DOCSLIB.ORG

Dysregulation of Post-Transcriptional Modification by Copy Number

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Dysregulation of Post-Transcriptional Modification by Copy Number Yoshikawa et al. Translational Psychiatry (2021) 11:331 https://doi.org/10.1038/s41398-021-01460-1 Translational Psychiatry ARTICLE Open Access Dysregulation of post-transcriptional modification by copy number variable microRNAs in schizophrenia with enhanced glycation stress Akane Yoshikawa1,2,ItaruKushima 3,4, Mitsuhiro Miyashita 1,5,6,KazuyaToriumi 1, Kazuhiro Suzuki 1,5, Yasue Horiuchi 1, Hideya Kawaji 7, Shunya Takizawa8,NorioOzaki 3, Masanari Itokawa1 and Makoto Arai 1 Abstract Previously, we identified a subpopulation of schizophrenia (SCZ) showing increased levels of plasma pentosidine, a marker of glycation and oxidative stress. However, its causative genetic factors remain largely unknown. Recently, it has been suggested that dysregulated posttranslational modification by copy number variable microRNAs (CNV-miRNAs) may contribute to the etiology of SCZ. Here, an integrative genome-wide CNV-miRNA analysis was performed to investigate the etiology of SCZ with accumulated plasma pentosidine (PEN-SCZ). The number of CNV-miRNAs and the gene ontology (GO) in the context of miRNAs within CNVs were compared between PEN-SCZ and non-PEN-SCZ groups. Gene set enrichment analysis of miRNA target genes was further performed to evaluate the pathways affected in PEN-SCZ. We show that miRNAs were significantly enriched within CNVs in the PEN-SCZ versus non-PEN-SCZ groups (p = 0.032). Of note, as per GO analysis, the dysregulated neurodevelopmental events in the two groups may have different origins. Additionally, gene set enrichment analysis of miRNA target genes revealed that miRNAs involved in glycation/oxidative stress and synaptic neurotransmission, especially glutamate/GABA receptor signaling, fi 1234567890():,; 1234567890():,; 1234567890():,; 1234567890():,; were possibly affected in PEN-SCZ. To the best of our knowledge, this is the rst genome-wide CNV-miRNA study suggesting the role of CNV-miRNAs in the etiology of PEN-SCZ, through effects on genes related to glycation/oxidative stress and synaptic function. Our findings provide supportive evidence that glycation/oxidative stress possibly caused by genetic defects related to the posttranscriptional modification may lead to synaptic dysfunction. Therefore, targeting miRNAs may be one of the promising approaches for the treatment of PEN-SCZ. Introduction educational achievement, leading to lifelong social and/or Schizophrenia (SCZ) is a neurodevelopmental disorder occupational dysfunction. Accumulating evidence sug- with a worldwide prevalence of around 1.0%, leading to gests that both genetic and epigenetic factors are involved – abnormalities in function and connectivity of key brain in the etiology of SCZ2 7. An integrative genetic and regions1. The most common symptoms are auditory epigenetic analysis would accelerate the elucidation of the hallucinations, delusions, withdrawal, flat affect, lack of etiology. motivation, and disorganized thinking. The onset, during Recent advances in genomic technologies have facili- early adolescence, strongly impacts school life and tated large-scale genomic studies. Previous research has successfully uncovered the polygenic nature of multiple common risk variants4,5,8 as well as the involvement of Correspondence: Makoto Arai ([email protected]) 9,10 1Schizophrenia Research Project, Department of Psychiatry and Behavioral rare but highly penetrant variants , and structural Sciences, Tokyo Metropolitan Institute of Medical Science, 2-1-6, Kamikitazawa, genetic variants called copy number variations (CNVs) – Setagaya-ku 156-8506 Tokyo, Japan in SCZ11 15. In the largest-scale CNV study to date, 2Department of Adolescent Psychiatry, Nihonbashi Sun Clinic, 2-1-21, Nihonbashi, Chuou-ku, Tokyo 103-0027, Japan using 41,321 subjects, deletions in 1q21.1, 2p16.3, 3q29, Full list of author information is available at the end of the article © The Author(s) 2021 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/. Yoshikawa et al. Translational Psychiatry (2021) 