An Epigenome-Wide Association Study of Early-Onset Major Depression in Monozygotic Twins Roxann Roberson-Nay 1,2,Danam.Lapato2,3,Aaronr.Wolen4, Eva E

Total Page:16

File Type:pdf, Size:1020Kb

An Epigenome-Wide Association Study of Early-Onset Major Depression in Monozygotic Twins Roxann Roberson-Nay 1,2,Danam.Lapato2,3,Aaronr.Wolen4, Eva E Roberson-Nay et al. Translational Psychiatry (2020) 10:301 https://doi.org/10.1038/s41398-020-00984-2 Translational Psychiatry ARTICLE Open Access An epigenome-wide association study of early-onset major depression in monozygotic twins Roxann Roberson-Nay 1,2,DanaM.Lapato2,3,AaronR.Wolen4, Eva E. Lancaster2,3, Bradley T. Webb 1,2,3, Bradley Verhulst1,2,5, John M. Hettema1,2 and Timothy P. York 2,3 Abstract Major depression (MD) is a debilitating mental health condition with peak prevalence occurring early in life. Genome- wide examination of DNA methylation (DNAm) offers an attractive complement to studies of allelic risk given it can reflect the combined influence of genes and environment. The current study used monozygotic twins to identify differentially and variably methylated regions of the genome that distinguish twins with and without a lifetime history of early-onset MD. The sample included 150 Caucasian monozygotic twins between the ages of 15 and 20 (73% female; Mage = 17.52 SD = 1.28) who were assessed during a developmental stage characterized by relatively distinct neurophysiological changes. All twins were generally healthy and currently free of medications with psychotropic effects. DNAm was measured in peripheral blood cells using the Infinium Human BeadChip 450 K Array. MD associations with early-onset MD were detected at 760 differentially and variably methylated probes/regions that mapped to 428 genes. Genes and genomic regions involved neural circuitry formation, projection, functioning, and plasticity. Gene enrichment analyses implicated genes related to neuron structures and neurodevelopmental processes including cell–cell adhesion genes (e.g., PCDHA genes). Genes previously implicated in mood and psychiatric disorders as well as chronic stress (e.g., NRG3) also were identified. DNAm regions associated with early-onset MD were found to overlap genetic loci identified in the latest Psychiatric Genomics Consortium meta-analysis of depression. 1234567890():,; 1234567890():,; 1234567890():,; 1234567890():,; Understanding the time course of epigenetic influences during emerging adulthood may clarify developmental phases where changes in the DNA methylome may modulate individual differences in MD risk. Introduction negative socioemotional outcomes including recurrent Major depression (MD) is highly prevalent, ranking MDEs3,4. Adolescence/young adulthood is characterized second in the global burden of disease, with the overall by neurophysiological changes (e.g., synaptic pruning, lifetime risk estimated to be 16.2% in the general popu- myelination) that significantly influence brain function lation1. MD is associated with increased mortality, parti- and behavior, which may increase risk for MD and other cularly suicide1. Among adolescents, MD is associated psychiatric conditions5. Thus, understanding the genetic with the greatest level of impairment of all psychiatric contributions to MD during this dynamic neurophysio- – conditions, with 16% of females and 12% of males logical period where peak incidence is observed2,6 8 is endorsing at least one major depressive episode (MDE) by critical to elucidating developmentally informed pathways age 182. An early age of onset confers increased risk for to mood disorders. Twin and family studies robustly demonstrate that genetic factors play a role in risk for MD, with heritability Correspondence: Roxann Roberson-Nay ([email protected]) estimates of roughly 35% for MD and 45% for early-onset 1Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, MD9; these heritability estimates indicate that MD is USA suitable for epigenetic study using twins10. Moreover, a 2Virginia Commonwealth University, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, USA large number of genetic loci have been identified for MD, Full list of author information is available at the end of the article © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a linktotheCreativeCommons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Roberson-Nay et al. Translational Psychiatry (2020) 10:301 Page 2 of 12 supporting the role of genetic factors in the etiology of comorbidities and medication usage as well as long-term MD11,12. Twin study variance component analyses also nicotine use. Moreover, it eliminates the well-known – indicate a considerable contribution of unique environ- epigenetic changes associated with aging36 38. The mental risk factors to MD9. Due to the substantial link developmental window of young adulthood also is asso- between environmental hardship and onset of an ciated with moderate conservation of DNAm that is MDE13,14, epigenetic mechanisms may, in part, mediate nonetheless responsive to environmental signals39, mak- the influence of environmental stress and combine with ing it an ideal sensitive period for the study of MD. – genetic liability to increase MD risk over the lifespan15 17. Epigenetic mechanisms refer to DNA, chromatin, and Methods RNA modifications that can influence the expression of Participants genes but do not alter the underlying genetic sequence. One hundred sixty-six MZ twins (83 pairs) were drawn Animal studies have been critical