The Tumor Suppressor CIC Directly Regulates MAPK Pathway Genes Via Histone Deacetylation Simon Weissmann1,2, Paul A
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Functions of the Mineralocorticoid Receptor in the Hippocampus By
Functions of the Mineralocorticoid Receptor in the Hippocampus by Aaron M. Rozeboom A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Cellular and Molecular Biology) in The University of Michigan 2008 Doctoral Committee: Professor Audrey F. Seasholtz, Chair Professor Elizabeth A. Young Professor Ronald Jay Koenig Associate Professor Gary D. Hammer Assistant Professor Jorge A. Iniguez-Lluhi Acknowledgements There are more people than I can possibly name here that I need to thank who have helped me throughout the process of writing this thesis. The first and foremost person on this list is my mentor, Audrey Seasholtz. Between working in her laboratory as a research assistant and continuing my training as a graduate student, I spent 9 years in Audrey’s laboratory and it would be no exaggeration to say that almost everything I have learned regarding scientific research has come from her. Audrey’s boundless enthusiasm, great patience, and eager desire to teach students has made my time in her laboratory a richly rewarding experience. I cannot speak of Audrey’s laboratory without also including all the past and present members, many of whom were/are not just lab-mates but also good friends. I also need to thank all the members of my committee, an amazing group of people whose scientific prowess combined with their open-mindedness allowed me to explore a wide variety of interests while maintaining intense scientific rigor. Outside of Audrey’s laboratory, there have been many people in Ann Arbor without whom I would most assuredly have gone crazy. -
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. -
Modes of Interaction of KMT2 Histone H3 Lysine 4 Methyltransferase/COMPASS Complexes with Chromatin
cells Review Modes of Interaction of KMT2 Histone H3 Lysine 4 Methyltransferase/COMPASS Complexes with Chromatin Agnieszka Bochy ´nska,Juliane Lüscher-Firzlaff and Bernhard Lüscher * ID Institute of Biochemistry and Molecular Biology, Medical School, RWTH Aachen University, Pauwelsstrasse 30, 52057 Aachen, Germany; [email protected] (A.B.); jluescher-fi[email protected] (J.L.-F.) * Correspondence: [email protected]; Tel.: +49-241-8088850; Fax: +49-241-8082427 Received: 18 January 2018; Accepted: 27 February 2018; Published: 2 March 2018 Abstract: Regulation of gene expression is achieved by sequence-specific transcriptional regulators, which convey the information that is contained in the sequence of DNA into RNA polymerase activity. This is achieved by the recruitment of transcriptional co-factors. One of the consequences of co-factor recruitment is the control of specific properties of nucleosomes, the basic units of chromatin, and their protein components, the core histones. The main principles are to regulate the position and the characteristics of nucleosomes. The latter includes modulating the composition of core histones and their variants that are integrated into nucleosomes, and the post-translational modification of these histones referred to as histone marks. One of these marks is the methylation of lysine 4 of the core histone H3 (H3K4). While mono-methylation of H3K4 (H3K4me1) is located preferentially at active enhancers, tri-methylation (H3K4me3) is a mark found at open and potentially active promoters. Thus, H3K4 methylation is typically associated with gene transcription. The class 2 lysine methyltransferases (KMTs) are the main enzymes that methylate H3K4. KMT2 enzymes function in complexes that contain a necessary core complex composed of WDR5, RBBP5, ASH2L, and DPY30, the so-called WRAD complex. -
A Misplaced Lncrna Causes Brachydactyly in Humans