11:331 Page 2 of 11 and 22q11.21 were confirmed as risk factors for SCZ Science Institute near Tokyo, for both genetic and bio- with high odds ratios (ORs) ranging from 3.8–67.714. chemical analyses. All subjects were ethnically Japanese. The Psychiatric Genomics Consortium (PGC)-CNV The patients were diagnosed according to the DSM-5 analysis group has demonstrated that genes disrupted by criteria (American Psychiatric Association) for SCZ or CNVs in SCZ are enriched within the synaptic net- schizoaffective disorders as per the consensus of at least works14. Kushima et al. performed the largest genome- two experienced psychiatrists. Patients with a history of wide CNV analysis in the Japanese population so far and drug addiction or alcohol abuse/dependence were confirmed an increased burden of rare exonic CNVs in excluded. Patients with accompanying diabetes mellitus 2458 SCZ and 1108 autism spectrum disorder (ASD) and chronic renal disease were also excluded because cases16. these diseases may affect the plasma pentosidine levels. MicroRNAs (miRNAs) are a family of short (~22 nt), Medical records were obtained with the approval of single-stranded, non-coding RNAs primarily involved in patients. The study was performed in accordance with the posttranscriptional downregulation of gene expres- the Declaration of Helsinki. This study was approved by sion via binding to the 3′UTR of target genes17.CNV- the research ethics committee of each participating miRNA genes generate the complexity of miRNA reg- institute and written informed consent was obtained ulation and function17,18. Genomic CNVs can induce the from all subjects. The subjects’ demographics are pre- aberrant expression of integral miRNAs and their target sented in Table 1. Of note, to define the PEN-SCZ and genes18, resulting in the dysregulation of posttransla- non-PEN-SCZ cases, plasma concentration of pentosi- tional modification, crucial for the adaptation to envir- dine was measured via high-performance liquid chro- onmental factors. Recently, it has been shown that the matography as described previously25. genetically dysregulated posttranslational modification caused by CNV-miRNAs contribute to the etiology of Identification of CNV-miRNAs neurodevelopmental disorders including SCZ, ASD, and Genomic DNA was extracted from blood samples. intellectual disability (ID)19. For example, in the context Array comparative genomic hybridization (array CGH) of SCZ, miRNA contents within CNV regions were sig- was performed to identify rare (<1%) CNVs in PEN-SCZ nificantly enriched compared to those in control sub- and non-PEN-SCZ patients; NimbleGen 720 K Whole- jects19.Additionally,inASD,miRNAspresentinCNV Genome Tiling Arrays (Roche NimbleGen, Madison, loci were shown to regulate synaptic transmission and WI, USA) were used for the CNV analysis. CNV calls may, thus, contribute to the disorder phenotype20. were conducted using the Nexus Copy Number software Genetic heterogeneity is a hallmark of SCZ; the genetic v9.0 (Bio Discovery, El Segundo, CA, USA) with the Fast architecture of this common disorder is largely com- Adaptive States Segmentation Technique 2 algorithm. plex21,22. To overcome the heterogeneity, the identifica- Quality control (QC) was performed as described pre- tion of SCZ subpopulations presenting with disturbances viously16,26.Briefly, QC scores for each sample based on of specific metabolic pathways is a promising strategy. We the statistical variance of the probe-to-probe log ratios previously reported an SCZ subtype with enhanced gly- were calculated and samples with QC >0.15 were cation/oxidative stress that showed accumulated plasma removed. As sample QC, subjects with excessive num- pentosidine (PEN-SCZ)23. Although around 20% of SCZ bers of autosomal CNVs were also removed from the patients were reported to be PEN-SCZ24, the genetic and analysis. After filtering out common CNVs (≥1%), rare epigenetic factors that account for the accumulation of CNVs (<1%) in 185 patients were used for further ana- pentosidine and their contribution to this phenotype have lyses. All genomic locations are given in GRCh38 not yet been fully elucidated. coordinates, and gene annotation was based on GEN- Here, we performed an integrative, genome-wide, CODE version 27. Of note, previously, we confirmed CNV-miRNA analysis to investigate the genetic factors that CNV calls from NimbleGen arrays are highly associated with SCZ with enhanced glycation/oxidative accurate with a validation rate >99%27.ThemiRNAs stress and to provide novel insights into the etiology of present in CNVs were then identified via the analysis of PEN-SCZ, focusing on the disturbance of post- the chromosomal coordinates as per the UCSC Genome transcriptional modification caused by CNV-miRNAs. Browser
