencing Working Group, NCI DCEG Cancer Genomics Research Laboratory, Stephen J. Chanock, 5 Neil E. Caporaso, 1 Margaret A. Tucker, 6 and Lynn R. Goldin 1 1Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 2Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 3Ad - vanced Biomedical Computing Center, Leidos Biomedical Research Inc.; Frederick National Laboratory for Cancer Research, Frederick, MD; 4Westat, Inc., Rockville MD; 5Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; and 6Human Genetics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA ©2016 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol.2015.135475 Received: August 19, 2015. Accepted: January 7, 2016. Pre-published: June 13, 2016. Correspondence: [email protected] Supplemental Author Information: NCI DCEG Cancer Sequencing Working Group: Mark H. Greene, Allan Hildesheim, Nan Hu, Maria Theresa Landi, Jennifer Loud, Phuong Mai, Lisa Mirabello, Lindsay Morton, Dilys Parry, Anand Pathak, Douglas R. Stewart, Philip R. Taylor, Geoffrey S. Tobias, Xiaohong R. Yang, Guoqin Yu NCI DCEG Cancer Genomics Research Laboratory: Salma Chowdhury, Michael Cullen, Casey Dagnall, Herbert Higson, Amy A. -
Deep Multiomics Profiling of Brain Tumors Identifies Signaling Networks
ARTICLE https://doi.org/10.1038/s41467-019-11661-4 OPEN Deep multiomics profiling of brain tumors identifies signaling networks downstream of cancer driver genes Hong Wang 1,2,3, Alexander K. Diaz3,4, Timothy I. Shaw2,5, Yuxin Li1,2,4, Mingming Niu1,4, Ji-Hoon Cho2, Barbara S. Paugh4, Yang Zhang6, Jeffrey Sifford1,4, Bing Bai1,4,10, Zhiping Wu1,4, Haiyan Tan2, Suiping Zhou2, Laura D. Hover4, Heather S. Tillman 7, Abbas Shirinifard8, Suresh Thiagarajan9, Andras Sablauer 8, Vishwajeeth Pagala2, Anthony A. High2, Xusheng Wang 2, Chunliang Li 6, Suzanne J. Baker4 & Junmin Peng 1,2,4 1234567890():,; High throughput omics approaches provide an unprecedented opportunity for dissecting molecular mechanisms in cancer biology. Here we present deep profiling of whole proteome, phosphoproteome and transcriptome in two high-grade glioma (HGG) mouse models driven by mutated RTK oncogenes, PDGFRA and NTRK1, analyzing 13,860 proteins and 30,431 phosphosites by mass spectrometry. Systems biology approaches identify numerous master regulators, including 41 kinases and 23 transcription factors. Pathway activity computation and mouse survival indicate the NTRK1 mutation induces a higher activation of AKT down- stream targets including MYC and JUN, drives a positive feedback loop to up-regulate multiple other RTKs, and confers higher oncogenic potency than the PDGFRA mutation. A mini-gRNA library CRISPR-Cas9 validation screening shows 56% of tested master regulators are important for the viability of NTRK-driven HGG cells, including TFs (Myc and Jun) and metabolic kinases (AMPKa1 and AMPKa2), confirming the validity of the multiomics inte- grative approaches, and providing novel tumor vulnerabilities. -
Gene Regulation by Cohesin in Cancer: Is the Ring an Unexpected Party to Proliferation?
Published OnlineFirst September 22, 2011; DOI: 10.1158/1541-7786.MCR-11-0382 Molecular Cancer Review Research Gene Regulation by Cohesin in Cancer: Is the Ring an Unexpected Party to Proliferation? Jenny M. Rhodes, Miranda McEwan, and Julia A. Horsfield Abstract Cohesin is a multisubunit protein complex that plays an integral role in sister chromatid cohesion, DNA repair, and meiosis. Of significance, both over- and underexpression of cohesin are associated with cancer. It is generally believed that cohesin dysregulation contributes to cancer by leading to aneuploidy or chromosome instability. For cancers with loss of cohesin function, this idea seems plausible. However, overexpression of cohesin in cancer appears to be more significant for prognosis than its loss. Increased levels of cohesin subunits correlate with poor prognosis and resistance to drug, hormone, and radiation therapies. However, if there is sufficient cohesin for sister chromatid cohesion, overexpression of cohesin subunits should not obligatorily lead to aneuploidy. This raises the possibility that excess cohesin promotes cancer by alternative mechanisms. Over the last decade, it has emerged that cohesin regulates gene transcription. Recent studies have shown that gene regulation by cohesin contributes to stem cell pluripotency and cell differentiation. Of importance, cohesin positively regulates the transcription of genes known to be dysregulated in cancer, such as Runx1, Runx3, and Myc. Furthermore, cohesin binds with estrogen receptor a throughout the genome in breast cancer cells, suggesting that it may be involved in the transcription of estrogen-responsive genes. Here, we will review evidence supporting the idea that the gene regulation func- tion of cohesin represents a previously unrecognized mechanism for the development of cancer. -