A misplaced lncRNA causes brachydactyly in humans Philipp G. Maass, … , Friedrich C. Luft, Sylvia Bähring J Clin Invest. 2012;122(11):3990-4002. https://doi.org/10.1172/JCI65508. Research Article Translocations are chromosomal rearrangements that are frequently associated with a variety of disease states and developmental disorders. We identified 2 families with brachydactyly type E (BDE) resulting from different translocations affecting chromosome 12p. Both translocations caused downregulation of the parathyroid hormone-like hormone (PTHLH) gene by disrupting the cis-regulatory landscape. Using chromosome conformation capturing, we identified a regulator on chromosome 12q that interacts in cis with PTHLH over a 24.4-megabase distance and in trans with the sex- determining region Y-box 9 (SOX9) gene on chromosome 17q. The element also harbored a long noncoding RNA (lncRNA). Silencing of the lncRNA, PTHLH, or SOX9 revealed a feedback mechanism involving an expression-dependent network in humans. In the BDE patients, the human lncRNA was upregulated by the disrupted chromosomal association. Moreover, the lncRNA occupancy at the PTHLH locus was reduced. Our results document what we believe to be a novel in cis– and in trans–acting DNA and lncRNA regulatory feedback element that is reciprocally regulated by coding genes. Furthermore, our findings provide a systematic and combinatorial view of how enhancers encoding lncRNAs may affect gene expression in normal development. Find the latest version: https://jci.me/65508/pdf Research article Related Commentary, page 3837 A misplaced lncRNA causes brachydactyly in humans Philipp G. Maass,1,2 Andreas Rump,3 Herbert Schulz,2 Sigmar Stricker,4 Lisanne Schulze,1,2 Konrad Platzer,3 Atakan Aydin,1,2 Sigrid Tinschert,3 Mary B. -
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). -
Figure S1. Representative Report Generated by the Ion Torrent System Server for Each of the KCC71 Panel Analysis and Pcafusion Analysis
Figure S1. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. A Figure S1. Continued. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. B Figure S2. Comparative analysis of the variant frequency found by the KCC71 panel and calculated from publicly available cBioPortal datasets. For each of the 71 genes in the KCC71 panel, the frequency of variants was calculated as the variant number found in the examined cases. Datasets marked with different colors and sample numbers of prostate cancer are presented in the upper right. *Significantly high in the present study. Figure S3. Seven subnetworks extracted from each of seven public prostate cancer gene networks in TCNG (Table SVI). Blue dots represent genes that include initial seed genes (parent nodes), and parent‑child and child‑grandchild genes in the network. Graphical representation of node‑to‑node associations and subnetwork structures that differed among and were unique to each of the seven subnetworks. TCNG, The Cancer Network Galaxy. Figure S4. REVIGO tree map showing the predicted biological processes of prostate cancer in the Japanese. Each rectangle represents a biological function in terms of a Gene Ontology (GO) term, with the size adjusted to represent the P‑value of the GO term in the underlying GO term database. -
Supplemental Materials ZNF281 Enhances Cardiac Reprogramming
Supplemental Materials ZNF281 enhances cardiac reprogramming by modulating cardiac and inflammatory gene expression Huanyu Zhou, Maria Gabriela Morales, Hisayuki Hashimoto, Matthew E. Dickson, Kunhua Song, Wenduo Ye, Min S. Kim, Hanspeter Niederstrasser, Zhaoning Wang, Beibei Chen, Bruce A. Posner, Rhonda Bassel-Duby and Eric N. Olson Supplemental Table 1; related to Figure 1. Supplemental Table 2; related to Figure 1. Supplemental Table 3; related to the “quantitative mRNA measurement” in Materials and Methods section. Supplemental Table 4; related to the “ChIP-seq, gene ontology and