Load more

Recommended publications
  • Cytogenomic SNP Microarray - Fetal ARUP Test Code 2002366 Maternal Contamination Study Fetal Spec Fetal Cells
    Patient Report |FINAL Client: Example Client ABC123 Patient: Patient, Example 123 Test Drive Salt Lake City, UT 84108 DOB 2/13/1987 UNITED STATES Gender: Female Patient Identifiers: 01234567890ABCD, 012345 Physician: Doctor, Example Visit Number (FIN): 01234567890ABCD Collection Date: 00/00/0000 00:00 Cytogenomic SNP Microarray - Fetal ARUP test code 2002366 Maternal Contamination Study Fetal Spec Fetal Cells Single fetal genotype present; no maternal cells present. Fetal and maternal samples were tested using STR markers to rule out maternal cell contamination. This result has been reviewed and approved by Maternal Specimen Yes Cytogenomic SNP Microarray - Fetal Abnormal * (Ref Interval: Normal) Test Performed: Cytogenomic SNP Microarray- Fetal (ARRAY FE) Specimen Type: Direct (uncultured) villi Indication for Testing: Patient with 46,XX,t(4;13)(p16.3;q12) (Quest: EN935475D) ----------------------------------------------------------------- ----- RESULT SUMMARY Abnormal Microarray Result (Male) Unbalanced Translocation Involving Chromosomes 4 and 13 Classification: Pathogenic 4p Terminal Deletion (Wolf-Hirschhorn syndrome) Copy number change: 4p16.3p16.2 loss Size: 5.1 Mb 13q Proximal Region Deletion Copy number change: 13q11q12.12 loss Size: 6.1 Mb ----------------------------------------------------------------- ----- RESULT DESCRIPTION This analysis showed a terminal deletion (1 copy present) involving chromosome 4 within 4p16.3p16.2 and a proximal interstitial deletion (1 copy present) involving chromosome 13 within 13q11q12.12. This
    [Show full text]
  • Prioritization and Evaluation of Depression Candidate Genes by Combining Multidimensional Data Resources
    Prioritization and Evaluation of Depression Candidate Genes by Combining Multidimensional Data Resources Chung-Feng Kao1, Yu-Sheng Fang2, Zhongming Zhao3, Po-Hsiu Kuo1,2,4* 1 Department of Public Health and Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan, 2 Institute of Clinical Medicine, School of Medicine, National Cheng-Kung University, Tainan, Taiwan, 3 Departments of Biomedical Informatics and Psychiatry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America, 4 Research Center for Genes, Environment and Human Health, National Taiwan University, Taipei, Taiwan Abstract Background: Large scale and individual genetic studies have suggested numerous susceptible genes for depression in the past decade without conclusive results. There is a strong need to review and integrate multi-dimensional data for follow up validation. The present study aimed to apply prioritization procedures to build-up an evidence-based candidate genes dataset for depression. Methods: Depression candidate genes were collected in human and animal studies across various data resources. Each gene was scored according to its magnitude of evidence related to depression and was multiplied by a source-specific weight to form a combined score measure. All genes were evaluated through a prioritization system to obtain an optimal weight matrix to rank their relative importance with depression using the combined scores. The resulting candidate gene list for depression (DEPgenes) was further evaluated by a genome-wide association (GWA) dataset and microarray gene expression in human tissues. Results: A total of 5,055 candidate genes (4,850 genes from human and 387 genes from animal studies with 182 being overlapped) were included from seven data sources.