to establishing a causal from a larger sample of twins (N = 430 pairs) enrolled in a association between early life environments, epigenetic parent study (R01MH101518)40, which was designed to alterations, and phenotypic outcomes. For example, the estimate genetic and environmental contributions to seminal work of Michael Meaney and his research team RDoC Negative Valence Systems constructs using classi- reveals the importance of maternal care in altering the cal twin modeling (i.e., disaggregating variance into expression of genes that regulate behavioral and neu- additive genetic, common shared, and unique non-shared – roendocrine responses to stress as well as synaptic environmental variation)41 51. Blood samples were col- – development in the rat hippocampus18 23. Indeed, a lected as part of the parent R01 for future use. Twins were number of animal and human studies demonstrate lasting primarily recruited through the Mid-Atlantic Twin Reg- epigenetic alterations occurring in the genomes of cells istry (MATR), a population-based registry40,41. All parti- including changes to postmitotic neurons that integrate cipants (parents/minor children, adults) provided written experience-dependent changes24. Thus, the timing of informed consent/assent. environmental stress plays an important role in sub- Of the 83 twin pairs initially identified as MZ via the sequent epigenetic consequences, with early life stress zygosity questionnaire, five pairs (6.3%) were determined paradigms in mice and humans demonstrating enduring to be DZ pairs using DNA-based markers and were – changes in epigenetic profiles18,21,25 29. removed (for zygosity determination, see Supplementary). A number of studies have utilized genome-wide plat- One or both twins from three additional pairs failed forms to determine DNA methylation (DNAm) differ- DNA-based quality control checks, reducing the final ences between MD cases and controls. However, as much analyzed sample to 75 MZ twin pairs (150 twins; see Table as 37% of DNAm variance can be accounted for by genetic 1 for details). Of the 150 twins, 39 twins endorsed a his- factors30 with recent studies indicating that common tory of at least one MD episode, resulting in 27 dis- genetic variation (i.e., methylation quantitative trait loci cordant, 42 concordant negative (i.e., both twins MD – [mQTLs]) influence DNAm levels31 35. Most MD case- unaffected), and 6 concordant positive (i.e., both twins control studies of DNAm do not account for allelic var- MD affected) pairs. All twins were raised together in the iation, which means genetic and environmental influences same home and were required to be free of psychotropic on DNAm cannot be disaggreagated. In contrast, the medications/medications with psychotropic effects at the quasi-experimental design afforded by monozygotic (MZ) time of study entry, although ~4.0% (n = 6) endorsed a twins greatly improves on the unmatched case-control history of psychotropic medication use, slightly lower design (see Supplementary Table 1). The use of MZ twins, than the national average42. See full study exclusionary both discordant and concordant pairs for outcome, criteria in Supplementary material. adjusts for much of the impact of unmeasured confounds such as genetic variation, uterine environment, age, sex, Measures race, cohort effects, and exposure to many shared envir- Major depression onmental events. All twin pairs completed a psychiatric history based on The current study utilized the robust MZ twin design an expanded version of the Composite International coupled with an analytically powerful approach
Recommended publications
  • Defining Functional Interactions During Biogenesis of Epithelial Junctions
    ARTICLE Received 11 Dec 2015 | Accepted 13 Oct 2016 | Published 6 Dec 2016 | Updated 5 Jan 2017 DOI: 10.1038/ncomms13542 OPEN Defining functional interactions during biogenesis of epithelial junctions J.C. Erasmus1,*, S. Bruche1,*,w, L. Pizarro1,2,*, N. Maimari1,3,*, T. Poggioli1,w, C. Tomlinson4,J.Lees5, I. Zalivina1,w, A. Wheeler1,w, A. Alberts6, A. Russo2 & V.M.M. Braga1 In spite of extensive recent progress, a comprehensive understanding of how actin cytoskeleton remodelling supports stable junctions remains to be established. Here we design a platform that integrates actin functions with optimized phenotypic clustering and identify new cytoskeletal proteins, their functional hierarchy and pathways that modulate E-cadherin adhesion. Depletion of EEF1A, an actin bundling protein, increases E-cadherin levels at junctions without a corresponding reinforcement of cell–cell contacts. This unexpected result reflects a more dynamic and mobile junctional actin in EEF1A-depleted cells. A partner for EEF1A in cadherin contact maintenance is the formin DIAPH2, which interacts with EEF1A. In contrast, depletion of either the endocytic regulator TRIP10 or the Rho GTPase activator VAV2 reduces E-cadherin levels at junctions. TRIP10 binds to and requires VAV2 function for its junctional localization. Overall, we present new conceptual insights on junction stabilization, which integrate known and novel pathways with impact for epithelial morphogenesis, homeostasis and diseases. 1 National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK. 2 Computing Department, Imperial College London, London SW7 2AZ, UK. 3 Bioengineering Department, Faculty of Engineering, Imperial College London, London SW7 2AZ, UK. 4 Department of Surgery & Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK.