pathway analysis” and “RNA-seq” and gene ontology analysis” in Materials and Methods section. Supplemental Figure S1; related to Figure 1. Supplemental Figure S2; related to Figure 2. Supplemental Figure S3; related to Figure 3. Supplemental Figure S4; related to Figure 4. Supplemental Figure S5; related to Figure 6. Supplemental Table S1. Genes included in human retroviral ORF cDNA library. Gene Gene Gene Gene Gene Gene Gene Gene Symbol Symbol Symbol Symbol Symbol Symbol Symbol Symbol AATF BMP8A CEBPE CTNNB1 ESR2 GDF3 HOXA5 IL17D ADIPOQ BRPF1 CEBPG CUX1 ESRRA GDF6 HOXA6 IL17F ADNP BRPF3 CERS1 CX3CL1 ETS1 GIN1 HOXA7 IL18 AEBP1 BUD31 CERS2 CXCL10 ETS2 GLIS3 HOXB1 IL19 AFF4 C17ORF77 CERS4 CXCL11 ETV3 GMEB1 HOXB13 IL1A AHR C1QTNF4 CFL2 CXCL12 ETV7 GPBP1 HOXB5 IL1B AIMP1 C21ORF66 CHIA CXCL13 FAM3B GPER HOXB6 IL1F3 ALS2CR8 CBFA2T2 CIR1 CXCL14 FAM3D GPI HOXB7 IL1F5 ALX1 CBFA2T3 CITED1 CXCL16 FASLG GREM1 HOXB9 IL1F6 ARGFX CBFB CITED2 CXCL3 FBLN1 GREM2 HOXC4 IL1F7 -
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Supplementary Figure S1. Results of flow cytometry analysis, performed to estimate CD34 positivity, after immunomagnetic separation in two different experiments. As monoclonal antibody for labeling the sample, the fluorescein isothiocyanate (FITC)- conjugated mouse anti-human CD34 MoAb (Mylteni) was used. Briefly, cell samples were incubated in the presence of the indicated MoAbs, at the proper dilution, in PBS containing 5% FCS and 1% Fc receptor (FcR) blocking reagent (Miltenyi) for 30 min at 4 C. Cells were then washed twice, resuspended with PBS and analyzed by a Coulter Epics XL (Coulter Electronics Inc., Hialeah, FL, USA) flow cytometer. only use Non-commercial 1 Supplementary Table S1. Complete list of the datasets used in this study and their sources. GEO Total samples Geo selected GEO accession of used Platform Reference series in series samples samples GSM142565 GSM142566 GSM142567 GSM142568 GSE6146 HG-U133A 14 8 - GSM142569 GSM142571 GSM142572 GSM142574 GSM51391 GSM51392 GSE2666 HG-U133A 36 4 1 GSM51393 GSM51394 only GSM321583 GSE12803 HG-U133A 20 3 GSM321584 2 GSM321585 use Promyelocytes_1 Promyelocytes_2 Promyelocytes_3 Promyelocytes_4 HG-U133A 8 8 3 GSE64282 Promyelocytes_5 Promyelocytes_6 Promyelocytes_7 Promyelocytes_8 Non-commercial 2 Supplementary Table S2. Chromosomal regions up-regulated in CD34+ samples as identified by the LAP procedure with the two-class statistics coded in the PREDA R package and an FDR threshold of 0.5. Functional enrichment analysis has been performed using DAVID (http://david.abcc.ncifcrf.gov/) -
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 -
Evolution of the DAN Gene Family in Vertebrates
bioRxiv preprint doi: https://doi.org/10.1101/794404; this version posted June 29, 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. RESEARCH ARTICLE Evolution of the DAN gene family in vertebrates Juan C. Opazo1,2,3, Federico G. Hoffmann4,5, Kattina Zavala1, Scott V. Edwards6 1Instituto de Ciencias Ambientales y Evolutivas, Facultad de Ciencias, Universidad Austral de Chile, Valdivia, Chile. 2David Rockefeller Center for Latin American Studies, Harvard University, Cambridge, MA 02138, USA. 3Millennium Nucleus of Ion Channels-Associated Diseases (MiNICAD). 4 Department of Biochemistry, Molecular Biology, Entomology, and Plant Pathology, Mississippi State University, Mississippi State, 39762, USA. Cite as: Opazo JC, Hoffmann FG, 5 Zavala K, Edwards SV (2020) Institute for Genomics, Biocomputing, and Biotechnology, Mississippi State Evolution of the DAN gene family in University, Mississippi State, 39762, USA. vertebrates. bioRxiv, 794404, ver. 3 peer-reviewed and recommended by 6 PCI Evolutionary Biology. doi: Department of Organismic and Evolutionary Biology, Harvard University, 10.1101/794404 Cambridge, MA 02138, USA. This article has been peer-reviewed and recommended by Peer Community in Evolutionary Biology Posted: 29 June 2020 doi: 10.24072/pci.evolbiol.100104 ABSTRACT Recommender: Kateryna Makova The DAN gene family (DAN, Differential screening-selected gene Aberrant in Neuroblastoma) is a group of genes that is expressed during development and plays fundamental roles in limb bud formation and digitation, kidney formation and morphogenesis and left-right axis specification. -