    [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]
  • Prediction of Meiosis-Essential Genes Based Upon the Dynamic Proteomes Responsive to Spermatogenesis
    bioRxiv preprint doi: https://doi.org/10.1101/2020.02.05.936435; this version posted February 6, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. Prediction of meiosis-essential genes based upon the dynamic proteomes responsive to spermatogenesis Kailun Fang1,2,3,8, Qidan Li3,4,5,8, Yu Wei2,3,8, Jiaqi Shen6,8, Wenhui Guo3,4,5,7,8, Changyang Zhou2,3,8, Ruoxi Wu1, Wenqin Ying2, Lu Yu1,2, Jin Zi5, Yuxing Zhang3,4,5, Hui Yang2,3,9*, Siqi Liu3,4,5,9*, Charlie Degui Chen1,3,9* 1. State Key Laboratory of Molecular Biology, Shanghai Key Laboratory of Molecular Andrology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China. 2. State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Institute of Neuroscience, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China. 3. University of the Chinese Academy of Sciences, Beijing 100049, China. 4. CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China. 5. BGI Genomics, BGI-Shenzhen, Shenzhen 518083, China. 6. United World College Changshu China, Jiangsu 215500, China. 7. Malvern College Qingdao, Shandong, 266109, China 8.
    [Show full text]
  • Supplemental Table 1. Complete Gene Lists and GO Terms from Figure 3C
    Supplemental Table 1. Complete gene lists and GO terms from Figure 3C. Path 1 Genes: RP11-34P13.15, RP4-758J18.10, VWA1, CHD5, AZIN2, FOXO6, RP11-403I13.8, ARHGAP30, RGS4, LRRN2, RASSF5, SERTAD4, GJC2, RHOU, REEP1, FOXI3, SH3RF3, COL4A4, ZDHHC23, FGFR3, PPP2R2C, CTD-2031P19.4, RNF182, GRM4, PRR15, DGKI, CHMP4C, CALB1, SPAG1, KLF4, ENG, RET, GDF10, ADAMTS14, SPOCK2, MBL1P, ADAM8, LRP4-AS1, CARNS1, DGAT2, CRYAB, AP000783.1, OPCML, PLEKHG6, GDF3, EMP1, RASSF9, FAM101A, STON2, GREM1, ACTC1, CORO2B, FURIN, WFIKKN1, BAIAP3, TMC5, HS3ST4, ZFHX3, NLRP1, RASD1, CACNG4, EMILIN2, L3MBTL4, KLHL14, HMSD, RP11-849I19.1, SALL3, GADD45B, KANK3, CTC- 526N19.1, ZNF888, MMP9, BMP7, PIK3IP1, MCHR1, SYTL5, CAMK2N1, PINK1, ID3, PTPRU, MANEAL, MCOLN3, LRRC8C, NTNG1, KCNC4, RP11, 430C7.5, C1orf95, ID2-AS1, ID2, GDF7, KCNG3, RGPD8, PSD4, CCDC74B, BMPR2, KAT2B, LINC00693, ZNF654, FILIP1L, SH3TC1, CPEB2, NPFFR2, TRPC3, RP11-752L20.3, FAM198B, TLL1, CDH9, PDZD2, CHSY3, GALNT10, FOXQ1, ATXN1, ID4, COL11A2, CNR1, GTF2IP4, FZD1, PAX5, RP11-35N6.1, UNC5B, NKX1-2, FAM196A, EBF3, PRRG4, LRP4, SYT7, PLBD1, GRASP, ALX1, HIP1R, LPAR6, SLITRK6, C16orf89, RP11-491F9.1, MMP2, B3GNT9, NXPH3, TNRC6C-AS1, LDLRAD4, NOL4, SMAD7, HCN2, PDE4A, KANK2, SAMD1, EXOC3L2, IL11, EMILIN3, KCNB1, DOK5, EEF1A2, A4GALT, ADGRG2, ELF4, ABCD1 Term Count % PValue Genes regulation of pathway-restricted GDF3, SMAD7, GDF7, BMPR2, GDF10, GREM1, BMP7, LDLRAD4, SMAD protein phosphorylation 9 6.34 1.31E-08 ENG pathway-restricted SMAD protein GDF3, SMAD7, GDF7, BMPR2, GDF10, GREM1, BMP7, LDLRAD4, phosphorylation
    [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]
  • Gene Expression Profiling of Corpus Luteum Reveals the Importance Of