    [Show full text]
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
    Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated.
    [Show full text]
  • 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).
    [Show full text]
  • Supplementary Table 1: Adhesion Genes Data Set
    Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like,
    [Show full text]
  • Supplementary Materials
    Supplementary materials Supplementary Table S1: MGNC compound library Ingredien Molecule Caco- Mol ID MW AlogP OB (%) BBB DL FASA- HL t Name Name 2 shengdi MOL012254 campesterol 400.8 7.63 37.58 1.34 0.98 0.7 0.21 20.2 shengdi MOL000519 coniferin 314.4 3.16 31.11 0.42 -0.2 0.3 0.27 74.6 beta- shengdi MOL000359 414.8 8.08 36.91 1.32 0.99 0.8 0.23 20.2 sitosterol pachymic shengdi MOL000289 528.9 6.54 33.63 0.1 -0.6 0.8 0 9.27 acid Poricoic acid shengdi MOL000291 484.7 5.64 30.52 -0.08 -0.9 0.8 0 8.67 B Chrysanthem shengdi MOL004492 585 8.24 38.72 0.51 -1 0.6 0.3 17.5 axanthin 20- shengdi MOL011455 Hexadecano 418.6 1.91 32.7 -0.24 -0.4 0.7 0.29 104 ylingenol huanglian MOL001454 berberine 336.4 3.45 36.86 1.24 0.57 0.8 0.19 6.57 huanglian MOL013352 Obacunone 454.6 2.68 43.29 0.01 -0.4 0.8 0.31 -13 huanglian MOL002894 berberrubine 322.4 3.2 35.74 1.07 0.17 0.7 0.24 6.46 huanglian MOL002897 epiberberine 336.4 3.45 43.09 1.17 0.4 0.8 0.19 6.1 huanglian MOL002903 (R)-Canadine 339.4 3.4 55.37 1.04 0.57 0.8 0.2 6.41 huanglian MOL002904 Berlambine 351.4 2.49 36.68 0.97 0.17 0.8 0.28 7.33 Corchorosid huanglian MOL002907 404.6 1.34 105 -0.91 -1.3 0.8 0.29 6.68 e A_qt Magnogrand huanglian MOL000622 266.4 1.18 63.71 0.02 -0.2 0.2 0.3 3.17 iolide huanglian MOL000762 Palmidin A 510.5 4.52 35.36 -0.38 -1.5 0.7 0.39 33.2 huanglian MOL000785 palmatine 352.4 3.65 64.6 1.33 0.37 0.7 0.13 2.25 huanglian MOL000098 quercetin 302.3 1.5 46.43 0.05 -0.8 0.3 0.38 14.4 huanglian MOL001458 coptisine 320.3 3.25 30.67 1.21 0.32 0.9 0.26 9.33 huanglian MOL002668 Worenine
    [Show full text]
  • Mutation Analysis of Genes Within the Dynactin Complex in a Cohort of Hereditary Peripheral Neuropathies
    Clin Genet 2016: 90: 127–133 © 2015 John Wiley & Sons A/S. Printed in Singapore. All rights reserved Published by John Wiley & Sons Ltd CLINICAL GENETICS doi: 10.1111/cge.12712 Original Article Mutation analysis of genes within the dynactin complex in a cohort of hereditary peripheral neuropathies a a Tey S., Ahmad-Annuar A., Drew A.P., Shahrizaila N., Nicholson G.A., S. Tey , A. Ahmad-Annuar , Kennerson M.L. Mutation analysis of genes within the dynactin complex in A.P. Drewb, N. Shahrizailac, , a cohort of hereditary peripheral neuropathies. G.A. Nicholsonb d and Clin Genet 2016: 90: 127–133. © John Wiley & Sons A/S. Published by M.L. Kennersonb,d John Wiley & Sons Ltd, 2015 aDepartment of Biomedical Science, The cytoplasmic dynein–dynactin genes are attractive candidates for Faculty of Medicine, University