A Homozygous Splicing Mutation in ELAC2 Suggests Phenotypic Variability Including Intellectual Disability with Minimal Cardiac Involvement Nadia A
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Old Data and Friends Improve with Age: Advancements with the Updated Tools of Genenetwork
bioRxiv preprint doi: https://doi.org/10.1101/2021.05.24.445383; this version posted May 25, 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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. Old data and friends improve with age: Advancements with the updated tools of GeneNetwork Alisha Chunduri1, David G. Ashbrook2 1Department of Biotechnology, Chaitanya Bharathi Institute of Technology, Hyderabad 500075, India 2Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA Abstract Understanding gene-by-environment interactions is important across biology, particularly behaviour. Families of isogenic strains are excellently placed, as the same genome can be tested in multiple environments. The BXD’s recent expansion to 140 strains makes them the largest family of murine isogenic genomes, and therefore give great power to detect QTL. Indefinite reproducible genometypes can be leveraged; old data can be reanalysed with emerging tools to produce novel biological insights. To highlight the importance of reanalyses, we obtained drug- and behavioural-phenotypes from Philip et al. 2010, and reanalysed their data with new genotypes from sequencing, and new models (GEMMA and R/qtl2). We discover QTL on chromosomes 3, 5, 9, 11, and 14, not found in the original study. We narrowed down the candidate genes based on their ability to alter gene expression and/or protein function, using cis-eQTL analysis, and variants predicted to be deleterious. Co-expression analysis (‘gene friends’) and human PheWAS were used to further narrow candidates. -
Searching the Genomes of Inbred Mouse Strains for Incompatibilities That Reproductively Isolate Their Wild Relatives
Journal of Heredity 2007:98(2):115–122 ª The American Genetic Association. 2007. All rights reserved. doi:10.1093/jhered/esl064 For permissions, please email: [email protected]. Advance Access publication January 5, 2007 Searching the Genomes of Inbred Mouse Strains for Incompatibilities That Reproductively Isolate Their Wild Relatives BRET A. PAYSEUR AND MICHAEL PLACE From the Laboratory of Genetics, University of Wisconsin, Madison, WI 53706. Address correspondence to the author at the address above, or e-mail: [email protected]. Abstract Identification of the genes that underlie reproductive isolation provides important insights into the process of speciation. According to the Dobzhansky–Muller model, these genes suffer disrupted interactions in hybrids due to independent di- vergence in separate populations. In hybrid populations, natural selection acts to remove the deleterious heterospecific com- binations that cause these functional disruptions. When selection is strong, this process can maintain multilocus associations, primarily between conspecific alleles, providing a signature that can be used to locate incompatibilities. We applied this logic to populations of house mice that were formed by hybridization involving two species that show partial reproductive isolation, Mus domesticus and Mus musculus. Using molecular markers likely to be informative about species ancestry, we scanned the genomes of 1) classical inbred strains and 2) recombinant inbred lines for pairs of loci that showed extreme linkage disequi- libria. By using the same set of markers, we identified a list of locus pairs that displayed similar patterns in both scans. These genomic regions may contain genes that contribute to reproductive isolation between M. domesticus and M. -
Laboratory Testing for Her2 Status in Breast Cancer