    bioRxiv preprint doi: https://doi.org/10.1101/673558; this version posted February 27, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 Gene expression profiling of corpus luteum reveals the 2 importance of immune system during early pregnancy in 3 domestic sheep. 4 Kisun Pokharel1, Jaana Peippo2 Melak Weldenegodguad1, Mervi Honkatukia2, Meng-Hua Li3*, Juha 5 Kantanen1* 6 1 Natural Resources Institute Finland (Luke), Jokioinen, Finland 7 2 Nordgen – The Nordic Genetic Resources Center, Ås, Norway 8 3 College of Animal Science and Technology, China Agriculture University, Beijing, China 9 * Correspondence: MHL, [email protected]; JK, [email protected] 10 Abstract: The majority of pregnancy loss in ruminants occurs during the preimplantation stage, which is thus 11 the most critical period determining reproductive success. While ovulation rate is the major determinant of 12 litter size in sheep, interactions among the conceptus, corpus luteum and endometrium are essential for 13 pregnancy success. Here, we performed a comparative transcriptome study by sequencing total mRNA from 14 corpus luteum (CL) collected during the preimplantation stage of pregnancy in Finnsheep, Texel and F1 15 crosses, and mapping the RNA-Seq reads to the latest Rambouillet reference genome. A total of 21,287 genes 16 were expressed in our dataset. Highly expressed autosomal genes in the CL were associated with biological 17 processes such as progesterone formation (STAR, CYP11A1, and HSD3B1) and embryo implantation (eg.
    [Show full text]
  • Complexin-1 Is a Biomarker of Synucleinopathy
    DMM Advance Online Articles. Posted 20 January 2017 as doi: 10.1242/dmm.028035 Access the most recent version at http://dmm.biologists.org/lookup/doi/10.1242/dmm.028035 BLOOD RNA BIOMARKERS IN PRODROMAL PARK4 AND REM SLEEP BEHAVIOR DISORDER SHOW ROLE OF COMPLEXIN-1 LOSS FOR RISK OF PARKINSON’S DISEASE Suna Lahut1,2, Suzana Gispert1, Özgür Ömür1,2, Candan Depboylu3, Kay Seidel4, Jorge Antolio Domínguez-Bautista1, Nadine Brehm1, Hülya Tireli5, Karl Hackmann6, Caroline Pirkevi2, Barbara Leube7, Vincent Ries3, Kerstin Reim8, Nils Brose8, Wilfred F. den Dunnen9, Madrid Johnson10, Zsuzsanna Wolf11, Marc Schindewolf12, Wiebke Schrempf13, Kathrin Reetz14, Peter Young15, David Vadasz3, Achilleas S. Frangakis10, Evelin Schröck6, Helmuth Steinmetz1, Marina Jendrach1, Udo Rüb4, Ayşe Nazlı Başak2¶, Wolfgang Oertel3¶, Georg Auburger1¶ 1Exp. Neurology, Goethe University Medical School, 60590 Frankfurt/Main, Germany; 2NDAL, Boğaziçi University, 34342 Istanbul, Turkey; 3Dept. of Neurology, Philipps University, Baldingerstrasse, 35043 Marburg, Germany; 4Dr. Senckenberg Chronomedical Institute, Goethe University, 60590 Frankfurt/Main, Germany; 5Dept. of Neurology, Haydarpaşa Numune Training and Research Hospital, 34668 Istanbul, Turkey; 6Institute for Clinical Genetics, Faculty of Medicine Carl Gustav Carus, TU Dresden, Fetscherstrasse 74, 01307 Dresden, Germany; 7Institute of Human Genetics, Heinrich Heine University, 40225 Düsseldorf, Germany; 8Dept. of Molecular Neurobiology and Center for the Molecular Physiology of the Brain, Max Planck Institute of Experimental Medicine, 37075 Göttingen, Germany; 9Dept. of Pathology and Medical Biology, Medical Center, University, 9700 RB Groningen, Netherlands; 10Institut für Biophysik and Frankfurt Institute for Molecular Life Sciences, Goethe University, 60438 Frankfurt/Main, Germany; 11Hemophilia Centre, Medical Clinic III, Institute of Transfusion Medicine and Immunhematology Goethe University, 60590 Frankfurt/Main, Germany; 12Dept.
