3D Interactions with the Growth Hormone Locus in Cellular Signalling and Cancer-Related Pathways
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Variation in Protein Coding Genes Identifies Information
bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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-NC-ND 4.0 International license. Animal complexity and information flow 1 1 2 3 4 5 Variation in protein coding genes identifies information flow as a contributor to 6 animal complexity 7 8 Jack Dean, Daniela Lopes Cardoso and Colin Sharpe* 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Institute of Biological and Biomedical Sciences 25 School of Biological Science 26 University of Portsmouth, 27 Portsmouth, UK 28 PO16 7YH 29 30 * Author for correspondence 31 [email protected] 32 33 Orcid numbers: 34 DLC: 0000-0003-2683-1745 35 CS: 0000-0002-5022-0840 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Abstract bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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-NC-ND 4.0 International license. Animal complexity and information flow 2 1 Across the metazoans there is a trend towards greater organismal complexity. How 2 complexity is generated, however, is uncertain. Since C.elegans and humans have 3 approximately the same number of genes, the explanation will depend on how genes are 4 used, rather than their absolute number. -
3D Interactions with the Growth Hormone Locus in Cellular Signalling and Cancer-Related Pathways
64 4 Journal of Molecular L Jain et al. 3D interactions with the GH locus 64:4 209–222 Endocrinology RESEARCH 3D interactions with the growth hormone locus in cellular signalling and cancer-related pathways Lekha Jain, Tayaza Fadason, William Schierding, Mark H Vickers, Justin M O’Sullivan and Jo K Perry Liggins Institute, University of Auckland, Auckland, New Zealand Correspondence should be addressed to J K Perry or J M O’Sullivan: [email protected] or [email protected] Abstract Growth hormone (GH) is a peptide hormone predominantly produced by the anterior Key Words pituitary and is essential for normal growth and metabolism. The GH locus contains f growth hormone locus five evolutionarily related genes under the control of an upstream locus control region f GH1 that coordinates tissue-specific expression of these genes. Compromised GH signalling f genome and genetic variation in these genes has been implicated in various disorders including f chromosome capture cancer. We hypothesised that regulatory regions within the GH locus coordinate f cancer expression of a gene network that extends the impact of the GH locus control region. We used the CoDeS3D algorithm to analyse 529 common single nucleotide polymorphisms (SNPs) across the GH locus. This algorithm identifies colocalised Hi-C and eQTL associations to determine which SNPs are associated with a change in gene expression at loci that physically interact within the nucleus. One hundred and eighty-one common SNPs were identified that interacted with 292 eGenes across 48 different tissues. One hundred and forty-five eGenes were regulated intrans . -
Reprogramming of Lysosomal Gene Expression by Interleukin-4 and Stat6 Brignull Et Al
Reprogramming of lysosomal gene expression by interleukin-4 and Stat6 Brignull et al. Brignull et al. BMC Genomics 2013, 14:853 http://www.biomedcentral.com/1471-2164/14/853 Brignull et al. BMC Genomics 2013, 14:853 http://www.biomedcentral.com/1471-2164/14/853 RESEARCH ARTICLE Open Access Reprogramming of lysosomal gene expression by interleukin-4 and Stat6 Louise M Brignull1†, Zsolt Czimmerer2†, Hafida Saidi1,3, Bence Daniel2, Izabel Villela4,5, Nathan W Bartlett6, Sebastian L Johnston6, Lisiane B Meira4, Laszlo Nagy2,7 and Axel Nohturfft1* Abstract Background: Lysosomes play important roles in multiple aspects of physiology, but the problem of how the transcription of lysosomal genes is coordinated remains incompletely understood. The goal of this study was to illuminate the physiological contexts in which lysosomal genes are coordinately regulated and to identify transcription factors involved in this