Laboratory Testing for Her2 Status in Breast Cancer Katherine Geiersbach, M.D. Assistant Professor, Department of Pathology May 28, 2015 Overview • Clinical relevance of Her2 status for treatment of breast cancer • Standard approaches for determining Her2 status in breast cancer • Current concepts and controversies in Her2 testing 2 Who gets breast cancer? • Breast cancer is one of the most common malignancies to affect women • About 1 in 8 women will be diagnosed with breast cancer at some point in her lifetime • Most cases of breast cancer are sporadic, but a small percentage (5-10%) are related to a heritable gene mutation, most commonly BRCA1 or BRCA2 • Having a first degree relative with breast cancer increases a woman’s chance of developing breast cancer • Screening mammography is recommended for older women – US Preventive Services Task Force: Every 2 years starting at age 50 – American Cancer Society, others: Every 2 years starting at age 40 How is breast cancer treated? • Surgery: excision with or without sentinel lymph node biopsy – Breast conserving: lumpectomy, partial mastectomy – Mastectomy • Chemotherapy: before and/or after surgery • Radiation • Targeted therapies – Hormone therapy: Tamoxifen, aromatase inhibitors – Her2 targeted therapy for cancers with overexpression of the gene ERBB2, commonly called Her2 or Her2/neu • Treatment is based on testing for ER, PR, and Her2 status, as well as cancer grade and stage. 4 Her2 targeted therapy • Herceptin (trastuzumab) • Others: pertuzumab (Perjeta), T-DM1 (Kadcyla), and lapatinib (Tykerb) • Recent data shows that a combination of pertuzumab, trastuzumab, and docetaxel (PTD) improved progression free survival compared to patients who had only trastuzumab and docetaxel (TD)1,2 source: http://www.perjeta.com/hcp/moa 1. -
Nuclear ELAC2 Overexpression Is Associated with Increased Hazard for Relapse After Radical Prostatectomy
www.oncotarget.com Oncotarget, 2019, Vol. 10, (No. 48), pp: 4973-4986 Research Paper Nuclear ELAC2 overexpression is associated with increased hazard for relapse after radical prostatectomy Cornelia Schroeder1,2,*, Elham Navid-Hill1,*, Jan Meiners2, Claudia Hube-Magg1, Martina Kluth1, Georgia Makrypidi-Fraune1, Ronald Simon1, Franziska Büscheck1, Andreas M. Luebke1, Cosima Goebel1, Dagmar S. Lang1, Sören Weidemann1, Emily Neubauer1, Andrea Hinsch1, Frank Jacobsen1, Patrick Lebok1, Uwe Michl3, Dirk Pehrke3,4, Hartwig Huland3, Markus Graefen3, Thorsten Schlomm3,4, Guido Sauter1 and Doris Höflmayer1 1Institute of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany 2General, Visceral and Thoracic Surgery Department and Clinic, University Medical Center Hamburg-Eppendorf, Hamburg, Germany 3Martini-Clinic, Prostate Cancer Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany 4Department of Urology, Charité - Universitätsmedizin Berlin, Berlin, Germany *These authors contributed equally to this work Correspondence to: Ronald Simon, email: [email protected] Keywords: ELAC2; HPC2; prostate cancer; prognosis; tissue microarray Received: November 17, 2018 Accepted: July 21, 2019 Published: August 13, 2019 Copyright: Schroeder et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT ELAC2 is a ubiquitously expressed enzyme potentially involved in tRNA processing and cell signaling pathways. Mutations of the ELAC2 gene have been found to confer increased prostate cancer susceptibility in families. ELAC2 protein expression was analyzed by immunohistochemistry in 9,262 patients and Kaplan-Meier curves of PSA recurrence-free survival were calculated in 8,513 patients treated with radical prostatectomy. -
A Yeast Phenomic Model for the Influence of Warburg Metabolism on Genetic Buffering of Doxorubicin Sean M
Santos and Hartman Cancer & Metabolism (2019) 7:9 https://doi.org/10.1186/s40170-019-0201-3 RESEARCH Open Access A yeast phenomic model for the influence of Warburg metabolism on genetic buffering of doxorubicin Sean M. Santos and John L. Hartman IV* Abstract Background: The influence of the Warburg phenomenon on chemotherapy response is unknown. Saccharomyces cerevisiae mimics the Warburg effect, repressing respiration in the presence of adequate glucose. Yeast phenomic experiments were conducted to assess potential influences of Warburg metabolism on gene-drug interaction underlying the cellular response to doxorubicin. Homologous genes from yeast phenomic and cancer pharmacogenomics data were analyzed to infer evolutionary conservation of gene-drug interaction and predict therapeutic relevance. Methods: Cell proliferation phenotypes (CPPs) of the yeast gene knockout/knockdown library were measured by quantitative high-throughput cell array phenotyping (Q-HTCP), treating with escalating doxorubicin concentrations under conditions of respiratory or glycolytic metabolism. Doxorubicin-gene interaction was quantified by departure of CPPs observed for the doxorubicin-treated mutant strain from that expected based on an interaction model. Recursive expectation-maximization clustering (REMc) and Gene Ontology (GO)-based analyses of interactions identified functional biological modules that differentially buffer or promote doxorubicin cytotoxicity with respect to Warburg metabolism. Yeast phenomic and cancer pharmacogenomics data were integrated to predict differential gene expression causally influencing doxorubicin anti-tumor efficacy. Results: Yeast compromised for genes functioning in chromatin organization, and several other cellular processes are more resistant to doxorubicin under glycolytic conditions. Thus, the Warburg transition appears to alleviate requirements for cellular functions that buffer doxorubicin cytotoxicity in a respiratory context. -