    [Show full text]
  • Single-Cell RNA-Seq of Dopaminergic Neurons Informs Candidate Gene Selection for Sporadic
    bioRxiv preprint doi: https://doi.org/10.1101/148049; this version posted August 31, 2017. 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. 1 Single-cell RNA-seq of dopaminergic neurons informs candidate gene selection for sporadic 2 Parkinson's disease 3 4 Paul W. Hook1, Sarah A. McClymont1, Gabrielle H. Cannon1, William D. Law1, A. Jennifer 5 Morton2, Loyal A. Goff1,3*, Andrew S. McCallion1,4,5* 6 7 1McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of 8 Medicine, Baltimore, Maryland, United States of America 9 2Department of Physiology Development and Neuroscience, University of Cambridge, 10 Cambridge, United Kingdom 11 3Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, 12 Maryland, United States of America 13 4Department of Comparative and Molecular Pathobiology, Johns Hopkins University School of 14 Medicine, Baltimore, Maryland, United States of America 15 5Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, 16 United States of America 17 *, To whom correspondence should be addressed: [email protected] and [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/148049; this version posted August 31, 2017. 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.
    [Show full text]
  • Metastatic Adrenocortical Carcinoma Displays Higher Mutation Rate and Tumor Heterogeneity Than Primary Tumors
    ARTICLE DOI: 10.1038/s41467-018-06366-z OPEN Metastatic adrenocortical carcinoma displays higher mutation rate and tumor heterogeneity than primary tumors Sudheer Kumar Gara1, Justin Lack2, Lisa Zhang1, Emerson Harris1, Margaret Cam2 & Electron Kebebew1,3 Adrenocortical cancer (ACC) is a rare cancer with poor prognosis and high mortality due to metastatic disease. All reported genetic alterations have been in primary ACC, and it is 1234567890():,; unknown if there is molecular heterogeneity in ACC. Here, we report the genetic changes associated with metastatic ACC compared to primary ACCs and tumor heterogeneity. We performed whole-exome sequencing of 33 metastatic tumors. The overall mutation rate (per megabase) in metastatic tumors was 2.8-fold higher than primary ACC tumor samples. We found tumor heterogeneity among different metastatic sites in ACC and discovered recurrent mutations in several novel genes. We observed 37–57% overlap in genes that are mutated among different metastatic sites within the same patient. We also identified new therapeutic targets in recurrent and metastatic ACC not previously described in primary ACCs. 1 Endocrine Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. 2 Center for Cancer Research, Collaborative Bioinformatics Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. 3 Department of Surgery and Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA. Correspondence and requests for materials should be addressed to E.K. (email: [email protected]) NATURE COMMUNICATIONS | (2018) 9:4172 | DOI: 10.1038/s41467-018-06366-z | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-018-06366-z drenocortical carcinoma (ACC) is a rare malignancy with types including primary ACC from the TCGA to understand our A0.7–2 cases per million per year1,2.