control. Results: As transcription factors and their target genes are often co-regulated, we performed meta-analyses of array-based expression data to identify regulators whose mRNA profiles are highly correlated with those of a core set of lysosomal genes. Among the ~50 transcription factors that rank highest by this measure, 65% are involved in differentiation or development, and 22% have been implicated in interferon signaling. The most strongly correlated candidate was Stat6, a factor commonly activated by interleukin-4 (IL-4) or IL-13. Publicly available chromatin immunoprecipitation (ChIP) data from alternatively activated mouse macrophages show that lysosomal genes are overrepresented among Stat6-bound targets. Quantification of RNA from wild-type and Stat6-deficient cells indicates that Stat6 promotes the expression of over 100 lysosomal genes, including hydrolases, subunits of the vacuolar H+ ATPase and trafficking factors. -
Transparent Machine Learning for Multi-Omics Analysis of Mental Disorders
UPTEC X20 015 Examensarbete 30 hp Juni 2020 Transparent Machine Learning for Multi-Omics Analysis of Mental Disorders Stella Belin Abstract Transparent Machine Learning for Multi-Omics Analysis of Mental Disorders Stella Belin Teknisk- naturvetenskaplig fakultet UTH-enheten Schizophrenia and bipolar disorder are two severe mental disorders that affect more than 65 million individuals worldwide. The aim of this Besöksadress: project was to find co-prediction mechanisms for genes associated with Ångströmlaboratoriet Lägerhyddsvägen 1 schizophrenia and bipolar disorder using a multi-omics data set and a Hus 4, Plan 0 transparent machine learning approach. The overall purpose of the project was to further understand the biological mechanisms of these Postadress: complex disorders. In this work, publicly available multi-omics data Box 536 751 21 Uppsala collected from post-mortem brain tissue were used. The omics types included were gene expression, DNA methylation, and SNP array data. The Telefon: data consisted of samples from individuals with schizophrenia, bipolar 018 – 471 30 03 disorder, and healthy controls. Individuals with schizophrenia or Telefax: bipolar disorder were considered as a combined CASE class. 018 – 471 30 00 Using machine learning techniques, a multi-omics pipeline was developed Hemsida: to integrate these data in a manner such that all types were adequately http://www.teknat.uu.se/student represented. A feature selection was performed on methylation and SNP data, where the most important sites were estimated and mapped to their corresponding genes. Next, those genes were intersected with the gene expression data, and another feature selection was performed on the gene expression data. The most important genes were used to develop an interpretable rule-based model with an accuracy of 88%. -
Variation in Protein Coding Genes Identifies Information Flow
bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 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-NC-ND 4.0 International license. Animal complexity and information flow 1 1 2 3 4 5 Variation in protein coding genes identifies information flow as a contributor to 6 animal complexity 7 8 Jack Dean, Daniela Lopes Cardoso and Colin Sharpe* 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Institute of Biological and Biomedical Sciences 25 School of Biological Science 26 University of Portsmouth, 27 Portsmouth, UK 28 PO16 7YH 29 30 * Author for correspondence 31 [email protected] 32 33 Orcid numbers: 34 DLC: 0000-0003-2683-1745 35 CS: 0000-0002-5022-0840 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Abstract bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 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-NC-ND 4.0 International license. Animal complexity and information flow 2 1 Across the metazoans there is a trend towards greater organismal complexity. How 2 complexity is generated, however, is uncertain. Since C.elegans and humans have 3 approximately the same number of genes, the explanation will depend on how genes are 4 used, rather than their absolute number. -