of Malaya, b neurodegenerative disorders given their functional role in retrograde Kuala Lumpur, Malaysia, Northcott transport along neurons. The cytoplasmic dynein heavy chain (DYNC1H1) Neuroscience Laboratory, ANZAC Research Institute, and Sydney Medical gene has been implicated in various neurodegenerative disorders, and School, University of Sydney, Sydney, dynactin 1 (DCTN1) genes have been implicated in a wide spectrum of Australia, cDepartment of Medicine, disorders including motor neuron disease, Parkinson’s disease, spinobulbar Faculty of Medicine, University of Malaya, muscular atrophy and hereditary spastic paraplegia. However, the Kuala Lumpur, Malaysia, and dMolecular involvement of other dynactin genes with inherited peripheral neuropathies Medicine Laboratory, Concord Hospital, (IPN) namely, hereditary sensory neuropathy, hereditary motor neuropathy Sydney, Australia and Charcot–Marie–Tooth disease is under reported. We screened eight genes; DCTN1-6 and ACTR1A and ACTR1B in 136 IPN patients using Key words: Charcot–Marie–Tooth – whole-exome sequencing and high-resolution melt (HRM) analysis.
    [Show full text]
  • Somatic Mutational Landscapes of Adherens Junctions and Their
    1 Somatic mutational landscapes of adherens junctions and their 2 functional consequences in cutaneous melanoma development 3 4 Praveen Kumar Korla,1 Chih-Chieh Chen,2 Daniel Esguerra Gracilla,1 Ming-Tsung Lai,3 Chih- 5 Mei Chen,4 Huan Yuan Chen,5 Tritium Hwang,1 Shih-Yin Chen,4,6,* Jim Jinn-Chyuan Sheu1,4, 6-9,* 6 1Institute of Biomedical Sciences, National Sun Yat-sen University, Kaohsiung 80242, Taiwan; 7 2Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung 80424, 8 Taiwan; 3Department of Pathology, Taichung Hospital, Ministry of Health and Welfare, Taichung 9 40343, Taiwan; 4Genetics Center, China Medical University Hospital, Taichung 40447, Taiwan; 10 5Institute of Biomedical Sciences, Academia Sinica, Taipei 11574, Taiwan; 6School of Chinese 11 Medicine, China Medical University, Taichung 40402, Taiwan; 7Department of Health and 12 Nutrition Biotechnology, Asia University, Taichung 41354, Taiwan; 8Department of 13 Biotechnology, Kaohsiung Medical University, Kaohsiung 80708, Taiwan; 9Institute of 14 Biopharmaceutical Sciences, National Sun Yat-sen University, Kaohsiung 80242, Taiwan 15 16 PKK, CCC and DEG contributed equally to this study. 17 *Correspondence to: Dr. Shih-Yin Chen ([email protected]) at Genetics Center, China 18 Medical University Hospital, Taichung, 40447, TAIWAN; or Dr. Jim Jinn-Chyuan Sheu 19 ([email protected]) at Institute of Biomedical Sciences, National Sun Yat-sen 20 University, Kaohsiung City 80424, TAIWAN. 21 22 Running title: mutational landscape of cadherins in melanoma 1 23 Abstract 24 Cell-cell interaction in skin homeostasis is tightly controlled by adherens junctions (AJs). 25 Alterations in such regulation lead to melanoma development.