Supplementary Data
Supplementary Fig. 1 A B Responder_Xenograft_ Responder_Xenograft_ NON- NON- Lu7336, Vehicle vs Lu7466, Vehicle vs Responder_Xenograft_ Responder_Xenograft_ Sagopilone, Welch- Sagopilone, Welch- Lu7187, Vehicle vs Lu7406, Vehicle vs Test: 638 Test: 600 Sagopilone, Welch- Sagopilone, Welch- Test: 468 Test: 482 Responder_Xenograft_ NON- Lu7860, Vehicle vs Responder_Xenograft_ Sagopilone, Welch - Lu7558, Vehicle vs Test: 605 Sagopilone, Welch- Test: 333 Supplementary Fig. 2 Supplementary Fig. 3 Supplementary Figure S1. Venn diagrams comparing probe sets regulated by Sagopilone treatment (10mg/kg for 24h) between individual models (Welsh Test ellipse p-value<0.001 or 5-fold change). A Sagopilone responder models, B Sagopilone non-responder models. Supplementary Figure S2. Pathway analysis of genes regulated by Sagopilone treatment in responder xenograft models 24h after Sagopilone treatment by GeneGo Metacore; the most significant pathway map representing cell cycle/spindle assembly and chromosome separation is shown, genes upregulated by Sagopilone treatment are marked with red thermometers. Supplementary Figure S3. GeneGo Metacore pathway analysis of genes differentially expressed between Sagopilone Responder and Non-Responder models displaying –log(p-Values) of most significant pathway maps. Supplementary Tables Supplementary Table 1. Response and activity in 22 non-small-cell lung cancer (NSCLC) xenograft models after treatment with Sagopilone and other cytotoxic agents commonly used in the management of NSCLC Tumor Model Response type -
Mccartney, Karen M. (2015)
THE ROLE OF PEROXISOME PROLIFERATOR ACTIVATED RECEPTOR ALPHA (PPARα) IN THE EFFECT OF PIROXICAM ON COLON CANCER KAREN MARIE McCARTNEY BSc. (Hons), MSc. Thesis submitted to the University of Nottingham for the degree of Doctor of Philosophy April 2015 Abstract Studies with APCMin/+ mice and APCMin/+ PPARα-/- mice were undertaken to investigate whether polyp development in the mouse gut was mediated by PPARα. Additionally, the effect of piroxicam treatment dependency on PPARα was assessed. Results showed the number of polyps in the colon was significantly higher in APCMin/+ PPARα-/- mice than in APCMin/+ mice, whilst in the small bowel the difference was not significant. Analysis of gene expression in the colon with Affymetrix® microarrays demonstrated the largest source of variation was between tumour and normal tissue. Deletion of PPARα had little effect on gene expression in normal tissue but appeared to have more effect in tumour tissue. Ingenuity pathway analysis of these data showed the top biological processes were growth & proliferation and colorectal cancer. Collectively, these data may indicate that deletion of PPARα exacerbates the existing APCMin/+ mutation to promote tumorigenesis in the colon. 95 genes from Affymetrix® microarray data were selected for further analysis on Taqman® low density arrays. There was good correlation of expression levels between the two array types. Expression data of two genes proved particularly interesting; Onecut homeobox 2 (Onecut2) and Apolipoprotein B DNA dC dU - editing enzyme, catalytic polypeptide 3 (Apobec3). Onecut2 was highly up-regulated in tumour tissue. Apobec3 was up-regulated in APCMin/+ PPARα-/- mice only; suggesting expression was mediated via PPARα. There was a striking increase in survival accompanied by a marked reduction in small intestinal polyp numbers in mice of either genotype that received piroxicam. -
1 Supplemental Methods 4Su RNA Isolation Cells Were Incubated In
Supplemental Methods 4sU RNA isolation Cells were incubated in 500uM 4-thiouridine for 2.5 mins before RNA extraction using Trizol. 