TFEB Links MYC Signaling to Epigenetic Control of Myeloid Differentiation and Acute Myeloid Leukemia
RESEARCH ARTICLE TFEB Links MYC Signaling to Epigenetic Control of Myeloid Differentiation and Acute Myeloid Leukemia Seongseok Yun1, Nicole D. Vincelette1, Xiaoqing Yu2, Gregory W. Watson1, Mario R. Fernandez3, Chunying Yang3, Taro Hitosugi4, Chia-Ho Cheng2, Audrey R. Freischel3, Ling Zhang5, Weimin Li3, Hsinan Hou6, Franz X. Schaub3, Alexis R. Vedder1, Ling Cen2, Kathy L. McGraw1, Jungwon Moon1, Daniel J. Murphy7, Andrea Ballabio8,9,10,11,12, Scott H. Kaufmann4, Anders E. Berglund2, and John L. Cleveland3 Downloaded from https://bloodcancerdiscov.aacrjournals.org by guest on September 23, 2021. Copyright 2020 American Copyright 2021 by AssociationAmerican for Association Cancer Research. for Cancer Research. ABSTRACT MYC oncoproteins regulate transcription of genes directing cell proliferation, metabolism, and tumorigenesis. A variety of alterations drive MYC expression in acute myeloid leukemia (AML), and enforced MYC expression in hematopoietic progenitors is sufficient to induce AML. Here we report that AML and myeloid progenitor cell growth and survival rely on MYC- directed suppression of Transcription Factor EB (TFEB), a master regulator of the autophagy–lysosome pathway. Notably, although originally identified as an oncogene, TFEB functions as a tumor suppressor in AML, where it provokes AML cell differentiation and death. These responses reflect TFEB control of myeloid epigenetic programs by inducing expression of isocitrate dehydrogenase-1 (IDH1) and IDH2, resulting in global hydroxylation of 5-methycytosine. Finally, activating the TFEB–IDH1/IDH2–TET2 axis is revealed as a targetable vulnerability in AML. Thus, epigenetic control by an MYC–TFEB circuit dictates myeloid cell fate and is essential for maintenance of AML. SIGNIFIcaNCE: Alterations in epigenetic control are a hallmark of AML. -
Molecular and Clinical Delineation of the 17Q22 Microdeletion Phenotype
European Journal of Human Genetics (2013) 21, 1085–1092 & 2013 Macmillan Publishers Limited All rights reserved 1018-4813/13 www.nature.com/ejhg ARTICLE Molecular and clinical delineation of the 17q22 microdeletion phenotype Tobias Laurell*,1,2,3,14 , Johanna Lundin4,5,14, Britt-Marie Anderlid2,5, Jerome L Gorski6, Giedre Grigelioniene2,5, Samantha JL Knight7,8, Ana CV Krepischi9, Agneta Nordenskjo¨ld4,10, Susan M Price11, Carla Rosenberg12, Peter D Turnpenny13, Angela M Vianna-Morgante12 and Ann Nordgren*,2,5 Deletions involving 17q21–q24 have been identified previously to result in two clinically recognizable contiguous gene deletion syndromes: 17q21.31 and 17q23.1–q23.2 microdeletion syndromes. Although deletions involving 17q22 have been reported in the literature, only four of the eight patients reported were identified by array-comparative genomic hybridization (array-CGH) or flourescent in situ hybridization. Here, we describe five new patients with 1.8–2.5-Mb microdeletions involving 17q22 identified by array-CGH. We also present one patient with a large karyotypically visible deletion involving 17q22, fine-mapped to B8.2 Mb using array-CGH. We show that the commonly deleted region in our patients spans 0.24 Mb and two genes; NOG and C17ORF67. The function of C17ORF67 is not known, whereas Noggin, the product of NOG, is essential for correct joint development. In common with the 17q22 patients reported previously, the disease phenotype of our patients includes intellectual disability, attention deficit hyperactivity disorder, conductive hearing loss, visual impairment, low set ears, facial dysmorphology and limb anomalies. All patients displayed NOG-related bone and joint features, including symphalangism and facial dysmorphology. -
Genome-Wide Association Study and Pathway Analysis for Female Fertility Traits in Iranian Holstein Cattle