ETV4 and AP1 Transcription Factors Form Multivalent Interactions with Three Sites on the MED25 Activator-Interacting Domain
Featured Arcle KDC YJMBI-65461; No. of pages: 21; 4C: ETV4 and AP1 Transcription Factors Form Multivalent Interactions with three Sites on the MED25 Activator-Interacting Domain Simon L. Currie 1,2, Jedediah J. Doane 1,2, Kathryn S. Evans 1,2, Niraja Bhachech 1,2, Bethany J. Madison 1,2, Desmond K.W. Lau 3, Lawrence P. McIntosh 3, Jack J. Skalicky 4, Kathleen A. Clark 1,2 and Barbara J. Graves 1,2,5 1 - Department of Oncological Sciences, University of Utah School of Medicine, Salt Lake City, UT, 84112-5500, USA 2 - Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, 84112-5500, USA 3 - Departments of Biochemistry and Molecular Biology, Department of Chemistry, and Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada 4 - Department of Biochemistry, University of Utah School of Medicine, Salt Lake City, UT, 84112-5650, USA 5 - Howard Hughes Medical Institute, Chevy Chase, MD, 20815-6789, USA Correspondence to Barbara J. Graves: Department of Oncological Sciences, University of Utah School of Medicine, Salt Lake City, UT, 84112-5500, USA. [email protected]. http://dx.doi.org/10.1016/j.jmb.2017.06.024 Edited by Prof. M.F. Summers Simon L. Currie, Jedediah J. Doane, Kathryn S. Evans and Barbara J. Graves Abstract The recruitment of transcriptional cofactors by sequence-specific transcription factors challenges the basis of high affinity and selective interactions. Extending previous studies that the N-terminal activation domain (AD) of ETV5 interacts with Mediator subunit 25 (MED25), we establish that similar, aromatic-rich motifs located both in the AD and in the DNA-binding domain (DBD) of the related ETS factor ETV4 interact with MED25. -
Genetic Pathways of Neuroregeneration in a Novel Mild Traumatic Brain Injury Model in Adult Zebrafish
New Research Disorders of the Nervous System Genetic Pathways of Neuroregeneration in a Novel Mild Traumatic Brain Injury Model in Adult Zebrafish Amanda L. Maheras,1,3 Brian Dix,2,3 Olivia M. S. Carmo,2,3 Aleena E. Young,2,3 Vanessa N. Gill,2,3 Julia L. Sun,1,3 Aleah R. Booker,1,3 Helen A. Thomason,1,3 Anastasia E. Ibrahim,2,3 Lauren Stanislaw,1,3 Jennifer C. Dallego,2,3 Cat N. Ngo,3,4 Audrey Chen,5 Barbara K. Fortini,6 and Rory D. Spence3 DOI:http://dx.doi.org/10.1523/ENEURO.0208-17.2017 1Scripps College, Claremont, CA 91711, 2Claremont McKenna College, Claremont, CA 91711, 3Keck Science Department, Claremont, CA 91711, 4Harvey Mudd College, Claremont, CA 91711, 5Ayala School of Biological Sciences, Department of Neurobiology & Behavior, University of California, Irvine, Irvine, CA, and 6School of Pharmacy, Keck Graduate Institute, Claremont, CA 91711 Visual Abstract Mild traumatic brain injuries (mTBIs) are one of the most prevalent neurological disorders, and humans are severely limited in their ability to repair and regenerate central nervous system (CNS) tissue postinjury. However, zebrafish (Danio rerio) maintain the remarkable ability to undergo complete and functional neuroregeneration as an adult. We wish to extend knowledge of the known mechanisms of neuroregeneration by analyzing the differentially expressed genes (DEGs) in a novel adult zebrafish model of mTBI. In this study, a rodent weight drop Significance Statement Mild traumatic brain injuries (mTBIs) are a major health concern in the United States, with ϳ2.8 million concussions reported annually by the CDC.