    [Show full text]
  • Whole-Exome Sequencing Associates Novel CSMD1 Gene Mutations with Familial Parkinson Disease
    Whole-exome sequencing associates novel CSMD1 gene mutations with familial Parkinson disease Javier Ruiz-Martínez, ABSTRACT MD, PhD Objective: Despite the enormous advancements made in deciphering the genetic architecture of Luis J. Azcona, BBA Parkinson disease (PD), the majority of PD is idiopathic, with single gene mutations explaining only Alberto Bergareche, MD a small proportion of the cases. Jose F. Martí-Massó, MD, Methods: In this study, we clinically evaluated 2 unrelated Spanish families diagnosed with PD, in PhD which known PD genes were previously excluded, and performed whole-exome sequencing anal- Coro Paisán-Ruiz, PhD yses in affected individuals for disease gene identification. Results: Patients were diagnosed with typical PD without relevant distinctive symptoms. Two dif- Correspondence to ferent novel mutations were identified in the CSMD1 gene. The CSMD1 gene, which encodes Dr. Paisán-Ruiz: a complement control protein that is known to participate in the complement activation and [email protected] inflammation in the developing CNS, was previously shown to be associated with the risk of PD in a genome-wide association study. Conclusions: We conclude that the CSMD1 mutations identified in this study might be responsible for the PD phenotype observed in our examined patients. This, along with previous reported studies, may suggest the complement pathway as an important therapeutic target for PD and other neurodegenerative diseases. Neurol Genet 2017;3:e177; doi: 10.1212/NXG.0000000000000177 GLOSSARY AD 5 Alzheimer disease; CCP 5 complement control protein; fPD 5 familial Parkinson disease; H&Y 5 Hoehn and Yahr; INDEL 5 insertions/deletions; LOPD 5 late-onset PD; PD 5 Parkinson disease; RBD 5 REM sleep behavior disorder; RLS 5 restless legs syndrome; SNV 5 single nucleotide variant; WES 5 whole-exome sequencing.
    [Show full text]
  • Whole Genome Sequencing of Familial Non-Medullary Thyroid Cancer Identifies Germline Alterations in MAPK/ERK and PI3K/AKT Signaling Pathways
    biomolecules Article Whole Genome Sequencing of Familial Non-Medullary Thyroid Cancer Identifies Germline Alterations in MAPK/ERK and PI3K/AKT Signaling Pathways Aayushi Srivastava 1,2,3,4 , Abhishek Kumar 1,5,6 , Sara Giangiobbe 1, Elena Bonora 7, Kari Hemminki 1, Asta Försti 1,2,3 and Obul Reddy Bandapalli 1,2,3,* 1 Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), D-69120 Heidelberg, Germany; [email protected] (A.S.); [email protected] (A.K.); [email protected] (S.G.); [email protected] (K.H.); [email protected] (A.F.) 2 Hopp Children’s Cancer Center (KiTZ), D-69120 Heidelberg, Germany 3 Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), D-69120 Heidelberg, Germany 4 Medical Faculty, Heidelberg University, D-69120 Heidelberg, Germany 5 Institute of Bioinformatics, International Technology Park, Bangalore 560066, India 6 Manipal Academy of Higher Education (MAHE), Manipal, Karnataka 576104, India 7 S.Orsola-Malphigi Hospital, Unit of Medical Genetics, 40138 Bologna, Italy; [email protected] * Correspondence: [email protected]; Tel.: +49-6221-42-1709 Received: 29 August 2019; Accepted: 10 October 2019; Published: 13 October 2019 Abstract: Evidence of familial inheritance in non-medullary thyroid cancer (NMTC) has accumulated over the last few decades. However, known variants account for a very small percentage of the genetic burden. Here, we focused on the identification of common pathways and networks enriched in NMTC families to better understand its pathogenesis with the final aim of identifying one novel high/moderate-penetrance germline predisposition variant segregating with the disease in each studied family.
    [Show full text]