Ann. Anim. Sci., Vol. 20, No. 3 (2020) 825–851 DOI: 10.2478/aoas-2020-0031 GENOME-WIDE ASSOCIATION STUDY AND PATHWAY ANALYSIS FOR FEMALE FERTILITY TRAITS IN IRANIAN HOLSTEIN CATTLE Ali Mohammadi1, Sadegh Alijani2♦, Seyed Abbas Rafat2, Rostam Abdollahi-Arpanahi3 1Department of Genetics and Animal Breeding, University of Tabriz, Tabriz, Iran 2Department of Animal Science, Faculty of Agriculture, University of Tabriz, Tabriz, Iran 3Department of Animal Science, University College of Abureyhan, University of Tehran, Tehran, Iran ♦Corresponding author: [email protected] Abstract Female fertility is an important trait that contributes to cow’s profitability and it can be improved by genomic information. The objective of this study was to detect genomic regions and variants affecting fertility traits in Iranian Holstein cattle. A data set comprised of female fertility records and 3,452,730 pedigree information from Iranian Holstein cattle were used to predict the breed- ing values, which were then employed to estimate the de-regressed proofs (DRP) of genotyped animals. A total of 878 animals with DRP records and 54k SNP markers were utilized in the ge- nome-wide association study (GWAS). The GWAS was performed using a linear regression model with SNP genotype as a linear covariate. The results showed that an SNP on BTA19, ARS-BFGL- NGS-33473, was the most significant SNP associated with days from calving to first service. In total, 69 significant SNPs were located within 27 candidate genes. Novel potential candidate genes include OSTN, DPP6, EphA5, CADPS2, Rfc1, ADGRB3, Myo3a, C10H14orf93, KIAA1217, RBPJL, SLC18A2, GARNL3, NCALD, ASPH, ASIC2, OR3A1, CHRNB4, CACNA2D2, DLGAP1, GRIN2A and ME3. -
Network Mining Approach to Cancer Biomarker Discovery
NETWORK MINING APPROACH TO CANCER BIOMARKER DISCOVERY THESIS Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University By Praneeth Uppalapati, B.E. Graduate Program in Computer Science and Engineering The Ohio State University 2010 Thesis Committee: Dr. Kun Huang, Advisor Dr. Raghu Machiraju Copyright by Praneeth Uppalapati 2010 ABSTRACT With the rapid development of high throughput gene expression profiling technology, molecule profiling has become a powerful tool to characterize disease subtypes and discover gene signatures. Most existing gene signature discovery methods apply statistical methods to select genes whose expression values can differentiate different subject groups. However, a drawback of these approaches is that the selected genes are not functionally related and hence cannot reveal biological mechanism behind the difference in the patient groups. Gene co-expression network analysis can be used to mine functionally related sets of genes that can be marked as potential biomarkers through survival analysis. We present an efficient heuristic algorithm EigenCut that exploits the properties of gene co- expression networks to mine functionally related and dense modules of genes. We apply this method to brain tumor (Glioblastoma Multiforme) study to obtain functionally related clusters. If functional groups of genes with predictive power on patient prognosis can be identified, insights on the mechanisms related to metastasis in GBM can be obtained and better therapeutical plan can be developed. We predicted potential biomarkers by dividing the patients into two groups based on their expression profiles over the genes in the clusters and comparing their survival outcome through survival analysis. -
Brain Region-Specific Gene Signatures Revealed by Distinct Astrocyte Subpopulations Unveil Links to Glioma and Neurodegenerative Diseases
This Accepted Manuscript has not been copyedited and formatted. The final version may differ from this version. A link to any extended data will be provided when the final version is posted online. Research Article: New Research | Disorders of the Nervous System Brain region-specific gene signatures revealed by distinct astrocyte subpopulations unveil links to glioma and neurodegenerative diseases Raquel Cuevas Diaz Duran1,2,3, Chih-Yen Wang4, Hui Zheng5,6,7, Benjamin Deneen8,9,10,11 and Jia Qian Wu1,2 1The Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA 2Center for Stem Cell and Regenerative Medicine, UT Brown Foundation Institute of Molecular Medicine, Houston, TX 77030, USA 3Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Ave. Morones Prieto 3000, Monterrey, N.L., 