    [Show full text]
  • Independent Centromere Formation in a Capricious, Gene-Free Domain of Chromosome 13Q21 in Old World Monkeys and Pigs
    Open Access Research2006CardoneetVolume al. 7, Issue 10, Article R91 Independent centromere formation in a capricious, gene-free comment domain of chromosome 13q21 in Old World monkeys and pigs Maria Francesca Cardone*, Alicia Alonso†, Michele Pazienza*, Mario Ventura*, Gabriella Montemurro*, Lucia Carbone*, Pieter J de Jong‡, Roscoe Stanyon§, Pietro D'Addabbo*, Nicoletta Archidiacono*, Xinwei She¶, Evan E Eichler¶, Peter E Warburton† and Mariano Rocchi* reviews Addresses: *Department of Genetics and Microbiology, University of Bari, Bari, Italy. †Department of Human Genetics, Mount Sinai School of Medicine, New York, New York 10029, USA. ‡Children's Hospital Oakland Research Institute, Oakland, California 94609, USA. §Department of Animal Biology and Genetics 'Leo Pardi', University of Florence, Florence, Italy. ¶Howard Hughes Medical Institute, Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195, USA. Correspondence: Mariano Rocchi. Email: [email protected] Published: 13 October 2006 Received: 3 May 2006 reports Revised: 31 July 2006 Genome Biology 2006, 7:R91 (doi:10.1186/gb-2006-7-10-r91) Accepted: 13 October 2006 The electronic version of this article is the complete one and can be found online at http://genomebiology.com/2006/7/10/R91 © 2006 Cardone et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which deposited research permits unrestricted
    [Show full text]
  • MALE Protein Name Accession Number Molecular Weight CP1 CP2 H1 H2 PDAC1 PDAC2 CP Mean H Mean PDAC Mean T-Test PDAC Vs. H T-Test
    MALE t-test t-test Accession Molecular H PDAC PDAC vs. PDAC vs. Protein Name Number Weight CP1 CP2 H1 H2 PDAC1 PDAC2 CP Mean Mean Mean H CP PDAC/H PDAC/CP - 22 kDa protein IPI00219910 22 kDa 7 5 4 8 1 0 6 6 1 0.1126 0.0456 0.1 0.1 - Cold agglutinin FS-1 L-chain (Fragment) IPI00827773 12 kDa 32 39 34 26 53 57 36 30 55 0.0309 0.0388 1.8 1.5 - HRV Fab 027-VL (Fragment) IPI00827643 12 kDa 4 6 0 0 0 0 5 0 0 - 0.0574 - 0.0 - REV25-2 (Fragment) IPI00816794 15 kDa 8 12 5 7 8 9 10 6 8 0.2225 0.3844 1.3 0.8 A1BG Alpha-1B-glycoprotein precursor IPI00022895 54 kDa 115 109 106 112 111 100 112 109 105 0.6497 0.4138 1.0 0.9 A2M Alpha-2-macroglobulin precursor IPI00478003 163 kDa 62 63 86 72 14 18 63 79 16 0.0120 0.0019 0.2 0.3 ABCB1 Multidrug resistance protein 1 IPI00027481 141 kDa 41 46 23 26 52 64 43 25 58 0.0355 0.1660 2.4 1.3 ABHD14B Isoform 1 of Abhydrolase domain-containing proteinIPI00063827 14B 22 kDa 19 15 19 17 15 9 17 18 12 0.2502 0.3306 0.7 0.7 ABP1 Isoform 1 of Amiloride-sensitive amine oxidase [copper-containing]IPI00020982 precursor85 kDa 1 5 8 8 0 0 3 8 0 0.0001 0.2445 0.0 0.0 ACAN aggrecan isoform 2 precursor IPI00027377 250 kDa 38 30 17 28 34 24 34 22 29 0.4877 0.5109 1.3 0.8 ACE Isoform Somatic-1 of Angiotensin-converting enzyme, somaticIPI00437751 isoform precursor150 kDa 48 34 67 56 28 38 41 61 33 0.0600 0.4301 0.5 0.8 ACE2 Isoform 1 of Angiotensin-converting enzyme 2 precursorIPI00465187 92 kDa 11 16 20 30 4 5 13 25 5 0.0557 0.0847 0.2 0.4 ACO1 Cytoplasmic aconitate hydratase IPI00008485 98 kDa 2 2 0 0 0 0 2 0 0 - 0.0081 - 0.0
    [Show full text]
  • And MUC1 in Mammary Epithelial Cells
    Linköping University Post Print Loss of ICAM-1 signaling induces psoriasin (S100A7) and MUC1 in mammary epithelial cells Stina Petersson, E. Shubbar, M. Yhr, A. Kovacs and Charlotta Enerbäck N.B.: When citing this work, cite the original article. The original publication is available at www.springerlink.com: Stina Petersson, E. Shubbar, M. Yhr, A. Kovacs and Charlotta Enerbäck, Loss of ICAM-1 signaling induces psoriasin (S100A7) and MUC1 in mammary epithelial cells, 2011, Breast Cancer Research and Treatment, (125), 1, 13-25. http://dx.doi.org/10.1007/s10549-010-0820-4 Copyright: Springer Science Business Media http://www.springerlink.com/ Postprint available at: Linköping University Electronic Press http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-63954 1 Loss of ICAM-1 signaling induces psoriasin (S100A7) and MUC1 in mammary epithelial cells Petersson S1, Shubbar E1, Yhr M1, Kovacs A2 and Enerbäck C3 Departments of 1Clinical Genetics and 2Pathology, Sahlgrenska University Hospital, SE-413 45 Gothenburg, Sweden; 3Department of Clinical and Experimental Medicine, Division of Cell Biology and Dermatology, Linköping University, SE-581 85 Linköping, Sweden E-mail: [email protected] E-mail: maria.yhr@ clingen.gu.se E-mail: [email protected] E-mail: [email protected] Correspondence: Stina Petersson, Department of Clinical Genetics, Sahlgrenska University Hospital, SE-413 45 Gothenburg, Sweden E-mail: [email protected] 2 Abstract Psoriasin (S100A7), a member of the S100 gene family, is highly expressed in high-grade comedo ductal carcinoma in situ (DCIS), with a higher risk of local recurrence. Psoriasin is therefore a potential biomarker for DCIS with a poor prognosis.