15-20ug RNA was biotinylated in a volume of 250μl containing 10mM HEPES (pH7.5), 5ug MTSEA Biotin-XX (Iris Biotech, dissolved in dimethyl formamide). After incubation in the dark for 90 mins, biotinylated RNA was chloroform extracted, phenol chloroform extracted and ethanol precipitated. It was re-suspended in RPB (300mM NaCl, 10mM Tris pH7.5, 5mM EDTA) and incubated with 50ul streptavidin-coated magnetic beads (Miltenyi Biotech) for 15 mins. Beads were washed 5x in (100 mM Tris-HCl pH 7.4, 10 mM EDTA, 1 M NaCl, and 0.1% Tween-20) pre-heated to 60oC. RNA was eluted in 100μl of 0.1M DTT for 15 mins at 37oC before final phenol chloroform extraction and ethanol precipitation. Metaprofiling of effects of XRN2 vs CPSF73 depletion (Figure 2A) Single-end 50-base-pair (bp) reads were screened for sequencing quality using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc); adapter sequences were then removed using Trim Galore using the default settings (https://www.bioinformatics.babraham.ac.uk/projects/trim_galore). Trimmed reads passing QC steps were aligned to the GRCh38 (Ensembl) human genome using Hisat2 with default parameters, incorporating known splice sites (Kim et al. 2015). Unmapped, multi-mapped and reads with low mapping quality scores (MAPQ <20) were discarded using SAMtools (Li et al. 2009). For metagene analysis, expressed protein-coding genes (>50 reads per gene) were selected and a window extending 20 kb downstream from the transcription end site (TES) was applied. -
Population Genomics in a Disease Targeted Primary Cell Model
Downloaded from genome.cshlp.org on October 8, 2021 - Published by Cold Spring Harbor Laboratory Press Letter Population genomics in a disease targeted primary cell model Elin Grundberg,1,2,12 Tony Kwan,1,2,12 Bing Ge,2 Kevin C.L. Lam,2 Vonda Koka,2 Andreas Kindmark,3 Hans Mallmin,4 Joana Dias,2 Dominique J. Verlaan,1,2,5 Manon Ouimet,5 Daniel Sinnett,5,6 Fernando Rivadeneira,7,8 Karol Estrada,7 Albert Hofman,8 Joyce M. van Meurs,7 Andre´ Uitterlinden,7,8 Patrick Beaulieu,5 Alexandru Graziani,5 Eef Harmsen,2 O¨ sten Ljunggren,3 Claes Ohlsson,9 Dan Mellstro¨m,9 Magnus K. Karlsson,10 Olle Nilsson,4 and Tomi Pastinen1,2,11,13 1Department of Human Genetics, McGill University, Montre´al H3A 1B1, Canada; 2McGill University and Genome Que´bec Innovation Centre, Montre´al H3A 1A4, Canada; 3Department of Medical Sciences, Uppsala University, Uppsala 75185, Sweden; 4Department of Surgical Sciences, Uppsala University, Uppsala 75185, Sweden; 5Sainte-Justine University Health Center, Montre´al H3T 1C5, Canada; 6Department of Pediatrics, University of Montre´al, Montre´al H3T 1C5, Canada; 7Department of Internal Medicine, Erasmus MC, Rotterdam 3015GE, The Netherlands; 8Department of Epidemiology, Erasmus MC, Rotterdam 3015GE, The Netherlands; 9Center for Bone Research at the Sahlgrenska Academy, Department of Internal Medicine, Go¨teborg University, Gothenburg 41345, Sweden; 10Clinical and Molecular Osteoporosis Research Unit, Department of Clinical Science, Lund University and Department of Orthopaedics, Malmo¨University Hospital, Malmo¨20502, Sweden; 11Department of Medical Genetics, McGill University, Montre´al H3H 1P3, Canada The common genetic variants associated with complex traits typically lie in noncoding DNA and may alter gene regu- lation in a cell type-specific manner. -
Transcriptomic and Epigenomic Characterization of the Developing Bat Wing
ARTICLES OPEN Transcriptomic and epigenomic characterization of the developing bat wing Walter L Eckalbar1,2,9, Stephen A Schlebusch3,9, Mandy K Mason3, Zoe Gill3, Ash V Parker3, Betty M Booker1,2, Sierra Nishizaki1,2, Christiane Muswamba-Nday3, Elizabeth Terhune4,5, Kimberly A Nevonen4, Nadja Makki1,2, Tara Friedrich2,6, Julia E VanderMeer1,2, Katherine S Pollard2,6,7, Lucia Carbone4,8, Jeff D Wall2,7, Nicola Illing3 & Nadav Ahituv1,2 Bats are the only mammals capable of powered flight, but little is known about the genetic determinants that shape their wings. Here we generated a genome for Miniopterus natalensis and performed RNA-seq and ChIP-seq (H3K27ac and H3K27me3) analyses on its developing forelimb and hindlimb autopods at sequential embryonic stages to decipher the molecular events that underlie bat wing development. Over 7,000 genes and several long noncoding RNAs, including Tbx5-as1 and Hottip, were differentially expressed between forelimb and hindlimb, and across different stages. ChIP-seq analysis identified thousands of regions that are differentially modified in forelimb and hindlimb. Comparative genomics found 2,796 bat-accelerated regions within H3K27ac peaks, several of which cluster near limb-associated genes. Pathway analyses highlighted multiple ribosomal proteins and known limb patterning signaling pathways as differentially regulated and implicated increased forelimb mesenchymal condensation in differential growth. In combination, our work outlines multiple genetic components that likely contribute to bat wing formation, providing insights into this morphological innovation. The order Chiroptera, commonly known as bats, is the only group of To characterize the genetic differences that underlie divergence in mammals to have evolved the capability of flight.