Study of Stem Cell Function Using Microarray Experiments
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector FEBS 29359 FEBS Letters 579 (2005) 1795–1801 Minireview Study of stem cell function using microarray experiments Carolina Perez-Iratxeta, Gareth Palidwor, Christopher J. Porter, Neal A. Sanche, Matthew R. Huska, Brian P. Suomela, Enrique M. Muro, Paul M. Krzyzanowski, Evan Hughes, Pearl A. Campbell, Michael A. Rudnicki, Miguel A. Andrade* Ontario Genomics Innovation Centre, Ottawa Health Research Institute, Molecular Medicine Program, 501 Smyth Road, Ottawa, Canada K1H 8L6 Accepted 10 February 2005 Available online 21 February 2005 Edited by Robert Russell and Giulio Superti-Furga If we understand the variety of cells of an organism as dif- Abstract DNA Microarrays are used to simultaneously mea- sure the levels of thousands of mRNAs in a sample. We illustrate ferent expressions of the same genome, it is clear that a collec- here that a collection of such measurements in different cell types tion of cell gene expression snapshots can give us information and states is a sound source of functional predictions, provided about that cell and of the relations between its genomeÕs the microarray experiments are analogous and the cell samples genes. For example, the concerted expression of two genes are appropriately diverse. We have used this approach to study across many cellular states would indicate their functional stem cells, whose identity and mechanisms of control are not well relation, even in the total absence of any other experimental understood, generating Affymetrix microarray data from more characterization. But to make this analysis meaningful two than 200 samples, including stem cells and their derivatives, from conditions apply to the gene expression data; it must be both human and mouse. -
Intimate Relations—Mitochondria and Ageing
International Journal of Molecular Sciences Review Intimate Relations—Mitochondria and Ageing Michael Webb and Dionisia P. Sideris * Mitobridge Inc., an Astellas Company, 1030 Massachusetts Ave, Cambridge, MA 02138, USA; [email protected] * Correspondence: [email protected] Received: 29 August 2020; Accepted: 6 October 2020; Published: 14 October 2020 Abstract: Mitochondrial dysfunction is associated with ageing, but the detailed causal relationship between the two is still unclear. Wereview the major phenomenological manifestations of mitochondrial age-related dysfunction including biochemical, regulatory and energetic features. We conclude that the complexity of these processes and their inter-relationships are still not fully understood and at this point it seems unlikely that a single linear cause and effect relationship between any specific aspect of mitochondrial biology and ageing can be established in either direction. Keywords: mitochondria; ageing; energetics; ROS; gene regulation 1. Introduction The last two decades have witnessed a dramatic transformation in our view of mitochondria, their basic biology and functions. While still regarded as functioning primarily as the eukaryotic cell’s generator of energy in the form of adenosine triphosphate (ATP) and nicotinamide adenine dinucleotide (reduced form; NADH), mitochondria are now recognized as having a plethora of functions, including control of apoptosis, regulation of calcium, forming a signaling hub and the synthesis of various bioactive molecules. Their biochemical functions beyond ATP supply include biosynthesis of lipids and amino acids, formation of iron sulphur complexes and some stages of haem biosynthesis and the urea cycle. They exist as a dynamic network of organelles that under normal circumstances undergo a constant series of fission and fusion events in which structural, functional and encoding (mtDNA) elements are subject to redistribution throughout the network. -