    [Show full text]
  • Open Data for Differential Network Analysis in Glioma
    International Journal of Molecular Sciences Article Open Data for Differential Network Analysis in Glioma , Claire Jean-Quartier * y , Fleur Jeanquartier y and Andreas Holzinger Holzinger Group HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036 Graz, Austria; [email protected] (F.J.); [email protected] (A.H.) * Correspondence: [email protected] These authors contributed equally to this work. y Received: 27 October 2019; Accepted: 3 January 2020; Published: 15 January 2020 Abstract: The complexity of cancer diseases demands bioinformatic techniques and translational research based on big data and personalized medicine. Open data enables researchers to accelerate cancer studies, save resources and foster collaboration. Several tools and programming approaches are available for analyzing data, including annotation, clustering, comparison and extrapolation, merging, enrichment, functional association and statistics. We exploit openly available data via cancer gene expression analysis, we apply refinement as well as enrichment analysis via gene ontology and conclude with graph-based visualization of involved protein interaction networks as a basis for signaling. The different databases allowed for the construction of huge networks or specified ones consisting of high-confidence interactions only. Several genes associated to glioma were isolated via a network analysis from top hub nodes as well as from an outlier analysis. The latter approach highlights a mitogen-activated protein kinase next to a member of histondeacetylases and a protein phosphatase as genes uncommonly associated with glioma. Cluster analysis from top hub nodes lists several identified glioma-associated gene products to function within protein complexes, including epidermal growth factors as well as cell cycle proteins or RAS proto-oncogenes.
    [Show full text]
  • FARE2021WINNERS Sorted by Institute
    FARE2021WINNERS Sorted By Institute Swati Shah Postdoctoral Fellow CC Radiology/Imaging/PET and Neuroimaging Characterization of CNS involvement in Ebola-Infected Macaques using Magnetic Resonance Imaging, 18F-FDG PET and Immunohistology The Ebola (EBOV) virus outbreak in Western Africa resulted in residual neurologic abnormalities in survivors. Many case studies detected EBOV in the CSF, suggesting that the neurologic sequelae in survivors is related to viral presence. In the periphery, EBOV infects endothelial cells and triggers a “cytokine stormâ€. However, it is unclear whether a similar process occurs in the brain, with secondary neuroinflammation, neuronal loss and blood-brain barrier (BBB) compromise, eventually leading to lasting neurological damage. We have used in vivo imaging and post-necropsy immunostaining to elucidate the CNS pathophysiology in Rhesus macaques infected with EBOV (Makona). Whole brain MRI with T1 relaxometry (pre- and post-contrast) and FDG-PET were performed to monitor the progression of disease in two cohorts of EBOV infected macaques from baseline to terminal endpoint (day 5-6). Post-necropsy, multiplex fluorescence immunohistochemical (MF-IHC) staining for various cellular markers in the thalamus and brainstem was performed. Serial blood and CSF samples were collected to assess disease progression. The linear mixed effect model was used for statistical analysis. Post-infection, we first detected EBOV in the serum (day 3) and CSF (day 4) with dramatic increases until euthanasia. The standard uptake values of FDG-PET relative to whole brain uptake (SUVr) in the midbrain, pons, and thalamus increased significantly over time (p<0.01) and positively correlated with blood viremia (p≤0.01).
    [Show full text]