64710, Mexico 4Department of Life Sciences, National Cheng Kung University, Tainan City, 70101, Taiwan 5Huffington Center on Aging, Baylor College of Medicine, Houston, TX 77030, USA 6Medical Scientist Training Program, Baylor College of Medicine, Houston, TX 77030, USA 7Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA 8Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX 77030, USA 9Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA 10Neurological Research Institute at Texas’ Children’s Hospital, Baylor College of Medicine, Houston, TX 77030, USA 11Program in Developmental Biology, Baylor College of Medicine, Houston, TX 77030, USA https://doi.org/10.1523/ENEURO.0288-18.2019 Received: 19 July 2018 Revised: 16 January 2019 Accepted: 12 February 2019 Published: 7 March 2019 J.Q.W. -
Pangolin Genomes and the Evolution of Mammalian Scales and Immunity
1 Pangolin genomes and the evolution of mammalian scales 2 and immunity 3 4 The International Pangolin Research Consortium (IPaRC) 5 Siew Woh Choo, Mike Rayko, Tze King Tan, Ranjeev Hari, Aleksey Komissarov, Wei Yee Wee, Andrey 6 A. Yurchenko, Sergey Kliver, Gaik Tamazian, Agostinho Antunes, Richard K. Wilson, Wesley C. 7 Warren, Klaus-Peter Koepfli, Patrick Minx, Ksenia Krasheninnikova, Antoinette Kotze, Desire L. Dalton, 8 Elaine Vermaak, Ian C. Paterson, Pavel Dobrynin, Frankie Thomas Sitam, Jeffrine J. Rovie-Ryan, Warren 9 E. Johnson, Aini Mohamed Yusoff, Shu-Jin Luo, Kayal Vizi Karuppannan, Gang Fang, Deyou Zheng, 10 Mark B. Gerstein, Leonard Lipovich, Stephen J. O’Brien and Guat Jah Wong 11 12 Supplemental Information 13 14 1.0 GENOME SEQUENCING, ASSEMBLY AND SCAFFOLDING 15 1.1 DNA Extraction and whole-genome sequencing 16 1.2 Data pre-processing and Next-Generation Sequencing (NGS) 17 libraries reads statistics 18 1.3 Genome assembly, scaffolding and gap closing 19 1.3.1 Malayan pangolin genome 20 1.3.2 Chinese pangolin genome 21 1.4 Genome size estimation 22 1.5 NGS reads based characterisation 23 24 2.0 EVALUATION OF GENOME ASSEMBLIES 25 2.1 Mapping Malayan pangolin transcripts to assembled genomes 26 2.2 CEGMA analysis 27 2.3 Pangolin genome comparison 28 29 3.0 GENOME ANNOTATION 30 3.1 Protein-coding gene annotation 31 3.1.1 Multiple functional assignments of genes 32 3.2 Pseudogene identification 33 3.3 Non-coding genes 34 3.4 Segmental duplications 35 36 4.0 SPECIATION TIME AND DIVERGENCE TIME 37 38 5.0 HETEROZYGOSITY 39 -
A Novel View of the Transcriptome Revealed from Gene Trapping In
Downloaded from genome.cshlp.org on September 29, 2021 - Published by Cold Spring Harbor Laboratory Press Letter A novel view of the transcriptome revealed from gene trapping in mouse embryonic stem cells Guglielmo Roma,1,4 Gilda Cobellis,1,2,4 Pamela Claudiani,1,4 Francesco Maione,1 Pedro Cruz,1 Gaetano Tripoli,1 Marco Sardiello,1 Ivana Peluso,1 and Elia Stupka1,3,5 1Telethon Institute of Genetics and Medicine (TIGEM), 80131 Napoli, Italy; 2Dipartimento di Patologia Generale, Seconda Universita’ di Napoli, 80100 Napoli, Italy; 3CBM S.c.r.l., Area Science Park, Basovizza- SS14, Km 163,5 Trieste, 34012 Italy Embryonic stem (ES) cells are pluripotent cell lines with the capacity of self-renewal and the ability to differentiate into specific cell types. We performed the first genome-wide analysis of the mouse ES cell transcriptome using ∼250,000 gene trap sequence tags deposited in public databases. We unveiled >8000 novel transcripts, mostly non-coding, and >1000 novel alternative and often tissue-specific exons of known genes. Experimental verification of the expression of these genes and exons by RT-PCR yielded a 70% validation rate. A novel non-coding transcript within the set studied showed a highly specific pattern of expression by in situ hybridization. Our analysis also shows that the genome presents gene trapping hotspots, which correspond to 383 known and 87 novel genes. These “hypertrapped” genes show minimal overlap with previously published expression profiles of ES cells; however, we prove by real-time PCR that they are highly expressed in this cell type, thus potentially contributing to the phenotype of ES cells.