Duplications of 17P FTNW
Duplications of 17p rarechromo.org 17p duplications A 17p duplication means that the cells of the body have a small but variable amount of extra genetic material from one of their 46 chromosomes – chromosome 17. For healthy development, chromosomes should contain just the right amount of genetic material (DNA) – not too much and not too little. Like most other chromosome disorders, having an extra part of chromosome 17 may increase the risk of birth defects, developmental delay and learning (intellectual) disability. However, the problems vary and depend on what and how much genetic material is duplicated. Background on Chromosomes Chromosomes are structures which contain our DNA and are found in almost every cell of the body. Every chromosome contains thousands of genes which may be thought of as individual instruction booklets (or recipes) that contain all the genetic information telling the body how to develop, grow and function. Chromosomes (and genes) usually come in pairs with one member of each chromosome pair being inherited from each parent. Most cells of the human body have a total of 46 (23 pairs of) chromosomes. The egg and the sperm cells, however have 23 unpaired chromosomes, so that when the egg and sperm join together at conception, the chromosomes pair up and the number is restored to 46. Of these 46 chromosomes, two are the sex chromosomes that determine gender. Females have two X chromosomes and males have one X chromosome and one Y chromosome. The remaining 44 chromosomes are grouped in 22 pairs, numbered 1 to 22 approximately from the largest to the smallest. -
Atlas Journal
Atlas of Genetics and Cytogenetics in Oncology and Haematology Home Genes Leukemias Solid Tumours Cancer-Prone Deep Insight Portal Teaching X Y 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 NA Atlas Journal Atlas Journal versus Atlas Database: the accumulation of the issues of the Journal constitutes the body of the Database/Text-Book. TABLE OF CONTENTS Volume 12, Number 6, Nov-Dec 2008 Previous Issue / Next Issue Genes BCL8 (B-cell CLL/lymphoma 8) (15q11). Silvia Rasi, Gianluca Gaidano. Atlas Genet Cytogenet Oncol Haematol 2008; 12 (6): 781-784. [Full Text] [PDF] URL : http://atlasgeneticsoncology.org/Genes/BCL8ID781ch15q11.html CDC25A (Cell division cycle 25A) (3p21). Dipankar Ray, Hiroaki Kiyokawa. Atlas Genet Cytogenet Oncol Haematol 2008; 12 (6): 785-791. [Full Text] [PDF] URL : http://atlasgeneticsoncology.org/Genes/CDC25AID40004ch3p21.html CDC73 (cell division cycle 73, Paf1/RNA polymerase II complex component, homolog (S. cerevisiae)) (1q31.2). Leslie Farber, Bin Tean Teh. Atlas Genet Cytogenet Oncol Haematol 2008; 12 (6): 792-797. [Full Text] [PDF] URL : http://atlasgeneticsoncology.org/Genes/CDC73D181ch1q31.html EIF3C (eukaryotic translation initiation factor 3, subunit C) (16p11.2). Daniel R Scoles. Atlas Genet Cytogenet Oncol Haematol 2008; 12 (6): 798-802. [Full Text] [PDF] URL : http://atlasgeneticsoncology.org/Genes/EIF3CID44187ch16p11.html ELAC2 (elaC homolog 2 (E. coli)) (17p11.2). Yang Chen, Sean Tavtigian, Donna Shattuck. Atlas Genet Cytogenet Oncol Haematol 2008; 12 (6): 803-806. [Full Text] [PDF] URL : http://atlasgeneticsoncology.org/Genes/ELAC2ID40437ch17p11.html FOXM1 (forkhead box M1) (12p13). Jamila Laoukili, Monica Alvarez Fernandez, René H Medema. -
An In-Depth Analysis of the HER2 Amplicon and Chromosome 17
Rondón-Lagos et al. BMC Cancer 2014, 14:922 http://www.biomedcentral.com/1471-2407/14/922 RESEARCH ARTICLE Open Access Unraveling the chromosome 17 patterns of FISH in interphase nuclei: an in-depth analysis of the HER2 amplicon and chromosome 17 centromere by karyotyping, FISH and M-FISH in breast cancer cells Milena Rondón-Lagos1,4, Ludovica Verdun Di Cantogno2, Nelson Rangel1,4, Teresa Mele3, Sandra R Ramírez-Clavijo4, Giorgio Scagliotti3, Caterina Marchiò1,2*† and Anna Sapino1,2*† Abstract Background: In diagnostic pathology, HER2 status is determined in interphase nuclei by fluorescence in situ hybridization (FISH) with probes for the HER2 gene and for the chromosome 17 centromere (CEP17). The latter probe is used as a surrogate for chromosome 17 copies, however chromosome 17 (Chr17) is frequently rearranged. The frequency and type of specific structural Chr17 alterations in breast cancer have been studied by using comparative genomic hybridization and spectral karyotyping, but not fully detailed. Actually, balanced chromosome rearrangements (e.g. translocations or inversions) and low frequency mosaicisms are assessable on metaphases using G-banding karyotype and multicolor FISH (M-FISH) only. Methods: We sought to elucidate the CEP17 and HER2 FISH patterns of interphase nuclei by evaluating Chr17 rearrangements in metaphases of 9 breast cancer cell lines and a primary culture from a triple negative breast carcinoma by using G-banding, FISH and M-FISH. Results: Thirty-nine rearranged chromosomes containing a portion of Chr17 were observed. Chromosomes 8 and 11 were the most frequent partners of Chr17 translocations. The lowest frequency of Chr17 abnormalities was observed in the HER2-negative cell lines, while the highest was observed in the HER2-positive SKBR3 cells. -
BRCA1 and BRCA2 Genes and Their Relationship to Breast and Ovarian Cancer
Cancer Lab BRCA Teacher Background on DNA Bioinformatics Lab BRCA1 and BRCA2 Genes and Their Relationship to Breast and Ovarian Cancer Note: The Teacher Background Section is meant to provide information for the teacher about the topic and is tied very closely to the PowerPoint slide show. For greater understanding, the teacher may want to play the slide show as he/she reads the background section. For the students, the slide show can be used in its entirety or can be edited as necessary for a given class. What Are BRCA1 and BRCA2 and Where Are the Genes Located? BRCA1 and BRCA2 genes in humans code for proteins that work to suppress tumors. The gene names come from BReast CAncer genes 1 and 2. The official names of these genes are breast cancer 1, early onset and breast cancer 2, early onset. Everyone, male and female, has these genes which normally work to repair DNA and are involved in cell growth and cell division. The BRCA1 gene codes for the BRCA1 protein which regulates the activity of other genes and is involved in embryonic development during cell division. The BRCA2 gene codes for the BRCA2 protein which is thought to help regulate cytokinesis during cell division and to regulate telomere homeostasis. By helping repair DNA, BRCA1 and BRCA2 proteins are thought to work in tandem to ensure the stability of the DNA in the cell. The BRCA1 protein combines with BRCA2 and other proteins to mend breaks in the DNA caused by natural and medical exposures to radiation and other environmental exposures and by recombination when the chromosomes exchange genetic material when replicating prior to cell division. -
The Genomic Landscape of Centromeres in Cancers Anjan K
www.nature.com/scientificreports OPEN The Genomic Landscape of Centromeres in Cancers Anjan K. Saha 1,2,3, Mohamad Mourad3, Mark H. Kaplan3, Ilana Chefetz4, Sami N. Malek3, Ronald Buckanovich5, David M. Markovitz2,3,6,7 & Rafael Contreras-Galindo3,4 Received: 8 March 2019 Centromere genomics remain poorly characterized in cancer, due to technologic limitations in Accepted: 23 July 2019 sequencing and bioinformatics methodologies that make high-resolution delineation of centromeric Published: xx xx xxxx loci difcult to achieve. We here leverage a highly specifc and targeted rapid PCR methodology to quantitatively assess the genomic landscape of centromeres in cancer cell lines and primary tissue. PCR- based profling of centromeres revealed widespread heterogeneity of centromeric and pericentromeric sequences in cancer cells and tissues as compared to healthy counterparts. Quantitative reductions in centromeric core and pericentromeric markers (α-satellite units and HERV-K copies) were observed in neoplastic samples as compared to healthy counterparts. Subsequent phylogenetic analysis of a pericentromeric endogenous retrovirus amplifed by PCR revealed possible gene conversion events occurring at numerous pericentromeric loci in the setting of malignancy. Our fndings collectively represent a more comprehensive evaluation of centromere genetics in the setting of malignancy, providing valuable insight into the evolution and reshufing of centromeric sequences in cancer development and progression. Te centromere is essential to eukaryotic biology due to its critical role in genome inheritance1,2. Te nucleic acid sequences that dominate the human centromeric landscape are α-satellites, arrays of ~171 base-pair monomeric units arranged into higher-order arrays throughout the centromere of each chromosome3–103 1–3.